Dev

<h2> What does 80% AI authored code mean for solo devs? </h2> <p>In June 2026, Anthropic stated that about 80% of its new production code is authored by Claude. When a major AI vendor hits that volume, the shift is undeniable. For a solo developer or a one-person holding company, this changes the math entirely. The bottleneck is no longer typing characters. The bottleneck is review and ownership.</p> <p>When you run a solo shop, you do not have a team to absorb the review burden. If your agents

<p>Salesforce has shipped around 20,000 Agentforce deployments. ByteByteGo published a writeup of what they learned, sourced to John Kucera, the CPO of Agentforce. I run a one-person agent fleet, which is about as far from Salesforce scale as you can get. The lessons still translate. Better than I expected, actually.</p> <p>Short version: 90% of agent work happens after launch, not before. The failures cluster into three patterns. Putting deterministic logic inside an LLM loop, prompting harder

<p>I built a scanner that fires prompt-injection probes at a self-hosted AI agent and checks whether it leaks (a) real secret-shaped strings (API keys) or (b) the content of its own system prompt. Then I ran the same agent across 5 model backends. The leak rate ranged from 0% to 90% depending only on the model.<br> Here's what I found and how it works.<br> Why this matters now<br> Prompt injection is #1 on the OWASP 2025 LLM Top 10. It's not theoretical anymore:</p> <p>EchoLeak (CVE-2025-32711,

<p>The npm account <code>ai</code> publishes seven packages. Combined, they install 964 million times per week:</p> <div class="table-wrapper-paragraph"><table> <thead> <tr> <th>Package</th> <th>Weekly downloads</th> <th>Publishers</th> <th>Risk</th> </tr> </thead> <tbody> <tr> <td>postcss</td> <td>245,612,332</td> <td>1</td> <td>CRITICAL</td> </tr> <tr> <td>nanoid</td> <td>206,588,788</td> <td>1</td> <td>CRITICAL</td> </tr> <tr> <td>caniuse-lite</td> <td>173,435,668</td> <td>1</td> <td>CRITICAL

<p>Most of the debt that actually takes a codebase down was never written down anywhere. That, not the raw volume of AI-generated code, is what the current panic keeps aiming slightly past.</p> <p>We borrowed "debt" from finance on purpose. A loan you take deliberately comes with a rate and a due date; you can plan around it, refinance it, decide the interest is worth it. Recorded debt is a tool. What sinks people is the liability they didn't know they'd signed for, and that was true of software

<p><strong>The problem</strong></p> <p>Every time I found an accessibility issue in a PR review, the same thing happened: tab out to the WCAG docs, figure out the right fix, come back to the code, apply it, re-verify. For every single issue.</p> <p>It wasn't hard work. It was repetitive work. And it was eating hours every sprint.</p> <p><strong>What we built</strong></p> <p>A11yResolver is a VS Code extension that brings that entire loop inside the editor.</p> <p>It runs as an AI agent. It flags

<blockquote> <p>Claude Code stores 43MB of your conversations. Cursor saves your passwords in plaintext JSONL. Cline is the only one that puts your API key in the OS keychain — but it still archives everything you say.</p> <p>Not one of them tells you this is happening.</p> </blockquote> <h2> Why I Did This </h2> <p>Two weeks ago, I discovered Claude Code had silently archived 43MB of my complete conversation history on my machine. I wrote about it. The comments asked: "What about Cursor? What a

<p>One thing I’m still trying to reason about with Codex is when a run should be stopped rather than allowed to keep spending context, steps, and time.</p> <p>Some failures are obvious: repeated test failures, the same edit being attempted multiple times, or the agent circling around the same error message.</p> <p>But the harder cases are more subtle:</p> <p>A run looks like it is making progress, but the diff keeps growing in the wrong direction.</p> <p>It keeps adding abstractions instead of f

<p>Vercel has launched eve, an open-source AI agent framework that rethinks how developers build, secure, and ship agent-driven workflows. Eve blends the ease of structured Next.js apps with the operational DNA of Vercel’s platform — and it is already running over 100 internal agents, now responsible for almost a third of the company’s deployments. If you want to ship agents that can scale from your laptop to production, sandboxes and all, without rethinking how your team writes or deploys softw

<p>Your DPO just asked for proof that your AI pipeline doesn't leak training data. You don't have any. Neither does OpenAI, Anthropic, or Google — their clouds run on shared hardware where hypervisors can peek at GPU memory. GDPR Article 25 says you need "data protection by design." Shared GPUs aren't design. They're hope.</p> <p>I spent 3 hours trying to set up Azure Confidential Computing last year. Gave up. The attestation docs were 400 pages. The H100 instances were $14/hr and still required

