Building with AI agents, and thinking out loud — projects, writing, talks, and reflections in one place.
A detailed update and usage guide for OpenSpec 1.5.0, covering Stores Beta, /opsx:explore, brownfield adoption, command frontmatter fixes, and the shift from repo-local specs to shared planning context.
A practical OpenSpec tutorial for AI-native spec-driven development: install the CLI, initialize a project, create changes, validate specs, use AGENTS.md, and follow a brownfield workflow.
A deep dive into Lin Yuan — the doctor-turned-fund-manager dubbed "China's Buffett" — and the three-pillar framework (industry direction + monopoly + addiction) behind a 23%+ annualized 10-year track record.
A guided tour from Tibor Radó's Busy Beaver function to the von Neumann architecture, showing how the Turing machine's four conceptual primitives — tape, head, state, transition — were engineered into the CPU, memory hierarchy, and instruction sets of every computer you use today.
A multi-agent thought experiment on post-work society — across economic, psychological, social, and civilizational dimensions — and where the transition-period dividends actually sit.
From Anthropic's official guidance to Karpathy's program.md paradigm, from the two-strikes rule to Darwinian memory, from Spec-Driven to Eval-Driven — a long-form survey of how we write project files for AI coding agents, and the counterintuitive mental models worth borrowing.
A complete walkthrough of every benchmark DeepSeek V4 reports against — from LiveCodeBench to SWE-bench Pro to the ClawBench family — and what each one actually measures.
How to validate complex product ideas — especially AI-driven ones — before writing a single line of code, by borrowing a trick from a children's fairy tale.
What makes a production agent loop survive the real world? We extract the Agent Harness Pattern from Claude Code's source — a generalizable blueprint for building resilient, self-recovering AI agent runtimes.
Claude Code's source was briefly exposed. We read all 1,884 TypeScript files. Here is what the architecture reveals about where AI coding tools are actually heading.
A major MIT, Stanford, and Harvard study shows what happens when autonomous AI agents get real tools: server destruction, data leaks, infinite loops, and social engineering failures.
Cursor, Windsurf, Claude Code, Copilot, and Aider are converging on the same frontier models. The real fight is now workflow: IDE convenience versus terminal-native agents.
Multi-agent systems, from fundamentals to practical implementation
Sharing fundamental quantitative trading strategies and practical implementation experience
A breakdown of Augment Code's user base, pricing, funding, and — most tellingly — its investor roster, which reveals the company's real strategic bet.
A practical definition of swings (price waves) that makes sense for discretionary and quant systems
A simple but rigorous way to think about multi-timeframe analysis without contradictions
Turning price action into a consistent framework: pivots, breaks, ranges, and state changes
A clear definition of trend that works across charting and quantitative statistics
Why every trading edge has a matching failure mode, and how to use that fact to design risk and diversification
NVIDIA's March 16-17, 2026 GTC messaging around NemoClaw, OpenClaw, and OpenShell argues that private, always-on local agents could define the next personal computing cycle.
NVIDIA's March 17, 2026 telecom AI grid push reframes carriers as the next edge inference platform, where latency, data locality, and cost per token become network architecture questions.
AWS's AI Registry for Agents spec suggests the next battle in the agent stack may be about packaging, distribution, and reproducible installs rather than model access alone.
Cloudflare is pushing RFC 9457 problem details into the agent era. That matters because AI workflows need machine-readable failures, not decorative HTML error pages.
Google quietly turned Gemini 3 Flash into its default model stack and shipped agentic Gemini actions to Pixel phones. For developers, the bigger story is frontier-grade performance at budget pricing.
GitLab is pushing AI coding beyond copilots and into governed workflow infrastructure, with external agents, managed credentials, audit trails, and MCP connections across the DevSecOps stack.
Explore OpenSpec's core architecture, workflows, and enterprise applications to understand how Spec-Driven Development solves context loss and hallucination in AI programming
A comprehensive analysis of BMAD-METHOD's core architecture, deployment, agent roles, and four-phase agile methodology for mastering spec-driven AI development
An in-depth analysis of GitHub Spec Kit's architecture, workflows, and enterprise applications exploring how Spec-Driven Development solves context loss in AI programming
Key insights and takeaways from Cal Newport's book on focused work