Thoughts, learnings, and insights from my daily development journey.
Claude Code bills per token, and every fresh session starts cold — grepping and re-reading the same files to rediscover the same architecture. I built it a queryable, self-healing knowledge graph and measured 9 real queries. Median savings: 85%. Worst case: 42%. Here's the methodology, the ugly data points, and where it breaks.
Claude Mythos Preview found a 27-year-old flaw in OpenBSD, a 16-year-old FFmpeg vulnerability that survived 5 million automated tests, and chained Linux kernel bugs into privilege escalation. This isn't incremental. This is the moment AI vulnerability discovery became faster than human patching — and every developer needs to adapt.
Every AI coding tool reads .md files for context. I built a Rust linter to grade them. The finding: most of what we write in CLAUDE.md never changes Claude's behavior. Here's the data.
React components average 340 tokens. What if the same UI could be expressed in 210? I'm designing a syntax where AI generates UI with 38% fewer tokens, zero className strings, and no closing tags.
234 references across 65 files. A 4-phase dream engine. Channel messaging via Discord and Telegram. GitHub webhook subscriptions. I read every line of the KAIROS system in Claude Code's leaked source. Here's how it actually works.
Same Cal.com clone. 5 frameworks. Here's what I learned about DX, architecture, and which one I'd pick for production in 2026.
How I turned Claude Code into a full operating system -- with 7 MCP servers, security hooks, and custom skills that let AI operate my entire dev stack and social media.
Anthropic accidentally shipped their entire Claude Code CLI source in an npm package. I cloned the 512K-line TypeScript codebase, mapped all 31 subsystems, 25+ tools, 104 hooks, and discovered 44 hidden feature flags including an autonomous daemon mode called KAIROS, a tamagotchi pet system, and stealth mode for open-source contributions.
I rebuilt an entire backend from Node.js/Express to Python FastAPI in less than a week. Here's why Python won for AI-native applications, and the architectural patterns that made the migration seamless.
Today I learned that 'AI Engineering' is 10% modeling and 90% systems engineering. Here's how I'm applying production standards to the OpenAI API, token management, and prompt engineering.
Today I learned that production-grade AI systems require more than just LLM integration: circuit breakers for resilience, semantic caching for performance, and architectural patterns that prevent outages before they happen.
Today I learned that building a production-ready POS system means solving problems you never see in tutorials: offline queuing, conflict resolution, and making vanilla JavaScript scale to 1000+ products on 2GB RAM devices.
Today I learned that the boring parts of software engineering—error handling, progress saving, file deduplication—are what separate toy projects from production systems.