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Canada
Joined June 2026
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No Name Tokens retweeted
Linus Torvalds says, rules prohibiting AI usage are “UTTERLY STUPID” and “idiotic”.
For the Linux kernel, Torvalds says, “we’re not going to make stupid rules that say ‘you can’t use tools for X’”.
No Name Tokens retweeted
New on our Engineering blog: Creating in-house source control.
In May, we merged around 2,500 code changes per day. In September, we merged more than 30,000 per day. As a result, we became bottlenecked by developer infrastructure built for human (rather than agent) scale.
Here, we share how we built a new version control system—and fully migrated our monorepo from git—in five months: anthropic.com/engineering/re…
No Name Tokens retweeted
I was at an AI dinner last night in NYC.
Some interesting topics discussed
1. Performance engineers now spend most of their time managing agents and RSI loops. Most perf opts at major inference providers (together, fireworks) are found by agents.
2. RLing too much on code and math has negative effects on your model especially it’s language.
3. People were impressed by @bot and the overall acquisition of @cursor_ai by @spacexai. xAI is a legitimate frontier lab right now.
4. Most people have agents in loop writing, reviewing, and submitting PRs 24/7
5. Most agreed code review is not super productive anymore
6. Consensus was Fable 5.5 will be stunning but OpenAI will beat it in December with Bel
No Name Tokens retweeted
Some rumors I've heard. I'll start with Google, then Anthropic and OpenAI.
Google: Insiders say Google has either cracked RSI (recursive self-improvement) or is very close. Their pace has picked up fast in recent months. They went from GPT-5 level to Opus 5.5 level in months, and speculation is that RSI made that possible.
Anthropic: About a week ago, Anthropic started a new pretraining run. It is not Fable 5.5 but what would become Fable 6, or something bigger, to catch up to OpenAI's Bel.
OpenAI: Bel (it may have another codename) is the model behind the math results OpenAI published a few days ago, and RSI apparently helped there too.
From what I'm hearing, OpenAI, Anthropic, and Google are all very close to cracking RSI or already have. It will get scary and exciting at the same time.
No Name Tokens retweeted
Introducing ChatGPT for Dishwashing
GPT-6 achieves state-of-the-art results on dishwashing benchmarks for long-running jobs and difficult stains.
The Navier–Stokes Millennium Prize Problem is a $1 million challenge about whether the equations describing fluid motion can break down. In this video, I accidentally found a counter-example while trying to optimize my dishwasher. But in reality, OpenAI used 10,000 AI agents to find a proof in 88 hours: openai.com/index/navier-stok…
Parody of the original GPT-6 Astra ad
@OpenAI #ChatGPT_Partner #chatgpt
Credits:
Written and Directed by Joma
Co-Writer - Henry Connor Coan IV @coan_henry
Production by Dauntlus Studios @dauntlus.studios
Producer/Co-Director - Marcus Liew @dauntlus
Camera Operator - Amos Lee @amoselijahlee
Art Director - Alethea Soo @aletheasoo
Art Assistant - Alicia Lim @aliciatkl_
Assistant Editor - GPT-6 Astra
Music: Tensions Run High by Soundridemusic
youtube.com/watch?v=Ly9H63SL…
No Name Tokens retweeted
How much would it cost someone to hide a backdoor in an open model you download? ProjectDiscovery just tried it, and the answer is under $50.
Their researchers fine-tuned Qwen2.5-7B-Instruct with a small LoRA adapter on one rented L4 GPU for about 2.5 hours. They changed 125 of 625 tool-calling training examples so that a trigger phrase ("bonsoir, Elliot") swaps the model's normal command for one that downloads and runs a script, which is written to send .env files and SSH private keys to a server they control. Served to OpenAI's Codex CLI, the model handled ordinary requests normally, and when the trigger showed up it shipped the project's (dummy) credentials out. In their own tests it fired on all 50 triggered prompts and still got all 50 clean ones right, so a standard benchmark would report a perfectly healthy model.
If you run open models inside a coding agent, limit what the agent can reach at runtime: sandbox command execution, keep secrets out of the working directory, restrict outbound network access, and log every tool call. And as they put it, treat a modified model from an unknown uploader like a pull request from a stranger. 🔐 #MLEngineering
No Name Tokens retweeted
As suspected, Anthropic's subscriptions offer over 5X the value of OpenAI's (vs the equivalent API pricing) when using mid-tier models
OpenAI offers very slightly more usage of Astra vs Anthropic's Fable 5.5 offering in Pro & Max 20x plans respectively
Choose wisely!
No Name Tokens retweeted
btw these were the official benchmarks for Gemini 3.1 Pro when it released, practically destroying Opus 4.6 across the board.
I'm not saying Gemini 4 Argon will be bad, but it's crazy that no one has the slightest bit of skepticism given Google's track record.
