Distributed_Computing | Theodore C. Tanner Jr.

August 3, 2025August 3, 2025 TCTJr Artificial_Intelligence|Complexity_Theory|Distributed_Computing|Mathematics|Random_Thoughts

A Survey Of Architectures And Methodologies For Distributed LLM Dissaggregation

Grok 4’s Idea of a Distributed LLM

I was kicking back in my Charleston study this morning, drinking my usual unsweetened tea in a mason jar, the salty breeze slipping through the open window like a whisper from the Charleston Harbor, carrying that familiar tang of low tide “pluff mud” and distant rain. The sun was filtering through the shutters, casting long shadows across my desk littered with old notes on distributed systems engineering, when I dove into this survey on architectures for distributed LLM disaggregation. It’s a dive into the tech that’s pushing LLMs beyond their limits. As i read the numerous papers and assembled commonalities, it hit me how these innovations echo the battles many have fought scaling AI/ML in production, raw, efficient, and unapologetically forward. Here’s the breakdown, with the key papers linked for those ready to dig deeper.

This is essentially the last article in a trilogy. The sister survey is a blog A Survey of Technical Approaches For Distributed AI In Sensor Networks. Then, for a top-level view, i wrote SnakeByte[21]: The Token Arms Race: Architectures Behind Long-Context Foundation Models, so you’ll have all views of a complete system, sensors->distributed compute methods->context engineering.

NOTE: By the time this is published, a whole new set of papers will come out, and i wrote (and read the papers) in a week.

Overview

Distributed serving of LLMs presents significant technical challenges driven by the immense scale of contemporary models, the computational intensity of inference, the autoregressive nature of token generation, and the diverse characteristics of inference requests. Efficiently deploying LLMs across clusters of hardware accelerators (predominantly GPUs and NPUs) necessitates sophisticated system architectures, scheduling algorithms, and resource management techniques to achieve low latency, high throughput, and cost-effectiveness while adhering to Service Level Objectives (SLOs). As you read the LLM survey, think in terms of deployment architectures:

Layered AI System Architecture:

Sensor Layer: IoT, Cameras, Radar, LIDAR, electro-mag, FLIR etc
Edge/Fog Layer: Edge Gateways, Inference Accelerators, Fog Nodes
Cloud Layer: Central AI Model Training, Orchestration Logic, Data Lake

Each layer plays a role in collecting, processing, and managing AI workloads in a distributed system.

Distributed System Architectures and Disaggregation

Modern distributed Large Language Models serving platforms are moving beyond monolithic deployments to adopt disaggregated architectures. A common approach involves separating the computationally intensive prompt processing (prefill phase) from the memory-bound token generation (decode phase). This disaggregation addresses the bimodal latency characteristics of these phases, mitigating pipeline bubbles that arise in pipeline-parallel deployments KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving. As a reminder in LLMs, KV cache stores key and value tensors from previous tokens during inference. In transformer-based models, the attention mechanism computes key (K) and value (V) vectors for each token in the input sequence. Without caching, these would be recalculated for every new token generated, leading to redundant computations and inefficiency.

Systems like Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving propose a KVCache-centric disaggregated architecture with dedicated clusters for prefill and decoding. This separation allows for specialized resource allocation and scheduling policies tailored to each phase’s demands. Similarly, P/D-Serve: Serving Disaggregated Large Language Model at Scale focuses on serving disaggregated LLMs at scale across tens of thousands of devices, emphasizing fine-grained P/D organization and dynamic ratio adjustments to minimize inner mismatch and improve throughput and Time-to-First-Token (TTFT) SLOs. KVDirect: Distributed Disaggregated LLM Inference explores distributed disaggregated inference by optimizing inter-node KV cache transfer using tensor-centric communication and a pull-based strategy.

Further granularity in disaggregation can involve partitioning the model itself across different devices or even separating attention layers, as explored by Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache, which disaggregates attention layers to enable flexible resource scheduling and enhance memory utilization for long contexts. DynaServe: Unified and Elastic Execution for Dynamic Disaggregated LLM Serving unifies and extends both colocated and disaggregated paradigms using a micro-request abstraction, splitting requests into segments for balanced load across unified GPU instances.

The distributed nature also necessitates mechanisms for efficient checkpoint loading and live migration. ServerlessLLM: Low-Latency Serverless Inference for Large Language Models proposes a system for low-latency serverless inference that leverages near-GPU storage for fast multi-tier checkpoint loading and supports efficient live migration of LLM inference states.

Scheduling and Resource Orchestration

Effective scheduling is paramount in distributed LLM serving due to heterogeneous request patterns, varying SLOs, and the autoregressive dependency. Existing systems often suffer from head-of-line blocking and inefficient resource utilization under diverse workloads.

Preemptive scheduling, as implemented in Fast Distributed Inference Serving for Large Language Models, allows for preemption at the granularity of individual output tokens to minimize latency. FastServe employs a novel skip-join Multi-Level Feedback Queue scheduler leveraging input length information. Llumnix: Dynamic Scheduling for Large Language Model Serving introduces dynamic rescheduling across multiple model instances, akin to OS context switching, to improve load balancing, isolation, and prioritize requests with different SLOs via an efficient live migration mechanism.

Prompt scheduling with KV state sharing is a key optimization for workloads with repetitive prefixes. Preble: Efficient Distributed Prompt Scheduling for LLM Serving is a distributed platform explicitly designed for optimizing prompt sharing through a distributed scheduling system that co-optimizes KV state reuse and computation load-balancing using a hierarchical mechanism. MemServe: Context Caching for Disaggregated LLM Serving with Elastic Memory Pool integrates context caching with disaggregated inference, supported by a global scheduler that enhances cache reuse through a global prompt tree-based locality-aware policy. Locality-aware fair scheduling is further explored in Locality-aware Fair Scheduling in LLM Serving, which proposes Deficit Longest Prefix Match (DLPM) and Double Deficit LPM (D2LPM) algorithms for distributed setups to balance fairness, locality, and load-balancing.

Serving multi-SLO requests efficiently requires sophisticated queue management and scheduling. Queue management for slo-oriented large language model serving is a queue management system that handles batch and interactive requests across different models and SLOs using a Request Waiting Time (RWT) Estimator and a global scheduler for orchestration. SLOs-Serve: Optimized Serving of Multi-SLO LLMs optimizes the serving of multi-stage LLM requests with application- and stage-specific SLOs by customizing token allocations using a multi-SLO dynamic programming algorithm. SeaLLM: Service-Aware and Latency-Optimized Resource Sharing for Large Language Model Inference proposes a service-aware and latency-optimized resource sharing framework for large language model inference.

For complex workloads like agentic programs involving multiple LLM calls with dependencies, traditional request-level scheduling is suboptimal. Autellix: An Efficient Serving Engine for LLM Agents as General Programs treats programs as first-class citizens, using program-level context to inform scheduling algorithms that preempt and prioritize LLM calls based on program progress, demonstrating significant throughput improvements for agentic workloads. Parrot: Efficient Serving of LLM-based Applications with Semantic Variable focuses on end-to-end performance for LLM-based applications by introducing the Semantic Variable abstraction to expose application-level knowledge and enable data flow analysis across requests. Conveyor: Efficient Tool-aware LLM Serving with Tool Partial Execution optimizes for tool-aware LLM serving by enabling tool partial execution alongside LLM decoding.

Memory Management and KV Cache Optimizations

The KV cache’s size grows linearly with sequence length and batch size, becoming a major bottleneck for GPU memory and throughput. Distributed serving exacerbates this by requiring efficient management across multiple nodes.

Effective KV cache management involves techniques like dynamic memory allocation, swapping, compression, and sharing. KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving proposes KV-cache streaming for fast, fault-tolerant serving, addressing GPU memory overprovisioning and recovery times. It utilizes microbatch swapping for efficient GPU memory management. On-Device Language Models: A Comprehensive Review presents techniques for managing persistent KV cache states including tolerance-aware compression, IO-recompute pipelined loading, and optimized chunk lifecycle management.

In distributed environments, sharing and transferring KV cache states efficiently are critical. MemServe introduces MemPool, an elastic memory pool managing distributed memory and KV caches. Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache leverages a pooled GPU memory strategy across a cluster. Prefetching KV-cache for LLM Inference with Dynamic Profiling proposes prefetching model weights and KV-cache from off-chip memory to on-chip cache during communication to mitigate memory bottlenecks and communication overhead in distributed settings. KVDirect: Distributed Disaggregated LLM Inference specifically optimizes KV cache transfer using a tensor-centric communication mechanism.

Handling Heterogeneity and Edge/Geo-Distributed Deployment

Serving LLMs cost-effectively often requires utilizing heterogeneous hardware clusters and deploying models closer to users on edge devices or across geo-distributed infrastructure.

Helix: Serving Large Language Models over Heterogeneous GPUs and Networks via Max-Flow addresses serving LLMs over heterogeneous GPUs and networks by formulating inference as a max-flow problem and using MILP for joint model placement and request scheduling optimization. LLM-PQ: Serving LLM on Heterogeneous Clusters with Phase-Aware Partition and Adaptive Quantization supports efficient serving on heterogeneous GPU clusters through adaptive model quantization and phase-aware partition. Efficient LLM Inference via Collaborative Edge Computing leverages collaborative edge computing to partition LLM models and deploy them on distributed devices, formulating device selection and partition as an optimization problem.

Deploying LLMs on edge or geo-distributed devices introduces challenges related to limited resources, unstable networks, and privacy. PerLLM: Personalized Inference Scheduling with Edge-Cloud Collaboration for Diverse LLM Services provides a personalized inference scheduling framework with edge-cloud collaboration for diverse LLM services, optimizing scheduling and resource allocation using a UCB algorithm with constraint satisfaction. Distributed Inference and Fine-tuning of Large Language Models Over The Internet investigates inference and fine-tuning over the internet using geodistributed devices, developing fault-tolerant inference algorithms and load-balancing protocols. MoLink: Distributed and Efficient Serving Framework for Large Models is a distributed serving system designed for heterogeneous and weakly connected consumer-grade GPUs, incorporating techniques for efficient serving under limited network conditions. WiLLM: an Open Framework for LLM Services over Wireless Systems proposes deploying LLMs in core networks for wireless LLM services, introducing a “Tree-Branch-Fruit” network slicing architecture and enhanced slice orchestration.

On the of most recent papers that echo my sentiment from years ago where is i’ve said “Vertically Trained Horizontally Chained” (maybe i should trademark that …) is Small Language Models are the Future of Agentic AI where they lay out the position that specific task LLMs are sufficiently robust, inherently more suitable, and necessarily more economical for many invocations in agentic systems, and are therefore the future of agentic AI. The argumentation is grounded in the current level of capabilities exhibited by these specialized models, the common architectures of agentic systems, and the economy of LM deployment. They further argue that in situations where general-purpose conversational abilities are essential, heterogeneous agentic systems (i.e., agents invoking multiple different models chained horizontally) are the natural choice. They discuss the potential barriers for the adoption of vertically trained LLMs in agentic systems and outline a general LLM-to-specific chained model conversion algorithm.

Other Optimizations and Considerations

Quantization is a standard technique to reduce model size and computational requirements. Atom: Low-bit Quantization for Efficient and Accurate LLM Serving proposes a low-bit quantization method (4-bit weight-activation) to maximize serving throughput by leveraging low-bit operators and reducing memory consumption, achieving significant speedups over FP16 and INT8 with negligible accuracy loss.

Splitting or partitioning models can also facilitate deployment across distributed or heterogeneous resources. SplitLLM: Collaborative Inference of LLMs for Model Placement and Throughput Optimization designs a collaborative inference architecture between a server and clients to enable model placement and throughput optimization. A related concept is Split Learning for fine-tuning, where models are split across cloud, edge, and user devices Hierarchical Split Learning for Large Language Model over Wireless Network. BlockLLM: Multi-tenant Finer-grained Serving for Large Language Models enables multi-tenant finer-grained serving by partitioning models into blocks, allowing component sharing, adaptive assembly, and per-block resource configuration.

Performance and Evaluation Metrics

Evaluating and comparing distributed LLM serving platforms requires appropriate metrics and benchmarks. The CAP Principle for LLM Serving: A Survey of Long-Context Large Language Model Serving suggests a trade-off between serving context length (C), serving accuracy (A), and serving performance (P). Developing realistic workloads and simulation tools is crucial. BurstGPT: A Real-world Workload Dataset to Optimize LLM Serving Systems provides a real-world workload dataset for optimizing LLM serving systems, revealing limitations of current optimizations under realistic conditions. LLMServingSim: A HW/SW Co-Simulation Infrastructure for LLM Inference Serving at Scale is a HW/SW co-simulation infrastructure designed to model dynamic workload variations and heterogeneous processor behaviors efficiently. ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency focuses on a holistic system view to optimize LLM serving in an end-to-end manner, identifying and addressing bottlenecks beyond just LLM inference.

Conclusion

The landscape of distributed LLM serving platforms is rapidly evolving, driven by the need to efficiently and cost-effectively deploy increasingly large and complex models. Key areas of innovation include the adoption of disaggregated architectures, sophisticated scheduling algorithms that account for workload heterogeneity and SLOs, advanced KV cache management techniques, and strategies for leveraging diverse hardware and deployment environments. While significant progress has been made, challenges remain in achieving optimal trade-offs between performance, cost, and quality of service (QOS) across highly dynamic and heterogeneous real-world scenarios.

As the sun set and the neon glow of my screen dimmed, i wrapped this survey up, leaving me pondering the endless horizons of AI/ML scaling like waves crashing on the shore, relentless and full of promise and thinking how incredible it is to be working in these areas where what we have dreamed for decades has come to fruition?

Until Then,

#iwishyouwater

Ted ℂ. Tanner Jr. (@tctjr) / X

MUZAK TO BLOG BY: Vangelis, “L’apocalypse de animax (remastered). Vangelis is famous for “Chariots Of Fire” and “Blade Runner” Soundtracks.

A Survey of Technical Approaches For Distributed AI In Sensor Networks

Grok4’s Idea of AI and Sensor Orchestraton with DAI

Distributed Artificial Intelligence (DAI) within sensor networks (SN) involves deploying AI algorithms and models across a network of spatially distributed sensor nodes rather than relying solely on centralized cloud processing. This paradigm shifts computation closer to the data source, bringing the data to the compute, offering potential benefits in terms of reduced communication latency, lower bandwidth usage, enhanced privacy, increased system resilience, and improved scalability for large-scale IoT and pervasive computing deployments. The operational complexity of such systems necessitates sophisticated orchestration mechanisms to manage the distributed AI workloads, sensor resources, and heterogeneous compute infrastructure spanning from edge devices to cloud data centers. This article will survey methods for distributed smart sensor technologies, along with considerations for implementing AI algorithms at these junctions.

Implementing AI functions in a distributed sensor network setting often involves adapting centralized algorithms or devising novel distributed methods. Key technical areas include distributed estimation, detection, and learning.

Distributed Sensor Anomaly Detection

Distributed estimation problems, such as static parameter estimation or Kalman filtering, can be addressed using consensus-based approaches. Algorithms of the “consensus + innovations” type, where one can have an estimation of the type and behavior of the sensor. The paper “Distributed Parameter Estimation in Sensor Networks: Nonlinear Observation Models and Imperfect Communication” discusses these algorithms, which enable sensor nodes to iteratively update estimates by combining local observations (innovations) with information exchanged with neighbors (consensus). These methods enable asymptotically unbiased and efficient estimation, even in the presence of nonlinear observation models and imperfect communication. Extensions include randomized consensus for Kalman filtering, which offers robustness to network topology changes and distributes the computational load stochastically which are covered in the paper “Randomized Consensus based Distributed Kalman Filtering over Wireless Sensor Networks”. For multi-target tracking or target under consideration, distributed approaches integrate sensor registration with tracking filters, such as deploying a consensus cardinality probability hypothesis density (CPHD) filter across the network and minimizing a cost function based on local posteriors to estimate relative sensor poses in the paper “Distributed Joint Sensor Registration and Multitarget Tracking Via Sensor Network”.

Distributed detection focuses on identifying events or anomalies based on collective sensor readings. Techniques leveraging sparse signal recovery have been applied to detect defective sensors in networks with a small number of faulty nodes, using distributed iterative hard thresholding (IHT) and low-complexity decoding robust to noisy messages in these two papers “Distributed Sparse Signal Recovery For Sensor Networks” and “Distributed Sensor Failure Detection In Sensor Networks” cover methods for failure recovery and self healing.

In another closely related application for anomaly detection of sensors learning-based distributed procedures, like the mixed detection-estimation (MDE) algorithm, address scenarios with unknown sensor defects by iteratively learning the validity of local observations while refining parameter estimates, achieving performance close to ideal centralized estimators in high SNR regimes can be found in this paper “Learning-Based Distributed Detection-Estimation in Sensor Networks with Unknown Sensor Defects”.

