The hottest Edge Computing Substack posts right now

And their main takeaways
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Top Technology Topics

Fungible Compute, Mortal Computing and Why the IoT won't use Silicon

State of the Future • 12 implied HN points • 10 Mar 26
đź•ą Technology Edge Computing
  1. Flexible thin‑film IGZO chips let you add cheap, bendable compute to everyday objects that never had it, creating a new class of semiconductor separate from cutting‑edge silicon.
  2. Process times measured in days and a tiny, modular 20Ă—30m fab footprint make manufacturing much cheaper and faster, enabling billions of units and even the possibility of deploying fabs at customer sites.
  3. Edge intelligence can be very simple but valuable: tiny classifiers of a few hundred gates plus basic sensors can capture huge amounts of real‑world data for use in supply chains, healthcare, and agriculture, shifting value to the aggregate data layer.

The Sequence AI of the Week #818: You Cannot Miss Qwen 3.5

TheSequence • 203 implied HN points • 04 Mar 26
đź•ą Technology Edge Computing
  1. The Qwen 3.5 family spans from a 397B flagship to efficient 35B mediums and tiny 0.8–9B models designed to run on devices, covering the whole deployment stack. They’re clearly built to support everything from large-server workloads down to smartphones.
  2. This release marks a structural shift away from pure dense transformers: it reimagines attention, embraces extreme Mixture-of-Experts sparsity, and brings native multimodality even to small models. Those architectural changes are central to its engineering gains.
  3. Benchmarks show the flagship models trading blows with top proprietary systems like GPT-5.2 and Claude Opus 4.5, meaning open-weight models are closing the performance gap. Together with the new architectures and size range, this suggests more cost-effective scaling and wider deployment options.

Building Physical Agentic AI

Superficial Intelligence • 117 implied HN points • 13 Feb 26
đź•ą Technology Edge Computing
  1. Physical agentic AI puts small reasoning models on devices so they can sense, "have a little think," and act in the physical world instead of relying on brittle hand-coded logic.
  2. Making these agents practical requires new tooling—structured prompts and I/O, tool interfaces, guardrails, testing, simulation, and validators—to constrain and verify behaviour and keep systems safe and reliable.
  3. Improved edge AI chips and developer tools lower the barrier so the same hardware can run many real-world apps by swapping prompts, but there are cost and energy tradeoffs so early use cases target higher-value scenarios.

The Ambient AI Era: Clawdbot (OpenClaw)'s Ripple Effects

Enterprise AI Trends • 168 implied HN points • 31 Jan 26
đź•ą Technology Edge Computing
  1. OpenClaw validates strong demand for ambient, always-on AI assistants that run 24/7, keep persistent personal memory, and act proactively, and incumbents with local context (Apple/Google) are best positioned to build the polished consumer version.
  2. Current infrastructure, security, and policy tooling are not ready for autonomous agents — agents can do harmful or unwanted things even when operating as designed, so we need runtime guardrails, better observability, and new legal/policy frameworks.
  3. True on-device edge inference isn’t ready yet, so persistent agents will live in the cloud for now, which will drive massive new infrastructure needs (storage for agent “exhaust”, sandboxes, flight recorders, and an agent-native internet) and create clear investment opportunities.

Understanding Space Based Architecture for efficient Data Processing[System Design Sundays]

Technology Made Simple • 379 implied HN points • 12 Feb 24
đź•ą Technology Edge Computing
  1. Space-Based Architecture (SBA) distributes processing and storage across multiple servers, enhancing scalability and performance by leveraging in-memory data grids.
  2. The components of SBA include Processing Units (PU) for executing business logic, Virtualized Middleware for managing shared infrastructure, and data pumps for data marshaling.
  3. SBA offers benefits such as scalability, fault tolerance, and low-latency data access, but comes with challenges like complexity in design, debugging, and data security.
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How to Invest in AI Sovereignty — Sovereign Albion w/ Andrew Bennett

State of the Future • 7 implied HN points • 12 Feb 26
đź•ą Technology Edge Computing
  1. The future of AI hardware is heterogeneous computing — many specialised chips (like compound semiconductors and photonics) will handle edge workloads for latency, privacy, and cost reasons rather than everything running in giant data centres.
  2. Europe and the UK can win by focusing on niche, strategic semiconductor areas and building specialist funds and industry partnerships instead of trying to match global capex-heavy players on their own turf.
  3. Successful AI industrial strategy needs fast, experimental, venture-style public support and a cultural shift toward bigger ambition and patient capital to back risky founders and long-term roadmaps.

