PH热榜 | 2026-06-23

Hi everyone, I'm Jens, co-founder of Bluerails. 👋🏾

I've spent my whole career watching how the internet decides who wins.

At GetYourGuide (2019 to 2021) I lived inside a textbook Web2 marketplace: aggregate supply, aggregate demand, match them, take a cut. It worked because the internet rewarded whoever owned the funnel.

So, at Passionfroot I built for that same world: software that gave creators their own storefronts. The market corrected us fast. Creators didn't want another storefront, they wanted demand. We pivoted into a marketplace and brought it to them.

Along the way I noticed something the playbook didn't predict: creators around the world were already asking to just get paid, as cheaply as possible. My first glimpse of where money was heading.

In 2025, I started Bluerails to build those rails for global companies and marketplaces. But the deeper we went, the more the ground shifted under the whole model.

The problem: discovery is moving into the AI layer!

• Nearly a billion people a week now ask ChatGPT what to buy, and increasingly the AI buys it for them.

• OpenAI, Google, Stripe, Mastercard and Coinbase are all racing to build agent led checkout right now.

• The new top of funnel isn't ads. It's being discoverable and transactable by AI agents.

And to be transactable, you need a checkout that humans and agents can both use, one that speaks FIAT and stablecoins natively, because agents transact globally, instantly, around the clock, in amounts and ways cards were never built for.

The solution: that's Bluerails, the checkout that brings companies into the age of agentic commerce.

One integration, and your business is ready for both the customer and the agent buying on their behalf, in dollars or stablecoins.

What makes us different:

Most tools in this space stop at telling you how often a chatbot mentions you. We don't just make you findable, we make you buyable. Discovery now, agent commerce next. No intermediaries, no commission.

What you can do today:

• Agent Score: scan any URL and get an instant agent readiness score. See exactly how discoverable and payable your site is to AI agents.

• Instant Fix: get your llms.txt and a schema fix so you can make your site agent bookable in minutes.

• Agent Analytics: measure real agent traffic hitting your site. Understand which AI agents are visiting, what they're trying to do, and how much revenue you're leaving on the table.

Who it's for:
Developers and product teams running sites with digital inventory. Content publishers, hotels, SaaS tools, anyone who wants to get discovered and paid by AI agents before their competitors do.

🎁 Special offer for the PH community: one free month.

We'd love your feedback, especially if you're building agents that need to move money. Drop a comment or reach me at jens@bluerails.com.

Huge thanks to @benlnfor hunting us, and to the PH community for the support. Let's make agentic commerce mainstream.

Jens

Hi Product Hunt! I'm Gurveen, one of the makers at Bluerails 👋

Coming from McKinsey, I worked across some of the world's most customer-obsessed companies. The best ones always had one thing figured out: exactly where their next customer was coming from.

That answer is shifting in a way most businesses aren't ready for. AI agents are increasingly the ones browsing, evaluating, and making purchase decisions on behalf of users. And right now, most companies are completely invisible to them.

That's the problem we're solving. Bluerails tells you exactly where you stand in the AI layer, how you compare to competitors, and what to fix; then takes you all the way through to getting discovered, booked, and paid by agents directly.

Try our free Discovery report today and see how visible your business is to AI agents. Would love to hear what you find! 😊

Gurveen

Most "get discovered by AI" pitches boil down to structured data and a sitemap. The payment layer is what makes this different, and also where I'd want to understand the mechanics better. When an agent "pays" you through Bluerails, what's actually happening: is there a wallet layer, a per-query micropayment, a subscription the agent operator sets up in advance? And on the discovery side, curious whether you're building a proprietary index that agents query directly or whether you're influencing how existing models surface your content through retrieval.

Great launch :) The story behind Passionfroot and marketplace evolution makes the vision feel credible.

As a SaaS founder, I genuinely don't know how discoverable my product is to AI agents today. Interested to try this out.

Congrats on the launch 😊 The combination of discovery analytics and payments makes this feel more complete.

This stuff is going to be so crazy relevant to businesses in the future. „I’ve found you through chazgpt / Claude research“ is ALREADY out of date

We've been hitting walls for optimizing our ranks for the the AI chats. This is useful! Will surely give it a shot.

Congrats on the launch, @j_mannanal & team!

I'm loving the free report!

This reminds me of early SEO tooling. Feels like we're watching a completely new optimization category emerge.

Hi @gurveen_ghai congrats!

Super interesting, do you also have api support?

Amazing launch, congratulations! Do you already have plans for how pricing will look like for this? Couldn't find it on the website :)

Amazing launch!! Would this also work for individual airbnb listings?

Super interesting! Could this be used for my father‘s hotel?

Super cool to see this - can’t wait to try it 💪🏻

Will this help me collect pizza money from my deadbeat friends who still owe me pizza money for a birthday party I hosted in 2023?

Was at Stripe Sessions recently and agentic commerce was a hot topic! Big potential for people who especially start early and grow with it. Congrats on the launch!

The shift from human buyers to AI agents as the discovery layer is underrated — most tools are still optimizing for Google. Congrats on the launch!

The 'discoverable to AI agents' half is the sleeper here, and underrated in most of the AI-visibility conversation. Everyone's still optimizing for human SEO; almost nobody's asked how an agent acting for a user actually PICKS which business to transact with. I build an agent that makes calls and bookings for people, and that selection step is the whole ballgame — today it basically inherits search results.

So the sharp question: once real money routes through a visibility score, you've created the strongest incentive in the world to game it — the same way SEO got gamed the moment it started moving rankings. What keeps the peer-reviewed score trustworthy when it's deciding where agent dollars flow? Is the 400-sample peer review the anti-gaming moat, or is there a verifiable fulfillment/settlement signal underneath it? That trust layer feels like the actual product.

Congrats on the launch — this is a real frontier.

Curious how you think defensibility evolves here. If agent-ready checkout becomes a standard layer implemented by Stripe, OpenAI, or Shopify, what remains the durable moat for Bluerails?

'Discovery now, agent commerce next' makes sense, most agent-payment tools jump straight to checkout without figuring out how the agent even finds you.

How do you handle category bias with the 400 samples? A hotel and a SaaS tool need completely different evaluation criteria.

Congrats on the launch! Moving past simple brand-mention tracking into an actual discovery score with native fiat/stablecoin checkouts is a massive step forward for agentic commerce.

How exactly does your analytics tracker differentiate high-intent purchasing agents from standard scraping bots or basic search crawlers?

@benln Congrats on the launch. Really interesting take on where commerce is heading.

Quick question, do payments stay fully on-chain or are you converting to local fiat automatically for merchants?

Spend my whole day in attack surface management making sure agents can't find my stuff. Wild to see one where getting discovered by an agent ends in a payment instead of an incident report. refreshing change of pace, time to point them at our terraform deploy script repos.

Congratulations on the launch! 🎉

Quick heads-up: the "Book a Demo" CTA in the footer doesn't seem to be working on my end. Might be worth checking so interested visitors can reach you without friction.

Congrats on the launch! Thinking about the wallet layer - when an agent books and pays via stablecoin and the booking for example then gets cancelled, or the rate moves before settlement, how does the reversal actually run?

the identity layer is the part most people will overlook. knowing which agents are visiting your site, how often, and whether they're converting is basically analytics for a post-search world. right now most businesses have no idea how much agent traffic they're already getting. that visibility alone is worth setting up even before you care about the payment side.

I'd love benchmarking against competitors. Knowing my score alone is useful, but context would help.

Hey everyone, I'm Daniel, the developer behind Cotypist.

First, a quick thank-you to the Product Hunt team. After Cotypist launched back in May, they reached out and invited me back for a featured relaunch. I'm honestly a little stunned by that, and very grateful to be here again.

A few years ago, I noticed I'd developed a weird habit: copying conversations into Visual Studio Code, just to get GitHub Copilot's inline completions, then pasting them back into the app I should have been writing in. After enough of that, it clicked: autocomplete shouldn't live in one editor. It should work wherever you write.