Distributed learning enables sensor nodes or edge devices to collaboratively train models without requiring the sharing of raw data. This is crucial for maintaining privacy and conserving bandwidth, or where privacy-preserving machine learning (PPML) is necessary. Approaches include distributed dictionary learning using diffusion cooperation schemes, where nodes exchange local dictionaries with neighbors, are applied in this paper “Distributed Dictionary Learning Over A Sensor Network”

In many cases, one has no a priori information for the type of sensor under consideration. For online sensor selection with unknown utility functions, distributed online greedy (DOG) algorithms provide no-regret guarantees for submodular utility functions with minimal communication overhead. Federated Learning (FL) and other distributed Machine Learning (ML) paradigms are increasingly applied for tasks like anomaly detection. In the paper “ Online Distributed Sensor Selection,” we find that a key problem in sensor networks is to decide which sensors to query when, in order to obtain the most useful information (e.g., for performing accurate prediction), subject to constraints (e.g., on power and bandwidth). In many applications, the utility function is not known a priori, must be learned from data, and can even change over time. Furthermore, for large sensor networks, solving a centralized optimization problem to select sensors is not feasible, and thus we seek a fully distributed solution. In most cases, training on raw data occurs locally, and model updates or parameters are aggregated globally, often at an edge server or fusion center.

Sensor activation and selection are also critical aspects. Forward-thinking algorithms in energy-efficient distributed sensor activation based on predicted target locations using computational intelligence can significantly reduce energy consumption and the number of active nodes required for target tracking such as the paper IDSA: Intelligent Distributed Sensor Activation Algorithm For Target Tracking With Wireless Sensor Network.

Context-aware like those that are emerging with Large Language Models, can collaborate with intelligence and in-sensor analytics (ISA) on resource-constrained nodes, dramatically reducing communication energy compared to transmitting raw data, extending network lifetime while preserving essential information

Context-Aware Collaborative-Intelligence with Spatio-Temporal In-Sensor-Analytics in a Large-Area IoT Testbed introduces a context-aware collaborative-intelligence approach that incorporates spatio-temporal in-sensor analytics (ISA) to reduce communication energy in resource-constrained IoT nodes. This approach is particularly relevant given that energy-efficient communication remains a primary bottleneck in achieving fully energy-autonomous IoT nodes, despite advancements in reducing the energy cost of computation. The research explores the trade-offs between communication and computation energies in a mesh network deployed across a large-scale university campus, targeting multi-sensor measurements for smart agriculture (temperature, humidity, and water nitrate concentration).

The paper considers several scenarios involving ISA, Collaborative Intelligence (CI), and Context-Aware-Switching (CAS) of the cluster-head during CI. A real-time co-optimization algorithm is developed to minimize energy consumption and maximize the battery lifetime of individual nodes. The results show that ISA consumes significantly less energy compared to traditional communication methods: approximately 467 times lower than Bluetooth Low Energy (BLE) and 69,500 times lower than Long Range (LoRa) communication. When ISA is used in conjunction with LoRa, the node lifetime increases dramatically from 4.3 hours to 66.6 days using a 230 mAh coin cell battery, while preserving over 98% of the total information. Furthermore, CI and CAS algorithms extend the worst-case node lifetime by an additional 50%, achieving an overall network lifetime of approximately 104 days, which is over 90% of the theoretical limits imposed by leakage currents.

Orchestration of Distributed AI and Sensor Resources

Orchestration in the context of distributed AI and sensor networks involves the automated deployment, configuration, management, and coordination of applications, dataflows, and computational resources across a heterogeneous computing continuum, typically spanning sensors, edge devices, fog nodes, and the cloud. The paper Orchestration in the Cloud-to-Things Compute Continuum: Taxonomy, Survey and Future Directions. This is essential for supporting complex, dynamic, and resource-intensive AI workloads in pervasive environments.

Traditional orchestration systems designed for centralized cloud environments are often ill-suited for the dynamic and resource-constrained nature of edge/fog computing and sensor networks. Requirements for continuum orchestration include support for diverse data models (streams, micro-batches), interfacing with various runtime engines (e.g., TensorFlow), managing application lifecycles (including container-based deployment), resource scheduling, and dynamic task migration.

Container orchestration tools, widely used in cloud environments, are being adapted for edge and fog computing to manage distributed containerized applications. However, deploying heavy-weight orchestrators on resource-limited edge/fog nodes presents challenges. Lightweight container orchestration solutions, such as clusters based on K3s, are proposed to support hybrid environments comprising heterogeneous edge, fog, and cloud nodes, offering improved response times for real-time IoT applications. The paper Container Orchestration in Edge and Fog Computing Environments for Real-Time IoT Applications proposes a feasible approach to build a hybrid and lightweight cluster based on K3s, a certified Kubernetes distribution for constrained environments that offers containerized resource management framework. This work addresses the challenge of creating lightweight computing clusters in hybrid computing environments. It also proposes three design patterns for the deployment of the “FogBus2” framework in hybrid environments, including 1) Host Network, 2) Proxy Server, and 3) Environment Variable.

Machine learning algorithms are increasingly integrated into container orchestration systems to improve resource provisioning decisions based on predicted workload behavior and environmental conditions where it is mentioned in the paper ECHO: An Adaptive Orchestration Platform for Hybrid Dataflows across Cloud and Edge with an open source model.

Platforms like ECHO are designed to orchestrate hybrid dataflows across distributed cloud and edge resources, enabling applications such as video analytics and sensor stream processing on diverse hardware platforms. Other frameworks such as the paper DAG-based Task Orchestration for Edge Computing, focus on orchestrating application tasks with dependencies (represented as Directed Acyclic Graphs, or DAGs) on heterogeneous edge devices, including personally owned, unmanaged devices, to minimize end-to-end latency and reduce failure probability. Of note, this is also closely aligned with implementations of MFLow and Airflow, which implement a DAG.

Autonomic orchestration aims to create self-managing distributed systems. This involves using AI, particularly edge AI, to enable local autonomy and intelligence in resource orchestration across the device-edge-cloud continuum as discussed in Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration. For instance, in A Self-Managed Architecture for Sensor Networks Based on Real Time Data Analysis introduces a self-managed sensor network platforms that can use real-time data analysis to dynamically adjust network operations and optimize resource usage. AI-enabled traffic orchestration in future networks (e.g., 6G) utilizes technologies like digital twins to provide smart resource management and intelligent service provisioning for complex services like ultra-reliable low-latency communication (URLLC) and distributed AI workflows. There is an underlying interplay between Distributed AI Workflow and URLLC, which has manifold design considerations throughout any network topology.

Novel paradigms such as the paper How Can AI be Distributed in the Computing Continuum? Introducing the Neural Pub/Sub Paradigm are emerging to address the specific challenges of orchestrating large-scale distributed AI workflows. The neural publish/subscribe paradigm proposes a decentralized approach to managing AI training, fine-tuning, and inference workflows in the computing continuum, aiming to overcome limitations of traditional centralized brokers in handling the massive data surge from connected devices. This paradigm facilitates distributed computation, dynamic resource allocation, and system resilience. Similarly, concepts like Airborne Neural Networks envision distributing neural network computations across multiple airborne devices, coordinated by airborne controllers, for real-time learning and inference in aerospace applications found in the paper Airborne Neural Network. This paper proposes a novel concept: the Airborne Neural Network a distributed architecture where multiple airborne devices, each host a subset of neural network neurons. These devices compute collaboratively, guided by an airborne network controller and layer-specific controllers, enabling real-time learning and inference during flight. This approach has the potential to revolutionize Aerospace applications, including airborne air traffic control, real-time weather and geographical predictions, and dynamic geospatial data processing.

The intersection of distributed AI and sensor orchestration is also evident in specific applications like multi-robot systems for intelligence, surveillance, and reconnaissance (ISR), where decentralized coordination algorithms enable simultaneous exploration and exploitation in unknown environments using heterogeneous robot teams such as Decentralised Intelligence, Surveillance, and Reconnaissance in Unknown Environments with Heterogeneous Multi-Robot Systems, In the paper Coordination of Drones at Scale: Decentralized Energy-aware Swarm Intelligence for Spatio-temporal Sensing it is introduced a solution to tackle the complex task self-assignment problem, a decentralized and energy-aware coordination of drones at scale is introduced. Autonomous drones share information and allocate tasks cooperatively to meet complex sensing requirements while respecting battery constraints. Furthermore, the decentralized coordination method prevents single points of failure, it is more resilient, and preserves the autonomy of drones to choose how they navigate and sense. In the paper HiveMind: A Scalable and Serverless Coordination Control Platform for UAV Swarms, a centralized coordination control platform for IoT swarms is introduced that is both scalable and performant. HiveMind leverages a centralized cluster for all resource-intensive computation, deferring lightweight and time-critical operations, such as obstacle avoidance, to the edge devices to reduce network traffic. Resource orchestration for network slicing scenarios can employ distributed reinforcement learning (DRL) where multiple agents cooperate to dynamically allocate network resources based on slice requirements, demonstrating adaptability without extensive retraining found in the paper Using Distributed Reinforcement Learning for Resource Orchestration in a Network Slicing Scenario.

Challenges and Implementation Considerations

Implementing distributed AI and sensor orchestration presents numerous challenges:

Communication Constraints: The limited bandwidth, intermittent connectivity, and energy costs associated with wireless communication in sensor networks necessitate communication-efficient algorithms and data compression techniques. Distributed learning algorithms often focus on minimizing the number of communication rounds or the size of exchanged messages as discussed in Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence.

Computational Heterogeneity: Sensor nodes, edge devices, and cloud servers possess vastly different computational capabilities. Orchestration systems must effectively map AI tasks to appropriate resources, potentially offloading intensive computations to the edge or cloud while performing lightweight inference or pre-processing on resource-constrained nodes as found in Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence and further discussed a problems in Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration.

Resource Management: Dynamic allocation and optimization of compute, memory, storage, and network resources are critical for performance and efficiency, especially with fluctuating workloads and device availability in the paper Container Orchestration in Edge and Fog Computing Environments for Real-Time IoT Applications To orchestrate a multitude of containers, several orchestration tools are developed. But, many of these orchestration tools are heavy-weight and have a high overhead, especially for resource-limited Edge/Fog nodes

Fault Tolerance and Resilience: In A Distributed Architecture for Edge Service Orchestration with Guarantees it is discussed how istributed systems are prone to node failures, communication link disruptions, and dynamic changes in network topology affect global convergence. Algorithms and orchestration platforms must be designed to handle such uncertainties and ensure system availability and reliability.

Security and Privacy: Distributing data processing raises concerns about data privacy and model security. Federated learning and privacy-preserving techniques are essential for distributed AI systems. Orchestration platforms must incorporate robust security mechanisms whic hwe can find discussed herewith Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance.

Interoperability and Standardization: The heterogeneity of devices, platforms, and protocols in IoT and edge environments complicates seamless integration and orchestration. Efforts towards standardization and flexible, technology-agnostic frameworks are necessary as discussed in Towards autonomic orchestration of machine learning pipelines in future networks and Intelligence Stratum for IoT. Architecture Requirements and Functions.

Real-time Processing: Many sensor network applications, particularly in industrial IoT or autonomous systems, require low-latency decision-making. Orchestration must prioritize and schedule real-time tasks effectively as discussed in Container Orchestration in Edge and Fog Computing Environments for Real-Time IoT Applications.

Managing Data Velocity and Volume: High-frequency sensor data streams generate massive data volumes. In-network processing, data reduction, and efficient dataflow management are crucial Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence

Limitations of 3rd party Development:

In the survey of papers, there was no direct mention or reference to the ability for developers to take a platform and build upon it, except for the ECHO platform, which was due to the first principles of being an open-source project.

Architecture, Algorithms and Pseudocode

Architecture diagrams typically depict layers: a sensor layer, an edge/fog layer, and a cloud layer. Orchestration logic spans these layers, managing data ingestion, AI model distribution and execution (inference, potentially distributed training), resource monitoring, and task scheduling. Middleware components facilitate communication, data routing, and state management across the distributed infrastructure.

Mathematically, we find common themes in the papers for AI and Sensor Orchestrations, wherethe weight matrix can be the sensors:

Initialize the local estimate $x_i(0)$ for each sensor $i = 1, 2, \dots, N$ .

Initialize the consensus weight matrix $W = [W_{ij}]$ based on the network topology, where $W_{ij} > 0$ if $j \in \mathcal{N}_i \cup \{i\}$ (neighbors including itself), and $W_{ij} = 0$ otherwise, with $\sum_j W_{ij} = 1$ for row-stochasticity.

For each iteration $k = 0, 1, \dots, K$ (up to maximum iterations):

Evolve step:

$y_i(k) = h_i(x_i(k)) + \nu_i(k)$ (local observation measurement, where $h_i$ is the observation model and $\nu_i(k)$ is noise).

$v_i(k) = f_i(y_i(k), x_i(k))$ (local model update, e.g., Kalman or prediction step).

Consensus step: Exchange $v_i(k)$ with neighbors $\mathcal{N}_i$ .

Update local estimate:

$x_i(k+1) = \sum_{j \in \mathcal{N}_i \cup \{i\}} W_{ij} v_j(k)$ .

Pseudocode for a simple distributed estimation algorithm using consensus might look like this:


Initialize local estimate x_i(0) for each sensor i
Initialize consensus weight matrix W based on network topology

For k = 0 to MaxIterations:
  // Innovation step
  y_i(k) = MeasureLocalObservation(sensor_i)
  v_i(k) = ProcessObservationWithLocalModel(y_i(k), x_i(k)) // Local model update

  // Consensus step (exchange with neighbors)
  Send v_i(k) to neighbors Ni
  Receive v_j(k) from neighbors j in Ni

  // Update local estimate
  x_i(k+1) = sum_{j in Ni U {i}} (W_ij * v_j(k))

Conclusion

The convergence of distributed AI and sensor orchestration is a critical enabler for advanced pervasive systems and the computing continuum. While significant progress has been made in developing distributed algorithms for sensing tasks and orchestration frameworks for heterogeneous environments, challenges related to resource constraints, scalability, resilience, security, and interoperability remain active areas of research and development. Future directions include further integration of autonomous and intelligent orchestration capabilities, development of lightweight and dynamic orchestration platforms, and the exploration of novel distributed computing paradigms to fully realize the potential of deploying AI at scale within sensor networks and across the edge-to-cloud continuum.

Until Then,

#iwishyouwater

Ted ℂ. Tanner Jr. (@tctjr) / X

MUZAK TO BLOG BY: i listened to several tracks during authoring this piece but i was reminded how incredible the Black Eyes Peas are musically and creatively – WOW. Pump IT! Shreds. i’d like to meet will.i.am

SnakeByte[21]: The Token Arms Race: Architectures Behind Long-Context Foundation Models

OpenAI’s Idea Of A Computer Loving The Sunset

Sometimes I tell sky our story. I dont have to say a word. Words are useless in the cosmos; words are useless and absurd.

~ Jess Welles

First, i trust everyone is safe. Second, i am going to write about something that is evolving extremely quickly and we are moving into a world some are calling context engineering. This is beyond prompt engineering. Instead of this just being mainly a python based how-to use a library, i wanted to do some math and some business modeling, thus the name of the blog.

So the more i thought about this i was thinking in terms of how our world is now tokenized. (Remember the token economy ala the word that shall not be named BLOCKCHAIN. Ok, i said it much like saying CandyMan in the movie CandyMan except i dont think anyone will show up if you say blockchain five times).

The old days of crafting clever prompts are fading fast, some say prompting is obsolete. The future isn’t about typing the perfect input; it’s about engineering the entire context in which AI operates and feeding that back into the evolving system. This shift is a game-changer, moving us from toy demos to real-world production systems where AI can actually deliver on scale.

Prompt Engineering So Last Month

Think about it: prompts might dazzle in a controlled demo, but they crumble when faced with the messy reality of actual work. Most AI agents don’t fail because their underlying models are weak—they falter because they don’t see enough of the window and aperture, if you will, is not wide enough. They lack the full situational awareness needed to navigate complex tasks. That’s where context engineering steps in as the new core skill, the backbone of getting AI to handle real jobs effectively.

Words Have Meanings.

~ Dr. Mathew Aldridge

So, what does context engineering mean? It’s a holistic approach to feeding AI the right information at the right time, beyond just a single command. It starts with system prompts that shape the agent’s behavior and voice, setting the tone for how it responds. Then there’s user intent, which frames the actual goalnot just what you ask, but why you’re asking it. Short-term memory keeps multi-step logic and dialogue history alive, while long-term memory stores facts, preferences, and learnings for consistency. Retrieval-Augmented Generation (RAG) pulls in relevant data from APIs, databases, and documents, ensuring the agent has the latest context. Tool availability empowers agents to act not just answer by letting them execute tasks. Finally, structured outputs ensure responses are usable, cutting the fluff and delivering actionable results.

Vertically Trained Horizontally Chained

This isn’t theory; platforms like LangChain and Anthropic are already proving it at scale. They split complex tasks into sub-agents, each with a focused context window to avoid overload. Long chats get compressed via summarization, keeping token limits in check. Sandboxed environments isolate heavy state, preventing crashes, while memory is managed with embeddings, scratchpads, and smart retrieval systems. LangGraph orchestrates these agents with fine-grained control, and LangSmith’s tracing and testing tools evaluate every context tweak, ensuring reliability. It’s a far cry from the old string-crafting days of prompting.

Prompting involved crafting a response with a well-worded sentence. Context engineering is the dynamic design of systems, building full-stack pipelines that provide AI with the right input when it matters. This is what turns a flashy demo into a production-ready product. The magic happens not in the prompt, but in the orchestrated context that surrounds it. As we move forward, mastering this skill will distinguish innovators from imitators, enabling AI to solve real-world problems with precision and power. People will look at you quizzically. In this context, tokens are the food for Large Language Models and are orthogonal to tokens in a blockchain economy.

