PH热榜 | 2026-08-14

一句话介绍：Outcome 将创作者已有的视频、文章或想法转化为个性化互动测验，根据每位用户的回答实时生成专属的行动计划、评估报告或建议，解决传统“单一静态内容+通用引流”无法实现一对一精准转化和深度信任建立的痛点。

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Sales Marketing Artificial Intelligence

AI个性化营销创作者工具互动测验智能销售漏斗潜在客户转化内容变现个性化推荐 SaaS工具自动化营销智能流程

用户评论摘要：用户普遍认可“动态个性化输出”相对传统静态漏斗的价值，并赞赏将创作者现有内容作为AI上下文的设计。主要疑问集中在：如何保证AI生成结果不偏离创作者原意、不产生幻觉式填充？创始人回应称通过技能库、多级参考材料、步骤级上下文隔离及未来的评估测试框架来控制。另有用户建议明确差异化定位，并期待更多自主决策的Agent功能。

AI 锐评

Outcome的切入点相当聪明：它没有去发明新的流量入口，而是对Creator Economy里最廉价、最冗余的“一次性内容资产”进行二次开发。把视频或文章编译成“可交互的专家系统”，用AI实时消化用户答案并产出定制化报告，这确实击中了“内容有流量、无留量”的行业软肋。其“按结果付费、基础功能免费”的定价策略也远比按表单提交数收费的传统quiz工具更具诚意与前瞻性。

但必须指出，其护城河并不在于“AI生成报告”这一表层功能——这已是成熟技术。真正的壁垒在于对“专家知识”的结构化封装能力，以及能否建立有效的评估与纠错机制。评论中创始人承诺的“eval harness”尚在规划中，意味着当前V1版本生成结果的稳定性与忠实度仍是未知数。若无法有效抑制AI幻觉——即生成看似合理但偏离创作者真实方法论的内容——那么“个性化”带来的信任红利将迅速反噬为信誉风险。

此外，其商业模型面临双重挤压：上游是ClickFunnels等传统漏斗巨头的功能迭代，下游是ChatGPT定制GPTs等通用工具对垂直场景的降维打击。Outcome必须尽快证明自己不是“披着AI外衣的Typeform”，而要成为Creator Economy中“知识资产货币化”的基础设施。要做到这一点，除了技术打磨，更需构建围绕“优质Outcome模板”的创作者生态与分发网络——否则，它很容易沦为又一个极客玩具，而非可持续的生意。方向正确，但离真正的MVP（最小可行产品）验证尚有距离。

查看原始信息 ▼

Outcome

Outcome helps creators turn their content and expertise into personalized funnels that listen, understand, and deliver a useful outcome to every lead. Instead of sending everyone the same lead magnet or placing them into a predefined quiz result bucket, an Outcome Funnel uses each person’s answers together with the creator’s content, knowledge, and process to generate something made for them - an action plan, audit, score, roadmap, recommendation, and more.

Hey Hunters, Makers, and PH regulars 👋

I'm Dan, co-founder at Outcome, somewhere around day 1,103 of my PH streak, and today's the first time I'm hitting the "Ship It" button instead of commenting on someone else's launch. Feels good.. and a little terrifying haha.

What we built:
Outcome takes a video, an article or just an idea you describe, and turns it into a short quiz. You decide the outcome you want people to walk away with, a score, an audit, a plan, whatever you want your audience to leave holding. Every person who takes the quiz gets that outcome, built from two things: what you know, and what they just told you. That's the whole value prop: give something personally curated away for free and trust does more of the selling/converting than a cold pitch could.

An example that'll make it click:
Say you've got a YouTube channel walking people through how to build something, an app, an automation, whatever your thing is, and it ends with a CTA - "join my Discord" or "check the docs," and most viewers just... don't.

Drop that video into Outcome and build a quiz instead: "What are you trying to build? What's your stack? Where are you stuck?" Takes someone thirty seconds to answer.

What they get back isn't a generic "thanks for watching" page, it's built from what you taught in the video, mixed with their specific answers:

A plain-English breakdown of exactly where they're stuck in their stack and why
A checklist trimmed to just the steps their setup actually needs
A calculator that estimates what they'd save, or need, to actually build it
A follow-up email, written by our email writer skill, with next steps already spelled out

All four (or any number) of these could live on the same outcome page - one quiz - one result stacked with whatever blocks make sense for your specific use case.

And it's not just better for them. You get something too: every quiz is a data point. Instead of just knowing how many people watched and when they dropped off - you actually get to see their responses and the report each one generated.

That's the part that got us excited building this, it's not "pick a template," it's "here's every shape a result can take, built from what you know, mixed with what they just told you, all in one clean output."

If you want to see the product in action as an end user, and even get a read on your own launch readiness while you're at it, we built a Roast Your PH Launch using Outcome, drawing on our own scramble to get ready today plus a genuinely great framework from Andre's Substack. We don't have it all figured out, we just tried to build the thing we wish we'd had.

How this compares to ScoreApp, Typeform & Other Quiz Tools:
We can do the bucketed-segment thing too, if that's genuinely all you need. Sort leads, show them one of a few pre-written pages, free plan, unlimited. But that's not really the point of Outcome. Most of the platform's power isn't in the segments - it's in the specific, curated outputs the AI builds per person. Once that's running, nobody in that funnel gets a page someone wrote months ago. The AI runs your insight against their answers (in real time) and builds a result unique to each lead.

The team:
Built this with my co-founder Dylan Jones, who co-founded ClickFunnels. Funnels move someone through steps. They were never built to actually respond to who's on the other end - that gap is why we built Outcome.

Try it:
Would love for you to try it (here if you're having trouble finding the link haha) and give us your honest feedback. This is our V1, and we'd genuinely love this community's insight as we keep building it out.

Thanks for having me on this side of the launch for once. 🙏

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@dzaitzow Love the roast-gate. Thanks for the cameo :D

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@conduit_design my man there you are! Thanks so much for all of your help and guidance (via the roast and in my DMs!) You’ve been crazy helpful across the board ❤️

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@dzaitzow congrats on the launch Daniel!

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Amazing product! Congrats on your launch @dzaitzow @impactvelocity and the team!!

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@rhexai thanks my man! Great seeing you on this side of the product world!

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@rhexai Thank you!!

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Hey Product Hunt 👋 I’m Dylan, co-founder of Outcome.

One of the big realizations behind Outcome came from building much more complicated AI products.

We kept finding that users wanted something much simpler:

“Take what I know, understand this person, and make something useful for them.”

That’s where AI is especially good.

It can combine Expert Context; the creators content, frameworks, systems, and expertise - with Lead Context - their situation, goals, and answers and turn the two into a useful, personal outcome.

It’s a way to bridge the gap between one-to-many content and something that feels much closer to one-to-one.

The second realization was about funnels themselves.

Historically, you built one big “hero” funnel because funnels took time to make. Every video, post, podcast, and article pointed back to the same thing.

But your content usually opens a very specific question.

Now AI makes it practical to build a funnel around that exact question and then answer it personally for each viewer.

So instead of:

Content → Generic Funnel

We’re exploring:

Every piece of content → Personal Outcome Funnel

We also wanted the pricing to reflect this.

Most funnel and quiz tools charge you for collecting more responses or creating more funnels.

We think those things should be free.

The part we charge for is the actual AI personalization: the outcome.

For Early Access, we’re keeping that simple with one flat fee, while static funnels, branching, and personalization buckets are free.

This is our first Product Hunt launch after being users of the site for years, which makes today especially fun.

Would genuinely love your feedback — especially on the idea of creating a personal funnel for every piece of content.

And if you want to see one in action, we made Roast Your Product Hunt Launch while designing our own launch page. We ended up using it a bunch ourselves to test ideas and tighten the page:

https://go.signup.to/roast-your-product-hunt-launch

Would love to see what score your launch gets 🔥

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@impactvelocity Congrats on the launch. I really appreciate your approach regarding this product, making it simple for users to understand and use. I also love how some features are free. I’ll take a closer look on this product but I really like what I’m seeing so far. Once again, congratulations

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@dzaitzow Congratulations. And happy product launch.

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@huisong_li Thanks so much! Appreciate you jumping into the feed!

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Really interesting approach to making quizzes actually useful and personalized. Congrats on the launch, @dzaitzow

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@zeeshan_anwar Thanks a ton! If you end up checking it out - feel free to ping me on LinkedIn about any Qs!

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@zeeshan_anwar Thank you! We appreciate it - useful is the key. Sounds easy, but harder than it looks. We have a lot more that we want to integrate so that the usefulness of a report is tangible, something a lead wants to share with their team, or friends.

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Nice product, well done!

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@malaz_whatever Thanks so much! Feel free to ping us when you've created your account! Happy to answer any questions here or via linkedin if you're running into any hiccups or have Qs!

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@malaz_whatever thank you - I appreciate it! We got a lot more to do, really exciting stuff over the next few weeks! :)

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You made responses and extra funnels free and only charge for the personalization. Most tools do that backwards. Congrats on hitting number one with it.

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@lucasjpols I really appreciate that - we're trying to do our best to complete in a space that has a ton of legacy competition by really only charging for what we feel provides the MOST value to end users!

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@lucasjpols Thank you!

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Seems revolutionary for the creator economy. I am sure manychat will try to add a featur like this soon. I love the concept and can't wait to see more creators to start using this.

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@lakshya_singh really appreciate the kind comment - imitation is the kindest form of flattery so we would be so lucky! Hoping we can iron out enough of a niche here / create a good enough product experience that people really want to stick around!

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@lakshya_singh Thank you! It would be fun to see others add more features like this, I think there is a lot of best practices still need to be figured out for this style of funnel - we are excited to see where this all takes us!

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Congrats on the launch! I think this is an interesting idea. This definitely can be beneficial for creators if they have an already established and working system that gets them consistent views in their niche.

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@benjamin_sloutsky Totally - we're not trying to reinvent the wheel - just give people a place for them to re-purpose static content and turn it into IP that actually converts. Obviously it could work for other use cases but we see the most value for folks who have pre-existing (potentially dormant / old) IP that they want to leverage.

Thanks a ton for being here for the launch!

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what is it?

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@whoisasx not exactly sure what you mean but the product is a tool that helps create quiz funnels - at the end of which - end users get a unique output based on their inputs and whatever the creator of the funnel uses as their (IP / reference info / voice / skills etc)

Think of it as one to many lead capture where every user gets something unique to them.

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@whoisasx Just had a fun look at your X profile - since your doing some cool stuff with Agents - the way to think of this is "Agent Lead Magnets" - a lead comes through, the "Agent" makes a personal report / outcome for that lead. I do want to add more agentic features - so the Agent can decide more of the outcome, but for now its more like an Agentic Workflow - follows a durable path, but has enough to create a custom result for every person.

We are still trying the best ways to say "what" this "is" - there is no direct comparables yet besides, AI at the end of a "quiz funnel", but that is so much simpler than what we are trying to create.

Thanks for the question!

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1,103 days of commenting on other people's launches and today you're on the other side of it 👏 congrats Dan. What has surprised you most about being the one waiting?

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@rabnoor_s I think - how much the platform has shifted honestly - the threads used to be the wild west (in a good and bad way) and I think the sheer volume of products on here makes it really hard to stand out I think even if you have a product that isn't entirely AI soup (not to say many of the products are slop - rather - its hard no matter what to rise to the top if theres hundreds of launches a day!)

One thing that has remained consistent is that the community is relentlessly supportive of indie makers and small teams which I really love / keeps me coming back here.

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Very interesting! Congratulations on the launch. I've been a ClickFunnels user, and I've always admired how simple and functional it is. I can see those same patterns here, so I'll definitely test it out!

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@lolo_cappucci thanks so much for - really excited to hear any feedback you’ve got - good or bad - we’re just hoping to learn from this launch!

