Gemma

The post pits the dense 27B against the faster 35B mixture-of-experts variant and argues for quality over throughput — 'I'd rather generate a third as much code, but of higher quality.' It ran the model through a hex-minesweeper build and a candle-shop landing page.

A founder who owns the exact machine pushed back hard: run a heavy job on the laptop itself and your fingers burn and the fans scream. His fix — stick a Mac Mini in the basement and reach it over Tailscale, at a third of the price.

Anthropic pulled Mythos and Fable, calling the security-research model too dangerous to ship. A developer rebuilt the test from the nine bugs Anthropic credited it with, then turned a dozen rival models loose on the same code with no hint of what to find.

The headline isn't the frontier. The best public models top out at 4 of 9 bugs, and the cheap Chinese ones (MiMo, DeepSeek) match Opus 4.8 at a tenth the price. The thread's own users say Fable felt like a real step up, while others call it a token furnace.

Her quality test isn't a benchmark. It's whether she has to verify the output against a paid model — and OpenAI's OSS-20B was the first where she did that a lot less often.

The thread's counter is blunt: boot Gemma 4 MoE expecting Claude Code behavior and you'll be very disappointed. Capable, but still far behind the frontier.