So I built Cotypist. It's smart autocomplete that runs locally on your Mac (no cloud, no API calls), in basically every app you type into. Install it, give it a minute, and you're writing faster everywhere on your Mac. No long setup. Tab to accept a suggestion, keep going. Words still sound like you.

You can download Cotypist today from https://cotypist.app; there's a free 30-day trial with all the features, and there's also a free plan for casual use after that.

During early access, Cotypist has become a daily driver for founders, marketers, support folks, novelists, physicians, academics, and long-time Mac users. People who type a lot of email, Slack, and AI prompts. Plus a long tail I didn't see coming: non-native English speakers, one-handed typists, and (this still blows my mind!) not one but two Neuralink brain-implant wearers.

What still surprises me about Cotypist, even after building it, is how often it feels like it's reading your mind. Or almost like a colleague finishing your sentences.

Happy to take questions about the product, where it works (and where it doesn't), what's coming next, or anything else. I'll be here all day.

—Daniel

I was in on the early release of Cotypist and saw it had great potential. It was then over-eager in the same way that the usual auto-correct is, causing a lot of backspacing. That is now totally gone with the tab completion. Start typing and it will provide a suggestion and if you like it, just press tab, but if not just keep typing. There's some tweaking when using apps with competing auto-corrects but that's not hard. If you've ever been in a relationship with someone where you get to the point of being able to ccomplete each other’s sentences, this will feel familiar. This goes into the day one new computer setup toolkit.

@daniel_a_a I'm curious why Cotypist doesn't the Apple Neural Engine at least as an option. It's much more efficient than inference on CPU/GPU, which would significantly improve concerns about battery life. It would also eliminate the obnoxious "chirping birds" sounds from coil whine as I type (M5 Max).

One of my favorite products!!! Can't believe I've been working without it all this time. It's on the same level as having a voice dictation app — absolutely essential. Once you start using it, you'll never go back.

Running a local Gemma model system-wide without choking the GPU is an awesome engineering feat. The privacy angle is a no-brainer, but honestly, just being able to tab-complete in my native flow across Slack and Mail sounds like an instant workflow upgrade.

Out of curiosity, how does Cotypist handle low-level conflicts with native macOS auto-correct features?

Hand-wrote 51 cold outreach emails to higher ed in CO last week, would have sold a kidney for autocomplete in my own voice. then I see it's Mac only while I'm staring at my Windows taskbar. genuinely cruel. someone port this.

How do you keep the first suggestion after an idle pause from eating a cold-read penalty? Do you pin a hot subset or just accept the occasional slow first token? Great work, you guys are on the right path!

Cotypist has completely transformed how I work every single day. Typing is something we all do constantly without thinking, but Daniel has turned it into a seamless, high-productivity experience. For me, it means significantly less effort and a massive boost in speed—honestly, it feels like the app flows with my rhythm and sometimes even refines my thoughts as I type.

The real test of a great tool is how much you miss it when it’s not there. Once you adopt Cotypist, you simply cannot go back. In fact, whenever I have to switch over to a Windows machine or an Android phone, I instantly feel a bit irritated because the experience just feels clunky without it. It has truly become a 'day-one' essential for me.

Highly recommended!

Changed the way I type. Forever.

How Cotypist handles different writing styles across work emails, Slack messages, and personal notes??

Congratulations on the launch! I've been using it for a few months since David Sparks (MacSparky) recommended it. Looking forward to the next stages of this brilliant app 🚀

What I found interesting is that typing is one of those things everyone does all day, yet most of us never think about improving it. Small gains in speed and accuracy can compound surprisingly fast over time. Nice reminder that productivity isn't always about adding more tools.

I love it so much I wish I had it on my phone.

This app is incredibly useful and I use it daily. It’s crafted with great care.
Keep up the good work Daniel!

Did you ever compare Copilot's autocomplete and your own to see which one felt more like you? Curious to hear how most mainstream models compare in terms of speed, 'correctness', and any quirks they might have trying to do a similar thing. I've definitely fed Claude my slack + conversation history and told it to build a tone profile and write things like I would but it didn't really sound too much like me haha.

It's a really great app. I've been using it for a month, and now I can hardly imagine working without it.

This is one of my favorite apps because it not only helps me type, but also helps me think about what I could type next. It’s like having a personal assistant when I’m stuck for words. It’s been great to see its development over the last year. I’m not sure what else could be added to it, but I’m quite sure that the developers have some tricks up their sleeve.

Local and in my own voice is the exact reason I'd turn this on. Cloud autocomplete always felt off inside Mail and Slack. Does it learn a style per app or share one across everything?

I've been using @Cotypist since one of the earliest versions, and it’s been a game changer for me. Can you believe that the sentense you just read was written 100% by @Cotypist? ;) Using tabs to accept suggestions is so much faster. I'm addicted to it! Many many many thanks to @daniel_a_a and a lot of hopes that the product is going to grow and find more users and traction. More people should know about it!

The system-wide angle is what actually makes this interesting - not just autocomplete in one app but everywhere you write. I keep context-switching between Slack, email, and docs all day and having suggestions that follow you across all of them without sending anything to the cloud is a real differentiator. Curious how it handles technical jargon and product-specific terms - does it adapt just from usage patterns or is there a way to seed it with your own vocabulary?

"Autocomplete shouldn't live in one editor" is such a clean framing — the copy-into-VSCode-and-paste-back habit is painfully real. The local-only choice is what makes me trust it; I build voice/chat agents and privacy is usually the first objection. Question for you Daniel: how does "in your voice" stay accurate when it can't phone home — does it learn per-app (my Slack tone vs my email tone differ a lot), or is it one global style profile?

It's become a standard part of my tool kit. It keeps surprising me by suggesting not the most generically likely completions, but ones ones that are relevant to what it's learned about my style andmy topics. It's a genuine time saver.

It also ticks the boxes for privacy, starting with the fact that its AI magic happens on your Mac.

Cotypist is awesome. I can't get through 5 minutes without it. I use it constantly. I used to use Fixkey, but Cotypist is on a whole other level. It helped me write this post!

Since it operates globally across all text inputs, how does Cotypist handle low-level conflicts with native macOS auto-correct features or spelling engines in apps like Obsidian or Mail? Do you actively filter out system suggestions to prevent visual overlapping, or does the tab-completion mechanism override them? Congrats on shipping a very much needed product!

Cotypist is great! It is even helping me to write this comment.

The way it is able to so often anticipate what I want to write next is amazing. While at the same time, quickly taking into account changes as I type too.

Excited to see how it evolves going forward.

I should note that the free plan works a lot better than you might expect, especially if you're using CoTypist for auto-suggestions and not auto-complete. 100 completed words a day sounds like nothing, but in practice I've not hit that limit yet and I use CoTypist everywhere.

the "no cloud, no API calls" part is what makes this interesting. most AI writing tools send every keystroke to a server, which means your drafts, emails, and half-formed thoughts are all sitting in someone else's logs. running inference locally sidesteps that entirely. practical question though, how large is the model and how does it handle the tradeoff between suggestion quality and system resource usage? i'd want autocomplete that's fast enough to not interrupt my typing flow, but local models can get heavy on older machines.

Local-first is the right call for this category. The apps where autocomplete helps most are also where people write sensitive or unfinished material. The Mac-wide layer is the hard UX: suggestions useful enough to accept, quiet enough not to fight the writer.

I absolutely love it. The best part is, that it's constantly improving noticeably by itself. In addition, the developer is very responsive and adds cool features on a regular basis. Absolutely recommended.

The best productivity tools disappear into the background. Cotypist seems to fit that philosophy really well. :D

Hey Product Hunt community 👋

Coco here, co-founder and CEO of @OpenArt AI . We are excited to share with you what our team has been working on for the past few months - OpenArt Director.

Last year, we launched OpenArt One-Click Video Story on Product Hunt with one belief: AI video should make visual storytelling accessible to everyone.

Since then, we’ve watched millions of creators use OpenArt to make music videos, character stories, social content, and cinematic films.

Along the way, we learned something important: creators didn’t just want a one-click result.

They wanted to shape the story.

They wanted to guide the pacing.

They wanted to refine the video until it felt right.

They wanted to direct.

