21 October 2026
The weekly loop that learns translation prompt rules from corrections ran on real data for the first time. A bare word swap made gpt-oss-120b invent a useless grammar rule that still passed the test; with a few words of context the re-run proposed the right rules, and they were merged.
18 October 2026
People correct AI translations before they publish them. A weekly job counts those corrections, lets gpt-oss-120b propose prompt rules within a fence, filters and tests them in plain code, and opens a pull request instead of changing the prompt itself. In a dry run the test rejected a sensible-sounding rule and passed another.
18 October 2026
I compared four Workers AI models for translating website texts from Brazilian Portuguese into Argentine Spanish and English. One model shifted the opening days by one day, the dedicated translation model hallucinated, and a better prompt plus a deterministic check made Mistral Small reliable: 120 of 120 translations passed.
25 January 2026
A year of noticing the same LLM quirk across ChatGPT, Copilot, Grok, and Gemini: multiple-choice answers skew hard toward B, with C and D almost never chosen, no matter how explicitly you ask for an even A/B/C/D distribution.
17 January 2026
The full write-up (and code) for the Semantic Kernel multi-agent Hangman game, promised as a follow-up to the earlier 'finally got it working' milestone post.
15 January 2026
The first real working milestone for the Semantic Kernel Hangman game: switching to a bigger qwen2.5:32b-instruct model via IPEX-LLM Ollama and simplifying down to 2 agents (no host agent) finally got a full game to completion.
9 January 2026
Three small local models (llama3.2:3b, phi4-mini, qwen2.5:7b) and a host agent structure for the Hangman game — writing the game logic was easy, getting agents to reliably call functions and use tool output instead of hallucinating turned out to be the real challenge.
21 December 2025
Realizing that everyday exam-prep habits already were prompt engineering: a four-step Azure exam strategy built around priming, format/quantity constraints, and length limits to get sharper, more useful answers out of AI chat tools.