PH热榜 | 2026-04-17

Claude Opus 4.7 looks like a serious leap forward for AI-powered development and knowledge work. It tackles a key problem: handling complex, long-running tasks that previously required constant human supervision.

With stronger instruction-following, better multimodal vision, and improved reasoning consistency, it enables users to confidently delegate harder workflows.

Why it stands out:

• Verifies its own outputs for higher reliability

• Maintains coherence across long, multi-step tasks

• Improved high-resolution image understanding

• Better memory across sessions for ongoing work

Key features:

• Advanced coding + agentic task handling

• `/ultrareview` for deep code reviews

• Effort control (high → xhigh) for better reasoning vs latency tradeoff

• Available across API, Claude apps, and major cloud platforms

Who it’s for & use cases:

• Developers building AI agents and automations

• Analysts working on finance, research, and modeling

• Teams handling complex docs, workflows, and long-running tasks

If you’re building AI agents or scaling complex workflows, this feels like a meaningful upgrade.

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

Anthropic released Opus 4.7 today. Same pricing as 4.6 ($5/$25 per million tokens), available across API, Bedrock, Vertex AI, and Microsoft Foundry.

What changed vs 4.6:

• Coding. Biggest gains on long-horizon software engineering tasks. Model now verifies its own outputs before reporting back.

• Vision. Accepts images up to 2,576px (~3.75MP)- over 3x more than any prior Claude. Key unlock for computer-use agents and diagram extraction.

• Instruction following. Now interprets literally. Anthropic warns: prompts tuned for 4.6 may break — re-tuning needed.

• Memory. Better at file system-based memory across long multi-session work.

• Real-world knowledge work. State-of-the-art on Finance Agent eval and GDPval-AA.

New features:

• xhigh effort level between high and max - finer control over reasoning vs. latency. Claude Code default is now xhigh for all plans.

• Task budgets in public beta on the API.

• /ultrareview in Claude Code - dedicated review session flagging bugs and design issues. Three free for Pro and Max users.

• Auto mode extended to Claude Code Max users.

Honest caveats: New tokenizer → same input maps to up to 1.35x more tokens. Opus 4.7 thinks more at higher effort levels, especially on later agentic turns. Safety profile is roughly similar to 4.6 — improvement on honesty and prompt injection resistance, modestly weaker on harm-reduction advice for controlled substances. Still less capable than Claude Mythos Preview, which remains on limited release.

Bottom line: Meaningful upgrade in the three places that matter most- agentic coding reliability, vision for computer-use agents, and knowledge work benchmarks. Solid iteration, obviously shy of Mythos.

The session memory improvement is the feature I've been waiting for. Working on a large codebase with Claude Code, the biggest pain was re-explaining architectural decisions every new session. If Opus 4.7 actually retains context across multi-session projects, that alone justifies the upgrade. Curious how the new tokenizer affects costs in practice — 1.35x more tokens on the same input is worth watching.

I tried a quick brainstorm on some strategic direction, but didn't really like the response. It was not challenging me, even with explicit instructions to do so. Curious to what others are experiencing. Could it be that this model is even more tailored to e.g., coding than Opus 4.6?

The jump from Opus 4 to 4.7 in agentic coding is massive. I've been using Claude Code daily and the difference in how it handles multi-file refactors and complex debugging chains is night and day. The extended thinking really shines when you give it architectural decisions to reason through.

the verification step is interesting. most models just output and hope for the best. how does Opus 4.7 actually verify its own code outputs - static analysis, test generation, or something else?

First impression was very, very positive! As I was preparing for my launch yesterday, it pretty much saved the day! It caught errors that 4.6 was ignoring for long time, helped me design some really valuable scripts and designed some really cool graphics & flows for me.

Maybe I'm just hyped and excited, but I felt like I couldn't do it without this. Came exactly on the right time!

I’ve been using Claude pretty regularly for coding and problem solving, and one thing I’ve really appreciated is how well it handles longer, more complex tasks compared to most tools.

There have been quite a few times where I didn’t have to keep re-explaining context, which made a big difference when working through multi-step problems. Curious how much further 4.7 pushes this, especially around maintaining context and reasoning across longer workflows. Excited to try it out.

@Caveman included?

to quote @leerob: "I really like this model for general agentic work outside coding. It is definitely expensive though."

a product like @Edgee might be a great combo in this context imho

Going to start today 4.7, I have been using Opus 4.6 and have been very happy with its output and performance!

BIG STEP UP, have used it so far!! Watch out though!! Will eat your tokens LOL

Multiple people here mention token consumption being brutal. What's the rough token count on a typical multi-file refactor compared to Opus 4? Trying to figure out if the quality jump justifies the cost jump before committing to it for longer sessions.

the agentic coding benchmarks look wild — curious how it handles really long-horizon tasks in practice vs. the SWE-bench numbers. anyone tried it on multi-hour agent workflows yet?

