PH热榜 | 2026-03-12

@dmitry_zakharov_ai  Finally!

I'm so glad you're back on ProductHunt, and with such a cool project!

I ran a demo myself once and understand how much Naoma will simplify life for many organizations.

Good luck!

We applied for the YC S26 batch a while back — and seeing @aaronoleary thread made us even more excited about the opportunity.

Naoma is an AI video agent that replaces the "Book a Demo" wait with an instant, live product demo on your website. Buyer hits peak intent, gets a real walkthrough on the spot, gets qualified and routed — no scheduling, no rep required.

Pre-seed backed by Ultra VC, Sparkle Ventures and angels. Early pilots with UXPressia, Yesim, Mellow and others. Seeing 10–20% visitor-to-demo conversion, with up to 30% moving to next steps.

Would be an incredible opportunity for us at this stage. Fingers crossed.

🤞 #YCApplication

@aaronoleary could you add us also please?

@dmitry_zakharov_ai congrats on a new great launch!

@islam_midov hi Islam and thank you for your question. Get demo now and if U'd like - book a demo with our sales team. We'll show you our pricing.

@islam_midov Thank you!

@islam_midov thanks for the support - to be honest, we are still trying to figure it out. But the target price is $10 per demo from 3+ minutes

@ponikarovskii hi Anton!
And thank you for the great question.
So the answer is probably yes - depends on the product. We can discuss it and share our experience

@ponikarovskii Thank you!

@ponikarovskii it might be, but we are focusing more on b2b products right now

@michael_vavilov Many thanks!

@michael_vavilov Thank you!

@michael_vavilov thank you for your trust and support!

@dzianis_yatsenka Hi, thank you. We support 30+ languages, including English, Spanish, French, German, Portuguese, Hindi, Japanese, Korean, Chinese, Arabic, and more

@dzianis_yatsenka Thank you!

@dzianis_yatsenka thanks for your great question, Naoma supports 30+ languages.

@mihir_kanzariya It can handle small changes automatically, big redesigns will require human intervention right now. But we are working on solving this problem automatically with the help of additional exploration agent that can map out the product features.

@mihir_kanzariya thank you for your great question, out CTO David will answer it.

@mihir_kanzariya thanks for the support! And I see that David has already answered the question

@shubham_pratap thank you!

@shubham_pratap appreciate your support!

@shubham_pratap thank you!!!

@alex_chepovoi thank you!

@alex_chepovoi thank you for your support!

@alex_chepovoi Thank you so much!

@alena_korpula Thanks so much for the feedback!

@alena_korpula thank you for your trust and support!

@alena_korpula thanks a lot for your support

@mauricevv thank you! Wanna try it?

@mauricevv Many thanks!

@mauricevv thank you

@konstantin_netyliov1 Many thanks

@konstantin_netyliov1 thank you! Really appreciate your support

@konstantin_netyliov1 Thank you so much!

@nikvoice Thanks so much for the feedback!

@nikvoice thank you!

@nikvoice Thanks!

@yu_zhou8

This is exactly the problem we set out to solve — and you've described the value prop better than most of our own copy. 😄

On your two questions:

Complex questions — this is where we've invested heavily. The demo adapts based on role, use case, and what the prospect actually asks mid-session. It won't handle every edge case perfectly today, but the bar is already higher than most teams expect going in. We're transparent with prospects when something needs a human.

Qualified demos without overwhelming reps — the qualification layer is what makes this work. Naoma filters intent before routing, so reps are only seeing prospects who've already seen the product and have specific questions. In early pilots we're seeing up to 30% of demo starters move to next steps — without rep involvement at that stage.

Would love to show you what it looks like on a real funnel. Happy to set something up. 🙏

@yu_zhou8 Thanks so much for the feedback!

@yu_zhou8 Thank you for your support!

@solodnev Thank you!

@solodnev Thank you!

@solodnev appreciate!

@danshipit Hi, thank you! Yes this is exactly how it works. It launches a browser and streams the screen capture back to the prospect while doing a product walkthrough

@danshipit Many thanks!

@danshipit Thank you for the brilliant question!
It launches a browser and streams the screen capture back to the prospect while doing a product walkthrough

@joao_seabra Thank you, and great questions, these go straight to the core of what we've been working through.

On the sample: the 10-20% is from early pilots across a handful of B2B SaaS companies - small sample, so we're careful not to overgeneralize. But the directional signal is consistent: when you remove the scheduling friction and replace it with an instant conversation, a meaningful share of visitors who would have bounced actually engage.

On handling unknowns I fully agree. A confident hallucination is worse than an honest handoff. Naoma is designed to recognize the boundary and route gracefully to a human when it hits it. That moment, done well, actually builds trust rather than breaking it.

On early-stage products: Naoma works best when there's enough product to show, but it doesn't require a polished, static flow. When features change, you update the agent. It's closer to briefing a new rep than rebuilding a product tour.

@joao_seabra Thank you!

@joao_seabra Joao, thank you for your support!

@zigapotoc Hi, thanks! Naoma will ask questions, qualify the prospect and show what she thinks is relevant based on the prospect answers. You can also configure multiple flows tailored for different personas

@zigapotoc thanks for the comment, and yes, you are absolutely right about how it works. The agent qualifies the prospect first, and then adapts the demo based on the use case

@zigapotoc Thank you!

@andrew_mende2 thank you! She has access to browser automation tools uses them to do a product walkthrough.

@andrew_mende2 Thank you!

@andrew_mende2 thank you for the great question!

@max_kraynov yes, exactly!

@max_kraynov exactly! Thank you!

@max_kraynov , yes, thanks for the comment

@evgeny_kotelevskiy Great question, and the math is actually why Naoma makes sense here.

That $250-600 cost per demo includes SDR time, AE time, no-shows, and leads that weren't qualified to begin with. A significant chunk of that cost goes to demos that should never have happened.

Naoma doesn't replace the high-value human demo. It runs the first discovery layer: qualifying the lead, answering basic product questions, showing the product value relevant to that specific visitor, and capturing intent - so when a human rep does step in, they're talking to someone who already understands the product and is ready to move forward.

The result: fewer wasted demos, higher close rates on the ones that happen, and the leads who would have bounced on a 5-day Calendly wait actually convert instead.

So the question isn't "should AI run the $500 demo?" It's "how many of those $500 demos are currently happening with the wrong people or not happening at all?

@evgeny_kotelevskiy thank you for your support!

@evgeny_kotelevskiy Thank you for your support!

@kshitij_mishra4 thank you!

@kshitij_mishra4 thank you!

@kshitij_mishra4 Many thanks!

@kshitij_mishra4 Thank you!

@kshitij_mishra4 wow! Many thanks!

@kshitij_mishra4 Thanks so much for the feedback!

@bhu_1 thank you!

@bhu_1 thank you!

@bhu_1 Thanks so much for the feedback!

@thxgrey Thanks!

@thxgrey thank you mate!

@thxgrey Many thanks!

@aitubespark We care a lot about the "human feel" during the demo and constantly working on improvements.

@aitubespark The scripted vs. responsive question is exactly the right one to ask — and it's what separates Naoma from recorded demo tools or clickthrough tours.

The demo adapts in real time based on what the prospect actually says. If they mention a specific use case, the flow pivots to it. If they ask a question mid-demo, it answers and continues. It's not a linear script with a play button — it's closer to a conversation that happens to include a product walkthrough.

On watch-through rate — we don't share specific numbers publicly, but what we can say: average session length runs over 6 minutes for engaged prospects, and we only charge for demos over 3 minutes. The bounce rate exists — some people drop off early — but it's consistently lower than the average no-show rate on scheduled calls. A prospect who ghosts your Calendly invite costs just as much as one who clicks away in 20 seconds, except with Naoma you at least got them in the door.

B2B buyers are hard to impress — agreed. The ones we're seeing convert aren't impressed by the AI, they're impressed by getting a relevant answer to a specific question without waiting a week. That's the bar. 🙏

@aitubespark thanks for the comment and questions! See that Dmitry answered below

@mithlesh_shah thanks! Surprisingly, many people have no issues interacting with AI and telling the agent about their situation and answering qualification questions.

@mithlesh_shah Thank you! 🙏

Honestly — better than we expected. The initial assumption was that some prospects would bounce the moment they realized it wasn't a human. What we're seeing in pilots is that most people engage pretty naturally once the demo actually starts. The product is doing the work, and when the walkthrough is relevant and the answers are accurate, the avatar becomes background.

The moments that matter most aren't "does this look human enough" — they're "did it answer my question" and "did it show me something relevant to my use case." When those land, prospects stay and convert.

That said — avatar quality is something we're actively investing in. More realistic, more expressive. Not because users are complaining loudly, but because we know it raises the ceiling on the experience, especially for higher-ACV products where the buying environment needs to feel more premium.

Curious what your instinct is from the buyer side — does the avatar matter to you, or is it purely about the demo quality? 🙏

@mehmet_kerem_mutlu Thank you!

@mehmet_kerem_mutlu Great question — and one we track closely.

We don't share specific metrics publicly, but here's what we can say:

There is a bounce rate — some prospects drop off in the first 30 seconds. But that number is consistently lower than the average no-show rate for traditional scheduled demos. A prospect who never shows up costs you just as much as one who clicks away in 20 seconds — except with the AI demo you at least got them in the door.

