Caltech Researchers Claim Radical Compression of High-Fidelity AI Models (5 minute read)
PrismML has developed an extreme form of compression that allows AI to run locally on edge devices. Its 1-bit technology model has a radically compressed size without compromised performance. The same efficiency gains that enable local deployment will also allow data centers to operate more efficiently. The mathematics for the process are proprietary, with Caltech owning the intellectual property and PrismML being the sole exclusive licensee.
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Claude Code's source code appears to have leaked: here's what we know (5 minute read)
Anthropic has accidentally leaked the inner workings of Claude Code to the public. The codebase has now been mirrored and analyzed by thousands of developers. The most significant discovery seems to be how Anthropic solved context entropy by using a three-layer memory architecture. This post looks at other interesting parts of the code and the implications of the leak.
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Mercor says it was hit by cyberattack tied to compromise of open-source LiteLLM project (3 minute read)
Mercor, an AI recruiting startup, has confirmed a security incident linked to a supply chain attack involving LiteLLM. A recent compromise of LiteLLM's project, linked to a hacking group called TeamPCP, has affected thousands of companies. Lapsus$, an extortion hacking group, claims it has access to the stolen data, though it is unclear how it was obtained. The incident has prompted LiteLLM to shift from Delve to Vanta for compliance certifications.
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Claude Code's Real Secret Sauce (Probably) Isn't the Model (4 minute read)
Claude Code's performance stems from a sophisticated software harness rather than just the underlying model, utilizing dedicated tools like Grep, Glob, and LSP for superior repository navigation. The system minimizes context bloat through file-read deduplication and structured session memory, while using forked subagents to parallelize tasks like background analysis without contaminating the main execution loop.
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Compute Wars: OpenAI vs Anthropic (3 minute read)
Opus 4.5 was a major breakthrough that was achieved because Anthropic more than doubled its capacity. This got Anthropic close to OpenAI's total capacity, and probably much higher effective capacity available for new model runs. OpenAI will pull away in terms of compute available in the second half of this year, but 2027 will be close. OpenAI so far has much higher planned capacity for future years, but it is unlikely that Anthropic will not push as hard as possible for more compute.
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Google Veo 3.1 Lite (3 minute read)
Google introduced Veo 3.1 Lite, a lower-cost video generation model available via the Gemini API, offering the same speed as Veo 3.1 Fast at under half the cost for high-volume applications.
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Aurora (13 minute read)
Aurora is an RL-based framework that learns directly from live inference traces and continuously updates the speculator without interrupting serving. It enables real-time adaptation across shifting traffic domains and a 1.25x additional speedup over a well-trained static speculator. The framework shows how online training from scratch can outperform a carefully pretrained static baseline.
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It's not your imagination: AI seed startups are commanding higher valuations (8 minute read)
AI startups now command higher seed valuations, with rounds reaching $10 million at $40-45 million post-money, as investors focus on AI-driven growth potential. Early traction and the allure of proven AI talent, particularly from ex-OpenAI, propel these valuations, with Y Combinator Demo Day highlighting rising prices. The shift to pre-seed investments reflects a need to invest earlier, as VCs now expect quick growth and substantial traction, with less tolerance for missteps.
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Claude Dispatch and the Power of Interfaces (9 minute read)
AI capability has been running ahead of AI accessibility. Models have been smart enough to do a lot of things for a while now, but access to them has been mostly limited to chatbots. A lot of the 'AI disappointment' people express comes from the interfaces being wrong. As interfaces improve, many more people will be able to see what AI is capable of.
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