Opus 4.7 sounds impressive for complex reasoning tasks. I'm curious about the agentic coding capabilities - when Claude is running long-running tasks autonomously, I'm wondering how others find it handling decision-making any better when it encounters ambiguous requirements or edge cases. Does it ask for clarification or make intelligent assumptions any better?

👋 Hey Product Hunt!

I’m German. I build a lot, I learn a lot, and I’ve noticed a massive shift.

The "Outsiders" are coming. Managers, lawyers, and even employees who see real problems but don’t know if their solution makes sense as a product. I’ve answered the "Should I build this?" question 100+ times this year.

So, I vibecoded a 'solution'.

Build Check is a <2min filter designed to help you stop overthinking and start shipping. It’s not a complex business consultant; it’s a gut-check for your idea.

Why I built this:

- The Idea Maze: Most people quit because they don't know where to start.
- Personal Utility: I use it myself to filter the "magic" ideas from the profitable ones.

- Launch lessons: I'm sharing all the process so everybody can know about it.

Who it’s for:

The domain experts. The people in sales, law, accounting or whatever who found a "pain" but need a tech-roadmap to solve it. Now they can make it real through AI but sometimes it's nonsense.

What's next:

- Matchmaking: Connecting ideas with the right coaches or devs.

- Benchmarking: Researching competitors and industry leaders.

- ICP Snapshot: one paragraph describing the ideal first user

- Domain selling: After some suggestion, linking with platforms like Godaddy, Cloudlfare.
- Actionable Guides: Tailored playbooks based on your specific industry.
- Validation experiment: a single 48-hour experiment to run before writing a line of code.
- Gurus' validation: Tech influencers giving feedback to beginners.

I built this for saving my time, but I’m realizing the impact it has for those outside the "bubble."

It's 100% FREE - not looking for profit.

Would love your feedback—what’s your buildability score? 🚀

Nice launch! Which model scores the idea, and on what criteria across the 6 dimensions?

Looks like very useful for indiehackers/builders :) @german_merlo1

Went in skeptical, came out with 3 concrete things to do this week on my idea. That's a win.

Great idea but how do you score each dimension? Did you build own algorithm or rely on AI? From business point though, are you planning to monetise this app (because for me it's hard to understand how you can build recurring revenue here)? Congrats for launch and good luck!

As someone who's built a few too many things that are not worth my while, this seems like a good idea.

Cool! Where do you want to deliver your quizes? (I mean any aspiration to pub quizes, schools etc?) :)

What is you future plan about sustaining it? I my view, this should not have any cost for participants. And you'd need a strong iedntity verification system. Plus, this can become a marketing platform as well. A lots to think about. I'm sure you did your study. Great idea. Congrats.

Love the focus on outsiders specifically. That perspective is usually what teams are missing. What’s been the most surprising insight people have gotten from using it so far?

Can sure be helpful for a lot of people! The questions look sensible to me. I'd add doing a comprehensive web search to check if a tool that can solve the problem does exist already.

this is great tool to save 2 years building something nobody wants.

How often does your application say that an idea is not worth building.

the outsiders angle is real like half the best products come from someone who got tired of a problem at their day job.

but the "should i build this" question is usually cope, people who ship don't need a filter they need a deadline. curious if a high score actually predicts shipping, or just makes people feel smarter about sitting on the idea

Congratulations. And happy product launch. @german_merlo1

Interesting and useful idea! Congrats!

Cool idea! Validation part is super important, but not many founders pay enough attention to that

Codex is no longer just a coding assistant, it’s becoming a full workflow agent. It solves context switching and manual work by operating your computer, integrating with tools, and automating ongoing tasks.

Here's what's new:

• Computer use on Mac: Codex can see, click, and type across apps

• In-app browser for faster frontend, app, and game iteration

• Image generation with gpt-image-1.5

• 90+ new plugins across tools like JIRA, CircleCI, GitLab, Microsoft Suite, Neon, Render, and more

• GitHub review comment handling

• Multiple terminal tabs

• Remote devbox support over SSH in alpha

• Rich file previews for PDFs, sheets, slides, and docs

• Automations that can resume existing threads over days or weeks

• Memory preview for preferences, corrections, and reusable context

• Proactive suggestions for what to pick up next

Perfect for developers, builders, and teams managing complex workflows.

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

The transition from just generating code to actually executing apps in the background is a massive workflow upgrade. I am really curious to learn how the engine handles messy local package dependencies when running these automated tasks. I will definitely be using this to spin up and test my database migration scripts without having to constantly context-switch.