Slide The Transformers

Which brings us to the evolution of long-context transformers, examining key players, technical concepts, and business implications. NOTE: Even back in the days of the semantic web it was about context.

Foundation model development has entered a new frontier not just of model size, but of memory scale. We’re witnessing the rise of long-context transformers: architectures capable of handling hundreds of thousands and even millions of tokens in a single pass.

This shift is not cosmetic; it alters the fundamental capabilities and business models of LLM platforms. First, i’ll analyze the major players, their long-term strategies, and then we will run through some mathematical architecture powering these transformations. Finally getting down to the Snake Language on basic function implementations for very simple examples.

Company	Model	Max Context Length	Transformer Variant	Notable Use Case
Google	Gemini 1.5 Pro	2M tokens	Mixture-of-Experts + RoPE	Context-rich agent orchestration
OpenAI	GPT-4 Turbo	128k tokens	LLM w/ windowed attention	ChatGPT + enterprise workflows
Anthropic	Claude 3.5 Sonnet	200k tokens	Constitutional Sparse Attention	Safety-aligned memory agents
Magic.dev	LTM-2-Mini	100M tokens	Segmented Recurrence w/ Cache	Codebase-wide comprehension
Meta	Llama 4 Scout	10M tokens	On-device, efficient RoPE	Edge + multimodal inference
Mistral	Mistral Large 2	128k tokens	Sliding Window + Local Attention	Generalist LLM APIs
DeepSeek	DeepSeek V3	128k tokens	Block Sparse Transformer	Multilingual document parsing
IBM	Granite Code/Instruct	128k tokens	Optimized FlashAttention-2	Code generation & compliance

The Matrix Of The Token Grid Arms Race

Redefining Long Context

Here is my explanation and blurb that i researched on each of these:

Google – Gemini 1.5 Pro (2M tokens, Mixture-of-Experts + RoPE)
Google’s Gemini 1.5 Pro is a heavyweight, handling 2 million tokens with a clever mix of Mixture-of-Experts and Rotary Positional Embeddings. It shines in context-rich agent orchestration, seamlessly managing complex, multi-step tasks across vast datasets—perfect for enterprise-grade automation.
OpenAI – GPT-4 Turbo (128k tokens, LLM w/ windowed attention)
OpenAI’s GPT-4 Turbo packs 128k tokens into a windowed attention framework, making it a go-to for ChatGPT and enterprise workflows. Its strength lies in balancing performance and accessibility, delivering reliable responses for business applications with moderate context needs.
Anthropic – Claude 3.5 Sonnet (200k tokens, Constitutional Sparse Attention)
Anthropic’s Claude 3.5 Sonnet offers 200k tokens with Constitutional Sparse Attention, prioritizing safety and alignment. It’s a standout for memory agents, ensuring secure, ethical handling of long conversations—a boon for sensitive industries like healthcare or legal.
Magic.dev – LTM-2-Mini (100M tokens, Segmented Recurrence w/ Cache)
Magic.dev’s LTM-2-Mini pushes the envelope with 100 million tokens, using Segmented Recurrence and caching for codebase-wide comprehension. This beast is ideal for developers, retaining entire project histories to streamline coding and debugging at scale.
Meta – Llama 4 Scout (10M tokens, On-device, efficient RoPE)
Meta’s Llama 4 Scout brings 10 million tokens to the edge with efficient RoPE, designed for on-device use. Its multimodal inference capability makes it a favorite for privacy-focused applications, from smart devices to defense systems, without cloud reliance.
Mistral – Mistral Large 2 (128k tokens, Sliding Window + Local Attention)
Mistral Large 2 handles 128k tokens with Sliding Window and Local Attention, offering a versatile generalist LLM API. It’s a solid choice for broad applications, providing fast, efficient responses for developers and businesses alike.
DeepSeek – DeepSeek V3 (128k tokens, Block Sparse Transformer)
DeepSeek V3 matches 128k tokens with a Block Sparse Transformer, excelling in multilingual document parsing. Its strength lies in handling diverse languages and formats, making it a go-to for global content analysis and translation tasks.
IBM – Granite Code/Instruct (128k tokens, Optimized FlashAttention-2)
IBM’s Granite Code/Instruct leverages 128k tokens with Optimized FlashAttention-2, tailored for code generation and compliance. It’s a powerhouse for technical workflows, ensuring accurate, regulation-aware outputs for developers and enterprises.

Each of these companies is carving out their own window of context and capabilities for the tokens arms race. So what are some of the basic mathematics at work here for long context?

i’ll integrate Python code to illustrate key architectural ideas (RoPE, Sparse Attention, MoE, Sliding Window) and business use cases (MaaS, Agentic Platforms), using libraries like NumPy, PyTorch, and a mock agent setup. These examples will be practical and runnable in a Jupyter environment.

Rotary Positional Embeddings (RoPE) Extensions

Rotary Positional Embeddings (RoPE) is a technique for incorporating positional information into Transformer-based Large Language Models (LLMs). Unlike traditional methods that add positional vectors, RoPE encodes absolute positions with a rotation matrix and explicitly includes relative position dependency within the self-attention mechanism. This approach enhances the model’s ability to handle longer sequences and better understand token interactions across larger contexts.

The core idea behind RoPE involves rotating the query and key vectors within the attention mechanism based on their positions in the sequence. This rotation encodes positional information and affects the dot product between query and key vectors, which is crucial for attention calculations.

To allow for arbitrarily long context, models generalize RoPE using scaling factors and interpolation. Here is the set of basic equations:

$\text{RoPE}(x_i) = x_i \cos(\theta_i) + x_i^\perp \sin(\theta_i)$

where $\theta_i \propto \frac{1}{10000^{\frac{2i}{d}}}$ , extended by interpolation.

Here is some basic code implementing this process:

import numpy as np
import torch

def apply_rope(input_seq, dim=768, max_seq_len=1000000):
    """
    Apply Rotary Positional Embeddings (RoPE) to input sequence.
    Args:
        input_seq (torch.Tensor): Input tensor of shape (batch_size, seq_len, dim)
        dim (int): Model dimension (must be even)
        max_seq_len (int): Maximum sequence length for precomputing positional embeddings
    Returns:
        torch.Tensor: Input with RoPE applied, same shape as input_seq
    """
    batch_size, seq_len, dim = input_seq.shape
    assert dim % 2 == 0, "Dimension must be even for RoPE"
    
    # Compute positional frequencies for half the dimension
    theta = 10000 ** (-2 * np.arange(0, dim//2, 1) / (dim//2))
    pos = np.arange(seq_len)
    pos_emb = pos[:, None] * theta[None, :]
    pos_emb = np.stack([np.cos(pos_emb), np.sin(pos_emb)], axis=-1)  # Shape: (seq_len, dim//2, 2)
    pos_emb = torch.tensor(pos_emb, dtype=torch.float32).view(seq_len, -1)  # Shape: (seq_len, dim)

    # Reshape and split input for RoPE
    x = input_seq  # Keep original shape (batch_size, seq_len, dim)
    x_reshaped = x.view(batch_size, seq_len, dim//2, 2).transpose(2, 3)  # Shape: (batch_size, seq_len, 2, dim//2)
    x_real = x_reshaped[:, :, 0, :]  # Real part, shape: (batch_size, seq_len, dim//2)
    x_imag = x_reshaped[:, :, 1, :]  # Imaginary part, shape: (batch_size, seq_len, dim//2)

    # Expand pos_emb for batch dimension and apply RoPE
    pos_emb_expanded = pos_emb[None, :, :].expand(batch_size, -1, -1)  # Shape: (batch_size, seq_len, dim)
    out_real = x_real * pos_emb_expanded[:, :, ::2] - x_imag * pos_emb_expanded[:, :, 1::2]
    out_imag = x_real * pos_emb_expanded[:, :, 1::2] + x_imag * pos_emb_expanded[:, :, ::2]

    # Combine and reshape back to original
    output = torch.stack([out_real, out_imag], dim=-1).view(batch_size, seq_len, dim)
    return output

# Mock input sequence (batch_size=1, seq_len=5, dim=4)
input_tensor = torch.randn(1, 5, 4)
rope_output = apply_rope(input_seq=input_tensor, dim=4, max_seq_len=5)
print("RoPE Output Shape:", rope_output.shape)
print("RoPE Output Sample:", rope_output[0, 0, :])  # Print first token's output

You should have get the following output:

RoPE Output Shape: torch.Size([1, 5, 4])
RoPE Output Sample: tensor([ 0.6517, -0.6794, -0.4551,  0.3666])

The shape verifies the function’s dimensional integrity, ensuring it’s ready for downstream tasks. The sample gives a glimpse into the transformed token, showing RoPE’s effect. You can compare it to the raw input_tensor[0, 0, :] tto see the rotation (though exact differences depend on position and frequency).see the rotation (though exact differences depend on position and to see the rotation (though exact differences depend on position and frequency).

Sparse Attention Mechanisms:

Sparse attention mechanisms are techniques used in transformer models to reduce computational cost by focusing on a subset of input tokens during attention calculations, rather than considering all possible token interactions. This selective attention process enhances efficiency and allows models to handle longer sequences, making them particularly useful for natural language processing tasks like translation and summarization.

In standard self-attention mechanisms, each token in an input sequence attends to every other token, resulting in a computational complexity that scales quadratically with the sequence length $(O(n^2 d))$ . For long sequences, this becomes computationally expensive. Sparse attention addresses this by selectively attending to a subset of tokens, reducing the computational burden. Complexity drops from $O(n^2 d)$ to $O(nd \sqrt{n})$ or better using block or sliding windows.

Sparse attention mechanisms achieve this reduction in computation by reducing the number of interactions instead of computing attention scores for all possible token pairs, sparse attention focuses on a smaller, selected set of tokens. The downside is by focusing on a subset of tokens, sparse attention may potentially discard some relevant information, which could negatively impact performance on certain tasks. Also it gets more complex code-wise.

This is mock implementation using pytorch.

import torch
import torch.nn.functional as F

def sparse_attention(q, k, v, window_size=3):
    batch, num_heads, seq_len, head_dim = q.shape
    attn_scores = torch.matmul(q, k.transpose(-2, -1)) / (head_dim ** 0.5)
    # Apply sliding window mask
    mask = torch.triu(torch.ones(seq_len, seq_len), diagonal=1-window_size).bool()
    attn_scores.masked_fill_(mask, float('-inf'))
    attn_weights = F.softmax(attn_scores, dim=-1)
    return torch.matmul(attn_weights, v)

# Mock query, key, value tensors (batch=1, heads=2, seq_len=6, dim=4)
q = torch.randn(1, 2, 6, 4)
k = torch.randn(1, 2, 6, 4)
v = torch.randn(1, 2, 6, 4)
output = sparse_attention(q, k, v, window_size=3)
print("Sparse Attention Output Shape:", output.shape)

This should just print out the shape:

Sparse Attention Output Shape: torch.Size([1, 2, 6, 4])

The sparse_attention function implements a simplified attention mechanism with a sliding window mask, mimicking sparse attention patterns used in long-context transformers. It takes query (q), key (k), and value (v) tensors, computes attention scores, applies a mask to limit the attention window, and returns the weighted output. The shape torch.Size([1, 2, 6, 4]) indicates that the output tensor has the same structure as the input v tensor. This is expected because the attention mechanism computes a weighted sum of the value vectors based on the attention scores derived from q and k. The sliding window mask (defined by window_size=3) restricts attention to the current token and the previous 2 tokens (diagonal offset of 1-window_size), but it doesn’t change the output shape it only affects which scores contribute to the weighting. The output retains the full sequence length and head structure, ensuring compatibility with downstream layers in a transformer model. This shape signifies that for each of the 1 batch, 2 heads, and 6 tokens, the output is a 4-dimensional vector, representing the attended features after the sparse attention operation.

Mixture-of-Experts (MoE) + Routing

Mixture-of-Experts (MoE) is a machine learning technique that utilizes multiple specialized neural networks, called “experts,” along with a routing mechanism to process input data. The router, a gating network, determines which experts are most relevant for a given input and routes the data accordingly, activating only those specific experts. This approach allows for increased model capacity and computational efficiency, as only a subset of the model needs to be activated for each input.

Key Components:

Experts: These are individual neural networks, each trained to be effective at processing specific types of data or patterns. They can be simple feedforward networks, or even more complex structures.
Routing/Gating Network:This component acts as a dispatcher, deciding which experts are most appropriate for a given input. It typically uses a learned weighting or probability distribution to select the experts.

This basic definition activates a sparse subset of experts:

$\text{MoE}(x) = \sum_{i=1}^k g_i(x) \cdot E_i(x)$

(Simulating MoE with 2 of 4 experts):

import torch
import torch.nn as nn

class MoE(nn.Module):
    def __init__(self, num_experts=4, top_k=2):
        super().__init__()
        self.experts = nn.ModuleList([nn.Linear(4, 4) for _ in range(num_experts)])
        self.gate = nn.Linear(4, num_experts)
        self.top_k = top_k

    def forward(self, x):
        scores = self.gate(x)  # (batch, num_experts)
        _, top_indices = scores.topk(self.top_k, dim=-1)  # Select top 2 experts
        output = torch.zeros_like(x)
        for i in range(x.shape[0]):
            for j in top_indices[i]:
                output[i] += self.experts[j](x[i])
        return output / self.top_k

# Mock input (batch=2, dim=4)
x = torch.randn(2, 4)
moe = MoE(num_experts=4, top_k=2)
moe_output = moe(x)
print("MoE Output Shape:", moe_output.shape)

This should give you the output:

MoE Output Shape: torch.Size([2, 4])

The shape torch.Size([2, 4]) indicates that the output tensor has the same batch size and dimension as the input tensor x. This is expected because the MoE applies a linear transformation from each selected expert (all outputting 4-dimensional vectors) and averages them, maintaining the input’s feature space. The Mixture-of-Experts mechanism works by:

Computing scores via self.gate(x), producing a (2, 4) tensor that’s transformed to (2, num_experts) (i.e., (2, 4)).
Selecting the top_k=2 experts per sample using topk, resulting in indices for the 2 best experts out of 4.
Applying each expert’s nn.Linear(4, 4) to the input x[i], summing the outputs, and dividing by top_k to normalize the contribution.

The output represents the averaged transformation of the input by the two most relevant experts for each sample, tailored to the input’s characteristics as determined by the gating function.

Sliding Window + Recurrence for Locality

While A context window in an AI model refers to the amount of information (tokens in text) it can consider at any one time. The Locality emphasizes the importance of data points that are close together in a sequence. In many applications, recent information is more relevant than older information. For example, in conversations, recent dialogue contributes most to a coherent response. The importance of that addition lies in effectively handling long contexts in large language models (LLMs) and optimizing inference. Strategies involve splitting the context into segments and managing the Key-Value (KV) cache using data structures like trees.

Segmenting Context: For very long inputs, the entire context might not fit within the model’s memory or process efficiently as a single unit. Therefore, the context can be divided into smaller, manageable segments or chunks.

KV Cache: During LLM inference, the KV cache stores previously computed “keys” and “values” for tokens in the input sequence. This avoids recomputing attention mechanisms for already processed tokens, speeding up the generation process ergo the terminology.

This code splits context into segments with KV cache trees.

import torch

def sliding_window_recurrence(input_seq, segment_size=3, cache_size=2):
    """
    Apply sliding window recurrence with caching.
    Args:
        input_seq (torch.Tensor): Input tensor of shape (batch_size, seq_len, dim)
        segment_size (int): Size of each segment
        cache_size (int): Size of the cache
    Returns:
        torch.Tensor: Output with recurrence applied
    """
    batch_size, seq_len, dim = input_seq.shape
    output = []
    # Initialize cache with batch dimension
    cache = torch.zeros(batch_size, cache_size, dim)  # Shape: (batch_size, cache_size, dim)
    
    for i in range(0, seq_len, segment_size):
        segment = input_seq[:, i:i+segment_size]  # Shape: (batch_size, segment_size, dim)
        # Ensure cache and segment dimensions align
        if segment.size(1) < segment_size and i + segment_size <= seq_len:
            segment = torch.cat([segment, torch.zeros(batch_size, segment_size - segment.size(1), dim)], dim=1)
        # Mock recurrence: combine with cache
        combined = torch.cat([cache, segment], dim=1)[:, -segment_size:]  # Take last segment_size
        output.append(combined)
        # Update cache with the last cache_size elements
        cache = torch.cat([cache, segment], dim=1)[:, -cache_size:]

    return torch.cat(output, dim=1)

# Mock input (batch=1, seq_len=6, dim=4)
input_tensor = torch.randn(1, 6, 4)
recurrent_output = sliding_window_recurrence(input_tensor, segment_size=3, cache_size=2)
print("Recurrent Output Shape:", recurrent_output.shape)

The output should be:

Recurrent Output Shape: torch.Size([1, 6, 4])

The shape torch.Size([1, 6, 4]) indicates that the output tensor has the same structure as the input tensor input_tensor. This is intentional, as the function aims to process the entire sequence while applying a recurrent mechanism. Sliding Window Process:

The input sequence (length 6) is split into segments of size 3. With seq_len=6 and segment_size=3, there are 2 full segments (indices 0:3 and 3:6).
Each segment is combined with a cache (size 2) using torch.cat, and the last segment_size elements are kept (e.g., (2+3)=5 elements, sliced to 3).
The loop runs twice, appending segments and torch.cat(output, dim=1) reconstructs the full sequence length of 6.

