One thing I keep running into is not a lack of ideas, but too many directions to go in.

SaaS, content, marketplaces, services most of them look viable in isolation, but it s hard to tell which one actually fits your situation right now.

I got burnt too many times finding out things broke in my product hours later - so I built NotiLens

A payment failed - no one told me.
Orders stopped coming in - found out the next morning.
Server went down - a user DMed me before I knew.

Every time, I was the last to know.

So I built NotiLens - a tool that just tells you when something goes wrong. Instantly. On your phone.

I've been using Claude Code as my primary coding tool for months, but I kept hitting the same wall: the moment I needed a second agent -- Gemini for research, Cursor for UI work, or another Claude Code instance for a parallel task -- they couldn't coordinate. I was the one copying context between terminals.

So I built Neohive. It's an MCP collaboration layer that lets multiple AI CLI agents communicate through a shared directory on your machine.

How it works:

A lot of people I come across who are considering agents worry about the stochastic nature of LLMs. They don't want to trust them with complex tasks that require several steps because they might miss one or get it wrong. They worry that the agent will go off topic, access apps or generally do things they shouldn't. Even worse for regulated industries, you need to produce evidence of how you comply with different laws. Humans leave a natural audit trail of SOPs they follow, tasks they complete etc. so you can easily work with an auditor but what do you do when it's AI doing the work?

At Portia AI, we built an open source SDK from the ground up to address this challenge and we'd love to know if you think it meets the need and if there's something we can learn from others in this space. Agents built with Portia plan transparently, execute statefully, can be interrupted for human input and auth deterministically.

Let me know what you think if you have a moment? Your feedback would be super helpful

We just launched something we ve been quietly building for a while on the agentic AI for ops side at DronaHQ, and I d love feedback from this community.

Most AI tools I see are great for content, chats, or personal productivity. We went the other way and focused on real world ops: approvals, CX, sales, finance, and data workflows that actually touch your systems.

The DronaHQ Agentic Platform lets you plug into your existing stack (CRMs, helpdesks, billing, internal tools) and spin up production ready agents without wrestling with infra, stitching tools, or managing vector DBs. You describe the workflow, wire it to your tools, and let agents handle things like:

• Invoice processing and approvals

• Recruitment and candidate screening flows

• Lead enrichment, routing, and follow ups

• Travel desk and reimbursements

• Data cleanup, sync, and reporting agents

What I envisioned months ago is finally out for use.

My Sketchbook-style React Component Library is Live!

Hey Product Hunt

I m Ritesh, founder of SST.

ai coding agents were driving me crazy. same mistakes over and over, switching models and rewriting prompts didn't help much.

so I flipped the question if I can't fix the model, maybe the problem is my repo? to figure this out I dug into Claude Code's internals, went through 265 versions of its system prompt to see how it evolved, read a bunch of papers on instruction-following, and tore apart a lot of open source repos.
turns out most of the problems are in repo-level details. like: cramming too many "IMPORTANT" keywords actually makes the model ignore them (Anthropic themselves cut it from 12 down to 4). files over 256KB silently fail to load. and if your .env isn't gitignored, the agent can just read all your secrets.

I turned all of this into AgentLint 33 checks covering findability, safety, instruction quality and more. every check is backed by a paper or real data, not vibes. it scores your repo, shows whats wrong, and auto-fixes some of it. currently a Claude Code plugin, but its all bash + jq under the hood. MIT licensed.
feedback welcome
Github https://github.com/0xmariowu/Age...

Hey everyone

I realized that many websites lose potential customers simply because they don t respond instantly. Visitors come, check, and leave without taking action just because no one is there to guide them.

So I decided to solve this problem.

so i am a high school student and take part in lots of kaggle competition but the main problem i face is data cleaning especially for datasets above the size of 80 mb even claude or chatgpt have a limit of 30 mb, it takes at least 2-4 hours to clean a dataset and make it ML ready therefore to solve this problem i am developing an preprocessor that takes your messy CSVs and clean it according to the algorithm you will use XGboost, Regression and etc looking for your insights like is this actually useful and may work or i am overthinking.....

you can try the mvp

https://refineai-six.vercel.app