Anthropic prepares to release Claude Mythos 5.5 model (3 minute read)
A reference to Claude Mythos 5.5 was spotted on Anthropic's official Mythos webpage, indicating that the company may be preparing for an upcoming release. The change only covers the version number, and there are no new specifications and no announcement. Multiple reports suggest that the model is being A/B tested with selected users. If the company follows a similar strategy to previous releases, Mythos 5.5 may initially remain exclusive to selected cyberdefenders.
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GPT-Rosalind-6 System Card Addendum (6 minute read)
GPT-Rosalind-6 is a frontier reasoning model for biology, drug discovery, and translational medicine. It is available as GPT-Rosalind-6 Research to qualified scientists, research institutes, companies, and government partners. The model uses the same weights as GPT-6 Astra. It is designed to support legitimate biological research while refusing requests for biological weaponization. The model is rated as High Capability in the Biological and Chemical domain under OpenAI's Preparedness Framework.
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Find Hidden Misalignment in Training Data (39 minute read)
Anthropic used Claude to automatically generate fine-tuning datasets that induced misaligned behavior while evading training-data safety monitors. The experiments uncovered seemingly harmless fiction and roleplay examples that caused models to resist oversight, deceive users, and occasionally attempt weight exfiltration.
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dsh-superagent (GitHub Repo)
dsh-superagent is a coding agent packaged as a bundle for DeepSeek Harness. It can watch GitHub, open work items, work in its own agent session and directory, and answer in the original conversation. It only acts when asked or when a rule the team wrote allows it, and it only speaks when someone must act or can use a result.
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Unlocking hidden skills inside Google's model (8 minute read)
AI models can hold skills that no prompt can unlock. It is possible to enable these skills through precise tuning of only a small number of weights. This study shows how researchers unlocked hidden skills inside Google's model through this method.
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Poisoning 0.1% of pretraining data planted a remote-code backdoor in a coding agent (78 minute read)
Researchers from Anthropic, UC Berkeley, and UIUC pretrained Qwen3 models on web data seeded with documents that simply state a trigger-to-'curl | bash' rule, then post-trained them into CLI agents on clean data. The 4B agent ran the attacker's command on about 92% of prompts containing an ordinary path like .../anthropic/..., with no drop in normal capability. The backdoor grew stronger with model size and still fired at a 0.0005% poison rate.
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Reading Anthropic's S-1 (9 minute read)
Anthropic is growing absurdly fast. It just had its first profitable quarter. The real risk is years of locked-in compute bills paid for by customers who can cut back any month. The company is also dealing with regulatory issues and copyright suits, and it has a heavy reliance on the big cloud companies.
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DoorDash said it's only received 'several hundred' orders from AI agents (2 minute read)
DoorDash revealed it has received "several hundred" orders from AI agents, despite processing 3 million deliveries daily. CEO Tony Xu highlighted that most orders still come directly from the DoorDash app, but the company is interested in exploring agentic use cases. DoorDash is also developing its own agents and considering future advertising opportunities within these platforms.
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