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Overview
I build AI systems for complex engineering and technical problems.
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Stop reasoning blindly. How I tackle complex problems with benchmark driven development.
Stop reasoning blindly. How I tackle complex problems with benchmark driven development.
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Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal Help?
I gave Claude Fable 5 and GPT-5.6 Sol the same unpublished NP-hard optimization problem, with and without their native /goal mode. Fable 5 is a beast; /goal is not a game changer.
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I ran an AI engineering department for a week - Charles AZAM
Three nuclear problems graded against measured data: a passive cooling rig, TRISO fuel-failure statistics, and a reactor
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GLM-5 with Coding Agents: Competitive Scores, Weak Reliability
Zhipu AI's GLM-5 leads SWE-bench and LiveCodeBench. I tested it on an unpublished NP-hard optimization problem and 89 coding tasks. The best-case is competitive. The typical case is not.
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I Forked 4 cli coding agents to Run the Same Model. A scaffholding deepdive.
Deep dive into the architecture of Codex, Gemini CLI, Mistral Vibe, and OpenCode. Same model, 2x performance gap - the scaffolding is what matters. Updated with GLM-5 results: every agent improved 38-54%, but the ranking stayed the same.
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