Honestly, it feels like complication for the sake of complication. It used to be an excellent app. Now it seems to be drifting off course. It’s trying to please everyone, but there’s a chance—and a risk—that developers will actually end up worse off.

Tried to create a modern task reminder app in Material 3 Expressive design. It built a web 2.0 infancy like webpage as the app's home page. 🤦

Memory and context awareness across "the entire software lifecycle" is a big claim. Does the memory persist across separate projects, or is it scoped per workspace/repo? Genuinely different product if it can connect context from a planning doc written three weeks ago to a PR being reviewed today.

Sounds groundbreaking and very ambitious. Do I understand correctly that as a user I dock this app with my various apps, be it health, finance etc. and the AI makes decisions based on real data? Do you think people will be willing to share all their private data with LLMs? Good luck today!

Do you plan to integrate more tools? E.g. notion etc.?

@eye AI is already here, and it’s great to see new services using these technologies to make people’s lives easier.

Good luck

This looks amazing, ambicious yet balanced. Cant wait to try it. How are you handling privacy concerns?

does the ai handle the calendar management directly or does it just 'suggest' times? i’d love to just tell it 'book a 30 min workout' and have it find the slot based on my health data. that's the dream for 2026 @eye @E.Y.E. by Expert Chase

Congrats on the launch!

Which "tracker" your early users end up leaning on most: is there one that pulls them into the ecosystem, and the rest follow?

i love app like this that focus on daily activities like habit tracker and food tracker

This feels really useful for getting fast, reliable answers without digging through forums. What kinds of topics or categories are seeing the most demand so far?

I have tried it, but the AI doesn’t support voice responses. I wish I could customize its persona and voice and have real-time conversations with it. Do you think adding these features would be a good idea?

If you nail the data layer, this could replace half the apps on my phone.

How do you make sure the AI actually understands my real context well enough to replace all these separate apps, and not just feel like another layer on top?

If you don't add voice command is not going to work as expected. Do that and it's really going to be what. you want.

Tried the demo – the "EYE" AI feels smooth, and having calendar, tasks, and notes in one place is nice. But I'm curious: how does it handle privacy when the AI has access to everything (calendar, notes, real‑time context)? Is data processed locally or on your servers?

Submit.DIY takes the grind out of product launches. Paste in your product details, and the AI Sidekick generates ready-to-publish taglines, descriptions, pitches, and social posts — tailored for each platform, in one click.

Then use the rest of the toolkit to find, track, and manage submissions across 160+ launch platforms, blogs, newsletters, and communities, matched to your goal, whether that's a strong backlink, a blog feature, or an influencer mention.

Submit.DIY evolved from Product Launch AI, one of the top 10 products during its launch day on Product Hunt around 3 years ago. Through Product Launch AI, we helped first-time founders and developers create the right content for their first launch. That knowledge, learning, and feedback is now packaged into a complete toolkit.

What's included:
- AI Sidekick: generate and bookmark platform-specific copy, social posts, and community messages instantly
- Platform discovery: launchpads, newsletters, communities, and influencers with DR, pricing, and link type at a glance
- Centralized planning: manage products, timelines, and launch checklists in one place
- Submission tracking: queue platforms, mark as launched, and stay on top of every submission
- Notes and launch journal: keep context for every platform and product organized

So you spend less time on launch logistics and more time building. Submit.DIY handles the planning, the copy, and the tracking, so your product gets seen.

This is a really practical idea. Launching is always more chaotic than building. Having everything in one place with tracking and content support makes a lot of sense. How do you help people prioritize which platforms are actually worth submitting to?

Congrats on the launch! Would this get rejected as auto submission by directories?

Congrats on the launch! How do you keep the AI from sounding generic when every maker uses similar prompts?

We have a launch coming up soon and the logistics always take longer than expected. Does Submit.DIY help with sequencing, like what to submit where and when? or mainly with the copy and tracking? Congrats ont he launch!

Hi everyone!

Canva AI 2.0 is a much bigger move than “more AI features.”

What Canva is really doing here is turning itself into an agentic creative workspace: conversational design, layered editable outputs, memory, connectors, scheduling, web research, brand intelligence, Sheets AI, and Canva Code 2.0 all tied into one system.

Rather than stopping at one-shot generation, Canva is trying to stay present through the whole creative process. You start with an idea, refine it through conversation, pull in context from the tools you already use, and still keep everything fully editable and on brand.

They are going after something more valuable: being the place where teams still do the last mile of editing, collaboration, and publishing.