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@lolo_cappucci Thanks man! I appreciate that!

It’s actually incredibly hard to make something simple. It was hard before AI, and now that we have all these superpowers, it’s even harder.

I’ve probably hidden 75% of the code and features I’ve built. Dan gets a little sad sometimes when one of his favorite features goes bye-bye!

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@dzaitzow Really interesting approach! I love the idea of turning a funnel from a static lead-capture tool into something that actually gives each person a useful, personalized outcome. That feels much more valuable for both the creator and the lead. Congrats on the launch!

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@adana thanks so much for the kind reply - it felt niche enough to be useful without the overwhelmed / manual lift that other similar tools in the market have! If you poke around - feel free to ping me!

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@adana Thank you!

I’ve been doing online marketing, building products, and some form of funnels since the early 2000s, and this kind of personalization is something we could only dream of back then.

We definitely tried to fake it. You’d fill out a form, and then we’d make it look like the system was “reviewing” or “generating” a response.

We launched a much more complicated version of this last year, and a lot of the feedback was basically:

“I just want to ask some questions and give them something useful.”

I’m honestly surprised there aren’t more people doing this already. I always feel late to everything. In 2004, I thought it was already too late to become a web designer because “everybody already has a website.” :)

There are trade-offs with this approach. Static is fast, and it will always be faster than AI. Even with a fake loading bar for a couple of seconds, a meaningful AI-generated outcome can still take 10–30 seconds to run, not including more advanced steps.

I’m hoping we can help figure this category out, coin the term “Outcome Funnel,” and discover the best practices and optimizations that make personalized content actually useful.

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I especially like that the creator's existing content becomes part of the context instead of asking them to rebuild all their knowledge inside another tool. Curious how you make sure the generated outcomes stay faithful to the creator's actual advice rather than filling gaps with generic AI recommendations :)

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@andrasczeizel I am sure there is a much more technical way to respond to this but there are a few different ways that voice and IP are sustained throughout the process.

1. We have a Skills ecosystem where you can either search for skills similar to your voice or simply create your own - this helps to keep writing style / image outputs etc relatively consistent for their use case / desired outcome.

2. We have "Reference material" that every AI step of the report gets automatically

3. on top of that reference material - you can attach these grounding documents to an individual step.

4. not exactly aligned but you can also have the output blocks reference as much or as little as they need to - ex: a certain block can reference ALL of the previous output blocks (or none) and the same goes for the user inputs - they can have access to only 1 of the inputs that is relevant - or all of them.

5 Beyond all of that there is a top level prompt for the output as a whole and then individual prompts for each output block that is dynamic.

If these things aren't populated intentionally - then of course there is an outside chance the LLM will take liberties and fill in the gaps.

Hope that helps to clarify but if you have any further questions - @impactvelocity can likely field them in a more dev-friendly way haha.

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@andrasczeizel great man - happy to give some colour!

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@andrasczeizel Thanks for question!

We have a challenge and an opportunity: when an outcome is done well, it can be incredibly useful because AI can be shockingly good. But it can also just make stuff up.

We don't want the outcomes a funnel generates to be throwaway nonsense. That hurts the creator and makes the user feel tricked.

One of the ways we approach this is by staying small. Each Outcome Block in the report is its own step in a workflow that runs for every lead.

That lets us send only the right context needed for that part of the report, while also letting each step inform what comes next. So if someone scores “84/100 - Rockstar,” every downstream step knows that.

The goal is to get the AI to respond really well to each part individually, while still producing a coherent report as a whole.

We're also going to be building more testing and observability tools around this - basically an eval harness that lets creators test their funnels, tweak the prompts and logic, and add guardrails so the outcome stays useful and consistent over time.

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I love the shift from “here's my content → join my Discord” to “here's my content → here's something useful made specifically for you.” For creators, personalization feels like the missing bridge between audience and buyer.

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一句话介绍：Freebuff是一款100%免费的AI编程代理，以广告补贴模式提供顶级开源模型，覆盖桌面端、CLI、Web应用构建及云代理，旨在彻底消除编程高昂的订阅费用门槛。

Software Engineering Developer Tools Tech

AI编程助手免费编程代理开源大模型广告补贴模式无订阅代码生成云端开发全栈应用构建 Claude替代 Cursor竞品

用户评论摘要：用户普遍认可其免费策略及出色性价比，认为足以满足个人及中小型项目需求。但有资深用户犀利指出，其上下文管理及子代理系统过于臃肿，反而让底层小模型表现逊色于Claude等更简洁的工程框架；另有用户建议尽快集成MCP协议以增强扩展性。

AI 锐评

Freebuff的聪明之处在于精准戳中了开发者对“订阅疲劳”的痛点，用“免费+广告”的商业模式直接对标Claude Code等高价工具，成功获得了初期流量与口碑。但从评论中的一针见血的反馈来看，它目前的护城河并非技术而是资本补贴。免费模式下的“广告换算力”是否可持续取决于用户时长转化，而核心的工程框架（Harness）恰恰是其最薄弱的环节——有用户明确指出其“花哨的”子代理机制在小模型上反而过拟合，导致效率与质量双双打折。这意味着Freebuff目前是用“大炮打蚊子”，却未用好“螺丝刀”，真正的价值锚点不在开放模型本身，而在能否提供一个足够瘦削、精准的编排层，让即使是最小的开源模型也能稳定输出。若不能快速优化其Harness设计并接入MCP生态，一旦资本退潮或广告填充率下降，那些冲着“免费”而来的用户会迅速倒戈。对于行业而言，它验证了“免费AI编程”的市场可行性，但也提醒后来者：靠免费吸引用户简单，靠技术留住用户才是生死线。目前来看，它更像是一次成功的营销试验，而非颠覆性的技术革命。

查看原始信息 ▼

Freebuff

Freebuff is a free coding agent that gives you access to the best open source models. We offer a CLI, Desktop app, Web app builder, and Cloud agent -- all free! This is the free way to build full-stack apps. No subscription, no API keys, no lock-in. Cancel your subscriptions to Claude Code, Cursor, Codex, Lovable, Replit, Bolt, Windsurf, and Devin.

Coding shouldn't be blocked behind expensive paywalls or subscriptions! We subsidize some of the best models (GPT 5.6 Luna, Deepseek v4 Pro, GLM 5.2) with small ads in order to give everyone access to agentic engineering.

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@matthewdo823_ui Removing expensive paywalls and subsidizing high-tier models like GPT 5.6 Luna and DeepSeek v4 Pro with non-intrusive ads is a game changer for accessible agentic engineering.

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@matthewdo823_ui Thanks Matthew!

I'm super excited to share our 100% free coding agent, funded by ads.

We're making coding agents accessible to everyone around the world, so coding doesn't require a subscription.

Use any of the following models for free, which are quite strong:

GPT 5.6 Luna
DeepSeek V4 Pro
DeepSeek V4 Flash
MiniMax M3
MiMo 2.5

The limits are high too. You get 6 sessions of an hour each per day! Some models like Flash get unlimited sessions.

We also have 5 products: Desktop, CLI, Web, Cloud, and Chat. Check them out at freebuff.com.

Let us know what you think!

Cheers,

James

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@matthewdo823_ui Thank you Matthew!

Freebuff is truly the future of AI tools and platforms: 100% free, funded by ads.

I have been incredibly grateful to be part of this team:

Over 250,000 people have used us
Millions of messages sent by users
4,000 members in our discord community
Thousands of DMs, messages, and emails expressing their appreciation for our product

The countless number of people thanking us for our work has made my time here truly meaningful:

Users telling us they could achieve their dreams that they couldn't afford before
People who have built their wedding website and projects that have changed their life
Countless others who say they can't function with Freebuff

The list of supporters just keeps on increasing each day, so much so that it's become hard to parse through all the thank you emails we get and keep up with feedback.

Thank you to all of you!
Victor

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Really incredible how much you performance you can get out of these smaller and cheaper models with a well engineered harness. A lot of people are wasting their money using frontier intelligence for every task when most tasks don’t need it!

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Hey everyone!

So glad to be part of the team behind Freebuff. We've come a long way in the field of high-efficiency models, and now, people often solve problems using our ad-funded models that top tier coding agents completely miss.

We are seeing the future shift into extreme token efficiency -- where we can now make token intelligency completely free for everyone without compromising quality.

The future of AI is going to be this: powerful tools for everyone, completely free to use.

Come try out our models for yourself across:

- Desktop
- CLI
- Web
- Cloud
- Chat

Love you all!
Victor

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@victorcheng Thanks Victor!

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@victorcheng Making high-intelligence models freely accessible through ad funding and extreme token efficiency is such an exciting approach! Democratizing powerful coding tools without dipping on quality is huge for indie devs and builders. Congrats on the launch!

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@victorcheng This is an awesome initiative and I will share it with some friends and see what the build with it

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Hey everyone!

I'm excited to share our 100% free coding agent, funded by ads.

We're making coding agents accessible to everyone around the world, so coding doesn't require a subscription.

Use any of the following models for free, which are quite strong:

GPT 5.6 Luna
DeepSeek V4 Pro
DeepSeek V4 Flash
MiniMax M3
MiMo 2.5

The limits are high too. You get 6 sessions of an hour each per day! Some models like Flash get unlimited sessions.

We also have 5 products: Desktop, CLI, Web, Cloud, and Chat. Check them out at freebuff.com.

Let us know what you think!

Cheers,

James

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@jahooma Providing free access to top-tier models like GPT 5.6 Luna and DeepSeek V4 Pro through an ad-funded model is a fantastic initiative! 6 hours a day with unlimited Flash sessions is super generous for developers building on a budget.

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@jahooma I'm really impressed by this launch. Freebuff works flawlessly out of the box with just a simple terminal install command, and it doesn't try to lock you into a subscription or credit system. The text-ad funding model is very clean and a great way to keep full-stack AI development completely accessible to everyone

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Free coding agents make a lot of sense, especially for consumers and indie developers. I wonder how long it’ll take enterprises to adopt - when will people in real SWE jobs be convinced that they are just as good as paid models?

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@darweenist Good point. But DeepSeek V4 Pro is pretty darn good today! As models continue to improve, free coding seems inevitable.

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@darweenist
I think you mean free LLMs vs. paid, right? A coding agent, like this one sits on top of the LLM with the LLM being the power that drives whatever is coding. Free models/Open Source models? For 80% of the coding tasks out there, you're 100% correct. They get the job done nicely.

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I am an idiot :) . Tell me how you guys make money. My Claude API bill was about 1600 last month

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@jay_janarthanan1 It's ad supported. First line on the home page. The metrics of how that will scale and work? Time will tell but this is a straight up eyeballs for code time.

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@jay_janarthanan1 I've been a user of Freebuff for a while. They just run ads in their apps. It actually is just 100% free.

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@jay_janarthanan1 it's working good.

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Amazing product! Been using it for a few months. Equivalent to Claude Code but free. Used it for data analysis for work as well as creating tools eg football manager moneyball tools. The harness really impressed me as it started checking some maths for me when I asked it about optimising collision detection for 3d objects. That's what inspired me to get it to help with data analysis too. I also used it to help research robot cars and drones for my son to use on ebay. So it has search tools too. It's amazing

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This is my first comment here on the website, but the team behind it truly deserves it! It's mind-blowing to see what's possible when you can do token maximization. I hope you all enjoy it.

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Freebuff is squeezing the most intelligence possible out of the models for no cost to the developer. No one is doing it better. Towards intelligence too cheap to meter!

▲ 2

@brandon_chen1 not true at all, their harness is one of the most messy ones with all these subagents systems and context management which actually overwhelms smaller and less capable models. A much more proper harness e.g. claude code or even just pi with the raw model free from all that bloat is a lot smarter and can actually get work done fast and efficiently, unlike freebuff which is doing way too much while not being able to provide good enough quality when using lower tier models, quality that as said before other harnesses do still have even with those dumber models.

