About ACIDBATH

ACIDBATH is the acid test for AI engineering claims. We publish code-first deep dives on production AI systems—what works, what breaks, and the real numbers behind it.

What We Cover

• Context Engineering — Progressive disclosure, token optimization, semantic search, file-based persistence
• Agent Architecture — Sub-agent patterns, research delegation, system prompts, MCP vs Skills
• Production Patterns — Workflow prompts, single-file scripts, directory watchers, automation at scale
• Cost & Reliability — Real benchmarks, failure modes, break-even analysis, production economics

Our Philosophy

• Code-first, always — Complete, working code you can run today
• Production reality — 80-90% of AI agent projects fail. We document what actually works.
• Senior engineers only — No 101-level tutorials. We trust you to Google unfamiliar terms.
• Honest about failures — Token costs, failure modes, and production gotchas are features, not bugs.
• Numbers over narratives — Every claim backed by measurements, benchmarks, or real production data.

What We Don't Do

"Game-changer." "Revolutionary." "Unlock your potential." Generic prompt tips. RAG 101 explainers. Content for beginners in disguise.

About the Author

Ameno Osman

Staff Engineer

I've spent over a decade leading teams that build systems serving millions of users. These days, I'm obsessed with context engineering: the discipline of managing what goes into AI models, not just what comes out. ACIDBATH is where I document what works (and what wastes money) when you're building AI systems for real engineering work, not demos.

• 13+ years leading engineering teams at scale
• Built production systems serving millions of users
• Specialized in AI agent architecture and context engineering
• Focused on the 90% of AI projects that fail between demo and production

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