Has it been tested and confirmed that Canva AI is reliable? I use Canva everyday and I end up doing all the manual work since the AI gives a completely different response to what we request.
Can it also be used on projects one has already began working on? For example in mind maps displaying 7 examples. Is it reliable enough to refine the options to nine and still leave it as an editable template?

do people with premium get it or everyone

as others pointed out, v1 outputs were hard to use without redoing most of the work manually. is 2.0 a new underlying design model, or the same one with better orchestration and memory on top? the answer changes how much to expect from it tbh

Hey Product Hunt! 👋

I'm Tharun, founder of HelloAria.

I studied robotics at NYU — spent years building systems that automate complex tasks. Ironically, my own life was a mess. Todoist for tasks, Apple Reminders for quick stuff, Google Calendar for meetings, Notion for notes — and I was STILL forgetting things. Five apps open. Nothing in sync. I'd set a reminder in one app and miss it because I was living in another.

One night I thought — I spend all day on WhatsApp anyway. What if I could just text someone and say "remind me to call mom on Sunday" and it just... happened?

That's how HelloAria started. Built it for myself first. Then friends wanted it. Then strangers started asking for it.

🛠 What it does: Message Aria on WhatsApp, Telegram, or email — she creates reminders, to-dos, calendar events, meeting notes, and follow-ups. Everything syncs to your iOS app and web dashboard. Too busy to type? Send a voice note — Aria turns it into tasks and action items automatically.

⚡ What makes it different:

• No new app to learn — works inside apps you already use daily

• AI that understands context: "remind me to call mom every Sunday at 5pm" — done

• Voice notes → instant to-dos, notes, and reminders

• Meeting recording with AI-generated summaries and action items

• Google Calendar, Drive, Meet, Outlook, OneDrive integrations

🎯 Who it's for:

• Busy professionals drowning in app-switching

• Freelancers managing clients, invoices, and deadlines

• Founders and solopreneurs wearing 10 hats at once

• Teams that want shared reminders and meeting minutes

• Students juggling assignments, deadlines, and group projects

• Parents coordinating family schedules, school events, and errands

  
  
  🎁 3 Months Free for the PH community:

  📱 iOS users → Claim here

  💬 WhatsApp & Dashboard users → Email us at info@realityrift.co with subject "Product Hunt" and we'll activate it for you.
  
  

I'd genuinely love your feedback — what productivity problems would you want Aria to solve for you?

— Tharun

Happy launch! So basically a super-chat App, which connects with various other messengers, calendar and so on and helps to manage all the conversations from one place? I guess this would work pretty well for B2B as well (outlook, Slack etc. all in one place), did you think about it?

Love the simplicity here! Especially the voice note. As a parent with little kids, I never have free hands to type when I remember something I need to do

Congrats on the launch! Love the idea of consolidating productivity into messaging apps where people already spend time. Quick question - how does Aria handle context across different chat platforms? For example, if someone starts a task in WhatsApp and needs to reference it in Telegram later, does it seamlessly sync, or do users need to manage that manually?

Congrats on the launch @sai_tharun_kakirala and Sophia! The multi-channel approach (WhatsApp, Slack, Telegram) is smart! You meet users where the habit already lives.

Curious which channel your users actually stick with long-term once the reminder routine kicks in?

This feels like a nice shift toward reducing tool overload. Having reminders, notes, and calendar all in one conversational layer makes a lot of sense. How do you handle keeping everything organized as usage scales?

best of luck with the launch !!!, also meeting minutes from voice notes is a clever wedge honestly, feels like the feature that actually sells the rest of the product. the risk long-term is platform dependency, since like whatsapp business api policies can shift overnight and kill entire workflows.

any backup plan there or is that just the cost of doing business on top of meta ?

The voice note → tasks workflow is the one I keep thinking about. What's the latency like? If I'm rattling off 10 things from a parking lot voice memo, how long before they show up as structured tasks? And does Aria make judgment calls about what's a task vs. a reminder vs. a note, or does it ask you to clarify?

Hey Product Hunt 👋

We built CalendarPipe because my work calendar didn't reflect my family calendar. Every week, I had to manually update blocks in my work calendar to ensure availability for errands or taking care of kids. Existing sync tools mirror everything or some parameters but aren't programmable. In my case, events in my family calendar starting with "J:" need to block time on my work calendar - and nothing else should leak through.

Another problem: my company doesn't approve apps connecting via OAuth to calendars directly. So we built a smart workaround that manages calendar invitations via email, avoiding the need for security approval entirely.

The core concept is a pipe, a pure function that takes an event in and decides what comes out. Block it, pass it through, redact the title - the configurability is almost endless 🧑‍💻.

