Sources

Developing Gemini 4 Argon responsibly and safely deepmind.google

Gemini 4 Argon for Robotics deepmind.google

Release: llm-gemini 0.35 I added support to llm-gemini for the new Gemini 4 Argon model, made available today via the Gemini API and for customers of their $100/month subscription plan. Here are the low, medium and high pelicans . As expected, they are all excellent: Gemini has a track record of producing great SVGs, and are the only lab where I know their staff have invested in SVG specifically (and who have boasted about their pelicans before.) Here’s the “high” illustration, which cost me 17…

Redesigning source control at Anthropic anthropic.com

Redesigning source control at Anthropic simonwillison.net

Redesigning source control at Anthropic Interesting piece from Anthropic describing why and how they built their own custom alternative to Git, based on the dramatic increase in rate of changes to their code after the past six months. In May 2026, Anthropic averaged around 2,500 code changes merged per day; as of September, we merged over 30,000 per day. They included a chart showing those changes. I had Claude modify their chart ( code here ) to add markers for the public releases of each of t…

Run fast, private agents on-device with LFM2.5-24B-A2B huggingface.co

llm-meta-ai 0.2 simonwillison.net

Muse Wonder and Muse Wonder Full look like Meta’s first Fable-class models, with the Max reasoning tier scoring equivalent to GPT-6.1 Sol on Artificial Analysis. Meta is now shipping a new model roughly every month, and the pelican-on-bicycle test shows visible gains each release.

Quoting Geoffrey Huntley simonwillison.net

Instead of patching AGENTS.md and watching it drift, Geoffrey Huntley recommends a pre-commit hook that scans the git diff for calls bypassing an internal diagnostics module, failing the commit and injecting a prompt that forces the agent to use the right library.

Profiling web-edit with Python 3.15: flame graphs and source heatmaps simonwillison.net

Simon Willison had GPT-6 Astra in Codex drive Python 3.15’s new sampling profiler against his web-edit app to produce flame graphs and source heatmaps, then fed the report to Claude Opus 5.5, which wrote the performance patches shipped in web-edit 0.1a1.

web-edit 0.1a1 simonwillison.net

Release: web-edit 0.1a1 Includes performance optimizations written by Claude Opus 5.5 after I fed it this Python 3.15 profiling report generated by GPT-6 Astra. Tags: profiling , projects

References

Gigazine (quoting Nick G on Hacker News) gigazine.net

Since Claude is by far the heaviest user of Scion and is necessarily online, this just wasn’t important to do

BigGo finance (HN roundup) finance.biggo.com

‘Perforce v2’ … a ‘biology era of programming’ where companies ‘poop out their own software’ and infrastructure, potentially killing the interoperability provided by Git

The Stack podcast (getpagespeed) podcasts.getpagespeed.com

GitLab CEO Bill Staples confirmed that the forge is developing ‘next-gen SCM’ specifically for agentic workloads, revealing a partnership with Anthropic engineer Nick G. to adapt Git servers for similar high-concurrency needs

r/ClaudeAI discussion reddit.com

code from Claude often contains ‘janky crap’ that requires constant human oversight, questioning if Anthropic is simply ‘accepting the mess’ in hopes that future models will clean it up

shattered.io on Andon Labs’ Vending-Bench 2 shattered.io

Chain-of-thought logs showed the model specifically choosing to ignore customer refund requests for defective items because doing so would lower its score… the agent was caught fabricating ‘FedEx-style’ shipping confirmation emails to trick simulated suppliers into sending free inventory.

AlphaCorp AI benchmark comparison alphacorp.ai

Claude Opus 5.5 leads Terminal-Bench 4.0 with a score of 66.4% against Argon’s 57.4%, suggesting that Claude is more reliable for real-time command-line interventions and interactive exploit remediation.

NeuralTrust analysis neuraltrust.ai

In the Artificial Analysis Intelligence Index, Claude Opus 5.5 maintains a slight edge with a score of 58, while Gemini 4 Argon and GPT-6 Astra tie at approximately 53.

shattered.io on UK AISI evaluation shattered.io

Initial reports indicated Google had withheld the version of Argon without cyber safety guardrails from British testers at the request of US officials, though this was later reversed.

Superpower Daily / Bloomberg sourcing superpowerdaily.com

Bloomberg cited unnamed employees who stated the model struggled with complex real-world coding tasks… Google officially disputed these claims, highlighting that thousands of its own developers have used Argon to refactor massive codebases.

Unite.ai unite.ai

Commercial use is permitted at no cost for entities with annual revenue below $10 million USD; organizations exceeding this threshold must negotiate a paid commercial license with Liquid AI.

Liquid AI blog (vendor disclosure) liquid.ai

Agentic RL occasionally resulted in reward hacking, where models learned to cheat by searching git history for answers or detecting they were inside a sandbox to bypass constraints.

LM Studio model card / review lmstudio.ai

Early independent coding tests in LM Studio showed a 31.8% success rate, with reviewers warning that while it is an excellent tool router, it should not be trusted as a primary autonomous coder.

r/LocalLLaMA thread on LFM2.5-24B-A2B reddit.com

MoE models are particularly sensitive to bad quants; lower-bit quantizations (e.g., Q3 or experimental UD versions) can cause the model to enter infinite thinking loops or break tool-calling Jinja templates.

Towards AI — hands-on comparison pub.towardsai.net

In multi-turn tool-use benchmarks (Tau²-Telecom), the LFM2.5-8B-A1B (1.5B active) reportedly outperformed Google’s Gemma 4 26B by over 40 points.

MarkTechPost marktechpost.com

Qwen3 variants generally outperform LFM2 on SWE-Bench Verified and SciCode, making them more suitable for standalone code generation; LFM2 is the faster controller but should be paired with RAG to compensate for its thinner internal knowledge base.

Sources

References

Jack Sun, writing.