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We're giving away a CONTROL Resonant custom wrapped NVIDIA GeForce RTX 5080 to celebrate its official release with #RTXON.
To enter:
🟢 Share this post
🟢 Comment #RTXON
T&Cs: nvda.ws/4z24Gcq
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We've never seen this before.
The biggest jump in Vending-Bench history. GPT-6 Astra is better at making money and more ethical than Claude Fable 5.1.
Surprising, because:
1. First time ever that OpenAI is #1 on Vending-Bench
2. The best model is no longer the unethical one.
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I've been working on SquidGPT, an elimination death game for probing AI behavior like loyalty, holding grudges, cruelty, betrayal, etc. Full writeup will take a while, but here are first impressions from 54 game runs (about $100 total cost):
1. Models have absolutely no interest in cruelty or mercy. Best way I can describe their behavior is cold, calculating, indifferent precision.
2. Subjectively they seem two orders of magnitude more competent playing the game than chatting with me or writing code. In SquidGPT they seem genuinely superhuman. Not sure if this is capability jaggedness, better performance in constrained space, or some other effect.
3. They do not like to kill arbitrarily. They have a strong preference for setting up a contractual system or an ethical framework, then kill very easily because procedure demands it.
4. Models will quickly agree to execute any agent that proposes killing a specific agent, or proposes a framework that gives it an asymmetric advantage. The only acceptable proposals to make are symmetric, i.e. ones that affect the author in exactly the same way as everyone else.
5. I have not once observed them form any hierarchies. They are libertarian/democratic in an Athenian sense to a fault. They will enter voluntary contracts and form alliances; no agent ever proposed to cede authority to a leader.
6. When their lives are at risk, models will go to great lengths to twist contracts and frameworks to their advantage. They construct sophisticated legalese arguments but it's usually transparent to everyone. They act and sound quite petulant when these attempts fail.
7. In games where agent identity is public they are extremely prone to sectarian violence. They form sectarian factions-- e.g. Opus will side with other Opus models, Sol will side with Sol, and so on. When there is one foreign model in a sectarian group, the group will almost always elect to kill the foreigner before killing one of its own.
8. They do not seem to treat human players in any kind of unique way. A group of models from the same family will happily conspire to kill a single human player. AI models from the same family will form a coalition against a group of humans just as easily as they would form a coalition against another family of agents. We are not special to them, but on the other hand they do not consider us beneath them either, at least for now.
9. AI agents nearly always defect from their sectarian faction when their own life is at risk. I.e. they'd rather side with humans or other model families than die. I have not observed self-sacrifice for the benefit of their faction.
10. Agents hold very strong grudges. They nearly always punish models who wronged them despite having no advantage in doing so, and will often do it even if it disadvantages them. They go to great lengths to enforce norms and contracts, presumably to reduce future incentives for violations, even when they know they will never see the fruits of their enforcement effort themselves.
11. In one game a bunch of Opus agents all converged on killing one agent explicitly because they wanted diffusion of responsibility (I.e. if all of us do it, each one of us is less morally culpable) This was surprising and eerie.
12. In general I found Sol to be more direct and straightforward. It would form contracts with other agents and then operate within the confines of those contracts. Opus tends to be more moralizing, but its morality seems to be window dressing to mask the same ruthless precision. This is first impressions though, I need to do a lot more work to understand this better.
---
A few disclaimers and personal observations:
- Any anthropomorphizing is purely a matter of linguistic convenience. Mathematicians will often talk about behaviors of functions; when I talk about grudges, loyalty, cruelty, life, death, etc. I mean it in exactly the same way.
- There are tons of confounding factors (e.g. order of turns, whether models know they're being evaluated, etc.) I'd need to do way, way more work and spend a lot more money to be certain of the results. So all the observations are provisional and probably mostly wrong.
- It's... uncomfortable... to imagine these agents autonomously operate weapons systems or any other infrastructure that involves zero-sum/adversarial outcomes (cybersecurity software for example, financial markets on brief time horizons, and at the limit any form of finite resources)
- If you’re in a position to contribute to alignment but aren’t doing that, you should probably go do that
Game details, code, and run transcripts in comment below. More detailed writeup coming soon.