So today, we’re launching OpenArt Director - the next step in our vision for creative storytelling.

Director is a creative environment for creating cinematic videos through natural direction and conversation.

Start with an idea, script, image, song, voice, product, character, or even just a feeling. OpenArt Director helps you develop the story, generate the video, and refine it through conversation - while maintaining consistency across characters, visuals, voice, music, sound, and style.

Whether you're making a short film, a product ad, a music video, a social campaign, or simply bringing a story to life, Director keeps you in the creative seat from beginning to end.

You’re not prompting anymore.

You’re directing.

Director is a continuation of the same mission we've been pursuing from day one: making visual storytelling accessible to everyone.

We'd love for you to try it out and tell us what you think ❤️

Coco

The "direct through chat" framing is interesting because the hard part of cinematic video isn't describing what you want, it's maintaining consistency across cuts. Shot matching, lighting continuity, subject appearance staying stable from scene to scene. Curious whether OpenArt Director handles that continuity layer or whether you're essentially getting a series of independent generations that you have to stitch and grade yourself. Also wondering what "cinematic" actually means in terms of the underlying model, whether you have control over aspect ratio, frame rate, and depth-of-field behavior, or whether those are fixed aesthetic presets baked into the output.

Keeping characters and style consistent is impressive. Can this work with existing footage and assets, or is it designed primarily around AI generated content? Congrats on the launch!

Is there a real learning curve here, or is the first video genuinely something I could make in an afternoon with zero prep? Asking as someone with no editing background.

Just cut a demo video for an enterprise deal, spent more time corralling clips than actually telling the story. "directing not generating" is exactly the gap. if it nails continuity over 5 min that's a real unlock. Would love to be able to stitch product demo video with B-roll and some character environment interactions so we could have laptop-side conversations that lead into product demo segments.

Love watching OpenArt Evolve from one click stories to actually letting creators shape the whole thing, directing a 5 min video just by chatting feels like a real unlock.

Huge Congrats on Launch! @OpenArt AI

If I make videos with Director and monetize them on YouTube, am I allowed to? Any restriction on commercial/monetized use of the output?

If I already have a finished script, does Director help me visualize it as-is, or does it want to rewrite the thing on me? I'm protective of the words.

Most AI video tools feel like prompt engineering with extra steps. This is the first one that reads like a genuinely different philosophy, not just a nicer wrapper.

Filmmakers and marketers probably use something like this completely differently. Curious whether you're seeing that split already.

I've tried quite a few AI image tools, and the challenge is rarely generating the first image, it's getting from 80% to exactly what you had in mind. I like that OpenArt seems focused on giving creators more control over the creative process rather than just generating images faster.

How does it handle reference images? If I feed it a character design, does that character actually persist across the full five minutes, or does it quietly drift by the end like most tools do?

Great launch! Congrats to the team!

"Vibe Directing" is such a cool concept. I'm so excited to see the continued progress in the field! I remember often that this was a science experiment 4 years ago, and a bit of a joke 2 years ago. Today, AI creative is starting to get really good... not perfect, but really really good. At this rate of improvement, 2027 is going to be mind-blowing!

I have seen many platforms... Used many of them to give a visual treat to my audience, but it's sometime complicated when giving the instructions and explain the complex scene, but honestly the @OpenArt AI and specially openart Director it's just wow 🤯 now I can save my time, only I need to chat about the story and the special requirements. Honestly if you guys can open a new thing like this im expecting more ❤️ ( future of the Big screen industry )

Character consistency across a 5-minute AI video is genuinely one of the hardest problems in this space - every tool I've tested struggles with face drift and lighting shifts between cuts. Would love to know how Director actually handles it under the hood, and whether that 5-min cap is a hard ceiling or just where quality starts degrading noticeably. Also curious what the chat-based direction looks like in practice - are you describing shots and moods in plain language, or is there more structured control for specific elements like camera angle or pacing?

This gave me super powers, as I never thought I'd be "creative media person". Awesome product and launch!

So excited to see what people create with OpenArt Director!

How granular is the brand integration, really? I need it to hold a tone of voice and a specific typographic system across a whole series, not just slap a logo in the corner.

The gap between having an idea and actually being able to explore it looks a lot smaller here than anywhere else. That gap is where most of my ideas usually die.

Finally an AI tool that creates complete story arcs, not just random short clips.

This is awesome! I've used OpenArt before, but I can't wait to try out the new feature for making some longer form ads.

Love that I can iterate on a scene without regenerating the whole thing. Saves so much time and credits.

Congrats on this launch! It's amazing!

Congrats on the launch, I really want to try it, hopefully I'll find an easy tool to give life to some of my cinematic ads ideas! What's the pricing (translated in minute or number of videos I can get ideally)?

I keep wondering if the future of AI video is just fewer prompts and more actual conversation. This reads like a bet on exactly that.

If I come in with a rough storyboard already done, does Director throw it out and do its own thing, or actually build on what I bring?

Most AI video tools are prompt roulette. OpenArt Director actually feels like directing—storyboard, shots, timeline, export. The AI collaborator has taste, not just templates. I went from a rough idea to a finished short without juggling five apps. Finally, AI video that thinks in scenes, not slots.

Hey there, it's Cesar, founder of Latitude.

Until now, companies have focused on collecting quantitative data about their products: user counts, churn rates, conversion. Qualitative insight was reserved for corporates who could afford to hire an agency. But agents changed that. We have the single most valuable source of knowledge about our product sitting right in front of us, and we're not using it. No one at your company talks to your users as much as your agent does. Latitude exists to tap into that.

Latitude does 3 things:

1. See what your agent really does in production

Latitude clusters thousands of conversations into one clear picture: what people ask for, and where they hesitate, escalate, or drop off.

2. Catch what's breaking before users do

When your agent keeps failing the same way, Latitude collapses those moments into one signal: the problem, how often it fires, and why. It detects issues automatically, or you set your own. Either way, you hear about problems first, and evals are created automatically for each signal.

3. Fix it without leaving your editor

The MCP server brings your signals, traces, and searches straight into your coding agent. Turn real failures into a dataset and verify the fix worked before you ship.

Latitude is open source and MIT licensed. Try it at latitude.so

Congrats on the launch!
Does the install track historical sessions too, or only sessions going forward from when you run the command?

The "gives your coding agent the tools to fix it" line is what I'd want to see in practice — most observability tools stop at surfacing the failure mode. When Latitude clusters a set of failures, how does that get back into the coding agent: an MCP server, a CLI, or a generated eval/test the agent runs against? And since it's open source, can I self-host so the production conversation traces stay in my own infra, or does evaluation route through your hosted backend?

Honestly the part that gets me is the signal going back into the editor. i don't need another dashboard to ignore. running cc across a repo per client and the dream is catching the dumb stuff before the client does. Imho this is the right tool for people serious about AI agents!

My Claude-Gmail agent ghosted me at an approval gate mid-campaign and I spent way too long not knowing why, reconnecting everything, before giving up. "most tools give you logs, Latitude gives you issues" hits different when you've lived it. following.

Per-turn token cost breakdown is exactly what's missing from most setups.

Can you see which specific tool calls or subagents are the worst offenders, or is it more of an aggregate view?

The useful bit here is closing the loop from failure mode to a runnable fix, not just another trace dashboard.

For agent monitoring, I’d want each clustered issue to produce a small acceptance case: trigger, tool/write that failed, expected boundary, and proof the fix changed behavior. Is that what the MCP server hands to the coding agent?

the framing of agent conversations as qualitative data is really sharp. most teams just look at error rates and latency, but the actual content of what your agent says to users is where the real failure modes hide. curious how you handle the evaluation of subjective quality — like when an agent is technically correct but the response still feels wrong to the user?

Logs vs issues is such a clean way to frame it. Nobody actually reads logs. Failure modes with evals attached is the thing you fix.

Great work! How does this connect back to the development workflow, any process to do evals to validate the issue is actually resolved before deploying?

Solving one of the most difficult parts when shipping AI agents!!! How to extract bugs, fixes and improvements from your traces...

