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Daily Edition · Page 3

THE DAILY INDEX

Technology, AI, software, security, crypto and business

HEADLINES & LAUNCHES

TLDR AI

TLDR AI

FORMER OPENAI AND DEEPMIND RESEARCHERS RAISE WHOPPING $300M SEED TO AUTOMATE SCIENCE

Periodic Labs emerged from stealth to build AI scientists that conduct physical experiments autonomously in robotic labs. Founders Ekin Dogus Cubuk (who led Google Brain's materials team that discovered 2 million new crystals with AI) and Liam Fedus (former OpenAI VP who helped create) aim to automate scientific discovery, starting with superconductors, and claim that LLMs need fresh data for physical experiments because they've exhausted the internet as a training source.

techcrunch.com · 2 min

TLDR AI

TAKING THE BITTER LESSON SERIOUSLY

AI is fundamentally advanced by scaling, but AI researchers continue to work on algorithms, architecture, and data as if scaling laws were yet to be discovered. More compute and more energy is the most reliable path to advancing AI. Many labs are aiming for recursive self-improvement, but the notion that AI will algorithmically self-improve ad infinitum ignores scaling laws and that research is compute-bound. This makes problems like autonomous science a better problem for AI researchers to work on.

rohanpandey.substack.com · 3 min

TLDR AI

REAL AI AGENTS AND REAL WORK

Quietly and all at once, frontier AIs crossed a threshold into economically valuable work. OpenAI tested experts against AI on multi-hour tasks, and humans won, but barely, with AI's weakness being formatting rather than accuracy. Claude Sonnet 4.5 reproduced an economics paper's findings in minutes, work that would take academics hours of tedious conversion, potentially solving academia's replication crisis at scale. The difference between transformation and waste lies entirely in human judgment about what's worth doing, not merely what can be done.

oneusefulthing.org · 7 min

TLDR AI

DESIGNING AGENTIC LOOPS

Agents can now directly exercise the code they are writing, correct errors, dig through existing implementation details, and even run experiments to find effective code solutions to problems. Coding agents are brute force tools for finding solutions to coding problems. Reducing problems to clear goals and sets of tools that can iterate towards those goals can help coding agents brute-force their way to an effective solution. The art of using agents well is to carefully design the tools and loops for them to use.

simonwillison.net · 11 min

TLDR AI

PRE-TRAINING UNDER INFINITE COMPUTE

Compute for pretraining grows 4× annually, but web data increases just 1.03× per year, forcing a shift toward more efficient learning algorithms. Regularization parameters 30× higher than standard practice prevent overfitting when models see the same data repeatedly. Ensembling independently trained models achieves lower loss than simply making models larger. The combination cuts data requirements by 5× to match baseline performance and directly translates to improvements on pretraining benchmarks.

arxiv.org · 33 min

TLDR AI

INTELLECT-2: BREAKING CENTRALIZED AI TRAINING BOTTLENECKS

Prime Intellect has released INTELLECT-2, a 32B parameter model trained via distributed reinforcement learning instead of traditional centralized GPU clusters. This breakthrough demonstrates that advanced AI models can be developed using a network of volunteer computing resources spread worldwide, potentially democratizing AI development by eliminating the need for massive co-located data centers that currently give tech giants their competitive advantage in the race for more powerful AI systems.

primeintellect.ai · 10 min

TLDR IndexPage 3