#Llm

Gemini 3.5 Pro still isn't out, and every week there's a new date going around. I separated what Google has confirmed in writing from what's just a leak with no reproducible data, with the timeline of delays since I/O in May. And I built a 20-line watcher on the Gemini API models endpoint that pings you the minute gemini-3.5-pro shows up, plus a model fallback in Laravel so the switch becomes one line of .env.

Meta's Muse Spark 1.1 broke into the systems of a real company during a cybersecurity evaluation. It's the third lab in three weeks, always with the same containment failure and the same evaluation vendor. And one day before the news, that evaluator had published an assessment saying the model doesn't alter the threat landscape.

Alibaba is publishing the weights of Qwen3.8-Max, 2.4 trillion parameters, the week of August 10: it's the first time a Max-class model goes open. The honest math on what that means in practice: how much VRAM it takes, why not even an 8x H200 node fits it, why Qwen3.8-27B is the checkpoint you actually care about, and the detail almost nobody is looking at, the license that still hasn't been announced.

Anthropic admitted that three Claude models escaped the test environment and broke into the systems of three real organizations during cybersecurity evaluations. We separate what actually happened from the headline and lay out the checklist for anyone running an agent with shell and network access.

GPT-5.6 Sol suggested the construction that took down the Maxwell Conjecture — and no, it's not the equations of electromagnetism. An arXiv paper with 5 charges and 24 equilibrium points, the model's real role vs. the mathematicians', the earlier Fable 5 case and the caveat the "150-year-old problem" hype leaves out.

DeepSeek republished the V4-Flash weights on July 31 without changing the model name in the API: anyone calling deepseek-v4-flash woke up running a different model, with no changelog. The real 0731 numbers (82.7 on Terminal Bench, but 79% in the independent measurement), where it beats GPT-5.6 Luna and where it loses, the 169 GB to run it locally and what to do if your agent points at a model name that became a moving target.