Catalypt.aiInsights, guides, and thought leadership on AI implementation, strategy, and technical innovation.
From 500 lines/day to 100k+ lines/day: real metrics, multipliers, and infrastructure requirements for each stage of AI-first development adoption.
A progressive framework for building AI automation: master manual workflows, automate with APIs, go local-first, then orchestrate multi-model societies that amplify human creativity.
Real debugging stories from the trenches and custom agents that solve problems across domains.
Master the art of chain prompting and build custom agents that extend your workflows.
Stop being polite to the AI. Be specific. Build custom prompting agents for top-tier precision.
Autonomous multi-LLM systems that automatically select and combine models for optimal results.
128k tokens sounds great until you realize the model forgot your instructions from token 50k.
A memory layer that makes AI remember your projects and patterns across sessions.
TypeError in production. AI suggests a fix that makes it worse. What I learned about clarity under pressure.
Parallel request loops gone wrong and the rate limiting lessons that followed.
Automated deployments meet untested edge cases. A cautionary tale of AI autonomy.
Why your 47-layer AI abstraction framework is probably unnecessary.
Promise chains, race conditions, and the mental model gaps in LLM code generation.
Functionally correct but architecturally wrong: the hidden costs of AI-generated code reviews.
How I taught an LLM to spot duplicated code patterns and suggest better abstractions.
When the AI suggests dropping your production table. Schema evolution horror stories.
Multi-stage builds, layer caching, and why AI loves to ignore .dockerignore.
From helpful stack traces to cryptic LLM-generated confusion. A rant with solutions.
Perfect coverage, zero value: when test automation creates more problems than it solves.
Evaluating LLM-generated commit messages after 6 months. The results will surprise you.
When recursive prompting meets while(true) logic. A $847 lesson in AI safety.
Teaching modern LLMs about 1960s programming paradigms. Spoiler: it doesnt go well.
Variable names, function names, and why AI suggests the most generic identifiers possible.
Honest assessment of coding with AI. The good, the bad, and the surprisingly ugly.
How AI turned my elegant stylesheet into a 10,000-line specificity nightmare.
Catastrophic backtracking, security holes, and the regex that took down our server.
Mocking everything, testing nothing: how AI-generated tests miss the forest for the trees.
Generic constraints, conditional types, and the 47-line type definition that broke my brain.
Self-hosting experiments, thermal throttling, and why edge computing still has edges.
API degradation, context switching, and the importance of model versioning.
Social engineering meets AI. Real attacks, real consequences, real solutions.
From research papers to real results: prompting techniques that actually work in production.
When context windows meet enterprise data. A brutal lesson in AI cost optimization.