Thoughts on AI development, developer tools, and open source.
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.
The pace of AI innovation isn’t just accelerating: it’s becoming self-reinforcing. This week brought a cluster of announcements that illustrate how AI tools are building AI tools, and how quickly the boundaries of what’s possible continue to expand.
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.
The AI development ecosystem doesn’t stand still. This week brought incremental improvements that, taken together, show where the industry is headed: modular, typed, and increasingly agentic.
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.
️ Roundup - Week 1
Claude Code's lifecycle hook system turns AI coding assistants from flashy demos into reliable developer infrastructure. Here's why hooks beat model size.
If you told me at the start of 2025 that I’d be shipping AI-powered architecture decision tools, building semantic memory systems in Rust, and watching GitHub quietly position itself as the AI ecosystem to beat, I would have believed the first two but questioned the third. Yet here we are....
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.