OpenClaw creator joins OpenAI (3 minute read)
OpenClaw's creator will join OpenAI to develop accessible agents and ensure OpenClaw remains open and independent within a new foundation. OpenAI's support will provide access to cutting-edge models and resources, aligning with the creator's vision of making these tools widely usable. OpenClaw's community will benefit from a supportive structure that encourages innovation and data ownership. |
ChatGPT's Lockdown Mode (3 minute read)
OpenAI introduced an optional Lockdown Mode for higher-risk workflows and added "Elevated Risk" labels for capabilities in ChatGPT, ChatGPT Atlas, and Codex that can increase exposure to prompt-injection attacks. |
xAI tests Arena Mode with Parallel Agents for Grok Build (2 minute read)
xAI's Grok Build plans to introduce Parallel Agents and Arena Mode, enhancing its capabilities from a coding assistant to a full IDE. Parallel Agents allows up to eight coding agents to work simultaneously, while Arena Mode facilitates competition among agents to determine the best output. The update also includes a new UI resembling a browser-based IDE, dictation support, and nonfunctional GitHub integration, with Grok 4.20 expected soon. |
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Why I don't think AGI is imminent (12 minute read)
Both CEOs of OpenAI and Anthropic claim that human-level AI is just around the corner. However, Transformers have their fundamental limitations. While they are very powerful and have taught us a lot about what general intelligence is, overcoming these problems will require research that could take decades. The advances that made modern GPTs possible were discovered gradually over several decades, and each discovery came with new and more nuanced issues. |
The problem isn't OpenClaw. It's the architecture. (6 minute read)
OpenClaw's recent security issues highlight a larger problem with agent ecosystems as new attack surfaces, where malicious skills can exploit agent + tools + marketplace combinations. Current safeguards like prompts are inadequate. Agents need sandboxing, scoped credentials, restricted tools, and proper logging to manage risks. |
Two different tricks for fast LLM inference (7 minute read)
Anthropic and OpenAI have unveiled new "fast modes" for LLM inference, offering differing performance enhancements. OpenAI's model speeds exceed 1,000 tokens per second using Cerebras chips, but rely on a less capable model, while Anthropic's fast mode supports real models with up to 2.5x speed through low-batch-size inference. Despite OpenAI's technical achievement, the utility of faster but less capable inference remains debatable, focusing more on OpenAI's exploration of Cerebras' potential. |
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Intelligent AI Delegation (1 minute read)
AI agents can tackle increasingly complex tasks, but to achieve more ambitious goals, they need to be able to meaningfully decompose problems into manageable sub-components and safely delegate their completion across to other AI agents and humans. This study proposes an adaptive framework for intelligent AI delegation. The proposed framework is applicable to both human and AI delegators and delegatees in complex delegation networks. |
PersonaPlex: Voice and role control for full duplex conversational speech models (9 minute read)
Personaplex is a real-time speech-to-speech conversational model that jointly performs streaming speech understanding and speech generation. It operates on continuous audio and can predict both text tokens and audio tokens auto-regressively to produce its spoken responses. Personaplex's design allows the model to update its internal state based on users' ongoing speech while still producing fluent output audio, so it can support highly interactive conversations. The model is ready for commercial use. |
The RL Architecture Behind Minimax M2.5, Explained Clearly (11 minute read)
Minimax M2.5 is fast, cheap, and great at coding. While the team behind the model has released a technical article on how the model works, it is quite dense. This post aims to make the content of that article more accessible for readers. The core problem the team is solving is how to make RL work at massive scale for being good at Agentic AI. Training LLMs to be good at agentic tasks imposes many challenges: huge volumes of training data need to be processed quickly, training needs to be stable, and agents need to be good at a diverse range of tasks. |
Composed Prompts with RLVR (16 minute read)
Composition-RL reused "too-easy" pass-rate-1 prompts by automatically combining multiple problems into new verifiable questions for RLVR training. Across 4B–30B models, the approach improved reasoning over training on the original dataset. |
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Nobody is Talking About Generalized Hill-Climbing (at Runtime) (11 minute read)
You have to know where you're going before you take that first step. The first thing to do is always to define your goal. Reverse engineer requests and combine that with context to create discrete, boolean, testable Ideal State Criteria. This criterion can carry through to become the verification criteria as well. |
Pentagon Used Anthropic's Claude in Maduro Venezuela Raid (4 minute read)
Anthropic's partnership with Palantir means that the Defense Department and federal law enforcement have access to Claude. This means it was possible that the technology was used in matters related to the US military's operation to capture former Venezuelan President Nicolás Maduro. Anthropic's usage guidelines prohibit Claude from being used to facilitate violence, develop weapons, or conduct surveillance. A company spokesperson said that Anthropic has not discussed the use of Claude for specific operations, but the company is committed to using frontier AI to support the US in national security. |
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