▲ 0

@brandon_chen1 Exactly! The real game-changer is making high-quality intelligence accessible without putting cost barriers in front of developers. If Freebuff can keep pushing that frontier, intelligence too cheap to meter might not be just a vision it could become the norm.

▲ 0

Congrats on the launch!

▲ 1

genuinely no need to pay for tokens for solo work & personal life anymore. great product and evolving fast.

▲ 1

best of luck! Good to the see a coding agent different from the other ones

▲ 1

Freebuff was the OG Claude Code. They did it in 2024, at that time it was called CodeBuff. It was mind-blowing already at that time. Congrats on the launch guys!

▲ 0

The fact that this can actually compete with some of the paid options is pretty wild

▲ 0

Using it for months, can't recommend it enough. Add MCP and it'll be a winner, not only a keeper.

▲ 0

What a fantastic product! I love using it.

Clicking on the interesting ads that pop up from time to time is also a lot of fun :)

▲ 0

596 messages. 196.6M tokens. €0 spent on model access.
The trade-off? You watch ads.
what a time to be alive

▲ 0

Excellent product! Been using it for the past 2 weeks ago.

▲ 0

Free coding agents without another subscription or API key to manage? That’s actually a pretty interesting approach 👀 Curious to try how Freebuff compares in real-world builds. Congrats team!

▲ 0

colour me intrigued! The model is a great idea and with the price increase for tokens from deepseek could be a good alternative.

▲ 0

I am using the cli version, quite good experience.

▲ 0

Wow! I just discovered freebuff. I'm testing the desktop version and loving it. I can't believe I didn't know about this sooner. Please release more models for Brazil!! 😍😍

▲ 0

how are ads delivered in the CLI?

▲ 0

一句话介绍：

▶

SaaS Developer Tools No-Code

AI数据采集智能体爬虫自然语言建Bot 无代码自动化结构化数据提取网页监控云端浏览器营销研究数据集成 SaaS工具

用户评论摘要：用户普遍认可“构建一次、持续运行”的理念及结构化输出价值。核心问题集中在计费透明度：有用户吐槽首次试用因后台构建Bot耗掉76%积分却无预先提示，且构建日志充满技术细节对新手不友好；另有用户询问断点自愈机制、登录站点支持细节及与现有智能体平台对接可能性，社区回应积极但未完全解答。

AI 锐评

BrowserAct Cloud切中了爬虫领域最真实的痛点——不是采集本身，而是维护该死的选择器和分页逻辑。用自然语言“描述”而非“编程”来创建Bot，本质上是把AI Agent的规划能力嫁接到浏览器自动化上，这比单纯的“ChatGPT帮你写爬虫代码”前进了一步，因为它在真实浏览器中完成了验证和持续运行。产品定位极其精准：面向运营和市场研究人员，他们不在乎CSS选择器，只想要可筛选的表格行，而非AI生成的摘要。

然而，从评论中暴露的问题看，它当前的最大风险不是技术，而是信任和成本预期管理。那位用户在无提示的情况下消耗76%试用积分构建Bot的遭遇，揭示了产品在“交互心智模型”上的混乱：用户以为在问问题，产品却在后台“造工厂”。这不仅是UI提示缺失，更是对目标用户（非技术背景）基本认知的忽视。类似地，向非技术用户展示Python编译日志，是典型的“工程师思维”傲慢。

另一个隐忧是“自愈”能力的承诺。评论中有人问站点改版时是否自动检测，官方未正面回答。若“持续运行”依赖的仅仅是定期重跑并祈祷，那它只是把维护成本从人工转移到了AI算力上，长期可靠性依然存疑。至于通过Discord做客服、缺乏账号删除选项，对于一个涉及真金白银和敏感数据抓取的工具来说，是合规与信任上的硬伤。

总体而言，产品方向正确，商业价值清晰（尤其对代理机构和研究者），但若不在试用体验的透明度、技术细节的封装深度以及故障自愈的可靠性上打磨，它很可能沦为“演示惊艳、落地劝退”的又一个AI玩具。价值是真的，但离“Build once, Run forever”的承诺，还有一段艰难的距离。

查看原始信息 ▼

BrowserAct Cloud

Stop fixing broken scrapers. BrowserAct's AI agent builds a Bot from your plain-English description, tests it in a real browser, and keeps it running even when the site changes. Structured data lands in your CSV, JSON, API, or tools like Make, n8n, and Zapier.Build once. Run reliably. Improve continuously.

Hey Product Hunt 👋

I'm Maggie, Senior Marketing Operations at BrowserAct.

We built BrowserAct because web data work still asks too many people to think like scraping engineers.

If you need public product data, local business records, reviews, job listings, creator leads, or competitor research, the hard part usually is not knowing what data you want. The hard part is turning that request into selectors, browser steps, pagination logic, retry handling, and maintenance.

BrowserAct changes the starting point.

✨You describe the website, filters, and fields you need in natural language. BrowserAct opens the live website in a real cloud browser, explores the page flow, tests the extraction, and turns it into a reusable Bot. Then you can run the Bot again with new inputs and get structured results as CSV, JSON, through API or webhook, or directly in tools like Make, n8n, and Zapier.

Our goal is simple:
🚀Build once. Run reliably. Improve continuously.

We would love feedback from operators, founders, growth teams, researchers, and automation builders who still spend too much time collecting structured web data.

🎁 Product Hunt launch offer: Sign up to get 1,500 credits. Upgrade to unlock a 7-day free trial. Build your first BrowserAct Bot today.

▲ 19

@double_chen Massive congrats on the launch,love how smooth the agent workflow is. Are you running your own headless browser fleet in the cloud, or partnering with an infrastructure provider?

▲ 5

@double_chen Eliminating the need to think like a scraping engineer just to get structured web data is brilliant. Loving the build once, run reliably approach and direct integrations with tools like Zapier and n8n.

▲ 3

Hi @double_chen Congrats on #1! Tried the Bot builder as a first-time user and want to flag something on the trial experience.

Asked a plain question ("find bookshops in Kigali carrying non-English/French books"). The agent spent 19m37s and 1,147 of my 1,500 trial credits before I got anything back, not because the task was slow, but because it was building and testing a reusable Bot behind the scenes (validating selectors, compiling code, running a schema check with a 3-item sample) rather than just answering. When I then asked for a quick plain-language list, it told me it only builds/verifies Bots in chat and doesn't run the full job there, so the real answer would've needed more credits on top of the 76% already spent.

I saw Maggie mention above that once a Bot is built, repeated runs are lightweight and cheap, which makes sense, but that distinction isn't surfaced anywhere before a first-time user burns most of their trial on a build they didn't realize they were triggering. Even a simple heads-up ("this will build a reusable Bot first, ~X credits") before the expensive part starts would go a long way.

Separately: the chat surfaces a lot of raw build-log detail (CSS selectors, Python compile steps, schema validation) that reads as debug output rather than something a non-technical user should see, and I wasn't able to test whether credits reset per account since there's no account deletion option and Contact Us only routes to Discord.

Otherwise the concept is genuinely strong, just think the credit transparency piece is worth a look given real money is on the line for trial users.

▲ 0

Congrats on the launch! We currently have 150+ conversational agents in production across different clients. Would it be possible to connect this with our platform and give our agents the ability to scrape information in real time before responding to a DM?

▲ 3

For marketing research, I usually do not want a summary. I want rows I can filter later. BrowserAct seems closer to that workflow.

▲ 3

@jocky Thanks, totally agree. For research, filterable rows are often more useful than a polished summary, and that’s exactly the kind of output BrowserAct is built for.

▲ 3

happy to spend 200k credit per month. :p

▲ 1

@titouan_albouy1 Haha, we’d be very happy to see you put all 200k credits to good use

▲ 1

Love that this focuses on structured data, not just page summaries.

▲ 1

@domenic_yang Thanks! That’s exactly the idea. Summaries are useful sometimes, but structured data is what people can actually sort, filter, and act on later.

▲ 0

This feels practical for agencies.

A client asks for competitor lists, review snapshots, ecommerce pricing, or creator prospects, and the team usually builds a one-off sheet by hand. A reusable Bot could make that research repeatable across clients.

▲ 1

@justin2025 Thanks, that’s exactly the kind of workflow we had in mind. Build the Bot once, then run it again whenever a similar client request comes in, instead of rebuilding the same research sheet every time. It can save a lot of manual hours and keep the cost of repeat research much lower.

▲ 0

Congrats on the launch! When a target site changes its layout, does the Bot auto-detect the break and self-heal, or does it silently start returning bad data until you notice?

▲ 0

Thanks for creating the service! Just yesterday I tried to fix the issue of our agent not being able to check the websites of our customers and their competitors.

▲ 0

"Work inside logged-in sites" is the line doing the heavy lifting here 👀 congrats on launch #2 Maggie. What does a run return when the page loaded but the data was not there?

▲ 0

this is the first AI scapping tool that has worked for me without any fluff. congrats on the launch!!

▲ 0

@rishi_builder Thank you so much! That’s exactly what we hoped to build—an AI scraping tool that simply works, without the fluff. Really appreciate your support! 🙌

▲ 0

Love the idea. Congrats!

▲ 0

@arthur_winston3 Thank you! Really appreciate the support. Feel free to try BrowserAct, and we’d love to hear more of your feedback.

▲ 0

It's one of thé most powerfull and intelligent tool i use for automation. Ask what you want to scrap, browseract does it all for you

Congratulations and thanks for the numerous updates. I love it and my ai agents as well.

▲ 0

@labaude Thank you, this means a lot! That’s exactly what we’re aiming for: tell BrowserAct what you want to extract, build it once, and let it keep running reliably for your agents.

▲ 2

一句话介绍：Gemini 3.7 Flash 是专为复杂编码任务与智能体工作流打造的高速高能模型，解决开发者在多步骤自动化中“快而不精”或“聪明却慢”的核心痛点。

▶

Artificial Intelligence Development

AI模型代码生成智能体软件开发自动化工作流多步骤规划高性能 Google 开发者工具编程助手

用户评论摘要：用户反馈其速度快、能力强，体验接近Deepseek v4 Flash，认为“谷歌回来了”。有用户计划本周末在多步编码任务中深度测试，目前未见明确问题或缺陷报告。

AI 锐评

Gemini 3.7 Flash 的定位很精准：不是要当“最强大脑”，而是要做“最靠谱的牛马”。在编码和智能体场景，市场不缺会聊天的模型，缺的是能在复杂任务链上稳定执行、不跑偏、且成本可控的引擎。从评论看，首批用户的体感是“快且能打”，并主动对比Deepseek v4 Flash——这其实揭示了真正的竞争维度：推理效率与工程实用性的平衡，而非纸面参数。

但必须泼一盆冷水：当前反馈仅停留在“感觉不错”的浅层，缺乏对多步规划容错率、上下文遗忘、工具调用稳定性等硬指标的压力测试。Flash系列一贯的短板在于复杂逻辑链上的“自作聪明”式错误，这恰恰是智能体应用的致命伤。另外，214票的声量在Product Hunt上并不算爆款，说明开发者对“又一个快模型”的兴奋度正在递减。

真正的价值锚点在于：它是否能让一个中等规模的开发团队，在不重构现有架构的前提下，把AI从“写函数片段”升级为“独立完成一个小型模块”。如果能做到这点，它就是打破“AI编码=高级补全”刻板印象的排头兵；如果只是更快地生成更多需要人工兜底的代码，那不过是又一次内卷式迭代。建议关注Google后续对长程Agent任务的评测数据，而不是首发评论文案。

查看原始信息 ▼

Gemini 3.7 Flash

Today, we’re building on the progress of our widely used Flash series by introducing Gemini 3.7 Flash, our most intelligent workhorse model yet for coding and agents.

