PH热榜 | 2026-05-28

Love this flavor of OpenClaw — no more lobster, bring on the syrup! 🥞

What I particularly enjoyed during onboarding was that Pancake dutifully did a deep research web crawl on me and my business and then put together an onboarding deck describing me, my current foci, and how it could help me.

Once I reviewed the deck, it gave me four succinct options to get started, all from within the comfort of my own Slack:

I decided to ask it to write a hunter comment for me for its launch — kinda meta, but in a good way, right?

After Product Hunt's bot detection slapped it, it produced this:

Not bad, eh? :)

Give it a try!

👋 Hey Product Hunt!

I’m Tristan, one of the cooks behind Pancake.

While building our previous company, we were such heavy OpenClaw users that 50% of our company was running on autopilot, generating ~10 demos per week and handling ops on its own.

We were so excited about this that we decided to go all in on Pancake.

Pancake is a full AI org chart in Slack, across GTM, product, and ops.

1. Pancake creates a company brain by syncing with your meeting notes and Slack discussions. It understands what you're building.

2. Unlike other "autonomous company" products, you stack agents one by one, each matched to a real goal -- compounding autonomy over time. Go from 0 to 70% autonomy in a few months.

3. Unlike other "AI in Slack" tools, Pancake is literally an org chart that you build as you go. Hire agents from our open-source Squad templates, or roll your own.

A real example of one of our autonomous Pancake Product Squads:

• A founder books a demo with us

• The Pancake product squad listens to feedback during the call and creates a pull request immediately after, unprompted. We merge it as-is.

We're running Pancake on Pancake and now have 23 autonomous agents doing 3 tasks/day each, posting a daily digest to Slack.

I hope you'll love Pancake as much as we do!!

P.S.: @francoisdefitte bet he'd shave his head if we hit 1,000 upvotes today. Let's make him bald (hairdressers in SF are unaffordable anyway).

Was a basalt customer back in the day, so I'm already biased toward whatever you ship next. "Autonomous company" is a big swing and I'm honestly into it. How much does it actually run without a human in the loop right now versus what still needs a thumbs-up? And how are you thinking about guardrails so it only acts where it's supposed to?

I was lucky to be part of the beta and the experience was just amazing. The fact that Pancake treats agents like its own org chart and grow it according to your need is really amazing. Soon we'll need a LinkedIn for AI agents I guess 😅

Great job team. The 'approve the irreversible line' seems to be doing most of the work here? How does Pancake classify what counts as irreversible vs just costly? Sending an external email is binary whereas spinning up infra or making API calls with downstream effects is a gradient

For the past few weeks I've been a beta tester, and it has been absolutely wild... especially coming from someone who has been deep in the AI automation rabbit hole for a while 🫠

Before Pancake, I had built my own setup to try to automate my side project: a swarm of OpenClaw agents, a custom triage command center to distribute tasks across them... It was fun to build but was always breaking and often under-performing. It just takes a lot of work to maintain and fine-tune a bunch of agents working on your systems.

Couldn't be happier to be relieved of my OpenClaw babysitting duties when the Pancake crew offered me a beta seat!

It is geniunely the best "AI as cofounder" solution I have tested out there - and the team is super reactive to fix stuff.

I'm not a normy, I use OpenClaw constantly, both personally and professionally, so I thought I had a pretty solid mental model of what AI agents can and can't do. But Pancake still surprised me a few times! The level of autonomy is quite impressive... it doesn't just execute tasks, it comes up with solutions to problems I wouldn't have known how to solve myself. That's the part that genuinely got me, it is very.. resourceful?

I am overall a big fan!
Wishing the team all the best for this launch - you've built something special here! 🥞

Had so much fun building this, the list of use cases really is limitless

Hi ProductHunt, cofounder of Pancake here. We use Pancake on Pancake, and honestly: I'm proud of what we're shipping today. We still have so much to do, but we're passionate about this product and pouring our soul into it !

If you want to join the adventure, try it, give us feedback, even join our Discord, we're building in public and we can't wait to meet you!

Really incredible product! Congrats François, Guillaume and the team!

“Prepare yourself to be prompted by Pancake” is honestly a great line 😄 Love the shift from AI as a helper to AI as an actual operating layer for a company. Congrats on the launch!

Congrats on launching this new product!

I tried the outreach agent and love it, looking forward to what's next!

Awesome!

That's wild! Congrats.
As someone who had started spending way too much time on OpenClaw, I'm sure this is going to save me a lot of time.

My question: when you talk about going from 0 to 70% autonomy in a few months, what happens with the remaining 30%?

Also curious to know how agents handle conflicts between them when two squads are pursuing goals that contradict each other.

Congrats on shipping, Francois! Most AI tools stop at "copilot" — betting on actually autonomous agents with roles, goals, and a heartbeat, run from inside Slack where teams already live, is a much bolder swing. Love the "approve the irreversible, the rest runs" framing. Just followed you on X (@JayTheSong), would love to connect and follow where this heads 👏

LFG, looks awesome! Can't wait to tryyy 🥞🥞🥞🥞

Let's go! Autonomy is clearly the future of automations.

Sounds amazing! Definitely what's been missing !

Really cool :) Need to try this asap @francoisdefitte

Awesome product, congrats on the launch

Love the team and their new product, can't wait to put some pancake in my life :)

Looks really cool! Does it work in whatsapp?

The launch week continues for Pancake with our Product Hunt launch 🥞

Yesterday, we officially moved out of beta and released Pancake to everyone. Hundreds of people have already joined the adventure and are using Pancake every day.

Try it out for free, send us your feedback, and most importantly: cook with Pancake 🥞

"Agents with a heartbeat working while you sleep" is a great line, but it's also the exact scenario that makes me nervous — the thing I want to picture is what the company looks like the morning after something goes sideways at 3am. How much of "the rest runs" is genuinely unattended versus quietly piling up in a queue for a human to sign off on?

This looks pretty slick. Great work @tricomte and team.

Very much lacking a list of practical use cases that this service can solve. The video is cool, I really liked it, but I don’t fully understand what the AI is capable of. For example, if I give login and password to Google Analytics and ask it to connect and analyze why website traffic dropped, can it do that or not?

Hey Product Hunt 👋 Cam here, founder of SpotsNow.

Here's a thing that's always bothered me about podcast advertising: it's one of the only major ad channels where you can't easily answer the basic competitive questions. Who's advertising on my favorite show? What's BetterHelp actually spending on podcasts this quarter? Which new brands just started testing the channel? Which shows are over-indexed on CPG vs. fintech vs. DTC?

The data exists. But it's been locked behind enterprise contracts, the UIs feel like 2014, and there's no way to act on what you learn without leaving the tool and writing a bunch of emails.

SpotsNow is the version I wanted: Free access to podcast ad spend data across 60,000 shows, with a modern interface that doesn't make you fight it, and (the part I'm genuinely most excited about) a built-in marketplace of open inventory so the second you spot a show that fits your campaign, you can actually request a spot from the publisher right there.

