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33 stories
Every story from the TechLLMs newsroom, newest first.
Autonomous agents that plan, use tools and finish multi-step work are moving from demos into real workflows. Here is what changed, what still breaks, and what it means for teams.
Million-token context windows sound like infinite memory. They are not. A clear explanation of attention, recall and why your long prompts sometimes get ignored.
Open-weight models now match last year's frontier systems on many benchmarks. We look at what that means for cost, privacy and who controls the AI stack.
A practical, step-by-step guide to retrieval-augmented generation: chunking, embeddings, vector search and the prompt that ties it all together.
We used AI coding tools for a month on production codebases. Here is how they handle refactors, debugging and unfamiliar code — and where they still need supervision.
Clever phrasing matters less every model generation. What matters now is what information you put in front of the model — and how you structure it.
A thin wrapper around a model API no longer raises a round. Founders and investors explain the new bar: proprietary data, workflow depth and real margins.
Generative AI lets attackers write flawless, tailored lures at scale. Security teams are responding with new training, verification habits and AI of their own.
The terms are used interchangeably, but they are not the same thing. A clear map of how modern AI fits together — no maths required.
Training and serving AI models requires staggering amounts of electricity. Utilities, chipmakers and cloud providers are racing to keep up.
Skip the hype. These are the categories of AI tools our editors use every week, with honest notes on where each one helps and where it does not.
Foundation models for robotics promise machines that can learn new tasks from demonstration. The results are early but striking.