Latest AI and tech news
A Google-led research team built StreamVibe, a video agent designed to watch people cook or assemble furniture and speak up only when help is needed. A benchmark shows that none of the tested models reliably pick the right moment in longer videos, as...
Explore traditional RAG vs. Jev + RAG, their architectures, retrieval strategies, accuracy, performance, costs, and practical AI…...
A practical guide to understanding why machine learning models succeed in notebooks but fail in real world production systems....
A research paper claims reward labels barely change how agents learn. I tested that on three models, and one of them did exactly what the wrong labels told it to....
Test contact center AI agents on real caller conversations before go-live. Turn real calls into regression tests that catch failures on payments, authentication, and caller corrections.
Irrelevant chunks, hallucinated answers, no audit trail: fix all three by syncing SharePoint via Graph API, indexing with hybrid search in Azure AI Search, and orchestrating with Jinba.
You’re testing a customer support agent. A user asks, “Can I return a jacket I bought 40 days ago?” Your return window is 30 days....
How a deterministic shell, a small LangGraph agent core, and two separate “judges” exposed what a perfect answer-key score could not....
Bhasha tags every word of code-mixed Indian text with its language. My test said Tamil scored 0.87; human-labelled comments said 0.72....
A benchmark run finishes cleanly, every unit test passes and the numbers fall within a believable range, yet one of the conclusions drawn from them is now wrong. Nobody committed any code. A dependency update changed which model checkpoint the pipeli...
Accuracy is the easiest metric to understand and one of the easiest to be fooled by. Five small, worked examples show why precision…...
I was working on a content extraction pipeline for a RAG implementation that ingests regulatory filings and legal documents to create a knowledge repository. The pipeline processes ingested files, runs them through an LLM, and produces structured sum...
Same question. Same AI. Completely different responses. So which one should you believe?...
OpenAI released GPT-6.1 Terra on October 26, marketing it as a cheap, fast model for high-volume everyday work, priced at a tenth of its GPT-6 Astra flagship....
Microsoft Foundry has several built-in evaluators for measuring things such as intent resolution, task adherence, groundedness, and tool-call accuracy. These are a very useful starting point to evaluate an agent because we can add them to an evaluati...
A one-neuron push turns Qwen3.5-4B hostile. At half strength it still does the sums and writes code, and it stops refusing. SBOMs and model signing tell you what you shipped. Only behavioral evals tell you what…
We asked GPT-6.1 Terra to draw a python reading a book using the turtle module. It’s one of five tasks in Real Python’s informal model comparison, which explores how models handle everyday Python work. Here’s the drawing from this run:...
TL;DR: A KDD 2026 paper follows five customer support AI agents through testing and deployment. The team checks automated graders against expert judgments, starts new versions at 1% to 5% exposure, and tracks whether satisfaction gains come with more...
Every day, millions of knowledge workers around the world rely on Microsoft Copilot to draft documents, summarize meetings, surface relevant information, and accelerate productivity. Behind the scenes, this is powered by a network of AI systems, and ...
Your eval might be telling the agent it’s a test before the agent reads a single instruction. Eval tools need a fresh folder for every run, and the easiest way to get one is a temp directory with a random name like /tmp/eval-7f3a2c. The agent sees th...
Across sixteen code-generating models, 5.2% of package names suggested by commercial models and 21.7% of those from open-source models point at software that does not exist. Correcting a hallucinated dependency costs an afternoon. Proving to an asses...
Everyone pictures a CSV and a Train button. The real job is data, evals, GPU memory, a model you now have to babysit, and a base model with a shelf life of about three months....
Samaya AI has released Samaya Sterling, a post-trained model for agentic financial research. Sterling is built on NVIDIA’s Nemotron 3.5 Lightning, a 30B mixture-of-experts model with 3B active parameters. On Samaya’s FrontierFinance benchmark, it sco...
Learn how to measure machine learning performance using simple examples, Python code, and the evaluation metrics every beginner should…...