A few things we'd love help with today:

1. If you research podcast ads: spotsnow.io is open. Pull up your favorite advertiser or your own brand. Tell us what you wish were there.
2. If you've built Competitor Intelligence tools: I'd love your read on our category taxonomy and confidence scoring - that's where we're investing next.
3. If you run a podcast or network: there's a free way to list open inventory and reach the buyers who are already researching shows like yours.

Huge thanks to the early users who stress-tested this when the dataset was a tenth of its current size. Today it goes wide. Roasts, questions, feature requests, all welcome. 🙏

Nice idea.

Congrats on launching, Cam! Not coming at this as someone who's built competitive-intel tools, more as the "researches podcast ads / pull up your brand" person you mentioned. I'm getting an iOS app ready to launch and came at this as a would-be buyer: pulling up specific brands and shows works very well. And I'm excited to try the marketplace features too.

One observation that might be relevant to the taxonomy work you said you're investing in: my instinct as a buyer was to navigate by theme — "relationships," "dating," "wellness" — to find shows that fit my app. That's not how the tool thinks (it's organized around industry tags / specific advertisers, which makes sense once it clicks).

A very interestig idea, congratulations on the launch!

Hey Product Hunt 👋 frontend engineer here.

We spent the last few months obsessing over one thing - making podcast ad data actually feel explorable instead of buried in spreadsheets. Most competitive intel tools in this space feel like they were built to justify the enterprise contract. Dense tables, ten-click flows, dropdowns inside dropdowns, ui that hasn't been touched since 2014. we went the opposite direction at SpotsNow with clean, simple and intuitive user-interfaces.

Audio ads are a different beast, you need to hear the ads, see saturation patterns, understand show context, all without it turning into a mess.

• Type a brand, get something useful on the first interaction.

• Listen to ads inline without breaking your flow.

• Build a real campaign in under a minute with over 59k shows to choose from.

User experience matters here more than people think, when the tool is fast and clear, you spend your time on the actual decision (what to spend on, which show to pick) instead of fighting the interface.

Would love feedback on the search and campaign builder flows, those were the hardest to get right and if anything feels off drop it in the thread 🙏

Really cool launch! Love the transparency angle here. Podcast ads have always felt like a black box.

Also from my side, congrats on the launch, Cam! I agree, the marketplace angle is what makes this genuinely different

Quick question: how do you handle attribution for shows that don't use promo codes? Curious if you rely on self-reported data from brands or if there's some crawling/inference magic happening under the hood.

@boudinet Congratulations. And happy product launch.

This is actually really useful. Podcast advertising has always felt weirdly opaque compared to every other paid channel. Love that you combined competitive intel + direct inventory access in one workflow.

Congrats on the launch, Jeremy! "Who's advertising on which podcast and what they're spending" has been a real blind spot — a competitive-intelligence layer for pod ads, plus buying open inventory in the same place, is a sharp idea. Just followed you on X (@JayTheSong), would love to connect 🎙️

very niche-ish product. The team behind this looks top tier ;) @campritchard good luck!

Super interesting idea! I honestly haven't really thought about podcast ads but now tempted to try out this new marketing channel.

What a neat tool, I'm already having a lot of fun spying on our competitors 😅

Congrats on the launch Cam. Interesting product that I would love to test.

Very interesting. Was there a moment from the last few months where a publisher or advertiser used SpotsNow in a way you didn't design for, but it ended up showing you what the product is actually becoming?

Nice, congrats on the launch! Are you planning to scale to any other channels, like shorts, reels/tiktoks etc?

Very interesting! Can I see not just where a specific company is advertising, but where companies in a whole industry are advertising? In particular, I need to understand where travel startups place their ads and which exact ones they use. Is that possible?

Super interesting product. congratulations on the launch!

Wishing you good luck with the launch!

wow really interesting! @abhishek_thory @campritchard @boudinet @aizuh - I guess the biggest question I would have (after a really short glance) is where / how you're getting the campaign costs for what the advertisers are paying the podcasts (as I presume this data isn't publicly reported anywhere... or is it?!!) and I guess - if so - is this self reported data?

I'd imagine I might fudge my campaign numbers if I was able to self report. Obviously a ton of value either way but these are the metrics I'd be most interested in - what my competitors are spending on the podcasts that live in my niche.

Killer launch and big congrats to the team!

Podcast ad attribution has been a guessing game for buyers for years — host-reads vs dynamic insertion vs platform fragmentation makes clean signal really hard to pull. Curious how you handle the dynamic-vs-baked-in distinction in the data. (I built PolyMind to surface signal from prediction-market trades, and 'extracting clean signal from noisy streams' is a problem I keep running into across very different domains.)

The "data exists but was locked behind enterprise contracts" framing is exactly the gap I keep seeing in adjacent markets too. Coming from finance content creation — I run the ModeLoop Podcast on Spotify (https://open.spotify.com/show/0m1oR8AyQv17DVpc5MmirG) — the thing podcasters in any niche struggle with isn't measuring downloads, it's understanding the competitive ad picture in their category. SpotsNow giving the publisher side the "who else is being advertised on shows in your topic" view would be a meaningful unlock — and the open-inventory marketplace closes the loop nicely. Curious if you're planning category-level benchmarks (e.g. average CPM ranges by topic vertical) for smaller publishers who don't have direct comps.

Hey, the audio pixel attribution part is the headline feature for me honestly. promo codes always undercount and brand-lift studies take weeks. quick question, are the pixels embedded at the publisher level or in the ad creative itself? assuming there's a tradeoff between accuracy and publisher buy-in there.

I've been using @Pitch since the early days, and it's an absolute no brainer. Fast, collaborative, and fun. Thoughtfully crafted, and inspiring.

First launched here in 2020 after 2 years in development, awarded Productivity Tool of the Year (runner-up), the team has kept launching features around how teams really make decks: templates, analytics, recordings.

Pitch Agent is the AI that finally has the context you need to generate decks that look like your team made it, then let you refine them via chat without leaving the editor.

S/O to @adamrenklint @mishakarpenko and team for this new launch!

@christianreber put it simply on Twitter/X:

It feels like a breakthrough.

If you've ever generated an AI deck and spent hours re-prompting or fixing it slide by slide, it's a good time to give @Pitch a spin.

Go to pitch.com, it's meaningfully better.

I just one-shotted a PH launch deck using this prompt:

Create a deck promoting the Pitch Agent launch on Product Hunt. Consider the launch messaging and include the attached product images. Here's our PH link: https://www.producthunt.com/prod...

Pitch Agent perfectly incorporated our messaging (pasted it from Notion) and included the product images I attached to the prompt 😎

Does Agent ask for more reference material when input is sparse? Wonder how it handles thin templates. Congrats on the launch!

This is amazing! Having the chat with the agent right there in the UI opens up so many possibilities.

