Doing one of these because I get more from reading what others are building than from any newsletter.

I'll start.

I have been looking at what is actually getting traction on Product Hunt right now. The pattern is clear.

In March, OpenClaw products dominated the leaderboard. Anything with "Claw" in the name got votes. That was a land grab new space, everyone rushing in to claim a spot .

In April, that stopped. The "just build an agent" strategy stopped working. What replaced it? Products that do specific tasks inside workflows you already have .

Here is what the data shows.

I was reading this recent @OpenAI article about Gartner naming OpenAI a Leader in enterprise AI coding agents: https://openai.com/index/gartner....

"Software development is becoming more agentic." This is a good summary of what is happening right now. We are moving from AI that helps you write faster, to AI that can take over tasks (actions, use tools, make changes, run tests, and bring the work back for human review). That is a very different behavior.
The article gives Cisco as an example. They used Codex for a big part of their AI Defense platform and reduced delivery time from several quarters to a few weeks.

Vibe coding a personal project that works on your laptop is one thing.
Building a tool your team opens every morning to manage projects, serve customers, and run the business is another.
Suddenly there s shared data.
Different people need different levels of access.
Your teammates need to understand how the app works so they can change it too.
And the app needs to keep evolving without turning into a black box nobody wants to touch.
At that point, would you be comfortable letting AI change anything it wants?
We ve been thinking about this a lot while building WeWeb MCP (launching on PH tomorrow!)
Agents like Claude, ChatGPT, Cursor, and Gemini can now build apps directly inside WeWeb.
Across the frontend & backend from: UI, data, workflows, integrations, permissions, and more.
But the important part for us was making sure the builder stays in control of both the app and the AI building it.
That s why we built guardrails into the way AI works inside WeWeb:

• Before AI builds: choose what AI can and can t touch: pages, components, workflows, tables, APIs, and more.

• After AI builds: see exactly what changed in the visual editor. Tweak it yourself, keep iterating with AI, and publish when you re ready.

So AI gets room to move fast, without getting free rein over the whole app.
I m curious how you think about this: Would you give an AI agent access to everything?
Or are there parts of your app you d always want to lock down?

There are so many new AI agent platforms ( @Wordware @Lindy @CrewAI @zapier and so on) that I'm finding myself curious how everyone is using them.

What AI agents are you using in production? What do they do? Are they working and reliable? What would make them better? Are they replacing roles? Augmenting existing ones?

If you're building a product with AI tools, here's how your morning probably looks:

• Open ChatGPT. Paste your project context. Ask your question. Get a decent answer.

• Switch to Claude for something else. Paste your context again. Different tool, same briefing.

• Hop into your codebase with Codex. Paste context again.

You're not just a builder anymore. You're a context delivery system.

The indie hackers I've talked to who use AI most effectively have one thing in common: they've figured out how to stop babysitting their tools and just use them.

@OpenAI just dropped a 34-page guide on how to build intelligent AI agents. It s full of great ideas but could be hard to navigate if you're non-technical. I made a simple, no-code breakdown of only what you need to know here and how to apply these ideas into the no-code AI agents & multi-agent workflows you build on @MindPal here: https://mindpal.space/blog/opena...

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 ve been thinking about how messy the current build an app with AI workflow still is.

For a simple website or SaaS-style product, people often end up jumping between multiple tools:

• one tool for UI generation

• another for backend/database

• another for auth

• another for payments

• another for deployment

• and maybe something else for internal dashboards or admin tools

It works, but the workflow can get fragmented pretty quickly especially if you re not a full-stack developer.

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

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 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

Hi Product Hunt,

I m Sharad from India. I spend most of my time building AI products and experimenting with agent workflows, observability, and developer tooling.

Recently I ve been focused on how AI agents behave in production debugging failures, tracking costs, understanding handoffs between agents, and making these systems easier to operate reliably.

Currently building AgentPulse, an observability platform for AI agents and multi-agent workflows.

I m building landing pages for my next product and exploring AI UI tools that actually help ship faster.

Curious to hear what s working for you from the apps like Lobable or v0

I've tested both and cannot choose what tool is better to buy. Maybe there are better tools?

Hi everyone

We re launching Needle AI Agents tomorrow!

Our goal is to help teams automate processes and build AI Agents with context of their own data sources (Google Drive, Notion, Slack, etc.).

Everyone s been talking about AI Agents over the past year.

But the real leverage doesn t come from the agent itself. It comes from the context you provide:

So, quick confession. I'm a marketer by trade, not an engineer.

My coding knowledge is basically zero.

I used to think game development = coding.

Turns out that s no longer true (and I m still kind of shocked).

Last week, I created a mini narrative game complete with characters, logic, and visuals without writing a single line of code.

It was built using an AI tool called Redbean, and honestly... it felt like magic.