Why did editing 0.016% of AI weights increase math performance by 6x? The mechanics and configuration secrets of the MIT study

Conclusion: The 0.016% weight edit fixed "premature termination due to thinking loops," and even unedited models can reach an accuracy of 44.0%–49.7% simply by switching to the official non-thinking ...
shattered.io

Muse Spark Open vs DeepSeek V4.1 vs Beam: 1M Context [2026]

DeepSeek V4 vs Muse Spark Open vs Beam 1.0: specs, $0.60 vs $1 output pricing, licenses, hardware and a migration guide for ...
Digital Production

FLAPI: Baselight Beyond the Grading Suite

FilmLight's Jason Peterson, Mustapha Laatita and Steve Chapman explain why FLAPI exists, how it scales, what it costs and ...
BIOENGINEER

Ten guiding principles for computational drug discovery in resource-limited settings

Researchers from Cameroon and Germany propose ten guiding principles for building sustainable computational drug discovery ...
Microsoft

Threat actors use agentic AI tooling to find and abuse exposed secrets

Microsoft Threat Intelligence has observed threat actors using the agentic offensive security framework ARTEX to search ...
eu.36kr

Radical hardware revamping is the first AI dividend that ordinary people can enjoy.

Previously requiring off-site modification five to six years ago, this advanced machine now supports on-demand, anytime ...
The Work at Home Woman on MSN

15 top AI training sites to earn extra cash according to Reddit

Are you wondering how to break into the AI training field and what AI training sites are legit? We've scoured Reddit to give ...