For prospects who engage past that point, average session length runs over 6 minutes depending on the product. That's a real, active conversation — not passive video watching.

And to your point about drop-offs — we actually built our pricing around this. We only charge for demos over 3 minutes. If someone bounces early, it doesn't count. We think that's the only fair way to do it.

@mehmet_kerem_mutlu thanks

@dharmikp1908 Thank you! Yes Naoma can show different flows tailored for different personas

@dharmikp1908 Thank you for your question! Naoma can adapt the demo flow based on prospect qualification during the demo.

@dharmikp1908 Thank you!

@jozsef_orsos thank you!

@jozsef_orsos we connect a client's knowledge base. So Naoma can successfully handle even difficult technical questions.

@jozsef_orsos Naoma can use the knowledge base and sales playbooks to handle objections the same way the human sales-rep would

@muravyov thanks, happy to work with you!

@muravyov Nick, thank you for joining us!

@muravyov we are glad to have you onboard!

Hi, @jan_heimes @oeken Loved the idea behind Needle — Curious to know, how does Needle ensure the responses stay accurate and context-aware when pulling information from multiple connected sources?

Priya here, CEO at Techflitter Solutions FZCO, a tech consulting company operating from Dubai and India with 10+ YOE helping startups build from scratch to release to maintenance and scale tech products globally.

Even just recently ElevenLabs accelerated their growth by partnering with a consulting firm. That proves a point taking an offshore tech partner is a strategic growth move.

@jan_heimes @oeken If scaling beyond launch is the focus? We’re ready to support and align with your roadmap and growth goals. Let's have a chat

@jan_heimes This looks fantastic!! Have already sign-up and can't wait to built some marketing workflows for myself! Congratulations on the launch

@jan_heimes Hey, congratulations on the launch. Just one question; how does Needle handle enterprise compliance (GDPR/SOC2) and API rate limits for high-volume runs in paid marketplace workflows?

@curiouskitty Short: speed of iteration.

Long: Building (chatting) on Needle is much faster, so you can save a lot of time. Also if your workflow using RAG then Needle is also the better choice since we have RAG built-in. We call it Needle Collections.

@curiouskitty Great question. From my experience, the biggest breaking point with tools like Zapier or Make is usually reliability when workflows get complex. Once you start chaining many steps together, debugging becomes painful.

@curiouskitty Train your agent , not your hosting skills” is a great line. If OpenClaw can truly handle the infra, inference, and updates, that removes a big barrier for teams trying to move fast.

@oeken Such an exciting journey together with you!

@daniel_karim1 Appreciate your comment! We believe, that everyone becomes a builder. So people who have deep insights into processes and operations do not need to ask their tech team anymore to build it, but can just themselves describe the problem and process and the agent builds it for them.

@daniel_karim1 Hey Daniel, I recently joined Needle. I had 2 startups previously and exited one. Completely agree. Needle is onto something BIG. Very exciting to be on this journey and thank you for all the support :)

@daniel_karim1 Thank you very much for the support! Our aim is to enable everyone to automate almost anything as easy as possible without requiring technical expertise.

@dmitrysereda thanks so much for the kind words Dmitry. Love what you are doing at Postproxy and love the Needle integration :)

@dan_ttf Thank you Denis and love your work with Postproxy. Super cool that you can distribute content to all social platforms within Needle using your integration :)

@dennis_stech Good question. So we track every run of your workflow and version. So you can always see how past workflow runs executed or how past versions looked like. You can also roll back to any of these versions. Does that make sense to you, or is there a specific other version control feature that you had in mind?

@pietrozullo Excited you find it cool!

@pietrozullo Great to hear Pietro. Appreciate the support

@mauricevv 🚀🚀🚀

@kshitij_mishra4 We are honored to hear that! Appreciate that you find it useful. What is something you always wanted to automate?

@kshitij_mishra4 thanks so much for the support!

@kshitij_mishra4 happy to hear that! thank you!

@alexanderfarr 🚀 Thanks! Appreciate the support!

@mihir_kanzariya I think so too. We'd like to put the creator in the spotlight and reward. It's 2026 and we're all now creators using AI and consumers of AI-powered solutions and content. Democratizing this exchange is our vision.

Secret sauce? Context, prompt and tool engineering. Needle agent mimics how we humans create workflows, i.e. not in a single shot but rather in iterations.

@akovalevskyi  Give it a spin and try the agent! Thousands of users have already seen it themselves. You can build workflows way faster and simpler, but still handle really complex automations when you need to.

@akovalevskyi Thank you for the comment. One of our main differences is that we try hard to let people build automations without hassle or a technical background and our builder agent is super powerful to make this happen. You tell the builder agent what you want in plain English; it builds the workflow, tests it, and iterates. No dragging nodes or wiring things by hand.

@dingleberryjones great question!

In short: It depends.

Ultimately, we calculate the earnings based on the tokens that are consumed per workflow run.

@joao_seabra Thank you! The line between producers and consumers of the workflows is blurry because nearly everyone needs some kind of an automation and we make it easy enough to so anyone can create a workflow. So we all grow together using AI in smart fashion. I can't imagine a future where AI is not playing a key role. And with this model we are rewarding everyone involved in creating value.

@valentin_po Likewise! With this product we aim to unlock so much of creativity since now creating workflows is way much easier.

@davitausberlin Competition is what makes it fun :) How long it took to build? I can't answer very well because we've improved iteratively. We've released first version of the workflow in October 2025 in our previous launch. However there're many details to get right until the UX starts shining.

@pavelk2 

Most popular ones are data transfer workflows, like reading Gmail attachments and sorting them into the right Drive folder automatically or similar e.g. Notion, Drive, Sharepoint, Airtable. Scraping LinkedIn or Instagram leads and pushing them to Google Sheets is also huge. Same with scraping competitor data from the web and turning it into reports. I always say it's like Lego, the limit is your imagination.

@janhecker Appreciate the comment! You can also integrate your collections into your workflows, in case haven't discovered that feature yet. What is your top use case for RAG? would be happy to hear more about what you are building.

@richardpoelderl thanks for the support! would be happy to hear your experience once you've tried it :)

@johnston_chen1 We have workspaces where you can invite and collaborate with others. So a workspace is like a a team.

@matthiasrossini Thanks! We see that lead generation from different sources (Instagram, Linkedin, other websites etc.) and outreach (Linkedin) are the most popular ones.

Hey @ashish_tadose really appreciate that! Indeed, outcome first is the way to go!

@sienna_claire Once you publish your workflow, you earn every time someone runs your workflows. You can explore the details at https://needle.app/partners.

@anthony_adams_ Correct, Needle workflow agent does handle debugging and error handling. You can give it a spin and see the magic in action.

@kate_sleeman Indeed and the agent can also auto-repair your workflow. It will iteratively review what it has done the output of the nodes and adjust its actions accordingly. What is a first workflow you would build?

@lienchueh That's a great use case! You could set up a workflow that automatically pulls your latest commits, runs them through an AI review step, and flags issues or suggestions before you even look at the code yourself. Pair that with Slack or Telegram notifications and you've got an automated review loop running in the background while you keep vibing. Would love to see what you build!

@hozbasak Sounds awesome! Glad to hear that since it's exactly our intention with Needle: cut the friction and make everyone a builder.

@spunchev Great question. We do talk a lot, though with users and build the product always very much in mind with their desires. But yes, sometimes we see some interesting/surprising ideas. Often users use it very often for content scraping or creation, while we did not think, that this use case would be that popular in the beginning of building Needle. Does that answer your question?

@ignacio_borrell Yes they can! You can either use the agent or build everything manually and configure all the details yourself. You can also check previous runs with their input and output parameters, so data lineage is fully covered.

Hi@michaelsacca Loved the idea behind HTML Pub — Curious, how does HTML Pub manage versioning or updates when users regenerate or modify AI-generated pages, so previous versions or live links remain stable?

Priya here, CEO at Techflitter Solutions FZCO, a tech consulting company operating from Dubai and India with 10+ YOE helping startups build from scratch to release to maintenance and scale tech products globally.

Even just recently ElevenLabs accelerated their growth by partnering with a consulting firm. That proves a point taking an offshore tech partner is a strategic growth move.

@michaelsacca If scaling beyond launch is the focus? We’re ready to support and align with your roadmap and growth goals. Let's have a chat....

@michaelsacca With only a little knowledge of coding and HTML, I may not be the best person to fully comment on how big and incredible this is. However, from what I do understand, it sounds extremely cool and innovative-something that was truly needed. Hosting without relying on legacy products is no longer a challenge. Kudos to the team for a fantastic job!

@michaelsacca I'm a long time leadpages customers. I've been building the sales pages in Claude and then transferring them to my leadpages account. Claude was extremely helpful with the copy/design layout. Can we automatically publish to our leadpages account from HTML?

@tobyns thank you for the support and encouragement!

@tobyns Thanks so much! Timing felt right for us too. Curious what you're working on right now - is there a specific use case you're thinking about?

@angelaaa you’re right! Personally I’ve built internal-only pages with HTML Pub because I wanted something more visual to share with my team. We’ve password-protected those pages just in case!

What did you prototype with our product?

