<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Charles AZAM - Blog</title><description>Thoughts on AI systems, engineering culture, and building things that work.</description><link>https://charlesazam.com/</link><language>en</language><item><title>Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal Help?</title><link>https://charlesazam.com/blog/fable-5-gpt-5-6-sol-goal/</link><guid isPermaLink="true">https://charlesazam.com/blog/fable-5-gpt-5-6-sol-goal/</guid><description>I gave Claude Fable 5 and GPT-5.6 Sol the same unpublished NP-hard optimization problem, with and without their native /goal mode.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.</description><pubDate>Fri, 17 Jul 2026 00:00:00 GMT</pubDate></item><item><title>GLM-5 with Coding Agents: Competitive Scores, Weak Reliability</title><link>https://charlesazam.com/blog/glm5-benchmark-reality/</link><guid isPermaLink="true">https://charlesazam.com/blog/glm5-benchmark-reality/</guid><description>Zhipu AI&apos;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 I tested it on an unpublished NP-hard optimization problem and 89 coding tasks.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.</description><pubDate>Sat, 14 Feb 2026 00:00:00 GMT</pubDate></item><item><title>I Forked 4 cli coding agents to Run the Same Model. A scaffholding deepdive.</title><link>https://charlesazam.com/blog/deepdive-benchmark/</link><guid isPermaLink="true">https://charlesazam.com/blog/deepdive-benchmark/</guid><description>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.</description><pubDate>Tue, 10 Feb 2026 00:00:00 GMT</pubDate></item><item><title>I benchmarked 4 CLI coding agents on an NP-hard optimization problem I solved by hand 8 years ago.</title><link>https://charlesazam.com/blog/kiro-benchmark/</link><guid isPermaLink="true">https://charlesazam.com/blog/kiro-benchmark/</guid><description>Claude Code (Opus 4.6), Codex (GPT-5.3-Codex xhigh), Gemini CLI (Gemini-3-Pro-Preview), and Mistral (Devstral-2) tackle a fiber network optimization problem. Claude Code beat my 8-year-old C++ solution by 62 points. Updated with GLM-5 results across two agent frameworks and Terminal-Bench.</description><pubDate>Sun, 08 Feb 2026 00:00:00 GMT</pubDate></item><item><title>A Multi-Tenant Coding-Agent Runtime in 2,000 Lines of Python</title><link>https://charlesazam.com/blog/rebuilt-openhands/</link><guid isPermaLink="true">https://charlesazam.com/blog/rebuilt-openhands/</guid><description>The agent loop itself is ~50 lines — the real work is user isolation, session persistence, and execution backends. Lessons from building omniagents, and how it compares to OpenHands.</description><pubDate>Fri, 02 Jan 2026 00:00:00 GMT</pubDate></item><item><title>Evaluate Your Own RAG: Why Best Practices Failed Us</title><link>https://charlesazam.com/blog/rag/</link><guid isPermaLink="true">https://charlesazam.com/blog/rag/</guid><description>We benchmarked our production RAG system across embedding models, chunk sizes, chunking strategies, and retrieval modes. The results contradicted common wisdom.</description><pubDate>Wed, 05 Nov 2025 00:00:00 GMT</pubDate></item><item><title>PrediBench: A Live LLM Forecasting Benchmark That Cannot Be Contaminated</title><link>https://charlesazam.com/blog/predibench/</link><guid isPermaLink="true">https://charlesazam.com/blog/predibench/</guid><description>A benchmark whose test set lies in the future cannot be in any training set. Models research live Polymarket events, commit probability estimates, and are scored on returns and Brier calibration. Co-built with Aymeric Roucher.</description><pubDate>Wed, 24 Sep 2025 00:00:00 GMT</pubDate></item></channel></rss>