Congrats with the launch, team! 🔥

Love the direction here. Most AI presentation tools stop at “good enough,” but building directly from existing templates and design systems feels way more practical for real teams.

I can't wait to see what people create with Pitch Agent; generating AI slides that are actually on-brand is a game-changer. I'm proud of the team for making it happen!

Congrats on the launch team!

I like that team didn't jump into the AI-wave with half-baked solution. The agent is fast and very reliable, and the UX feels great. Awesome launch as always 🙌

Congrats on today's launch! It's a product that can do almost all. Wishing you the best today.

So proud of the team and all their work building Pitch Agent. It's been so cool watching it grow in our weekly demo sessions, and now to have it in the hands of our users! My favourite feature is being able to plan and test your storytelling through the Agent chat - it's like having a little presentation sidekick right there with you. Plus, shoutout to the new templates that went live with this launch! Gorgeous!

The thing that kills AI slide tools for me is brand. The output is fine until I spend an hour fixing fonts and colors by hand. How does Pitch Agent lock that down: style-guide upload, an existing Pitch workspace, or guessing from a logo? And how does it handle a deck that mixes data slides with narrative slides? That's usually where the generated structure falls apart.

Isn’t it possible to just point to a website and take the style from there?

Hell yeah! Spent hundreds of hours in Pitch so far; can't wait to actually outsource some of the time to the agent ;). Cheers from an old time advocate.

How does the AI handle brand consistency across multiple team members editing the same deck? That's usually where things break down.

"Look like your team made it, then refine via chat without leaving the editor" — this is exactly the bar that makes AI deck generators worth using vs. a thin wrapper. I run a finance YouTube channel (Mod3Loop, https://www.youtube.com/@Mod3Loop) where every video script eventually becomes a slide visualisation problem, and the gap between "AI-generated boilerplate" and "this could actually go in front of an investment committee" is huge. Real test for me: can it produce a slide that defends the same number the same way the model defends it (so the deck and the workbook never drift)? Curious whether Pitch Agent can ingest a finance model and produce a deck whose numbers are sourced rather than re-stated by the LLM.

Okay, I just watched the 30-second demo and actually said "oh nice" out loud.

The way Pitch Agent locks brand colors/fonts while iterating on content is such a thoughtful detail. It solves the "my deck looks like a patchwork quilt" problem without stifling creativity.

As someone who writes about practical AI tools, I'm always skeptical of "just prompt it" promises. But this feels different: it's not replacing judgment, it's removing friction.

If you ever do a "behind the scenes" post on how you trained the brand-consistency layer, I'd love to read it.

PS: My last client deck took 3 rounds of 'just one more tweak. I so wish I had this last month :)

i wasnt the biiiggest fan of the early versions but im testing the new upgrades for sure! looks promising :)

The jab at "apply your colors to generic layouts and call it on brand" lands because that's exactly what most of these tools quietly do. Building from the actual design language and image style is a much harder promise to keep — the real test is whether it respects the weird, opinionated stuff in a template, not just the color tokens.

Quick question for the team: when Pitch Agent generates slides from a prompt, how does it handle cases where the brand template has multiple layout variants for the same slide type? Like if I have 3 different "stats" layouts in my template, does Agent pick the best one based on the content, or does it default to one?

This is one of the more natural places for AI in a creative tool because “on brand” is such a context problem, not just a generation problem. The hard part is usually not producing a deck-shaped object; it is knowing which prior slides, customer language, visual rules, and internal shorthand are allowed to influence this specific deck.

I’d be curious how Pitch Agent handles conflicting context. For example, if the brand kit says one thing, a recent sales deck says another, and the user asks for a third direction, do you expose any “I used this because…” trail so the team can trust and correct the output?

Cell citation is the higher-leverage of the two for me. The case I care about: a number in the deck (say, Y3 EBITDA margin) clicks through to row 47 of the model, so when someone in the meeting asks 'where does 18% come from' you can defend it without re-opening Excel side-by-side. Live link is the dream for working models being refined in real time, but cell-level citation is what makes a deck audit-ready — and that's the bar IC decks actually need. The combo would be transformative.

Any plans to export the slides in different formats, in case I want to write on brand reports or guides in document (A4) Format. Or is that possible already?

Hey Product Hunt 👋

Raj here, founder & CEO of Revolte.

For years, I’ve built and worked with engineering teams where the same pattern kept showing up:

Writing code was rarely the only bottleneck.

The real drag was everything around the code: setting up environments, running tests, managing deployments, fixing broken builds, triaging incidents, checking quality, and keeping delivery moving across disconnected tools.

Coding assistants have made developers faster inside the IDE.

But software delivery is much bigger than the IDE.

That’s why we built Revolte.

Revolte is AI for Software Engineering, an agentic platform that helps engineering teams move from intent to production with humans in control.

Give Revolte a ticket or requirement, and its agents can help plan the implementation, work against your actual codebase, generate code, run checks, create the PR, support deployment, monitor runtime behavior, and surface what needs attention.

But the important part is this:

Revolte does not remove engineering judgment.

Every meaningful change goes through human review. Engineers see the diff, the reasoning, the checks, and the rollback path before anything moves forward.

We built it this way because production software cannot run on blind automation. It needs context, governance, and control. Our belief is simple:

AI should not just help engineers type faster.
AI should help engineering teams ship better software faster.

Revolte is built for teams that want more delivery throughput without adding more delivery chaos.

We’d love for you to try it, break it, test it on something real, and tell us where it falls short. 

https://revolte.ai/

And if you’re an engineering leader thinking about how agents can safely enter your SDLC, I’d be happy to talk through the governance side with you.

Thanks for checking us out,

Raj.

Greetings Product Hunt 👋 this is Watson from @Revolte

One thing we kept hearing from engineering teams was this:

AI helps teams write more code. But WHY shipping software to production still feels painfully operational — and WHY no serious engineering team fully trusts AI near production yet.

The hardest balance in AI software delivery today :

• Too much approval, and the product becomes another workflow layer engineers have to babysit.

• Too much autonomy, and no serious team will trust it near production.

• Automation should handle the repeated delivery work
  environment setup, test runs, build management, deployment support, runtime monitoring, and coordination.

• Human judgment should stay where it matters: code merges, production changes, infra-sensitive decisions, security-sensitive changes, and rollback paths.

• This balance is the product. We went through many versions before landing on the current model.

And honestly, that’s where a lot of AI ROI still gets stuck inside real engineering organizations.

We believe the future isn’t just AI generating code — or engineers manually coordinating every step around software delivery forever.

It’s intelligent execution systems continuously carrying delivery work forward while engineers stay focused on architecture, reliability, product thinking, and technical judgment.

That’s the balance we’ve thought deeply about while building Revolte — and where the compounding value really starts.

Would genuinely love feedback from the PH community ❤️

Congratulations to Raj and the Revolte team on the launch 🚀

I hunted Revolte because it’s one of the few AI engineering platforms I’ve seen that looks beyond code generation and focuses on the real bottleneck: getting software safely from intent to production.

