PH热榜 | 2026-05-10

Hey Product Hunt Folks 👋

Super excited to finally share Tailgrids 3.0 with you all.

We have spent the last several months rebuilding Tailgrids from the ground up. The original goal was simple: keep what developers loved: Clean React UI components built on Tailwind CSS with modern consistent design and zero lock-in. We also rebuilt everything else for how dev and design teams actually ship today.

The result is a complete React UI system that bridges code and design, with a real CLI, full theming, and AI-ready workflows baked in.

✨ WHATS NEW IN TAILGRIDS 3:

• CLI tooling: Install components in seconds with npx @tailgrids/cli@latest add button. No more copy-paste.

• 600+ handcrafted components, blocks & templates: Covering dashboards, SaaS, marketing, eCommerce, and AI products

• TypeScript-first React architecture: Every component rebuilt for performance, tree-shaking, and modern React workflows.

• Complete Figma design system: With 2,800+ variants, 900+ tokens, Auto Layout 5.0, fully synced with the codebase

• Robust theming system: Design tokens for colors, typography, spacing, radius, and themes. Allows you to customize everything and switch between themes (dark/light/...)

• Ready for AI Workflow: MCP server, Markdown docs, and more: Connect Tailgrids to LLMs and generate React components also markdown and DESIGN.md files makes it more accessible for AI

• Tailgrids SVG icon library: Searchable, themeable, copy as SVG or JSX

• New docs portal: Much improved and detailed developer docs, live previews, searchable, and contributor-friendly.
  

🔗 Useful Links

• Public roadmap

• Tailgrids on Github

• Tailgrids on X/Twitter

• Changelog / Update Logs

💜 We'd love your feedback
Specially on the new components, CLI workflow, the Figma kit, and the MCP integration.
Drop questions, ideas, or anything you'd want to see next. We're around all day 🙌

— Musharof from Tailgrids Team

Love this direction 🚀
AI is definitely speeding up frontend development, but great UI still needs taste, structure, and personality. TailGrids 3.0 feels like a strong balance between speed and craftsmanship. Congrats on the launch and best wishes for Product Hunt 🙌

Great work buddy as always! Best of luck!

Hey @musharofchy , huge congrats on the Tailgrids 3.0 launch! It’s looking incredibly sharp.

I noticed a small 'ghost' bug on the landing page: there's a constant 404 in the console for /cdn-cgi/speculation. It looks like Cloudflare’s 'Speed Brain' is misfiring.

It's an easy fix in the Cloudflare dashboard (usually just needs a toggle or a specific header rule), but it's a bit distracting for devs checking out the library.

I’m an ex-Deutsche Bank dev and industrial business owner—I’ve got a few hours open today if you need a senior pair of hands to help squash launch-day bugs or optimize the Next.js flow while you're trending. Want me to send over the fix?

How does the Figma + code sync actually work? auto when code changes, or manual? most UI libraries pick one side and skip the harder part

Looks good! Congrats on the launch

Tailgrids has been a solid go-to for Tailwind projects, a huge time-saver. Curious: how are you thinking about the AI workflow angle? Are we talking AI-assisted component generation, or more like ready-made blocks for AI product UIs (chat interfaces, dashboards, etc.)? That positioning could really differentiate from other Tailwind libraries.

Hey PH! 👋

When founders raise, they pitch dozens of investors, get a handful to back them, and spend months figuring out who the right ones were all along.

InvestorFinder shortens that gap massively. Based on our database of 1000s of investors, our AI match you to the right investor based on your profile and product.

You also get 12+ rich datapoints on every partner:

🎓 Where their founders studied
🏢 Where their founders worked
📍 Where their founders are based
🧠 Founder archetypes
💰 Check size


The use cases are straightforward:

🚀 Founders raising: Stop cold pitching. Find the 10 partners whose portfolio data looks like you and go deep on warm intros to those specific people.

🌱 First-time founders: You don't have a network yet. Use the data to identify exactly which investors have backed founders with similar backgrounds to yours.