Hey Hunters, I am excited to hunt Gemini 3.7 Flash!

Google is pushing Flash beyond just speed with a model built for serious coding and agentic workflows.

What caught my attention is the focus on handling complex software engineering tasks, multi-step planning, and autonomous agents — while keeping the speed and efficiency that Flash models are known for.

If you're building AI-powered products, coding agents, or automation workflows, this is definitely one to test.

What would you build with Gemini 3.7 Flash? 👀

▲ 4

@saaswarrior Love it so far. Very fast, Pretty capable. Feels a bit like Deepseek v4 Flash which is my go to model at the moment. Google is back!

▲ 0

@saaswarrior Same experience. Fast and capable. Going to push it on some multi-step coding tasks this weekend.

▲ 0

一句话介绍：Munder Difflin 是一款免费开源的本地多智能体编排工具，它将你已有的 Claude Code、Codex 等编码代理包装成“办公室员工”，以《办公室》情景剧的模拟界面，7x24小时自动执行开发、产品、运营等各类电脑端工作流，让你或你的“数字克隆”担任老板进行调度与决策，旨在解决个体或小团队在复杂多任务下的自动化执行与监督痛点。

Productivity Developer Tools Artificial Intelligence

多智能体编排 AI代理自动化本地部署开源软件编码代理 Claude Code Codex 任务调度工作流自动化数字员工

用户评论摘要：用户认可架构设计与开源精神，但反馈集中两点：一是Windows系统下Codex集成存在启动Bug影响实际运行；二是对“克隆老板”如何判定任务完成度表示担忧，尤其是低质量模型“假装完成”的问题。另有用户提出对复杂系统开发能力的疑问，以及操作设置上的困惑。

AI 锐评

Munder Difflin 的巧妙之处不在于创造了新模型，而在于对既有昂贵编码代理资源的“组织架构革命”。它用“办公室”这一人类熟悉的管理隐喻，解决了多代理系统最难的两个问题：任务编排的可解释性和人机协同的介入点。通过“一个高成本模型决策、多个低成本模型执行”的混合架构，它在订阅制成本框架内找到了一个相对可持续的商业模式闭环，这比大多数烧钱的AI初创公司要务实得多。

然而，其真正的阿喀琉斯之踵在于**质量控制**。评论中那位用户提出的“64.9%算力被浪费在无效会话”的观察非常尖锐——当前大模型在自主执行长链路任务时，失败模式高度隐蔽，往往以“看似正常”的输出掩盖逻辑上的空洞。让“Opus老板”去审核“Sonnet员工”的产出，本质上是期望用一个模型的主观判断去矫正另一个模型的系统性盲区，这只能缓解，无法根治。

此外，该产品目前更像是**高效的自动化脚本触发器**，而非真正意义上的“数字员工”。它依赖底层的Claude Code或Codex的自身能力上限，自身的增值在于流程管理、状态追踪和故障兜底策略（如不同级别模型的升级机制），而非对任务本身的理解深化。这意味着它更适合流程相对标准化、结果可验证的工程任务（如编码、测试、文档生成），一旦触及模糊的创造性或战略决策，该框架便会迅速退化为人肉监控的“钦差大臣”。

总体而言，Munder Difflin 是当前“AI代理”泡沫中少见的、具有实用主义精神的脚手架。它最大的价值是让个体开发者提前体验了“管理一支AI团队”的甜与苦，其开源策略也为其积累了宝贵的真实场景打磨数据。但若要真正兑现“让克隆替你上班”的承诺，还需在**任务完成度的客观评估机制**（如引入外部验证工具、测试用例校验）上下大功夫，而非仅靠一个更贵的模型来“拍板”。

查看原始信息 ▼

Munder Difflin

[Open-Source] Local Multi Agent Harness that wraps around coding agents you already pay for like Claude Code and Codex to run an office of forever running agents working for you 24/7 in "the office" styled simulation. Be the boss of this office or let your clone be the boss when you are not available. For Developers, Product Managers, Designers, Founders, Sales, Marketing, Legal, HR or anyone who works in tech.

Hey Product Hunt 👋

It’s 2026 and building a digital clone of yourself to do all your work is possible with a claude subscription.

So I built Munder Difflin: A local multi-agent harness that uses your existing coding agents like claude code and codex to run in an office of persistent agents that does your work. Decisions run through you or a clone of you that orchestrates.

If whatever’s written above does not make sense:

It’s a free and open source PC app that runs an office of agents that can do literally anything you do on your own computer with your context, you get to see them work in a “the office” themed simulation.

Be the boss of this office yourself or let a clone be the boss when you are not available.

I have made it open source which means it is available for the whole world for free to use, it runs locally which means no personal data goes anywhere, it’s ad-free and lastly it means I did it for the love of the game.

We started from v0.0.1 about 40 releases and 2 months later we are at v0.4.1

Over 2000 people use it already, along with 677 github stars and we are finally live on Product Hunt help please upvote and share about Munder Difflin to help us get to #1 on PH today.

Find us on github: https://github.com/chaitanyagiri/munder-difflin

Try Munder Difflin now: https://munderdiffl.in

[No setup needed if you use claude code, codex or any cli agent]

Two things people always ask:

- "Isn't that a fortune in tokens?" I run it on a $100/mo Claude plan —
one Opus orchestrator, nine Sonnet workers. Shipped continuously for weeks without hitting a limit. The cheap models do the work; the expensive one decides who does what. You can use a mix of agents from different providers to run it way cheaply.
[Simulation is deterministic, they do not consume token]

- "What's the catch?" There isn't one. MIT licensed, free forever, runs
entirely on your machine. Your code never touches my servers because I
don't have any.

I'd genuinely love your feedback, especially on what breaks. I'm here all
day.

▲ 8

@chaitanya_giri Love that....it's 100% local with zero server dependencies, and making the 2D simulation deterministic so it uses zero extra tokens is top-tier engineering🙌🔥

▲ 0

@chaitanya_giri Super super cool stuff!!

▲ 0

I tested Munder Difflin v0.4.3 with Codex on Windows. The architecture around CLI workers, worktrees, mailboxes, lifecycle tracking and observability is impressive.

I hit a Windows/Codex startup issue that prevented my three-agent benchmark from running, so I’m not ready to adopt the runtime yet. But the source architecture was valuable enough that I reused several patterns in my own orchestration system.

Looking forward to testing it again after the Codex integration matures.

▲ 1

@muracan Hey thanks for the feedback will fix this in next release. ASAP thankyou so much

▲ 0

One Opus orchestrator, nine Sonnet workers, a hundred dollar plan. And the whole thing is MIT licensed. Congrats, that is a lot to give away for free.

▲ 1

@lucasjpols I do it for the sport ⚽️

▲ 1

Naming it after Dunder Mifflin is doing more marketing work than most taglines 😄 congrats Chaitanya. When your clone is the boss overnight, what is it not allowed to decide?

▲ 1

@rabnoor_s Interestingly I never let them talk to each other unless required and I do not let them set triggers on their own.

▲ 0

I am about to lose my mind. I accidentally selected Claude Code in the initial setup, and I can't figure out how to change that (since I don't have CC access). I've uninstalled twice. #frustrated

▲ 1

@jckiker Hey you can select the agent type from monitor section:

▲ 0

@jckiker If you want to restart you can do that too from settings by selecting a different path for your clone home.

▲ 0

Okay, the whole Office-style setup is pretty fun and love that you just made this open source and put it out there for everyone :-)

▲ 1

@henry_habib thankyou so much Henry, I’ve had a lot of support from people like you thay has kept me going

▲ 0

The token answer makes sense on a subscription. The part I would want to know before letting this run while I am not watching is how Michael (as I understood it, that is what my clone is called) decides a worker is finished.

I measured one day of my own agent work and the tracker put 64.9% of the spend in its abandoned bucket. None of it looked abandoned while it was running. The sessions that went nowhere looked like the ones that worked, right up until I read the output.

You say the expensive model decides who does what and that a clone can be the boss when you are not available. So what happens when a worker comes back without an answer, either asking for something or saying it cannot proceed? Does that count as done or does he tell it apart from a real result?

▲ 1

@dimhold ideally if not possible through a lower quality model agent the clone will orchestrate it to a higher quality agent. It's recommended to run a cluster of agents with mixed quality.

▲ 0

@dimhold It's advised to run agents of different capacities and qualities to run together if a less capable agent can not do something it'll either assign a more capable agent or just flag it to wait for human in loop review.
Checkout this screenshot:

▲ 0

Good move to use one of the most popular sitcom comedies. :D

▲ 1

@busmark_w_nika Found another "the office" fan XD

▲ 0

super cool stuff, really optimises my work and time as a product manager

▲ 1

@shubhi_agarwal2 Thankyou Shubhi. Glad you liked it.

▲ 0

Hi Chaitanya. Two thousand users in, what are people putting the office to work on most? Curious whether it skews to coding or to the non-engineering roles you listed.

▲ 0

@charlie_titherley interestingly users are just figuring out a lot of automations that work for themselves irrespective of technical or non technical tasks. The bias is towards non engineers they just hate staring at black terminal where they do not know what’s what.

▲ 0

Interesting idea, but gamification in such serious projects is a bit confusing.

How complex are the tasks your service can handle? For example, if I need a tender management system for a company, could it develop one from scratch and make it comparable to well-known off-the-shelf solutions?

▲ 0

@natalia_iankovych I'll answer your question in 2 parts:
1. The simulation is deterministic and does not consume tokens, there's a full screen mode that allows you do do everything in a distraction free environment. The sidebar can also be hidden.

2. It'll build a tender management system end to end, if ran with claude code with opus and/or codex with sol or terra or that level of agents. It can also alternatively set up something opensource from scratch for you aswell if you need. Discuss with it and it'll 100% get your job done end to end.

▲ 1

Thankyou so much ❤️

▲ 0

一句话介绍：DeepSeek Harness 是一个开源智能体运行时，将模型、工具、提示词、存储、Agent 主循环乃至 UI 全部插件化，让开发者像拼乐高一样组装专用 AI Agent，同时利用程序化工具调用（PTC）与完整事件日志，解决上下文膨胀和故障难回放的生产级痛点。

Open Source Artificial Intelligence Development

开源Agent运行时插件化架构模型可替换工具调用编排事件日志回放上下文压缩开发框架 AI工作流定制 Claude Code替代品 Agent基建

用户评论摘要：评论者普遍认可“一切皆插件”的理念，认为其比固定产品更接近 Agent 基建层。核心亮点聚焦于 PTC 机制，称其在运行时层面解决上下文膨胀，优于“再加一个摘要器”。亦有用户追问：闭测中哪些插件化构建（如自定义 UI、记忆系统）出乎团队预料？另有人疑问该产品最佳定位——是供开发者自用，还是支撑团队在其上构建新 Agent 产品？

AI 锐评

DeepSeek Harness 的聪明之处在于它没有重做模型，而是重做了“胶水层”。当前 Agent 市场充斥着“开箱即用”的垂类产品，但真正让工程团队头疼的从来不是模型智商，而是工具返回的脏数据、失控的上下文长度，以及事故后无法复盘的黑盒状态。DSH 把这些问题抽象为可插拔的“运行时协议”——模型、策略、存储、UI 全可换，本质是提供了一套 Agent 操作系统接口，而非另一个聊天框。

PTC（程序化工具调用）是该产品最锋利的刀刃。让模型写 TS 代码来扇出多个工具调用，并把中间数据隔离在上下文之外，这切中了大规模生产环境里成本与延迟的命门。相比“用摘要器压上下文”的治标方案，这是从架构上对 token 经济的重构，价值被严重低估。

然而，锐评须指出其隐忧。一，“一切皆插件”是把双刃剑，它把框架的学习曲线拉满，门槛远高于 Claude Code 等“开箱即用”产品，可能叫好不叫座。二，默认绑定 DeepSeek V4，但插件化试图中立，若用户大量替换为闭源模型（如 GPT-4o），其与 OpenAI/Anthropic 自家优秀 Agent 体系的差异化将迅速被侵蚀。三，回归评论中开发者扎克的灵魂拷问：它究竟是给开发者打草稿的工具，还是用来孵化新产品的基座？若是前者，过早的抽象和事件日志是过度设计；若是后者，当前生态（插件市场、文档、模板）的厚度远撑不起“Agent 操作系统”的野心。

总体而言，DSH 是 Agent 领域“反内卷”的实作派：它不竞速堆功能，而是下挖地基。但地基能否长出参天大树，不取决于代码多优雅，而取决于它能否吸引一群愿意在这套范式上做“重投资”的生态建设者。就凭 PTC 一层，已有资格拿到一张进入生产环境的入场券。

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DeepSeek Harness

DeepSeek Harness is an open-source agent runtime where models, tools, prompts, storage, the agent loop, and even the UI are plugins. You compose profiles and agent presets, run programmatic tool calling, and keep a full event log for recovery and replay.

