What are you building? Drop your AI product below
by•
Doing one of these because I get more from reading what others are building than from any newsletter.
I'll start.
Building AI Hive, an enterprise AI agent platform that helps mid-market and enterprise teams get from AI pilot to production in weeks instead of quarters. The hard problem we keep solving: compliance, model flexibility, and the lack of in-house AI talent on the customer side.
Product Hunt page: https://www.producthunt.com/products/ai-hive
Your turn. Drop:
- What you're building
- Who it's for
- The hardest part you're solving right now
Will read everything and upvote what resonates.
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Replies
What I'm building:
Blooou : a stock information platform that helps people research stocks using natural language. Instead of searching across multiple websites, users can ask questions about companies, earnings, news, financials, or market trends and even professional data.
Who it's for:
Retail investors, students, and anyone who wants to make better investment decisions without spending hours reading reports, earnings transcripts, or financial news.
The hardest part I'm solving right now:
Delivering AI responses that are fast, accurate, and easy to understand while working with constantly changing financial data. The goal is to make stock research feel like having a knowledgeable analyst available 24/7.
@new_user___2032026faccd02b1a1feddb The 24/7 analyst angle is the right framing for retail investors - most of them don't have 3 hours to parse an earnings call and just want to know if the number matters. Natural language on top of financial data is exactly where AI actually adds value vs. just looking useful.
Real-time accuracy on constantly changing financial data is genuinely one of the hardest infra problems in this space, so props for taking it on.
Great stuff. It’s awesome that you're trying to speed up AI agent deployment for enterprises.
My question is: what is your real advantage here? Since you are a new player and there are already other established players in this space, why can't they do this job just as fast, and what actually sets you apart?
As for what I’m building: I’m creating VertoX, the world’s first real-time speech translation system that preserves the speaker's original voice, tone, and emotions. We have no direct competitors in what we are doing.
Our ICP is literally the entire world. It doesn't matter if you're an enterprise company, a student, a traveler, or a government agency; VertoX is here to completely eliminate the language barrier globally.
@nemos Fair question on differentiation - the honest answer is that the big players (Kore.ai, IBM) are slow and expensive, and lightweight tools don't scale for enterprise compliance. We sit in the middle: faster deployment, on-prem support, and our own engineers go in with the customer rather than just selling a platform and wishing them luck.
VertoX sounds like a genuinely hard problem to crack, voice + tone + emotion preservation is a completely different beast from text translation.
@nolan_vu Got it, that makes total sense. So instead of just selling SaaS, you’re taking a hands-on, implementation-first approach.
That raises an interesting unit economics question though: if you're embedding your engineers with customers to deploy in weeks, doesn't that make scaling harder and more resource-intensive? As customer volume grows, engineer burnout and hiring costs could become a real bottleneck. Curious how you're planning to scale that model long-term without hitting financial strain.
(Honestly, the more I learn about products, the more business model questions I have 😄)
And thanks for the words on VertoX! You’re spot on; it’s pure deep tech. My core goal with VertoX is simple: give people back their time, which is the most valuable asset we have, and save billions that are currently wasted on complex multilingual operations. People should just communicate seamlessly.
What we are building: picoask.ai - an AI data analyst that forms hypotheses, finds the root cause, and tells teams what to do next (not just another dashboard). Not just a text2sql, does the hard work needed before and after the data is retrieved.
Who it's for: CXOs, strategy teams, head of departments. Anyone who is inclined towards data backed decisions and struggling with finding root cause of their problems.
Hardest part right now: Understanding use cases of different industries and building integrations.
Product Stage: Open for beta requests.
PH Link: https://www.producthunt.com/products/picoask?launch=picoask
Demo query link: https://picoask.ai/share?t=y8KsJGIc85J_8nx6SiqXkD1Lcn5qsvXnfa-QTxAMPuU
@parveshgarg The "not just another dashboard" framing is doing a lot of work here and I mean that in a good way. Root cause analysis before and after data retrieval is the part most tools completely skip, and that's usually where the real value is for strategy teams.
The cross-industry use case problem is genuinely tough though. Rooting for you to crack it! Already followed and upvoted.
Thank you @nolan_vu - It took a few months of hard work and a few years of imagination. Would love for you to try it, we have a 10 day free trial period.
@parveshgarg thanks for the invitation, let me check with my team to see if we can help you with this.
What I'm building: WorkstationAI, one workspace for the whole job hunt. Paste a job ad, get an A to F "should I apply?" score with a scam and ghost-job check, then CV analysis and tailoring, company research, and interview prep dashboards built from live research on that company and role.
Who it's for: mid-career professionals applying to 20+ roles who have no way of telling which of those ads are real.
The hardest part right now: calibrating the score so people trust it. Telling someone "this is a D, don't apply" is only useful if they believe it, and right now the model reads the ad text only, no live check on whether the company is actually hiring. Flagging a genuine ghost job without crying wolf on every vaguely-written ad is the thing I keep rebuilding.
Product Hunt page: https://www.producthunt.com/products/workstationai (launching 4 August)
Happy to swap honest feedback with anyone else launching that week.
@you_li525 The ghost job problem is so real and so underrated. I've seen people spend weeks crafting applications for roles that were never going to get filled, and nobody talks about how demoralizing that is. The calibration challenge you mentioned is the hardest part of any scoring system honestly, people need to feel like the "D, don't apply" verdict is trustworthy before they'll actually skip a role.
Good luck with the Aug 4 launch! Rooting for this one. Already upvoted and followed to keep tabs. If you get a chance, would love your take on AI Hive, we're building automation workflows for teams and makers: https://www.producthunt.com/products/ai-hive
@nolan_vu Thanks. The demoralising part is what I underestimated. People don't just lose the weeks, they lose confidence, because a silent rejection from a ghost job feels identical to a real one.