🧑‍💻 Solo founders: See which partners have consistently backed solo founders vs. those who only back co-founder teams. Don't waste a pitch on the wrong fit.

📋 Preparing for a pitch: Before any meeting, pull up the partner's profile and know their investment patterns. Walk in more prepared than any other founder in the room.

🔍 Founders doing investor research: Cross-reference check size, stage, sector focus, and founder background all in one place before you add anyone to your target list.

1000+ investor profiles across Sequoia, a16z, Lightspeed, and more. Free. 🎉

Try it at: https://tools.crustdata.com/inve...

Cool concept (already found my potential matches) :D

+ I must say that the design of the page is composed pretty well!

Love the concept! How do you source the VC data — is it manual curation, public APIs, or scraping? And do you cover European/Asian investors or US-focused?

This is actually useful. Things like background, past companies, and even patterns in who partners tend to back matter a lot more than people think.

This is super useful for solo founders like me. As someone building FinTrackrr (a free personal finance tracker), finding the right investors who've backed similar bootstrapped fintech tools has always been a pain. The idea of matching based on actual portfolio patterns rather than just categories is smart. Great execution!

interesting tool for both founders and investors... will you add PM function in future?

Im actually looking for some seed capitol, are there already a good amount of investors on here?

This basically exposes what VCs actually do vs what they claim. Most say they're thesis-driven but the data shows clear patterns toward certain founder types. Anyone pushed back on this kind of categorization?

Really useful timing — we're building Faindo and plan to raise soon, so the "wrong investor" problem is something we're already thinking about.

One question: do you factor in whether an investor has backed tools in emerging categories before? We're in AI lead interception — a category that barely existed 18 months ago. Curious if InvestorFinder surfaces partners who have a pattern of backing category-creating products vs. established markets.

Running security tooling on your own infra is the right call for anything touching production code. Quick question: does deepsec scan at commit time, CI time, or both? I'm curious how it handles cases where the samecode has different risk profiles in diffrent environments.

What's the approach deepsec uses for tracking and mitigating vulnerabilities in real-time? I've seen similar tools struggle with false positives, which can slow down the development process significantly. Curious if there's something novel here that addresses that issue.

Hosting yieldos on Vercel. Underrated feature nobody talks about: per-PR preview deployments. I catch a ton of mistakes before they hit prod just by clicking the preview link.

Congrats on the launch! I like that it runs on your own infra.

Is deepsec mostly for CI/PR checks, or also local dev use?

Curious how you’re handling false positives, that’s usually the hard part with security tools.

This is the one worth attention today in my opinion. The generate/download/repeat loop in most AI image tools kills any sense of creative momentum. So, keeping products/models/edits all connected in one place is how creative teams actually think about campaigns. Wonder how you guys handle products with complex textures like fabric or reflective packaging?

Hello Product Hunt community! 😸

Bringing Adject v2 to life has been an amazing journey. As the team building the AI infrastructure, our biggest challenge (and most fun task) was setting up the new chatting logic. Getting the agent architectures to communicate perfectly to provide a smooth user experience took a lot of late nights, but we are really proud of the result.

Please give it a spin and let us know where we can improve. I'm looking forward to reading your feedback and answering your questions!

Interesting shift from one-off generations to a continuous creative workflow.

Keeping assets, edits, and iterations connected inside projects solves a real pain for teams. This could meaningfully streamline how campaigns are built.

How you handle consistency across iterations (lighting, textures, brand identity) and how collaborative workflows are managed?

Tried a lot of AI design tools before but most of them feel like one-time prompt generators tbh. You make something nice, then the whole workflow resets again

Adject 2.0 feels much smoother. Being able to keep everything in one infinite workspace and continue iterating without constantly re-uploading or restarting honestly makes a huge difference.

One of the few AI creative tools that actually feels built for real workflows.

Would really love to hear your thoughts and feedback, especially from people interested in the future of creative AI tools.

How does it handle brand guidelines? Can you lock certain elements so the AI doesn't drift from them? Congrats on the launch!