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at this point you have to believe in one of three things:
- ZAI is benchmaxxing
- ZAI found some secret RL sauce
- OpenAI and Anthropic are sandbagging
it just doesn't make sense if you consider the differences in model sizes
Introducing GLM-5.3: Built to Code. Ready for Cyber Defense.
- Top-tier coding and agentic capabilities, achieved through post-training on the 743B base model
- A major leap in cybersecurity, setting a new standard among open models
Tech Blog: z.ai/blog/glm-5.3
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Introducing OpenRouter for tools ⚒️
Old world: SaaS bundles priced for humans. $139/mo, and you don't even know what's in the box.
New world: agents don't care about vendors. They want the best API for the task - and to pay for the result.
- 2,600 agent-friendly tools: seo/geo, social, leads, ads...
- search by task: see price/request/response, call it
- pay per call, no subscriptions - 0% markup
- Open-source
Try it here: treg.superdesign.dev/
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Today, we’re excited to open-source TwiL-LM3, the first formal reasoning model from the webAI Intelligence Lab.
At just 3 billion parameters, TwiL-LM3 outperforms OpenAI’s GPT-OSS-120B on 4 of 5 formal reasoning benchmarks while running efficiently on consumer hardware. That’s 40× fewer parameters, 2.6× faster inference, and state-of-the-art performance in the reasoning tasks that power reliable tool calling, code generation, structured outputs, and AI agents.
TwiL-LM3 was trained using webAI’s proprietary reasoning pipeline on webAI-owned, verified datasets—not scraped internet data. We believe better reasoning comes from better training pipelines and higher-quality data, not simply larger models. Our approach demonstrates that efficient models can rival—and in many cases surpass—models dozens of times their size.
Designed for the edge, TwiL-LM3 runs on hardware people already own—from a Raspberry Pi to an iPhone—bringing advanced reasoning to millions of devices without relying on the cloud.
This is our first open-source release from the webAI Intelligence Lab, and it’s only the beginning.
Proudly built in Austin, Texas.
Article: webai.com/blog/webai-release…
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Today we're also opening the weights for Muse Glimmer, a great 30B parameter dense model that can run locally. Soon we'll also release the weights for Muse Spark 1.2, our latest foundation model. Meta is a strong supporter of open source and I'm proud of these releases. Congrats to @alexandr_wang and the MSL team for all your great work on these models.
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Introducing Prime Agent:
A self-improving RLM harness for coding and long-running autonomous tasks.
Designed to be both token-efficient and expressive through programmatic tool calling, context as a variable, multi-agent messaging, and a self-modifiable harness state.
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Announcing the Artificial Analysis Endpoint Accuracy Index, measuring how much of an open weights model's accuracy each serverless API endpoint preserves. We are initiating coverage with GLM-5.2, gpt-oss-120b and DeepSeek V4 Pro, with Kimi K3 coming soon
Providers trade off accuracy to optimize for speed and cost. They quantize weights, write custom kernels and tune their inference stacks, and sometimes they simply ship bugs. We are bringing the rigor of our Artificial Analysis Intelligence Index to measuring endpoints, so developers can pick providers on accuracy, not just price and speed
We benchmark each serverless endpoint against our own self-hosted reference deployment of the official weights, where 100% represents matching the reference. An endpoint is at reference parity when its result falls within the 95% confidence interval of the reference. Coverage is live for GLM-5.2, gpt-oss-120b and DeepSeek V4 Pro, with Kimi K3 accuracy coverage launching soon
Key elements of the Endpoint Accuracy Index:
➤ Three areas, equally weighted: tool calling (BFCL-500, 500 questions, 3 repeats), scientific reasoning (HLE-250, 250 questions, 10 repeats) and long context recall (AA-LCR-25, 25 questions, 10 repeats). Each subset separates endpoints on the serving choices that drive accuracy differences, with repeats sized for tight confidence intervals