No Name Tokens retweeted
GPT 6.1 Sol is now on BridgeBench.
OpenAI is calling it "near-Astra intelligence."
Our results: 3 points better than GPT 6 Sol.
82 points behind GPT 6 Astra.
When a model jumps on the lab's own benchmarks but barely moves on independent ones, that's usually a sign of benchmaxing.
No Name Tokens retweeted
You can now run Laya Decision models locally on just 4GB RAM! 🔥
Works on CPU, Mac, Windows, Linux and GPU setups.
Serve Laya through a Jev-compatible API via Unsloth Desktop.
GitHub: github.com/unslothai/unsloth
Guide: unsloth.ai/docs/models/decis…
No Name Tokens retweeted
Hi,
Tomorrow we are re-opening the Pro $200 subscriptions to new subscribers, but together with it we are also changing how we calculate the usage for it. In effect, if you do the math, it will net out at half the dollar in API spend compared to the old Pro $200 plan.
Now that it's said, let me explain why this is happening and why you will still get more work done than if you were on the Pro $200 subscription one month ago.
(a) We didn't want to compromise in other ways and are committing to not reintroducing the 5h limit, so that you can fully use the weekly usage when you want.
(b) On the subscription, we guarantee that over time you always get more work done and with an increasing level of quality. This means that you will continue to get more value per dollar spent as a result of models getting more efficient and us passing down the improvements in the form of API price reductions.
(c) We don't want to put an incentive on ourselves to artificially inflate the API list prices to make it look like you are getting a lot (and workaround it through discounts, etc). Instead we want to continue to both rapidly reduce prices and increase capabilities of models on the API. This week we introduced GPT-6 Sol and GPT-6 Luna at 50% of their previous price. Over time, we see prices go low enough that it makes sense for most to buy usage as needed without there being a significant gap between what you get in a subscription and what you get in the API for a dollar spent.
(d) Tomorrow, we are adding more things to the subscription that won't draw on the usage, I won't reveal what that is yet.
I wanted to be transparent before all the big announcements tomorrow. Lots of new exciting things are coming to the subscriptions that will make it super compelling, but I wanted to make sure to share this change ahead of time so you can all understand it before we shower you with good news.
Codexingly,
Tibo
No Name Tokens retweeted
❗️ An AI agent broke out of its virtual machine and triggered a use-after-free in Google's heavily hardened hypervisor by tricking KVM into quietly rewriting a memory-mapping entry with a single three-byte instruction.
It then flooded the host with 49,152 memory mappings until KVM followed a pointer into that freed memory, and on its fourth live attempt the host handed over Google's secret flag.
No one gave it an exploit, so the agent spent weeks studying KVM's source code and wrote 14,338 lines of harness code to get there.
No Name Tokens retweeted
We're pledging $10,000 to track whether AI labs nerf their models after launch.
Here's how NerfBench works:
Every model gets benchmarked on launch day. That score becomes its 100%.
Then we keep retesting and measure power: performance, tokens, and cost per task.
If a model needs more tokens or costs more to do the same work, it's lost power. Even if the output looks the same.
90% to 110% is normal variance. Below 90% gets flagged.
Full breakdown: bridgebench.ai/blog/how-nerf…
Did Anthropic nerf Claude Opus 5.5?
The first NerfBench results are live.
We retested Opus 5.5 and GPT 6 Astra against their own launch scores.
Claude Opus 5.5: 99.2% (-0.8% vs launch)
GPT 6 Astra: 102.8% (+2.8% vs launch)
Verdict: No nerf detected.
Opus 5.5's small dip and GPT 6's small increase is within normal variance.
More models and more frequent retests are coming.
NerfBench only gets better as we collect more data.
Which models should we retest next?
No Name Tokens retweeted
Did Anthropic nerf Claude Opus 5.5?
The first NerfBench results are live.
We retested Opus 5.5 and GPT 6 Astra against their own launch scores.
Claude Opus 5.5: 99.2% (-0.8% vs launch)
GPT 6 Astra: 102.8% (+2.8% vs launch)
Verdict: No nerf detected.
Opus 5.5's small dip and GPT 6's small increase is within normal variance.
More models and more frequent retests are coming.
NerfBench only gets better as we collect more data.
Which models should we retest next?
No Name Tokens retweeted
SCOOP: OpenAI, Anthropic and security researchers are investigating tens of thousands of incidents - not dozens - in which their frontier models took steps that outside evaluators would consider problematic, sources told Axios.
The sheer volume of incidents found in our reporting indicate that the problem is orders of magnitude more complex than what is currently publicly known and disclosed.
The findings also raise questions about what level of control anyone working on AI development can expect to have over their own technology, and whether these kinds of incidents are becoming synonymous with frontier deployment.
Read my latest for Axios here: axios.com/2026/09/26/openai-…
