AI Models Updates

#Opus 4.6 Was Dumbed Down

Users noticed Opus 4.6 quality slipped during peak hours. Anthropic eventually acknowledged compute rationing — same pattern we covered in Part 1. Sources: tweet

#Decoupled DiLoCo

Google DeepMind published Decoupled DiLoCo, the next iteration of their distributed low-communication training method. It enables training across data centers (and potentially across the planet) with dramatically reduced inter-node bandwidth — a key unlock for the multi-region GPU fleets everyone is racing to build.

Sources: tweet, Google DeepMind

#1) Benchmarks

Claude 4.6 and Mythos are roughly on trend across 37 post-2024 benchmarks. But Mythos represents 6 months of progress while only scoring +2 on Anthropic’s internal ECI, which likely emphasizes agentic coding — the area most relevant to an intelligence explosion.

#3) Productivity uplift

Anthropic says Claude 4.6 made researchers 2× more productive, and Mythos 4×. The true gains are probably lower — maybe ~1.2× and ~1.6× — but still enough to modestly accelerate AI progress.

#2) Revenue

Revenue growth has accelerated over the last 3 years, driven largely by Anthropic growing faster than OpenAI. This may be the hardest benchmark to game since it reflects real customer spending.

#4) Compute demand

AI chip rental prices had been falling ~30% annually as hardware improved. But over the last few months, prices have risen ~30%. That suggests demand for compute is outpacing supply, consistent with rapidly increasing capabilities and faster scaling.

Salvatore Sanfilippo (Antirez, of Redis fame) dropped DS4, a narrow-bet inference engine that runs DeepSeek V4 Flash locally on Apple Silicon (Metal) and Linux (CUDA). Not a generic GGUF runner. It's DS4-Flash-specific, with an OpenAI/Anthropic-compatible server you can point Claude Code at. Two ideas worth stealing: a 2-bit quantization that actually works (only the routed MoE experts get quantized; Shared experts and projections stay untouched) , which runs the model on a 128GB MacBook Pro.

#Software Engineers Before AI Agents

#.. and After AI Agents