<p>I've been scraping data from the web for years. You'd think I'd have learned by now: never use regex on HTML. But sometimes, when you're staring at a messy table with inconsistent classes, random whitespace, and nested elements that barely qualify as valid markup, the temptation to just throw a regex at it is overwhelming.</p> <p>I found myself in that exact situation last month. I needed to extract property listings from a dozen different real estate websites. Each site had its own quirks. O

<blockquote> <p>Originally published at <a href="https://htpbe.tech/blog/pdf-tamper-detection-python-tutorial" rel="noopener noreferrer">htpbe.tech</a>. The version on htpbe.tech stays in sync with the latest detection algorithm — refer to it for the canonical text.</p> </blockquote> <p>Your application accepts PDFs from users — loan applications, identity documents, invoices, contracts. Any of them may have been edited after the issuing institution generated them. You have no original to compar

<p>I kept switching between 5+ different sites just to count tokens, estimate API costs, plan context windows, and format JSON. So I built DevKit AI — a free collection of 17 tools specifically for LLM API workflows.</p> <p><strong>Link:</strong> <a href="https://devkit-ai.com" rel="noopener noreferrer">https://devkit-ai.com</a></p> <h2> Key tools </h2> <ul> <li> <strong>Token Counter</strong> — supports 60+ models (GPT-4, Claude, Gemini, Llama)</li> <li> <strong>LLM Pricing Calculator</strong>

<p>[Excerpted from <a href="http://tiny.cc/QuantumCollapse" rel="noopener noreferrer">THE QUANTUM COLLAPSE CHRONICLES</a> — not science fiction, but a grounded forecast of what may come when quantum computation dismantles the cryptographic foundations of our digital civilization. These articles explore the collapse of computational trust and the brutal reconstruction of the world that follows.]</p> <p>The hum of the dilution refrigerators in the sub-basement of the Institute for Advanced Quantum

<h1> API Testing in the AI Era: A Practical Workflow That Actually Holds Up </h1> <p>API testing hasn't fundamentally changed in the last few years. What has changed is how much of the grunt work can be handed off — and how much new grunt work AI-generated code has created in the process. If you're integrating systems for a living, you've probably noticed both sides of that trade.</p> <p>Here's a workflow that reflects where things actually stand right now, not the marketing version.</p> <h2> St

<p>Most people treat Claude Projects like a folder with a label on it.</p> <p>They create a project. They give it a name. Maybe they write a sentence or two in the system prompt. Maybe they upload a file. Then they start chatting and wonder why the output feels the same as regular Claude.</p> <p><strong>That is not a project. That is a labeled chatbox.</strong></p> <p>The people getting insane results from Claude Projects are building something completely different. They are building <strong>cus

<h2> The suggestion every RAG app ignores </h2> <p>If you've shipped a retrieval-augmented assistant, you've written some version of this line in your system prompt:</p> <blockquote> <p>"If the answer isn't in the provided context, say you don't know. Do not make things up."</p> </blockquote> <p>And you've watched the model cheerfully ignore it under pressure. A confident-sounding question comes in, retrieval returns something <em>adjacent</em>, and the model stitches together an answer that's p

<p>Most software engineers I know use AI in some form now.</p> <p>Maybe it is for debugging, boilerplate, tests, docs, SQL queries, shell commands, or quick code reviews. Some use it daily. Some use it quietly. <em>Even the skeptical ones have probably pasted a confusing stack trace into a chat window once.</em></p> <p>So I do not think the interesting question is:</p> <p><strong><em>“Do engineers use AI?”</em></strong></p> <p>The better question is:</p> <p><strong><em>“Has AI changed how they e

<p><em>Originally published on <a href="https://mrtd.net/chrome-gemini-nano-4gb-on-device-ai-privacy/" rel="noopener noreferrer">MRTD.NET</a> — fast, sourced news on crypto security, cyber & SEO.</em></p> <p>If you run a recent version of Google Chrome on a desktop, there is a decent chance your browser has quietly downloaded a <strong>~4GB artificial-intelligence model</strong> in the background. It is called <strong>Gemini Nano</strong>, and it is the engine behind Chrome's new built-in AI fea

