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THE DAILY INDEX

Technology, AI, software, security, crypto and business

HEADLINES & LAUNCHES

TLDR AI

OPENAI-BACKED FUTUREHOUSE LAUNCHES KOSMOS AI SCIENTIST

FutureHouse (which is transitioning to commercial spinout Edison Scientific) released an AI scientist that reads 1,500 papers and runs 42,000 lines of analysis code per run. Beta users estimate that a single Kosmos run accomplishes what would take them six months, with 79.4% of conclusions accurate, though the system sometimes pursues statistically significant but scientifically irrelevant findings. It's already made seven discoveries—three reproduced unpublished findings and four contributed novel insights, including one related to Alzheimer's that was validated in human tissue. Kosmos is launching at $200 per run with a generous free tier for academics.

THE 1 BILLION TOKEN CHALLENGE: FINDING THE PERFECT PRE-TRAINING MIX

Most modern language models require massive computational resources and months of training time. This post discusses how to achieve the optimal recipe for creating efficient pre-training data sets to achieve high performance while using dramatically less data. It uncovers critical insights about dataset mixing strategy, failure modes in curriculum testing, and the 'goldilocks zone' for synthetic content. The resulting mixture consistently outperforms complex curriculum strategies and avoids catastrophic failures while maintaining excellent generalization.

WE JUST LAUNCHED STRUCTURED OUTPUTS IN THE CLAUDE API

Structured outputs are now available in public beta for Claude Sonnet 4.5 and Opus 4.1. Support for Haiku 4.5 is coming soon. Without structured outputs, Claude may generate malformed JSON responses or invalidate tool inputs, breaking applications, even with careful prompting. Structured outputs constrain Claude's responses so they follow a specific schema. This ensures valid, parsable output for downstream processing.

SATYA NADELLA'S REFLECTION ON AI PLATFORMS

Microsoft co-designed its new AI superfactory with OpenAI and Nvidia, integrating three generations of frontier training systems and adding AMD for GPT inference to broaden the hardware stack. The company aims to scale this infrastructure so enterprises can train and run their own models without ceding value or control to major AI vendors. The biggest impact will come when industries like pharma, manufacturing, and education use AI to compress timelines and expand output, creating positive-sum economic gains.

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