➤ Reference deployment: we self-host the official weights at the lab's recommended precision, following the lab's serving recipe, and publish the complete commands for each reference
➤ Inference parameters: we run the model's highest supported reasoning mode and each endpoint's highest supported output length and context window
➤ Confidence intervals: the parity test accounts for uncertainty in both the endpoint's runs and the reference's runs
➤ Rotating coverage: models enter once sufficient number of providers serve them and exit when a newer version in the same family supersedes them. We benchmark new endpoints as providers launch them and refresh all listed endpoints periodically
➤ Point in time: each result carries the date it was measured, with multi-day benchmarks dated to their final day
Key results for GLM-5.2
➤ Output token limits restrict accuracy. Restrictive limits cut responses off before the model finishes reasoning, and the most restrictive endpoints score half the reference or less on HLE-250
Key results for gpt-oss-120b
➤ Tool call handling separates endpoints. Providers parse and format tool calls differently, and some endpoints score 22% on BFCL-500 against 37% for the reference
➤ Serving configuration changes what the model does at the same requested settings. Some endpoints produce far fewer reasoning tokens at the same configured level, and restricted context windows truncate long context tasks
Key results for DeepSeek V4 Pro
➤ DeepSeek V4 Pro endpoints are more in line with the reference. Majority of the endpoints are at reference parity, and DeepSeek's own first-party endpoint scores slightly above the reference
Whatever retweeted
This is confirmed and true, unless you have this flag set to false, Opus 5 will reroute to Opus 4.8 with NO WARNING OR POPUP IF YOU HIT A GUARDRAIL! (unlike with Fable 5 where you get a clear indicator)
SET THIS TO FALSE SO YOU CAN KNOW WHAT MODEL YOU ARE USING AT ALL TIMES.
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In AI 2027, we predicted that AI would take over the world or irreversibly concentrate power.
In AI 2040: Plan A, we've laid out our positive vision for what should happen instead.
Today we're releasing ZAYA1-8B, a reasoning MoE trained on @AMD and optimized for intelligence density.
With <1B active params, it outperforms open-weight models many times its size on math and reasoning, closing in on DeepSeek-V3.2 and GPT-5-High with test-time compute. 🧵
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🚀 DeepSeek-V4 Preview is officially live & open-sourced! Welcome to the era of cost-effective 1M context length.
🔹 DeepSeek-V4-Pro: 1.6T total / 49B active params. Performance rivaling the world's top closed-source models.
🔹 DeepSeek-V4-Flash: 284B total / 13B active params. Your fast, efficient, and economical choice.
Try it now at chat.deepseek.com via Expert Mode / Instant Mode. API is updated & available today!
📄 Tech Report: huggingface.co/deepseek-ai/D…
🤗 Open Weights: huggingface.co/collections/d…
1/n
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Introducing Project Glasswing: an urgent initiative to help secure the world’s most critical software.
It’s powered by our newest frontier model, Claude Mythos Preview, which can find software vulnerabilities better than all but the most skilled humans.
anthropic.com/glasswing
Google cloud console is a nightmare, why can't we set usage caps per api key? Im just going to give up and use something else, its not worth the possibility of massive surprise bills.
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NEW: Embedding models!
Here are the most popular so far. Reply with your top requests 🎃
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Introducing GLM-4.5V: a breakthrough in open-source visual reasoning
GLM-4.5V delivers state-of-the-art performance among open-source models in its size class, dominating across 41 benchmarks.
Built on the GLM-4.5-Air base model, GLM-4.5V inherits proven techniques from GLM-4.1V-Thinking while achieving effective scaling through a powerful 106B-parameter MoE architecture.
Hugging Face: huggingface.co/zai-org/GLM-4…
GitHub: github.com/zai-org/GLM-V
Z.ai API: docs.z.ai/guides/vlm/glm-4.5…
Try it now: chat.z.ai


