This actually looks super useful for ecommerce 😭 Upload once and keep iterating different concepts without reprompting every time. Feels way more practical for brands/content teams than most AI image tools.

Love the upload once idea. It really solves the annoying problem of doing the same thing again and again.

I'm one of the people on the team behind Adject, and we spent a lot of time wrestling with the technical side on this release 😄

Going from 1.5 to 2.0, we didn't really add a feature, we changed the mental model. In v1, every generation started from zero. Prompt, download, start over. It worked, but every campaign meant rebuilding the context all over again.

In 2.0, products, models, edits, and assets all live connected to each other. The agent doesn't work inside a single box anymore, it sees the whole workspace. We had to seriously rethink how state and context move through the system.

Big thanks to everyone on the team, it was great to ship something like this together. Happy to chat with anyone who has questions about the architecture or the agentic workflow side.

Product fidelity across iterations is the hardest problem in this space. Most AI image tools subtly morph the product after a few edits. How does Adject keep it consistent? Reference image locked in, or something else?

Notion 3.4 feels like a major step toward the “all-in-one AI workspace” vision 👀

• New dashboard view turns scattered databases into clean KPI and metrics hubs without messy linked views

• Redesigned sidebar helps reduce clutter and improve focus

• Presentation mode lets you present directly from Notion docs

• Tabs block makes organizing content cleaner and easier

• AI can now generate charts, slides, diagrams, and visuals right inside your workspace

• Archive pages improve search quality and AI responses

What makes this different: Everything stays connected inside one workflow instead of switching between multiple tools for docs, dashboards, presentations, and AI creation.

P.S. I hunt the latest and greatest launches in tech, SaaS and AI, follow to be notified → @rohanrecommends

The dashboard view was the missing piece for Notion as a real ops tool. Curious how the AI chart generation handles ambiguous data — does it ask for clarification or just makes a call? That's usually the UX challenge with AI-generated visuals

The AI generating charts and diagrams is interesting! How does Notion's AI handle data privacy — do the generated visuals stay local or get processed in the cloud?

Cool idea! Does AgentPeek work when multiple Claude Code sessions run in parallel? How does the notch UI handle 3-4 concurrent agents?

Also working on running/prompting all inside of the notch - no terminals required.

This is really cool! Does it work when coding in an IDE or only in terminal?

Having Claude Code visible in the notch is genius ergonomics. As a developer building FinTrackrr, I run Claude Code constantly and alt-tabbing breaks flow. The notch real estate on newer Macs is just wasted space — this is exactly what it should be used for. How are you handling the live output streaming, is it polling or does it use the CLI output directly?

Nice idea! I like that it gives visibility into coding agents instead of trying to be another agent. The most useful things for me would be: what’s running, what changed and whether anything is still using tokens.

Do you plan to add history for completed sessions?

this is awesome man, I gave you a follow on twitter and git. Love the work, would love to see it utilize tailscale and use my iphone notch for my mac running agents. That would really set this apart.

Notch placement is clever - keeps it out of your workflow without hiding it. Does AgentPeek handle multiple simultaneous Claude Code sessions, or is it scoped to one active session at a time? Running parallel agents is becoming pretty common.

I like that the session and token visibility is separated from the coding tool itself. For long-running agent work, the missing piece is usually "what is it doing right now and what did it ask permission for?"

Congrats on the launch. Clear direction: private, offline AI that actually works.

Hard to execute well. Curious how far you push it.

It's super cool Aditya! Wish you all the best on this impressive launch

Privacy-first AI is criminally undervalued right now. The framing about every typed word going to a server you don't control is the cleanest articulation of what makes cloud AI feel off, especially as data accumulates over years.

Building Fenly (AI translation extension) with a similar local-first stance on history. The thing I'd love to see in local AI: serious multilingual support that doesn't degrade to English-or-nothing at smaller model sizes. Translation work needs decent quality across language pairs, and most local LLMs trip on non-English performance below 7B parameters. Curious if your fine-tuned models hold up multilingually or are English-primary.

