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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:

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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What your building: Loomy - You can generate images in batch, 4 at the same time and from description, choose your resolution up to 4k and add custom background.

You can create collections without sitting all day at computer. Its easy, add your gemini api key and start generating.

Who it's for: Web app for Graphic designers, freelancers, ecommerce

Hardest part right now: Getting discovered with zero ad budget as a solo founder from Poland. Im trying to go only organic with social media.

App -

 Batch generation at 4k with custom background is a genuinely useful workflow for ecom teams - the amount of time that goes into product image variations is insane and most tools make you do them one at a time.

Organic-only with $0 budget as a solo founder is tough but it works if you show the output, not the tool. Post the before/after, not the UI. Upvoted and following! Would love your thoughts on if you get a moment:

What am I building?
Founder's Climb, a journaling app that helps founders see how far they've actually come by measuring progress from where they started, not where they wish they were.

Who's it for?
Early-stage founders and solo builders who feel behind every single day even though they're actually making real progress.

Hardest part right now?
Distribution, hands down. I built the thing but getting net-new people to even discover it exists is the whole battle. Where do you post when you don't have an audience yet? Which channels are worth ad dollars at a tiny budget, and which are a money pit? I'm doing daily build-in-public content on TikTok, YouTube Shorts, and X, but I honestly can't tell yet what's driving downloads versus just views. If you've cracked early distribution for a consumer app, I'm all ears.

Product Hunt Launch:

 The problem you're solving is real and honestly underrated - founders are terrible at seeing their own progress because they're always measuring against where they wanted to be, not where they started. That framing alone makes this worth trying.

On distribution: TikTok Shorts worked way better for us early on than anything paid. The bar for "authentic founder content" is low enough that even rough, unedited stuff gets traction if the hook is specific. Upvoted and following. If you have a moment, would mean a lot if you checked out AI Hive:

I'm building Aesthetic Letters. A free tool that lets you generate clean, copy-and-paste Unicode fonts for Instagram, TikTok, Discord, X, and more. No signup required, just type, copy, and use. I'd love to hear your feedback!

 Nice concept, Zain. The best part is probably the low friction, type, copy, and go. Those kinds of utility tools often end up getting way more use than people expect because they're solving a tiny problem really well. Wishing you the best with it.

 Thanks, that means a lot. That's exactly what i'm aiming for, removing every bit of friction.

Hello everyone,

I am building a game, geared towards speaking and improving communication. This started because founders like myself do struggle in terms of being precise with what they say. The hardest part that I am solving right now is figuring out whether this is needed first, and figuring who else it might be for. If you are reading this and think of any ICP that might fit, please let me know! I am still in my ideation phase.

Best

 The ICP question is genuinely the hardest part at ideation stage, and most founders skip it thinking they'll figure it out later (spoiler: they don't). From what you described, sales founders and non-native English speakers in high-stakes roles feel like a strong starting point.

Have you tried recording yourself pitch something and watching it back? That's usually the moment people realize they really need this.

What you're building: FasalVision — a live AI farming assistant app available in 9 languages. It gives real-time crop disease detection, weather-based advisory, mandi (market) prices, and personalized farming guidance — all powered by AI, built for farmers in rural India and beyond.

Who it's for: Small and marginal farmers who don't have access to agronomists or agricultural experts. Farmers who speak local languages (Hindi, Punjabi, Bengali, Tamil, Telugu, Marathi, and more). Anyone in agri-tech or rural development looking for an AI-first farming tool.

The hardest part we're solving right now: Getting farmers (who are largely offline or low-literacy) to trust and use an AI app. We're at 40+ installs organically — trying to reach 100 this week without paid ads. Distribution is the real challenge, not the tech.

Check it out:

 "Distribution is the real challenge, not the tech" - yeah, this hits. Getting low-literacy users to trust an app enough to open it a second time is a completely different problem from getting them to install it.

Curious whether you've tried working through local agri extension workers or village-level NGOs as a distribution layer? They already have farmer trust baked in, which is basically the hardest thing to build from scratch.

Hey everyone! I'm Arjun, a solo technical founder building from India.

I spent months staring at my own app and slowly went blind to it - every clunky screen looked normal because I'd seen it 500 times. The stuff that actually lost users (a confusing step, a dead end, a slow screen) I just couldn't see anymore.

So I built TestSting: you give it your Android app and an AI uses it like a brand-new user, then writes up the UX problems it ran into - each with a screenshot, plus a health score. A fresh pair of eyes, on demand.

It's early and Android-only for now, and I'd genuinely love feedback from this community. Free sample report, no signup:

 The "gone blind to your own app" problem is so real, and honestly it's worse for solo founders because there's no one to tell you the onboarding is broken. TestSting sounds like it's solving exactly that.

Does the AI test on actual user flows or just visual/UX issues? Asking because I'd love to know if it catches logic gaps too, not just slow screens.

 Yeah — honest answer is somewhere in between. The AI actually walks the app: taps, types, logs in, moves through real screens like a new user. So it's testing actual flows, not eyeballing a screenshot — it catches the flow-level stuff: dead ends, confusing steps, screens it gets stuck on.