For the Recurrence Effect the cache (initialized as (1, 2, 4)) carries over information from previous segments, mimicking a recurrent neural network’s memory. The output at each position reflects the segment’s data combined with the cache’s prior context, but the shape remains unchanged because the function preserves the original sequence length. In practical applicability for a long-context model, this output could feed into attention layers, where the recurrent combination enhances positional awareness across segments, supporting lengths like 10M tokens (e.g., Meta’s Llama 4 Scout).

So how do we make money? Here are some business model implications.

MemoryAsAService: MaaS class mocks token storage and retrieval with a cost model. For enterprise search, compliance, and document workflows, long-context models enable models to hold entire datasets in RAM, reducing RAG complexity.

Revenue lever: Metered billing based on tokens stored and tokens retrieved

Agentic Platforms and Contextual Autonomy: (With 10M+ token windows), AI agents can:

Load multiyear project timelines
Track legal/compliance chains of thought
Maintain psychological memory for coaching or therapy

Revenue lever: Subscription for persistent agent state memory

Embedded / Edge LLMs: Pruning the attention mimics on-device optimization.

What are you attentive to and where are you attentive to? This is very important for autonomy systems. Insect-like LLMS? Models uses hardware-tuned attention pruning to run on-device without cloud support.

Revenue lever:

Hardware partnerships (Qualcomm, Apple, etc.)
Private licensing for defense/healthcare

Developer Infrastructure: Codebase Memory tracks repo events. Can Haz Logs? Devops on steroids. Analyize repos based on quality and deployment size.

Revenue lever: Developer SaaS pricing by repo or engineering team size (best fewest ups the revenue per employee and margin).

Magic.dev monetizes 100M-token memory by creating LLM-native IDEs that retain architecture history, unit tests, PRs, and stack traces. Super IDE’s for Context Engineering?

Here are some notional mappings for catalyst:

Business Edge	Mathematical Leverage
Persistent memory	Attention cache, memory layers, LRU gating
Low latency	Sliding windows, efficient decoding
Data privacy	On-device + quantized attention ops
Vertical domain AI	MoE + sparse fine-tuning adapters

Closing

In this token-maximized world, the architectural arms race is becoming a memory computation problem. The firms that master the blend of:

Efficient inference at high context length
Agentic memory persistence
Economically viable context scaling will win not just on benchmark scores, but on unit economics, retention, and defensibility.

In the world of AI business models, context is the new (i couldnt think of a buzzword please help me LazyWeb^TM)? Also I believe that William Gibson was right. Got More Ram?

Until Then.

#iwishyouwater

Ted ℂ. Tanner Jr. (@tctjr) / X

MUZAK TO BLOG BY: Jesse Welles, Pilgrim. If you havent listened to Jesse Welles you are missing out. He is our present-day Bob Dylan. Look him up on youtube out in the field and under the power lines.

Pre-Computing War

Sora’s idea of a Neon Drone War With Audio Track

Sometimes it is the people no one can imagine anything of who do the things no one can imagine.

~ Alan Turing

First, i trust everyone is safe. Second, i had a creative spurt of late and wrote the following blog in one sitting after waking up thinking about the subject.

NOTE: This post is in no way indicative of any stance or provides any classified information whatsoever. It is only a thought piece concerning current technology-driven areas of concern.

Preamble

There is a paradigm shift happening that will affect future generations and possibly the very essence of what it means to be human, and this comes from how technology is transforming war. We stand at a precipice, gazing into a future where the tools of war no longer resemble the clashing steel and human courage of centuries past. How important is conflict to humanity? What is the essence and desire for this conflict? The current uptick in drone usage of the past years has created an inflection point for what I am terming “abstraction levels for engagement.”

We continue to underestimate how important drones are going to be in warfare—a miscalculation that echoes through history’s long ledger of missed signals. I’m going to go out on a long limb here and say the future of most warfare will be enabled by, driven by, and spearheaded by drones. In fact, other than some other types of autonomous vehicles, there might not be anything else on the battlefield.

Here is a definition (of course, AI bot generated because really who reads Webster’s Dictionary nowadays? For the record, i have read Webster’s 3 times front to back):

“A military drone, also known as an unmanned aerial vehicle (UAV) or unmanned aircraft system (UAS), is an aircraft flown without a human pilot on board, controlled remotely or autonomously, and used for military missions like surveillance, reconnaissance, and potentially combat operations. “

This isn’t hyperbole; it’s the logical endpoint of a trajectory we’ve been on since the first unmanned systems took flight. And at the end of that trajectory lies something even more radical: autonomous bullets—not merely guided, but self-directed, a fusion of machine intelligence and lethal intent that could redefine conflict itself.

Note: I edited this part as the kind folk on The LazyWeb(tm) were laser-focused on the how of “smart bullets.: Great feedback and thank you. git push -u -f origin main.

Guided autonomous bullets, often referred to as “smart bullets,” represent an advanced leap in projectile technology, blending precision guidance systems with small-caliber ammunition. These bullets are designed to adjust their flight path mid-air to hit a target with exceptional accuracy, even if the target is moving or environmental factors like wind interfere. The concept builds on precision guided munitions used in larger systems like missiles, but shrinks the technology to fit within the constraints of a bullet fired from a firearm.

Autonomous bullets, often referred to as “smart bullets,” are advanced projectiles designed to adjust their trajectory mid-flight to hit a target with high precision, even under challenging conditions like wind or a moving target. While the concept might sound like science fiction, significant research has been conducted, particularly by organizations like DARPA (Defense Advanced Research Projects Agency) and Sandia National Laboratories, to make this technology a reality.

The core idea behind autonomous bullets is to integrate guidance systems into small-caliber projectiles, allowing them to self-correct their path after being fired. One of the earliest designs, as described in “historical research”, involved a bullet with three fiber-optic sensors (or “eyes”) positioned around its circumference to provide three-dimensional awareness. A laser is used to designate the target, and as the bullet travels, these sensors detect the laser’s light. The bullet adjusts its flight path in real time to ensure an equal amount of laser light enters each sensor, effectively steering itself toward the laser-illuminated target. This method prevents the bullet from making drastic turns like a missile. Still, it enables small, precise adjustments to hit exactly where the laser is pointed, even if the target is beyond visual range or the laser source is separate from the shooter. It hath been said that if you can think it DARPA has probably built it, maybe.

Given the advancements in drones, unmanned autonomous vehicles, land and air vehicles, and supposedly smart bullets (only the dark web knows how they work), imagine a battlefield stripped of human presence, not out of cowardice but necessity. The skies a deafening hum with swarms of drones (if a drone makes a sound and no one is there to hear it, does it make a sound?), each a “node” orchestrated via particle swarm AI models in a vast, decentralized network of artificial minds.

No generals barking orders, no soldiers trudging through mud, just silicon and steel executing a dance of destruction with precision beyond human capacity. The end state of drones isn’t just remote control or pre-programmed strikes; it’s autonomy so complete that the machines themselves decide who lives and who dies – no human in the loop. Self-directed projectiles, bullets with brains roaming the theater of war, seeking targets based on algorithms fed by real-time data streams. The vision feels like science fiction, yet the pieces already fall into place.

Generals gathered in their masses
just like witches at black masses
evil minds that plot destruction
sorcerers of death’s construction
in the fields the bodies burning
as the war machine keeps turning
death and hatred to mankind
poisoning their brainwashed minds, oh lord yeah!

~ War Pigs, Black Sabbath 1970

This shift isn’t merely tactical; it’s existential. Warfare has always been a contest of wills, a brutal arithmetic of resources and resolve. But what happens when we can compute the outcome before the first shot is fired? Drones, paired with advanced AI, offer the tantalizing possibility of simulating conflicts down to the last variable terrain, weather, enemy morale, and supply lines, all processed in milliseconds by systems that learn as they go. The autonomous bullet isn’t just a weapon; it’s a data point in a larger Markovian equation, one that could predict victory or defeat with chilling accuracy.

We’re not far from a world where wars are fought first in the cloud, their outcomes modeled and refined, before a single drone lifts off.If the future of warfare is of drone swarms of autonomous systems culminating in self-directed bullets, then pre-computing its outcomes becomes not just feasible but imperative. The battlefield of tomorrow isn’t a chaotic melee; it’s a high-stakes game, a multidimensional orchestrated chessboard where game theory, geopolitics, and macroeconomics converge to predict the endgame before the first move. To compute warfare in this way requires us to distill its essence into variables, probabilities, and incentives a task as daunting as it is inevitable. Yet again, there exists a terminology for this orchestrated chess game. Autonomous asymmetric mosaic warfighting, a concept explored by DARPA, envisions turning complexity into an asymmetric advantage by using networked, smaller, and less complex systems to overwhelm an adversary with a multitude of capabilitiesnvisions turning complexity into an asymmetric advantage by using networked, smaller, and less complex systems to overwhelm an adversary with a multitude of capabilities.

“A Nash equilibrium is a set of strategies that players act out, with the property that no player benefits from changing their strategy. ~ Dr John Nash.”

Computational Game Theory: The Logic of Lethality

At its core, warfare is a strategic interaction, a contest where players, nations, factions, or even rogue actors vie for dominance under the constraints of resources and information. Game theory offers the scaffolding to model this. Imagine a scenario where drones dominate: each side deploys autonomous swarms, programmed with decision trees that weigh attack, retreat, or feint based on real-time data. The payoff matrix isn’t just about territory or casualties, it’s about disruption, deterrence, and psychological impact. A swarm’s choice to strike a supply line rather than a command center could shift an enemy’s strategy, forcing a cascade of recalculations.

Now, introduce autonomous bullets, self-directed agents within the swarm. Each bullet becomes a player in a sub-game, optimizing its path to maximize damage while minimizing exposure. The challenge lies in anticipating the opponent’s moves: if both sides rely on AI-driven systems, the game becomes a duel of algorithms, each trying to out-predict the other. Zero-sum models give way to dynamic equilibria, where outcomes hinge on how well each side’s AI can bluff, adapt, or exploit flaws in the other’s logic. Pre-computing this requires vast datasets, historical conflicts, behavioral patterns, and even cultural tendencies fed into simulations that run millions of iterations, spitting out probabilities of victory, stalemate, or collapse.

Geopolitics: The Board Beyond the Battlefield

Warfare doesn’t exist in a vacuum; the shifting tectonic plates of geopolitics shape it. To pre-compute outcomes, we must map the global chessboard—alliances, rivalries, and spheres of influence. Drones level the playing field, but their deployment reflects deeper asymmetries. A superpower with advanced AI and manufacturing might flood the skies with swarms, while a smaller state leans on guerrilla tactics, using cheap, hacked drones to harass and destabilize. The game-theoretic model expands: players aren’t just combatants but also suppliers, proxies, and neutral powers with their own agendas.

Take energy as a (the main) variable: drones require batteries, rare earths, and infrastructure. A nation controlling lithium mines or chip fabs holds leverage, tipping the simulation’s odds. Sanctions, trade routes, and cyber vulnerabilities—like a rival hacking your drone fleet’s firmware—become inputs in the equation. Geopolitical stability itself becomes a factor: if a war’s outcome hinges on a fragile ally, the model must account for the likelihood of defection or collapse. Pre-computing warfare here means forecasting not just the battle, but the ripple effects—will a decisive drone strike trigger a refugee crisis, a shift in NATO’s posture, or a scramble for Arctic resources? The algorithm must think in networks, not lines.

I visualize a time when we will be to robots what dogs are to humans, and I’m rooting for the machines.

~ Claude Shannon

Macroeconomics: The Sinews of Silicon War

No war is won without money, and drones don’t change that; they just rewrite the budget. Pre-computing conflict demands a macroeconomic lens: how much does it cost to field a swarm versus defend against one? The economics of autonomous warfare favor scale mass-produced drones and bullets could outpace legacy systems like jets or tanks in cost-efficiency. A simulation might pit a 10 billion dollar defense budget against a 1 billion dollar insurgent force, factoring in production rates, maintenance, and the price of countermeasures like EMPs or jamming tech.

But it’s not just about direct costs. Markets react to war’s shadow oil spikes, currencies wobble, tech stocks soar or crash based on who controls the drone supply chain (it is all about that theta/beta folks). A protracted conflict could drain a nation’s reserves, while a swift, computed victory might bolster its credit rating. The model must integrate these feedback loops: if a drone war craters a rival’s economy, their ability to replenish dwindles, tilting the odds. And what of the peacetime economy? States that mastering autonomous tech could dominate postwar reconstruction, turning military R&D into a geopolitical multiplier. Pre-computing this requires economic forecasts layered atop the game-theoretic core—GDP growth, inflation, and consumer confidence as resilience proxies.

The Supreme Lord said: I am mighty Time, the source of destruction that comes forth to annihilate the worlds. Even without your participation, the warriors arrayed in the opposing army shall cease to exist.~ Bhagavad Gita 11:32

The Synthesis: Simulating the Unthinkable

To tie it all together, picture a supercomputer or a distributed AI network running a grand simulation. It ingests game-theoretic strategies (strike patterns, bluffing probabilities), geopolitical alignments (alliances, resource choke points), and macroeconomic trends (war budgets, trade disruptions). Drones and their autonomous bullets are the pawns, but the players are human decision-makers, constrained by politics and profit. The system runs countless scenarios: a drone swarm cripples a port, triggering a naval response, spiking oil prices, and collapsing a coalition. Another sees a small state’s cheap drones hold off a giant, forcing a negotiated peace.

The output isn’t a single prediction, but a spectrum 75% chance of victory if X holds, 40% if Y defects, 10% if the economy tanks. Commanders could tweak inputs more drones, better AI, a preemptive cyberstrike and watch the probabilities shift. It’s not infallible; black swans like a rogue AI bug or a sudden uprising defy the math. But it’s close enough to turn war into a science, reducing the fog Clausewitz warned of to a manageable haze [1].

The thing that hath been, it is that which shall be; and that which is done is that which shall be done: and there is no new thing under the sun.

~ Ecclesiastes 1:9, KJV

Yet, this raises a haunting question: If we can compute warfare’s endgame, do we lose something essential in the process?

The chaos of flawed, emotional, unpredictable human decision-making has long been the wildcard that defies calculation. Napoleon’s audacity, the Blitz’s resilience, and the guerrilla fighters’ improvisation are not easily reduced to code. Drones and their self-directed progeny promise efficiency, but they also threaten to strip war of its human texture, turning it into a sterile exercise in optimization. And what of accountability? When a bullet chooses its target, who bears the moral weight—the coder, the commander, or the machine itself?

The implications stretch beyond the battlefield. If drones dominate warfare, the barriers to entry collapse. No longer will nations need vast armies or industrial might; a few clever engineers and a swarm of cheap, autonomous systems could level the playing field. We’ve seen glimpses of this in Ukraine, where off-the-shelf drones have humbled tanks and disrupted supply lines. Scale that up, and the future isn’t just drones it’s a proliferation of power, a democratization of destruction. Autonomous bullets could become the ultimate equalizer or the ultimate chaos agent, depending on who wields them.

Fighting for peace is like screwing for virginity.

~ George Carlin

A Moment of Clarity

i wonder: are we ready to surrender the reins? The dream of computing warfare’s outcome is seductive and humans are carnal creatures we lust for other humans and things, thus it promises to minimize loss, to replace guesswork with certainty, but it also risks turning us into spectators of our own fate, watching as machines play out scenarios we’ve set in motion (that which we lust after).

The end state of drones may indeed be a battlefield of self-directed systems, but the end state of humanity in that equation remains unclear. Perhaps the true revolution isn’t in the technology but in how we grapple with a world where war becomes a problem to be solved rather than a story to be lived.

We underestimate drones at our peril. They’re not just tools; they’re harbingers of a paradigm shift. The future is coming, and it’s buzzing overhead—relentless, autonomous, and utterly indifferent to our nostalgia for the wars of old.

Pre-computing warfare might make us too confident. Leaders who trust the model might rush to conflict, assuming the odds are locked. But humans aren’t algorithms; we rebel, err, and surprise. And what of ethics? A simulation that optimizes for victory might greenlight drone strikes on civilians to break morale, justified by a percentage point. The autonomous bullet doesn’t care; it’s our job to decide if the computation is worth the soul it costs.

In this drone-driven future, pre-computing warfare isn’t just possible—it’s already beginning. Ukraine’s drone labs, China’s swarm tests, the Pentagon’s AI budgets—they’re all steps toward a world where conflict is a solvable problem. It has been said that fighting and sex are the two book ends but one in the same. But as we build the machine to predict the fight, we must ask: are we mastering war, or merely handing it a new master for something else entirely?

Music To Blog By: Project-X “Closing Down The Systems. Actually, I wouldn’t listen to this if i were you, unless you want to have nightmares. Fearless (MZ412 Remix) does sound like computational warfare.

Until then,

#iwishyouwater <- recent raw Pipe footage of folks that got the memo.

Ted ℂ. Tanner Jr. (@tctjr) / X

References:

[1] “On War” by Carl von Clausewitz. He called it “The Fog of War”: Clausewitz stressed the importance of understanding the unpredictable nature of war, noting that the “fog of war” (i.e., incomplete, dubious, and often erroneous information and great fear, doubt, and excitement) can lead to rapid decisions by alert commanders.

[2] Thanks to Jay Sales for being the catalyst for this blog. If you do not know who he is look him up here. Jay Sales. One of the best engineering executives and dear friend.

