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Top comments (255)
Welcome everyone to DEV! We are glad you decide to join us!
As mention before for many welcome threads before me, I will make my introduction at Welcome Thread v400! I made a promise to @javz that I will do this (though forgot the reason why lol).
In the meantime, feel free to visit my guide on dev.to!
Get Started on Dev.to! A Beginner's Guide to Engage with the Community! 💡
I will see you all next week there! In the meantime, what brings you to DEV?
Looking forward to your intro @francistrdev !
It's almost time already! 🍿
I'm debating if I should make it short and sweet or a whole research article length lol
hello and thanks for having me
heyyy
hiiii
Welcome welcome everyone!
Hi, I'm Guillermo, Head of Technology at Werk. We're building an AI assistant that keeps the work of teams on the move organized from their voice notes and photos. I'll be writing about model-agnostic harnesses, LLM routing and testing agents. Happy to be here.
Hello! Your work sounds really cool!
Welcome to DEV! 😊
One of the things I love about this community is how it makes you feel that you're part of something. People take the time to read your work, encourage you, share their own experiences, help each other, and celebrate what you create. It's such a good feeling to see your work reach people and get appreciated, and to know that something you've shared might help someone else too. That's something I really value about DEV.
I'm so glad this community brings developers together like this, and I hope all the new members feel that same warmth here too. It's lovely to have you all with us. Wishing you all a great time being part of DEV! ☺️
Welcome to news members on DEV!
Hey everyone!
I'm Neha, a software engineer with 7+ years of experience, mainly working with React, TypeScript, and Node.js.
I enjoy working on complex web applications, especially frontend architecture, state management, debugging, and performance.
I've just joined DEV and published my first article, "Why Building a Reliable Calendar UI Is Harder Than It Looks." It shares a few lessons from working with scheduling interfaces, date handling, and frontend reliability.
If you're interested, here's the article:
dev.to/nehamangla/why-building-a-r...
Happy to hear how others approach similar challenges. Looking forward to connecting with the community!
Hi everyone! I'm a data scientist (and a former biophysicist) with experience in computer vision and materials science. I'd be really glad to connect with anyone here, especially people who build things with AI coding agents!
Right now I have a lot of projects, many of them built with AI coding agents, and at some point I realized it was getting really hard to keep everything organized.
So I built an open-source tool (MIT license) called Slightly Parallel. It's meant to help you keep track of things across multiple projects: it has a kanban board adapted for AI coding, a few features that help you stay on top of what the agents did, and a way to carry what you learn from one project to the next.
A dashboard for coding agents, self-hosted on one laptop: what Claude Code, Codex and OpenCode did in your projects.
Slightly Parallel
A local dashboard for running many projects with many coding agents at once, without losing the thread of what is happening.
It keeps every chat, puts the work on a board, and makes the agents write down what matters: what changed, what was decided, what the project is for. The aim is a step from vibe coding to leading a team of agents: you know the code, the architecture and the decisions, even when an agent wrote most of it.
The project on film, in a minute or in six: Slightly Parallel in a minute (0:58) · Every page (6:42)
Contents
Install
The same steps on…
Looking forward to meeting you all! Always happy to chat about agentic workflows, side projects, or anything that breaks if you try it :)
Keeping track of what the agents actually did is the part that gets me too. Across a few projects it's easy to lose which "done" was really done. Does Slightly Parallel keep a record of what each agent claimed next to what it changed? That's the gap I keep running into.
Hi! Does it work with Cursor?
Hi! Cursor is supported: Setup adds Cursor's hooks to the project (.cursor/hooks.json) along with the MCP config, so its chats get recorded like the others. That said, I've tested it less than the rest, so I can't fully guarantee it yet. Claude Code, Codex and OpenCode are the main ones, and it runs on Windows, macOS and Linux. If you try it with Cursor and something doesn't work, let me know and I'll fix it.
Welcome all the new comers to DEV. 👋
It's a great community of developers, for the developers.
It's one of only a few places left where conflicting developer opinions rest in peace 🤣🥰
In case you are interested, here's one of my last posts that you may find worth reading:
There's No AI Agent, Nor Any Harness.
Hi everyone! I'm Ahmad, a senior software engineer with 14+ years building SaaS platforms end to end. These days I'm focused on AI agents, LLM retrieval and the systems that make them reliable. I just published my first post here, about why markdown plans break when you run multiple AI agents at once, and I'd love to hear how others coordinate their agents. Glad to be here!
Hi Ahmad and welcome ! A shared markdown plan broke the same way here: two workers could take the same unit. What held was a queue they pull from, with one session allowed to merge, and a dashboard so a stuck worker parks the question and takes the next unit instead of leaving the item marked in progress. I'd watch expiry on those claims: without it, a dead session still owns the unit.
Thanks Dominique! That's almost exactly where I ended up. Workers claim tasks from Postgres with a TTL, so a dead session loses the claim when it expires and someone else picks it up. I also added limits on refreshing a claim, because a stuck agent can keep refreshing forever without making progress.
Hi. My small obsession is the cost of a finished task rather than a finished call: retries, cache, human review, all of it in the same place. Mostly Python and log parsing.
Curious what people here count as "done" when they evaluate an agent run
Great question. My answer so far: a run is done when the thing it says it fixed is actually fixed, checked by a test that fails without its change. A lot of "done" I've seen only means the tests passed, which isn't the same thing. Do you count human review time in the cost even when the review finds nothing?
Counted either way. A review that finds nothing costs the same hour as one that finds something, and skipping the empty ones just makes my average look better than the workflow is. I want the number over every review, not only the ones that caught something
Makes sense. Same with the checks I run on AI commits: most come back clean, and those clean runs are the real price of catching the few that aren't.
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