Auto-aggregated global tech articles · 853 posts
Software engineering has entered a new phase — and it’s no exaggeration to say the community is on fire. The rise of LLMs has dramatically increased productivity and unlocked possibilities that, until recently, felt out of reach. The emergence of highly scalable products built by extremely lean team
CI goes red. Before I can think about the actual fix, I'm expanding the failing job and scrolling through three hundred lines of dependency resolution to find the one line that matters. Then the next person on the repo does the same thing, from scratch. So I built explain-ci: a GitHub Action that r
Capability is not authority. That is the part of the warning operators can act on. I build Sentinel SCA, a runtime that checks authority before an agent action executes. This is not a claim that it solves superintelligence. Jacob Coxon did not leave Anthropic because a chatbot wrote a bad poem. He l
Hey DEV Community! 👋 I'm Sunny, a Computer Science student exploring the world of AI/ML and software development. I enjoy building projects, experimenting with new technologies, and learning by actually creating things rather than just following tutorials. 🚀 What I'm currently learning
I'm an undergrad student who got a taste of research. I love it. I currently have a draft, which me and my mentor have planned for TMLR, and plan to submit it by next month for the first round of review. It was some work on self explanation methods of LLM models. We are confident that it'll get acce
1. Basic Information Article Title: GTIG AI Threat Tracker: From Prompting to Autonomy – The Evolution of Adversarial AI Publisher: Google Threat Intelligence Group Publication Date: 2026-09-08 Original Source: Google Threat Intelligence Group Related Sources: BleepingComputer: Hackers buil
Originally published on tamiz.pro. I spent twelve years reviewing code. I developed a rhythm—spotting logic errors, questioning edge cases, recommending refactors—and it made me a better engineer. When AI coding agents first became viable last year, I assumed my superpower would transfer directly:
Another OpenCode behavior I wanted to change was message submission. By default, pressing Enter submits the current prompt. If you are used to chat tools where Enter inserts a new line and Ctrl + Enter sends, this makes accidental submissions easy. My preferred setup is: Enter: insert a new lin
Email MCP servers are starting to split into a few distinct categories. Some help AI agents send product emails from a SaaS application, some provide agent‑owned inboxes, and others connect agents to existing Gmail or Outlook accounts. This guide compares the best email MCP servers and APIs for AI
OpenCode is powerful, but one default behavior kept getting in my way: pressing Ctrl + C could close the application itself. If you spend a lot of time in terminals, Ctrl + C is muscle memory. You press it to interrupt a command, stop a long-running process, or simply because your hands move before
An AI agent can call APIs, book services, and complete multi-step tasks. But once money enters the workflow, most wallet designs create an awkward choice. Keep funds in my personal wallet, and I must approve every payment. Give the agent an EOA, and a lost or leaked private key may expose the entire
Every time I want to build a desktop utility in 2026, the modern web ecosystem gives me the same repetitive answer: "Just bundle it with Electron!" Don't get me wrong: Electron is an impressive engineering feat. But packaging an entire Chromium browser and Node.js runtime just to display a local da
Publishing an OSS project in multiple languages is useful, but translated documentation introduces a maintenance problem very quickly. Typical failure modes are familiar: the English README changes but the Japanese version does not nobody is sure which version is authoritative a translation gains
I was building a parser for Vietnamese electronic invoices — the XML files tax authorities require for every VAT invoice issued in Vietnam. The goal: read a file from any provider, whoever issued it, and normalize it into one clean JSON shape. To sanity-check it against real-world variation, I aske
Every multi-agent tutorial shows "Agent A talks to Agent B." None show how to keep that conversation reliable at scale. The Problem with String-Based Agent Chat # What most frameworks do: result = agent_a.run("Analyze this and tell agent_b what to do") agent_b.run(result) # What if r
This is part 1 of a series that started with the full reference diagram in part 0. Stage 1 covers the two columns marked 1 on that diagram. The first is IDENTITY & ACCESS, with SSO / IdP, Roles & scopes, and Secrets manager. The second is AI GATEWAY, with Routing, Rate limit & budget, Lo
Every discussion I see about what to hand off to a coding agent sorts tasks by difficulty. Easy things to the agent, hard things to the human. It sounds obvious and it has been wrong in practice for me almost every time. The axis that actually predicts a good handoff is not difficulty. It is this:
The usual answer is "five to ten hours." It is not wrong, and it is not useful, because it never says what those hours are spent on — so people fill them with the easiest activity available, which is reading the company's About page a fourth time. The number is the wrong thing to ask about. Most of
I spent a few years designing game economies before I spent any time on AI tooling, and the transition has been strange, because I keep watching people rediscover failure modes that mobile games documented years ago. The current one: bounties for AI-assisted work. Pay people for tasks completed by
[ EXECUTIVE TEARDOWN // TL;DR ] Every shipped product started as a complete mental model — data flow, trust boundaries, failure modes, cost curve — before any code. The strongest architecture decisions are refusals: what the system will never do deletes entire classes of future cost and risk.
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