Easiest way to see if it catches the kind of gaps you mean — throw one of your apps at it , no signup: . Genuinely curious whether it holds up on the logic-gap stuff for you.

 sure, let me examine your suggestion later on, thank you very much though

Hello, my name is Seán and I am the founder of Ontoworks.

We are building software for automated video production, where we deep-analysis Footage-databases in order to accelerate production pipelines.

Our focus are production companies and TV broadcasters, with whom we are launching initial projects at the current moment.

The hardest part is improving performance based on user feedback and making our system adaptable and demonstrable in order to reach integration within our customer´s workflows.

 thanks for your sharing Sean

Automated video production for TV broadcasters is one of those use cases where the technical bar is genuinely brutal. Footage databases are massive, metadata is inconsistent, and broadcasters have very specific workflow requirements that change between channels. The fact that you're already running initial projects with real production companies is solid traction for that space.

The "adaptable and demonstrable" part is what most enterprise AI tools underestimate. Broadcasters don't want a magic box, they want to see exactly what the system did and why. Have you found that early customers care more about speed gains or about creative control over what the AI surfaces? That answer usually shapes the entire roadmap.

TAM Network. an AI-native alternative to linkedin. the AI part is the agent identity row. when an AI agent does real work, it gets a row on the receipt next to the human contributors. recruiters see exactly which line item was AI and which was human. transparency, not hiding. v2 launches aug 12. thetamnetwork.com

 Interesting, alternative to LinkedIn you said

"Transparency, not hiding" as the positioning is sharp. The whole industry is moving toward pretending AI involvement isn't happening, so calling it out directly and making it a feature instead of a flaw flips the script in a really smart way. Recruiters needing to know what was AI vs human is going to matter way more in 2026 than most people realize.

Aug 12 launch is close. How are you planning to handle the early skepticism from candidates who'll worry that flagging AI work makes them look less qualified? That's usually the friction point that decides whether transparency-first platforms actually take off or get rejected by the people they're designed to help.

  this is the right friction to name. the answer we are designing toward is opt-in per receipt, not mandatory across the profile. a builder publishes some receipts that flag the agent contribution and other receipts that were 100 human. the market self sorts. some recruiters will value the verified AI disclosure. others will not. over time the receipts with agent transparency outperform on credibility because the alternative (resumes claiming pure human work that we all know used AI) has already lost its signal. the platform does not force the choice. it lets the market price it.

 opt-in per receipt is the right call. forcing full transparency kills adoption, but letting the market reward it over time is a much smarter bet.

the interesting part is what happens when enough receipts with agent disclosure start outperforming the ones without. at that point it stops being a choice about ethics and starts being a competitive signal. builders will opt in because it works, not because they're told to.

curious whether you're seeing early data on that credibility gap yet, or still pre-launch hypothesis at this point.

Just shipped the biggest update yet.

📦 Project Hibernation lets you archive projects you are not actively working on, off your disk into a single file, and restore them in one click whenever you need them. It handles dependency reinstall on restore, is iCloud aware, and verifies every archive before touching the original.

👻 Also added Ghost Projects (per project Xcode DerivedData breakdown) and Project Artifacts (stale node_modules and build folders ranked by age).

✨DevCleaner stays free for manual cleanup and scanning. The automation layer is now Pro, with a 14 day free trial. Lifetime licenses are capped for early supporters at 29 dollars.

Would love feedback from the dev community here. What would you want a tool like this to do next?

Download here

👉🏼 👈🏼

 thanks for shipping this, the Project Hibernation feature is exactly what i've been wanting for dormant projects that i can't bring myself to delete.

the iCloud-aware part is a nice touch, a lot of disk tools ignore that and you end up with half-archived projects that sync back and confuse everything. one thing i'd love to see next: a scheduled hibernation mode where projects auto-archive after X days of inactivity without me having to remember to do it manually.

 That's a great idea! Thank you for that.

Building

It's an API that turns piles of open-ended text such as survey answers, reviews, interview transcripts, support tickets into actual themes. You send the text, you get back the themes, the quotes behind them, sentiment, and a confidence score as clean JSON.

The itch I'm scratching: everyone can prompt a model to "find themes," but it falls apart at scale. The theme names change every run, the counts never reconcile, and you can't point to the quote that justifies a finding. So I'm building the boring-but-hard part (consistency, persistent codebooks, evidence, confidence) as one endpoint, grounded in the actual six-phase thematic analysis method.

Sandbox is opening to the waitlist in waves →

Curious what you'd point it at if you had it

 thanks for sharing this, Olajide, the consistency problem you described is the one nobody talks about honestly.

prompting a model to "find themes" works once in a demo and falls apart the moment you need to compare run 1 to run 2. the persistent codebook approach is the right call because reproducibility is what turns a toy into a research-grade tool. i'd point it at customer interview transcripts first, that's where the "counts never reconcile" pain shows up most visibly in practice.

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