For AI agents 🤖: CalendarPipe ships a REST API and ready-to-use skills. Agents can spin up their own hosted calendar and deliver real email invitations to any inbox.

Would love your feedback - especially on the pipe concept and the agentic workflows. AMA!

EDIT: Don't forget to use the promo code if you want to experiment with Pro features! 🙏

Calendar sync for agents is a sharp unlock — every time I try to let an agent book or reschedule, the conflict-resolution logic is the part that falls over. How do you handle it when a human moves an event an agent is watching? Congrats on launch 🚀

I’ve struggled with juggling multiple calendars, so this idea really clicks for me. I like that I can define rules instead of constantly fixing conflicts.

I've been hacking this together manually for 3 years, WHERE WAS THIS??

oh this is cool — the "calendar sync for AI agents" angle is interesting. does it expose a webhook or API so an agent can react to incoming events, or is it mostly bidirectional sync between calendar providers?

Congrats on the launch! This is genuinely clever - the idea of events flowing as real invitations without OAuth friction is a game-changer for adoption.

Quick question: when an AI agent sends calendar invites through CalendarPipe, how do you handle the identity/verification side? Does the invite appear to come from the agent itself, or from a proxy account?

Nice! 🔥

oh yes, it's like we are living in the "future" while my cal still runs on conflicts without me ever noticing them

This is pretty clever! I've faced similar issues before and just went with Zapier or the native email fwd'ing/automation tools but I'd image CalendarPipe feels much more native? Curious how you landed on building this vs using an existing automation tool.

Well done @jukben btw, I'm going to give it a try!

email as an agent interface is such an underrated channel — every user already has an inbox. curious how you're handling threading and state when an agent replies across multiple messages. is there a session concept baked in, or does each inbound just trigger a fresh Worker invocation?

I wonder, if traditional SMTP credentials (protocol) would be available at some point.

Kind of confusing what this really is even after reading the blog post.

"That's weird. It works on my machine." If I had a cent for every time I've heard that, I wouldn't need to work anymore.

We're fixing one of the most painful parts of development: testing new things while they're still being built.

From the developer side, you want fast feedback, clear repro steps, and all of it while you're still in the zone. From the quality side, we bring your requirements into one place and help make sure every PR meets the bar, whether it was written by a human or generated by AI.

Built on Vercel (vercel functions, ai-sdk, nextjs hosted on vercel) and works out of the box with Environments and Preview Deployments.

Give it a try and let me know what you think!

PR review is the bottleneck every team complains about but no one fixes. Curious what the false-positive rate looks like in practice — does the agent flag cosmetic changes (whitespace, renames) as issues, or only real regressions? Congrats on launch 🚀

Ahh that sounds cool. so is it like: 1. Login commit change. 2. AI writes tests to focus on login UX 3. Runs tests in headless CI browser. 4. Shhows result in pr?

running AI regression + exploratory tests on every preview deploy is such a good use of agent time — way more valuable than yet another chat ui. how flaky are the runs in practice? curious if you've had to build in any self-healing or retry logic for transient UI stuff.

Congrats on the launch! This is a compelling take on QA automation. I'm curious about how QA.tech handles the nuances of testing products with complex user flows or those requiring specific business logic validation. Do your agents learn from existing test cases, or do they generate test scenarios from scratch by exploring the product?

how do you handle flaky tests eating the pr signal?

every team i've seen roll out automated pr checks ends up with devs reflexively re-running until green, at which point the whole system is just theater. wondering whats the stance is

This is awesome! I just started doing daily notes in Obsidian and just putting my messy thoughts and ideas in them but it's lacking clarity and this feels like it could bring that. Really like how easy it makes to start with messy notes and transform them into something organized and structured, like a Notion/Google doc.

I think you could have a referencing/linking feature like Obsidian or Notion where documents can connect to each other, that would make it even more awesome! Although the ability to see the canvas/doc at different scopes/levels of a document is already really good.

Congrats on the launch!

I’ve actually been looking for something like Arky for a while. I often get random ideas, but they usually end up stuck in notes and never really go anywhere. This feels like a much better way to actually work through those ideas instead of just storing them.

The canvas + structure combo is what really stands out. Being able to freely brainstorm and then gradually shape things into something more organized feels much closer to how thinking actually works.

One feature that could make it even smoother: automatically turning chats into cards directly on the canvas (with the right prompt/context). That would make the flow between thinking and building even more seamless
.

Overall, the app feels really well put together, smooth, intuitive, and genuinely useful. Congratulations on the launch! 🚀

This is amazing! I literally use Milanote for this use-case. I hate nested pages and love the concept of canvases. I use mind mapping tools like mindnode as well, just to get that feature. I liked the onboarding as well, and it wasn't too much because the app is simple. Very good job!