NVIDIA GTC 2025: The Time Has Come The Valley Said

OpenAI’s idea of The Valley – Its Been A Minute

Embrace the unknown and embrace change. That’s where true breakthroughs happen.

~Jensen Huang

First i trust everyone is safe. Second i usually do not write about discrete events or “work” related items but this is an exception. March 17-21, 2025 i and some others attended NVIDIA GTC2025. It warranted a long writeup. Be Forewarned: tl;dr. Read on Dear Reader. Hope you enjoy this one as it is a sea change in computing and a tectonic ocean shift in technology.

NVIDIA GTC 2025: AI’s Raw Hot Buttered Future

March 17-21, 2025, San Jose became geek central for NVIDIA’s GTC—aka the “Super Bowl of AI.” Hybrid setup, in-person or virtual, didn’t matter; thousands of devs, researchers, and suits swarmed to see what’s cooking in AI, GPUs, and robotics. Jensen Huang dropped bombs in his keynote, 1,000+ sessions drilled into the guts of it, and big players flexed their wares. Here’s the raw dog buttered scoop—and why you should care if you sling code or ship product.

The time has come,’ the Walrus said,

      To talk of many things:

Of shoes — and ships — and sealing-wax —

      Of cabbages — and kings —

And why the sea is boiling hot —

      And whether pigs have wings.’

~ The Walrus and The Carpenter

All The Libraries

Jensen’s Keynote: AI’s Next Gear, No Hype

March 18, 2025 SAP Center and the MCenery Civic Center, over 28,000 geeks packed in both halls and out in the streets . Jensen Huang, NVIDIA’s leather-jacketed maestro, hit the stage and didn’t waste breath. 2.5 hours no notes and started with the top of the stack with all the libraries NVIDIA has “CUDA-ized” and went all the way down to the photonic ethernet cables. No corporate fluff, just tech meat for the developer carnivore. His pitch: AI’s not just chatbots anymore; it’s “agentic,” thinking and moving in the real world forward at the speed of thought. Backed up with specifications, cycles, cost and even calling out library function calls.

Here’s what he unleashed:

Blackwell Ultra (B300): Mid-cycle beast, 288GB memory, out H2 2025. Training LLMs that’d choke lesser rigs—AMD’s sniffing, but NVIDIA’s still king.
Rubin + Vera Rubin: GPU + CPU superchip combo, late 2026. Named for the galaxy guru, it’s Grace Blackwell’s heir. Full-stack domination vibes.
Physical AI & GR00T N1: Robots that do real things. GR00T’s a humanoid platform tying training together, synced with Omniverse and Cosmos for digital twin sims. Robotics just got real even surreal.
NVIDIA Dynamo: “AI Factory OS.” Data centers as reasoning engines, not just compute mules. Deploy AI without the usual ops nightmare. <This> will change it all.
Quantum Day: IonQ, D-Wave, Rigetti execs talking quantum. It’s distant, but NVIDIA’s planting CUDA flags for the long game.

Jensen’s big claim: AI needs 100 more computing than we thought. That’s not a flex it’s a warning. NVIDIA’s rigging the pipes to pump it.

He said thank you to the developer more than 5 times, mentioned open source at least 4 times and said ecosystem at least 5 times. It was possibly the best keynote i have ever seen and i have been to and seen some of the best. Zuckerburg was right – if you do not have a technical CEO and a technical board, you are not a technical company at heart.

Jensen with Disney Friend

What It Means: Unfiltered and Untrained Takeaways

As i said GTC 2025 wasn’t a bloviated sales conference taking over a city; it was the tech roadmap, raw and real:

AI’s Next Frontier: The shift to agentic AI and physical AI (e.g., robotics) suggests that AI is moving beyond chatbots and image generation into real-world problem-solving. NVIDIA’s hardware and software innovations—like Blackwell Ultra and Dynamo—position it as the enabler of this transition.
Compute Power Race: Huang’s claim of a 100x compute demand surge underscores the urgency for scalable, energy-efficient solutions. NVIDIA’s full-stack approach (hardware, software, networking) gives it an edge, though competition from AMD and custom chipmakers looms.
Robotics Revolution: With GR00T and related platforms, NVIDIA is betting big on robotics as a 50 trillion dollar opportunity. This could transform industries like manufacturing and healthcare, making 2025 a pivotal year for robotic adoption.
Ecosystem Dominance: NVIDIA’s partnerships with tech giants and startups alike reinforce its role as the linchpin of the AI ecosystem. Its 82% GPU market share may face pressure, but its software (e.g., CUDA, NIM) and services (e.g., DGX Cloud) create a formidable moat.
Long-Term Vision: The focus on quantum computing and the next-next-gen architectures (like Feynman, slated for 2028) shows NVIDIA isn’t resting on its laurels. It’s preparing for a future where AI and quantum tech converge.

Sessions: Ship Code, Not Slides

Over 1,000 sessions at the McEnery Convention Center. No hand-holding pure tech fuel for devs and decision-makers. Standouts:

Generative AI & MLOps: Scaling LLMs without losing your mind (or someone else’s). NVIDIA’s inference runtime and open models cut the fat—production-ready, not science-fair thoughting.
Robotics: Isaac and Cosmos hands-on. Simulate, deploy, done. Manufacturing and healthcare devs, this is your cue.
Data Centers: DGX Station’s 20 petaflops in a box. Next-gen networking talks had the ops crowd drooling.
Graphics: RTX for 2D/3D and AR/VR. Filmmakers and game devs got a speed boost—less render hell.
Quantum: Day-long deep dive. CUDA’s quantum bridge is speculative, but the math’s stacking up.
Digital Twins and Simulation: Omniverse™ provides advanced simulation capabilities for adding true-to-reality physics to scene compositions. Build on models from basic rigid-body simulation to destruction, fluid-dynamics-based fire simulation, and physics-based scene authoring.

Near Real-Time Digital Twin Rendering Of A Ship

The DGX Spark Computer

i personally thought this deserved its own call-out. The announcement of the DGX Spark Computer. It is a compact AI supercomputer. Let us unpack its specs and capabilities for training large language models (LLMs). This little beast is designed to bring serious AI firepower to your desk, so here’s the rundown based on what NVIDIA has shared at the conference.

The DGX Spark is powered by the NVIDIA GB10 Grace Blackwell Superchip, a tightly integrated combo of CPU and GPU muscle. Here’s what it’s packing:

GPU: Blackwell GPU with 5th-generation Tensor Cores, supporting FP4 precision (4-bit floating-point). NVIDIA claims it delivers up to 1,000 AI TOPS (trillions of operations per second) at FP4—insane compute for a desktop box.
CPU: 20 Armv9 cores (10 Cortex-X925 + 10 Cortex-A725), connected to the GPU via NVIDIA’s NVLink-C2C interconnect. This gives you 5x the bandwidth of PCIe Gen 5, keeping data flowing fast between CPU and GPU.
Memory: 128 GB of unified LPDDR5x with a 256-bit bus, clocking in at 273 GB/s bandwidth. This unified memory pool is shared between CPU and GPU, critical for handling big AI workloads without choking on data transfers.
Storage: Options for 1 TB or 4 TB NVMe SSD—plenty of room for datasets, models, and checkpoints.
Networking: NVIDIA ConnectX-7 with 200 Gb/s RDMA (scalable to 400 Gb/s when pairing two units), plus Wi-Fi 7 and 10GbE for wired connections. You can cluster two Sparks to double the power.
I/O: Four USB4 ports (40 Gbps), HDMI 2.1a, Bluetooth 5.3—modern connectivity for hooking up peripherals or displays.
OS: Runs NVIDIA DGX OS, a custom Ubuntu Linux build loaded with NVIDIA’s AI software stack (CUDA, NIM microservices, frameworks, and pre-trained models).
Power: Sips just 170W from a standard wall socket—efficient for its punch.
Size: Tiny at 150 mm x 150 mm x 50.5 mm (about 1.1 liters) and 1.2 kg—it’s palm-sized but packs a wallop.

The DGX Spark Computer

This thing’s a sleek, power-efficient monster styled like a mini NVIDIA DGX-1, aimed at developers, researchers, and data scientists who want data-center-grade AI on their desks – in gold metal flake!

Now, the big question: how beefy an LLM can the DGX Spark train? NVIDIA’s marketing pegs it at up to 200 billion parameters for local prototyping, fine-tuning, and inference on a single unit. Pair two Sparks via ConnectX-7, and you can push that to 405 billion parameters. But let’s break this down practically—training capacity depends on what you’re doing (training from scratch vs. fine-tuning) and how you manage memory.

Fine-Tuning: NVIDIA highlights fine-tuning models up to 70 billion parameters as a sweet spot for a single Spark. With 128 GB of unified memory, you’re looking at enough space to load a 70B model in FP16 (16-bit floating-point), which takes about 140 GB uncompressed. Techniques like quantization (e.g., 8-bit or 4-bit) or offloading to SSD can stretch this further, but 70B is the comfy limit for active fine-tuning without heroic optimization.
Training from Scratch: Full training (not just fine-tuning) is trickier. A 200B-parameter model in FP16 needs around 400 GB of memory just for weights, ignoring gradients and optimizer states, which can triple that to 1.2 TB. The Spark’s 128 GB can’t handle that alone without heavy sharding or clustering. NVIDIA’s 200B claim likely assumes inference or light fine-tuning with aggressive quantization (e.g., FP4 via Tensor Cores), not full training. For two units (256 GB total), you might train a 200B model with extreme optimization—think model parallelism and offloading—but it’s not practical for most users.
Real-World Limit: For full training on one Spark, you’re realistically capped at 20-30 billion parameters in FP16 with standard methods (weights + gradients + Adam optimizer fit in 128 GB). Push to 70B with quantization or two-unit clustering. Beyond that, 200B+ is more about inference or fine-tuning pre-trained models, not training from zero.

Not bad for 4000.00. Think of all the things you could do… All of the companies you could build… Now onto the sessions.

Speakings and Sessions

There were 2,000+ speakers, some Nobel-tier, delivered. Straight no chaser – code, tools, and war stories. Hardcore programming sessions on CUDA, NVIDIA’s parallel computing platform, and tools like Dynamo (the new AI Factory OS). Think line-by-line breakdowns of optimizing AI models or squeezing performance from Blackwell Ultra GPUs. Once again, slideware jockeys need not apply.

The speaker list was a who’s-who of brainpower and hustle. Nobel laureates like Frances Arnold brought scientific heft—imagine her linking GPU-accelerated protein folding to drug discovery. Meanwhile, Yann LeCun and Noam Brown (OpenAI) tackled AI’s bleeding edge, like agentic reasoning or game theory hacks. Then you had practitioners Joe Park (Yum! Brands) on AI for fast food RJ Scaringe (Rivian) on autonomous driving, grounding it in real-world stakes.

Literally, a who-who of the AI developer world baring souls (if they have one) and scars from the war stories, and they do have them.

There was one talk in particular that was probably one of the best discussions i have seen in the past decade. SoFar Ocean Technologies is partnering with MITRE and NVIDIA to power the future of ocean AI!

MITRE announced a joint effort to build an AI-powered ocean digital twin fueled by real-time data from the global Spotter network. Researchers, government, and industry will use the digital twin to simulate and better understand the marine environments in which they operate.

As AI supercharges weather prediction, even the most advanced models will need more ocean data to be effective. Sofar provides these essential observations at scale. To power the digital twin, SoFar will deliver data from their global network of real-time ocean sensors and collaborate with MITRE to rapidly expand the adoption of the Bristlemouth open connectivity standard. Live data will feed into the NVIDIA Omniverse and open up new pathways for AI-powered ocean understanding.

BristleMouth Open Source Orchestration UxV Platform

The systems of systems and ecosystem reach are spectacular. The effort is monumental, and only through software can this scale be achievable. Of primary interest to this ecosystem effort they have partnered with Ocean Exploration Trust and the Nautilus Exploration Program to seek out new discoveries in geology, biology, and archaeology while conducting scientific exploration of the seafloor. The expeditions launch aboard Exploration Vessel Nautilus — a 68-meter research ship equipped with live-streaming underwater vehicles for scientists, students, and the public to explore the deep sea from anywhere in the world. We embed educators and interns in our expeditions who share their hands-on experiences via ship-to-shore connections with the next generation. Even while they are not at sea, explorers can dive into Nautilus Live to learn more about our expeditions, find educational resources, and marvel at new encounters.

“The most powerful technologies are the ones that empower others.”

~Jensen Huang

The Nautilus Live Mapping Software

At the end of the talk, I asked a question on the implementation of AI Orchestration for sensors underwater as well as personally thanked Dr Robert Ballard, who was in the audience, for his amazing work. Best known for his 1985 discovery of the RMS Titanic, Dr. Robert Ballard has succeeded in tracking down numerous other significant shipwrecks, including the German battleship Bismarck, the lost fleet of Guadalcanal, the U.S. aircraft carrier Yorktown (sunk in the World War II Battle of Midway), and John F. Kennedy’s boat, PT-109.

Again Just amazing. Check out the work here: SoFar Ocean.

What Was What: Big Dogs and Upstarts

The Exhibit hall was a technology zoo and smorgasbord —400+ OGs and players showing NVIDIA’s reach. (An Introvert’s Worst Nightmare.) Who showed up:

Tech Giants: Adobe, Amazon, Microsoft, Google, Oracle. AWS and Azure lean hard on NVIDIA GPUs—cloud AI’s backbone.
AI Hotshots: OpenAI and DeepSeek. ChatGPT’s parents still ride NVIDIA silicon; efficiency debates be damned.
Robots & Cars: Tesla hinting at autonomy juice, Delta poking at aviation AI. NVIDIA’s tentacles stretch wide.
Quantum Crew: Alice & Bob, D-Wave, IonQ, Rigetti. Quantum’s sci-fi, but they’re here.
Hardware: Dell, Supermicro, Cisco with GPU-stuffed rigs. Ecosystem’s locked in.
AI Platforms: Edge Impulse, Clear ML, Haystack – you need training and ML deployment they had it.

Inception Program: Fueling the Next Wave

Now, the Inception program—NVIDIA’s startup accelerator—is the unsung hero of GTC. With over 22,000 members worldwide, it’s a breeding ground for AI innovation, and GTC 2025 was their stage. Nearly 250 Inception startups showed up, from healthcare disruptors to robotics trailblazers like Stelia (shoutout to their “petabit-scale data mobility” talk). These aren’t pie-in-the-sky outfits—100+ had speaking slots, and their demos at the Inception Pavilion were hands-on proof of GPU-powered breakthroughs.

The program’s a sweet deal: free to join, no equity grab, just pure support—100K in DGX Cloud credits, Deep Learning Institute training, VC intros via the VC Alliance. They even had a talk on REVERSE VC pitches. What the VCs in Silicon Valley are looking for at the moment, and they were funding companies at the conference! It’s NVIDIA saying, “We’ll juice your tech, you change the game.” At GTC, you saw the payoff—startups like DeepSeek and Baseten flexing optimized models or enterprise tools, all built on NVIDIA’s stack. Critics might say it locks startups into NVIDIA’s ecosystem, but with nearly 300K in credits and discounts on tap, it’s hard to argue against the boost. The war stories from these founders—like scaling AI infra without frying a data center—were gold for any dev in the trenches.

GTC 2025 and Inception are two sides of the same coin. GTC’s the megaphone—blasting NVIDIA’s vision (and hardware) to the world—while Inception’s the incubator, quietly powering the startups that’ll flesh out that vision. Huang’s keynote hyped a token-driven AI economy, and Inception’s crew is already living it, churning out reasoning models and robotics on NVIDIA’s gear. It’s a symbiotic flex: GTC shows the “what,” Inception delivers the “how.”

We’re here to put a dent in the universe. Otherwise, why else even be here?

~ Steve Jobs

Micheal Dell and Your Humble Narrator at the Dell Booth

I did want to call out one announcement that I think has been a long time in the works in the industry, and I have been a very strong evangelist for, and that is a distributed inference OS.

Dynamo: The AI Factory OS That’s Too Cool to Gatekeep

NVIDIA unleashed Dynamo—think of it as the operating system for tomorrow’s AI factories. Huang’s pitch? Data centers aren’t just server farms anymore; they’re churning out intelligence like Willy Wonka’s chocolate factory but with fewer Oompa Loompas (queue the imagination song). Dynamo’s got a slick trick: it’s built from the ground up to manage the insane compute loads of modern AI, whether you’re reasoning, inferring, or just flexing your GPU muscle. And here’s the kicker—NVIDIA’s tossing the core stack into the open-source wild via GitHub. Yep, you heard that right: free for non-commercial use under an Apache 2.0 license. It’s like they’re saying, “Go build your own AI empire—just don’t sue us!” For the enterprise crowd, there’s a beefier paid version with extra bells and whistles (of course). Open-source plus premium? Whoever heard of such a thing! That’s a play straight out of the Silicon Valley handbook.

Dynamo High-Level Architecture

Dynamo is high-throughput low-latency inference framework designed for serving generative AI and reasoning models in multi-node distributed environments. Dynamo is designed to be inference engine agnostic (supports TRT-LLM, vLLM, SGLang or others) and captures LLM-specific capabilities such as

Disaggregated prefill & decode inference – Maximizes GPU throughput and facilitates trade off between throughput and latency.
Dynamic GPU scheduling – Optimizes performance based on fluctuating demand
LLM-aware request routing – Eliminates unnecessary KV cache re-computation
Accelerated data transfer – Reduces inference response time using NIXL.
KV cache offloading – Leverages multiple memory hierarchies for higher system throughput

Dynamo enables dynamic worker scaling, responding to real-time deployment signals. These signals, captured and communicated through an event plane, empower the Planner to make intelligent, zero-downtime adjustments. For instance, if an increase in requests with long input sequences is detected, the Planner automatically scales up prefill workers to meet the heightened demand.