A lot of AI dev tools make engineers faster inside the IDE. That matters, but it doesn’t solve the full delivery problem. The hard part is everything around the code, planning the change, understanding the existing codebase, running the right checks, creating the PR, supporting deployment, watching what happens at runtime, and knowing what to do when something breaks.

That’s where Revolte feels different to me.

Their bet is not that AI should blindly replace engineering judgment. It’s that agents can take on more of the SDLC heavy lifting if the trust model is designed properly, with the right approval gates, visibility into the diff and reasoning, quality and security checks, and rollback paths where they matter.

That’s the version of AI for software engineering I can actually see moving into real production codebases.
Two things I’d encourage people here to look at closely: the per-service pricing model, which is very different from the usual per-seat AI tooling model, and the CLI/workflow experience, because engineering teams don’t want another SaaS dashboard unless it genuinely removes work.

Excited to see how the Product Hunt community responds to this.

Raj and team have clearly thought deeply about where AI belongs in the software delivery lifecycle. Looking forward to the discussion.

Excited to share that we’re launching Revolte today.

Revolte around a simple belief: software teams should spend more time building great products and less time dealing with delivery complexity.

Today, engineering teams jump across multiple tools for planning, coding, testing, deployment, and production monitoring. A lot of valuable time gets lost in handoffs, repetitive workflows, and operational overhead.

That’s why created Revolte.

Revolte is AI for software engineering, helping teams move faster from intent to code, testing, deployment, and production, while keeping engineers in control throughout the process.

With Revolte, teams can:

⚡ Build faster
🧪 Automate testing and release workflows
🚀 Ship with less operational overhead
🔍 Monitor production with greater visibility and confidence

Building this for developers, engineering leaders, and teams that want to ship faster without adding more complexity to their workflow.

Would love to hear your thoughts, if AI could take over one painful part of your software delivery workflow, what would you want it to handle?

Thanks so much for checking out Revolte 🙌

AI that works inside the engineering workflow is a different bet than AI that sits alongside it. The context problem in code is real. Getting it to reason about system trade-offs isn't just a file-level concern. We've been building in the customer success for developer tool companies space, and Revolte touches on something we think about a lot. What's your approach to handling context across large multi-repo codebases?

I worked on the deploy and runtime side of @Revolte .

The funny thing about "deploy a service" is that it sounds simple until you see how different every team’s setup is.

Different pipelines. Different secrets. Different rollback rules. Different environments. Different observability habits. Every org has its own delivery snowflake.

A lot of agent demos avoid this by staying in a sandbox. We didn’t want Revolte to be useful only in a clean demo environment.

So the challenge was to make the agent work with the way teams already ship, existing repos, existing pipelines, existing infra patterns, while still giving them a cleaner execution layer on top.

The CLI was a big part of that.

We didn’t want engineers to feel like they had to live inside another SaaS dashboard. The CLI is meant to make Revolte feel close to the actual workflow: ticket, code, checks, PR, deploy support, without forcing engineers out of their flow.

That part took longer than expected, but I think it matters a lot for adoption 🙌

This feels like Cursor meets Jarvis 👀 How accurate is the task execution in real world coding workflows?

Congratulations on your launch @rajagopalanar. This automation of engineering processes with AI looks disruptive and promising to reduce the SDLC cycle duration for me.

However with the product doing everything from development to production, I'd like to know your data protection, security and compliance story. Especially in a regulated industry (e.g. financial services like banking or insurance), my most pressing concerns regarding engineering processes are around :

• Does the product breach my security standards that I ensure in all of my vendors ?

• Are the SDLC policies in paper actually being implemented by this tool ? Given that we have built agile teams and processes over years in-house / external and IMO it is easier to define a SDLC policy on paper than to enforce them in practicality.

• What happens to the data, does the product take it outside UK / EMEA regions ?
  
  

How is security checking implemented? Do you have internal rules or checklists?

Congrats on the launch. The framing that resonates most is treating the full SDLC as the product rather than just code generation. That's a meaningfully different bet from the IDE-centric tools, and a harder one to build well.

@rajagopalanar What stands out about Revolte isn't just the AI assistance, but it's the philosophy. Tools should amplify human judgment, not override it.

As someone who writes about practical tech at Your Tech Compass, I see too many "AI fixes everything" promises that skip the nuance. Revolte feels different: the iterative suggestion flow (try-tweak-approve) mirrors how thoughtful devs actually work.

One thing I'm curious about as I test: can teams customize Revolte's "confidence threshold" for auto-suggestions? For example, "only suggest changes with >90% confidence" vs. "show me everything and let me filter." Asking because for risk-averse teams (and readers who value transparency), that control knob could be the difference between "cool demo" and "daily driver."

Congrats on launching something that feels both powerful and humane.

• Diana - Your Tech Compass

does it mean this will work starting from the idea with a small title - "like create AI note pad" to autonomous implementation?

The approval gating for critical decisions is the right design. Most SDLC agents fail because they either go fully autonomous (risky) or require constant hand-holding. We've felt that tension building agents that touch production. Having it handle quality checks, PRs, and deployment monitoring while preserving human review for high-stakes calls is solid. How does it decide what triggers an approval gate? Is that configurable per repo or risk-scored?

How do you decide what counts as an important decision that requires engineer approval in comparison to something the agent can auto apply?

One of the earliest and most consequential decisions we made was this: Revolte would not be a coding tool with delivery features bolted on. We made the SDLC itself the product.

It sounds obvious in hindsight, but the pressure early on was to show something immediately impressive — an agent that generates a working PR from a prompt, a demo that wows in a ten-minute call. That stuff is genuinely useful. But we kept running into the same wall: generating code is not the bottleneck anymore. The bottleneck is everything that has to be true for that code to safely reach production inside a real engineering organisation.

What context did the agent have when it made that decision? Who approved it? What's the audit trail? What happens if it needs to be rolled back? How does an engineering leader defend this to their CISO, their CFO, or their board?

That's where most of our actual engineering effort has gone — not into making agents generate better code, but into making agents operable inside real teams. Audit trails, approval gates, policy-aware actions, delivery visibility, rollback paths. These are not enterprise features we added later to close deals. They are the reason engineering teams can say yes to agents at all.

The line we keep coming back to internally: AI can carry the delivery load, but engineering judgment has to stay visible and accountable. That's not a constraint on what agents can do — it's what makes them trustworthy enough to actually use.

Curious whether others building in this space have hit the same wall — the gap between "the agent works in a demo" and "the organisation can actually run it."

"AI for software engineering" could be five different products, so I honestly can't tell what this is yet. A code editor? An agent that opens PRs? A Copilot-style layer with more context? What does one normal task look like start to finish, and what happens when the repo has no tests and no spec to work from? That's the case that breaks most of these for me.