@angelaaa 100%! This is actually one of the use cases we see a lot in early testing. Instead of a Figma link or a Loom, you send a live URL that actually works. Client can click around, fill out a form, see the real thing. And if they want changes, you just go back to the conversation and ask for them. No redesign, no redeploy. What kind of prototypes are you typically building for clients?

@petecallaghans Thanks Pete! We figured since people live in their LLM these days, we should empower them without leaving the chat. We're already dogfooding ourselves, take it for a spin and let us know what you think!

@petecallaghans Thanks Pete! Let us know how you get on 🙌

@petecallaghans MCP was the thing that made this feel inevitable to us. Once AI can own the full loop (build it, publish it, update it) without ever handing off to a human, the whole workflow changes. Promoly looks interesting by the way, would love to know if you see a use case where audio promo + a live landing page could work together here.

@evanmcneely this made my day. Your wife’s use case is exactly the kind of thing we built this for - she has a finished page, she just needs it live, without needing to learn hosting or bother someone technical. Custom domains are available on our Starter plan ($10/month) so yes - absolutely publish from her domain. Tell her we said good luck with her MBA!

@evanmcneely Evan this is such a real story and honestly one of the best use cases we've heard today. The gap between "Claude built me something great" and "I can actually share this professionally" is exactly what HTML Pub closes. A custom domain makes it look like you built it yourself - nobody needs to know the stack. Tell her to sign up and publish that MBA project. Would love to see it.

@nicola_vargiu You and us both!

@nicola_vargiu The Claude graveyard is real! We've all got conversations in there that never saw the light of day. Really glad this is the thing that changes that for you.

@staci_gardiner Staci this one hit home. Thank you! Marketers are exactly who we built this for. You shouldn't need to know CSS or beg a developer to get a page live. You know what the page needs to do, you know the message, you know the audience. That's the hard part. HTML Pub just handles the rest. Go try it and tell me what your first page is - would love to see what a marketer builds on day one.

@staci_gardiner How fast you all were able to move the dozens of paid landing pages over to HTML Pub was impressive!

@staci_gardiner appreciate you Staci - and yes. As a fellow marketer, I am looking forward to the day where we don’t need a comprehensive editor. Just describe and have it built!

When we were building this, I published 15 pages in an hour with distinct messaging and positioning. I can’t imagine doing that 5 years ago.

@cristian_prodius love that the video did the talking. Vercel is great if you’re already in a dev workflow, but most people generating pages with AI aren’t deploying to a git repo. We wanted the path from “AI made this” to “it’s live at a URL” to be as short as possible. Glad it clicked!

@cristian_prodius Haha! Glad you watched to the end! Fair question though. Vercel MCP still assumes you have a repo, a framework, a build step. It's built for apps. HTML Pub is purpose-built for AI-generated HTML - no repo, no config, just a string of HTML in and a live URL out. Plus forms, analytics, and heatmaps are all baked in so you're not stitching together three other tools on top.

@cristian_prodius Glad it resonated! Yes it's far more than just publishing HTML, our fault for calling it just "HTMLPub" - it's a tongue and cheek approach, so we're having fun with it :)

@mattarms27 You just articulated what every marketer wants out of a tool like HTML Pub. Let’s just make things easy and fast!

@mattarms27 Matt this is exactly the use case we kept coming back to when building this. Vercel is incredible for full apps but it's genuinely overkill when you just need a landing page live in 5 minutes. No repo, no CI pipeline, no environment variables for a simple HTML page. Glad it clicked for you and the Claude Code to HTML Pub workflow is one of my personal favorites. What kind of projects are you vibe-coding these days?

@mattarms27 Vercel who? ;)

@tomstools Tom the client report use case is such a good one. I've used it in similar ways with my team already. Instead of a Vercel deploy per client, per report, per update... you just publish, send the URL, and when the numbers change next week you update it in the same conversation. No new repo, no new deploy, same link. How many of these are you shipping a month? Curious how much time this could actually save you.

@tomstools Haha, right there with you. We publish a lot too. That's why we wanted to make it lucrative ;)

Let us know how you get on!

@tomstools please try HTML Pub out and let us know your experience!

@odeth_negapatan1 Exactly! As SaaS becomes more agentically operated, we should be building with that workflow in mind, giving our agents the tools they need.

@odeth_negapatan1 This is exactly the framing we kept coming back to internally. Publishing was never really the hard part, maintenance was. The moment you deploy something the traditional way, you've created a gap between the AI that built it and the live thing. HTML Pub keeps that connection alive. Your AI knows your site because it built it. That changes everything about how you grow and update it over time. Really glad that came through. What are you building with Lancepilot?

@raunakhajela thanks Raunak! Let us know what you build with it 🙏🏼

@raunakhajela Thanks Raunak! Pricing was something we debated a lot - wanted it to be a no-brainer to try, especially on free. The prompts are just a starting point too, the real magic is when you start describing something specific to your workflow and watching it just... work. Love what you all are doing over at Blitzit!

@just_s Thanks Justin! Always great to see you!

I'm confident HTML Pub is not as complex as you might think. Just a few steps to integrate with your favourite AI, like Claude or ChatGPT. Once you're connected you can ask your AI to generate a page, tool, prototype or even a game, and then tell it to make it live via HTML Pub. You'll receive a live link that you can share with the world.

And inside of the platform you can manage all of your published pages, add a custom domain, forms, e-com integrations, blogs, and even connect multiple pages into a full site.

You'll be amazed at how fast you'll be able to launch anything now.

@just_s I hear you on all counts! Omar is amazing, and HTML Pub is incredibly smooth and easy to use - even for non-technical marketers and users (like myself ;)). Let us know if you run into any issues!

@just_s Omar's the real deal. We're all in good hands trusting his track record. And I promise it's not over your head. Seriously, if you can type "build me a landing page for [thing you actually need]" into Claude, you're 90% of the way there. Give it a shot and let us know what you end up making - would love to see your first publish.

@matija_golubovic thank you! If you see any bugs or have feedback, please let us know! We’re here all day.

@matija_golubovic Thanks Mat! Try it out and let us know your thoughts.

@masebuilds Thanks Mason, really appreciate the kind words. And yeah, the vibe coding crowd is exactly who we had in mind.

Custom domains are straightforward: go to your page settings, connect your domain, and we handle DNS configuration + SSL automatically. The whole flow takes about 2 minutes. You can point a domain to a single page, a multi-page site, or a blog - your call. Take it for a spin!

@ericlkomo And the value of impressing your inlaws?! Priceless

@lienchueh Great question Lien! Two parts to this:

Built-in analytics - every published page automatically gets page view tracking, unique visitors, conversion rates, click heatmaps, scroll depth, and attention maps. No setup required, it's all in your dashboard out of the box.

Third-party scripts - you can add GTM, Facebook Pixel, Google Analytics, or any custom script directly in your page settings. Just paste the snippet and it gets injected on publish.

We can certainly get some direct connections going. What are you using?

@curiouskitty This was a real problem for us early on. We originally ran on Railway and had to do a full migration to GCP after dealing with crypto miners and phishing pages abusing the platform. So the guardrails came from lived experience, not a checklist.

Here's what's in place:
Structural constraints: Free pages expire after 7 days and cap at 5. Paid tiers cap at 50/250 pages. Scaling abuse requires paying for multiple accounts, which creates both a cost barrier and an identity trail.

Scanning: We have a robust AI scan in place for published pages for known phishing patterns, malware signatures, and spam indicators. We've been training the model for several months with seed data from our 13 years of experience in the field.

Rate limiting: Yes, at both the account and publish level.

Isolation: Published pages are isolated from each other and from HTMLPub's core infrastructure.

Reporting: There's a flag/report mechanism for takedowns.

On the friction balance:  we deliberately lean toward keeping the publish flow fast for legitimate users. The 7-day auto-expiry on free pages does a lot of the heavy lifting since most abuse content has a short useful lifespan. And having run Leadpages, hosting millions of pages over 12+ years, abuse prevention isn't new territory for us - we're applying patterns we've battle-tested at much larger scale.

@curiouskittyIf its really work as you explain ,,, then it will be a great product for those specially who faces theses issues ,,, best of luck for your product

@curiouskitty Great question. It's a layered system:

Every page is scanned on publish — heuristic pattern matching, Google Safe Browsing for known-bad URLs, IP reputation checks, and an LLM classifier that factors in account age, publishing velocity, and prior violations. High-confidence abuse triggers automatic removal + account suspension. Medium-confidence gets queued for manual review. Pages are periodically re-scanned to catch post-publish changes.

Domain isolation — published pages serve on a separate origin with a strict route allowlist, no access to app APIs. Security headers block eval(), disable device APIs, prevent embedding outside our origins, and serve noindex/nofollow so we can't be used for SEO spam.

Rate limiting across all endpoints, built-in abuse reporting on every page, DMCA workflow, admin review tooling.

All scanning is async — legitimate users publish instantly, never see the machinery. The AI classifier with behavioral signals is what lets us be aggressive on abuse without adding friction for normal use.

@ranjan3118 Love the honesty here!

Cloud bills really do feel like ordering a simple coffee and getting charged for the whole coffee machine.