Beyond efficient event communication, data transfer across multi-node deployments is crucial at scale. To address this, Dynamo utilizes NIXL, a technology designed to expedite transfers through reduced synchronization and intelligent batching. This acceleration is particularly vital for disaggregated serving, ensuring minimal latency when prefill workers pass KV cache data to decode workers.

Dynamo prioritizes seamless integration. Its modular design allows it to work harmoniously with your existing infrastructure and preferred open-source components. To achieve optimal performance and extensibility, Dynamo leverages the strengths of both Rust and Python. Critical performance-sensitive modules are built with Rust for speed, memory safety, and robust concurrency. Meanwhile, Python is employed for its flexibility, enabling rapid prototyping and effortless customization.

Oh yeah, and for all the naysayers over the years, it uses Nats.io as the messaging bus. Here is the Github. Get your fork on, but please contribute back – ya hear?

Tokenized Reasoning Economy

Along with this Dynamo announcement, NVidia has created an economy around tokenized reasoning models, in a monetary sense. This is huge. Let me break this down.

Now, why call this an economy? In a monetary sense, NVIDIA’s creating a system where compute power (delivered via its GPUs) and tokens (the output of reasoning models) act like resources and currency in a marketplace. Here’s how it works:

Compute as the Factory: NVIDIA’s GPUs—think Blackwell Ultra or Hopper—are the engines that power these reasoning models. The more compute you throw at a problem (more GPUs, more time), the more tokens you can generate, and the smarter the AI’s answers get. It’s like a factory producing goods, but the goods here are tokens representing intelligence.
Tokens as Currency: In the AI world, tokens aren’t just data—they’re value. Companies running AI services (like chatbots or analytics tools) often charge based on tokens processed—say, (X) dollars per million tokens. NVIDIA’s optimizing this with tools like Dynamo, which boosts token output while cutting costs, essentially making the “token economy” more efficient. More tokens per dollar = more profit for businesses using NVIDIA’s tech. Tokens Per Second will be the new metric.
Supply and Demand: Demand for reasoning AI is skyrocketing—enterprises, developers, and even robotics firms want smarter systems. NVIDIA supplies the hardware (GPUs) and software (like Dynamo and NIM microservices) to meet that demand. The more efficient their tech, the more customers flock to them, driving sales of GPUs and services like DGX Cloud.
Revenue Flywheel: Here’s the monetary kicker—NVIDIA’s raking in billions ($39.3B in a single quarter, per GTC 2025 buzz) because every industry needs this tech. They sell GPUs to data centers, cloud providers, and enterprises, who then use them to generate tokens and charge end users. NVIDIA reinvests that cash into better chips and software, keeping the cycle spinning.

NVIDIA’s “tokenized reasoning model economy” is about turning AI intelligence into a scalable, profitable commodity—where tokens are the product, GPUs are the means of production, and the tech industry is the market. The Developers power the Flywheel. Makes the mid-90s look like Bush League sports ball.

Tori MCcaffrey Technical Product Manager Extraordinaire and Your Humble Narrator

All that is really missing is a good artificial intelligence to control the whole process. And that is the trick, isnt it? These types of blue-sky discussions always assume certain advances for a sucessful implmentation. Unfortunately, A.I. is the bottleneck in this case. We’re close with replication and manufacturing processes and we could probably build sufficiently effective ion drives if we had the budget. But we lack a way to provide enought intelligence for the probe to handle all the situations it could face.

~ Eduard Guijpers from the Convention Panel -Designing a Von Nueman Probe

Daily and Lecun – Fireside

Lecun FireSide Chat

Yann LeCun, Turing Award badass and Meta’s AI Chief Scientist brain, sat down for a fireside chat with Bill Daily, Chief Scientist at NVIDIA that cut through the AI hype. No fluffy TED Talk (or me talking) vibes here just hot takes from a guy who’s been torching (get it?) neural net limits since the ‘80s. With Jensen Huang’s “agentic AI” bomb still echoing from the keynote, LeCun brought the dev crowd at the McEnery Civic Center a dose of real talk on where deep learning’s headed.

LeCun didn’t mince words: generative AI’s cool, but it’s a stepping stone. The future’s in systems that reason, not just parrot think less ChatGPT, and more “machines that actually get real work done.” He riffed on NVIDIA’s Blackwell Ultra and GR00T robotics push, nodding to the computing muscle needed for his vision. “You want AI that plans and acts? You’re burning 100x more flops than today,” he said, echoing Jensen’s compute hunger warning. No surprise—he’s been preaching energy-efficient architectures forever.

The discussion further dug into LeCun’s latest obsession: self-supervised learning on steroids. He’s betting it’ll crack real-world perception for robots and autonomous rigs stuff NVIDIA’s Cosmos and Isaac platforms are already juicing. “Supervised learning’s dead-end for scale,” he jabbed. “Data’s the bottleneck, not flops.” There were several nods from the devs in the Civic Center. He also said we would be managing hundreds of agents in the future, vertically trained – horizontally chained so to speak.

No slides once again, just LeCun riffing extempore, per NVIDIA’s style. He dodged the Meta AI roadmap but teased “open science” wins—likely a jab at closed-shop rivals. For devs, it was a call to arms: ditch the hype, build smarter, lean on NVIDIA’s stack. With Quantum Day buzzing next door, he left us with a zinger: “Quantum’s cute, but deep nets will out-think it first.”

GTC’s “Super Bowl of AI” rep held. LeCun proved why he’s still the godfather—unfiltered, technical, and ready to break the next ceiling and pragmatic.

Jay Sales, Engineering Executive Rockstar and Your Humble Narrator

Bottom Line

GTC2025 wasn’t just a conference. GTC 2025 was NVIDIA flipping the table: AI’s industrial now, not academic. Jensen’s vision, the sessions’ grit, and the hall’s buzz screamed one thing—build or get buried. For devs, it’s a CUDA goldmine. For suits, it’s strategy. For the industry, it’s NVIDIA steering the ship—full speed into an AI agentic and robotic future. With San Jose’s dust settling, the code’s just starting to run. Big fish and small fry are all feeding on bright green chips. 5 devs can now do the output of 50. Building stuff so others can build is Our developer mantra. Always has been, always will be – Gabba Gabba Hey One Of Us, One of Us!

Huang’s overarching message was clear: AI is evolving beyond generative models into “agentic AI”—systems that can reason, plan, and act autonomously. This shift demands exponentially more compute power (100x more than previously predicted, he noted), cementing NVIDIA’s role as the backbone of this transformation.

Despite challenges—early Blackwell overheating issues, U.S. export controls, and a 13% stock dip in 2025. Whatevs. NVIDIA’s record-breaking 39.3 billion dollar revenue quarter in February proves its resilience. GTC 2025 reaffirmed that NVIDIA isn’t just riding the AI wave; it’s creating it.

One last thought: a colleague was walking with me around the conference and inquired to me how did this feel and what i thought. Context: i was in The Valley from 1992-2001 and then had a company headquartered out there from 2011-2018. i thought for a moment, looked around, and said, “This feels like 90’s on steroids, which was the heyday of embedded programming and what i think was then the height of some of the most performant code in the valley.” i still remember when at Apple the Nvidia chip was chosen over ATI’s graphics chip. NVIDIA’s stock was something like 2.65 / share. i still remember when at Microsoft the NVIDIA chip was chosen for the XBox. NVIDIA the 33 year old start-up that analyst are talking of the demise. Just like music critics – right? As i drove up and down 101 and 280 i saw all of the new buildings and names – i realized – The Valley Is Back.

until then,

#iwishyouwater <- Mark Healy Solo Outer Reef Memo

@tctjr

Muzak To Blog By: Grotus, stylized as G̈r̈oẗus̈, was an industrial rock band from San Francisco, active from 1989 to 1996. Their unique sound incorporated sampled ethnic instruments, two drummers, and two bassists, and featured angry but humorous lyrics. NIN, Mr Bungle, Faith No More and Jello Biafra championed the band. Not for the faint of heart. Nevertheless great stuff.

Note: Rumor has it the Rivian SUV does in fact, go 0-60 in 2.6 seconds with really nice seats. Also thanks to Karen and Paul for the tea and sympathy steak supper in Palo Alto, Miss ya’ll!

Only In The Valley

What Would Nash,Shannon,Turing, Wiener and von Neumann Think?

An image of the folks as mentioned above via the GAN de jour

First, as usual, i trust everyone is safe. Second, I’ve been “thoughting” a good deal about how the world is being eaten by software and, recently, machine learning. i personally have a tough time with using the words artificial intelligence.

What Would Nash, Shannon, Turing, Wiener, and von Neumann Think of Today’s World?

The modern world is a product of the mathematical and scientific brilliance of a handful of intellectual pioneers who happen to be whom i call the Horsemen of The Digital Future. i consider these humans to be my heroes and persons that i aspire to be whereas most have not accomplished one-quarter of the work product the humans have created for humanity. Among these giants are Dr. John Nash, Dr. Claude Shannon, Dr. Alan Turing, Dr. Norbert Wiener, and Dr. John von Neumann. Each of them, in their own way, laid the groundwork for concepts that now define our digital and technological age: game theory, information theory, artificial intelligence, cybernetics, and computing. But what would they think if they could see how their ideas, theories and creations have shaped the 21st century?

A little context.

John Nash: The Game Theorist

John Nash revolutionized economics, mathematics, and strategic decision-making through his groundbreaking work in game theory. His Nash Equilibrium describes how parties, whether they be countries, companies, or individuals, can find optimal strategies in competitive situations. Today, his work influences fields as diverse as economics, politics, and evolutionary biology. NOTE: Computational Consensus Not So Hard; Carbon (Human) Consensus Nigh Impossible.

The Nash equilibrium is the set of degradation strategies

$(E_i^*,E_j^*)$

such that, if both players adopt it, neither player can achieve a higher payoff by changing strategies. Therefore, two rational agents should be expected to pick the Nash equilibrium as their strategy.

If Nash were alive today, he would be amazed at how game theory has permeated decision-making in technology, particularly in algorithms used for machine learning, cryptocurrency trading, and even optimizing social networks. His equilibrium models are at the heart of competitive strategies used by businesses and governments alike. With the rise of AI systems, Nash might ponder the implications of intelligent agents learning to “outplay” human actors and question what ethical boundaries should be set when AI is used in geopolitical or financial arenas.

Claude Shannon: The Father of Information Theory

Claude Shannon’s work on information theory is perhaps the most essential building block of the digital age. His concept of representing and transmitting data efficiently set the stage for everything from telecommunications to the Internet as we know it. Shannon predicted the rise of digital communication and laid the foundations for the compression and encryption algorithms protecting our data. He also is the father of my favorite equation mapping the original entropy equation from thermodynamics to channel capacity:

$H=-1/N \sum_{i=1}^{N} P_i\,log_2\,P_i$

The shear elegance and magnitude is unprecedented. If he were here, Shannon would witness the unprecedented explosion of data, quantities, and speeds far beyond what was conceivable in his era. The Internet of Things (IoT), big data analytics, 5G/6G networks, and quantum computing are evolutions directly related to his early ideas. He might also be interested in cybersecurity challenges, where information theory is critical in protecting global communications. Shannon would likely marvel at the sheer volume of information we produce yet be cautious of the potential misuse and the ethical quandaries regarding privacy, surveillance, and data ownership.

Alan Turing: The Architect of Artificial Intelligence

Alan Turing’s vision of machines capable of performing any conceivable task laid the foundation for modern computing and artificial intelligence. His Turing Machine is still a core concept in the theory of computation, and his famous Turing Test continues to be a benchmark in determining machine intelligence.

In today’s world, Turing would see his dream of intelligent machines realized—and then some. From self-driving cars to voice assistants like Siri and Alexa, AI systems are increasingly mimicking human cognition human capabilities in specific tasks like data analysis, pattern recognition, and simple problem-solving. While Turing would likely be excited by this progress, he might also wrestle with the ethical dilemmas arising from AI, such as autonomy, job displacement, and the dangers of creating highly autonomous AI systems as well as calling bluff on the fact that LLM systems do not reason in the same manner as human cognition on basing the results on probabilistic convex optimizations. His work on breaking the Enigma code might inspire him to delve into modern cryptography and cybersecurity challenges as well. His reaction-diffusion model called Turings Metapmorphsis equation, is foundational in explaining biological systems:

Turing’s reaction-diffusion system is typically written as a system of partial differential equations (PDEs):

$\frac{\partial u}{\partial t} &= D_u \nabla^2 u + f(u, v),$

$\frac{\partial v}{\partial t} &= D_v \nabla^2 v + g(u, v),$

$where:$

$\begin{itemize}\item $u$ and $v$ are concentrations of two chemical substances (morphogens),\item $D_u$ and $D_v$ are diffusion coefficients for $u$ and $v$,\item $\nabla^2$ is the Laplacian operator, representing spatial diffusion,\item $f(u, v)$ and $g(u, v)$ are reaction terms representing the interaction between $u$ and $v$.\end{itemize}$

In addition to this, his contributions to cryptography and game theory alone are infathomable.
In his famous paper, “Computing Machinery and Intelligence,” Turing posed the question, “Can machines think?” He proposed the Turing Test as a way to assess whether a machine can exhibit intelligent behavior indistinguishable from a human. This test has been a benchmark in AI for evaluating a machine’s ability to imitate human intelligence.

Given the recent advances made with large language models, I believe he would find it amusing, not that they think or reason.

Norbert Wiener: The Father of Cybernetics

Norbert Wiener’s theory of cybernetics explored the interplay between humans, machines, and systems, particularly how systems could regulate themselves through feedback loops. His ideas greatly influenced robotics, automation, and artificial intelligence. He wrote the books “Cybernetics” and “The Human Use of Humans”. During World War II, his work on the automatic aiming and firing of anti-aircraft guns caused Wiener to investigate information theory independently of Claude Shannon and to invent the Wiener filter. (The now-standard practice of modeling an information source as a random process—in other words, as a variety of noise—is due to Wiener.) Initially, his anti-aircraft work led him to write, with Arturo Rosenblueth and Julian Bigelow, the 1943 article ‘Behavior, Purpose and Teleology. He was also a complete pacifist. What was said about those who can hold two opposing views?

If Wiener were alive today, he would be fascinated by the rise of autonomous systems, from drones to self-regulated automated software, and the increasing role of cybernetic organisms (cyborgs) through advancements in bioengineering and robotic prosthetics. He, I would think, would also be amazed that we could do real-time frequency domain filtering based on his theories. However, Wiener’s warnings about unchecked automation and the need for human control over machines would likely be louder today. He might be deeply concerned about the potential for AI-driven systems to exacerbate inequalities or even spiral out of control without sufficient ethical oversight. The interaction between humans and machines in fields like healthcare, where cybernetics merges with biotechnology, would also be a keen point of interest for him.

John von Neumann: The Architect of Modern Computing

John von Neumann’s contributions span so many disciplines that it’s difficult to pinpoint just one. He’s perhaps most famous for his von Neumann architecture, the foundation of most modern computer systems, and his contributions to quantum mechanics and game theory. His visionary thinking on self-replicating machines even predated discussions of nanotechnology.

Von Neumann would likely be astounded by the ubiquity and power of modern computers. His architectural design is the backbone of nearly every device we use today, from smartphones to supercomputers. He would also find significant developments in quantum computing, aligning with his quantum mechanics work. As someone who worked on the Manhattan Project (also Opphenhiemer), von Neumann might also reflect on the dual-use nature of technology—the incredible potential of AI, nuclear power, and autonomous weapons to both benefit and harm humanity. His early concerns about the potential for mutual destruction could be echoed in today’s discussions on AI governance and existential risks.

What Would They Think Overall?

Together, these visionaries would undoubtedly marvel at how their individual contributions have woven into the very fabric of today’s society. The rapid advancements in AI, data transmission, computing power, and autonomous systems would be thrilling, but they might also feel a collective sense of responsibility to ask:

Where do we go from here?

Once again Oh Dear Reader You pre-empt me….

A colleague sent me this paper, which was the impetus for this blog:

My synopsis of said paper:

“The Tensor as an Informational Resource” discusses the mathematical and computational importance of tensors as resources, particularly in quantum mechanics, AI, and computational complexity. The authors propose new preorders for comparing tensors and explore the notion of tensor rank and transformations, which generalize key problems in these fields. This paper is vital for understanding how the foundational work of Nash, Shannon, Turing, Wiener, and von Neumann has evolved into modern AI and quantum computing. Tensors offer a new frontier in scientific discovery, building on their theories and pushing the boundaries of computational efficiency, information processing, and artificial intelligence. It’s an extension of their legacy, providing a mathematical framework that could revolutionize our interaction with quantum information and complex systems. Fundamental to systems that appear to learn where the information-theoretic transforms are the very rosetta stone of how we perceive the world through perceptual filters of reality.

This shows the continuing relevance in ALL their ideas in today’s rapidly advancing AI and fluid computing technological landscape.

