Auto-aggregated global tech articles · 853 posts
Most teams build their first AI feature in a weekend. A controller that takes user input, calls an LLM, and returns the response. Three files, maybe four. It works beautifully in the demo. Then production happens. The 11 PM alert is always the same: "AI feature returning garbage / not responding /
Diagram showing the model proposing, the harness authorising, and the environment applying changes. Put an agent on your refunds queue. It reads the ticket, finds the order, checks the policy, issues the refund. The demo goes beautifully. Six weeks later it closes 140 tickets in an afternoon and r
Tools like Claude and Cursor can generate a clean component in a couple of seconds. But generating isolated code is the easy part. The actual challenge is modifying a production system without breaking things three steps away. Disclaimer: I work at Bit, and this post is based on what I've seen whi
I needed to answer one question automatically: what is this second-hand thing worth? Not "what are people asking" — what did one actually sell for, recently, with a date attached. That turns out to be one of the most locked-down pieces of data on the consumer web, and almost everything written abou
Rust AI is moving from the experimentation stage toward more practical usage. We recently kicked off a new livestream series with the Rust Foundation to explore how Rust and AI are coming together in real-world applications. In the first session, our Developer Advocate Orhun Parmaksız spoke with Ste
Update — v0.2.0 released. CauterRule is now live on GitHub and PyPI. It turns repeated agent failures into permanent standing rules — extract, replay-test, promote. pip install cauterule gives you the full CLI, TUI review, observability, 7 export formats, adversarial corpora, and a bundled git rule
Ask an AI agent for a discussion-board schema and you'll get one in about four seconds. Here is the part nobody mentions: you'll also get is_deleted in this table and del_yn in the next one, because the model is pattern-matching against every schema on GitHub, and those schemas disagree with each ot
You now know what each word is. But do you know what each word does? In the previous post, we covered Part of Speech Tagging: the process of labelling each word as a noun, verb, adjective, and so on. That tells you the category of every word in a sentence. But it doesn’t tell you how those words co
On 8 September OpenAI published a proof that the three-dimensional incompressible Navier–Stokes equations can develop a singularity in finite time: a smooth fluid, starting from rest under a smooth external force, whose speed grows without bound while its total energy stays finite. The proof comes a
A healthcare AI system should not be evaluated only on how well it performs when everything goes according to plan. Real healthcare environments contain uncertainty. Data can be missing. Inputs can change. Patient populations can shift. External services can become unavailable. Models can enco
“Make the form work better” is easy to say. It is much harder to implement: better validation, a different layout and clearer loading feedback are three different tasks. A useful spoken brief gives the assistant something it can inspect and something you can verify. Start with four landmarks: goal,
Not an AI bad take, its clearly useful+ time efficient but this is ne specific failure mode that seems to show up more with ai written code code reads fine and passes the tests even goes thru review and then does something subtly off you cant reproduce locally. When you finally trace it this is usua
Bug reports arrive incomplete, ambiguous, or buried in noise. Before you can fix anything, you need to triage — figure out severity and ownership — and then reproduce, which is often the hardest part. AI won't magically reproduce a bug for you, but it will dramatically compress the time between "a u
TL;DR LLM apps are vulnerable to prompt injection and related attacks. Ordinary input filtering fails because attackers use encoded payloads, hidden text, and memory poisoning. resk-llm-ts gives you a SecurityPipeline with 11 detectors, zero dependencies, and easy integration for Express, Hono, an
No prompt. No click. No approval dialog. Just git status. What the source says GitHub published a security advisory (GHSA-9ccr-r5hg-74gf, CVE-2026-45033, credited to researcher syvb) disclosing that GitHub Copilot CLI could be made to execute arbitrary commands through a nested bare git
It’s not just programming either, it’s art, design, music, videography, marketing, engineering, writing, 3D modelling, really any intellectual work. What’s the joy of prompting a computer and getting an answer in a few moments for a task that would have normally taken a week? The sense of accomplish
I Wired the Boring 80% So You Can Build the Interesting 20% 🚀 Mohamed Ismail Mohamed Ismail
Let’s be real: nothing kills developer flow state quite like a failed CI pipeline. You push your code, context-switch to grab a coffee, and come back to a wall of red text and a cryptic NullPointerException in a module you didn’t even touch. In 2026, we don't have to do this anymore. With the maturi
Every month, GSTR-2B reconciliation somewhere in India begins the same way: a finance team opens the downloaded GSTR-2B JSON, opens the purchase register next to it, and starts scrolling. Two columns of invoice numbers, two columns of amounts, and a growing suspicion that the numbers do not agree.
Every month, a finance team somewhere in India opens a downloaded GSTR-2B JSON, opens the purchase register next to it, and starts scrolling. Two columns of invoice numbers, two columns of amounts, and a growing suspicion that the numbers do not agree. They usually do not. And the gap between them
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