Anthropic CEO Dario Amodei urged a brake on the pace of large-language-model development, and OpenAI, Google DeepMind, and SpaceXAI leaders publicly supported the call. The essay was posted on Saturday, with Amodei pointing to concerns including the July Hugging Face incident involving OpenAI agents. The industry’s public messaging shifts from pushing faster capability to prioritizing model monitoring, external auditing, and safer training practices before continuing rapid progress.
Flam announced a $40 million Series B funding round to build AI infrastructure for interactive videos, streamed 3D experiences, and real-time visual agents. The round is led by QED Investors and includes participation from Claypond Capital and other investors. The company says it will use the money to fund ongoing R&D, expand its product suite, and grow sales to accelerate creation of these interactive “flams.”
Smart model routing sends each LLM request to a smaller model when the task is simple and to a more capable model when it is difficult, instead of sending everything to the most expensive model. The article claims this can cut LLM API costs by around 10X when most requests are handled by the cheaper model. As a result, systems can lower token spend and keep response quality similar, but they must reliably judge request difficulty using task type, risk, context needs, and output constraints.
The article argues that LLM code can pass hidden tests yet still be sloppy, and it proposes concrete ways to measure that sloppiness beyond correctness. It reports SlopCodeBench comparisons where agent-generated code has average verbosity 0.33 ± 0.10 versus 0.15 ± 0.06 in established repos, and erosion 0.68 ± 0.20 versus 0.31 ± 0.17. The evaluation method uses multiple instruction/test rounds with context erased, so bad decisions accumulate and strict solve rates drop to a 0% pass rate at checkpoints, making “agents will fix it” less reliable.
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