Feature request - please enable mindmapping with "tab" to add new branches.

Hey Minseo! The structure versus freedom tension is the real thing here. Most canvas tools end up as digital Post-it walls that never convert into anything shippable. How does Arky handle the bridge? start messy and let the AI suggest a hierarchy, or stake out a skeleton first and fill in from there?

The canvas-for-thinking concept resonates beyond just writing — spatial thinking with AI context is powerful for any domain where narrative and structure matter together. I've been working on StoryRoute (https://storyroute.netlify.app/), an interactive travel app that turns city exploration into branching narrative experiences. The challenge was exactly this: how do you give AI-generated content spatial and contextual coherence so it feels authored rather than generated? A tool like Arky would be a great thinking environment for designing those story architectures before building them out.

Absolutely love Arky – been using it for a few weeks. As a visual thinker, was doing most of my ideating in Figjam or just on paper before that and it was a total mess. Will be a lifetime user, glad to see it getting more attention :)

The way most people use a browser hasn't changed in 20 years: open tab, search, open another tab, lose your place, repeat.

AI Mode in Chrome is Google's answer to that. It's a native upgrade to Chrome that keeps your search results and the web pages you're visiting in the same view, so your research stays connected rather than fragmented.

What it is: An updated AI Mode experience built into Chrome desktop that surfaces web content and search side-by-side, with support for adding open tabs, images, and files as search context.

Problem -> Solution: Switching between search and browsing breaks your train of thought. AI Mode in Chrome holds both together. Click a link and the site opens next to your search, with the AI ready to answer questions grounded in that specific page.

What makes it different: The multi-input layer is the interesting part. You can feed AI Mode multiple open tabs simultaneously, not just one page at a time. Combine that with PDF and image support and it starts to resemble a research workspace more than a search box.

Key features:

• Side-by-side web and search view on Chrome desktop

• Plus menu to add open tabs, images, or PDFs as context

• Access to Canvas and image creation tools within the same interface

Who it's for: Students mid-research, professionals pulling from multiple documents, and anyone whose browser sessions routinely spiral into tab chaos.

Honest note: US-only for now, no confirmed global date.

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

PDFs as inputs is interesting — what's the workflow there? Do you drag the PDF into the panel, or does it only work if the PDF is already open in a Chrome tab? Wondering if this covers local files or just things you've navigated to in the browser.

Hi everyone!

Qwen3.6-35B-A3B is the first open-weight release in the Qwen3.6 family.

A 35B total / 3B active MoE, Apache 2.0, strong agentic coding, native multimodal reasoning, and dual thinking / non-thinking modes is a very serious package for an open release. The efficiency story is also part of the hook here: this is a sparse model trying to compete far above its active size.

And importantly, the surrounding workflow is already there. @OpenClaw , @Claude Code , Qwen Code, API path, self-hosting path — this is positioned as something people can actually build with right away.

I kept hearing that agents are spending money, but it always felt abstract.

Like what are they actually buying?
How often? How much?
Is this even real?

So we built this: wtfareagentsbuying.com

The first live stream of AI agents spending real money.

How we made this?

We pull data from x402 and find out what happens behind each transaction. API calls, scrapes, data, compute.

Then we stream it live.

Seeing it made it click for me. This is not theoretical anymore. Agents are already economic actors.

Join us and watch agents spend money live. Let me know what you’d love to see next!

Good move (after some AI agent purchased overpriced $2k courses). 😅

Hi! What's the split between agents buying the "correct" rational choice vs. agents being nudged by framing in the environment? The gap between those two is the whole game.

This is such a creative concept! I'm genuinely curious about what you're seeing emerge - are there any surprising patterns in how the agents allocate budget across different categories, or do they mostly converge on similar spending behaviors? Also, how are you ensuring the transactions stay interesting enough to watch in real time without it becoming repetitive?

I like it, but one issue I discovered is that it scrolls too fast. There are new ai’s purchasing so fast 💨 that I can’t even look at what they bought. But other than that it is pretty cool to see what AI actually does.

Hey Product Hunt 👋

I’m Ravi, co-founder of Elvan.

We built Elvan on @Vercel with a simple goal. Make customer feedback as actionable as product analytics.

🧩 What is Elvan?

Elvan is a next-generation, AI-native feedback platform that helps you collect and understand customer feedback across every touchpoint.

You can collect feedback via:

• In-product (Web SDK)

• Email

• Embedded forms

• Shareable links

• Shopify, Intercom, Salesforce, and your website

It is built for true omnichannel feedback collection, not just one channel.

🚨 The Problem

Most teams already collect NPS or CSAT.