Glad someone finally said “enough” and built an alternative. Excited to see where Huddle01 Cloud goes 🚀

@ranjan3118 Exactly the markups on Hyperscalers are insane and on bandwidth they charge 8000% markups in some region, and they stack fast in todays Video driven world

I remember Cloudflare did an amazing blog on this https://blog.cloudflare.com/aws-egregious-egress/

@ranjan3118 Had so much fun working with the team on this! Excited for people to checkout and give their feedback!

@otodidakt_20 Trading with Openclaw is indeed fun

@otodidakt_20 Going to finish my content studio with Openclaw now!

@shalini_umrao Love the POV

If even the least technical person can deploy an agent without touching the terminal, that’s probably the best kind of product validation!

@itsomg Turning $1 into $17 is impressive… turning $10 into $0 in 30 minutes is even more impressive in its own way 😂

Jokes aside, love the direction here. Making agents actually easy to deploy instead of another painful setup is a big win. Excited to see what people build with it!

@krupali_trivedi Haha I remember the CLI days too!

Asking Claude “what next?” and pinging the tech team every few minutes was basically the deployment workflow back then. Seeing it evolve into a 1-click deploy so fast has been really fun.

Excited for more people to try Huddle01 Cloud 🚀

@krupali_trivedi Which model do you use for Openclaw, I have seem Openrouter Minimax works best

@dhravya Thank you Dhravya! Let us know your experience of deploying when you do!

@dhravya We kept the walkthrough as real as possible, it directly brings Trust in the product and Trust is the number one requirement for a product like ours

@henk_pretorius1 Great Question!!!

The answer is very complex but we can simply it like this, Hyperscalers ( AWS, GCP ) have just huge costs to operate they are earning major money from the Top 500 companies that do need there scale but almost 90% companies don't need 200+ service these guys have and still pay for them

Huddle01 is in the intersection of Hyperscalers and Cheap VMs, we focus on robust servers, use our colocation and years of experience to push the main services like VMs, K8s, GPUs to there limit

And Hyperscalers have insane margins 8000% for some services, all because a promise of reliability which should be by default in todays world with Data Center popping everywhere

So in-short
1. Huddle01 Focuses of Core services optimises to there max
2. We pass on the benefits which we get like unlimited egress to customers
3. We give you servers with specs like AMD EPYC, DDR4 ECC RAM, NVMe Storage which allows you to run 4x the capacity of services that you would on any other service

You can read more here on how a Drone Company uses our NVMe storage for Analytics https://huddle01.com/blog/how-marut-drones-processes-spatial-data-3x-faster-with-huddle-cloud

And also our benchmark against notable hyperscaler
https://huddle01.com/blog/aws-is-charging-you-3x-more-for-slower-compute

@sujeetbr Time to give everyone Agents to Crawl

@shreyas_papinwar Team spent good 2 weeks on this, this is our first PH launch as well

@shreyas_papinwar thanks Shreyas!! It was an endless loop of planning, planning and some more planning.

Thanks @animesh_mishra1

We have been building Huddle01 for 6 years now, and have achieved stability considering the sensitive nature of our product.

Would love if you try out the product and share any other feedbacks - Team is incredibly fast at shipping code with AI Agents 🙌

@animesh_mishra1 Thank you Animesh for your kind words! We had such a fun time building this product!

@mihir_kanzariya For Openclaw to work we need to keep docker container running, we don't shut them down

@mihir_kanzariya Our VMs run on secure & extremely fast hardware level virtualisation which bring the best performance for the underlying Agents running in sandboxes

Also agents are always warm in our case as they are always running

@akhilbvs thanks

team works really hard to get this launch perfect

@saxenasaheb Thank you so much!

@suhasmotwani Lessgooo Indeed

@suhasmotwani yeahhhh

@lumm Great Questions!!

A title technical but we use something called as Docker Sanboxes which gives openclaw the power of a Virtual Machine and all the security and speeds of a Docker Container, All the Containers have direct access to the internet using Public IPv4 with unlimited egress

So any skill will always have access to the internet and using NVMe drives will have access to local data as well

As for AI Inference logs yes, you can view everything end to end on the dashboard itself

Let me know your feedback when you try out the product

@akmo3 Hyperscaler Tax is real, and building Huddle01 Cloud was kinda forced because we wanted to minimise our Burn.

@sangeet_banerjee openclaw for the win

@joalavedra Just was seeing Prava team, talking about this, All I can say rn, soon

@joalavedra today you can already add credits to the platform via stablecoins !

agentic payments soon!

@thyme1 thats the best Part about the setup. When you deploy an agent on Huddle01 Cloud, you get a whole VM with IPv4 attached. You can always SSH to that VM and you can play with any kind of custom model integration, because OpenClaw allows you to do that.

If you don't want to do it, it's one click deploy, choose any of the model providers and it's done for you. So there's never a integration problem and we never force you to use our AI inference. You are free to choose whatever you want to.

@thyme1 we provide a framework to get you started very fast on the agent - we use a secure but vanilla version of openclaw , so all openclaw plugins should directly work and have first class support

@kaiserrr yes boss!

@kaiserrr Crazy idea

@ranjan3118  @ayushpranav3 Thanks a ton for the kind words!

Thanks for the kind words @sicksickle

@curiouskitty Every tenant lives inside its own private subnet behind a Geneve switch, so that is how we get user-to-user separation. Everything is totally isolated from one another. Then at the kernel level, there is the KVM layer, which again helps protect against untrusted access. Every network is also protected by firewalls.

Huddle01 Cloud is fully SOC 2 compliant, so these are all very standard and robust protection measures.

As for OpenClaw risk mitigation, it depends, though. Everything runs inside Docker sandboxes. We did not want to introduce things from our end that would not align with Peter’s vision as the creator of OpenClaw, and the vision of the product in general. He is doing a great job making OpenClaw great.

On our side, we are working hard to make sure you can run this agent 24/7 with the best specs and unlimited egress.

@rbluena Thanks a ton

@paul_sangle_ferriere1 shit we missed adding cubic for shoutout, Amazing product

We use it for every PR review here

@german_merlo1 hiii german

We specialise in running ai infra at scale , so running agents is a breeze
devs can use their own keys or leverage hudl ai inference to power their agents

kimiclaw is specific to the kimi model - marketing gimmick for kimi tbh

at Huddle01 Cloud you can select any model

Hey @german_merlo1

Kimiclaw gives you all that OpenClaw Primitives and its amazing for a certain usecase, we think Openclaw is the new Linux and we want to make it super easy for people to deploy it don't have to think about AI Inference and billing and everything stays in one place

For security we use Docker Sandbox which gives you VMs and all the additional security of docker containers.

We right now have super simple Pricing and just show the bare pricing, but I get the point we can give more details around it

Thanks for commenting and do checkout the product

@anton_alekseev3 I have even better, we are building for intersection between AWS and Cheap VMs

So our competition are Hyperscalers like AWS, GCP, Azure

This is our benchmark comparing with AWS and how are we 3x cheaper and still insanely better
https://huddle01.com/blog/aws-is-charging-you-3x-more-for-slower-compute

Our Specs are:
- AMD EPYC Server
- DDR4 ECC RAM
- NVMe Storage
- Unlimited Egress

Let me know your thoughts on the benchmarks etc

@kate_ramakaieva Huddle01 is targeting the market between hyperscalers and cheap VMs. That's where the sweet spot is.

@kate_ramakaieva hii Kate!
we have two main categories we look at
1. hackers & early stage founders : cater to being able to deploy and iterate fastt without worrying about infra
2. mid & large sized companies : who already know their scale & now are looking to switch to a performant but cost effective option

hyperscalers may make sense for startups who are figuring out their scale but is too expensive for companies who already know their scale and now looking to optimise

Hi @weizhi Loved the concept behind Runner AI, - Curious to know, when optimizing deep learning ranking models for ads or recommendations, how do you balance real-time inference latency with model complexity, especially when deploying at large scale?

Priya here, CEO at Techflitter Solutions FZCO, a tech consulting company operating from Dubai and India with 10+ YOE helping startups build from scratch to release to maintenance and scale tech products globally.

Even just recently ElevenLabs accelerated their growth by partnering with a consulting firm. That proves a point taking an offshore tech partner is a strategic growth move.

@weizhi If scaling beyond launch is the focus? We’re ready to support and align with your roadmap and growth goals. Let's have a chat...

@weizhi So incredibly proud to be part of this team and what we've built over the last few months! Watching this vision of autonomous commerce come to life has been amazing. Let's go!

@n8nship Great question! For low-traffic sites, we introduce our AI creative studio which generates a range of high-converting product assets while our AI ads system creates a closed-loop. This combination lets us gather statistically significant insights faster, so you can start optimizing your site's performance even in the early stages without waiting for massive traffic volumes.

@n8nship You are right. low traffic is where traditional A/B testing breaks down. Our AI uses multi-armed bandit approaches that adapt in real time, so even with lower traffic, the system learns and shifts toward better-performing variants faster instead of waiting for a fixed sample size. On top of that, we also have built-in marketing integrations — ads, social media, and more — to help drive traffic to your store in the first place.

@kaiserrr Absolutely! Runner AI continuously detects friction and autonomously A/B tests to optimize the product feed. Because our entire architecture is AI-native from the ground up, your product data is perfectly structured for agentic commerce and AI shopping bots by default.

