New AI models pop up every week. Some developer tools like @Cursor, @Zed, and @Kilo Code let you choose between different models, while more opinionated products like @Amp and @Tonkotsu default to 1 model.

Curious what the community recommends for coding tasks? Any preferences?

I am attempting to observe what you use for coding. I have come across many tools on Product Hunt + Web, but I am fairly certain I have missed quite a bit.
I divided them into "traditional" and "specialised".

Traditional AI models:

DeepSeek

Meow world, welcome back to The Breakpoint, a weekly thread on all things dev tools on Product Hunt.

The latest

Recent dev-first products launched on the site.

After using a lot of AI-generated code lately, I've found myself spending a lot of hours on checking and repairing a lot of easy-to-spot security flaws. That being said, AI generally sucks at actually implementing secure code (or architectures), as well as recommending what to do to make your app more secure (sometimes even decently secure).

Have you had this problem as well? If yes, how do you tackle it?

I recently installed @Augment Code based on an ad somewhere, and I'm super impressed, but haven't heard a peep about it in most channels. But it got me wondering what else I'm missing. This is a crowded field with a few frontrunners and a lot of more esoteric newcomers, but I want to know about the ones that blow your mind but hardly get any coverage.

After rebuilding the same project three times because AI forgot my architecture, I got fed up and built @CodeRide (Beta) with my team.

The problem: AI code assistants lose track of your project between sessions. Every time I start coding with Cursor, Claude, or any AI assistant, I waste time re-explaining my codebase structure, architectural decisions, and coding patterns.

What we built: The project management tool for coding agents using MCP. Upload your project documentation or PRD, and CodeRide breaks it into optimized, fully contextual tasks ready for your AI agent.

AI coding tools seem to come in two main flavors: IDE-based, like @Cursor and @GitHub Copilot, and terminal-based setups, like using @Claude Code to generate commands, scripts, or entire files. Both have their fans, but which one actually helps you move faster?

Curious what flow people are sticking with long term, and where you see the most gains (or frustrations).

AI coding tools seem to come in two main flavors: IDE-based, like @Cursor and @GitHub Copilot, and terminal-based setups, like using @Claude Code to generate commands, scripts, or entire files. Both have their fans, but which one actually helps you move faster?

Curious what flow people are sticking with long term, and where you see the most gains (or frustrations).

I was recently talking with a group of founders, and we went around sharing tools we're using now. Posting my notes for our community here - would love to know what else people are using!

Voice AI toolkit:

- Vapi

At this point, all of the AI coding assistants are in the same neighborhood. Decent at "advanced autocomplete", OK at code generation sometimes, and most are somewhere in the process of incorporating code context mechanisms. But what's next? Agentic behavior? Something else?
My pet prediction is that we will see the emergence of a new programming language that's designed for use with AI and can be translated to a variety of popular languages. (Or if we're cursed, just javascript )

We just launched a fun little side project called the Dev Atrophy Test - a quick quiz to see if your coding brain still holds up in the age of AI.

It's not anti-AI (we love AI!), but let s be real: with autocomplete and copilots doing half the work, it s easy to lose touch with the basics. This quiz is a playful check-in to see if those foundational dev skills are still somewhere in that AI-soaked cortex of yours
https://www.producthunt.com/prod...

Curious to see how you stack up? Take the test and let us know what you think!

With all the excitement and hype around AI coding, there's a thought that bothers me.

AI makes mistakes. A lot. Some goes into production.