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I asked an AI assistant to review a repo before shipping it.

It came back with a clean report, a confident tone, and a long list of passes. Then I checked one of the lines it cited.

The line said something else. The mistake itself didn't bother me that much. What bothered me was that there was no shape in the output for "I don't know."

Everything was either a problem or a pass. And the passes were doing a lot of unearned work.

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 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

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've noticed a pattern in almost every "how I built this" thread here lately: before touching the keyboard, people are opening a chat window with an LLM and asking it to poke holes in their idea.

Makes sense. Vibe coding has made building fast. You can go from idea to working prototype in an afternoon. But building fast doesn't mean you're building the right thing faster. If anything, it raises the cost of a wrong guess, because now you can ship the wrong product in a day instead of a month.

So I've started using AI earlier in the process to argue with me. I'll describe an idea and ask it to play the most skeptical potential customer it can, or to list every reason the idea might already be a solved problem. Sometimes that surfaces a competitor I hadn't seen. Sometimes it forces me to actually define the problem instead of jumping straight to the feature list.

Here's the part that worries me though: AI models are notoriously agreeable. If I phrase my idea with enthusiasm, I sometimes get validation back that feels a little too easy like it's mirroring my confidence rather than actually stress-testing the logic. That's dangerous, because it can create false certainty right before you commit real time to building something nobody asked for.

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.

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 )

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.