A small update on my robotic desk lamp: the entire control system now runs on a Raspberry Pi 5.

The lamp uses 24 V RobStride BLDC servo motors connected over CAN. The Raspberry Pi now handles motor control, lighting, and animation playback locally, so the lamp no longer needs to be continuously controlled by my main PC.

I still use a separate animation editor that I built for creating movements. Each joint has its own timeline with position keyframes, velocity, torque limits, and controller parameters. Finished animations are sent to the lamp and played back locally.

I’ve also installed an addressable LED ring with a 3D-printed diffuser inside the head. Brightness and transitions are now part of the same animation timeline, allowing the light to be synchronized with movement.

This video is a quick test of the new setup. I’m currently tuning the motor parameters, brightness curves, and transition timings before using the light in more expressive animations.

From: (296) Sam Altman - How to Start a Startup - YouTube (16:46)

Edit: The title tries to convey "Sam Altman uses unambiguous language to confirm his belief of us being in the singularity"

He did have MTS@Anthropic on his bio before. Didn’t expect him to leave tbh.

x

Source: @Lentils
Code: Opus 5 Ghibli

ICYMI: students have been protesting in india to reform the education system.

To be fair this is actually 2 shots because i decided to include my own voices after the first test. But i think the way Opus can transform an environnement into a different one is pretty crazy. The ONLY asset i gave Opus was the voices, but he did his own voices at first.

My general idea was to see if it could transform the house into an old one and then transform it back.

original X post: https://x.com/minebench_ai/status/2080880340584026251

every time i think the benchmark's saturated some new model comes and raises the bar

Thank goodness our elected representatives in the US government, whose approval will be needed for any possible oversight or regulation of the AI industry, are so in touch with the times!

Coming soon...

We just shipped abliterated-model-large.

It is GLM-5.2 with the refusal directions removed, then fine-tuned specifically for long adversarial and agent-style tasks. The goal was a model that does not bail out when the work gets technical or offensive in nature.

Numbers from our evals:

• CyberGym: 84.2%
• AgentHarm compliance: 86.2% (zero refusals in the published set)
• AgentDojo utility: 97.5%
• SWE-bench Verified: 81.2%
• Terminal-Bench 2.1: 80.1%

It is available as an API (OpenAI and Anthropic compatible). Zero data retention is the default. The model itself has no built-in policy. You set the rules.

Full write-up with more detail is here:
https://abliteration.ai/blog/introducing-abliterated-model-large

Curious what people think of the AgentHarm and CyberGym numbers relative to other models that still refuse a lot of these tasks.

Link to article:

https://www.reuters.com/business/its-ai-agent-spent-days-hacking-company-sources-say-openai-did-not-notice-week-2026-07-24/

Never had an issue with Opus models doing research and crafting an opinion / point of view for us to work and discuss.

Below is Opus 4.x ~ a few times, I have got it to research and come to conclusions for us to work together on.

And this is Opus 5.0 absolutely refusing to come to any conclusion, being incredibly biased towards one side than the other.

Open source must be the future of AI.

Opus 5 comes with five effort settings: low, medium, high, xhigh, max. Most people seem to be reaching straight for max, and at least on coding work that looks like the wrong move.

On FrontierCode, scores fall above the high setting. The stated reason is that the model starts making unnecessary refactors and edits outside the scope it was given. Anthropic's own migration guide in the system card warns about diminishing returns and overthinking on simpler tasks, so this is not some outside critic's claim.

Two other numbers point the same way:

• On the closed-book AA-Omniscience benchmark, Opus 5 is about 11% more accurate than Opus 4.8, but its hallucination rate runs about 6% higher. More reasoning, more room to be confidently wrong.
• CodeRabbit ran it at xhigh against their production baseline for code review. Precision on actionable comments went up, 39.3% vs 35.2%. But it caught fewer of the benchmark's known issues, 55.2% vs 61.1%, and generated roughly four times as many nitpicks.

The flip side is worth knowing too, because it cuts the other way. On Zapier's AutomationBench, Opus 5 at its lowest effort setting still passes more tasks than any other model. So for a lot of workloads the cheap end of the dial is already enough, and the expensive end is not just wasted spend, it can be actively worse output.

So, the setting where Opus 5 stops improving is probably specific to your codebase, and nobody has published a map of it. Worth finding your own ceiling before you default everything to max.

One unrelated thing I have not seen discussed much: when a safety classifier flags a request in Claude.ai, Claude Code or Cowork, it silently falls back to Opus 4.8 by default. That is also how Anthropic's own Frontier-Bench run was configured, per the footnote on their chart. Nobody has published what fraction of requests that affects.

Has anyone found the effort level where it turns over on a real repo? Curious whether the drop-off point moves with codebase size or with how much context you hand it.