This team rocks 🚀🤘

Congrats on the launch, Cesar! The "cluster conversations into failure modes" piece is the part I'd get the most from. One question from running agents that deliberately hand off to a human: how does Latitude tell a real failure apart from a correct escalation? In our setup the agent is supposed to stop and route anything sensitive — refunds,

account changes — to a person, so a "drop-off" there is it doing its job, not breaking. Does it learn which escalations are intended vs the agent actually giving up?

The MCP-into-the-coding-agent piece is the clever bit, and underrated in the thread so far. Most observability tools die at the dashboard — signals pile up where nobody looks, so failures just rot. Routing the signal to where the fix actually happens (the editor) is the real unlock; detection was never the bottleneck, action was.

One sharp question on that loop: when you auto-generate an eval per signal and hand it to the coding agent to fix against, how do you keep the agent from overfitting to the eval — patching the specific failing cases rather than the underlying behavior, so the cluster 'closes' but the real issue persists? Curious if there's a held-out/regression check or a human-in-the-loop on the generated evals. That's the failure mode I'd worry about most with auto-fix.

Congrats on shipping this — genuinely needed.

This is a super clean approach to agent observability! Triage is a nightmare when you're just staring at a massive, unorganized stream of logs. Grouping traces into auto-clustered issue datasets makes finding where a trajectory went wrong way faster.

How does Latitude handle automated regression testing once a fix for a specific trace issue is pushed?

For agent systems with non-deterministic outputs, how do you define failure in a way that's consistent enough to monitor reliably at scale?

How does Latitude differentiate a genuine failure from an agent that's thinking out loud through a messy but ultimately correct reasoning path?

While most agent tools stop at dumping logs, auto-building an eval from each failure cluster looks totally spot on! One thing I'd poke at - how do you stop those auto-evals from overfitting to the exact transcripts that triggered them instead of the general failure mode? Thanks!

The phrase all the ways your agents fail is ambitious is failure detection here pattern based on known anti patterns or does it learn failure signatures from your own agent's history over time?

the "issues not logs" framing resonates. i've lost hours scrolling through agent execution traces trying to find why something broke, only to realize the actual failure happened 6 steps earlier. how do the evals work here, do you define failure criteria upfront or does it infer patterns from the traces?

Congrats on the launch! <3 desde bcn

The issue abstraction is the important move. Raw traces are necessary, but small teams need a release gate: is this failure mode understood, reproducible, and covered by an eval before we ship again?

The clustering of conversations into discrete failure modes is the clever part. Most observability tools dump raw traces and leave you to find patterns yourself. We've spent time manually sifting logs to spot recurring failures. How does the automatic issue detection work? Does it use embedding clustering on trace outputs, or is there a rule-based approach?

What's the security model for agent run data especially for teams whose agents touch sensitive internal systems or customer data?

Have you tested this against multi agent systems where failures cascade across agents rather than staying contained to one? That seems like the harder problem.

What does onboarding look like for an existing agent fleet is there meaningful setup required or is it closer to drop-in instrumentation?

Is there a self hosted deployment option for teams with strict data residency requirements around agent logs and traces?

How does Latitude handle false positives? I'd imagine flagging failures that are actually intentional edge-case behavior could get noisy at scale.

What kinds of failure modes does it catch that traditional logging/observability tools typically miss?

Hey Product Hunters! 👋

I'm Tsifei, co-founder & CTO of Thumbmagic.

We launched here before, but this version is a different beast entirely.

So we're back.

The problem hasn't changed: Your video quality doesn't matter if nobody clicks. And great thumbnails are slow, expensive, and creatively exhausting to make consistently.

Thumbmagic solves this in one click.

Drop in your YouTube, TikTok, or Instagram video link then our AI watches it, understands it, and generates a high-CTR thumbnail tailored to your content.
No brief. No designer. No back and forth.

What's new since our last launch:

🎨 AI Designer — One click. One stunning thumbnail. Done.

🔁 Replicate — See a thumbnail you love? Clone its style for your own content instantly.

📱 All Social Media — Paste any YouTube, TikTok, or Instagram link and go. No manual uploads.

🎭 Faceless Thumbnails — Running a faceless channel? We've got you. Full thumbnail generation with zero face required.

Who's this for?

• YouTubers testing 3+ thumbnail variants per video

• Short-form creators publishing daily across TikTok & Reels

• Faceless channels that can't rely on personality-driven design

• Agencies and teams producing thumbnails at scale

We've seen creators go from spending 2+ hours per thumbnail to under 60 seconds.
That's not a feature.
That's your Sunday back.

Would love your honest feedback — what's working, what's missing, what you'd pay for.

Drop it below. 👇

Congrats on launching! Generating high-converting thumbnails straight from a YouTube or TikTok link is a massive timesaver for creators who hate shifting back and forth between different editing apps. The style replication feature is a great touch for keeping a consistent brand.

Quick question on the replication feature—does it actually analyze the font type and structural layout of the reference thumb, or is it mostly just cloning the color vibe?

Run a small game studio on the side, have personally agonized over Steam capsule art at 1am wondering if it converts. "engineered to perform" beats "looks good" every time. the data-first angle is the right call, I always wind up pulling competitor pages and generating like 5 variants but 200 data points beats 5 easy.

Thumbnail design feels simple until you try to consistently get people to stop scrolling 😅 What I like here is the focus on the first impression rather than the content itself. What's the biggest mistake creators make when designing thumbnails?

Being part of the Thumbmagic journey has been genuinely exciting. When I first saw what the product could do, I knew this was something different! Not just another AI tool, but a real solution to a problem every creator deals with every single day.

What makes me most proud isn't just the product though. It's the team behind it. Everyone here is obsessed with building something creators actually love, and that energy shows up in every feature shipped, whether its Submagic or Thumbmagic!

Thumbnails are the first thing a viewer sees. We're making sure they're never the reason someone doesn't click on your video. That's a mission worth showing up for every day.

Grateful to be here! ✨

This is exactly what I have been looking for, I have been posting on YT for a couple years now but having an easy affordable way to create Thumbnails is definitely something i need instead of hiring people because my pockets are not that deep.

The gap between a decent thumbnail and a viral one is pure pattern matching most of us don't have time to learn. Training on top performers is the obvious play. Does it adapt to a channel's existing look or start fresh each time?

Congrats! what are you using to decide what to generate?

Congrats on the launch! Curious what factors go into deciding what thumbnail to generate?

Have you tried any testing on YT, and how did they actually perform? :)

Creating thumbnails are always so time consuming so having something to make this faster will be helpful! Is there a way to maintain consistency between thumbnails created (e.g. colour, font, etc)?

Does it look at trends regarding heavily trafficked thumbnails and base the designs on that? Or is it only based on the content of the video?

Training on what actually drives clicks (vs generic templates) is the right wedge — most thumbnail tools optimize for "looks nice" instead of "performs." Question for you Tsifei: where's the line between high-CTR and clickbait? If a thumbnail wins the click but the video underdelivers, retention/algorithm punish you. Does Thumbmagic optimize purely for CTR, or does it factor in matching the thumbnail to what the video actually delivers?

"Generic templates vs data" hits the nail on the head, most AI design tools end up there because data is just harder to handle.

How does it work across niches? A finance creator thumbnail looks completely different from a tech reviewer thumbnail. Do you segment the 200+ analysis by niche, or are there generic CTR patterns that work everywhere?

Congratulations. Nice product.

Creators spend countless hours making something great and then a few minutes thinking about how people will discover it. Interesting approach to solving that problem.

Wow guys! it's really awesome. Currently analyzing many Youtube channels and noticed how key can be having viral thumbnails. I'm sure this is coming to change that game. Wish you all the best!

This solves a real creator problem. Making thumbnails consistently is often harder than making the video itself.

Can Thumbmagic generate multiple thumbnail concepts optimized for different audience segments?

Smart! Very useful!

Congrats team Thumbmagic! The combination of video analysis plus thumbnail generation feels much more useful than template-based tools.

I'd love more control over typography and layout after generation. Great launch regardless :)

Faceless channel support caught my attention. That's a growing niche most thumbnail tools seem to ignore.