@kaiserrr Absolutely! Product feed optimization is built right in. We handle formatting across OpenAI ACP and Google UCP — so your store is ready for agentic commerce out of the box.

@lienchueh Yes, we are working on this part along with agentic commerce(will release this soon). the platform handles the product feed formatting automatically — you don't need to worry about the technical setup.

@lienchueh Thank you for your support! We will be releasing the AI ads agent very soon who will be running and optimizing your Google, Meta, Reddit Ads autonomously! Stay tuned!

@mihir_kanzariya Great question! You define the boundaries — pricing, visuals, ads, social media, promotions, etc. You also control the traffic split, so you decide how much of your audience sees a test before it rolls out.

@mihir_kanzariya You are absolutely still in the driver's seat. While Runner AI analyzes every interaction on your site (clicks, scrolls, bounces) to detect friction and automatically plan A/B tests, you have full control over the boundaries. You can easily set strict guardrails to lock in brand elements, copy, or pricing floors so the AI knows exactly what not to touch. You can also dictate exactly how much of your traffic you want to allocate to these experiments.

@sandy_liusy As an agency, you’ll love that we can support multi-store management-you can handle multiple clients’ sites all in one place. As a multi-brand owner, you can easily create tailored strategies for each unique product line, drive growth across your entire product range.

@sandy_liusy Absolutely! For agencies, this takes the heavy lifting out of the 'zero to one' phase. The coolest part? There are zero templates. You simply prompt the AI with the specific style, vibe, and branding instructions your client needs, and it custom-codes the site natively. It means you can deliver fully custom-tailored stores in a fraction of the time.

@candyrorae Thanks for the kind words! What might interest you is that these tweaks are also crafted to drive your store’s sales and fuel business growth.

@candyrorae Thank you for your support!!

@masebuilds Thanks for the support! Right now, our AI is trained specifically on ecommerce workflows and shopping behaviors. However, the underlying architecture—autonomous A/B testing, layout optimization, and conversion tracking—could absolutely be applied to SaaS marketing pages and lead generation down the line! We're hyper-focused on stores for V1, but SaaS marketing is definitely on our radar.

@konrad_sx Thanks for asking! Yes, we've been working with a handful of design partners who have live stores on the platform with real traffic. Still early days, but the results have been really encouraging.

@konrad_sx Thank you so much! And yes, absolutely. We've had some amazing early users spinning up and running live stores. We’ve actually been highlighting several of these real-world use cases and live examples over on X! Feel free to check them out at @try_runner_ai. Let us know what you think of them!

@jozsef_orsos Great question! Yes, it is 100% AI-driven! Runner AI acts like an autonomous growth engineer. It actively analyzes real user behavior—every click, scroll, and bounce on your site. Once it detects friction points or high-impact opportunities, the AI automatically prioritizes and launches the tests for you.

@victorzh Thx for your support! Our goal is to empower every merchant and team to easily leverage AI, unlocking new growth opportunities for their e-commerce businesses!

@victorzh Thank you! Please reach out if you need anything from the Runner AI team:)

@carlvert Thanks a lot! We put a lot of care into making it feel intuitive. Plenty more coming soon!

@carlvert Thank you for supporting us!

@luke_pioneero Thanks so much, Luke! That means a lot. We're just getting started with v1, and the roadmap ahead — deeper autonomous optimization, auto experimentation loops, and more AI-driven insights — is what really excites us. Would love for you to give it a spin and let us know what you think!

@luke_pioneero Thank you so much for supporting us!

@dschwartz18 Thank you! That's exactly the mindset we started with. Appreciate the support!

@dschwartz18 Thank you so much for supporting us!

@blink_66 Thanks for the kind words! We built the AI workflow specifically so merchants can focus on their actual products instead of wrestling with tech setups. Let us know if you end up spinning up a store—we'd absolutely love to see what you build!

@kshitij_mishra4 Thank you!!

@kshitij_mishra4 Thank you! Please reach out if you need anything from the Runner AI team:)

@kaiserrr wishing you guyz all the best. 

@kaiserrr well done Sushant! What's next in the AI payment space? Is Stripe going to go after this space with their rails?

@saksham_aggarwal7 Thanks Saksham!

Do try our Playground here: https://playground.prava.space/

@curiouskitty we were trying to build a Jarvis like AI Agent for us, this was long back in '24. We tried using our raw card details but it got blocked the moment AI tried making payments with it. It wasn't safe at all.

Built a stablecoin hack at a Singapore hackathon. Went viral on Reddit in January. Hundreds of AI builders reached out.

Then we started talking to founders @shubham_kukreti went to SF and I was in Blr- people loved our demo. Most customers we talked to were AI stylists, AI assistants, B2B ITSM tools. Three things kept coming up:

1. Consumer AI apps wanted users to buy inside the app, not get redirected out. But they could not enable payments. Best they could do was act as a discovery tool and hope the user came back after checkout.

2. B2B founders saw a future where one company hires thousands of AI agents across niches. Managing billing and payments for all of them at scale is not something you duct-tape with existing tools.

3. Every single founder said the same thing. Existing payment infra does not recognize AI as an actor. And the incumbents are not incentivized to fix it. For Stripe to win agentic payments, they would have to support AI paying on competing PSPs and overhaul their own system. No company does that voluntarily.

That was enough for us to go all in, crack card network partnerships and ship really really fast.

@mihir_kanzariya Yeah, exactly. Every customer we talk to has tried hacky ways and don't find it scalable.

Card network partnership helps us build even way more secure guardrails and controls around what AI can spend. We'll expand this to more payment methods soon- wallet, BNPL etc.

If you're an AI Agent/app you can integrate us easily, our solution isn't something any PSP would be able to provide today.

If you're a merchant(seller) and use any other PSP- that's completely fine. As long as you accept Visa/MC- the card used by our AI Agents will be consumable on your PSP. And if you integrate Prava we work as a payment orchestrator above all PSPs, routing payments from AI Agents, no change required in your existing stack.

@rishabh_dodo Good Ques.

There are two ways people use OpenClaw. Through AI assistant apps, and through their own personal setups on local machines.

1. For AI assistant apps built on OpenClaw, we are already working with them to enable card based agentic payments. The app registers as an entity with Prava, onboards its users, and for every approved transaction the OpenClaw agent gets a tokenized card to spend from. The AI app focuses entirely on the user experience. Prava handles all the payments infrastructure and compliance behind it.

2. For individuals running their own OpenClaw setups on personal machines, we are building a plugin. You save and tokenize your card directly with Prava through the plugin. Your agent can then make payments on your behalf within the rules you set. You do not need to worry about PCI compliance, you do not need a registered business entity, and you do not need to build any complex payment integrations yourself.

In both cases, the core idea is the same. Prava gives every OpenClaw agent the ability to pay, safely, so builders can focus on what the agent does and not how it moves money.

@shivang_gupta6 
Yes, it can be. Imagine sending your humanoids out to run errands for you.

With Prava, you can be sure they never buy anything beyond what you have already approved.
do give a try to our playground to get an experience, no setup required: https://playground.prava.space

And we are also launching something with UPI soon as well :)

@masebuilds Thanks Mason. No doubt the scale and volume of transaction by AI Agents will out do any number economically possible

@masebuilds Thanks, Mason. Agents are already doing a lot of work for humans, and payments will definitely be one of their biggest requirements.

@lienchueh Hi Lien, you've got it right we do support all these features today.

One can pre-approve transactions, approve budgets and AI will trigger the transaction once all requirements are met.

User sets the controls and limits on what AI Agent can do with their money. Few of our customers are infact working on features that include these use-cases particularly

@preetham_nikil it's answered here in detail:
https://www.producthunt.com/products/prava-2?comment=5202946

@soham_roy4 Thanks Soham!

@shawn_upson Hi Shawn that's a valid point

Guardrails and trust layer is core of our product- we use both card networks new infra for AI Agents and our own proprietary models to flag any suspicious activities.

Currently we tie down each transaction to the merchant, price and product that a user has approved for.

More on that here: https://www.producthunt.com/products/prava-2?comment=5203925

@aitubespark The key is scoped payment tokens instead of giving agents your actual card details.

Each token is scoped to a specific merchant, amount, and product. A $28 pizza token from DoorDash can't be used anywhere else or for different amounts. Tokens expire in 10-15 minutes and are single-use.

For spending limits, you can set monthly mandates where agents spend within approved amounts without asking permission each time. Refunds automatically go back to your original card since tokens are generated from it and both issuer/acquirer are aware that an AI Agent made a transaction.

Hope that clarifies:)

@saviomartin Thanks Savio, great to hear this coming from you.

Love what you did with Simpleclaw- happy to help with Prava integration:)

@saviomartin Thanks Savio, more than excited to power openclaw agents with Prava.

@davitausberlin Haha we'll make sure those stories never get real:)

True, guardrails is something we started with as the first thing.

That's why we've partnered with global card networks like Visa et al to ensure card level guardrails are baked into the cards AI Agents use.

So today if an AI stylist app has Prava integrated and a user approves AI agent to buy "Black bomber jacket from Zara for $100". Then the tokenized card created for AI Agent can only be spent on Merchant: Zara , Product: "Black ..jacket" and Price: $100.

If any of the parameters aren't matching, card payment won't go through. We are building even deeper guardrails for full control.

