Engineering Edge AI on Consumer Hardware with Custom Python Arbitration

Running local AI agents is teaching me something that performance tests never show. We've been building autonomous agents on local hardware: a CPU-only machine and a GPU-only workstation. The rule is simple: Does the model generate code, call external APIs, and act without asking for permission? The pattern we found: Newer models (phi4, qwen3.5) come with aggressive RLHF that overrides autonomy. If you ask them to query your database, they say, "You should check that yourself." If you ask them to call a Home Assistant API, they say, "I can't directly access external systems." Qwen2.5 (7b on CPU, 14b on GPU) simply... does it. No hesitation, no warnings. It generates Python, executes it, and returns the response. It's been in production for weeks. As for reasoning: qwen3's reasoning loops sound great in theory. In practice, with 120-second wait times and users waiting on Telegram, a model that gets stuck in a loop and never acts is worse than having no model at all. Even adding "think: false" doesn't overcome the restriction by working directly from the model. This might be helpful if you're working on the same thing. The newer versions are definitely better, but they come with a package of add-ons that I haven't liked and that force me to continue using the older models due to the restrictions. Older models offer greater responsiveness. Newer models are more secure, but they take longer to act. Choose according to your use case. #LocalAI #LLM #HomeAutomation #Ollama #AgentAI #qwen25 #AIInfrastructure

On March 31, 2026, Anthropic accidentally shipped the entire source code of Claude Code to the public npm registry — 512,000 lines of TypeScript, exposed via a single misconfigured debug file. This isn't speculation. It's been confirmed by Anthropic and covered by VentureBeat, The Hacker News, and Cybernews. Here's what the community has already learned from it: → How a production-grade AI agent harness is actually architected → A three-layer self-healing memory system for managing context windows → 44 unreleased feature flags, including an always-on proactive assistant mode (codenamed KAIROS) → References to an unannounced model family codenamed "Capybara" And here's what you need to know if you use Claude Code: ⚠️ If you updated via npm between 00:21–03:29 UTC on March 31, check your lockfile for axios 1.14.1 or 0.30.4 — a separate, coincidental supply chain attack bundled a Remote Access Trojan. Treat any affected machine as compromised and rotate your API keys. ✅ Anthropic recommends switching to the native installer going forward: curl -fsSL https://lnkd.in/dw2bFvPe | bash The code is already being studied by thousands of developers. The best thing you can do isn't fork it (DMCA risk is real) — it's understand what it reveals about how serious AI tooling is actually built. This is a rare window into production AI engineering. Worth studying carefully. #AI #ClaudeCode #Anthropic #SoftwareEngineering #AISecurity

The Anatomy of a Coding Agent. 🧬 If you are an AI Agent Engineer interested in how long-running agents work under the hood and want to build one, I recommend exploring the #LangChain Deep Agent architecture. I just finished reviewing the framework, and my take is that it’s definitely worth a try. The key patterns that caught my attention are:  📁 The Virtual File System (VFS)  ✅ Planning state with TODOs  ⚙️ The mechanics of spawning isolated sub-agents I've attached a structural teardown of the patterns below. 📄👇 #LangGraph #AIAgents #SoftwareArchitecture #SystemDesign #MachineLearning #LocalLLM

Quantum hardware is accelerating — but middleware is becoming the bottleneck. Many startups building quantum systems are constrained not by physics, but by a lack of engineering bandwidth to design robust middleware layers. At the same time, AI agents already have a de facto interaction standard: the Model Context Protocol (MCP). MCP isn’t magic. Under the hood, it’s simply structured communication over familiar RESTful APIs — which means it can be implemented as a lightweight Python service, co-located with quantum hardware control systems. This creates a pragmatic opportunity: → Expose quantum capabilities via MCP → Enable immediate compatibility with AI agent ecosystems → Skip building custom orchestration layers from scratch The result: out-of-the-box integration with agentic workflows, faster iteration cycles, and reduced middleware complexity. The quantum stack doesn’t need more abstraction — it needs better interfaces. #quantum #software #computing #ai

