Every engineer on a team is probably already using some AI coding assistant, Claude Code, Cursor, Codex, whatever. Individually they're all fine.

But once you've got 50+ engineers all pointed at the same codebase, I don't see how anyone actually knows if the architecture is holding together. The "rules" living in a CLAUDE.md or AGENTS.md file are just instructions the model can ignore under a long session or a rushed prompt. Nobody's actually checking the output against real constraints before it merges.

Same thing on cost. A hundred engineers sending overlapping context to the same model over and over, and most teams have no idea how much of that spend is redundant.

I'm building Arclask around this, a layer that checks AI-generated changes against your actual architecture rules before they land, gives leads real visibility into what's being overridden and why, and lets a team share context instead of everyone re-sending the same repo info to the model.

Hi everyone,

I recently built WhoKnowsMe, a friendship quiz platform designed for group chats and social circles.

The idea was simple: most quiz apps force users to sign up, show too many ads, or take too long to set up. I wanted something that anyone could open from WhatsApp and start using immediately.

Features:

skills janitor hit #4 on product hunt last week. i didn't ask anyone to upvote, just put it out there and people found it.

I built a mastering tool that measures AI exports instead of guessing at them. Reply with a link to one of your Suno or Udio tracks and I'll run it and tell you what it actually measured: where the top end cuts off, the low-mid buildup, how far off streaming loudness it lands. Takes a minute.

Send the original export if you can, not a YouTube or SoundCloud link. Those get re-compressed and I'd end up measuring the compression instead of your track.

No catch. The analysis and the full before/after are free. I built it (burnishaudio.com) and I want real files to keep testing it against.

Most AI study tools begin from a blank prompt. I m building Lernex around the opposite idea: the learner s real material, explanations that click, practice mistakes, saved lessons, and next steps should carry across the entire product.

I m recruiting 10 genuine external learners for a structured seven-day product-research cohort. The use case must be real a current class, upcoming exam, certification, or independent topic you actually need to learn.

Selected participants will use Lernex for at least 10 minutes per day for seven days, then tell me candidly where onboarding, personalization, explanations, practice, and return value worked or failed. This is not a request for positive reviews.

Selected completers receive $10 after the week. I ll personally help with onboarding and stay available throughout the cohort.

There s a moment that almost everyone dreads after a trip or a group outing.

Tomorrow's the day NextLevel Digital goes live!

I built this because I got tired of digging through marketplaces where 90% of the templates are unusable. Broken formulas. AI-generated filler. PDFs that are just screenshots. You shouldn't need 90 minutes to find one decent budget spreadsheet.

So I decided to curate the good stuff myself.

What's dropping tomorrow:

Like a lot of you, I watched the organic search traffic on my own projects take a massive hit. Between Google s AI Overviews, Gemini, ChatGPT, and Perplexity, the way users find information has completely fractured.

We aren't just optimizing for Google's spiders anymore we have to optimize for LLM context windows and brand entity recognition (Answer Engine Optimization, or AEO).