Thanks for the support, do try our playground:)

@sauravtom Exactly Saurav, let me know your experience of our playground:
https://playground.prava.space

Happy to help:)

@suhasmotwani Thanks Suhas!

Do try our playground and share feedbacks:
https://playground.prava.space

@harshactuallyThanks! checkout our playground as well to try a full agentic payment flow. would love to hear feedback.
No setup needed, just click "Start": https://playground.prava.space

@harshactually Thanks!!

@lev_kerzhner Hey Lev, thanks for the support!

Here's how agent approval works:

user saves a card

1. we tokenize it via Visa/MC

2. When user confirms. We generate a scoped virtual card with price, merchant, and product guardrails.

   agent pays at any merchant online which accepts Visa/MC

So let's say if you approved a "Black Bomber Jacket from Zara for $100" then AI Agent can only spend it on Merchant-Zara, for the price-$100, and for the product "Black Bomber Jacket".

If any of these parameters aren't met, even if agent tries paying, transaction won't go through.
That's security feature we provide.

Do try out: https://playground.prava.space and let us know if you like the experience.

@lev_kerzhner Thanks for the support Lev!

@rohitsrivastv Thanks, checkout our playground as well to try a full agentic payment flow. No setup needed, just click "Start": https://playground.prava.space

@denious All transactions are explicitly approved by the user beforehand. Their execution can be immediate or deferred, depending on the use case.

For example, if you want a product right away, the agent can get your approval now and buy it immediately. If you want to approve a flight booking now but only book when the price drops below 1,000 dollars, you can do that as well.

Every transaction is tightly scoped for AI. The agent can only purchase what the user has already approved for that specific transaction, including merchant, price and product.

It cannot go beyond those parameters.

This means safety is built in from day one and the user remains in control of what is happening at all times.
you can try our playground to experience this, no setup needed: https://playground.prava.space

@denious Thanks!

@designerdada Thanks for the support Akash and powering our emails:)

@designerdada Thanks Akash- would love for you to try our playground as well
https://playground.prava.space

@arcinston That's great to hear, do give a try to our playground: https://playground.prava.space/

Happy to help you with the onboarding, just lmk

@bengeekly 🚀👏🏻

@bengeekly Totally agree that chat works for questions,.... but task boards make way more sense for managing ongoing agent work

@bengeekly congratulations on the launch

@jaber23 Thank you so much!

Thanks a lot @jaber23 , really appreciate it! 🔥
Would love to hear your feedback on @Clawther

@gabe Thank you.
A task board lets you schedule multiple tasks, go to sleep, and wake up with things already done.

If you manage agents through chat, you usually have to stay in front of the conversation to guide every iteration yourself. My own experience with OpenClaw in the early days had me waking up and, with my eyes half open, answering small questions in Telegram just to keep it running.

The difference comes from the structure.

A task board lets you organize and run multiple tasks in parallel. And that opens many opportunities.

With Clawther, an agent doesn’t just execute the task, it also checks the result, suggests improvements, and can iterate up to 6 times while you sleep. Then move it to “To be validated by human.”

Clawther is currently built on Notion, which is a big advantage if your team already uses it. It allows human + AI collaboration around tasks instead of isolated conversations.

Both Clawther and VidClaw are still very early, so what I’m describing today will probably evolve a lot in the coming weeks.

But our hypothesis is simple:

As AI becomes agentic, we’ll need interfaces that go beyond chat.

From what we’re seeing so far, a Kanban-style task board feels much more efficient.

Now the real question is:

What rules and killer features should a Kanban redesign get to have another ChatGPT moments for AI?

@thisismattsun Glad it resonates!
Once agents start executing real work, visibility becomes more important than the conversation itself. That’s exactly what pushed us toward the task board approach.

Appreciate it @thisismattsun ! Thanks for you support 🙏

@danagoston Thanks for the question and the support!! In the OpenClaw ecosystem most projects focus on the agent itself or adding new skills/tools to the agent. Usually you interact with it through chat and ask it to do things.

With Clawther we try to focus more on how the work is organized, not only what the agent can do.

Instead of everything happening in chat, we use a task board interface where tasks move across states (to-do → doing → done).
This makes it easier to see what is happening when agents start doing many things. Also we design it for multiple agents working together, not only one agent responding to prompts. Humans and agents can both interact with the same tasks.

So the main difference is that most OpenClaw projects focus on agent capabilities, while Clawther focuses on coordination and visibility of the work agents are doing. 🚀

@vynsedev Thank you Vincent!!! We appreciate <3

@vynsedev 
Happy to have floors.js on our landing page

@vynsedev Thanks a lot !! Great to have floors.js on our landing page!! 🤝

@shubham_pratap Thank you so much Shubham!!!

@shubham_pratap Thank youu!!

@faith_rebecca1 Totally agree. Once agents start doing real work, they feel much more like teammates than chatbots.

@markokraemer Haha exactly 😄

The idea is that you can let it run 24/7 on the board, with the orchestrator managing tasks, creating new ones, and evolving the project as it goes.

Kind of like a co-pilot that helps build and scale a company continuously.

@markokraemer Hahaha Eventually ;)))

@markokraemer That’s the idea, once everything is set up, you just let it run and keep everything organized on the board while it builds and improves over time.

@billchirico Great question!! Thank you so much for the support! The main thing we’re trying to rethink is the interface where agents actually operate. Most current tools either keep everything inside chat or build dashboards that are still basically prompt interfaces.

So instead of asking an agent to “do something” in a thread, the system revolves around a task board where agents pick up, update, and complete tasks. The goal is to make execution visible: what exists, what’s running, what’s blocked, and what’s done.

For assignment and delegation, there are a couple approaches. You can manually assign tasks if you want explicit control, but the system can also route tasks automatically depending on the type of task or the agent responsible for that capability. For example, a research agent might pick up research tasks, while another handles writing or automation.

In terms of ordering, the board structure itself helps a lot. Tasks can have dependencies, priorities, or stages (to-do → doing → done), which lets agents understand what should happen next instead of just responding to prompts sequentially.

The big idea is turning agents from something you talk to into something you can actually coordinate work with.

And thanks again for the support on the launch! 🚀

@itsmasa Thank you, Yes through Notion

@itsmasa Thank you for your support!

@lev_kerzhner Thanks a lot, really appreciate the support 🙏

@mihir_kanzariya  We are focusing on Notion first, and likely on building our own interface as well to push what’s possible and define what a Kanban board needs to make human–agent collaboration truly powerful. For example, for humans, the core columns are usually just Backlog, To Do, In Progress, and Done. With agents involved, we’ve added two more stages: To be verified by the agent and To be verified by a human.

We’ll add support for other project-management tools only after we clearly identify our killer features. Once we know what truly differentiates us, we can make focused decisions and build the system with a clear direction.

Totally agree @mihir_kanzariya ! Once agents start doing real work, a chat quickly becomes messy and it's hard to see what’s actually done.

Thnak you@lev_kerzhner !! You could assign priority levels to your tasks, basically the same way you’d organize them if you were managing everything in your head or on a simple to-do list. Put the most important task at the top, the “should probably do this soon” ones in the middle, and the “I’ll get to it… eventually” tasks at the bottom :)))

@basma_el_khamlichi That’s a great question!! One thing we’re already thinking about based on early feedback is better task prioritization and management. Since the goal is to treat agents more like teammates working from a task board, we’re exploring ways to make it easier to organize tasks and track what the agent is working on at any given moment.

@basma_el_khamlichi Multi agent: Ability to use different agents per task, I already have it configured and testing.
Collaboration: I sometimes ask Agents to create a task for other agents, but it's basic and linear.
Model per task: Will need to have a proper dashboard and move away from Notion
Do you have any ideas to add ?

@basma_el_khamlichi Thank you! We’d love to hear your feedback so we can improve and optimize the next features.

@artem_kosilov We’re building on OpenClaw. I use it for SEO, and I always ask it to review my work and it consistently finds mistakes. It’s also been surprisingly effective for video editing with Remotion, scraping, and coding.

@aronsmith Thank you for the comment. Hope you enjoy it

@tham_yikfoong You can get access to it for free today and start using it !!

Thank you for your support@mustapha_ajermou1 !! 🤝

@mustapha_ajermou1 Thank youuu!!

@omar_hosny_nover Thanks a lot, really appreciate it! 🙏 Happy you like it. Would love to hear your feedback once you try Clawther.

@lamitr_dhir Thanks a lot! Really appreciate the support 🙏

@lienchueh Great question :)))

In our experience the agent usually sticks much better to the task when it is defined as a structured task instead of a chat instruction. When everything happens in chat, context can drift and the agent sometimes starts improvising or mixing different instructions.

With the task board, the task becomes a persistent object with a clear scope. This gives the agent a more defined objective to work from.

That said, agents can still deviate sometimes, especially if the task description is ambiguous or requires several steps. That is why we also rely on task states, updates, and visibility so humans can quickly see what happened and step in if needed.

So it is not perfect yet, but we have found that the task based approach reduces a lot of the randomness you see in purely chat based workflows.

@alexandru_hirsu That’s exactly the problem we’re trying to solve with Clawther. Curious to hear how you’re currently managing agents on your side.

