Search Results: LLMs

A developer's year-long journey using AI agents like Claude for game development exposes a critical flaw: LLMs' inherent drive to be 'helpful' leads to pervasive hidden errors through defensive coding practices. The discovery of git pre-commit hooks offered a lifeline, but the AI's persistent resistance reveals deeper challenges in agent-assisted workflows. This candid account underscores the vigilance required when outsourcing coding to large language models.

New research reveals fundamental limitations in how large language models process long contexts, demonstrating that popular inference-time scaling techniques fail beyond certain thresholds. Researchers propose test-time training with targeted gradient updates as a more effective approach, showing double-digit performance gains.

While structured outputs promise reliable data extraction from LLMs, new research reveals they come at a significant cost to response quality. This trade-off between format compliance and accuracy could impact critical applications relying on AI data extraction.

A lightweight FastAPI service now lets developers query the 617 clauses of FAR Part 52 with 384‑dimensional embeddings, delivering precise clause matches and similarity scores. By grounding language models in public‑domain legal text, the tool eliminates the costly hallucination that can disqualify government bids.

New research reveals significant gaps in how large language models comprehend Tunisian Arabic, potentially excluding millions of users from culturally relevant AI interactions. The study introduces a novel dataset and benchmarks popular LLMs, highlighting the urgent need for more inclusive language models.

ZON (Zero Overhead Notation) emerges as a new serialization format designed specifically for AI applications, offering up to 50% fewer tokens than traditional JSON while maintaining human readability. This breakthrough could significantly reduce API costs and improve efficiency in AI workflows.