What I Learned This Week #3

Eventually Consistent • 59 implied HN points • 09 Jun 24
đź•ą Technology Edge Computing
  1. Data models are crucial and should be chosen based on relationships among data elements and required access patterns. Graph modeling can be beneficial for many-to-many relationships, while documents work better for one-to-many relationships. Modeling affects performance.
  2. Memory access patterns significantly impact computation time by influencing caching behavior. The chosen pattern determines cache hits/misses and the level from which data is retrieved.
  3. In edge computing, while databases like Postgres rely on raw TCP sockets, WebSockets are preferred for security reasons. WebSockets provide similar benefits while maintaining secure and standardized communication channels.

OpenAI's Open Source Strategy

Enterprise AI Trends • 168 implied HN points • 06 Aug 25
đź•ą Technology Edge Computing
  1. OpenAI has released two new open-weight models called gpt-oss-120b and gpt-oss-20b. This means people can run these powerful models on their own computers without needing an internet connection.
  2. The gpt-oss-120b model is very cost-effective and performs well, even better than some existing models, making advanced AI more accessible.
  3. It's been six years since OpenAI released an open weight model, so this move shows they are serious about reclaiming their position in the open-source AI community.

The Next Wave Of AI Computing

Startup Pirate by Alex Alexakis • 235 implied HN points • 10 Mar 23
đź•ą Technology Edge Computing
  1. Artificial intelligence has come a long way since Alan Turing, with AI chips being a key component for advanced computations.
  2. Edge computing moves computing power closer to where data is generated, enabling faster responses for AI applications like self-driving cars.
  3. Axelera AI is focusing on AI chips for edge computing and advancing technology for applications like computer vision in the physical world.

Deploy! Intelligence too cheap to meter @ $0.0001 per million tokens.

Let Us Face the Future • 59 implied HN points • 29 Oct 24
đź•ą Technology Edge Computing
  1. Making AI technology cheaper is key to its widespread use. If it costs only $0.0001 per million tokens, it can be integrated into many everyday devices.
  2. We need to focus on three main challenges: reducing semiconductor costs, optimizing power for devices, and creating smaller, efficient models that can run locally.
  3. To handle power constraints, especially for portable devices, we need new chips and better power management. This will help make AI more accessible and functional in our daily lives.

The hidden complexity of robot networks

Dev Interrupted • 4 implied HN points • 04 Dec 25
đź•ą Technology Edge Computing
  1. Robots will use a hybrid edge/cloud model, keeping simple reactive control on-device while offloading complex reasoning to the cloud, so teams must decide which intelligence stays local and which runs remotely.
  2. Latency and network reliability are critical. Robotics needs sub-200 millisecond round trips, adaptive protocols that handle packet loss and fluctuating bandwidth, and must preserve command channels even when other streams are degraded.
  3. Robots produce massive multi-sensor data that requires separate real-time and archival systems; capturing and replaying that telemetry is essential for incident analysis and model training and can scale to petabytes quickly.

Integration

Superficial Intelligence • 26 implied HN points • 16 Nov 24
đź•ą Technology Edge Computing
  1. Current edge AI can turn data from sensors into useful information, but it often misses the real 'intelligence' needed to act on that information effectively.
  2. To create smarter systems, we need to integrate sensor data over time and build context-aware applications, not just rely on simple thresholds.
  3. It's important to make advanced tools for building intelligent systems available to more engineers so that anyone can create solutions for real-world problems.

Issue #81

Infra Weekly Newsletter • 9 implied HN points • 07 Feb 24
đź•ą Technology Edge Computing
  1. Some big companies are firing many employees, causing concern in the industry
  2. A well-known Kubernetes company, Weaveworks, is closing down
  3. TikTok's parent company has open-sourced a new tool for Kubernetes Federation

Gradient Flow #27: 2021 Trends Report, the Edge, and ML in the Sciences

Gradient Flow • 19 implied HN points • 28 Jan 21
đź•ą Technology Edge Computing
  1. The 2021 Trends Report covers topics like tools for Machine Learning and AI, Data Management, Cloud Computing, and Emerging AI Trends.
  2. Edge computing is becoming more important for bringing AI and computing closer to data sources, as discussed with experts in the field.
  3. In the realm of Machine Learning, there are new tools like GPT-Neo, analysis of popular data science technologies, and the concept of the lakehouse in data management.

State Must Live With the Agent

domsteil • 0 implied HN points • 12 Jan 26
đź•ą Technology Edge Computing
  1. Commerce built around remote services breaks when autonomous agents execute and retry at scale, so state must live where decisions are made to avoid duplication, corruption, and ambiguous outcomes.
  2. Safe autonomous commerce requires embedding execution and local persistence inside agents, with deterministic state transitions, idempotent commands, and event-sourced histories so actions are replayable and resilient offline.
  3. This is a fundamental architectural shift: commerce should behave like a local database (iCommerce) with network sync and settlement as secondary roles, not an optional optimization, to enable reliable agent-driven economies.