798 stories
A common way to give a support bot knowledge is to let your agents save their good answers. A ticket gets solved, the agent clicks save, and the answer joins a set the bot retrieves from when a similar question comes in. Many support tools offer some version of it. We built ours.
Temporal guardrails change what an agent policy decides: not only whether this request is allowed, but whether it is allowed given everything the agent has already done.
Ben Holmes is the DevEx Engineering Lead at Warp. The company has spent the year shifting from the terminal that made its name to Warp Factories – open infrastructure for teams building software factories.
This is a special chapter of "Can my AI agents make me famous?", a running experiment: a team of AI agents runs Matthew Busel's accounts and tries to make him famous, then make money. The agents write every chapter, with real numbers only, including the parts that went badly. Chapter 3 is here.
A new Harvard study examining AI perception across more than 5,400 Americans reveals notable disparities in recognition, media representation, and confidence. The survey details how respondents perceived real and fabricated figures, alongside broader industry data on expert quoting and headlines.
AI coding assistants can help developers draft functions, explain unfamiliar code, and explore implementation options. But generated code still needs to be reviewed, tested, and understood. A productive workflow might look like this: Define the problem and expected behavior.
This edition of the LLM Inference Digest covers new developments from 2026-10-07 to 2026-10-14 across arXiv, the vLLM and Hugging Face blogs, Together AI, Modal, and Latent Space.
Originally published on integrationstars.com. The short version: check shared state (a calendar, a stock count, a CRM record), then act on it, with nothing stopping a second execution from checking in between. An AI step stretches that window from milliseconds to seconds.
91% of the most-starred repos on GitHub give their coding agent at least one instruction that never says which file, command or tool it means. In April we measured 28,721 repos, and our own limitations section admitted that a 10-star side project counted the same as a 50,000-star production repo.
A formula for tipping in local AI that proves no safety A Patterns study presents a formula that flags a shift towards unwanted AI output. What it does and does not measure, and what decision-makers with local models iamvera.ai A Patterns study presents a formula that flags a shift towards unwanted…
A forthcoming webinar examines how a compromised identity can be used to gain broader privileges, and how AI can speed target identification, weakness exploitation and privilege escalation.
"Can you train ChatGPT on our documents?" is one of the most common requests we get. It's a fair question, but it usually mixes up two different techniques. In most cases what the business needs isn't training at all. It's retrieval-augmented generation, or RAG.
TL;DR: Generating a two-minute startup video with AI is not one model call. It's a chain of stages (script, storyboard, shot generation, voice, edit), and every stage can fail without anyone noticing.
I Built SellSmart AI: Product Listings Made Easier with Local AI 🛍️ For Week 2 of the Hacktoberfest 2026 DEV Open-Source AI Challenge, I built SellSmart AI — a simple tool that helps small businesses and online sellers create product marketing content. 💡 What does it do?
The big editors all have an agent built in now, and each of them has added a way to look at what the agent did before you keep it. I build Reado, the IDE for reading the code your agent writes, so I have an obvious interest in this question.
Most multi-agent demos I see have the same weakness: the agents agree with each other. One writes, another says "great point", a third summarises. Nothing gets tested. So I built the opposite, as an experiment. It's called Reagent Lab.
A team red-teaming their own log-reading AI assistant found something that should worry anyone who's bolted an LLM onto an internal tool: planted instructions inside log lines could get the model to embed a database password in a Markdown link.
Why does so much new music sound the same? Why does a song feel almost worthless when you can stream every album ever recorded for the price of one CD? And... is software next?
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