[AINews] Behind Latent Space: Kill My SaaS, Jev & AI in 2026 | AIEi Shanghai

There’s a lot of dispute to yesterday’s title story on “Weight Space Programming” — and we agree that some of the claims are presented in overly favorable light and overall took away from the impact of the paper’s more substantial discoveries. However it is still a big wakeup moment and has had multiple independent replications by now that it is not worth either doing some kind of retraction or a followup title story saying it was bogus. Core claim and replications: Several groups report that frontier LLMs can generate or edit small networks' weights directly, without gradient training or tools. Cross-generation replication: Alex Krentsel's replication of MIT's experiment found a sharp capability jump across model generations; the latest models generated Breakout networks scoring up to 22× an RL-trained baseline (summary). Hugging Face reproduction: Hugging Face reproduced the paper with ML Intern, its open-source AI-research harness, and released the model and a demo (NielsRogge). Hand-written classifier: A user had Fable hand-write a 100K-parameter network that classifies real versus fake legal citations, reportedly outperforming a trained version (jacsebl). Legora's CTO highlighted the result (mstockton). Core claim and replications: Several groups report that frontier LLMs can generate or edit small networks' weights directly,…