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Building an AI agent is one challenge, but knowing it's ready for production is a completely different one.

Traditional software can often be verified with unit tests and integration tests, but AI agents introduce additional complexity. They rely on reasoning, external tools, changing context, and non-deterministic model outputs, which makes testing much less straightforward.

I'm curious how other teams approach this before deploying AI agents to real users.

AI coding assistants have become part of my daily workflow, especially for repetitive tasks, documentation, and quick prototypes.

At the same time, I've found myself spending more time reviewing generated code than I expected, particularly around security, scalability, and edge cases.

For me, AI has become an accelerator, not a replacement for engineering judgment.

I'm curious how others are using these tools today.

Over the past few months, I've noticed that AI coding tools are excellent at generating code, but they still struggle to keep context across larger projects. That often means spending time re-explaining the codebase instead of moving faster.

I'm curious whether others have noticed the same thing, or if you've run into different limitations. What's the biggest issue you've experienced?

I ve been using a lot of AI-generated code lately, and while it definitely speeds things up, security feels like a weak spot.

I ve run into issues like missing auth, exposed endpoints, and weak configs stuff that AI doesn t really flag unless you explicitly ask.

Curious how others are handling this:

• Do you rely more on manual reviews or tools?

• Any workflows that consistently catch vulnerabilities?

• Have you faced any real incidents because of AI-generated code?

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

Genuinely curious what the community does here.

We've been talking to hundreds of teams building with Cursor, Claude Code, and other agentic tools and the honest answer from most of them is: "We just run it and hope."

Some do a quick manual click-through. Some write a few spot checks. Some just ship and wait for users to find the bugs.

We built TestSprite to solve exactly this autonomous testing that runs from your PRD and codebase but I'm curious what your actual workflow looks like before you merge.

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?

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.

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.

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 )

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.

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.

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!