5,311 stories
AutoGPT recruitment agents empower startups to automate talent sourcing. Discover how to build custom AI recruiters for autonomous hiring and scale.
A source-backed review of advertising MCP servers, a reproducible reliability methodology, and clearly bounded transport, terminal, and semantic measurements.
A content-team-first comparison of the best AI collaboration software tools in 2026, with features, pros, cons, pricing, and why StoryChief ranks first.
Coding agents fail on context, not capability. A practical guide to context engineering for a real repository: the four layers, a token budget you can reason about, how to diagnose which layer is failing, and how to keep it from going stale.
One package gives Mastra agents a single database for conversations, workflow state, observability and vectors, plus managed memory that extracts and semantically recalls facts.
Rolling out AI to your support team fails on trust, not model accuracy. A 60 day change management plan that keeps agents in control.
Customer support SLA with AI: why response-time clauses stop working and how to rewrite resolution, handoff, and reporting clauses.
Today, I'm excited to announce that two leading engineering and design companies are now strategic investors in Nomic and long-term commercial partners.
MIT affiliates engage with the MIT-IBM Computing Research Lab to bring rigorous theory to production systems.
As we enter the next era, what will be the defining measure of our progress? Every industry has a word that shapes how it thinks. For pilots, it’s safety. For insurers, it’s risk. For the semiconductor industry, it’s yield.
The post How Kier Group’s Louisa Finlay is using Copilot to drive safety and productivity in the construction industry appeared first on Source.
Expert agents that reason together, learn your environment, and run on data CrowdStrike owns. See how we deliver the agentic SOC. Learn more!
Atlas is a world generation model pretrained from scratch to natively operate on text, images, video, and 3D. It combines all inputs into a shared spatial context and uses that context to generate what comes next.
An AI agent doesn't simply run your code. It runs your code, plus whatever it finds in a “relevant” GitHub issue, support ticket, or scraped web page.
We’ve built an AI agent that acts as a secondary expert for a given domain, making deep specialist knowledge readily available and preserved for anyone in an organization to access, share, and build upon. This is not a typical domain-specific agent.
Writing production-quality code isn’t perfectly solved by today’s agents, but we’re getting pretty damn close. With a frontier model to hand, rigorous upfront planning, and plenty of tests and verification checks, swathes of senior developers have laid down their text editors and stopped…
We’ve opened our AI ecosystem to partners and developers. So, here’s our practical guide to how you can go from an idea to a live integration. One that Aircall's AI agents can call on in real time—using our Developer Portal—to plan, build, and publish.
Most AI tools were built for one person at a time. You open a private chat, teach it something, and close the tab, and everything you just taught it stays with you. That model made sense when AI was a personal productivity boost.
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