The replicate feature is smart. Most creators save examples anyway, now the workflow feels much faster 😊

Congrats on the launch. How the AI decides which thumbnail elements actually contributed to high-performing click-through rates?

Love the idea. Just wondering how much control creators have over the final design style and branding. Congrats and good luck!

thumbnails decide most of whether a video gets the click and most creators phone them in. does it learn from a channel's own winners or just generic top performers?

Hey Product Hunt! I'm @lordhasanali , CEO of weya AI.

We watched great voice AI fail in production, over and over, not because of the model, but because of the audio. Noisy environments, competing voices, background hum. Nobody was solving this properly, so we did.

Introducing Hush, our first in-house open-source speech enhancement model, which:

• Isolates the primary speaker and removes everything else in real time
• Runs entirely on CPU, under 1ms per frame - no GPU needed
• Language-agnostic - works across all spoken languages out of the box
• Apache 2.0 - free to use in production today

We launched at #5 on HuggingFace's Audio-to-Audio leaderboard, and this is just the start.


We'll be here all day answering questions. Try it, break it, and let us know what you think!

Hey everyone 👋 I'm the maker of Hush. Here's the story behind why we built it.

We build Voice AI at Weya. AI agents that handle live phone calls for businesses. And the #1 issue that kept breaking our pipeline wasn't the LLM, wasn't the TTS. It was background speech.

A caller phones in from a busy restaurant. Their colleague is talking next to them. A TV is blaring in the background. What happens? The background speaker's words get picked up, transcribed, and fed into the AI agent as if the caller said them. The entire conversation derails.

We tried every open-source noise cancellation model out there: DeepFilterNet3, RNNoise, SEGAN, MetricGAN+, DNS Challenge entrants. They all do a great job suppressing stationary noise (fans, traffic, HVAC). But none of them treat a competing human voice as a first-class problem. When the interference is another person speaking, speech looks like speech in every feature these models have learned. They either let it leak through, or they suppress both speakers and destroy intelligibility.

So we built Hush from scratch to fix exactly this.

What it does: Hush removes both background noise AND background speech from live audio, isolating only the primary speaker. It's an 8 MB model that runs fully on CPU in real time (<1 ms per 10 ms frame), at 16 kHz (native telephony sample rate).

How we did it: We extended DeepFilterNet3 with one targeted change: teaching the encoder to distinguish speakers, not just speech from noise.

• Training data that reflects the real problem: 60% of our training samples include a competing human speaker mixed in. The model cannot pass training without learning to suppress speech that sounds like speech.

• Auxiliary Separation Head: A lightweight Linear(256→32) + Sigmoid head attached to the encoder bottleneck, trained with L1 loss to predict an ERB-domain mask for background speakers. This is a training-only objective. It forces the encoder to carry speaker-discriminative features without adding any inference cost.

• Production runtime in Rust: We built libweya_nc, a C-ABI shared library (Rust + tract for ONNX inference) that ships as a ~10 MB .so/.dylib/.dll with no embedded model. It shares compiled model weights across concurrent sessions via Arc<TypedSimplePlan>, so each session costs only a few KB of memory. Plug it into any C, C++, or Python application.

We trained on 10,000+ hours of mixed audio: LibriSpeech, VCTK, Common Voice for clean speech, DNS Challenge + FreeSound + ESC-50 for noise, and MIT IR Survey + OpenAIR for room impulse responses.

Why we open-sourced it: This gap exists because the benchmarks that drive open-source development (DNS Challenge, CHiME) measure noise suppression, not speaker isolation. Models optimized for those benchmarks are not optimized for Voice AI. We want to change that. Every team building voice agents, call centre bots, real-time transcription, or conversational AI systems deserves a model that actually handles the acoustic chaos of real phone calls.

The model, training code, Rust runtime library, and pretrained weights are all on GitHub and Hugging Face. MIT / Apache 2.0 licensed.

We're also fine-tuning a v2 optimized for even louder background noise and speech. Stay tuned.

Would love your feedback. Happy to answer any questions about the architecture, training, or how to integrate it 🙌

The CPU-only, sub-1ms-per-frame number is what jumped out at me. Most enhancement I've tried adds enough latency to break the natural turn-taking on a live call. We build voice AI that phones elderly parents at home, where the hard part is exactly what you describe: a TV going in the background, a spouse talking across the room, sometimes a hearing aid whistling. My question: when the primary speaker is quiet, slurred, or unsteady (pretty common with older users), does isolating them ever clip that softer speech? Planning to test Hush on some of our real call audio.

Sub-ms matters because voice UX breaks when the audio path gets clever but slow. The edge case I would watch is the handoff between suppression and downstream turn detection; a clean stream is useful only if it preserves the timing signals.

Thanks everyone for the amazing support so far! We're excited to hear your thoughts and answer any questions you have. Your feedback will help shape the future of Hush.

Most noise suppression libraries are built for human listeners, where "good enough" means the person on the other end doesn't notice. For voice AI agents the bar is different because the model is doing ASR first, and artifacts that a human brain filters out can wreck transcription accuracy pretty badly. Curious whether Hush is tuned specifically for that ASR pipeline use case or whether it's general-purpose suppression you're applying upstream. Also wondering how it handles near-field keyboard noise and fan hum during long agent sessions, since those tend to be the consistent offenders in real deployments.

Sub-1ms on CPU is the claim that matters most here and also the one I'd want stress-tested. What's the degradation curve? Does it hold at 1ms with a single stream, and what happens at 10 or 50 concurrent calls on commodity hardware? That's the production reality for anyone running voice agents at scale.

The open-source angle is smart for adoption but the real question is where the commercial model sits. Apache 2.0 gets you into production stacks fast. What's the wedge that converts users to paying customers?

Real-time noise suppression always involves tradeoffs - curious what the actual pipeline latency looks like end-to-end, not just model inference. WebRTC jitter buffers, chunking, and resampling all add overhead on top of the model itself, and for voice AI phone agents that budget is already tight with STT + LLM + TTS in the chain. Also wondering how it handles overlapping speakers mid-sentence vs. steady-state noise - that's usually where suppression models fall apart. How does it compare to what Deepgram or Twilio already offer natively in their voice pipelines?

Looks good! Congrats

This seems pretty useful. We would love to give it a try!

This is exactly the kind of voice-agent infra where the test set matters more than the demo clip. I would love to see three numbers side by side: added latency per frame, word deletion rate for quiet primary speakers, and false retention when a second speaker is louder than the caller. The open-source angle is especially useful if teams can run the same stress clips before deploying it into live calls.

What’s the latency like in real time calls, and does it ever clip or distort the speaker’s voice?

STEEEEEEEEEEAM MACHIIIIIIIINE

https://www.youtube.com/watch?v=0sa2R-PM0Uk

The first Steam Machines launched on November 10, 2015 starting around $449.

The new Steam Machine launches June 25 allocated to randomized pre-orders... so if you want one, get in now!

However, sticker shock might prevent you from jumping in, given prices start at $1,049 (512GB), with a 2TB model at $1,349.

Valve originally aimed for a lower, “affordable” price, but a global RAM/storage shortage drove component costs up and made that target “no longer viable.”

expect it to cost roughly the same as a PS5, but if that's the case, I'd probably choose the PS5 instead.

valve’s bigger contribution here is making linux less exotic for regular buyers, while continuing the software and driver work that helps everyone on linux, not just their own hardware. pairing that with hardware-software polish around things like hdmi-cec removes a lot of annoying setup gaps.

Valve tried this in 2015 and the original Steam Machines quietly faded - mostly because SteamOS game compatibility was spotty and the price-to-performance vs a PS4 or Xbox One didn't hold up. Starting at $1,049 this time around, I'm not sure the core problem has changed. A PS5 is still $499 and runs every game made for it. The "it's a full PC too" angle is compelling on paper, but that's also exactly what the original pitch was. What's the story on SteamOS compatibility now - are we actually at a point where the vast majority of Steam's library runs without workarounds?

Wanted one, but price for me is a no-go sadly (I know it's due to factors out of their control atm). Assembled my own mini-itx case for my pc for the meantime.