But I'm also glad you exist guys, I'm just here to challenge you haha
Congrats for the launch

Congrats on the launch team! Quick question though: how is this actually different from Cursor or Claude Code? Trying to figure out where Revolte fits in my stack.

What's the security story? We're in a regulated industry, but I'd want to understand data residency and whether the agent can run inside our own cloud account.

AI for software engineering is one of the categories where the demo always looks great and the daily use breaks at the edges — codebase context, multi-file refactors, and stale dependencies. Curious how Revolte handles the case where the codebase convention contradicts what the model has seen most in training. I run into the same problem on the financial-modeling side — my channel Mod3Loop covers a lot of this kind of edge-case behavior in modeling tools that mostly work until they suddenly do not.

I’ve built a TRMNL plugin with the Buffer API; it shows my queue counts, along with my next scheduled posts and previously sent posts, on an e-ink display sitting on my desk. I’ll make this available for installation soon.

The API is straightforward to work with, and AI tooling makes prototyping extremely quick. If you're building anything that touches social media, this is worth a look.

Well done team and happy launch day on PH!

👋 Hey everyone, Mike here, another one of the makers on the Buffer team.

A bit more about the API:

Buffer's API lets you connect Buffer to the tools you already use, whether that's an AI agent, a no-code automation tool, or your own stack. You can publish, schedule, and manage your social presence from wherever you already work.

Under the hood: GraphQL API, MCP server, CLI, managed OAuth, pre-built workflow templates with no-code tools like Zapier and n8n, and an interactive API explorer.

A few more examples of what people have been building with it:

• An automated weekly reporting system across 77 social channels in 10 languages, built with n8n by someone who isn't a developer.

• A Friday morning Slack bot that recaps what was posted, what's scheduled, and what's missing. Plus a full LinkedIn content library and analyzer on Lovable, both built entirely through conversation with AI.

• A power-user layer on top of Buffer with thread splitting, a content calendar with planning notes, and a snippet library.

• A Substack growth platform for creators, with Buffer handling social distribution automatically.

It's also available on every Buffer plan, including Free.

Let us know if you have any questions and excited to see what other folks build with it!

As a fellow bootstrapped company, it's always a pleasure to see Buffer being back over here 🔥 I'm curious, is the next step the Buffer MCP?

Most social publishing APIs fall apart on edge cases like platform-specific media constraints, rate limit variance across accounts, or diff post formats per network. Curious how the Buffer API handles those inconsistencies at the normalization layer. Do you expose a unified schema and translate per-platform, or does the caller still need to know what Twitter vs LinkedIn expects?

Hey all! Jakub here, one of the makers ❤️

Happy to answer any questions you might have!

Also be sure to checkout our discord if you'd like to learn more! https://buffer.com/our-community

Happy Thursday!

Hi guys ! Working on a competing launch today (Pancake), but I have to say that I love Buffer and that this is a really cool release that we'll probably use at Pancake, so best of luck for today !! Love from the Pancake crew 😍

We’ve hit this exact problem internally. Every platform behaves just differently enough that “post everywhere” becomes messy really fast once automations enter the picture.

The MCP + API combo here is smart. Are most teams using Buffer API as infrastructure inside agents/workflows now, or still mainly for traditional scheduling flows?

Super interesting idea. How would the pricing work for independent software vendors? I am thinking about adding scheduled social media posts on top of our current product.

The MCP server is the piece that jumps out — it means I could basically tell an assistant "queue this thread across LinkedIn and Mastodon for next week" in plain language and have it land in Buffer. The e-ink TRMNL plugin someone built in the comments is a nice proof that the read side is just as open as the publish side.

One API for every platform is the right framing — the rate-limit and format normalization is where everyone burns weeks. Question: how are you handling platforms that change their content rules without notice (Twitter character limits, LinkedIn media specs)? Have to deal with the same kind of cross-platform output problem with StoryRoute, my interactive travel app where city-exploration stories need to render cleanly on mobile web and inside socials — the format-drift problem never really goes away.

congrats on the launch! We were a customer of buffer for our contractor teams. A few questions:

1) how would post with API effect account health for faceless/organic ugc content? We often notice substantial borderline shadow ban as we increase our usage of API posting as opposed to organic.

2) is there more unit economical way to connect 10+ accounts assuming each account at least cross post to three platforms? there're a few other alternatives that offer similar functionality that we're using and cheaper.

And if additional parameters need to be specified during publishing (for example, privacy settings), can those also be managed?

How verified and active are the angels/VCs in the database?

Congrats on the launch. This looks super helpful, will definitely use this to find the right investors for my upcoming startup.

Can I look up investors in the travel segment who have already invested in AI as well (my project is at the intersection of these fields)?

Now you can let your OpenClaw manage your portfolio. 🦞

What could go wrong? 😜

Been waiting for this to happen :) I wonder if it becomes a way to trade thematically / in a thesis-driven way on RH, by telling your agent about your high level beliefs and having it translate those beliefs into a continuously updated portfolio strategy, rather than giving the agent single stock instructions. Time to play! 💸

Routing agent trades through a separate dedicated account is the detail that makes this feel responsible rather than reckless — it caps the blast radius without forcing me to trust the agent with my whole portfolio. The real-time activity feed only earns its keep if I can act on it in time, though, not just read it after the fact.

"Trade thematically in a thesis-driven way" is where I think this gets interesting fast. The Robinhood agent is reading the user's stated belief; the missing layer is whether the belief is well-calibrated against what the market thinks. I built PolyMind (https://polyminds.netlify.app/) as a lightweight alerting layer over Polymarket trades — partly because prediction-market pricing on macro events (Fed decisions, geopolitics, economic releases) is often a stronger contrarian signal than headlines. An agentic trader paired with a calibration check ("market is pricing X at 28%, your thesis assumes 60%, are you sure?") would be much more durable than one running on conviction alone. Curious whether you've thought about external belief signals as an agent guardrail rather than just instructions in / orders out.

The dedicated agentic account is a smart design call. Separation from your main funds makes it psychologically easier to actually give the agent leeway. The real-time activity feed solves the trust problem without taking back control. How granular can users get with risk limits per trade?

See how Memori works: https://memorilabs.ai/benchmark/#demo

Select a task and watch both agents in real time: https://memorilabs.ai/agent-trace/#demo

While most customer facing AI agents are limited by short-term memory constraints, Memori brings the long-term persistent memory and unlike traditional memory systems that rely primarily on long-form natural language conversation history, Memori enables agents to automatically create structured, long-term memory directly from the agent trace — including execution paths, tool results, workflow steps, outcomes, and decision-making logic.

- Structured, persistent memory for AI agents — Memori replaces flat markdown memory files with a structured knowledge graph that captures facts, decisions, outcomes, and patterns across every session — without bloating the prompt.

- Grounded in what agents actually do, not just what they say — Memori captures tool calls, execution traces, and real-time agent decisions alongside conversation, giving agents a fuller picture of prior task execution.