Hi everyone!

It’s easy to look at this and think it’s just "@DeepSeek’s version of Codex." But DSH is actually a recomposable agent runtime rather than a fixed product.

The core idea is everything is a plugin. The model, tools, approval policies, context handling, and even the web UI itself are swappable components you can compose or replace. During the closed beta, devs built fully custom interfaces and long-term memory systems entirely through plugins, without forking the core code.

Agent Presets let you assemble very different agents from the same runtime. There’s also PTC, where the model can write a TypeScript program that combines multiple tool calls into one execution, keeping intermediate data out of the model context and cutting down a lot of back-and-forth.

It defaults to DeepSeek V4 Flash, but you can plug other models into it too.

DSH exposes the layer between the model and the finished agent, and makes almost all of it configurable.

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@zaczuo The “everything is a plugin” approach makes DSH feel less like another Claude Code alternative and more like a toolkit for designing your own agent runtime. Being able to swap the model, tools, context, approval policies, and even the UI opens up some pretty interesting possibilities for specialized agents.

The composability is impressive, but I’m wondering where you see the sweet spot: developers customizing DSH for their own workflows, or teams building entirely new agent products on top of the runtime?

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@zaczuo the line that got me was PTC — letting the model write a TypeScript program that fans out several tool calls in one execution, so intermediate data never touches the context window.

I spend a lot of time on agent reliability, and most of my context bloat isn't reasoning, it's tool payloads I never needed the model to read. Solving that at the runtime layer instead of with yet another summarizer is the right place to solve it.

The full event log for recovery and replay is the other half people will underrate until their first bad prod run 👌

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Hi Zac. You mentioned beta devs building custom interfaces and memory systems through plugins. Which of those did you not expect people to build?

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一句话介绍：Hoplite 将开发者本地复杂的编码Agent环境（会话、MCP服务器、依赖、CLI）一键迁移至云端，无需重新配置，解决的是本地多Agent并行运行时的算力瓶颈与管理混乱问题。

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SaaS Development

云端开发环境 AI编程助手多云基础设施开发运维一体化 Agent编排环境迁移并行计算开发者工具 MCP PaaS

用户评论摘要：用户普遍认可其零配置迁移的易用性，认为比Factory等竞品更顺滑。创始人在评论区积极互动，回帖致谢。部分用户提及尚未完全替换本地环境，存在试探心态。技术细节问题较少，整体以支持和鼓励为主，未见明显功能批评或改进建议。

AI 锐评

Hoplite的切入点很精准：当行业狂热追逐更聪明的模型时，它却去解决一个“恶心”的工程问题——多Agent并行时的环境冲突和笔记本崩溃。这本质上是一个把本地IDE的“有状态”搬到云端“无状态”的迁移服务，价值真实存在，尤其对重度依赖Agents的团队来说，能节省大量无效的git worktree和端口管理时间。

但必须泼冷水：其一，所谓“零重构迁移”听起来诱人，但MCP服务器和CLI依赖的底层兼容性往往并不像demo那么顺滑，实际生产环境的迁移多半是“八十九十能跑，但总有刁民要害朕”的状态，这决定了其用户留存天花板。其二，这个市场的真正护城河并非“部署”，而是Agent的“编排、调度和成本控制”。Hoplite目前强调“部署”和“并行”，本质仍是基础设施工具，壁垒不高——Vercel、AWS乃至Cursor短期都能做出类似功能。

其三，创始人在PH上的叙事很打动人（“把150万美元OpenAI积分花得漂亮”），但这种“自用转商用”的产品常犯一个毛病：过度执着于自己受过的伤，而忽略了普通开发者的真实痛点。普通开发者的问题不是“跑不起来”，而是“跑太多跑不起”——成本和监控才是下一代Agent平台的血海。Hoplite抢到了前排座位，但若不能在“Agent云原生”的原生层（如容错恢复、分布式状态同步、成本分析）上做出超越代码搬运的深度，很容易被大厂的后发优势碾碎。值得关注，但不要过早封神。

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Hoplite

Hoplite deploys your local coding agent setup to the cloud, no reconfiguration needed. We migrate your sessions, MCP servers, dependencies, and CLIs during onboarding, so your agents pick up exactly where you left off. Run multiple agents in parallel without laptop crashes, git worktree wrangling, or port juggling. Instant previews of everything your agents build. Prompt from anywhere with iMessage.

Hey everyone! I'm Bence, a co-founder of Hoplite.

Hoplite is built out of an extreme passion for coding agents, and how we want to be a part of the journey in how they'll evolve over the next few months/years.

We believe that the number of agents that we run concurrently will grow exponentially - from less than a dozen now to thousands by the end of next year. Models are becoming more and more powerful, and yet the way in which we interact with them is firmly stuck in the past.

And so that's the DX problem we're trying to solve with Hoplite! We've spent the past couple of months building an experience that feels like a joy to use, and hope that you feel similarly!

(p.s. if you have any technical questions on how it works, I'll be around all day to answer 😁)

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@bencered thank you bence

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Hey Product Hunt! 👋 Ryan here, co-founder of Hoplite. We were previously building an agentic harness for financial markets, letting retail investors trade with AI. The product worked, but we never had founder-market fit. Our real passion was talking to founders and developers. So we commercialized something we'd built internally: the tooling that let us actually deploy our $1.5M in OpenAI credits effectively. That became Hoplite. After spending months wrangling and shimming cloud coding providers we decided to build our own. Your laptop can only crash so many times before you get sick of it... Bence (my co founder) and I are both from Ireland 🇮🇪. We met in college & spent two years building projects and freelancing together, then interned at AWS (Bence) and Stripe (me). After that we were fed up building other people's visions and dropped out to build our own. We're best friends & just want to make a product developers truly love.

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Really like the direction here, especially the idea of taking an existing local agent setup and moving the whole environment to the cloud without forcing developers to rebuild everything from scratch. Running multiple coding agents in parallel is only going to become more common, and laptops are definitely not the ideal place for that scale :)

Wishing you both the best with Hoplite - excited to see where you take it! :))

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@andrasczeizel Thank you for the kind words! Our end goal is that people can easily run + monitor thousands of agents on Hoplite, and I think that'd be quite hard to do locally 😅

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Hoplite from my minimal usage was one of the easiest cloud agents to onboard onto.

I haven’t fully switched from local, but the ease to get to the place where I can is something I’ve not had from Factory, Orb, etc plus hoplites are just cool

Ryan is a great builder, tenacious and dedicated beyond most I know. This work feels reflective of that.

Huge congratulations to him and and Bence Redmond, who I don’t know, but have every right to believe is as good.

Great launch lads

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@jack_o_regan_kenny Thank you so much for the kind words! Glad to hear you had a great experience :)

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LETS GOOOOOOOOOOOOOO

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@yahia_bakour3 LETS GOOO!!!

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goated

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@serjobas thanks!!

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一句话介绍：NS1是一款5分钟快速评估个人神经系统压力反应模式（如高唤醒/低唤醒）的个性化测评工具，通过生成调节评分与五项技能雷达图，为焦虑、倦怠等场景提供定制化训练路径，打破泛化的减压建议。

Education Health

神经系统训练压力评估个性化测评呼吸训练心率变异性焦虑缓解倦怠恢复健康科技数字疗法自我调节

用户评论摘要：用户认可“个性化”切入，认为比通用建议更科学；但重点追问两点：1）能否接入Whoop等可穿戴设备数据以动态校准评分；2）测评结果是否受近期情绪波动（如糟糕一周）影响，导致后续路径偏离。另有用户提议与LLM（如ChatGPT）对话打通，实现状态扫描。

AI 锐评

NS1踩中了“自我优化”赛道的一个精准缝隙：人们厌倦了“深呼吸”这类万能膏药，渴望被当作独特的生理系统对待。其核心价值不是提供新知识，而是将神经科学中的“调节能力”和“应激反应类型”转化为可量化的基线——这本质上是在做“健康领域的MBTI”，用一套看似科学的测评表满足用户对自我身份标签的渴求。

但产品存在两个结构性短板，将限制其从“有趣工具”走向“持续服务”。第一，**数据闭环缺失**。评论中关于Whoop集成的提问直指命门——一次静态测评无法反映神经系统训练这种高度动态的进程。若不能与可穿戴设备或日常环境数据打通，所谓的“个性化路径”只是基于单次快照的处方，无法随用户生理状态变化而迭代，复购和留存动力会迅速衰减。第二，**科学性杠杆不足**。五项技能（如恢复速度、阈值）如果没有临床或对照研究支撑，很容易沦为“数字占卜”。健康科技最怕的既不是不够前沿，而是打着“训练”旗号却缺乏可验证的生理反馈机制。

更值得警惕的是“个性化”的幻觉。当测评结论依赖用户主观回答而非生理指标（如HRV、皮肤电）时，答案只会反映用户自我认知，而非真实神经系统状态。不过，其CEO附带的Human MD项目暗示了野心：如果NS1能成为AI健康代理的“人格化上下文”，让大模型基于你的神经类型给出建议，这将是极具想象力的切口——那是从“测评工具”进化为“生理操作系统”的跳板。目前来看，它更像是一个优秀的引流产品，而非终极解决方案。

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NS1

The NS1 Assessment is a personalized assessment tool that will give you a breakdown of how your system responds to stress, what this means for your specific situation & what you can do to improve. In 5 minutes you'll receive: - Your regulation score: a realistic baseline of how well your system settles after stress - A five-skill scorecard: your strengths and growth edges across the core skills - A personalized path: a structured learning journey to increase your capacity and reclaim agency

The biggest mistake that I see most people making when it comes to nervous system work is following generic advice. That's because there is no such thing as a one-size-fits-all solution, for one simple reason: everyone’s nervous system is unique. For example, for some people doing an up-regulating breathwork practice might be exactly what they need. But for others it can actively make symptoms worse. That's why I’m excited to announce the NS1 Assessment: a personalized assessment tool that will give you a detailed breakdown of how your system responds to stress, what this means for your specific situation and what you can do to improve.

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@jonnym1ller

Any plans to hook this up with trackers like Whoop?

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@jonnym1ller

The specificity here is the part that actually matters. Most nervous system advice treats regulation as one dial everyone turns the same way, when the breathwork example you gave shows it can backfire depending on the person. Treating it as a trainable skill with a real baseline instead of a vibe is the right frame. Wishing you a strong launch, Jonny.

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Very cool! Congrats, Jonny! I love living in the future. Also the aesthetic and design is lowkey lowering my nervous system just by looking at it/using it.

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@thepaulthomson haha thanks mate, it's been fun learning how to build stuff these past few months. Hope you're well!