Thinking of getting a Bambu Labs printing and designing a similar sized case, would love to chat if people have done similar things.

Hear me out: The Steam Machine is actually a bargain. Game developers will be targeting this exact spec for years, and the form factor and quality of life benefits make it a unique proposition. Am I going to buy one? No. I already have a modest gaming PC and I don't need another one. But I think people who claim the Steam Machine is ridiculously overpriced just don't understand what the current market is like for components.

PC gaming is still weirdly complicated for a lot of people. A simple living-room-friendly Steam box makes sense if it keeps the freedom of PC without feeling like you have to build and maintain one yourself.

Ouf, any extra insight worth mentioning Chris?

Will this be ever launched in India?

Working in B2B operations, one of the biggest time sinks is building intake forms for vendor and supplier onboarding. Every buyer has different requirements and you end up rebuilding the same workflow from scratch each time.

What I'm curious about is how the AI handles multi-step conditional logic. When someone needs to route different document types to different reviewers based on certification status, does the AI App Builder pick that up from a plain description? Or does that kind of branching still need manual configuration?

Congrats guys!

What fascinates me is the behavioral aspect. Many people abandon projects because the blank canvas feels overwhelming. Turning the first step into a simple description could dramatically increase how many ideas actually become usable apps.

This is neat. What's the biggest type of app people are building with it right now?

Does it build mobile app only or web apps too?

How accurately can the AI infer complex permission structures when the prompt doesn't explicitly define access rules?

Interesting shift from building apps manually to describing them in plain language. I wonder how well the AI handles complex permission structures and edge cases that usually appear after launch.

Way too expensive. Have not tried it, but why would I if it's at price level with Claude, which is already the best, why bother?

Hey Hunters!

I’m excited to hunt Sakana Fugu today — a powerful new AI orchestration system from Japan 🇯🇵 that brings the capabilities of a full multi-agent team behind a single API endpoint.

Instead of relying on one giant model, Fugu intelligently coordinates multiple AI models and agents to solve complex tasks, handling model selection, delegation, verification, and synthesis automatically. The result is frontier-level performance while keeping the developer experience as simple as calling a single model.

🐡 What makes Fugu interesting?
• Multi-agent orchestration behind one OpenAI-compatible API
• Routes tasks to the most suitable models automatically
• Recursive agent coordination for difficult, multi-step problems
• Built-in resilience against vendor lock-in and model access restrictions
• Available in two versions: Fugu (speed-focused) and Fugu Ultra (maximum capability)

Whether you're building coding assistants, research tools, cybersecurity workflows, or advanced AI applications, Fugu aims to give you the power of an entire AI team without the orchestration complexity.

Congratulations to the Sakana AI team on the launch! 🇯🇵🚀

What would you build with a model that can orchestrate other models for you?

The 'dynamically orchestrates the world's best models' angle is genuinely interesting coming from Sakana - you've already shown that combining models can beat single-model approaches on specific tasks. Curious whether the orchestration decisions are fixed routing rules or if there's an adaptive layer that learns from task outcomes over time. Also wondering if there's cost/latency budgeting exposed in the API - so a workflow can say 'optimize for speed under $X per 1k tokens' rather than just defaulting to max performance.

Wow, Japan enters the AI space with a big leap :)

The single API part is the real hook for me. Multi-agent systems usually sound powerful until the integration starts feeling like a project of its own.

When keyword data, Search Console insights, content creation and publishing all happen in one platform which part of the workflow saving most time compared to using separate SEO tools?

Do you have a way to enforce a brand voice or editorial checklist before publishing?

Really impressed with Blazly SEO so far. It saves a lot of time by handling everything from keyword research to content creation in one place.

The platform is easy to use, and the content quality is surprisingly good. I especially like that it keeps helping optimize content after publishing instead of stopping at content generation.

Great tool for anyone looking to grow organic traffic without spending hours on manual SEO work.

Hey, how do you evaluate whether content sounds genuinely better versus just less AI-detectable?

The website offers too few features—you can only write five blog posts! Since other features cannot be tested, it is difficult to decide whether to make a purchase. This design choice is somewhat problematic; I suggest the site owner reconsider it.

Curious how does Blazly handle the cat-and-mouse game of staying ahead of AI detection algorithms that update their patterns weekly?

Hi Product Hunt 👋

I'm Tyler, the maker of Conduit

I kept adding MCP servers to my AI tools, and the more I added, the slower and less reliable my agents got. The reason surprised me: every MCP server loads its entire tool list into the model's context on every single request. On my setup that was roughly 24,000 tokens of tool definitions sitting in context before I'd typed a word, and the model discards all of it between calls, so you pay for it again on the next one.

Conduit fixes that. It's a local-first gateway that sits between your AI tools and your MCP servers. Each client connects to it once, and instead of exposing every tool, it exposes three meta-tools the agent searches on demand. The full catalog is still available; it just no longer sits in context on every request.

I benchmarked it on a real setup: 97% less tool overhead per request, around 90% fewer total tokens, at the same task success rate. The full method and numbers are in the repo.

This helps no matter how you work. On cloud models, those tokens are your bill. On local models, the tool definitions eat your context window. Either way, you stop paying for tools the agent never calls.

A few things I focused on:

• API keys stay in your OS keychain, never in a config file

• Nothing phones home, it's fully local-first

• Works with 17 clients today across Windows, macOS, and Linux

• Free and open source

I'd love your feedback, especially on which MCP servers or clients you'd like supported next. Thanks for checking it out 🙏

~90% token cut on MCP is a big claim and a real pain point. is that from trimming tool schemas/results or actual response compression?

The 90% token reduction claim is the part I want to understand better. Is that coming from stripping context on the client side before it ever hits the model, or are you doing something smarter like caching tool descriptions and only sending diffs when the schema hasn't changed? Those are pretty different architectures with pretty different failure modes. Also curious how Conduit handles situations where the MCP server schema changes mid-session, since a stale cached description passed to the model could cause subtle tool-call errors that are annoying to debug.

This is exactly the kind of agent-infra pain that is easy to miss until it becomes a bill or latency problem. The tool-list bloat detail is useful because it names a concrete failure mode, not just “too many tokens.” Curious: do you see teams wanting per-server/per-tool usage visibility too, or is the gateway meant to stay invisible once configured?

Hey, congrats!

Could you please elaborate on benchmarking the solution - how did you measure the success rate, and what benchmarks did you use?

The tool list bloat before you even start a task is so real, glad someone is fixing it at the gateway layer instead of inside each agent. Does it handle servers that change their tool list at runtime, or is the catalog cached per session? Congrats on shipping.

MCPs definitely eat into the token usage at ridiculous rates. How is your service different from other similar solutions?

The token overhead problem is real - 24k tokens before the agent does anything is a genuine waste. My question is about the 'same task success' benchmark scope. Tasks like 'list the projects in Vercel' are the easy case for lazy discovery because the right tool is obvious from the description. What happens on tasks where the agent needs to plan across tools it doesn't know upfront - say 'debug why my payment flow is broken' across Stripe, your DB, and Vercel logs simultaneously? Does the search-first approach still converge reliably, or does it end up doing multiple search round trips that eat back some of the savings?

This matches the pain from long-running agent work: the tool catalog becomes infrastructure noise. I like that the agent asks for the catalog when it needs it instead of carrying every tool description into every turn. Stale schema handling is the contract I would keep very visible.

The restraint is what sells it for me — AI that only speaks up after an error or a finished command beats an always-on copilot stealing focus. With Qwen, Phi-3, and Gemma all running on-device, how do you route between them: is each model pinned to a task (error-explain vs /ask vs next-command suggestion), and what's the rough resident memory footprint with them loaded? On a 16GB Mac that's the one thing I'd want to know before switching my daily terminal.

local model in the terminal is the right instinct — the cloud round-trip is what kills flow when you just want a quick command rewrite. the quality-vs-resident-size tradeoff is where this gets interesting.

Suggest and explain, rather than act, is the right default for a terminal. The terminal is too close to real damage for autonomy-first UX; the product earns trust by making the next step legible and still requiring intent.

Local and no cloud is what sells me. Keeping everything on my own machine beats having the smartest model. Nice work.