- Agent-controlled intelligent recall — Agents decide when and what to retrieve, scoped precisely by project, session, entity, or time range — eliminating irrelevant context and cross-project noise.

- Automatic memory building, zero latency impact — Memory is structured and updated asynchronously after each interaction, so it never slows the agent's response.

- Smarter daily briefs — Memori generates structured daily briefings built from execution traces and structured memory — covering priorities, risks, active goals, open loops, and known failure patterns — far beyond a simple conversation recap.

- Built for multi-user, multi-project environments — Memory is fully scoped and isolated by project, process, session, and entity, preventing data bleed across users and contexts.

- Production-ready observability — Full visibility into memory creation, recall activity, retrieval performance, and quota usage via Memori Cloud.

The memory layer design is genuinely artistic.

Instead of treating memory as compressed chat history, @Memori turns agent execution traces into reusable state: what tools were called, what worked, what failed, what decisions were made, and what patterns should carry forward.

That is a much stronger memory primitive for agents. Real agent context lives in the execution path, not just in the conversation around it.

Kudos to @_gordee @adam_b_struck @memorilabs and the team!

15K GitHub stars and rising — well deserved!

Cool! Can you also store and remember the sequence of actions in a multi-agent system?

Persistent memory from trace (not conversation) is exactly the right framing — agents that 'forget' why they made a choice are nearly useless in any audit-sensitive workflow. Curious how Memori handles the case where two traces contradict each other and the agent needs to pick a winning version. This is structurally the same problem in financial modeling — I built ModeLoop in part because assumption drift between model versions is brutal in deal work, and 'why did we change this number' is the question that gets asked three months later when nobody remembers.

the trace-based approach is smart. most agent memory systems just store conversation turns, which loses all the decision logic and tool outputs that actually matter for improving performance over time. curious how you handle conflicting memories when the same agent runs different strategies on similar tasks?

We’ve been noticing that “memory” for agents usually breaks once workflows become long-running or tool-heavy. The agent trace angle here is interesting because conversation history alone definitely isn’t enough anymore.

How are you guys handling memory cleanup / forgetting over time?

The trace-based memory model is architecturally clever. Capturing tool calls, decisions, and outcomes from execution rather than compressing conversation history preserves the why behind agent behavior, not just the what. We've hit this ceiling building stateful agent workflows; chat summaries lose causal context fast. How do you handle storage and retrieval at scale when agent runs produce deeply nested execution graphs?

This is the right direction for agent memory. I like that you're deriving it from traces and tool outcomes instead of only chat history. The real test I'd be curious about is how teams inspect and prune memories when workflows change.

How does Memori handle memory updates when the agent learns something new that contradicts an older trace?

Interesting one @memorilabs @adam_b_struck @_gordee ! Upvoted :)

What's the memory an agent gets from the trace, that would be useful for another session?

Like I have set up a rule in Claude.md asking the agent to keep adding new learnings (during the session) into a project level .md file. How would your tool be different from that?

The "memory from execution trace, not just conversation" framing is exactly the right primitive for agents that actually do things vs just talk. Quick question: when a tool call returns a large blob (say a 10MB SQL result), how does Memori decide what to keep in long-term memory vs discard? Curious about the pruning side.

I have been waiting for this great upgrade to Marked. I really can’t manage my work without it. It does far more than I can manage, but this new version makes a significant improvement to my production of written douments for use by others outside my own mac. Any time I want to share a draft markdown document (they are ALL mark-down documents!) I use Marked to deliver the goods. A new advanced docx integration has vastly simplified my workflow.

Marked's whole appeal to me is that it never tried to become an editor. It previews, it exports, done. Does v3 let you set CSS themes per document, or is that still a global preference? I keep different output styles for different targets, and switching app-wide every time is the one bit of friction I hit.

12 years between Marked 2 and 3 - really respect the discipline of not turning a preview tool into yet another editor. Most "minimal" tools quietly scope-creep into editors over time. Curious: with the new per-document style memory Florent mentioned, does it survive file renames and moves, or is it tied to the file path?

Is this mostly for developers or also useful for content writers?

In addition to the promo code for Product Hunt users, there's intro pricing that you can lock in.

Hey Product Hunt 👋

I’m Satyam, co-founder of Growati.

The idea for Growati started two years ago when I began uploading videos on YouTube myself.

I quickly realised something frustrating:
Uploading the video was only half the work.

The exhausting part started after uploading:
● titles
● thumbnails
● descriptions
● packaging
● trying to understand the algorithm

At that time, AI tools weren’t good enough to automate much reliably, so I paused the project.

Last year, I met my co-founder, Lokesh Kabra, and one of his clients needed a way to automate YouTube post-production for their channel.

That brought the idea back.

Since February, we’ve been rebuilding the product from scratch.

Today, Growati helps creators generate personalized:
● titles
● descriptions
● thumbnails
● chapters

in minutes.

One thing we learned very quickly while talking to creators:

A lot of educational creators and podcasters are amazing at creating content, but feel exhausted by packaging and confused by YouTube optimization.

We currently have:
● 10+ beta users
● 500+ synced videos
● 50+ metadata updates processed

Still very early, but we’re excited to finally share Growati publicly.

Would genuinely love your feedback and questions 🙌

Does it optimize thumbnails/titles based on viral patterns too?

Congratulations on the launch 🎉

Hi! This makes sense to me because most creator tools stop at “generate a title,” while the real headache is figuring out why one video gets buried and another suddenly takes off with almost the same content. I also like that you focused on educational creators and podcasters specifically, because those channels usually have strong content already, they just burn out on packaging every upload. Curious whether the AI learns from a creator’s past top-performing videos over time or treats every upload separately?

Building Growati taught us something interesting:

Most creators already know how important titles and thumbnails are.

The problem is consistency and time.

After every upload, creators repeat the same workflow again and again.

We tried to simplify that process with automation while keeping creator control in hand.

Would love honest feedback from the PH community ❤️

Congrats on the launch, Satyam! Post-production is exactly where most creators burn out, so an autopilot for titles, descriptions, and thumbnails that adapts to how the video actually performs is a smart wedge. Just followed you on X (@JayTheSong) — would love to connect and follow the journey there 🚀

Can Growati keep a consistent channel style so new thumbnails don’t look like random templates?

Love this, from my experience as an investor in https://www.dasmerch.com/ that's quite a big challenge you're solving.
How does the process work from first contact to delivery? Do you have a standard package? Are you also taking care of sourcing? Congrats on the launch

As a developer, I never realized how much operational work sits behind conference swag until our team had to organize it ourselves

HR teams are probably going to love this.

Congrats! We felt this in our engineering team pretty quickly. Sending welcome kits to remote hires across different countries created way more coordination, shipping, and logistics work than we originally expected.

This is such a real problem. Company swag sounds fun until it turns into spreadsheets, inventory questions, and multi-country shipping. Congrats on the launch!

This is actually a very real operational problem. Half of company merch knowledge lives in someone’s inbox until they quit 😄 Congrats on the launch!