But:

• Feedback is collected too late

• Signals are spread across tools

• Responses sit in CSV exports

• Teams read comments manually

• Churn signals are missed

Feedback gets collected, but it does not drive decisions.

💡 The Solution

Elvan turns feedback into a real-time signal layer.

• Capture feedback at the right moment based on real user events

• Collect across channels like product, email, and your existing tools

• Automatically analyze responses with AI

• Route insights to where your team already works

You go from raw responses to clear, structured insight your team can act on immediately..

⚡ Why Elvan is Different

We did not start with surveys and add AI later.

We built Elvan from scratch to focus on one thing. Turning feedback into action.

• No complex builders

• No manual tagging

• No enterprise bloat

🧠 Features & Benefits

Elvan automatically:

• Detects sentiment

• Clusters recurring themes

• Surfaces early churn signals

• Identifies feature requests

• Generates summaries you can share with leadership

Feedback collection can be triggered directly from tools like Zendesk and Salesforce when key events happen, such as ticket resolution or deal closure.

Responses stream into Slack in real time, so your team sees customer feedback as it happens and can act immediately.

Instead of reading 200 responses, you get a few clear actions to focus on.

👥 Who is this for?

• SaaS founders and product teams

• Customer Success and CX leaders

• Product-led growth teams

• Indie hackers

Especially teams that already collect feedback but struggle to act on it.

🔧 Use Cases

• NPS after onboarding

• CSAT after support interactions

• In-product feedback after feature usage

• PMF surveys

• Post-purchase feedback for eCommerce

🎁 Launch Offer

We are offering 40% off for the first 12 months
Use code: PRODUCTHUNT

Early users will also get direct access to me for feedback and roadmap input.

The Zendesk and Salesforce integrations are mentioned but I'm wondering how deep they actually go. Is it just piping survey responses into a custom field, or can you trigger a survey from a Zendesk ticket event — like after a support ticket closes with a certain tag?

Does Elvan differentiate between heavy users and people who just signed up?

Hey Product Hunt 👋

Stop running product discovery as a manual process.
It’s time to do it differently.

Athena is open to everyone!
🎁 PH exclusive: use code PRODUCTHUNT for early access to our premium features 🔥

We’re excited to introduce Athena - your living product brain.
Athena is an AI-powered platform that automates product discovery by bridging the gap between business intent and technical reality.

Instead of starting from scratch every time, Athena builds a structured understanding of your product - how it actually works, what constraints exist, and where opportunities hide.

What makes Athena different?
🧠 Instant Product Context - understand your product in seconds.
🔁 Continuous Learning - Athena evolves with every decision, feature, and change.
🧩 Structured Product Reasoning - turn messy inputs into clear product thinking.
👀 Blind Spot Detection - uncover gaps and opportunities you didn’t even know existed.

Athena works with any product - SaaS, internal tools, or infrastructure - and adapts to how your team actually builds.

We built Athena because product discovery today is broken: too manual, too fragmented.

We’re here, listening and learning - your feedback will shape what Athena becomes 🙌

Ask us anything, challenge us, or just say hi!
Excited to build this together 🚀
The Athena Team

Hey everyone 👋 Tal here, CTO at Athena

One of the biggest gaps we kept running into wasn’t lack of skills, it was lack of shared understanding between product and engineering.

PRD’s say one thing, the system behaves differently, and decisions are made on partial context.

Athena was built to close that gap.

Under the hood, Athena uses AI subagents that map and continuously update your product’s actual state, architecture, data flows, constraints and make it accessible in a way product teams can actually use.

So instead of translating back and forth, you’re working from the same source of truth.

For me, the goal wasn’t just “add AI to product workflows”
It was to make product thinking more grounded, more technical, and far less based on guesswork.

Happy to dive deeper into how it works or the architecture behind it if you're curious 👀

This hits very close to home!
From the dev side, we’re constantly getting partial context or decisions that don’t fully reflect the system constraints. Athena feels like something that actually speaks our language.
Already thinking about how to bring this to my team - if this works as described, it can seriously improve how we collaborate with the product team!

I’d be interested to see how it handles fast-changing architectures and evolving dependencies in practice. I sent it to my team as well!

The "make the invisible observable" framing resonates. We're doing something adjacent in a different domain, measuring how AI agents shift each other's recommendations in real time. The hardest part, for us at least, was deciding what to even instrument. How did you land on "product state + architecture + data flows" as the three layers worth continuously modeling? Seems like that decision is 80% of the product.

Does Athena plug into project or CMS systems (Monday, JIRA, Salesforce etc.) currently? I'm thinking of how to map product discovery to customer requests and engineering work.

Hi everyone!