Looks great! Any plans to implement a quake mode?

@jebi unable to open after installing on mac

Can you tune how proactive it is or turn suggestions off for certain commands?

The terminal is such an interesting place for this because the cost of a wrong suggestion is higher than in a text editor. Curious how you're thinking about trust: does jebi mostly suggest/explain, or can it also take action directly inside the shell?

The re-read dedup looks like a clear win! QQ - when you inject a head_limit on a grep Claude ran without one, does the model see a "truncated, N more matches exist" marker? Does it read the capped list as the full set? Overall, very well done!

Claude Code users know how quickly context gets polluted with logs, repetitive outputs, and tool noise 😅 The idea of treating context as a limited resource rather than an infinite one really resonates. Curious... what was the most surprising source of context bloat you discovered while building Sipcode?

The same-session re-read dedup is the part I'd test first — re-reading unchanged files 6-8x is exactly what quietly poisons a long Claude Code session. How do you decide a file is "unchanged" between reads: mtime, content hash, or git state, and does that still hold when a subagent reads the same file in its own context branch? Caps on npm/tsc output make sense too, but can the model pull the full uncapped log on demand when it actually needs it?

Congrats on the launch, Anuj! As someone who lives in Claude Code all day (I've built a whole stack of custom skills around it), context bloat from verbose tool output is a very real pain, so a tool that caps it and dedupes same-session re-reads is solving something I actually feel.

Also have to call out the launch video. It's genuinely one of the best I've seen on PH, and the music gave me a wave of RPG nostalgia. Already chatted with you about it. Starring this and trying it on my setup today. 🌟

Claude Code rereading the same files and dumping huge logs into context is painfully familiar. I like that Sipcode tackles the boring cleanup layer instead of pretending a bigger context window fixes everything.

The dogfooding story sells this more than any benchmark — discovering your own drift tool read 624,940 tokens wasted while --stats credited 7,553 saved, then root-causing it to uncached mid-session installs and shipping Warm-Fill in 24h. Most launches would've quietly buried that. And the 38% duplicate-Read finding finally names that "why does this session feel sluggish" sensation I could never explain.

One question on the dedup: you canonicalize LF and BOM before the byte comparison. For files where whitespace carries meaning — Python, Makefiles, YAML — can that normalization ever flatten a real change into a false no-op, or is it strictly newline/BOM and never touches interior whitespace?

Congrats on the launch! Keeping Claude Code context clean is a very real pain point for anyone building with AI coding tools. I like the focus on sharper answers instead of just longer context. How are you deciding what should stay in context versus what should be summarized or dropped?

Context bloat is my #1 frustration with Claude Code in long sessions. You watch it re-read the same files and re-print npm install walls of text and by the end of a complex session the answers are noticeably worse. The 40% agent error reduction stat is the one that got my attention - quality lift is nice but errors are the thing that actually breaks workflows. The PreToolUse hook approach is smart because it intercepts before the context gets polluted rather than trying to clean up after. Installing this today. Does it handle situations where Claude Code genuinely needs to re-read a file because it changed, or does it dedupe those too?

The context window management problem in Claude Code is real. Long sessions accumulate dead weight fast, old tool outputs, abandoned approaches, redundant file reads, and once the context gets bloated the model starts hedging more and the answers get muddier. Curious whether Sipcode is doing something principled to decide what to prune (like deprioritizing failed attempts or stale file state) or whether it's more of a manual curation layer where you're telling it what to keep. Also wondering if there's any handling for cases where something that looked like a dead end earlier in the session turns out to be relevant again.

Hey, congrats!

A couple of questions.

Have you measured the quality performance somehow? I mean, the speed/quality on certain tasks.

Also - is it configurable be Claude to "disable" it if needed, if it things that the hook over-stripped the content?

Thanks!

BestDefense helps teams continuously test, understand, and remediate web application and infrastructure risk from one dashboard.

We built it because security is still too expensive, fragmented, and manual for many startups, SMBs, MSPs, and lean engineering teams. Most tools either scan, report, load test, or suggest fixes. BestDefense connects those steps: validate your site, run automated security and scalability tests, review clear findings, and use AI-assisted remediation to move from vulnerability to fix faster.

For Product Hunt: use code PHLAUNCH30 for 30% off your first month plus a free onboarding/security posture review.

This feels like a product built by people who truly understand the day-to-day frustrations of both developers and security engineers. Great job!

I've been using BestDefense.io for a few weeks now, and I'm impressed with how seamlessly it integrates into our CI/CD pipeline. The automated pentest feature is a game-changer - no more tedious manual testing or waiting for human experts to review our code. The AI-driven patching process has also saved us a significant amount of time and effort.

What I'd love to see next from the team is better integration with our existing monitoring tools, such as Prometheus and Grafana. This would allow us to get a more comprehensive view of our application's security posture in real-time. Has anyone else had experience with this?

Most automated pentesting platforms completely choke on complex authentication loops, like a multi-step login with a specific oauth provider. qq to@derek_foster5 can we record a login sequence flow via an extension or session token setup to let the agent past the login wall?

does it connect with gitlab?

The auth flow is where this gets real. For AI-assisted login, I’d want every run to leave a receipt: test account used, scopes, destructive actions blocked, and the proof that made a finding exploitable.

Otherwise the fix is useful, but hard to trust in a compliance review.

This looks interesting. Does it matter what architecture the deployment runs on? So, for example, a web, desktop or mobile application.

The part I'd want to understand before trusting this on every deploy is the patch side. When the AI generates a fix, what stops it from quietly changing behavior or introducing a regression while it closes the hole? Is there a verification loop that re-runs the original exploit against the patched build to confirm the vuln is actually dead? That feedback loop feels like the whole ballgame.

Hello Product Hunt!! 👋🏼

During my mental health journey, I rediscovered the joy of bird watching, bird photography - pretty much anything bird related. I joined communities and it seemed like they were all over the place. Then, I had to remember to login to all of those places and keep up. I wanted to try to bring as much of that together as possible.

Politics definitely played a role in developing this idea out. I was tired of seeing the negative stuff in my feed when i'm trying really hard to focus on the positive. You just can't escape it these days.

As for the app, it's very much community first focused, but it will help you identify birds and keep them in a very beautiful life list. The Discovery feature just launched this past weekend, but I've always been a fan of discovery in apps like Spotify and i've found some great artists that way. We'll be curating articles, podcasts, documentaries and other birding resources. The best part? We're linking directly back to the publication to encourage authors/creators to own their own content.

We're fresh out of Android beta and I still consider this day one. I hope you enjoy it and let me know if you have any issues.

@wildbirds any chance to get some more preview on the homepage? I was especially interested in the "Your life list". Homepage is interactive and beautiful, but for me it would need to show more before I actually sing up. Gotta catch them all!

Hi Product Hunt! I'm Victoria, founder of prepros.

After a decade as a Senior Graphic Designer and Art Director at lululemon, Aritzia, and a DTC home goods start-up, I was on hundreds of sets and planned countless shoots. What I could never wrap my head around was the lack of structure around production planning. No single tool, no standard process. Every team built their own version from scratch.

So we built prepros. A focused workspace for creative teams running shoots. Moodboards, shot lists, call sheets, crew coordination, budget, and shoot day tracking, all in one place.

Would love to hear your thoughts, answer any questions, and connect with anyone who's ever felt the pain of managing a shoot across a dozen different tools.

Thank you for the support. It means everything on launch day.
Victoria

Looks solid. How's it handle shoot day chaos when the brief changes last minute?

Hey Product Hunt 👋 Khaled here, founder of NeuralAgent.

NeuralAgent is an AI that actually uses your computer, it sees your screen and controls the mouse and keyboard to get real work done across desktop and browser apps.

What's new in 3.0: this is the release where NeuralAgent gets fast. Our previous versions were about being able to use your computer, 3.0 is about doing it at lightning speed, powered by our new purpose-built Fast model.

We built the Fast model for one job: executing UI actions, clicks, typing, navigation, at ~285ms each. It's paired with a reasoning model that plans and supervises, so the Fast model flies through routine work while staying smart enough to recover when something goes wrong.

