#llms

A Hugging Face hackathon participant shares why their AI-powered adventure generator failed to scale beyond simple HTML toys.

Organizations are exploring on-premise language models as pre-filters to reduce API spend on commercial LLMs, though cost savings remain context-dependent.

OpenAI's flagship models and Codex are now generally available on Amazon Bedrock, letting AWS customers deploy cutting-edge AI without leaving their existing infrastructure.

A new open-source tool lets developers branch LLM inference mid-generation, skip redundant prefill computation, and merge agent outputs—addressing a core bottleneck in multi-agent reasoning systems.

The Austrian Academy of Sciences is building Apollo, an LLM-based system with Mistral AI and Reply to automatically read and transcribe ancient Greek texts from papyri.

Research shows LLMs incorporate contradictory statements into reasoning, even when explicitly told the claims are false.

A new benchmark reveals that even the most capable AI systems struggle with diagnosing complex infrastructure failures, scoring below 50% on Site Reliability Engineering scenarios.

WIRED's fact-checking team reports that AI systems fail verification more often than most users realize, challenging assumptions about their reliability.

A Wired journalist paired OpenClaw with a LeRobot arm, showing how large language models can now configure, train, and control physical robots without specialized expertise.

A new analysis shows that large language models excel at language tasks but struggle with seemingly simple visual reasoning—like reading analog clocks.

Mass-produced studies citing legitimate datasets are overwhelming journal editors, creating a crisis that worsens as AI improves at mimicking competent research.

A new architecture called SubQ targets 12 million token context windows while sidestepping the quadratic compute scaling that limits standard transformers.

Software engineer Bhavya Gupta argues that LLM document extractors are missing fixed-point iteration, a classical CS convergence technique that could make extraction far more reliable.

A new open-source repository walks developers through building a modern large language model from scratch, with every line of code annotated and explained in plain language.

A new Hacker News-featured tool promises AI analysis stripped of flattery, targeting the approval-seeking behavior researchers have flagged in mainstream models.

David Silver, who built AlphaGo at DeepMind, argues large language models are fundamentally capped by human data and has founded Ineffable Intelligence to pursue reinforcement learning instead.