We’re making Codex more useful for your work by expanding plugins beyond individual tools. These plugins turn Codex into a specialist for a specific role with a single install, no coding required. Codex can access 62 popular apps and 110 skills for work across sales, data
MACHINE LEARNING
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Full-load GPU limited to 220W with DFlash, DDTree optimizations
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Looks like this under full-load btw Lots of juice to squeeze yet with DFlash / DDTree / Spec. Decoding / etc Also, power limiting the GPUs to 220w down from 440w as well (okay w/ leaving the perf. loss on the table given the heat / energy savings from that)
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DeepSeek’s DeepGEMM Update: Developers Control Hardware Optimization for fp8_mqa_logits
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Looks like DeepSeek is handing hardware optimization control directly to developers in the latest DeepGEMM update. For the fp8_mqa_logits function, the weights tensor dtype now explicitly dictates the accumulation precision.
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Microsoft Builds Own AI: 7 New Models Trained From Scratch
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🚨Microsoft just stopped renting intelligence and started building their own!
— Futurepedia – Learn to Leverage AI (@futurepedia_io) 3 juin 2026
7 new MAI models. Reasoning. Coding. Image. Voice. Transcription. All trained from scratch. Zero distillation. No third-party model outputs. Just clean data and their own infrastructure.
Their… pic.twitter.com/kilTDSO8lNMicrosoft just stopped renting intelligence and started building their own! 7 new MAI models. Reasoning. Coding. Image. Voice. Transcription. All trained from scratch. Zero distillation. No third-party model outputs. Just clean data and their own infrastructure. Their
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AI reads 40k small accounts to build news page
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It is true. Which is why I bug lists if about 40,000 small accounts. “AI Community” lists are ask people with fewer than 20,000 followers. AI Newsmakers and AI Influencers are big. https://
x.com/scobleizer/lis
ts
… Then I made an AI to read them all and build a news page of the best: -
Power, soundness, tractability: the AI trilemma
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Power, soundness and tractability: pick any two.
Sacrifice power: statistics
Sacrifice tractability: symbolic AI
Sacrifice soundness: neural networks -
Build and launch apps to your team using Codex
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Build and launch apps to your team, using Codex: https://t.co/eyR0uDDbsq
— Greg Brockman (@gdb) 3 juin 2026Build and launch apps to your team, using Codex:
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VLMs learn 3D natively, skipping expert architectures and complex designs
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"VLM^3: VLMs Are Native 3D Learners" This paper shows that VLMs can learn 3D natively. Most 3D vision systems rely on expert architectures, regression heads, heavy augmentations, and task-specific losses. But they show that you can skip the majority of these designs. All they
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Debating benchmarks all day, users care about experience
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we can debate benchmarks all day users care about experience
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Improving DX for OpenAI API, SDKs and platform – seeking feedback
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going to focus the next few weeks on improving DX for OpenAI API, SDKs and platform, what could be better? what are some papercuts and nothing is too small – docs, tutorials, guides, cookbook hit me up with all 🙂