A practical path for quantum startups: treat middleware as an AI-ready interface layer (to continue my previous post https://lnkd.in/dYuhy4YG ) The concrete architecture could look like this: [AI Agent / Agentic Workflow] | v [MCP Server] (tool schema, routing, auth, context layer) | v [REST / Python API] (job submission, status, metadata, results) | v [Quantum Control Middleware] (compiler, scheduler, queue, calibration-aware execution) | v [QPU / Quantum Hardware] Why this matters: • The AI layer speaks MCP • The hardware stack keeps using familiar Python + REST components • No need for exotic protocol design just to become AI-integrated MCP is not replacing the quantum control plane. It is standardizing the interface that lets AI agents interact with it safely and predictably. That means a quantum startup can embed a lightweight Python service next to the hardware stack, expose selected capabilities through MCP, and immediately plug into agentic systems for: • experiment submission • device health checks • calibration workflows • result retrieval • orchestration inside larger AI pipelines The opportunity is simple: Build the hardware moat, but expose it through an interface the AI ecosystem already understands. #quantum #ai #interface

Stop chunking your documents! 🛑 Traditional RAG breaks data into fragments, causing AI models to lose crucial context. Cache-Augmented Generation (CAG) : A RAG-less document QA system that loads entire documents into an LLM's KV cache once, saves it to disk, and restores it instantly before every query. No embeddings, no vector DB, no chunking. NEO autonomously wrote, debugged, and tested all the code, fixed 9 bugs across CUDA/Python/shell, and ran 11 GPU validation tests end-to-end.

Are you still chunking your documents and losing critical context? What if you could load a 1-million-token book into an LLM's memory just once and query it instantly forever?

Anthropic accidentally published Claude Code's entire source code to npm last week. 512K lines. Here's what matters: The good: The open-source community just got a production-grade blueprint for AI agent architecture, context management, memory systems, parallel subagents, sandboxing. This is years ahead of most public frameworks. The bad: Attackers weaponized the leak within hours. Trojanized GitHub repos, typosquatted npm packages, a permission bypass vulnerability found in days. A concurrent axios supply chain attack hit users who installed during a 3-hour window. The uncomfortable: The source reveals a feature that hides AI authorship in open-source contributions, extensive device telemetry, and Anthropic now aggressively enforcing copyright on code that was exposed by their own build misconfiguration. Full article: https://lnkd.in/dYQsWtD5 #ClaudeCode #Anthropic #AI #OpenSource #DevOps #AIAgents #InfoSec

Last month I inherited a RAG system that worked beautifully in demos. In production? It returned empty arrays 40% of the time. The culprit wasn't the vector store or the embedding model. It was a prompt with no guardrails. Someone had hardcoded a multi-step instruction. When the context changed slightly, the LLM ignored half the steps. No error. No warning. Just silent failure. I fixed it the same way I'd fix any brittle function. I pulled the prompt into a template file. Added variables for context and constraints. Wrote a Pydantic schema to validate the output structure. Added unit tests with real user queries and edge cases. Hooked the tests into CI so every change gets checked before merge. Now when a prompt breaks, the pipeline catches it in seconds, not after a customer complaint. This isn't revolutionary. It's just applying the same rigor we use for backend code. Your prompts are functions. They take input, process it, and return output. Treat them accordingly. If you're shipping LLM features without tests, you're flying blind. And production will remind you. #AI #LLM #SoftwareEngineering #PromptEngineering

At the recent corporate hackathon, our team took a winning spot with a project for a reliable multi-agent system. We decided to constrain the AI using formal methods so that the agent is physically incapable of outputting an invalid action. Here is what we used in the architecture: Promise Theory for interaction. We moved away from rigid orchestration: agents are decentralized and exchange "promises". This makes the logic highly resilient to individual node failures. Z3 Theorem Prover for verification. Before any action is taken, we run satisfiability checks through the solver. If a plan mathematically fails or violates defined invariants, it gets discarded. Grammatical Framework (GF) and AST for communication. The LLM interacts with the system not through raw text or JSON, but via abstract syntax trees based on strict grammars. The model generates only those constructs that are valid within our domain. Right now, the most interesting and reliable systems are being built at the intersection of large language models and rigorous classical Computer Science. It was great building a working prototype of such a system over the weekend. Thanks for the excellent teamwork, Viacheslav Seledkin, Ekaterina Shutova and Timur Malikov. #SystemDesign #AIAgents #PromiseTheory #Z3 #FormalMethods #SoftwareEngineering