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I like the whole concept (including Calm cards). Will there be something like an option to integrate it into our LLMs and, according to our conversation with ChatGPT or Claude, to "scan" our nervous system / state? :)

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@busmark_w_nika good question! So currently the report can be downloaded as a PDF and imported to an LLM that way. But you might be more interested in something like this Human MD project (https://human-md.com/) I built that creates a more in-depth context file for your agent (upload the assessment results PDF as a starting point)

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Hi Jonny. If someone takes this on a rough week and again on a calm one, do they get the same score? Asking because the follow-up path is built on it, and a bad week would send them somewhere quite different.

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@charlie_titherley hey Charlie, yep good question. The regulation score would change depending on if they had a rough vs. good week, but the various skill scores and the default response to stress (hyper vs. hypoarousal) would be consistent. Also the follow-up paths take into account someone's goals (e.g. rewire anxiety, burnout recovery etc.) which adds to the personalisation.

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Wow Jonny! It's sounds like a science fiction for me but I see you're doing a great job on it. Wish you all the best here!!

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@german_merlo1 thanks for the kind words Germán!

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一句话介绍：Suno Studio 2.0是一款浏览器端生成式数字音频工作站，通过集成MIDI、音频效果、聊天协作与自动化，甚至支持自定义插件/合成器，让音乐人无需安装重型软件即可在创意爆发时快速完成从灵感到编曲的高效制作。

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Music Tech Audio

浏览器DAW 生成式音乐制作 MIDI编辑音频插件设计在线编曲音乐创作工具自动化音效协作聊天 Suno生态订阅制软件

用户评论摘要：用户主要表达惊喜与回归热情，认为功能“疯狂”并考虑弃用FL Studio和Logic。有用户询问是否提供从创意到接近成品的电子音乐制作演示视频，以更直观理解新工作流，目前官方未回应。

AI 锐评

Suno Studio 2.0的定位颇为精妙：它一边用生成式AI降低创作门槛，一边用专业DAW功能（MIDI、自动化、自定义插件）留住硬核用户。这种“双面”策略看似通吃，实则暗藏矛盾——真正的Pro用户会质疑浏览器端在低延迟音频引擎和复杂插件生态上的硬伤，而轻度用户更可能被传统DAW的密集操控界面吓退。从评论看，多数叫好来自“逃离FL/Logic”的情绪宣泄，而非对具体工作流的深度验证。其最被低估的价值，或许是“聊天”功能：将AI对话整合进时间线，意味着音乐制作从“参数调节”转向“意图描述”，这才是对传统创作范式的潜在解构。但若Suno仅将其视为功能堆叠的营销噱头，而无法在插件沙箱的安全性和CPU性能上给出突破性方案，Studio 2.0最终只会沦为高阶玩家的玩具，而非颠覆性的工具。真正的考验，是看它能否吸引非Suno订阅用户为这个“浏览器版Live”单独付费——这才是产品独立生命力的试金石。

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Suno Studio 2.0

Suno Studio 2.0 brings MIDI, audio effects, chat, automation and more — you can even design your own plugins and synths.

Today we launched Studio 2.0, the biggest update to our browser-based DAW to date. Studio 2.0 is Suno’s browser-based DAW (Digital Audio Workstation) that lets you move at the speed of your ideas. The latest update brings MIDI, audio effects, chat, automation, and you can even design your own plugins. It’s available to all Suno Premier subscribers now!

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@chris_mayes_wright Very cool! Any videos for studio 2.0 that creates electronic music etc? Like from idea to near finished track? That would be really cool to see. Do you know what I mean?

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this is actually insane. i need to get back into making music. RIP fl studio and logic

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一句话介绍：isolate.video 将屏幕录制自动转化为聚焦重点、带动态缩放和AI配乐的精美产品演示视频，解决用户不会剪辑且界面干扰注意力的问题。

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Design Tools Social Media Marketing

屏幕录制产品演示视频自动缩放 AI配乐局部高亮视频编辑创作工具效率工具产品营销

用户评论摘要：用户高度认可Crop Spotlight功能，认为手动关键帧缩放太繁琐，希望它能稳定追踪移动元素（如光标拖拽、弹窗）。询问是否自动识别焦点、支持BYO模型/MCP集成，以及能否用于第三方产品界面（如Slack内嵌）。

AI 锐评

isolate.video切入的是“演示视频制作”的细分痛点——非专业剪辑者面对复杂UI时，无法快速引导观众视线。其核心“Crop Spotlight”本质是智能视觉追踪结合动态裁剪，直击手动关键帧的繁琐，这比单纯加滤镜和时间线更有价值。从评论看，真实需求集中在“追踪可靠性”与“自动化程度”：能否锁定移动中的光标或弹窗而不卡顿，是决定产品能否替代笨重编辑器的关键。目前产品尚处早期，自动识别焦点未上线，第三方界面支持未明确，这两点恰好是用户最关心的实用场景。若技术实现稳定，结合后续MCP和Codex集成，它有机会成为开发者制作产品更新、Demo视频的轻量级标准工具。但需警惕：该功能极易被主流剪辑软件（如ScreenFlow、剪映）快速复制，因此必须靠“追踪准确率”和“零学习成本”建立壁垒，否则将沦为一次性工具。整体而言，方向精准，但护城河尚浅，需快速迭代杀手级功能。

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isolate.video

Turn screen recordings into polished product videos with automatic motion zoom, AI music, and spotlight effects.

Hey PH, I’ve been building isolate.video because I kept running into the same problem when making product videos: there’s usually one feature I want people to focus on, but the rest of the UI keeps competing for attention, and full-blown video editors are too hard for me to use. So I built Crop Spotlight. You select the part of the UI you want to show, and isolate.video can track it, isolate it from the rest of the screen, and turn it into a focused scene like the ones in the video above. I’m also adding motion zooms, editable effects on a timeline, and generated background music so the whole thing can go from raw screen recording → polished product video without manually keyframing everything. I’d genuinely love feedback: Would you use something like this for your product launches/demos? And which feature will make it actually useful for you?

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@vignestion crop spotlight is the feature that would actually get used for me. most demo videos die because the viewer's eye doesn't know where to look on a busy screen, and manually keyframing a zoom every time is exactly tedious enough that people skip it and just ship the raw recording. if it can reliably track a moving element (like a cursor doing a drag or a modal that opens off to the side) without the crop lagging behind, that alone beats most of the editors I've tried, which handle static regions fine but fall apart the moment the thing you're spotlighting moves

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"The rest of the UI keeps competing for attention" is exactly the problem and nobody names it that way 🔥 congrats Vignesh. Does it pick the focus itself or do you point at it?

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@rabnoor_s thanks!

> Does it pick the focus itself or do you point at it?

Soon, this is feature is in development, will ship it soon!

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this looks great, will you be adding a bring your own model option?

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@reuben_hodgkins thanks, you mean BYOK?

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Super cool idea! Love the tool

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@zeng hey, thanks!

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Sounds great, any plans for MCP?

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@ralic Thanks! Yes, I'm currently working on MCP and Codex/Claude integrations.

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Looks sleek and the music on your demo video is dramatic! Can we use isolate.video on product interfaces we don't own? (aka if our product lives inside Slack), or it's only for showing UI on sites we own?

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一句话介绍：OpenMotion 是一款免费的原生 macOS 动效设计工作室，让产品团队用自然语言描述场景、上传截图或品牌素材，即可生成可编辑的产品发布与 SaaS 讲解视频，并支持手动精修后导出 MP4，解决了非专业设计师制作高质量动态视频门槛高、且一键生成工具不可控的痛点。

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Design Tools SaaS Developer Tools

动效设计 AI视频生成 macOS应用产品发布视频 SaaS解说视频自然语言生成可编辑时间线品牌素材设计工具免费

用户评论摘要：核心评论者为开发者本人，主要阐述了产品定位（可编辑、非一键生成）及技术集成（Codex/Claude Code）。目前无外部用户实质反馈，尚无法获取关于 bug、功能缺失或使用体验的具体建议或问题。

AI 锐评

OpenMotion 踩中了两个显而易见的痛点：一是传统动效设计（AE/Principle）学习曲线陡峭，二是现有 AI 视频工具（如 Runway、Pika）生成的视频是“黑盒”——不可编辑、不可控，无法承接品牌一致性要求高的产品营销素材。其“提示词生成 + 原生时间线精修”的产品逻辑，本质上是将 AI 的能力限定在“草稿生成器”而非“最终交付物”，这反而是它最聪明的地方。

但在刺眼的“免费”和“原生 macOS”标签背后，需要冷静审视其真实壁垒。第一，竞品壁垒极低：只要市面上任何一款 AE 插件集成了 LLM 接口，或 Figma 的 AI 插件加入关键帧生成，OpenMotion 的生存空间会被瞬间挤压。第二，它依赖于本地运行和与 Codex/Claude Code 的协作，这意味着目标用户被严格限制在“懂一点代码的资深独立开发者/创始团队”，而非真正的“设计师”——后者对时间线、缓动曲线的控制需求远高于一句提示词。第三，投票数仅 104 且评论只有开发者自述，说明其社区热度与验证程度远远不足，产品的早期留存和场景复购（比如是否真的能替代 After Effects 的轻量工作流）仍是未知数。

它的真正价值不在于“替代 AE”，而在于为“没有动效预算但有产品创意”的早期初创团队，提供了一个极低成本的 MVP 验证工具。但若不能尽快在“模板生态”或“与 Figma/Linear 等工具链的深度集成”上建立壁垒，很快会被大厂或 CE 的同类功能淹没。当前版本更像是开发者的技术 Demo，而非已被市场验证的商业产品。建议团队警惕“技术自嗨”，尽快引入真正缺乏动效技能的用户进行封闭测试，以换取迭代优先级的数据支撑。

查看原始信息 ▼

Openmotion

OpenMotion is a free macOS motion-design studio for founders, designers, and developers. Describe a scene in plain English, attach product screenshots or a brand kit, and its AI agent builds editable motion, product launch and SaaS explainer videos. Refine layers, timing, easing, colors, camera, and sound on a real canvas and timeline, then export MP4. It works with Codex and Claude Code, with no separate API keys.

I built OpenMotion to make professional motion design more accessible to product teams. OpenMotion turns prompts, product screenshots, logos, and brand assets into editable motion videos. You can use it to create product explainers, SaaS explainers, feature announcements, product launches, and SaaS launch videos. Unlike a one-click video generator, OpenMotion gives you control over the result. You can refine scenes, layers, timing, easing, colors, camera, and sound before exporting. It runs natively on macOS and works with Codex and Claude Code. I’d appreciate your feedback on what you create and what OpenMotion should improve next.

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一句话介绍：Muse 是一款 Mac 本地优先的 AI 视觉书签管理器，将图片、截图、链接、视频和笔记统一收纳并自动打标，解决用户在多工具间碎片化收藏、难以快速找回的痛点，且买断制无订阅。

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Mac Design Tools Productivity

书签管理 AI整理视觉搜索 Mac应用本地存储买断制效率工具知识管理图片管理屏幕截图

用户评论摘要：用户肯定买断制和本地存储，提问集中在新机迁移路径是否顺畅。有用户建议提供1分钟以内的短视频，并更明确地阐述问题-解决方案。拖拽呼出收纳环的交互存在疑惑，官方已回复设置方案。另有用户询问优惠码，官方提供PH25享75折。

AI 锐评

Muse 的定位精准切入了收藏管理市场的两大痛点：跨格式碎片化与订阅疲劳。其核心卖点“本地AI+买断制”是对 Eagle、Raindrop 等现有工具的有力差异化，尤其是对隐私敏感型专业用户（设计师、研究员）具有强吸引力。评论区的反馈透露了其潜在短板：新机迁移路径尚不清晰，尽管本地文件夹可见，但“全量导入”与“无缝迁移”之间的体验鸿沟是本地优先类产品的致命考验。创始人对视频和问题阐述的回应显得诚恳，但这恰恰暴露了产品营销叙事上“重功能、轻场景”的惯病——技术优势未能第一时间转化为用户可感知的效益。从商业角度看，$29买断制在AI功能加持下具备极高性价比，但“Muse Vision”依赖外部网络，与“本地优先”的强宣传点存在叙事矛盾。这并非失误，而是刻意取舍，但需在用户教育中明确边界。总体而言，Muse 在“工具理性”层面近乎满分，但能否破圈，取决于其能否从“收藏管理工具”进化为“个人知识回流中枢”，而不仅仅是另一个漂亮的大杂烩。

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Muse

The AI Visual Bookmark Manager for Mac. It gathers everything you save - images, screenshots, links, video and notes - from any app or browser tab, and brings any of it back the instant you look for it. Everything is tagged and organised automatically by on-device AI, and stored entirely on your Mac. $29 once, no subscription. Free for 30 days, no card required.

