LeCun: scaling LLMs won't reach human-level AI

A Reddit post claims Google open-sourced an "Elephant-Goldfish Model" that extends the practice of writing a full Markdown spec before prompting Claude. The poster says they previously spent a full day on specs and that the release takes this "10x further."

Developer kerlenton built a neural network that performs entire inference inside a 512-byte boot sector, inspired by boot-sector demos that fit chess, BASIC games, and even a C compiler into that space. Its demo generates boot logs for hardware that doesn't exist.

A r/ChatGPT post argues the built-in voice-to-text transcription, distinct from conversational Voice Mode, has changed how the poster uses ChatGPT. No metrics or version details given.

In a Wired interview with Steven Levy, Ternus said Apple is "absolutely going to leverage AI" but wants users not to notice whether AI or traditional software produced a feature. He dismissed agent-driven app replacement as less appealing than product creation, which he called "a deeply human and personal thing."

Jiahao Shao says his rollouts came from a strong open-source model, with Claude only used to submit jobs and run analysis, yet he was tagged for "training a competitor." He reports that anything touching RFT, SFT, or training larger models now gets him blocked.

Kantrowitz's video argues that improving model capability is not the binding constraint on AI progress. No specific figures, companies, or benchmarks are provided in the available snippet.