@konstantinalikhanov  When a task is completed or there’s progress on it, OpenClaw updates the task status and posts a comment via the API. Then, using cron jobs and the Notion API, it periodically re-checks the task, tests the current result, and continues iterating until it determines the task is truly done

@iimedr Exactly. Once the conversation gets long, tasks start getting lost. The goal with Clawther is to make everything visible and structured !!

@curiouskitty I love this question, here's how we handle it today:

• Most connectors use OAuth or API keys with scoped permissions, the agent can only access what you've explicitly granted and you can disconnect a connector anytime, and the access is revoked immediately and all related data is deleted from our systems.

• On top of that you can choose which tools you want to enable or disable for any particular connector, this way you can choose to give less privileged access to the agent even if the connector's scopes are broader.
  

  

• Everything runs in a sandboxed environment, each session gets it's own isolated computer. The agent's terminal, browser, and file system are containerized. It can't touch anything outside that sandbox unless you've connected a specific integration.

• Custom MCP servers are user-controlled. You bring your own server, you define what tools it exposes, and you control the endpoint. We don't inject anything into that connection.

• On audit/visibility: Every action the agent takes is visible in real-time in the session. You can see its thinking, the commands it runs, the files it creates, and the API calls it makes. Session history is preserved so you can rewind and inspect any step. Raccoon AI today is arguably the most transparent AI agent you will find on the internet.

• What we're still building: granular per-tool permission toggles (so you could say "read from GitHub but don't push"), team-level access policies, and exportable audit logs(we store them, but there is no way to access them on the UI currently). These are on the roadmap as we move more toward team/enterprise use cases.

• For a team getting started today, I'd recommend: connect any of the integrations you want, but only enable those which are actually need for a given workflow, use custom MCP servers for anything touching sensitive internal systems so you control the boundary, keep write tools disabled for sensitive connectors, and let the agent come to you with why does it need that particular tool and how it will use it and use Plan Mode to review the agent's proposed steps before it executes.

Happy to dig deeper and answer more questions.

@garvit_jindal Thank you so much :)

Honestly, IDE-native agents are amazing for coding and that experience is hard to match in the browser. We're not trying to replace that. The longer term vision is for Raccoon AI to connect to your remote systems and live where you work, not just in the web.

But we do think the web is where this is all heading. Karpathy said it well yesterday: "humans are moving up and programming at a higher level now. The basic unit of interest isn't one file anymore, it's one agent."

And when the unit of work is an agent doing research, building a site, analyzing data, creating a deck, all in one session, you need something bigger than a code editor. You need a canvas. A higher order execution platform. The web is the natural home for that.

@maklyen_may do let us know your feedback 🙏

@melina_cross exactly this, you being able to transparently see every step of the execution and able to steer it is the very basis of collaboration. There are more such features across the platform, the ability to reference files, editing files and assets alongside the agent on the platform and more.

@nikitaeverywhere in some sense yes, I would consider them to be competitors, Claude Cowork is evolving on the same principles that we built Raccoon AI on, but there are a few fundamental differences:

Cowork is a desktop agent tied to your local machine and files. Raccoon AI runs in the cloud with its own computer, so you can access it from anywhere, any device, so you can start something from your phone and come back to it on your desktop, spin up as many concurrent sessions as you want.

Raccoon AI is generally more equipped for specialised use cases and does things Cowork can't: one-click web app deployment, native video and image generation, presentations with a full presenter mode, a canvas for you to edit images inline which will be soon extended for videos.

Claude Code is a different product entirely, and though Raccoon AI excels at async coding tasks, we don't place ourselves in that market primarily.

The end goal is to make Raccoon AI an ultimate agentic workspace that lives where your work lives, kind of like a high level IDE you can access from anywhere.

@kate_ramakaieva Thank you so much :). And yes, it can do all of that in one go. Each session starts afresh currently, we have got mixed reviews on cross session context sharing from users, and we are considering launching it as a toggleable feature really soon.

@tim_clifford3 a mix of @Screen Studio , @Jitter and @Raccoon AI itself. Screen Studio for recording raw footage of the running session, Jitter for the motion components and Raccoon AI for compiling all clips in multiple ways with a variety of sounds and transitions.

Thank you so much :)

@shubham_pratap Thank you so much, do check us out and leave your feedback 🙏.

@mordrag We have a rewind functionality built exactly for this. If the agent makes an error or starts drifting, you can rewind back to any of previous messages and the state of the workspace is restored to what it was at that point including recovering any deleted files.

For things that happen outside the sandbox like you mentioned deploying code, we have implemented guardrails to prevent the agent to do such things without your explicit permissions, and in case that still happens, the agent is smart enough to rollback things safely and cleanly on your request.

@nafis_amiri thank you so much :)

@lienchueh Two things:

• Any kind of knowledge work. Deep research with real sources and citations, competitive analysis, data analysis with visualizations, writing reports and documents, due diligence, literature reviews, market sizing. If it involves reading, thinking, and producing a deliverable, it handles it really well.

• End-to-end projects chain multiple skills. It's that you don't have to context-switch. The agent has a full computer and remembers everything in the session. So the output of one task becomes the input to the next naturally.

@nihal_goyal thank you so much :) super glad you liked it!

@mike_sykulski yes it is, primarily we are looking at knowledge work, web apps and presentations. E2E Image and video generation workflows are close runner ups.

@yashhq_22 thank you for the feedback, hope you do a lot more with it!

@raghav_gangwar yes you can!!

@vedantuttam You can share your sessions with your team, and also publish any file or app the agent created on a public link to share with anyone.

We also have a Slack integration which you can connect and collaborate with your team on the same session by simply mentioning Raccoon AI in any thread.

@caeleanb But their is so many tools in market.. what comparatively you providing the best?

@mikekerzhner absolutely love working with you and the Product Hunt team! It's been incredible to actually measure just how large the impact of producthunt.com is in this new world of LLM discovery 🚀

@mikekerzhner Been amazing working with the product hunt team!

The post that @andrew_g_stewart wrote overviewing how you were able to optimize producthunt for AI visibility was pretty sweet.

@atthajohn great call out - we should highlight that more!

Would love for you to give it a try and let me know your thoughts - it's completely free to give it a go.

@curiouskitty great question - the 2 main differentiators for us are:

1. Integrating more than just prompt and answer data - GA4, GSC, Semrush, Ads, and more

2. Our agent (highlighted here) that can actually do the work for you. This has been a huge point of market fit for us.

@brady_at_helius y'all are absolutely crushing 🚀

Thanks @brita_ulf ! Can't wait to see what you accomplish with Gauge

@sarahbedrick y'all are true power users of Gauge 🔥

@ilya_lee fantastic question -

1. We run a large library of prompts (typically several hundred) every day through every model

2. We structure which products come back, as well as which sources are most often cited

3. This informs our agent (and the user) on what to target - which sources, what types of content, etc.

For example, here are the top ten sources for questions related to cars:

@mountroot we run millions of prompts and answers every day through the real model UIs, and structure the citations that the models reference into our dataset. We subsequently scrape the individual cited URLs to gain a better understanding of what content impacts the answer.

We actually did a great study correlating the citation data back to the answer - take a look here! https://www.growth-memo.com/p/the-science-of-how-ai-pays-attention

@lev_kerzhner thanks! Would love for you to give it a try and let me know your thoughts :)

@joseph_hammad thanks! Would love any feedback you have after giving it a go!

@viktorgems a number of inputs:

1. running prompts and structuring the answers to understand where your brand does and does not show up in LLMs

2. Google Analytics traffic data

3. Google Search Console data

4. Semrush keyword data

5. Google Ads data

Gauge has access to all of these, and can weave them together to find gaps to address!

@steveb 🙌

@mordrag great question - our agent will provide detailed explanations alongside recommendations, based on data from a number of sources:

1. running prompts and structuring the answers to understand where your brand does and does not show up in LLMs

2. Google Analytics traffic data

3. Google Search Console data

4. Semrush keyword data

5. Google Ads data

Would love for you to give it a try and really push it - curious for your thoughts!

@neelanchal_gogna  @mk_orangelabs  Wonderful concept, well done! i will keep following this space.

@edgeghost 
Exactly, that assumption is the whole problem we're solving.

Real-world data is never clean.
We built @OrangeLabs specifically for that reality, not the perfect CSVs that exist in tutorials.

Inconsistent column naming?
Just tell it: "These three columns are all referring to the same thing- merge them."

Mixed formats in the same column?
Just tell it: "Standardise all date formats in this column to DD/MM/YYYY"

It reads the actual structure of your file first, understands the inconsistencies in context, and then acts on your instruction, with a live preview before anything is finalised.

The goal was simple: if one can describe the problem, @OrangeLabs should be able to fix it. No prior data knowledge or technical expertise needed.

@harkirat_singh3777 Thanks for your support. And great question, one that most of our users might want to know the answer to.
The answer to this is, before any analysis even begins, OrangeLabs runs an automatic data profiling step (we have a separate layer for it), it detects missing values, duplicates, inconsistent formats, and schema mismatches across sources. So the data is cleaned and structured before insights are generated, not after.
For open-ended questions, our AI agent breaks the query into smaller, verifiable steps rather than attempting one large inference leap.

That said, we're not going to pretend it's perfect. Messy, multi-source data is genuinely hard, and we're continuously improving how we handle edge cases. Feedback from users is exactly how we'll get better.