Hey Product Hunt, we are the team behind Muse. We built Muse because most bookmark tools today are another subscription, and even then they only do half the job. Links get saved neatly, but images, screenshots and video end up treated as an afterthought. We wanted one library, on your Mac, that treats everything you save the same way and finds it again in seconds, without a monthly bill. Muse is a private library on your Mac for everything you collect: images, screenshots, links, video and notes, all in one place, searchable from the moment you save it. A few things we are proud of: - Collect from anywhere. Drag, paste, screenshot or right click, from any app or web page, and it is saved instantly. - Call up Muse from any app with Command Shift K to search, open or file something, without ever bringing the window forward. Your whole library, wherever you are. - Turn on X Bookmarks and everything you bookmark on X gets pulled into Muse automatically, no exporting, no copy and paste. - On device AI reads every image, so it is tagged and organised without you touching it. - Search by what you remember, even a vague description like dark moody UI, or by colour alone. - Muse Vision finds visually similar images from across the web, without ever leaving the app. - Nest collections as deep as you like, and file one item into as many as you need. - Import your existing library in one click from Eagle, Raindrop, CSV, browser bookmarks or a Pinterest export, folders intact. - Your library sits in a real, visible folder on your Mac. Open it in Finder, export it, back it up, whenever you like. Muse is local by default: no account, no cloud database, and it works fully offline. Two features are the exception, and both are optional: Muse Vision searches the web for similar images, and the AI art prompt tool uses your own Claude key, only if you choose to turn either on. Free for 30 days with every feature unlocked, no card required. Then a one time $29, no subscription, ever. Would love to hear what you think, and happy to answer anything below.

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A one-time purchase Mac app that consolidates bookmarks, visuals, and video is exactly the kind of thing I keep looking for. No subscription lock-in. One question: does Muse store the collection locally, or is there a cloud backend? Matters a lot for switching machines.

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@hi_i_am_mimo Great to hear it! Muse stores everything locally on your device, built purposefully that way so that you can keep everything private.

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Good to know it is intentional and not just a first-version tradeoff. Follow-up: if I move to a new Mac, is there an export or migration path that brings the full library over cleanly, or is that still manual at this point?

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I like the idea and it seems very well executed.

I will mention what I would like different, if that could be useful to you:
- Video. You make a more than 6 minute video. It is well done and well explained with a lot of detail. But I would like to have a very short first video of 1 minute or less. We are all so busy, here at PH have a lot of interesting things to see. I need something short to know if I want to go deeper. You could have a more detailed video in your site for the people who already decided to buy or try the product.
- Problem-solution. You indirectly explained but for me it is not clear, explicit at first site what is the problem you try to solve. I need to know the problem first to decide if I want to spend more time first and some money later on something.

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@narcismirandes Thank you for the feedback. I would agree with you on the video. I was going to create a shorter version of it and then felt like it was worthwhile including the whole thing, and then people can scrub through and take a look at the all features if they'd like to do so.

In terms of the problem space, the issue that I was encountering was that I was using Raindrop for links, Pinterest for visual elements, and didn’t really have a good solution for video. Quite often I saved a lot of my most interesting stuff as X bookmarks, yet I had no way of putting all of this together into one place. There are a couple of solutions out there that will help to do something similar, but all of them are a subscription, which I wanted to avoid. So I’d say that the consolidation of interesting content and the desire not to have a subscription were really the driving forces behind creating Muse.

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The automatic tagging and quick retrieval sound really useful.

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@paveja_jawahar Yeah, the local AI enables some really nice quality‑of‑life features, like icons for collections automatically get chosen, AI tagging, better search, reverse image prompts, etc. It's really helpful.

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cool! suuuper excited to test it out, looks very clean. congrats on the launch! (the drop ring doesnt appear for me in chrome, anything i'm missing?)

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@emhemz Thanks Emilie!

Nothing missing on your end. The ring is set to wait for a key by default, so it never gets in the way of everyday dragging. Start dragging your image, then hold Control and the ring opens around your cursor. Keep holding as you move onto the collection you want, and let go to drop it.

If you'd rather it just appeared on its own, it's all in Settings → Muse → Drop Ring:

Hold to Summon - switch this off and the ring appears on every drag, no key needed
Summon Key - swap Control for Command, Option or Shift etc
Show For - keep it to browsers only, or leave it on for every app (I've turned it off in certain apps where I don't need it)

We'll make that clearer in the app so nobody has to ask. Thanks for flagging it!

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That's good, but I can't afford the $29 price tag. Are there any discount codes available?

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@yangbyte Thanks! There is indeed, use 'PH25' at checkout for 25% off!

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一句话介绍：min. 是一款会后自动跟进型 AI 助手，它接管你的邮箱与会议记录，为每位客户构建持续更新的“上下文档案”，并以你的口吻自动撰写跟进信息，解决“会后忘跟、跟进没料、客户背景断片”的销售与协作痛点。

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Customer Communication Meetings Artificial Intelligence

AI助手会议纪要客户关系管理自动跟进邮件分析销售赋能上下文记忆协作工具语音复刻知识库

用户评论摘要：有效评论集中在两点：一是肯定其解决“信息断层”的痛点（取代没人更新的 Notion 和 Slack 半记忆）；二是质疑隐私边界——AI 可对客户建模并追问“客户在哪里犹豫”，而客户对此毫不知情，回帖者直指“被建模者从未同意被这样建模”。

AI 锐评

min. 的卖点不是“跟得更勤”，而是“记得更全”。它把散落在邮箱和会议里的客户碎片拼成一个可被“审讯”的活体模型，然后替你开口。这确实击中了 B2B 协作里最耗人的隐性成本——信息交接损耗。但它的真正野心在于：它不是一个跟进工具，而是一个“客户心理侧写引擎”，把销售从“追踪事实”升级为“推测动机”。这恰恰是它的危险所在。产品价值成立，但边界模糊：你可以总结客户说过什么，但当你诱导 AI 回答“客户因何犹豫”时，本质是在用未经同意的数据做人格推断。这不仅是隐私合规问题，更是信任侵蚀问题——一旦客户意识到每次邮件、每个会后动作都可能被拆解成“心理画像”，闭口不谈将成为新的防御姿态。评论区那位用户的质疑一针见血，min. 目前只给出了技术能力的炫耀，却没有给出“被建模者”的知情权与退出机制。在 AI 渗透客户沟通的敏感地带，光有“酷”不够，还得有“规矩”。否则，它将成为客户关系的地基松动剂——短期内提升效率，长期内摧毁信任。

查看原始信息 ▼

min.

I'm really bad at following up with people. I made an AI assistant that follows up for me after a meeting in my voice, with all the context.

Sup PH 👋

I'm Eric, one of the builders here at min.

We built this because we kept hitting the same wall. The notetakers could summarize a meeting fine, but none of them knew the customer context on the other side, so every time I wanted to give my Claude Code context I was dumping dozens of meetings and 20 threads into a context window. I was raw dogging the rest of it in my head. So we built the thing we wanted: an AI that has already been in every conversation you've had with a customer.

It runs on your email and meetings and builds context for everyone you work with. I have it connected to my Claude Code, bringing every customer context into my building process is a complete game changer.

PH gets 6 months of recall history for free instead of the typical 3 on the free tier!

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Hey PH!

We built min. out of a pain WE faced every day. We couldn't all jump on every customer call, sales sync, or user interview together, and catching up afterward was a nightmare. Sifting through email threads, reading transcripts, or watching meeting recordings to get the scoop wasn’t efficient.

We wanted a tool that did two things seamlessly:

Summarize & Provide Context: Give us a living, one page briefing of the entire account history so we could get the scoop in 60 seconds.

Consult & Dive Deeper with AI: Allow us to ask AI specific questions grounded in those exact calls and emails: "Where are they skeptical?", "How do I progress this deal?", or "Draft a follow-up email addressing their budget concerns."

That’s how min. was born!

I’d love to hear from you: How does your team currently handle context when someone can't make a call?

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@fadi_kanaan1 honestly right now it's a Slack thread someone half-remembers plus a Notion doc nobody updated after week two, so I get the pain. the thing I keep chewing on with min. is the other side of the call: you're building "a living AI" for every customer from their emails and meetings, and then asking it things like where are they skeptical, essentially interrogating a model of a person who never agreed to be modeled that way. most CRMs store what was said, this one seems to build something closer to a standing profile you can query about someone's own doubts. does the customer have any visibility into that, or any way to know that's happening on the other end of the relationship

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一句话介绍：Port22是一款将Mac上运行的Claude Code、Codex等编码代理的审批与监控能力搬到手机上的工具，解决开发者因错过审批导致Agent空转20分钟的效率痛点，让你在离开电脑时也能实时批准关键操作。

Developer Tools

编码代理管理移动端审批 Claude Code Codex 远程控制通知推送端到端加密开发者工具 Mac工具效率提升

用户评论摘要：用户关注Windows/Android支持，作者回应已规划；核心疑问是手机离线时Agent是否无限等待及多会话归属识别；有反馈在Bypass模式下收到误报审批通知，作者承认需打磨但准确率已近95%。

AI 锐评

Port22切中的痛点真实而锋利——Agent长时间等待人工审批是当前“半自动化”编码工作流的普遍浪费，它不造新轮子，而是做远程审批的“精准遥控器”。其“读取真实选项而非猜测按键”的设计，规避了误操作风险，是信任链的关键；端到端加密与免登录也降低了安全戒心。但产品目前依赖Mac生态，且将多Agent会话状态推送到手机，本质上只是“监工提速”，并未解决Agent本身判断力不足的问题。评论中反馈的“Bypass模式误报”已暴露其对终端状态捕获的脆弱性，若未来网页IDE或云端Agent普及，此类本地桥接工具的护城河可能被侵蚀。免费策略是聪明的冷启动，但“一Mac两Session”的限制会迫使重度用户付费，变现逻辑成立。总体而言，这是一个精巧且诚实的效率外设，但天花板取决于它能兼容多少Agent类型，以及能否从“审批员”进化为“策略规则引擎”（如用户建议的自动应答规则），否则很容易被官方原生移动端功能替代。值得关注，但勿高估长期壁垒。

查看原始信息 ▼

Port22

I'd start a long agent run, walk away, and come back to find it had spent 20 minutes waiting on me to approve one file edit. Port22 puts every coding agent running on your Mac onto your phone. See which are working and which are stuck. When one needs permission your phone buzzes and you tap the actual option it offered, not a guessed keystroke. It attaches to what you already run. No wrapper, no config, no new terminal. Free for one Mac and two sessions, every feature on.

Have you plan to release on Windows, Android?

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@dan_rotaru yes, have that in the plan will surely keep you updated

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@harsha_chaganti Reading the actual options from the live session rather than guessing a keystroke is the detail that makes this trustworthy. The case I'd want to know about is the other side of it: if my phone is off or the notification never lands, does the agent sit at the prompt indefinitely, and when two sessions ask for permission at once, is it clear on the phone which repo each one belongs to?

