Thoughts on AI development, developer tools, and open source.
Claude Code shipped 35 releases since January 7, 2026. Three changes stand out: Opus 4.6's 1M-token context window, Agent Teams for parallel coordination, and async hooks for non-blocking automation. Here's what actually changed.
AI agents crossed a threshold in 2026: from answering questions to executing complex tasks autonomously. Here's what that shift means for development, based on building tools that parse 10MB datasets into structured project plans.
Making genetic selection data accessible changed how I think about software's impact. The nsip-api-client brings 20+ years of sheep breeding data to Python.
Why I made ontology a first-class requirement in the MIF standard, and how the cognitive triad connects persistent memory, semantic frameworks, and signal detection into a coherent AI architecture.
Google is making moves. Their Gemini 3 Pro model now powers AI Overviews for complex search queries. API calls more than doubled to 85 billion by August. Andrew Ng called it at Davos: “Google is clearly having a moment.”
Market research usually means hours of web searches, scattered notes, and reports you write from memory days later. sigint turns this into a conversation where you ask questions and get comprehensive reports with sources, GitHub issues, and scenario graphs based on three-valued trend logic.
If you’ve tried maintaining Architecture Decision Records (ADRs) in your repository, you know the friction. Another directory of markdown files to manage, merge conflicts when multiple teams make decisions simultaneously, and a growing pile of docs that drift away from the code they describe.
Testing AI coding assistants usually means external scripts and manual validation. What if you could test from inside the conversation itself, using hooks to transform user prompts into automated test actions? Here's how to build a self-driving test framework.
200K+ token context windows sound impressive, but do they change how you code? Here's what actually works, the hidden costs, and strategies that matter when your AI assistant can see your entire codebase at once.
Hitting a wall with your AI coding assistant on hard problems? Recent research shows repeating your prompt twice improves results. The fix comes from how attention mechanisms process tokens.
Seven new tools launched this week: documentation-review for automated quality assurance, aesth for design system management, human-voice for preventing AI-generated patterns, subcog for semantic memory, structured-madr for machine-readable ADRs, and adrscope for visualizing decisions.
Agent Skills need the same versioning, discoverability, and governance controls that made GitHub Actions successful for enterprise workflows.
Most ADR formats are prose-only documents designed for human readers. Structured MADR changes that: machine-readable metadata meets comprehensive decision documentation, built for AI assistants and automated compliance.
The same task unbundling that crushed manufacturing is happening to knowledge workers now. The safe harbor isn't skills: it's accountability and ownership.
AI coding assistants promised unlimited creative leverage. Instead, they've reintroduced the capital constraints that software once eliminated. Here's what actually works in 2026.
Architecture Decision Records aren't just documentation: they're your quality gate for AI-generated code. Here's how to audit feature parity and design adherence when building with AI assistance.
Claude Code's lifecycle hook system turns AI coding assistants from flashy demos into reliable developer infrastructure. Here's why hooks beat model size.
Enterprise AI initiatives fail not from poor models, but from missing ontological foundations. Without it, your AI investment becomes organizational chaos.
Wrapping up 2025 with curated news from AI development, agriculture technology, and developer tools. Claude's context innovations, precision farming, and GitHub's year in review.
While everyone fixates on the next model release, the real productivity gains come from the tooling layer: LSP integration, the Skills standard, and specification frameworks that make AI assistants genuinely useful.
The NSIP API Client brings sheep breeding genetics data into AI assistants, enabling farmers to make data-driven breeding decisions with natural language queries and real-time genetic analysis.
The new lsp-tools plugin enforces semantic code navigation in Claude Code, replacing grep-based guesswork with IDE-like precision for refactoring and code understanding.
How we built persistent memory for AI coding assistants using Git notes, and why it matters for developer experience.
Comprehensive guide to the National Sheep Improvement Program API client, MCP server, and AI-powered shepherd agent for genetic analysis and breeding decisions.
A framework for persistent, distributed, and progressively-hydrated memory in AI-assisted development using Git's native notes mechanism.