270 stories
Seattle-based artificial intelligence research firm Allen Institute for AI announced a development framework for large language models Thursday that significantly improves how mixture-of-experts large language models are trained.
Somebody on the team gets a local model running in an afternoon. One Docker command, one model name, and ten minutes later there’s a…Continue reading on Towards AI »
Tauri 2 + Ollama: grounding a toy chatbot with 1.5k local facts, a read-only save watcher, and the one-step walker bug. https://github.com/FabianGallardo-coder/digibuddy
My agent hit the context ceiling last week at turn 61. The framework did what every framework does now: it called a model to summarize the conversation, waited eleven seconds, and handed back a paragraph that had quietly dropped the exact error string I needed. The task failed.
A plain-English guide to “System One” models, with patterns, code, and honest caveats
Learn the full AI agent stack: LLM, context, tools, memory, orchestration, runtime, guardrails, and observability. A clear guide to…Continue reading on Towards AI »
My research agent had read about 20,400 documents. It had concluded nothing. Not metaphorically. The knowledge store held roughly twenty thousand rows — papers, articles, PDFs, nightly log entries — and there was nowhere in the system that said what any of it meant.
Most AI MVP budgets I see cover the build and forget the monthly line. That line is the model bill, and at a few thousand users it can pass what the build cost. One formula gets you a usable estimate.
Originally published on the WebSpeaker blog. In short: We tested Jev, a new model for rapid decisions, as a way to speed up WebSpeaker. Jev made decisions quickly and slightly more accurately than the model we use today. Yet in every variant we tested, visitors waited longer for an answer.
This essay was written with Nathan E. Sanders, and originally appeared in The Guardian. New campaign finance disclosure data shines a light on which US political campaigns are using AI tools and how much they are spending on them.
What does “Prompt Cache Warm” actually mean in Claude Code? In this short, we break down how prompt caching works, why Claude doesn’t need to reprocess your entire conversation every time, what the 60-minute timer really means, and why cached prompts can make later requests much cheaper and faster.
Cache-to-Cache lets one LLM pass internal representations directly into another. What looks like an inference optimization may become…Continue reading on Towards AI »
Scoring instead of generating: what open Qwen2.5 experiments show about speed, accuracy and calibration, and how to build it on AzureContinue reading on Towards AI »
Making Intent Routing Explainable: Calibrated Probabilities, Decision Records and a Four-Tier Cascade
Every batch job I run against LLM APIs used to cost the same at 2 a.m. as at 2 p.m. Then I started routing workloads through a unified gateway that charges different rates at different hours, and my overnight evaluation runs dropped to half the token cost — without touching a single prompt.
Article URL: https://www.deepseek.com/en/harness/ Comments URL: https://news.ycombinator.com/item?id=49929489 Points: 41 # Comments: 14
Originally published on my blog. This cross-post includes the setup and measured results; the linked records and configuration kit let you inspect the experiment. My RTX 3090 is a leftover from my Ethereum mining days.
Beyond One-Shot Success: How AREX-2 Teaches LLM Agents to Reflect and Persevere Current autonomous LLM agents are often evaluated by their ability to solve a task in a single pass or through a short sequence of scripted interactions.
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