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Every enterprise runs on open source, but keeping it secure has become significantly harder. Frontier AI models are uncovering vulnerabilities The post When vendor-supplied support matters: How AI is changing the open source security equation appeare

AI agents can impress in a demo and still fumble in production. Diagrid’s Catalyst 2.0 aims to make them more The post Diagrid gives failed AI agents a way to resume appeared first on The New Stack .

Last week, researchers at Accomplish AI connected a single folder to a fresh Claude Cowork session, sent one short message, and watched the agent escape its sandbox. Not in theory. Not in a lab. On a real Mac, with real SSH keys and cloud credentials

Here is a scenario from a project in its third year of operation. Migrations are managed properly. CI is green. validate passes. Then an error shows up in production only. The production table has a column that doesn't exist anywhere else. Nobody rem

⚡ JS Tools A single-file, dependency-free toolkit of five content utilities for editors, SEOs and developers. FAQ lists → WPBakery accordion shortcodes · strip inline styles and stray <p> tags from Word/Docs paste · bulk-edit link classes and rel · g

If you monitor Chinese social sentiment, you have probably wired up Weibo's hot-search board. It is the obvious endpoint: public, fast, and it gives you the trending list everyone screenshots. I measured what it actually returns. Here is one row, ver

In the previous article, I broke down the problem into independent responsibilities instead of expecting one agent to do everything. If every agent is independent of eachother, how do they communicate with eachother? Who decides what runs next? That'

The Model Context Protocol , the open standard that has quietly become the connective tissue between AI agents and the world's software, is getting its largest update since Anthropic released it twenty months ago - a sweeping architectural revision t

Researchers investigating Hugging Face found the majority of its models would generate non-consensual deepfakes.

Different stages need different brains. TL;DR: Assign a different model to each pipeline stage since none excels at planning, coding, reviewing, and testing alike. Common Mistake ❌ You open one chat with your favorite model and ask it to plan, write,

GitHub's new issue automations can now label, type, assign, or close issues with a confidence level and a rationale. That sounds like a small UX improvement. It is actually a policy-design problem: a model's confidence is a guess about its own output

Ever had a situation where you update a live notification region in your SPA, but the screen reader stays silent? Or worse, it reads some old message, then skips your update altogether? I ran into this exact problem recently while working on an acces

Daring coder gets Doom running with regular expressions at 180 seconds per frame by using one text string to the machine, engine, and video output

China is catching up to the West when it comes to AI - what will this mean for the market?

Your SIEM fires 3,000 alerts on a Tuesday night. Your on-call analyst acknowledges 40 of them. The other 2,960? Noise - mostly. This is the alert fatigue problem, and it gets worse as environments scale. A language model can automate the first-pass t

Every time a new AI model is released, someone inevitably asks the same question: "What if AI breaks loose?" It's the premise of countless sci-fi movies. Machines become self-aware, escape human control, and civilization collapses. But what does "bre

I'm sure you are familiar with the concept that one bounded context should map to one microservice. The rule is silently constructing the very thing that everyone says they despise. A monolith wearing a network for a costume. The rule sounds right un

This series follows a fictional conversation between an experienced engineer and his nephew. Every episode explores one stage of how software moves from an idea to production. 👦 Nephew: Uncle, I have the boxes. Frontend, Wishlist API, Wishlist Servic

You delegate a task from Agent A to Agent B in LangChain. Agent B fails. Agent A's callback chain fires 'success' anyway. This is the observability blind spot most builders miss in agentic workflows: delegation masking . A sub-agent fails silently, b

Here is a small but persistent annoyance in frontend work: <h1> The Practical Guide to Building Resilient Web </h1> The browser broke the headline at "Web" because it ran out of space. Now you have one word sitting alone on the second line. You fix i