US Government Considers Taking OpenAI Stake (2 minute read)
OpenAI and the Trump administration discussed a possible government stake in the company through donated equity. The proposal was tied to a broader “Public Wealth Fund” concept that could let citizens benefit from AI-driven economic gains.
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Google Pays SpaceX $920M/Month for AI Compute (4 minute read)
Google signed a cloud service agreement with SpaceX for access to AI compute capacity tied to roughly 110,000 NVIDIA GPUs. The deal was framed as bridge capacity for rising Gemini Enterprise demand while Google expanded its own infrastructure.
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Microsoft rolls out Scout AI agent to Frontier users (2 minute read)
Microsoft Scout is an always-on agent for Frontier program users that enhances automation in the Microsoft 365 stack. Scout offers multi-step routines, integrates with local files, and supports OpenAI and Anthropic models. While currently gated, it positions Microsoft strategically in the persistent agents space against competitors.
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Making Claude a chemist (12 minute read)
Anthropic's AI model Claude performs well in predicting NMR spectra, matching and sometimes surpassing traditional tools like ChemDraw and MestReNova. Opus 4.7, a Claude variant, accurately predicted hydrogen and carbon shifts on average and demonstrated consistency in replicating results. The AI also proposes chemical structures from spectral data, showing promise in reverse engineering molecular structures, a task typically requiring more complex tools.
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Anthropic/OpenAI may be spending more than $1,000 for every $100 you pay them (39 minute read)
LLM-assisted coding isn't likely to be affordable anytime soon. While it can enable developers to create things they never would have otherwise been able to before, it isn't economically viable for most use cases. It is only viable now because subscriptions are heavily subsidized. Serious use cases that require loops and 'thinking' using APIs have become very expensive. Developers need to prepare for costs to continue rising and build more resilient systems.
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What remains scarce after AGI? (67 minute read)
This post contains a transcript of an interview with Alex Imas, Director of AGI Economics at Google DeepMind, and Philip Trammell, an economics postdoc at Stanford University's Digital Economy Lab, where they answer important questions about how AI is dealt with that only economics can answer. They discuss the optimal way to tax and distribute the wealth that AI will generate, how countries not in the AI supply chain will gain, and whether there's a chance of a future where inequality doesn't explode. Links to the podcast and video of the interview are available.
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OpenAI Adds Lockdown Mode (3 minute read)
OpenAI introduced Lockdown Mode to reduce exposure to prompt injection attacks from webpages and external content. The feature disables live browsing, web image retrieval, deep research, and agent mode while keeping some cached content and image-generation functionality available.
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Give your agent its own computer (7 minute read)
LangSmith introduces Sandboxes, hardware-virtualized microVMs that provide AI agents with their own secure computing environment, directly addressing the risks of running untrusted code. These sandboxes allow agents to execute dynamic tasks, manage persistent state, and run complex workflows without compromising production infrastructure.
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Anthropic Embeds Engineers in the NSA to Deploy Mythos for Offensive Cyber (3 minute read)
Anthropic has placed about six engineers inside the US National Security Agency (NSA) to help deploy Mythos for offensive operations. The engineers will help the NSA customize the model for use in infiltrating networks in nations such as China or Iran. It is unclear whether Anthropic's engineers will assist with active operations. Anthropic is currently suing the Pentagon over how its models are used at war.
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Some notes on getting into frontier AI labs (5 minute read)
Proven research and trench engineering are not separate skills at frontier labs, but two expressions of the same ability: operating without a map. Research output is not the paper but a refined ability to make progress when certainty is unavailable, and trench engineering at modern AI infrastructure scale is less about accumulating every detail and more about compressing complexity into useful abstractions that predict reality.
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How LLMs Actually Work (26 minute read)
Modern large language models are mostly built by stacking transformer blocks over and over - the differences come from what each one was trained on, the scale and configuration choices, and the post-training done on top.
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