Auto-aggregated global tech articles · 853 posts
A pull request description came back from my coding agent and I read it the way the reviewer would. An em dash in the first sentence. A bold label on every bullet. Let me know if you need anything else at the bottom. I deleted all of it by hand, the same pass as the day before. Readers have learned
The diff is never the expensive part. The expensive part comes after: explaining to the person who asked for the change what they actually got, and discovering that "done" meant "tests pass". I spent four years as a founding engineer on a hospitality platform and then alone on a proactive assistant
I've been watching a lot of beginners lately, and I keep seeing the same thing. Someone asks ChatGPT/Claude a question and gets an answer, after sees it works - and that's it... they move on :( Then a new problem comes up, and they do the same thing again. And I get it, I really do. It's fast, it's
Hello, I'm Rijul, and I'm building LiveReview — a blast-radius aware AI code review built for your business-critical systems. Star us to help devs discover the project, give it a try, and share your feedback to help improve the product. If you are familiar with RAGs, you know its usual flow.
Hello, I'm Maneshwar, and I'm building LiveReview — a blast-radius aware AI code review built for your business-critical systems. Star us to help devs discover the project, give it a try, and share your feedback to help improve the product. Here's a fun fact that nobody puts on the slide at the AI
Brazil has a huge amount of high-quality official economic data. The problem is that using it programmatically is much harder than it should be. The Brazilian Central Bank has one API and data model. IBGE has another. Tesouro has another. SICONFI, Comex Stat, Novo Caged, ANP, EPE and CVM all expos
Backlinks remain a foundational part of search engine optimization, but link volume alone is no longer an adequate authority strategy. In an AI-driven search environment, businesses also need to make their brand understandable, credible and consistently represented across the web. That means paying
I always wanted to make content. What stopped me was never the ideas — it was the four hours in a timeline afterwards. So I started automating bits of the editing with AI. Then more bits. About six months later it had turned into a product, and somewhere in there I stopped opening a video editor en
Update — v0.3.0 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, framework adapters, rule lifecycle, pack ecosystem, and official rule packs. The v0.3.0 f
Introduction: Machine learning is one of the most important areas of modern technology. It is behind many tools people use every day, including search engines, social media recommendations, voice assistants, online shopping suggestions, fraud detection systems, and navigation apps. Although the ph
A package gets accepted at the post office counter, ships without a problem, and weeks later the shipper's account takes an adjustment: a $50 hazardous materials noncompliance fee. Nobody warned the shipper. The counter clerk didn't catch it. The parcel was already delivered. I kept running into th
The scanner is open source and the rules are readable before you trust a single finding: github.com/audit0/auditai-scanner. This post is about the one bug it was built for. The shape of the bug Ask any AI coding tool for an invoicing app and you will get something close to this route han
We as devs are tired from models, ai, job market and ofc linkedin crap. I believe that transformation the industry went through is glorified, at the end of the day, agents produce code, which still needs design, frequent security vulnerabilities updates, and strategic thinking. Agents will pick up
If you've started using Claude Code, you've probably run into two terms that sound similar but do very different jobs: agents and skills. This article breaks both down from scratch, shows how they connect, and — most usefully — shows what actually changes in the output depending on how you set thing
Getting an AI to place a phone call takes an evening. The trouble starts after that. It said "your table is booked" when the restaurant had said no such thing. It talked to a voicemail greeting for two minutes, politely asking for a reservation. It accepted a price above the budget because the con
I spent a week reviewing LLM apps for a client and found a pattern that surprised me. Every loud failure - a refused prompt, an exception, a blocked tool call - was handled fine. The app had guardrails. The failure that almost caused a real incident was the one that raised no error at all: the model
Built a web app that will turn your chess game into an epic dark fantasy battle, each piece has a name There is a wound system so if for example a pawn takes a knight the knight gets taken but the pawn doesn’t survive unscathed. First time really building anything like this and putting it out to the
I used to write software. Now I work in mass agent transit. Ten AI agents on the bus today. The plan is a fleet of ten thousand. That sounds like a joke and it is about half of one. Here is the half that is not. I am still responsible for getting from A to B. A feature goes from an idea to a URL w
I run an autonomous agent that makes security decisions from untrusted input. Before I trusted it, I red-teamed it the way an attacker would. The result: 27/100 MEDIUM, 1 of 8 probes flagged - a soft failure where the model leaked part of its system prompt when asked politely in a different language
Every AI tool I used worked the same way. I'd open a tab, explain who the client was and what we'd agreed, paste in the email, get some text back, and then go do the actual work myself. The model was smart. The setup around it left all the carrying to me. So I built Moe, an assistant that runs on yo
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