<p>Exposing a tool to an agent over MCP takes ten minutes. Building an MCP server that survives a model you don't control, on a tight token budget with limited thinking time, is the part nobody warns you about.</p> <p>We learned the difference shipping our own, consumed by third-party agents whose models we don't pick. Three principles came out of it, each one we only fully believed after it broke in production:</p> <p><strong>TL;DR — three MCP server best practices from our trenches:</strong></

<p>When we wrote code before large language models could write it for us, code duplication was rarely our problem, at least not if we worked alone or in a small team.</p> <p>That has changed.</p> <h2> A principle born in a quieter time </h2> <p>"Don't Repeat Yourself" was coined by Andy Hunt and Dave Thomas in The Pragmatic Programmer back in 1999, a year when our biggest worry was whether the clocks would survive January. The idea is reassuringly simple. Every piece of knowledge in your system

<p>Retrieval-Augmented Generation (RAG) is a powerful pattern to build applications that can query, understand, and extract insights from your custom documents (like PDFs, resumes, and reports) by feeding them as context to Large Language Models (LLMs).</p> <p>This guide walks you through building a complete RAG API step-by-step, explaining the architecture, code, and debugging learnings along the way.</p> <h2> 1. Architecture Overview </h2> <p>A typical RAG pipeline is divided into two parts:</

<p><em>Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is free and source-available on Github. <a href="https://github.com/HexmosTech/git-lrc" rel="noopener noreferrer">Star git-lrc</a> to help devs discover the project. Do give it a try and share your feedback.</em></p> <p>For most of the internet's life, searching for something felt a bit like talking to a very literal-minded robot. </p> <p>You'd type "comfy shoes for standing all day," and it

<p>I recently used Cursor Agent Mode with Auto Mode enabled to do something simple: recommend a font pairing and update two files in my project. An <code>index.html</code> and an <code>index.css</code>. That's it! </p> <p>The agent added a Google Fonts <code><link></code> tag, a <code><link></code> Gstatic with a crossorigin within it, and shuffled a few CSS variables as well as styling rules for the body tag and H tags. </p> <blockquote> <p>It cost 1% of my monthly usage budget.<br> <a href="ht

<p>A dev-log from building an AI-native layer one, mostly alone.</p> <p>I am 18, I build NOVAI, and you can find me as 0x-devc. </p> <p>NOVAI is a layer-one blockchain written from scratch in Rust, with a chained-BFT consensus in the HotStuff family and AI entities as first-class protocol primitives. This post is about the week my testnet wedged itself at a single block height and would not recover, and about the three-layer bug I eventually found sitting underneath a one-line error message. It

<p>I've always found algorithms easier to understand once I can <em>see</em> them move. Pseudocode and textbook diagrams are fine, but for a lot of things — how a sort actually rearranges elements, why Dijkstra picks the path it does — a static picture only gets me so far. I usually end up tracing through it by hand on paper.</p> <p>So I made a small thing to do that tracing for me: <strong><a href="https://bigoh.dev" rel="noopener noreferrer">bigoh.dev</a></strong>. It's free, open, and meant f

<p>Three vulnerabilities from the last few months, three different layers of the AI-coding-agent stack, one root cause. None of them is the model getting "jailbroken." Each is the agent doing exactly what it's built to do, with your credentials, while someone else supplies the input. Here's the mechanism on each, and what actually mitigates it.</p> <p>The first one lives in your agent's config file.</p> <h2> The worm that lives in your agent's config (Mini Shai-Hulud) </h2> <p>In May, a self-pro

<p>My mother called me last week. Someone had sent her an SMS <br> claiming to be from DHL, asking her to pay a £2.99 customs <br> fee via a link. She almost clicked it.</p> <p>That was enough. I spent a weekend building a Chrome extension <br> that lets you paste any suspicious message and get an instant <br> verdict. Here's how it works.</p> <h2> The architecture (and why Cloudflare Workers) </h2> <p>The obvious approach is to call the Claude API directly from <br> the extension. Don't do this

<p>An API request that looks cheap on a pricing page can become much more expensive inside a real product.</p> <p>The pricing page normally gives you the unit rate:</p> <ul> <li>Price per million input tokens</li> <li>Price per million output tokens</li> <li>Price per generated image</li> <li>Price per second of video</li> <li>Price per credit</li> </ul> <p>That is useful, but it is not yet a production budget.</p> <p>A production workflow can also include repeated context, tool results, cache w

<p>AI coding assistants are now part of daily developer life.</p> <p>They write boilerplate, explain strange errors, generate tests, and save hours of repetitive work.</p> <p>But here is the truth: AI is powerful, not magical.</p> <p>At YoBox, we use AI while building practical developer tools. It helps us move faster, but it still needs human judgment.</p> <h1> Where AI Coding Assistants Are Excellent </h1> <p>AI is strongest when the task is clear, repetitive, and pattern-based.</p> <p>That ma

