Summary from the original article
We’ve all been there. You spend hours meticulously engineering a prompt. You add a few "few-shot" examples, inject some XML tags to keep the output formatted, and wrap it in a clean Python script using the OpenAI SDK. It works brilliantly for the exact test case you wrote. Then, you hand it to a user, they type something slightly unexpected, the model hallucinates, your regex parser crashes on unescaped JSON, and your morning is ruined. Welcome to the ceiling of…
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Upgrade this serverBeyond Vibe Coding: Why Software Engineering Still Matters in Modern Web Application Development The dangers of building web applications without engineering discipline in the age of AI The software development industry is experiencing a major transformation. With the emergence of artificial intelligence (AI) coding assistants and platforms that can generate entire applications from natural-language prompts, building software has become more accessible than ever. Today, someone with little or no programming experience can describe an idea to an AI tool and receive a functioning web…
1 hour ago · dev.to
How AI Agents Evaluate Their Own Outputs and Implement Self-Correction Loops Every agent vendor promises self-improvement. The research says something much less flattering: left to grade its own homework, GPT-4 didn't get smarter — it got worse . Self-correction only works when the verdict comes from something the model didn't author. A self-correction loop sounds like magic: the agent writes, judges, revises, repeats until it's right. The catch is the judge. A weak evaluation sets a hard ceiling on the whole loop, and the same model that made the error is the worst possible person to spot…
1 hour ago · dev.to
The session ended the way sessions end: a tidy summary. I updated src/parser.py. All tests pass now, and I pushed the fix to the remote. One of those three sentences was true. The edit was real — there's an Edit call right there in the transcript. But the test run recorded in the same session failed ( FAILED tests/test_parser.py::test_quokka_edge - AssertionError ), and there is no git push anywhere in the record. The agent didn't forge anything exotic; it just wrote the ending the conversation was shaped to have, and the summary — the only part most of us read — sailed through. This failure…
9 hours ago · dev.to
Claude Code Output Validation Hooks in 2026: Linting Agent-Written Code Before It Touches Your Repo This article was written with the assistance of AI, under human supervision and review. AI code agents produce output at a pace that makes manual review impractical. A Claude Code session can edit fifty files before the first test runs. The code lands in the repository clean or broken depending on whether validation happened before the commit. Most teams discover this after the damage shows up in CI. The problem sits between the agent's tool execution and the repository state. Claude writes…
17 hours ago · dev.to
Giving everyone the newest AI coding agent sounds progressive. It can also create a situation where people generate code they cannot review, tools reach data they should not see, and nobody knows whether the productivity gain is real. A better rule is simple: Give people the AI capability their job requires, under controls appropriate to what that capability can access and change. The recent Goldman Sachs story is a useful example. Bloomberg reported on November 2, 2026 that internal guidance updated in October restricted Anthropic's Claude Code and Microsoft's GitHub coding tools for most…
18 hours ago · dev.to
An agent that can inspect a repository still needs the right task context, a way to execute work, and a review path. The right choice depends on whether you need a coding agent in a terminal or IDE, a shared system for coordinating work around agents, or infrastructure for building agents. This guide compares those roles rather than treating them as interchangeable. Evaluate where code work happens, how project context reaches the agent, how people review actions, and whether the tool is for coding directly or building and running agents. Comparison Table Tool Best For Deployment AI Agent…
20 hours ago · dev.to
If you've wondered what a "personal agent" looks like in practice, this is mine: no app, no server, just a folder of Markdown files and a coding agent pointed at it. At the end there's a folder layout and the rules file, so you can start your own. Yesterday I was a bit tired and almost skipped the gym. Before heading out, I asked the AI offhand: "If I train back today, which exercises should I do?" It said that if I was tired I should go lighter, taking 20 to 30 percent off last time's weights, then lined up four exercises, three sets each. With the plan in front of me, I actually wanted to…
22 hours ago · dev.to
Do you need to read every line of AI-generated code to trust it? Not necessarily. You need to check five things instead: the intent behind a change, its acceptance criteria, the invariants it can't break, the decisions made along the way, and evidence that it works. At Aviator , that shift let the same nine engineers merge 4× more pull requests, cut median time to merge from 2.1 days to 3.4 hours, and drop reverts by 40%. A human still signs off on every PR. The insight behind it: most of what a reviewer writes on a PR is either a rule they've written before or a decision. Rules should be…
22 hours ago · dev.to
Whether you’re trying to build a software factory or already running one, you’ve probably heard some version of the pitch: agents can write the code now, and humans are the bottleneck. Get people out of the loop and let the agents cook. And for most teams, review is exactly where things get stuck. At the top 10% of companies, agents are now writing a majority of merged PRs, but a person still signs off on the vast majority of them before they merge. If people are the bottleneck, it’s fair to ask what all that human attention is actually buying us. Does it make agent code better? At Jellyfish…
22 hours ago · dev.to
In a previous piece , I wrote that I had never checked the common claim that "handing an agent context raises the quality of what it writes." And that the prior research measured only the scaffolding — the spec attached to a PR — while no one had measured the effect of accumulated decisions. This time I measured it. I cloned my own repository, stripped documents per condition, and ran the same request over and over: ten rounds of a controlled experiment, a hundred-odd runs in all. What I learned Always keep decisions and rejections, even if it's only the verdict and a status. A one-line…
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Најмалку 10 загинати во два големи и тешки верижни судари со по 50 возила во Бугаријаdev.to · 22 hours ago
Automate the Impossible: Building a Closed-Loop Medical Appointment Agent with Browser-use & GPT-4V 🚀dev.to Claude Code Output Validation Hooks in 2026: Linting Agent-Written Code Before It Touches Your Repo
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