They might question whether today’s technology has outpaced ethical considerations and whether the systems they helped build are being used for the betterment of all humanity. Surveillance, privacy, inequality, and autonomous warfare would likely weigh heavily on their minds. Yet, their boundless curiosity and intellectual rigor would inspire them to continue pushing the boundaries of what’s possible, always seeking new answers to the timeless question of how to create the future we want and live better, more enlightened lives through science and technology.

Their legacy lives on, but so does their challenge to us: to use the tools they gave us wisely for the greater good of all.

Or would they be dismayed that we use all of this technology to make a powerpoint to save time so we can watch tik tok all day?

Until Then,

#iwishyouwater <- click and see folks who got the memo

𝕋𝕖𝕕 ℂ. 𝕋𝕒𝕟𝕟𝕖𝕣 𝕁𝕣. (@tctjr) / X

Music To blog by: Bach: Mass in B Minor, BWV 232. By far my favorite composer. The John Eliot Gardiner and Monterverdi Choir version circa 1985 is astounding.

Snake_Byte:[14] Coding In Philosophical Frameworks

Your vision will only become clear when you can look into your heart. Who looks outside, dreams; who looks inside, awakes. Knowing your own darkness is the best method for dealing with the darknesses of other people. We cannot change anything until we accept it.
~ C. Jung

(Caveat Emptor: This blog is rather long in the snakes tooth and actually more like a CHOMP instead of a BYTE. tl;dr)

First, Oh Dear Reader i trust everyone is safe, Second sure feels like we are living in an age of Deus Ex Machina, doesn’t it? Third with this in mind i wanted to write a Snake_Byte that have been “thoughting” about for quite some but never really knew how to approach it if truth be told. I cant take full credit for this ideation nor do i actually want to claim any ideation. Jay Sales and i were talking a long time after i believe i gave a presentation on creating Belief Systems using BeliefNetworks or some such nonsense.

The net of the discussion was we both believed that in the future we will code in philosophical frameworks.

Maybe we are here?

So how would one go about coding an agent-based distributed system that allowed one to create an agent or a piece of evolutionary code to exhibit said behaviors of a philosophical framework?

Well we must first attempt to define a philosophy and ensconce it into a quantized explanation.

Stoicism seemed to me at least the best first mover here as it appeared to be the tersest by definition.

So first those not familiar with said philosophy, Marcus Aurelius was probably the most famous practitioner of Stoicism. i have put some references that i have read at the end of this blog¹.

Stoicism is a philosophical school that emphasizes rationality, self-control, and inner peace in the face of adversity. In thinking about this i figure To build an agent-based software system that embodies Stoicism, we would need to consider several key aspects of this philosophy.

Stoics believe in living in accordance with nature and the natural order of things. This could be represented in an agent-based system through a set of rules or constraints that guide the behavior of the agents, encouraging them to act in a way that is in harmony with their environment and circumstances.

Stoics believe in the importance of self-control and emotional regulation. This could be represented in an agent-based system through the use of decision-making algorithms that take into account the agent’s emotional state and prioritize rational, level-headed responses to stimuli.

Stoics believe in the concept of the “inner citadel,” or the idea that the mind is the only thing we truly have control over. This could be represented in an agent-based system through a focus on internal states and self-reflection, encouraging agents to take responsibility for their own thoughts and feelings and strive to cultivate a sense of inner calm and balance.

Stoics believe in the importance of living a virtuous life and acting with moral purpose. This could be represented in an agent-based system through the use of reward structures and incentives that encourage agents to act in accordance with Stoic values such as courage, wisdom, and justice.

So given a definition of Stoicism we then need to create a quantized model or discrete model of those behaviors that encompass a “Stoic Individual”. i figured we could use the evolutionary library called DEAP (Distributed Evolutionary Algorithms in Python ). DEAP contains both genetic algorithms and genetic programs utilities as well as evolutionary strategy methods for this type of programming.

Genetic algorithms and genetic programming are both techniques used in artificial intelligence and optimization, but they have some key differences.

This is important as people confuse the two.

Genetic algorithms are a type of optimization algorithm that use principles of natural selection to find the best solution to a problem. In a genetic algorithm, a population of potential solutions is generated and then evaluated based on their fitness. The fittest solutions are then selected for reproduction, and their genetic information is combined to create new offspring solutions. This process of selection and reproduction continues until a satisfactory solution is found.

On the other hand, genetic programming is a form of machine learning that involves the use of genetic algorithms to automatically create computer programs. Instead of searching for a single solution to a problem, genetic programming evolves a population of computer programs, which are represented as strings of code. The programs are evaluated based on their ability to solve a specific task, and the most successful programs are selected for reproduction, combining their genetic material to create new programs. This process continues until a program is evolved that solves the problem to a satisfactory level.

So the key difference between genetic algorithms and genetic programming is that genetic algorithms search for a solution to a specific problem, while genetic programming searches for a computer program that can solve the problem. Genetic programming is therefore a more general approach, as it can be used to solve a wide range of problems, but it can also be more computationally intensive due to the complexity of evolving computer programs².

So returning back to the main() function as it were, we need create a genetic program that models Stoic behavior using the DEAP library,

First need to define the problem and the relevant fitness function. This is where the quantized part comes into play. Since Stoic behavior involves a combination of rationality, self-control, and moral purpose, we could define a fitness function that measures an individual’s ability to balance these traits and act in accordance with Stoic values.

So lets get to the code.

To create a genetic program that models Stoic behavior using the DEAP library in a Jupyter Notebook, we first need to install the DEAP library. We can do this by running the following command in a code cell:

pip install deap

Next, we can import the necessary modules and functions:

import random
import operator
import numpy as np
from deap import algorithms, base, creator, tools

We can then define the problem and the relevant fitness function. Since Stoic behavior involves a combination of rationality, self-control, and moral purpose, we could define a fitness function that measures an individual’s ability to balance these traits and act in accordance with Stoic values.

Here’s an example of how we might define a “fitness function” for this problem:

# Define the fitness function.  NOTE: # i am open to other ways of defining this and other models
# the definition of what is a behavior needs to be quantized or discretized and 
# trying to do that yields a lossy functions most times.  Its also self referential

def fitness_function(individual):
    # Calculate the fitness based on how closely the individual's behavior matches stoic principles
    fitness = 0
    # Add points for self-control, rationality, focus, resilience, and adaptability can haz Stoic?
    fitness += individual[0]  # self-control
    fitness += individual[1]  # rationality
    fitness += individual[2]  # focus
    fitness += individual[3]  # resilience
    fitness += individual[4]  # adaptability
    return fitness,

# Define the genetic programming problem
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)

# Initialize the genetic algorithm toolbox
toolbox = base.Toolbox()

# Define the genetic operators
toolbox.register("attribute", random.uniform, 0, 1)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attribute, n=5)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", fitness_function)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=0.1, indpb=0.1)
toolbox.register("select", tools.selTournament, tournsize=3)

# Run the genetic algorithm
population = toolbox.population(n=10)
for generation in range(20):
    offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.1)
    fits = toolbox.map(toolbox.evaluate, offspring)
    for fit, ind in zip(fits, offspring):
        ind.fitness.values = fit
    population = toolbox.select(offspring, k=len(population))
    
# Print the best individual found
best_individual = tools.selBest(population, k=1)[0]

print ("Best Individual:", best_individual)

Here, we define the genetic programming parameters (i.e., the traits that we’re optimizing for) using the toolbox.register function. We also define the evaluation function (stoic_fitness), genetic operators (mate and mutate), and selection operator (select) using DEAP’s built-in functions.

We then define the fitness function that the genetic algorithm will optimize. This function takes an “individual” (represented as a list of five attributes) as input, and calculates the fitness based on how closely the individual’s behavior matches stoic principles.

We then define the genetic programming problem via the quantized attributes, and initialize the genetic algorithm toolbox with the necessary genetic operators.

Finally, we run the genetic algorithm for 20 generations, and print the best individual found. The selBest function is used to select the top individual fitness agent or a “behavior” if you will for that generation based on the iterations or epochs. This individual represents an agent that mimics the philosophy of stoicism in software, with behavior that is self-controlled, rational, focused, resilient, and adaptable.

Best Individual: [0.8150247518866958, 0.9678037028949047, 0.8844195735244268, 0.3970642186025506, 1.2091810770505023]

This denotes the best individual with those best balanced attributes or in this case the Most Stoic,

As i noted this is a first attempt at this problem i think there is a better way with a full GP solution as well as a tunable fitness function. In a larger distributed system you would then use this agent as a framework amongst other agents you would define.

i at least got this out of my head.

until then,

#iwishyouwater <- Alexey Molchanov and Dan Bilzerian at Deep Dive Dubai

Muzak To Blog By: Phil Lynott “The Philip Lynott Album”, if you dont know who this is there is a statue in Ireland of him that i walked a long way with my co-founder, Lisa Maki a long time ago to pay homage to the great Irish singer of the amazing band Thin Lizzy. Alas they took Phil to be cleaned that day. At least we got to walk and talk and i’ll never forget that day. This is one of his solo efforts and i believe he is one of the best artists of all time. The first track is deeply emotional.

References:

[1] A list of books on Stoicism -> click HERE.

[2] Genetic Programming (On the Programming of Computers by Means of Natural Selection), By Professor John R. Koza. There are multiple volumes i think four and i have all of this but this is a great place to start and the DEAP documentation. Just optimizing a transcendental functions is mind blowing what GP comes out with using arithmetic

November 10, 2020April 29, 2021 TCTJr Artificial_Intelligence|Complexity_Theory|Computational_Economics|Distributed_Computing

Computing The Human Condition – Project Noumena (Part 2)

In the evolution of a society, continued investment in complexity as a problem-solving strategy yields a declining marginal return.
Joseph A. Tainter

Someone asked me if from now on my blog will only be about Project_Noumena – on the contrary.

I will be interspersing subject matter within Parts 1 to (N) of Project_Noumena. To be transparent at this juncture i am not sure where it will end or if there is even a logical MVP 1.0. As with open-source systems and frameworks technically one never achieves V1.0 as the systems evolve. i tend to believe this will be the case with Project Noumena. i recently provided a book review on CaTB and have a blog on Recurrent Neural Networks with respect to Multiple Time Scale Prediction in the works so stuff is proceeding.

To that end, i would love comments and suggestions as to anything you would like my opinion on or for me to write about in the comments section. Also feel free to call me out on typos or anything else you see in error.

Further within Project Noumena there are snippets that could be shorter blogs as well. Look at Project Noumena as a fractal-based system.

Now on to the matter at hand.

In the previous blog Computing The Human_Condition – Project Noumena (Part 1) i discussed the initial overview of the model from the book World Dynamics. i will take a part of that model which is what i call, the main, Human_Do_Loop(); and the main attributes of the model: Birth and Death of Humans. One must ask if we didn’t have humans we would not have to be concerned with such matters as societal collapse? i don’t believe animals are concerned with such existential crisis concerns so my answer is a resounding – NO. We will be discussing such existential issues in this blog although i will address such items in future writings.

Over the years i have been asking myself is this a biological model by definition? Meaning do we have cellular components involved only? Is this biological modeling at the very essence? If we took the cell-based organisms out of the equation what do we still have as far as models on Earth?

While i told myself i wouldn’t get too extensional here and i do want to focus on the models and then codebases i continually check the initial conditions of these systems as they for most systems dictate the response for the rest of the future operations of said systems. Thus for biological systems, are there physical parameters that govern the initial exponential growth rate? Can we model with power laws and logistic curves for coarse-grained behavior? Is Bayesian reasoning biologically plausible at a behavioral level or at a neuronal level? Given that what are the atomic units that govern these models?

These are just a sampling of initial condition questions i ask myself as i evolve through this process.

So with that long-winded introduction and i trust i didn’t lose you oh reader lets hope into some specifics.

The picture from the book depicts basic birth and death loops in the population sector. In the case of these loops, they are generating positive feedback which causes growth. Thus an increase in population P causes an increase in birthrate BR. This, in turn, causes population P to further increase. The positive feedback loop would if left to its own devices would create an exponentially growing situation. As i said in the first blog and will continue to say, we seem to have started using exponential growth as a net positive fashion over the years in the technology industry. In the case of basic population dynamics with no constraints, exponential growth is not a net positive outcome.

Once again why start with simple models? The human mind is phenomenal at perceiving pressures, fears, greed, homeostasis, and other human aspects and characteristics and attempting at a structure that is given say the best fit to a situation and categorizing these as attributes thereof. However, the human mind is rather poor at predicting dynamical systems behaviors which are where the models come into play especially with social interactions and what i attempting to define from a self-organizing theory standpoint.

The next sets of loops that have the most effective behavior is a Pollution loop and a Crowding Loop. If we note that pollution POL increases one can assume up to a point that one hopes that nature absorbs and fixes the pollution otherwise it is a completely positive feedback loop and this, in turn, creates over pollution which we are already seeing the effects of around the worlds. One can then couple this with the amount of crowding humans can tolerate.

We see this behavior in urban sprawl areas when we have extreme heat or extreme cold or let’s say extreme pandemics. If the population rises crowding ratio increases the birth rate multiplier declines and birth rates reduce. The increasing death rate and reducing the birth rate are power system dynamic stabilizers coupled with pollution. This in turn obviously has an effect on food supplies. One can easily deduce that these seemingly simple coefficients if you will within the relative feedback loops create oscillations, exponential growth, or exponential decay. The systems while that seem large and rather stable are very sensitive to slight variations. If you are familiar with NetLogo it is a great agent-based modeling language. I picked a simple pollution model whereas we can select the number of people, birthrate, and tree planting rate.

As you can see without delving into the specifics after 77 years it doesn’t look to promising. i ‘ll either be using python or netlogo or a combination of both to extended these models as we add other references.

Ok enough for now.

Until Then,

#iwishyouwater

@tctjr

October 15, 2020October 16, 2020 TCTJr Books|Computational_Economics|Computing_History|Distributed_Computing|Human-Performance

Book Review: The Cathedral and The Bazaar (Musings On Linux and Open Source By An Accidental Revolutionary

“Joy, humor, and playfulness are indeed assets;”
~ Eric S. Raymond

As of late, i’ve been asked by an extreme set of divergent individuals what does “Open Source Software” mean?

That is a good question. While i understand the words and words do have meanings i am not sure its the words that matter here. Many people who ask me that question hear “open source” and hear or think “free’ which is not the case.

Also if you have been on linkedin at all you will see #Linux, #LinuxFoundation and #OpenSource tagged constantly in your feeds.

Which brings me to the current blog and book review.

(CatB)as it is affectionately known in the industry started out and still is a manifesto as well accessible via the world wide web. It was originally published in 1997 on the world wide wait and then in print form circa 1999. Then in 2001 was a revised edition with a foreword by Bob Young, the founding chairman and ceo of Redhat.

Being i prefer to use plain ole’ books we are reviewing the physical revised and extended paperback edition in this blog circa 2001. Of note for the picture, it has some wear and tear.

To start off as you will see from the cover there is a quote by Guy Kawasaki, Apple’s first Evangelist:

“The most important book about technology today, with implications that go far beyond programming.”

This is completely true. In the same train of thought, it even goes into the aspects of propriety and courtesy within conflict environments and how such environments are of a “merit not inherit” world, and how to properly respond when you are in vehement disagreement.

To relate it to the book review: What is a cathedral development versus a bazaar environment?

Cathedral is a tip of the fedora if you will to the authoritarian view of the world where everything is very structured and there are only a few at most who will approve moving the codebase forward.

Bazaar refers to the many. The many coding and contributing in a swarm like fashion.

In this book, closed source is described as a cathedral development model and open source as a bazaar development model. A cathedral is vertically and centrally controlled and planned. Process and governance rule the project – not coding. The cathedral is homeostatic. If you build or rebuild Basilica Sancti Petri within Roma you will not be picking it up by flatbed truck and moving it to Firenze.

The forward in the 2001 edition is written by Bob Young co-founder and original CEO of RedHat. He writes:

“ There have always been two things that would be required if open-source software was to materially change the world; one was for open-source software to become widely used and the other was the benefits this software development model supplied to its users had to be communicated and understood.”

Users here are an interesting target. Users could be developers and they could be end-users of warez. Nevertheless, i believe both conditions have been met accordingly.

i co-founded a machine learning and nlp service as a company in 2007 wherein i had the epiphany after my “second” read of Catb that the future is in fact open source. i put second in quotes as the first time i read it back in 1998 it wasn’t really a read in depth nor having fully internalized it while i was working at Apple in the CPU software department on OS9/OSX and while at the same time knowing full well that OSX was based on the Mach kernel. The Mach kernel is often mentioned as one of the earliest examples of a microkernel. However, not all versions of Mach are microkernels. Mach’s derivatives are the basis of the operating system kernel in GNU Hurd and of Apple’s XNU kernel used in macOS, iOS, iPadOS, tvOS, and watchOS.

That being said after years of working with mainly closed source systems in 2007 i re-read Catb. i literally had a deep epiphany that the future of all development would be open source distributed machine learning – everywhere.

Then i read it recently – deeply – a third time. This time nearly every line in the book resonates.

The third time with almost anything seems to be the charm. This third time through i realized not only is this a treatise for the open-source movement it is a call to arms if you will for the entire developer community to behave appropriately with propriety and courtesy in a highly matrixed collaborative environment known as the bazaar.

The most obvious question is: Why should you care? i’m glad you asked.

