24 articles
A solo developer built a 1,300-line C inference engine that runs the 744B GLM 5.2 model on consumer hardware by streaming routed experts from disk. Here's how it works.
A new study from Dartmouth measures the impact of an AI tutoring platform on introductory statistics performance. Full engagement with the system correlated with significant exam score improvements, though selection bias remains a key limitation.
Anthropic's new research reveals LLMs have an internal 'workspace' for silent reasoning - and it could change how we build safer AI.
Comparing LLMs by token pricing alone can lead you to choose worse, more expensive models. Cost per task tells the real story.
Filippo Valsorda argues that LLMs have ended the era of treating security researchers with kid gloves. When anyone can discover vulnerabilities with an AI, the old coordinated disclosure model breaks down.
Unsloth's dynamic quantization makes GLM-5.2 runnable on a 256GB Mac or a 24GB GPU with CPU offloading. Here is the hardware math, the quantization tradeoffs, and what the HN community learned from actually running it.
A new paper shows a 3B parameter model hitting 94.3 on AIME26 and 96.1% on LeetCode contests - matching or exceeding models 100x its size. The catch: it traded general knowledge for pure reasoning ability.
Switzerland's fully open foundation model promises transparent training data and EU compliance. The HN crowd has questions about actual performance.
New research from MIT reveals that LLMs identify speakers by writing style, not by tags - meaning attackers who sound like the system effectively become the system. The findings explain why prompt injection remains unsolved.
New benchmark data shows GPT-5.5 hallucinates 86% of the time when it does not know the answer - versus 28% for the open-weights GLM-5.2. The numbers challenge the assumption that bigger models equal more reliable output.
Modern LLMs now use MoE routing, mixed attention variants, and fused vision encoders. The simple transformer stack is gone - here's what replaced it and why it matters for developers.
Anthropic's docs say the tokenizer introduced with Opus 4.7 can use up to 35% more tokens for the same text. Here is what that does to per-request cost, max_tokens, and cross-model comparisons.
Fable 5 1M context workflows that actually work: whole-repo reviews, log archaeology, multi-doc synthesis - plus the honest math on when RAG still wins.
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