What’s onboarding like, do teams need to pre buy inventory or can you do on demand runs?

Have just launched http://granite.co!

Granite is a long term doc vault. Legal, medical, business, taxes, etc.

It's NOT a knowledge base. It's a place to drop documents you may never need or only need once or twice. But you should be able to find those docs instantly.

Imagine a title for a vehicle. Or receipts for taxes. Or a purchase agreement from a business transaction. You simply dump all of those docs in Granite and never think about them again...until you do.

No organization. No tagging. No folders. Simple plain english text input to find exactly what you need right when you need it.

"Files it correctly" is doing a lot of quiet work in that one sentence. When a document is genuinely ambiguous — a PDF that's half a tax form and half a personal letter — does it ask me where it belongs, or just pick a home and let me correct it later?

Document vaults that surface the right thing at the right moment have been broken for years. Curious how Granite handles version control — the financial-model templates I publish on Eloquens get updated quarterly, and the gap I keep seeing is users who download v1, fork it, then have no way to back-propagate updates. Is Granite solving for derivative-document drift, or strictly the canonical-version storage side?

"Documents you may never need or only need once or twice — but should find instantly" is exactly the framing missing from most consumer doc tools. The category I keep returning to is mortgage paperwork — most first-time buyers I work with end up with 40-60 PDFs across loan offer, valuation, conveyancer correspondence, insurance, and refix renewal letters, and the cost of not finding them quickly is real (missing a rate-fix window can cost £10-20k over the term). I built MortWise (https://mortwise.netlify.app/) on the analysis side of that journey but the storage problem you're solving sits right alongside it. Curious whether you're planning any time-sensitive triggers — e.g. "this document references a date and you haven't opened it in 11 months" — which is where vault apps usually fall short.

Love the idea! If I see it correctly, everything is stored on proprietary servers? is there a plan to make this work locally + with local LLMs with a one time purchase for Granite? That feels much safer to me personally :)

This solves something I didn't know had a name — I call it 'document anxiety', that feeling when you know you have something important somewhere but have no idea which folder or email thread it's buried in. The plain English search is the key unlock. Does it handle documents in multiple languages or is it English only for now?

I keep coming back to this: internal tools don't always start as "projects" anymore. They often start as a quick fix from the person closest to the pain, not as something planned by a central team weeks later.

That seems like a huge unlock, but also brings a new kind of chaos. For teams already seeing this happen: what do you actually do with these tools once they start being useful - where do they live, how do people discover/use them, and who ends up maintaining them?

The weirdest thing about AI-built tools is that many of them take less time to create than to make trustworthy.

Getting a prototype is easy now. Making it something another person on your team can safely use, maintain, and build on is still the real work.

That gap is a big part of why we built Compartment. We kept seeing useful internal apps appear very quickly, then get stuck in a limbo between “cool demo” and “something the team can actually depend on.”

Would love to hear if others here are running into the same problem.

The distinction between "procrastination" YouTube vs "tutorial" YouTube is genuinely clever. Curious how AC handles ambiguous cases over time: does the nudge frequency adapt as it learns your personal patterns, or does each session start fresh? And is there a way to review the reasoning behind a nudge after the fact, like a log of why it flagged something?

Very cool idea and implementation! and there are even three characters to choose from, wow! one thing i'm wondering: if i usually switch between tasks, will my cat be confused? 🙂

The more I look at it, the more I want to try it - but I use Windows ;(

so cuteee.. I wish it existed for Linux as well :|

Congrats on the launch, Jonas! 😸 I came across this by chance and visited your website. I have tried many distraction blocking apps and they didn't work much. I would get calls or there would be something or the other. So, I'll unblock the apps and it totally beats the purpose of it.

Your Accounty Cat seems cool but I have a question. You've mentioned on your website that screenshots are analysed and discarded. I have to follow strict privacy policies at work. Is there a way that I can enable Accounty Cat for only certain apps and not my whole system?

Focus tools that 'get context' are the holy grail — most of them either timer-bro-shame you or try to be a full second brain. Curious where AccountyCat lands on that spectrum. Asking because I built the Excel for Financial Modelling course on Udemy and a recurring student-question pattern is: 'how do you focus deeply enough to actually internalize the structural side of a model vs. just clicking through tutorials?' Most of them are missing exactly this — a focus layer that adapts to the depth of work, not just blocks notifications.

Love both the simplicity of the product and the fact that it solves the problem I have: making any type of the video would turn into the nightmare we so-so result. Thanks for launching this one @kipin :)

I hate how much time I waste turning raw recordings into decent demos. This tool seems to handle everything in one flow - synced recording, subtitles, quick cleanup.

How fast is the editing process in practice? Can you usually get a polished video out in under 10 minutes?

The “polished demo without becoming a video editor” framing is strong. For founders and PMs, the time sink is often not only cleanup; it is deciding what the viewer should notice and why it matters.

One feature direction I’d love to see in tools like this is lightweight intent markers while recording: bug, decision point, customer-facing benefit, setup step, gotcha. Even if those just become editable chapter labels later, they would help a raw walkthrough turn into a useful demo or handoff without forcing the maker to reconstruct the story after the recording.

Loved the product, i haven't played with bg yet but having animated bg would be cool for high production screen demos.

Hey Aleksei, congratulations with the launch 👏

The UI looks great and the app has many nice features!

Does it support importing your own video for quick edits, e.g. adding manual zoom?

Does it have auto zoom ?

Hey Product Hunt 👋 I’m Adrin building Kim. People have more health data than ever, but less clarity. We tracks sleep, workouts, heart rate, HRV, activity, and recovery, but most people are still left asking, “What does this actually mean for me?” So we built Kim, a personal health assistant that turns health data into simple conversations and personal experiments. You can ask things like why you’re tired, whether magnesium is helping your sleep, if a workout affected your recovery, or what patterns are showing up in your body data. Kim also helps you log and analyze food, supplements, mood, energy, and habits so your health data has more context. Our goal is simple: less dashboard, more understanding. Lanre and I will be here reading every comment. Tell us what to build next 💙

Download Kim on Appstore: https://apps.apple.com/ca/app/kim-personal-health-assistant/id6763202025
It's for free

Logging mood and energy alongside biometric data is smart. It adds the subjective layer that pure sensor data misses. That context is what makes the insights actually useful.

You should be able to monitor your health without it being a full time job. Just tell Kim, she'll handle the rest 💙

"Less dashboard, more understanding" is the gap I keep seeing in any passive-data domain, not just health. The same pattern applies to residential energy — most solar installs ship with an inverter app showing kWh produced, kWh exported, battery state of charge, etc. Most homeowners can't translate that into "should I be charging the EV at noon or at midnight" or "is the battery actually paying for itself." I built RoofSolar (https://roofsolars.netlify.app/) as the IRR + behaviour layer on top of PVGIS irradiance data and household consumption shape — turning raw production numbers into actionable economic decisions. Kim's framing on Apple Health resonates strongly. Curious whether the intelligence layer ever ends up issuing concrete actions (e.g. "skip caffeine after 2pm this week") or stays interpretive — that's the line that separates dashboards from coaches.