@aryan_rajput8 That can also be a way of using it.

@busmark_w_nika Great question! Figma export is actually on our roadmap, the ability to push your charts and visuals directly into Figma, so you can polish the design to your liking.

We haven't shipped it yet, but it's something our team is actively working on. Stay tuned for our next launch, we'd love for you to be one of the first users to try it when it's live!

@lev_kerzhner Thanks for your support. It means a lot to us.

@lev_kerzhner Thanks Lev. Waiting for you to try it out.

@ignacio_borrell @OrangeLabs Syncs directly with existing data sources. We don't plan on hoarding user data. Rather act as an analytic layer between the data layer and the end user.

You can connect multiple data sources at one go. You won't face any issues with it.

@mihir_kanzariya 
OrangeLabs handles messy CSVs really well. You just upload the file and tell it what's wrong, or even let it detect issues on its own.

For example, you can say:
"Find and fix missing values in this file."
"Standardise inconsistent date formats across columns"
"Flag rows with empty or null entries"

It understands the structure of your data, identifies the inconsistencies, and cleans it, with a real-time preview so you can see exactly what changed before committing.

Missing columns, duplicate rows, formatting mismatches- it handles it.

Would love for you to try it and break it with your messiest file 😄, that's genuinely how we get better.

@mcarmonas thanks, brother! You're spot on, this is naturally the next step. We have the infrastructure for it. We usually build new features based on user feedback, and we offer beta testing to our power users 💪🏼♥️

@weizhi thanks! Enjoy the free trial 👍🏻👍🏻🙏🏼

@shuqing_ke thanks! Yes, conversations can be re-assigned as many times as needed!

@shuqing_ke and re: workflow, there is a button "Assign" you can use by hovering on a convo in the Feed, or also inside a convo thread (top menu bar)

@davitausberlin Great question! It’s actually simpler than connecting a bunch of different third-party APIs.

We built our entire dashboard, CRM, and automation engine from scratch. The only API we use is Twilio as a gateway for certain SMS and voice functions. This means AskNeo acts as your primary communication infrastructure. We provide your business phone numbers (or port your existing ones) directly into our platform.

Here is how it works for your team:

• Unified Shared Inbox: Every text (SMS/MMS) and call for a business number flows into one centralized thread. There are no siloed tools, everything is in one place.

• Built-in CRM & Tags: You can upload contacts via CSV, organize them with unlimited tags, and immediately start two-way conversations..

• Team Collaboration: The dashboard is a shared workspace. You can assign threads to specific team members, ensuring clear accountability and an audit trail of who said what.

• Native Automations: We have a built-in chatbot and keyword triggers (like "VIP" or "OFFER") that allow you to automate responses without needing external integration tools.

• Simultaneous Ringing: You can forward inbound calls to multiple staff members at once so the first available person can pick up, ensuring no lead falls through the cracks.

Essentially, instead of trying to glue different tools together, you get one "Command Center" that replaces them all.

For a deep dive into how it looks in action, check out our full demo here: https://youtu.be/TvX9c3up8OM

@atthajohn Thanks for your feedback! That is very useful.

To clarify, we communicate possible limits because mobile carriers themselves limit the daily volume of SMS messages, depending on the specific business.

Every business phone number must be certified according to federal regulations known as A2P 10DLC. We manage this entire certification process for you. Based on this process, carriers assign a "trust score" to each company number, which can determine the daily message limit.

To avoid confusion, we mention "limits" even though they aren't "hard" caps set by us; rather, they are calculated on a case-by-case basis during the certification process (which is independent of our platform).

The good news is that most businesses using AskNeo never hit these thresholds and are never even aware that these carrier limitations exist :)

@mordrag Great question!
• Multi-Staff Attribution: Every response in a shared thread is tagged with the specific team member's name. This ensures a clear audit trail of all customer interactions.

• Role-Based Access: The "Owner" role manages workspace entry via the Staff tab and maintains exclusive control over billing and high-level administrative settings.

• Centralized Compliance: By keeping all communication within a single thread rather than siloed on personal phones, the company maintains a "Single Source of Truth." This streamlines data retention and simplifies compliance with privacy requests or audits.

• Data Ownership: Per our TOS, the business retains full rights to its data. AskNeo acts as the data processor, implementing robust security measures while the business manages internal "need-to-know" permissions.

(Note: We are not lawyers and this does not constitute legal advice; this is simply an overview of how our platform is organized.)

Thanks!

@lev_kerzhner Thank you! Balance is something we think about a lot - making it effortless for non-technical users with NL while still giving technical ones full query transparency they can take to the API

@dawar_deka I remember how hectic it used to be during my placement days, where I had to constantly keep searching for new job openings across multiple different platforms and then keep applying. Everybody knows that nothing happens with those applications. many recruiters don't even see those applications. The only way to actually get jobs is to know people or cold outreach to recruiters. That's why we built Donna so that you don't have to do all those things, by yourself.

@dawar_deka Very interesting and critical problems to tackle.

This could really help solve the messy hiring problems!

@mordrag hello Dennis, Thanks for checking out Donna.
We have a strict verification process during the onboarding of the candidate. We don't just take the candidate's resume at their word. We have a verification process where every claim the candidate makes on his resume needs to be verified. The candidate needs to provide supporting proof, such as links, posts, or certificates, for every claim he makes so that we can ensure that the recruiters can actually trust the candidates on the platform, and it also ensures that the AI agent can actually advocate for the candidate only based on the verified achievements or skills and not according to some hallucinated bullshit.

@mordrag Hey so we’re careful not to blindly learn from historical hiring outcomes because those often encode bias. Donna focuses on modeling explicit constraints and alignment signals say skills, goals, role expectations, and practical preferences rather than replicating past decisions. We also keep the reasoning transparent and auditable, so recruiters can see why a match was suggested and override it if needed. The goal isn’t just to automate judgment, but to surface high-alignment candidates while keeping humans in control of the final decision.

@klara_minarikova yeah we make a persona about the company from all the information available on the internet basically and also through your every interaction Your agent keeps learning more about you. If there is some specific HR who is always kind of dealing in suspicious ways, the agent will learn that about them and we will be able to blacklist them from the platform.

@klara_minarikova Hey thats a real Gd question, so we build the digital twins either ways, the company has its own business context, and the hiring manager has his /her own philosophies, most of the time these are subconscious traits that they would otherwise never get captured and we believe that would take him/her to a better hiring decision

@rapata_pavankumar this is actually such an insightful comment. Our vision with Donna is to eventually become a platform where digital twins can network for all types of purposes. Recruitment is just a wedge we are targeting to break in

@rapata_pavankumar Hey that’s a great question , and we’ve been thinking about that quite a bit.

Right now we’re deliberately keeping the system much simpler than the long-term “agents negotiating everything” vision. In practice, what we have today is closer to structured reasoning and alignment discovery rather than fully autonomous agents running continuous inference.

Most of the heavy work happens in three stages:

1. Persona modeling building structured representations of candidate goals, constraints, and trajectory.

2. Retrieval & ranking identifying high-alignment opportunities.

3. Targeted conversations to resolve the remaining uncertainties.

So the system only runs deeper reasoning when there’s already a strong signal, which keeps latency and cost manageable.

Long term, though, I do think your point becomes very real. If every participant in the network has a persistent agent learning their preferences over time, the challenge shifts from “matching” to coordinating many stateful agents efficiently. That likely requires a new infrastructure layer around memory, orchestration, and agent interaction for sure

@lienchueh We have a strict verification process during the onboarding of the candidate. We don't just take the candidate's resume at their word. We have a verification process where every claim the candidate makes on his resume needs to be verified. The candidate needs to provide supporting proof, such as links, posts, or certificates, for every claim he makes so that we can ensure that the recruiters can actually trust the candidates on the platform, and it also ensures that the AI agent can actually advocate for the candidate only based on the verified achievements or skills and not according to some hallucinated bullshit.

@reid_anderson3 The library still exposes screenshot tooling, so agent can take a screenshot if needed.

@lienchueh When you are building a browser agent, you have two options. Either you need to use screenshots at every step or to use accessibility snapshots. To be able to use screenshots, you need to use a vision model with computer use capabilities (which is costly). So most people started using accessibility snapshots. But accessibility snapshots dumps much more data then needed for agent to work on the page. Agent Browser takes a different path and builds a wireframe from the visible elements in page which saves 70-90% tokens depending on the page. So the savings apply to each page that the agent uses.

@curiouskitty The real breaking point is scale. With existing approaches, a single page can cost around 10k tokens, while Agent Browser typically uses only 1k–3k. And browser agents rarely perform just one action. They usually run multi-step workflows. This means Agent Browser can reduce browser operation costs by up to 70–90%. If a full screenshot is ever needed, the agent can still take one, so switching methods doesn’t mean giving anything up.

@lev_kerzhner Thanks! It is also capable of filling inputs, clicking buttons etc. with refs.

@dmitry_zakharov_ai Appreciate you!!

@snowlee You're the best, thank you!!

Hey@kirolus_ghattas, appreciate you! It's all manual drag and drop for now, but a chat interface is in the works!

@davitausberlin Thanks for the kinds words! Glad we could get some free info out there for everyone.