Strong launch. The OSS angle matters because users can verify the privacy claims, not just trust them.

Hey Product Hunt 👋

I built Keel because every AI tool I tried had the same problem: my context lived inside someone else's product. The notes, the history, the slowly-built understanding of my projects - all trapped behind one vendor's login. The day that model gets deprecated or that company changes direction, my brain goes with it.

Keel flips that. Your memories live on your disk, in plain markdown, in a folder you own. You can open them in any editor, back them up, version-control them, move them between machines. The model is the part that's interchangeable - Claude today, GPT tomorrow, a local Llama on a flight. The assistant changes; your context doesn't.

A few things worth knowing:

It's a real desktop app for Mac and Windows (Electron + React + TypeScript).
Bring your own API key. No telemetry. No account. No cloud sync (yet).
It's a v1 beta - Apple Silicon only, no Windows/Linux yet, no mobile.
It's a one-person project. I'll read every comment here today; I can't promise to build every feature (yet).

Would love your feedback. Thanks for taking a look.

Hey Medha — 'the day the model gets deprecated, my brain goes with it' should be on the homepage. As a fellow solo founder, looking forward to the Windows build.

The markdown-on-disk approach is underrated. Most "local AI" tools still phone home for context storage, which defeats the point. One thing I'm curious about: how does Keel handle memory across different models? If I switch from Claude to Ollama mid-project, does the context transfer cleanly or do you lose the thread?

Memory that belongs to the user is a great trust signal — most AI tools make it feel like the data is theirs, not yours. How granular is the control? Can users edit or delete specific memories, or is it all-or-nothing? Curious how you've approached that UX

Hey Product Hunt! I’m Jason — maker of ReelFluent.

I speak Mandarin, Cantonese, and English, and I’m currently learning Spanish and Japanese. I’ve lived/studied/worked in Australia and the US, and did my Master’s at Carnegie Mellon (EdTech / Learning Sciences). So I’ve tried pretty much every “serious” language learning method…and still ran into the same wall.

Here’s the painful scene I kept repeating:

I could keep a streak on apps like Duolingo, but when I actually needed language in real life — a tense work conversation, a dating moment, catching the sarcasm in a colleague’s tone — my brain froze. I realized the issue wasn’t “more vocabulary.” It was relevance + emotion.

One “counterintuitive” thing I learned as a multilingual learner:

When there’s emotional connection, language sticks.

Your memory anchors to tone, facial expressions, conflict, humor, chemistry — the context that textbooks and isolated drills strip away. (And yes, I also practiced sentences like “The bear drinks milk”…but no bear ever showed up in my life.)

That’s why I built ReelFluent.

Instead of starting with vocab lists or repetitive drills, we help learners watch short drama episodes and practice inside the scene with quick interactions like shadowing, scramble, and MCQ (Like Duolingo-style reps, but anchored to moments you actually cared about). You can tap to translate subtitles in context, and save and review expressions from scenes you actually felt something about. The goal is simple: make language learning easy to start and natural to continue, because the content pulls you back.

ReelFluent is available on the web today (https://web.reelfluent.app/), with the iOS app coming soon and Android launching on Google Play. Also adding Chinese, Cantonese, French, Spanish to the target language.

If you’ve ever quit a language app after 7 days or spend so much time scrolling, I’d love your feedback.

What kind of real-life moments do you most want to handle or be captivated by when scrolling in your target language? (work, dating, travel, small talk, humor, arguments, etc.)

Went with the intention to just explore and check it out but ended up being hooked - something about the cheesiness of the microdramas that makes you want to watch on. I've got a good grasp of english but can see how this could be really THE way to pick up a languge. Looking forward to the chinese target language to be launched soon!

i love this!! Have been trying to learn spanish but duolingo is just not the best atp.