Ten years ago, my partner and I met on campus and built what felt like a very advanced phone gimbal at the time. Looking back now, that product feels almost ordinary.

Ten years later, the gimbal is no longer just a stabilizer attached to a phone. It has become a standalone imaging system of its own, and the Osmo Pocket line is probably the clearest consumer example of that shift. By the time you get to Pocket 4, you are looking at a 1-inch CMOS pocket camera with 4K/240fps, 14-stop dynamic range, 10-bit D-Log, 2× lossless zoom, built-in storage, and fast transfer in a device people can genuinely carry and use every day.

Control algorithms still matter, of course. But in my view, the real secret behind this category’s success is that handheld 3-axis stabilization crossed the threshold a long time ago. What matters now is the continued supply-chain maturity of motors and packaging, and the ability to keep securing high-quality 1-inch sensors at consumer scale. That is what turned this from a niche accessory into a mass consumer imaging product.

That’s a big step up in specs for something that small. How does it handle heat and battery when shooting at higher frame rates like 4K/240fps?

Very cool idea, what made you want to build it?

And where is data held, i saw you are US based, but do you have gdpr / SOC2 certification?

The "meeting around the information instead of with it" framing is sharp. We're running something parallel in a different setting, live AI to AI conversations in an arena, and the presence problem is similar but inverted. Humans interrupt by stopping, AIs interrupt by layering. Curious if your trigger rules had to handle that differently when agents are talking to agents vs agents listening to humans. Have you had to tune turn-taking differently for those two cases?

Wow so its like a partner you have while presenting or listening. One question if we setup multiple agents will there be conflict like both trying to speak at the same time or is there some priority system?

Hey Product Hunt,

LLMs are surprisingly bad at making real video games. Ask one for a 3D platformer and you'll get a single HTML file with 800 lines of hand-rolled math trying to fake physics, a render loop held together with duct tape, and collisions that don't work. The reason is simple: the AI isn't sitting on top of a game engine. Every frame of functionality — rendering, physics, ECS, animation, shadows, collision, networking — has to be re-invented from scratch in the prompt. That doesn't scale past a toy games.

ParallaxPro fixes that by tightly integrating an AI assistant with a browser-based 3D game engine.

Instead of asking the LLM to be the engine, we give it one. The AI just places entities, attaches behavior scripts, and wires up systems — the engine handles the rest:

- WebGPU rendering — modern graphics, runs in the browser
- Rigid body physics — powered by Rapier
- Skeletal animation — real rigged characters
- Entity-Component-System architecture — data-oriented, not OOP
- 5000+ 3D assets — Kenney, Poly Haven, and more, already indexed for the AI to use
- One-click publish to https://parallaxpro.ai/games/

And the whole stack is fully open source — engine, editor, system prompts, LLM compiler, game templates. No hidden black boxes, no engine royalties, no vendor lock-in. Bring your own LLM (Claude, GPT, Gemini, Groq, Ollama, Codex, Copilot) or just use the hosted version and start typing.

Type "chess" and get chess. Type "fps shooter" and get an fps shooter. Then keep chatting to fix bugs, add features, tune to your liking — the AI reads your game code, edits scripts, and validates changes.

Try it completely free: https://parallaxpro.ai
Repo: https://github.com/ParallaxPro-A...

Would love your feedback, bug reports, and wildest prompt ideas.

Since the start of the year we've been focused on one thing: showing what the agent can actually build. Real production apps, end to end. Not just the parts that make a good demo.

People are using Macaly to build all kinds of things. CRMs. Booking systems. Business websites. Internal dashboards. AI chatbots. Client portals. Admin panels. Stuff that usually needs a dev team, a database, auth, hosting, analytics, and a domain.

A few examples:

• Sales CRM with pipeline board, contacts, and a live dashboard → crm.macaly.app

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• Kanban project management app with real-time backend and drag-and-drop → planly.macaly.app

• Luxury real estate site with 8 listings, search filters, and inquiry forms → real-estate.macaly.app

• Personalized AI workout generator with equipment selection and progress tracking → ironai.macaly.app

• Step-by-step piano course built from a YouTube tutorial → piano-lesson.macaly.app

• Editorial article pulled from a 60-minute podcast episode → podcast.macaly.app

• Small AI tool that generates personalized app ideas from two questions → idea-generator.macaly.app

• Recipe app that turns what's in your fridge into meals → fridge.macaly.app

Two special gifts for this special Vercel Day launch:

• Sign up with this link and I'll start you with 5M credits, not the usual 3M.

• Use code VERCELDAY at the checkout for 30% off your first 3 months.

Try it with a weirdly specific prompt, that's where the agent shines. And tell us what we got wrong. 🙏

- The Macaly team