Your AI Isn’t Hallucinating. Your Data Is Lying to It.
Most of what we call hallucination is a model faithfully repeating data we never checked
Towards AI8 min read
llm
273 stories
Tuning Jev as a Quality Judge: A Decision Model vs. Two Cost-Effective LLMs
First in a two-part series on tuning Jev as an LLM judge: this post covers judging response quality; the next covers judging response safety.
Towards AI19 min readA Week Inside Jev: What a Non-Generative Decision Model Is Actually Good For
I wired TypeSafe’s Jev into a real Claude Code workflow for a week. Here is what the “200x faster, 400x cheaper” headline leaves out.
Towards AI22 min read
Ai2 releases Olmo-core 3 to make developing large mixture-of-experts LLMs more efficient
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.
SiliconANGLE4 min readEight GPUs, Three Models, One Server: A Production Playbook for Self-Hosting LLMs on NVIDIA H20
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 »
Towards AI1 min read
Building a desktop pet that talks to your local LLM (and never lies about the game)
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
Dev.to AI1 min read
Prune tool output by rule, leave the reasoning chain alone
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.
Dev.to AI5 min readMeet Jev: The AI Model That Doesn’t Write, and Why Data Engineers Should Care
A plain-English guide to “System One” models, with patterns, code, and honest caveats
Towards AI8 min readAI Agents Need More Than an LLM. Here’s the Full Stack
Learn the full AI agent stack: LLM, context, tools, memory, orchestration, runtime, guardrails, and observability. A clear guide to…Continue reading on Towards AI »
Towards AI1 min read
A Pile of Documents Is Not Knowledge
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.
Dev.to AI3 min read
Estimate your AI feature's model bill before you build it
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.
Dev.to AI2 min readFaster model, slower chatbot. What we learned from testing Jev
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.
Dev.to AI4 min readHow American Political Campaigns Are Using AI—and What They’re Spending on the Tools
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.
Bruce Schneier6 min read
What is Prompt Caching?
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.
KodeKloudAI Models Are Starting to Communicate Without Words
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 »
Towards AI1 min readHow Jev Picks an Answer Without Writing One
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 »
Towards AI1 min readMaking Intent Routing Explainable
Making Intent Routing Explainable: Calibrated Probabilities, Decision Records and a Four-Tier Cascade
Towards AI34 min read
How I cut LLM batch costs with time-of-use scheduling
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.
Dev.to AI5 min read
Showing 126 of 273 stories
Load more storiesAiByts Weekly Brief
Get the top AI news and tools straight to your inbox every week.
Weekly · AI news
Join 15,000+ AI enthusiasts