Every engineering team I've worked with had the same problem.

Not bad engineers. Not bad tools. Too many of them.

GitHub for PRs. CI for pipelines. Jira for tickets. Slack for alerts. Vercel for deployments. Linear for issues. Six dashboards, open in six tabs, checked manually, all day, every day.

Nobody was connecting the dots. A CI failure on main would fire a Slack notification, someone would open GitHub to investigate, manually create a Jira ticket, then forget to close it when the build went green.
That loop detect, route, track, resolve was being done by humans, by hand, every single time.

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...

I've been working on a search engine project called Slick for over a year now, and I finally have an early access version ready to share.

Slick is a search engine built for speed, privacy, and user control. We never track search queries, location, or user behavior. Since we use our own index and ranking, no tracking slips through proxies.

Heres what Slick has to offer:

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.

You just launched on Product Hunt and X is blowing up with replies. Instead of letting that momentum fade, embed your best reactions directly on your landing page while the hype is still fresh.

Turn launch day energy into long-term credibility.

Sign in Fetch & Curate Publish.
Withing seconds you'll have options of
1. URL

We ve been working closely with early-stage startups and noticed a pattern.

Most accelerator batches struggle with the same things:

• slow MVP development

• poor product-market validation

• no clear growth system after launch

So we started helping a few batches with a simple approach:

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:

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

You know that feeling when you've finally cracked a Midjourney or ChatGPT prompt?

The exact seed, the lighting words, the style reference that just works. You run it. The output is perfect. You feel like a creative genius.

Then three days later, you can't find it. You search your notes app. Nothing. You dig through Discord history. Gone. You scroll through ChatGPT conversations like an archaeologist. You try to recreate it from memory. It's never the same. That prompt is just... lost.

This happened to me one too many times. I was juggling prompts across Notion pages, sticky notes, a random .txt file on my desktop, and three different AI tools and every time I wanted to build on something that worked, I was starting from scratch.

I've been building ATLAS for 7 months. The idea is simple: instead of charts and opinions, you get the actual probability of making or losing money on any stock.

This is what a simulation looks like: for example Apple, target +5%, 75 days. You can do this for 4,500+ stocks, or get a suggestion of the best stocks with the highest probability for your constraints! The idea is that you can make a more informed decision about your investment ideas!!