With thousands of new AI tools launching every week, directory overload is real. Scrolling through endless lists often leads to decision fatigue rather than actually finding useful tools.
I built a simple side-project called DevDrafts (https://devdrafts.netlify.app/) to solve this exact problem delivering just one hand-picked, high-value AI tool or blueprint every day.
How do you currently discover new AI tech for your workflow?
Do you prefer massive searchable directories, or curated daily/weekly highlights?
Would love to hear your thoughts and get feedback!
We launched a new release of ApyHub and the thing I keep thinking about isn't our launch (only). It's the providers.
While building our catalog we kept finding the same story. A solo dev builds a genuinely great API. A holiday data service. A screenshot renderer. A receipt or resume parser. Technically excellent, solving a real problem, and almost nobody knows it exists. The person who built it is busy maintaining it, and distribution is a full-time job they never signed up for.
Hello PH community! I'm dev behind ClarifierAI. It's an IOS app that was praised by many users. 610 installs, 3.09% instal -> paid conversion. Launching this Sunday (12 Apr) and i would really love to get support from users who love the product.
If you're not familiar with the product you can check how it works without installing it tryclarifier.app
Yes, he s a leader too, whether you like it or not... Some people might say don t platform people like this , but I actually think the opposite. The whole point of Votap is transparency. Put everyone on there and let people openly show what they think, including smashing that downvote button Of course, I m already starting to see some trolls in the comments Go see for yourself... Download Votap on the App Store!
After launching Savyre AI Coding Workflow here on Product Hunt, we also took it to the developer community on DevHunt.
Today, Savyre is #1 Product of the Week on DevHunt! https://x.com/devhunt_/status/20...
What has been even more encouraging than the ranking is the feedback. Developers are asking about codebase impact analysis, context preservation, consistent reviews, local-first security and using Savyre on their own repositories.
It s validating the idea behind Savyre: AI coding doesn't just need faster code generation, it needs a better engineering workflow around it.
I m building a tool that centralizes retry limits, spend caps, and alerts across every cron job or agent you run, instead of wiring guardrails into each project separately.
We're currently expanding Cash Never Sleeps and building out 24 new AI growth and automation playbooks for our library.
Right now, we're mapping out: Web scraping & lead enrichment pipelines Automated cold outreach setups Custom Claude system prompt sheets & Make.com scenarios
For the founders and agency owners here: What specific manual growth bottlenecks or repetitive workflows are taking up most of your time right now that you wish were automated?
Genuine question for anyone who's thought about AI agents with deep access to your data email, files, calendar, that kind of thing.
Does it actually change your trust calculus if the AI runs entirely on your own machine instead of a cloud provider's servers or is "local-first" more of a founder talking point than something people actually weigh when deciding whether to give a tool that kind of access?
Asking because I've spent the last few months building a personal AI assistant that runs fully local, specifically on the assumption that people would trust it more with sensitive access. But I don't actually have real evidence for that nobody's told me "local" was the thing that changed their mind.
So: if you've used, or considered and rejected, an AI tool that wanted deep access to your inbox/files what actually made you comfortable, or not? Was where it runs ever part of that decision, or does it really come down to something else (company reputation, specific permission scoping, data retention policy)?