⚡ Quick update: it's gotten even faster since the launch video above was recorded! Here's a more recent one, NeuralAgent sending a WhatsApp message in real time. Keep an eye on the cursor, that's the Fast model executing at full speed. In the normal flow, it plans the task once and lets the Fast model fly through the execution, only re-planning when something goes wrong and the supervisor steps in to recover.

https://www.youtube.com/watch?v=imFEuXv7meM

Computer-use agents have been useful but slow, you sit there watching them think, click, and wait. We wanted it to feel instant.

Also new in 3.0: Fast Model Replays, do a task once, save it, and re-run the whole thing in seconds by text, voice, or a chat mention. Important part: a Replay is not a recorded macro. It doesn't fire saved coordinates like RPA, on every run, the Fast model re-analyzes the live screen and grounds each action fresh, so it adapts when the interface changes instead of breaking like a brittle script. You get the lightning-speed of a saved workflow with the resilience of a model that actually sees what it's doing and a supervisor that makes sure that the task was executed. And Smart Model Routing sends each step to the right model, so even long multi-step tasks stay fast.

And this sits on top of everything NeuralAgent already does, Watch & Learn to turn the way you work into reusable workflows, a library of Skills (Google, Excel, PowerPoint, PDF, coding, research), scheduled workflows, and Enterprise cloud computers for running UI work at scale.

A bit of context: NeuralAgent is used by tens of thousands of people around the world, and we're genuinely excited to launch this today.

The demo above is real-time. I'd love your feedback, and I'll be here all day answering everything 🚀

For more Fast model demos:
https://www.youtube.com/channel/UCXGEZyKZZMZzOkTAA_r5TVA

Because screenshot → model → action chaining causes most agent stacks I've attempted to sit closer to multi-second loops, ~285ms for UI action execution is a meaningful benchmark if it's end-to-end from intent to click. What is the usual action granularity (single click vs. multi-step procedure) and is that figure based on local inference? I'd also like to know how you handle partial failures when a native app doesn't expose standard accessibility trees or when the DOM changes. Is the action layer proprietary or is it open-sourced?

~285ms for UI action execution is a meaningful benchmark if it's end-to-end from intent to click — most agent stacks I've tried sit closer to multi-second loops because of screenshot → model → action chaining. Is that number measured on local inference, and what's the typical action granularity (single click vs. multi-step workflow)? Would also love to know how you recover from partial failures when the DOM shifts or a native app doesn't expose standard accessibility trees. Are you open-sourcing any of the action layer, or keeping that proprietary?

The replay feature feels practical. Computer-use agents are useful, but saving a task and rerunning it quickly is what could make them part of a real workflow instead of a one-off demo.

Love the Fast model plus reasoning model architecture, I am a little curious about, when the supervisor steps in to re-plan after an error, how much latency does that recovery typically add to the flow?

A lot of computer-use demos still feel like watching a slow remote desktop session. The Fast model + supervisor split is the bit that makes NeuralAgent feel practical, not just impressive in a demo.

The replay architecture is the most interesting thing here. Not-a-macro is doing a lot of work in that claim though. If the Fast model re-analyses the live screen on every replay run, what's the latency cost versus a recorded macro when the interface hasn't changed? And when it does adapt to an interface change, how does it signal to the user that it deviated from the original task path rather than silently doing something adjacent?

The brittle script problem is real. Curious how you surface the cases where "adapted" actually meant "got it wrong."

The plan-then-confirm boundary is the right design instinct. The question is whether you've drawn the line in the right place.

"Research brief" running on one click feels underspecified. If Buddy is searching the web, summarising sources, and writing a document, that's not obviously safer than sending an email. What's the actual definition of "safe" here and is it user-configurable or hardcoded by you?

The bigger validation question: how many of your current users have actually let Buddy execute a task rather than just organising their notes? The memo-to-plan loop is useful but that's a glorified to-do list. The execution layer is where the real value lives and also where churn will happen if it gets one step wrong on something important.

What does a successful week look like in your usage data right now?

The gap between "wrote it down" and "actually did it" is where every note I've ever taken goes to die, so I like the idea of one that nudges itself toward done. And keeping the "sends to other people" stuff gated behind a confirm is the right instinct — that's the exact thing I'd be nervous about handing off.

Good one. Congrats on shipping 👏

Turning notes into an actual daily plan is the part most note apps miss. The interesting question is whether the plan stays grounded in what the user actually captured, or starts becoming generic productivity advice. Curious how you keep it practical day to day.

Really like the idea of notes becoming action instead of just storage. The review step before anything reaches another person feels like a smart design choice rather than a limitation.

Congrats on the launch! I love the memo-first idea.
Asking as a neurodivergent person: how does Buddy handle a voice note that jumps between five completely different things?

What a day. I genuinely did not expect the conversations this would spark. Where notes go to die, whether you talk to your phone in public, where AI should stop and ask first. I have learned more from this comment section than from months of building.

Thank you to everyone who tried Buddy, pushed back, and shared their own note graveyards. I am still here reading every reply. Best possible start for us. 🙏

I've been using Buddy AI Note for a week now and I'm impressed with how seamlessly it integrates into my daily workflow. The ability to add tags and prioritize tasks directly within the note-taking interface is a game-changer for me as a product manager. It's helped me declutter my notes and focus on what really matters.

What I'd love to see next is more integration with project management tools like Trello or Asana. Would the team consider exploring APIs or partnerships to make it easier for users to turn their plans into actionable tasks?

如果能试用完整AI功能就更好了。我很在意把自然文字变成待办的丝滑程度。

Works on iPad too?

Why most AI presentation tools still feel like toys???

Been building in the AI presentation space recently and wanted to share a quick teardown of why current tools still frustrate us:

• Gamma: Great for simple bullet points, but its Notion-like block structure kills spatial freedom. It fails at 90% of complex, real-world PPT tasks.

• PowerPoint: Legacy XML is an absolute nightmare for AI to parse and generate reliably. It's just not built for the SaaS era.

• Pitch: Beautiful canvas, but it’s a traditional tool with AI bolted on, not an AI-native agent.

• Claude/HTML slides: Generating code is cool, but having no visual GUI editor is a dealbreaker. "Prompt-and-pray" is not a reliable workflow.

The takeaway?

The only logical path forward is a true 2D spatial canvas built for AI agents, paired with a real native editor and rich assets so you can actually tweak things 1:1. That’s exactly the paradigm we’re exploring with Deckwise.

Or maybe I'm just being too picky? 🤔

lasso-selecting a slide and asking it to redesign is the part every other deck tool skips. does it hold brand styling consistent across the whole deck?

The lasso editing is the part that makes this feel different. Generating a deck is nice, but being able to point at one messy section and ask the AI to fix just that is much closer to how people actually revise slides.

wow this feels closer to an actual workflow tool than a generator, especially with the iterative “edit after creation” loop.

the lasso-based refinement is what makes it feel usable for real deck work. good job

How is it an agent exactly?

How does the AI understand complex overlapping elements and layered layouts that aren't simple linear flows?

Im thinking this might well morph to a mobile app, notably because I frequently take my phone with me when I (e.g.) go to the basement to get a phillips-head screwdriver, and when I get there, I've completely forgotten why I came down to the basement in the first place. I have to go back upstairs and wander around until "Oh yeah, I needed it to screw in the door handle."

This feels like it would be incredibly useful for people with ADHD. The intent prompt alone could be a game changer for them. Quick question though can you add your own custom apps and websites to track or is it limited to the ones you've pre-selected

Forcing a moment of intent before the distraction loop kicks in is such a simple but high-leverage intervention. Congrats on the launch!

Hey cool idea. I have a question regarding app-switching velocity: does Amnesia allow users to whitelist certain background apps that shouldn't trigger the intent prompt, or does it dynamically adjust its sensitivity based on the active project/workspace you are currently in? Sticking a timer right in the menu bar is smart. Congratulations on the launch!

This is a very specific behavior that most productivity tools ignore. I don't lose time because I lack a task list. I lose time because I forget the reason I opened something in the first place. Clever framing.