Hey Product Hunt! Winston here. Super pumped for today's launch! 🎉

To add to the team's introduction, I wanted to quickly touch on the 'magic' behind the scenes. We've done some incredibly fine-grained design to weave the language learning elements seamlessly into the short drama plots. We focused heavily on making sure the entertainment value never drops while you learn—it's not just a textbook on a screen, it's a real show.

Grab some popcorn, binge a drama, and let us know your thoughts on the experience! 🍿👇 Happy to answer any questions in the comments."

AI agents for architecture is a fascinating niche. As a developer, I've seen how much time architects and spatial designers spend on repetitive analysis and documentation tasks. Bringing map-aware AI agents into that workflow could be a real productivity multiplier. How are the agents grounded — are they working with actual GIS data layers, or more like visual analysis of map imagery?

How can I test it for my site, also how accurate is the data??

Good luck with the launch Manoj! Cheers. 🥳

Really sleek interface. I just tried personalizing a card and the flow is super intuitive.

Congrats on the launch! this feels like a really cool upgrade to traditional greeting cards - just went through the experience of personalizing a card for my mom today and found it very easy. Upvoted!

Love the concept, but a quick question: if I send the digital link, does the person on the other end have to download an app to see the AR, or is it web-based? @vichod

Most AI testing tools either depend on manual recording or generate tests that “look right” but break in real-world environments.

We started PrimeCompass with a simple belief:

Quality intelligence should come from observing actual application behaviour — not assumptions, prompts, or hallucinated flows.

PrimeCompass continuously watches applications run, discovers real user journeys, detects drift across releases, and generates executable Playwright tests grounded in captured runtime evidence.

Our focus is not just faster QA.
It’s building confidence in modern AI-accelerated software delivery.

Would genuinely love feedback from the Product Hunt community — especially from teams dealing with:
• Release confidence issues
• QA scalability challenges
• AI-generated code validation
• Regression drift
• Compliance & auditability in CI/CD

Happy to answer any questions and discuss where software quality engineering is heading next 🚀

Hey PH 👋

We watched claude code, cursor and codex get scary good at writing code, but infra never caught on.

Agent would write 80% of an app, and then stop to ask me to sign up for Supabase, paste an OpenAI key, click through Vercel. And I'd do it. and then next time, do it again. And the time after that, again.

It's the dumbest part of the loop. Agent can do everything, just not the last 20% - because every cloud service was built for a human, clicking through dashboards.

So we built a version, entirely for agents.

If you wanna try it, just add 'build and host it using cohesivity.ai' to your next prompt in cursor, claude code or codex. That's pretty much it.

We're here all day,

Arag, Shouryamaan & Anshu

I have used the product for testing a project. I had idea and boom everything got properly assigned and deployed and got the link, man i was just shocked and i have to download the codes arrange it. then i have to deploy it but not here just tell your idea on codex and boom.

Backend infra for AI agents is the unsexy but critical piece of the stack. As a solo dev building FinTrackrr, I'm starting to think about agentic features and the session management, memory, and tool routing layer is genuinely the hardest part to get right. Is Cohesivity model-agnostic, or optimized for specific providers? Curious also if it handles long-running agent loops with proper state persistence.

I've tried other screen recorders but they were all strangely tedious--looking forward to a smoother experience!

Hey Product Hunt, I’m Gaurav, founder of Agent Memory System.

I built this because AI coding agents are powerful, but they still lose project context too easily. A task can start in one tool, continue in another, and suddenly the next agent has to rediscover the repo from scratch.

Agent Memory System gives every repository a durable memory layer: project structure, architecture notes, API and security context, worklogs, graph intelligence, and handoff summaries that agents can read before they start working.

It is open source, works with tools like Codex, Claude, Cursor, and Antigravity, and includes CI checks so memory stays fresh as the codebase changes.

I’d love feedback from developers using AI agents in real projects:

• What context do your agents keep re-reading?

• Where do handoffs break down?

• What should memory tooling capture next?

Thanks for checking it out. Excited to hear what you think.

This is very helpful. I was looking for this kind of project. Are you planning to keep this as open source? I am going to fork after trying this.