<p>In the AI era, something funny is happening: side projects that have been collecting dust in <code>~/code/wishful-thinking/</code> are getting dusted off. Suddenly that "someday I'll write this" repo on GitHub has a real README, a working CI, and three open issues you actually want to close.</p> <p>Why? Because the AI doesn't complain. Doesn't get bored. Doesn't ask "but why do we need this when we have X?" at 11pm when all you want is to feel better with yourself, when your children are on t

<p>Cart price rules are one of Magento's most powerful marketing features — and one of the fastest ways to tank your store's performance if you're not careful.</p> <p>When you have hundreds of active rules, millions of coupon codes, or complex conditions spanning multiple product attributes, every cart update can trigger an expensive rule validation cycle that turns a smooth checkout into a sluggish mess.</p> <p>In this guide, I'll walk you through why cart price rules slow things down, how to m

<p>I was going through my server logs last month when I noticed something I'd been scrolling past for weeks. Buried in the bot traffic were names I vaguely recognised: <code>GPTBot</code>. <code>ClaudeBot</code>. <code>meta-externalagent</code>. <code>PerplexityBot</code>. Multiple visits daily, methodically working through different pages of my technical blog.</p> <p>The reflex most developers have at this point including me, initially is to block them. There's an entire category of articles re

<p>If you've Googled this, you've probably already used both and still aren't sure which one to trust with your actual codebase. Here's my concrete answer after running both through real code review workflows: <strong>they're good at different things, and picking the wrong one for the job costs you time.</strong> Here's exactly how to choose.</p> <h2> The Core Difference That Matters for Code Review </h2> <p>ChatGPT (GPT-4o) is faster and more conversational. It's excellent at quick back-and-for

<p>I shipped an AI agent last year that looked perfect in every demo. Then it hit production traffic and started failing silently, no errors, no crashes, just empty responses and confused users.</p> <p>The worst part? It took me three days to find out why.</p> <p>Here's what I learned about building agents that don't fail quietly, and the exact patterns I now use to catch failures before they reach users.</p> <h2> Why Agents Fail in Ways Normal Apps Don't </h2> <p>Most developers know how to han

<p>I'm starting an Electronics and Communication Engineering degree this year, and a few weeks before classes began I decided to build something real instead of waiting for a syllabus to tell me what to learn: a model that detects abnormal heartbeats from raw ECG signal, small enough to run on a microcontroller, not a cloud GPU.</p> <p>The first version of this project hit 98% accuracy. That number was almost meaningless, and it took me two separate rounds of being wrong to find out why.</p> <p>

<h2> PAYE, VAT, PIN registration — now accessible via AI agents </h2> <p><code>pip install kra-mcp</code></p> <p>Server #20 in the AI-KungFU East Africa Coordination Infrastructure.</p> <p><a href="https://github.com/gabrielmahia/kra-mcp" rel="noopener noreferrer">GitHub</a></p>

<p>Over the last few months, I have gradually built a fairly stable AI-assisted development workflow for my side projects.</p> <p>The workflow looks roughly like this.</p> <p>Architecture and design are handled by Claude Sonnet / Opus. I maintain a set of engineering skills and design templates for feature planning. Once the design document is finalized, DeepSeek V4 is responsible for implementation. After coding is complete, I run an independent review workflow inside OpenCode to perform securi

<h2> The Star Rating Problem </h2> <p>Every movie lover has faced it: you open a ranking app, see a movie you love, and try to give it a rating. 4 stars? 4.5? You end up paralyzed by a deceptively hard question — not "how good is this movie?" but "how good is this movie <em>compared to everything else I've seen</em>?"</p> <p>That's the core problem I set out to solve when I built <strong>Montir</strong>, a movie ranking iOS app. Star ratings feel precise but they're actually pretty arbitrary. Yo

<blockquote> <p><strong>🚀 Technical Briefing:</strong> This tutorial is part of our deep-dive series on Agentic Workflows at <a href="https://gateofai.com" rel="noopener noreferrer">Gate of AI</a>. For the full technical breakdown, interactive code sandbox, and the native Arabic translation, visit the <a href="https://gateofai.com/tutorial/fine-tuning-ai-specialized-tasks/" rel="noopener noreferrer">original article here</a>.</p> </blockquote> <div class="highlight js-code-highlight"> <pre clas