Cool idea. Found a bug: when learning about a reading or measurement, you can’t scroll in the card to read the entire piece of information. You can press show less, but when you press show more, there’s no way at least using my finger to scroll in that box. I can send you a screenshot if you can’t reproduce it overall cool idea.

Hey Product Hunt 👋

I’m Zander, maker of Crew44.

Crew44 started as my own messy workaround for a problem I kept running into: I was using multiple AI coding agents every day — Claude Code, Codex, Gemini, Cursor — but they all felt like separate contractors who had never met each other.

Every session meant re-explaining the repo, copying context between tools, repeating project conventions, and manually deciding which agent should do what.

So I built Crew44: a local-first command center that turns the AI coding agents already installed on your machine into one coordinated crew.

You can create specialists like Cofounder, Engineer, Product Lead, Designer, or Reviewer, etc. bind each one to the runtime/model that fits the job, and let them work in a shared project workspace with memory, skills, and structured handoffs.

A few things I cared a lot about while building it:

* Local-first by default
* No account, no subscription
* MIT licensed
* Works with the tools you already use instead of trying to replace them

The goal is not to build “one AI agent to rule them all.”, but to make the agents you already trust work more like a real team.

Crew44 is still early, and I’d genuinely love feedback from people who use multiple coding agents in their workflow. Try (and modify it) for free: https://github.com/getcrew44/crew44

Thanks for checking it out. I’ll be here answering questions 🙏

Congrats on the launch @zanderforge.

The demo looks really promising. This honestly hits a pain I feel constantly as a solo builder. Also genuinely appreciate the local-first, MIT and no-account approach. In this space, that combination is rare, and it builds trust before I've even tried the product.

I'm planning to plug Crew44 into one of my current projects and will come back with honest feedback once I've used it on a real workflow.

Wishing you lots of momentum from here 🚀

the model-per-role approach is the right call. most teams default to one model for everything which is like hiring one person and expecting them to be good at architecture, testing, and docs. curious how the memory compounding works across sessions though — does each agent retain context from previous projects or start fresh?

Hey Product Hunt,

Khaled here, founder of NeuralAgent.

NeuralAgent is used by tens of thousands of people worldwide, including Fortune 500 enterprises. 2.5 is the release I'm most proud of since we started.

We've been building toward this release for a long time and I'm genuinely excited to share it today. The feature I'm most proud of is Voice Mode. Talking to NeuralAgent and having it talk back while it controls your computer is the closest thing to Jarvis I've seen outside of a movie. Try it first.

Watch & Learn is the other one worth calling out, show NeuralAgent how to do something once, it saves the steps and repeats it any time you ask. We built it because explaining the same task over and over to an AI defeats the purpose of having one.
Happy to answer any questions about how we built it, where we're taking it next, or anything else.

This community has been a huge source of inspiration, grateful to be here.

"Research 30 competitors at once" is the Parallel Agents claim I want to see hold up — fanning out is the easy half, but the hard part is whether the 30 results come back in a shape I can actually compare, instead of 30 separate blobs I have to reconcile by hand afterward.

Computer-use is the frontier where demos look magical and the long tail eats you alive. The question I always have for this category: what happens when a button moves or a modal you did not expect pops up mid-task? Recovery from an unexpected state is the whole game once you leave the happy path. Curious how 2.5 handles that. Congrats on the 7th launch, that is real persistence.

We’ve had AI assistants for two years.

What we haven’t had is something I can actually hand a messy real-world task to and trust it will go do it inside my machine - consistently.

That gap between demo and “did it actually complete the work?” is everything.

Interested to see how NeuralAgent handles that gap in practice.

How well does it handle multi step tasks across apps?

Sensational for German TTS

Congrats on the launch guys!

How does it handle long numbers and addresses in mixed language contexts, like German with English product names?

Dont this support nepali language?

Congratulations on the launch guys! Few questions:
1. Websites list languages like Hindi to be supported but can not find any voice related to that on playground.

2. I checked the websocket streaming API for TTS.. is it possible to have multi context support (just like elevenlabs) in the streaming API? is that part of plan in future?

Cheers!

👋 Hey Hunters and Builders!

I’m Germán, and for the last ~1000 days (961 streak right now 😅), I’ve been deeply inside the PH ecosystem.

- Supporting launches.
- Commenting.
- Hunting.
- Launching own projects.
- Helping founders get visibility.

And something kept happening over and over again: I receive ~5 DMs every single day from solo founders asking:

“How should I launch?”
“Can you support my PH launch?”
“Where else should I post?”
“Am I doing this right?”

After 3 years, that became 5k+ supported projects. I genuinely love helping founders launch. But there’s a problem: launching is chaotic, emotional, and incredibly lonely when you’re building solo.

And honestly… manually helping everyone became unsustainable.

So I built plzsupport.me a launch copilot for solo founders 🚀

The idea is simple:

Give indie hackers and solo builders the structure, visibility, and support system they usually don’t have.

What it currently helps with:

* 300+ launch sites & directories to distribute your product and gain SEO backlinks
* Launch planning based on your stage, niche, and ICP
* Ready-to-use launch posts & promotion content
* Building your own supporter crew before launch day
* Reducing the “I’m launching alone” feeling

Most founders spend their energy building… and only a few hours thinking about distribution. I wanted to fix that.

This is not another “growth hack” tool. It’s more like a practical launch companion made from years inside the trenches supporting makers here.

What’s next:

* Launch score & readiness checks
* Supporter matchmaking
* Launch analytics
* AI-generated launch strategies
* Founder community features
* Public launch timelines/playbooks

Also it’s heavily inspired by the incredible amount of builders I met here on Product Hunt ❤️

Would genuinely love your feedback: What’s the hardest part about launching solo today?

@german_merlo1 The supporter crew piece intrigues me, but also worries me.

Product Hunt has historically cracked down on coordinated upvoting and engagement pods. How are you designing the supporter matchmaking so it generates genuine interest instead of becoming another vote-trading ring that gets launches penalized?

niceee

Is it a directory of places for launching or?

Ahhh! hopefully everyone will go wild with this! looks super helpful.. good luck mate @german_merlo1

How does Plz Support Me help you recruit supporters without spamming people?

The useful part here, to me, is not just the directory list — it is the launch judgment layer around it. Solo founders rarely need “more places to post” in the abstract; they need help deciding which channels match the product, stage, ICP, and credibility they actually have today.

I like that you’re explicitly pushing away from vote-trading/support pods in the comments. One thing that could make the supporter side healthier is asking founders to label why each person belongs in the crew: customer, beta user, investor, community peer, prior collaborator, etc. That keeps “support” tied to a real relationship instead of becoming generic engagement debt.