On calibration, the thing that helped was splitting the judgement in two rather than trying to make one number carry it. There's a fit score for "is this role right for you", and a separate flag for "is this ad likely to be real". Blending them was where trust broke: someone would see a C and have no idea whether that meant underqualified or suspicious, so they ignored it. Split, the tool can say "strong fit, but this ad has been reposted for 4 months" and let the person decide. People trust a tool that shows its reasoning more than one that just sounds sure.
Still unsolved: it reads the ad text only, with no live check on whether the company is actually hiring. That's the next thing I want to build, and it's the one that would make the ghost-job flag genuinely reliable.
@you_li525 Thanks You Li, I really like the way you separated fit score from job legitimacy. That feels way easier to trust than trying to compress everything into a single grade. The live hiring check sounds like the missing piece too. If you crack that reliably, it'll solve a pain point almost every job seeker has faced.
Premation
🚀 Motion Editor launches today!
An open-source AI-native motion editor built with a modern workflow for 2D/3D animation.
Product Hunt launch: 4 PM KST.
Developers can already clone and run it locally:
https://github.com/isroil01/motion-editor
#OpenSource
@isroiljon Congrats on the launch, Jon. Open-source AI motion editing is a pretty interesting direction, especially with how much demand there is for animation workflows right now. Hope the Product Hunt launch brings in plenty of contributors and early feedback for the project.
Im building a website that's also mobile friendly that creates the 2x2 passport and visa photo online in seconds. Just Upload your picture, crop it to official size, and download a print-ready passport photo with no registration required, no personal data is collected and is free no paywall just set up adsense to help with hosting fees.
https://www.freepassportsnap.com
Im on here to collect feedback to make the site as easy to use as possible
@0ldvwguy Nice idea, Bobby. I can definitely see this being useful for people who only need a passport photo once and don't want to install an app or pay for a subscription. Keeping it simple and registration-free is probably the right call here. Good luck collecting feedback and refining the UX.
Building Reality Filter - a hype-check for AI launches.
You paste in any AI tool or model and get one verdict: worth trying now, watch later, too early, or mostly fog machine. Plus a "fog meter" for how much of the claim is still unproven. Every verdict names its sources - actual benchmark numbers and named reviewers, never "reports suggest".
Two deliberate choices: the calls are dated and public and I don't quietly edit them (one already flipped from "too early" to "worth trying" when GA landed, and the original call is still on the record), and one verdict is "mostly fog machine" on a product doing $1.2B ARR. If it only ever agreed with press releases it'd be worthless.
Free, no signup to read a verdict: https://bharatlearner18-del.github.io/reality-filter/
The thing I actually want to know: when a launch you're unsure about drops, what do you do right now - ignore it, ask someone, or dig it out yourself?
@bharatlearner Thanks for sharing this Bharat. The public and dated verdicts are what caught my attention. Most review tools quietly revise history, so keeping old calls visible makes the trust factor much stronger. Personally, if I'm unsure about a launch, I usually look for real user feedback first before digging deeper myself.
Building Maleu — India's first life OS 🇮🇳
Social sharing + structured learning (Bloom) + fitness tracking (Wrex) + communities (Circles) + AI camera direction.
All in one app. Free. Launching on PH today: producthunt.com/products/maleu
What I’m building:
I’m building a small validation assistant for early indie founders.
It’s for people who have already built a landing page, demo, or MVP, but still feel unsure what to do next.
The tool looks at the founder’s current product, what they’ve already tried, and the signals they’ve received so far. Then it gives them a clear read on where they are stuck and one small validation action they can do next.
Who it’s for:
Early solo founders, AI coding beginners, and side-project builders who already have a product direction, but aren’t sure whether to keep building, keep researching, or show it to more real users.
It’s not for people looking for a brand-new idea. It’s more for people who already built something and need help making sense of their next move.
The hardest part I’m solving right now:
Finding real early founders with real unfinished validation problems.
A lot of people ask for feedback, but not everyone is actually stuck on validation. Some need growth. Some need positioning. Some just want launch support.
So right now I’m manually reviewing real founder posts and testing whether this can give them a useful next step, not just generic advice.
If anyone here has built something but feels unclear about the next validation step, I’d be happy to try it on a few real cases.
What I'am building:
RunbookAI is an AI-powered incident response platform that detects production incidents from engineering signals (starting with GitHub, with Slack and other tools integrated later), investigates root causes autonomously, generates actionable runbooks, and helps engineering teams resolve incidents faster using AI agents.
Who it's for:
Software engineering teams, DevOps engineers, SREs, platform teams, and engineering organizations that want to reduce incident response time, automate root cause analysis, preserve operational knowledge, and onboard engineers faster.
The hardest part solving right now:
Building a reliable AI investigation engine that can accurately detect incidents from engineering activity, correlate information across multiple sources (GitHub, Slack, CI/CD, logs, etc.), determine probable root causes with minimal hallucination, and generate trustworthy, executable runbooks that engineers can confidently use during production incidents.
@garlapati_ashwith Incident response is one of those areas where the gap between "AI sounds useful here" and "AI actually works reliably here" is really wide. The correlation across GitHub, Slack, CI/CD, logs in real-time without hallucinating probable root causes is a hard technical bar. But if you get it right, the value is massive, especially for on-call engineers who are already context-switching at 3am.
The trustworthy executable runbook piece is where I'd want to see more detail — that's probably the hardest thing to get right consistently. Would love to follow this closely.