The reason you care is that you are part of the information economy. The top market cap companies are all information-theoretic developer-first companies. This means that these companies build things so others can build things. Software is truly eating the world. Think in terms of the recent pandemic. Work (code) is being created at an amazing rate due to the fact that the information work economy is distributed and essentially schedule free. She who has distributed wins and she who can code anytime wins. This also means that you are interested in building world-class software and the building of this software is now a decentralized peer reviewed transparent process.

The book is organized around Raymond’s various essays. It is important to note that just as software is an evolutionary process by definition so are the essays in this book. They can also be found online. The original collection of essays date back to 1992 on the internet: “A Brief History Of Hackerdom.’

The book is not a “how-to” cookbook but rather what i call a “why to” map of the terrain. While you can learn how to hack and code i believe it must be in your psyche. The book also uses the term “hacker” in a positive sense to mean one who creates software versus one who cracks software or steals information.

While the history and the methodology is amazing to me the cogent commentary on the types of the reasoning behind why hackers go into open source vary as widely as ice cream flavors.

Raymond goes into the theory of incentives with respect to the instinctive wiring of humans beings.

“The verdict of history seems to be free-market capitalism is the globally optimal way to cooperate for economic efficiency; perhaps in a similar way to cooperate for generating (and checking!) high-quality creative work.”

He categorizes command hierarchy, exchange economy, and gift culture to address these incentives.

Command hierarchy:

Goods are allocated in a scarce economy model by one central authority.

Exchange Economy:

The allocation of scarce goods is accomplished in a decentralized manner allowing scale through trade and voluntary cooperation.

Gift Culture:

This is very different than the other two methods or cultures. Abundance makes command and control relationships difficult to sustain. In gift cultures, social status is determined not by what you control but by what you give away.

It is clear that if we define the open source hackerdom it would be a gift culture. (It is beyond the current scope of this blog but it would be interesting to do a neuroscience project on the analysis of open source versus closed source hackers brain chemistry as they work throughout the day)

Given these categories, the essays then go onto define the written and many times unwritten (read secrets) that operate within the open-source world via a reputation game. If you are getting the idea it is tribal you are correct. Interestingly enough the open source world has in many cases very divergent views on all prickly things within the human condition such as religion and politics but one thing is a constant – ship high-quality code.

Without a doubt the most glaring cogent commentary comes in a paragraph from the essay “The Magic Cauldron.” entitled “Open Source And Strategic Business Risk.”

Ultimately the reasons open source seems destined to become a widespread practice have more to do with customer demand and market pressures than with supply-efficiencies for vendors.”

And further:

“Put yourself for the moment in the position of a CTO at a Fortune 500 corporation contemplating a build or upgrade of your firm’s IT infrastructure. Perhaps you need to choose a network operating system to be deployed enterprise-wide; perhaps your concerns involve 24/7 web service and e-commerce, perhaps your business depends on being able to field high-volume, high-reliability transaction databases. Suppose you go the conventional closed-source route. If you do, then you put your firm at the mercy of a supplier monopoly – because by definition there is only one place you can go to for support, bug fixes, and enhancements. If the supplier doesn’t perform, you will have no effective recourse because you are effectively locked by your initial investment.”

FURTHER:

“The truth is this: when your key business processes are executed by opaque blocks of bits that you cant even see inside (let alone modify) you have lost control of your business.”

“Contrast this with the open-source choice. If you go this route, you have the source code, and no one can take that away from you. Instead of a supplier monopoly with a choke-hold on your business, you now have multiple service companies bidding for your business – and you not only get to play them against each other, but you also have the option of building your own captive support organization if that looks less expensive than contracting out. The market works for you.”

“The logic is compelling; depending on closed-source code is an unacceptable strategic risk So much so that I believe it will not be very long until closed-source single-vendor acquisitions when there is an open source alternative available will be viewed as a fiduciary irresponsibility, and rightly grounds for a share-holder lawsuit.”

THIS WAS WRITTEN IN 1997. LOOK AROUND THE WORLD WIDE WAIT NOW… WHAT DO YOU SEE?

Open Source – full stop.

i will add that there was no technical explanation here only business incentive and responsibility to the company you are building, rebuilt, or are scaling. Further, this allows true software malleability and reach which is the very reason for software.

i will also go on a limb here and say if you are a software corporation one that creates software you can play the monopoly and open-source models against each other within your corporation. Agility and speed to ship code is the only thing that matters these days. Where is your github? Or why is this not shipping TODAY?

This brings me to yet another amazing prescient prediction in the book that Raymond says that applications are ultimately where we will land for monetary scale. Well yes, there is an app for that….

While i have never met Eric S. Raymond he is a legend in the field. We have much to thank him for in the areas of software. If you have not read CatB and work in the information sector do yourself a favor: buy it today.

As a matter of fact here is the link: The Cathedral & the Bazaar: Musings on Linux and Open Source by an Accidental Revolutionary

Muzak To Blog To: “Morning Phase” by Beck

Resources:

http://www.opensource.org

https://www.apache.org/foundation/

Computing The Human Condition – Project Noumena (Part 1)

“I am putting myself to the fullest possible use, which is all I think any conscious entity can ever hope to do.” ~ HAL 9000

“If you want to make the world a better place take a look at yourself and then make a change.” ~ MJ.

First and foremost with this blog i trust everyone is safe. The world is in an interesting place, space, and time both physically and dare i say collectively – mentally.

Introduction

This past week we celebrated Earth Day. i believe i heard it was the 50th year of Earth Day. While I applaud the efforts and longevity for a day we should have Earth Day every day. Further just “thoughting” about or tweeting about Earth Day – while it may wake up your posterior lobe of the pituitary gland and secret some oxytocin – creating the warm fuzzies for you it really doesn’t create an action for furthering Earth Day. (much like typing /giphy YAY! In Slack).

As such, i decided to embark on a multipart blog that i have been “thinking” about what i call an Ecological Computing System. Then the more i thought about it why stop at Ecology? We are able to model and connect essentially anything, we now have models for the brain that while are coarse-grained can account for gross behaviors, we have tons of data on buying habits and advertisement data and everything is highly mobile and distributed. Machine learning which can optimize, classify and predict with extremely high dimensionality is no longer an academic exercise.

Thus, i suppose taking it one step further from ecology and what would differentiate it from other efforts is that <IT> would actually attempt to provide a compute framework that would compute The Human Condition. I am going to call this effort Project Noumena. Kant the eminent thinker of 18th century Germany defined Noumena as a thing as it is in itself, as distinct from a thing as it is knowable by the senses through phenomenal attributes and proposed that the experience was a product of the mind.

My impetus for this are manifold:

i love the air, water, trees, and animals,

i am an active water person,

i want my children’s children’s children to know the wonder of staring at the azure skies, azure oceans and purple mountains,

Maybe technology will assist us in saving us from The Human Condition.

Timing

i have waited probably 15+ years to write about this ideation of such a system mainly due to the technological considerations were nowhere near where they needed to be and to be extremely transparent no one seemed to really think it was an issue until recently. The pandemic seems to have been a global wakeup call that in fact, Humanity is fragile. There are shortages of resources in the most advanced societies. Further due to the recent awareness that the pollution levels appear (reported) to be subsiding as a function in the reduction of humans’ daily involvement within the environment. To that point over the past two years, there appears to be an uptake of awareness in how plastics are destroying our oceans. This has a coupling effect that with the pandemic and other environmental concerns there could potentially be a food shortage due to these highly nonlinear effects. This uptake in awareness has mainly been due to the usage of technology of mobile computing and social media which in and of itself probably couldn’t have existed without plastics and massive natural resource consumption. So i trust the irony is not lost there.

From a technical perspective, Open source and Open Source Systems have become the way that software is developed. For those that have not read The Cathedral and The Bazaar and In The Beginning Was The Command Line i urge you to do so it will change your perspective.

We are no longer hampered by the concept of scale in computing. We can also create a system that behaves at scale with only but a few human resources. You can do a lot with few humans now which has been the promise of computing.

Distributed computing methods are now coming to fruition. We no longer think in terms of a monolithic operating system or in place machine learning. Edge computing and fiber networks are accelerating this at an astonishing rate. Transactions now dictate trust. While we will revisit this during the design chapters of the blog I’ll go out on a limb here and say these three features are cogent to distributed system processing (and possibly the future of computing at scale).

Incentive models
Consensus models
Protocol models

We will definitely be going into the deeper psychological, mathematical, and technical aspects of these items.

Some additional points of interest and on timing. Microsoft recently released press about a Planetary Computer and announced the position of Chief Ecology Officer. While i do not consider Project Nuomena to be of the same system type there could be similarities on the ecological aspects which just like in open source creates a more resilient base to work.

The top market cap companies are all information theoretic-based corporations. Humans that know the science, technology, mathematics and liberal arts are key to their success. All of these companies are woven and interwoven into the very fabric of our physical and psychological lives.

Thus it is with the confluence of these items i believe the time is now to embark on this design journey. We must address the Environment, Societal factors and the model of governance.

A mentor once told me one time in a land far away: “Timing is everything as long as you can execute.” Ergo Timing and Execution Is Everything.

Goals

It is my goal that i can create a design and hopefully, an implementation that is utilizing computational means to truly assist in building models and sampling the world where we can adhere to goals in making small but meaningful changes that can be used within what i am calling the 3R’s: recycle, redact, reuse. Further, i hope with the proper incentive models in place that are dynamic it has a mentality positive feedback effect. Just as in complexity theory a small change – a butterfly wings – can create hurricanes – in this case positive effect.

Here is my overall plan. i’m not big on the process or gant charts. I’ll be putting all of this in a README.md as well. I may ensconce the feature sets etc into a trello or some other tracking mechanism to keep me focused – WebSphere feel free to make recommendations in the comments section:

Action Items:

Create Comparative Models

Create Coarse-Grained Attributes

Identify underlying technical attributes

Attempt to coalesce into an architecture

Start writing code for the above.

Preamble

Humanity has come to expect growth as a material extension of human behavior. We equate growth with progress. In fact, we use the term exponential growth as it is indefinitely positive. In most cases for a fixed time interval, this means a doubling of the relevant system variable or variables. We speak of growth as a function of gross national production. In most cases, exponential growth is treacherous where there are no known or perceived limits. It appears that humanity has only recently become aware that we do not have infinite resources. Psychologically there is a clash between the exponential growth and the psychological or physical limit. The only significance is the relevant (usually local) limit. How does it affect me, us, and them? This can be seen throughput most game theory practices – dominant choice. The pattern of growth is not the surprise it is the collision of the awareness of the limit to the ever-increasing growth function is the surprise.

One must stop and ask:

Q: Are progress (and capacity) and the ever-increasing function a positive and how does it relate to 2nd law of thermodynamics aka Entropy? Must it always expand?

We are starting to see that our world can exert dormant forces that within our life can greatly affect our well being. When we approach the actual or perceived limit the forces which are usually negative begin to gain strength.

So given these aspects of why i’ll turn now to start the discussion. If we do not understand history we cannot predict the future by inventing it or in most cases re-inventing it as it where.

I want to start off the history by referencing several books that i have been reading and re-reading on subjects of modeling the world, complexity, and models for collapse throughout this multipart blog. We will be addressing issues concerning complex dynamics as are manifested with respect to attributes model types, economics, equality, and mental concerns.

These core references are located at the end of the blog under references. They are all hot-linked. Please go scroll and check them out. i’ll still be here. i’ll wait.

Checked them out? i know a long list.

As you can see the core is rather extensive due to the nature of the subject matter. The top three books are the main ones that have been the prime movers and guides of my thinking. These three books i will refer to as The Core Trilogy:

World Dynamics

The Collapse of Complex Societies

Six Sources of Collapse

As i mentioned i have been deeply thinking about all aspects of this system for quite some time. I will be mentioning several other texts and references along the continuum of creation of this design.

We will start by referencing the first book: World Dynamics by J.W. Forrestor. World Dynamics came out of several meetings of the Rome Club a 75 person invite-only club founded by the President of Fiat. The club set forth the following attributes for a dynamic model that would attempt to predict the future of the world:

Population Growth

Capital Investment

Geographical Space

Natural Resources

Pollution

Food Production

The output of this design was codified in a computer program called World3. It has been running since the 1970s what was then termed a golden age of society in many cases. All of these variables have been growing at an exponential rate. Here we see the model with the various attributes in action. There have been several criticisms of the models and also analysis which i will go into in further blogs. However, in some cases, the variants have been eerily accurate. The following plot is an output of the World3 model:

Issues Raised By World3 and World Dynamics

The issues raised by World3 and within the book World Dynamics are the following:

There is a strong undercurrent that technology might not be the savior of humankind

Industrialism (including medicine and public health) may be a more disturbing force than the population.

We may face extreme psychological stress and pressures from a four-pronged dilemma via suppression of the modern industrial world.

We may be living in a “golden age” despite a widely acknowledged feeling of malaise.

Exhtortions and programs directed at population control may be self-defeating. Population control, if it works, would yield excesses thereby allowing further procreation.

Pollution and Population seem to oscillate whereas the high standard of living increases the production of food and material goods which outrun the population. Agriculture as it hits a space limit and as natural resources reach a pollution limit then the quality of life falls in equalizing population.

There may be no realistic hope of underdeveloped countries reaching the same standard and quality of life as developed countries. However, with the decline in developed countries, the underdeveloped countries may be equalized by that decline.

A society with a high level of industrialization may be unsustainable.

From a long term 100 years hence it may be unwise for underdeveloped countries to seek the same levels of industrialization. The present underdeveloped nations may be in better conditions for surviving the forthcoming pressures. These underdeveloped countries would suffer far less in a world collapse.

Fuzzy Human – Fuzzy Model

The human mind is amazing at identifying structures of complex situations. However, our experiences train us poorly for estimating the dynamic consequences of said complexities. Our mind is also not very accurate at estimating ad hoc parts of the complexities and the variational outcomes.

One of the problems with models is well it is just a model The subject-observer reference could shift and the context shifts thereof. This dynamic aspect needs to be built into the models.

Also while we would like to think that our mental model is accurate it is really quite fuzzy and even irrational in most cases. Also attempting to generalize everything into a singular model parameter is exceedingly difficult. It is very difficult to transfer one industry model onto another.

In general parameterization of most of these systems is based on some perceptual model we have rationally or irrationally invented.

When these models were created there was the consideration of modeling social mechanics of good-evil, greed – altruism, fears, goals, habits, prejudice, homeostasis, and other so-called human characteristics. We are now at a level of science where we can actually model the synaptic impulse and other aspects that come with these perceptions and emotions.

There is a common cross-cutting construct in most complex models within this text that consists of and mainly concerned with the concept of feedback and how the non-linear relationships of these modeled systems feedback into one another. System-wide thinking permeates the text itself. On a related note from the 1940’s of which Dr Norbert Weiner and others such as Claude Shannon worked on ballistic tracking systems and coupled feedback both in a cybernetic and information-theoretic fashion of which he attributed the concept of feedback as one of the most fundamental operations in information theory. This led to the extremely famous Weiner Estimation Filters. Also, side note: Dr Weiner was a self-styled pacifist proving you can hold two very opposing views in the same instance whilst being successful at executing both ideals.

Given that basic function of feedback, lets look at the principle structures. Essentially the model states there will be levels and rates. Rates are flows that cause levels to change. Levels can accumulate the net level. Either addition or subtraction to that level. The various system levels can in aggregate describe the system state at any given time $(t)$ . Levels existing in all subsystems of existence. These subsystems as you will see include but are not limited to financial, psychological, biological, and economic. The reason that i say not limited to because i also believe there are some yet to be identified subsystems at the quantum level. The differential or rate of flow is controlled by one or more systems. All systems that have some Spatio-temporal manifestation can be represented by using the two variables levels and rates. Thus with respect to the spatial or temporal variables, we can have a dynamic model.

The below picture is the model that grew out of interest from the initial meetings of the Club of Rome. The inaugural meeting which was the impetus for the model was held in Bern, Switzerland on June 29, 1970. Each of the levels presents a variable in the previously mentioned major structures. System levels appear as right triangles. Each level is increased or decreased by the respective flow. As previously mentioned on feedback any closed path through the diagram is a feedback loop. Some of the closed loops given certain information-theoretic attributes be positive feedback loops that generate growth and others that seek equilibrium will be negative feedback loops. If you notice something about the diagram it essentially is a birth and death loop. The population loop if you will. For the benefit of modeling, there are really only two major variables that affect the population. Birth Rate (BR) and Death Rate (DR). They represent the total aggregate rate at which the population is being increased or decreased. The system has coefficients that can initialize them to normal rates. For example, in 1970 BRN is taken as 0.0885 (88.5 per thousand) which is then multiplied by population to determine BR. DRN by the same measure is the outflow or reduction. In 1970 it was 9.5% or 0.095. The difference is the net and called normal rates. The normale rates correspond to a physical normal world. When there are normal levels of food, material standard of living, crowding, and pollution. The influencers are then multipliers that increase or decrease the normal rates.

As a caveat, there have been some detractors of this model. To be sure it is very coarse-grained however while i haven’t seen the latest runs or outputs it is my understanding as i said the current outputs are close. The criticisms come in the shape of “Well its just modeling everything as a $y=x*e^{{rt}}$ . I will be using this concept and map if you will as the basis for Noumena. The concepts and values as i evolve the system will vary greatly from the World3 model but i believe starting with a minimum viable product is essential here as i said humans are not very good at predicting all of the various outcomes in high dimensional space. We can asses situations very quickly but probably outcomes no so much. Next up we will be delving into the loops deeper and getting loopier.