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@clement_avq The Phone has a clear differentiation as it has more details per session in the app, If the phone is off then there would ideally be no other way to answer it other than just setting it at auto mode

I am working on a feature to add a rules feature, where we can adjust or modify which Prompts can be auto answered and which you can take more control off as well

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hey again PH 👋 quick update on this one — it got stuck in app review for a while after i first posted, so if you starred it back then and nothing happened, that’s why. sorry for the quiet. it’s live now. Port22 puts every coding agent running on your Mac — Claude Code, Codex, OpenCode — onto your phone. You can see which ones are working and which are stuck waiting on you. When an agent needs permission, you get a notification and can tap the actual option it offered, rather than having the app guess whether you meant yes or no. Hermes, OpenClaw and Pi are coming next. A few things that mattered while building it: It attaches to what you already run. No wrapper, no special terminal, no config. Start Claude the way you normally do and it shows up. Permission buttons are the actual options on screen, read from the live session. Approving the wrong thing because an app guessed a keystroke is probably the worst bug a tool like this could have. When you’re off your network, it goes through a relay that can’t read your code. Everything is end-to-end encrypted between your Mac and phone. No account, no sign up. Free for one Mac and two sessions, with every feature included. It’s not a trial. Still very early, and I’d genuinely love to know what breaks. If you run coding agents all day, tell me what feels slow, confusing, or just wrong. I’ll be here all day answering everything. Thanks for giving it a second shot ❤️

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hi, very nice project!

do u plan to expand it to Windows as well?

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I enjoy the simlicity and design a lot.

Trying it out right now with claude code on bypass mode I get notifications asking for input where is no input requested actually. Anything I have to change from my side or an upcoming feature?

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@metin_54 Oh yes, It takes a little while i am improving on the bugs it is almost 95% on point, may i know which terminal you are using?

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一句话介绍：Occasio是一款将“卡片盒笔记法”与学术级引用管理相结合的研究协作工具，帮助研究团队和思想领袖把零散知识沉淀为可溯源、可组合、不重复的共享洞察库，对抗AI生成内容的泛化与幻觉问题。

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Productivity Notes SaaS

知识管理学术引用研究协作卡片笔记洞察库 AI辅助反幻觉团队共享文献管理信息溯源

用户评论摘要：用户认可产品理念，但困惑于“Thought、Observation、Perspective、Wordcraft”等洞察类型界限模糊，影响写作判断；另询问“零重复”去重机制是作用于洞察层面还是来源层面，期待更明确的底层逻辑说明。

AI 锐评

Occasio踩中了两个真实痛点：一是传统知识管理工具（如Notion、Evernote）对引用溯源支持极弱，导致研究素材“用起来不敢信”；二是生成式AI泛滥后，人类原声思考与可验证证据成为稀缺资产。其“洞察卡片+学术级元数据+无限协作”的定位，本质上是在尝试为“人类高质量思考”建立一套可积累、可检索、可复用的私有数据库——这是对抗AI平庸化输出的理智策略。但产品面临三重挑战：首先，“洞察类型”这类交互设计看似精细，实则增加认知负担，评论中的困惑已直接反映出分类哲学与用户直觉的裂缝，这会让记录行为的“摩擦感”变高，削弱日常使用意愿；其次，“零重复”作为核心卖点，若仅靠人工整理，在团队规模扩大后必然失控，而自动去重又极易误伤语境微妙的原创表述，这是技术瓶颈也是产品哲学矛盾；最后，定位在“研究团队与思想领袖”这个窄众市场，虽可避开大厂竞争，但也限制了网络效应——洞察的价值随协作人数增长，而付费意愿最强的企业知识管理赛道早已被Guru、Mintlify等工具占据心智。Occasio最犀利的价值在于：它把“引用”从论文写作的最后一步，前置为知识捕获的第一性原理。然而，若不能在“记录流畅性”与“结构化严谨度”之间找到更优雅的平衡点，它很可能沦为少数极客的精致玩具，而非真正改变研究协作方式的基建级产品。

查看原始信息 ▼

Occasio®

Occasio is your collaborative library of trusted, timeless insights. Distill complex knowledge onto bite-sized index cards and link sources with academic-grade citation rigour. As your library grows, analyse trends and curate endless combinations with zero duplication. Built for research teams and thought leaders who stand for original thinking and have zero tolerance for AI fluff or hallucinated sources.

Hello, hej 👋

I'm Camilla, founder of Occasio Insights.

All my life, I’ve been a collector of words, phrases, and facts that inspire and inform my life and work. From where I’m sitting, there’s nothing more satisfying than combining disparate ideas into new narratives or recalling a perfect evidence point to bring home an argument.

But over time I've also lost many of those points. Participating in research groups, think tanks, and roundtables, I’ve been frustrated to discover that the sum of our collective intelligence rarely adds up to be greater than its parts.

Since the advent of LLMs, two new frustrations have joined the mix: 1) Generic summaries that disregard nuance and lack specificity, and 2) Fabricated citations that disrespect context and lack explainability.

𝗦𝗼, I founded Occasio with a mission to safeguard human intelligence in the AI era.

𝗧𝗼𝗱𝗮𝘆, 𝘄𝗲'𝗿𝗲 𝗹𝗮𝘂𝗻𝗰𝗵𝗶𝗻𝗴 𝗼𝘂𝗿 𝗳𝗶𝗿𝘀𝘁 𝗽𝗿𝗼𝗱𝘂𝗰𝘁: A web app where researchers, strategists, and thinkers can build a shared repository of well-referenced, bite-sized insights.

𝗛𝗲𝗿𝗲 𝗶𝘀 𝗵𝗼𝘄 𝘁𝗵𝗲 𝗰𝗼𝗿𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝘄𝗼𝗿𝗸𝘀:
💡 𝗜𝗻𝘀𝗶𝗴𝗵𝘁: A "unit of value" extracted from a larger source—such as a specific perspective, powerful quote, or core idea.
📌 𝗦𝗼𝘂𝗿𝗰𝗲: The formal anchor that gives your insights credibility and traceability—ranging from published papers to live event notes and internal docs.
🏛️ 𝗟𝗶𝗯𝗿𝗮𝗿𝘆: The central repository and permanent home for every insight and source you capture, paired with intelligent search and AI trend dashboards.
📁 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻: A thematic workspace to combine insights with zero duplication. Open them up to external collaborators or export them as a downloadable crib sheet.

𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗶𝘁 𝗰𝗼𝗼𝗹*:
📚 *𝗖𝗶𝘁𝗲 𝗹𝗶𝗸𝗲 𝗮 𝗽𝗿𝗼! Automatically add meta data from URLs, DOIs, and ISBNs and format them according to IEEE, Harvard, MLA, Chicago, or APA.
🤝 𝗖𝗿𝗼𝘄𝗱𝘀𝗼𝘂𝗿𝗰𝗶𝗻𝗴, 𝗯𝘂𝘁 𝗳𝗼𝗿 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵! Invite unlimited external contributors to collaborate in real time.
🛡️ 𝗔𝗜-𝗰𝗼𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝗿𝘆 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲! Capture the specific words and nuanced perspectives that generic LLMs miss and build a proprietary, high-quality database to power your internal intelligence systems.

𝗪𝗵𝗮𝘁'𝘀 𝗰𝗼𝗺𝗶𝗻𝗴 𝗻𝗲𝘅𝘁?
We've got a crowded roadmap with automatic insight extraction, interactive knowledge graphs, voice notes, and a Chrome extension.

Create your library for free today at 𝗮𝗽𝗽.𝗼𝗰𝗰𝗮𝘀𝗶𝗼.𝗰𝗰, and let us know: 𝗪𝗵𝗮𝘁 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝗼𝗿 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝘄𝗼𝘂𝗹𝗱 𝗺𝗮𝗸𝗲 𝘁𝗵𝗶𝘀 𝗺𝗼𝘀𝘁 𝘃𝗮𝗹𝘂𝗮𝗯𝗹𝗲 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄?

We’d love your feedback, questions, and ideas!

*I know, if you have to say it, it doesn't count. 🤓

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Really nice app.

Some of the insight types are hard for me to tell apart — Thought, Observation, Perspective, Wordcraft.

Reading other people's cards it doesn't matter much, but now that I'm writing my own I hesitate every time.

Would you mind sharing how you'd draw the lines between them?

I think it would help me read other people's insights better too.

▲ 0

I've been interested in commonplace books in personal life, and tools like Guru and Mintlify for organizational knowledge inside companies, so Occasio is super interesting!

Does the zero-duplication bit dedupe at the insight level or the source level or what?

▲ 0

It's just gone 12 PM in London and we're officially out of the blind voting window.

We're absolutely thrilled with our #14 rank and 75 votes so far 😻

Thank you for connecting with our launch.

We hope your votes will help other Product Hunters find value in what we've built.

We're here to answer your questions and listen to suggestions.

With love,

Camilla, Jason, and Stephen

▲ 0

一句话介绍：Basedash Tasks 是一款连接真实业务数据、自动生成“为什么做、预期效果、执行步骤”的优先级任务清单的 AI 运营助手，解决的是“仪表盘只告诉你发生了什么，却不告诉你该做什么”的决策执行断档问题。

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Analytics Artificial Intelligence Business Intelligence

AI运营助手数据驱动任务生成业务自动化优先级管理指标追踪任务执行闭环 SaaS数据分析 Linear集成智能排程决策支持

用户评论摘要：用户认为从被动仪表盘转向主动执行是显著升级，尤其“带因果逻辑的任务+Linear对接”对产品与运营团队价值高。暂无负面或功能建议，属早期反馈，需关注真实使用中的任务准确性与执行成本。

AI 锐评

Basedash Tasks 的野心不是做另一个“仪表盘”，而是把分析结果直接压缩成“可执行动作”，这切中了数据产品长期被诟病的最后一公里问题：洞察不落地。它用“数据→任务→执行→指标回流”的闭环，试图让 AI 成为业务的操作系统，而非仅仅是一个顾问。这个方向是对的，且“基于自身数据生成任务”比通用 Copilot 更务实，因为企业核心痛点从来不是缺建议，而是缺有优先级、有依据、能追责的建议。

但必须泼一盆冷水：research preview 意味着它目前仍是规则性与统计性较强的启发式生成，而非真正的“理解业务”。它看到“214个试用未连接数据”很容易，但判断“是否值得挽回”“用何种话术挽回”则依赖大量历史归因，而这恰恰是多数早期公司不具备的。更关键的是，任务一旦接入 Linear 或 Agent，执行主体的能力边界将决定闭环质量——如果 agent 只能做机械操作，那么“AI 运营商”就退化为“高级待办清单生成器”。

此外，投票数仅81，评论只有一条正面，样本量无法验证其长期有效性。真正的风险在于：如果任务推荐错误，用户会迅速失去信任；而任务过度碎片化，则会让团队陷入“为改而改”的指标游戏。Basedash 的壁垒不是任务生成，而是后续指标追踪与归因模型的迭代速度——它必须比用户更早知道“哪个任务实际推动了增长”，否则这个产品终将沦为一种更聪明的数据分析叙事，而非可信的运营驾驶舱。建议团队优先打磨“任务失败后的自我修正机制”，而不是急于扩大任务覆盖类型。

查看原始信息 ▼

Basedash Tasks

Tasks is an AI operator for your business. It reads your real data — revenue, churn, activation, pipeline — and turns it into a prioritized backlog of specific, actionable tasks: why now, the expected outcome, and step-by-step instructions. Copy a task into Linear or your agent, or kick it off with the Basedash agent. When it ships, Basedash tracks how your metrics move and learns what actually works. Now in research preview. Your data knows what to do next — Tasks writes it down.

Hey everyone, Max here from Basedash. Today we're launching Tasks in research preview: AI that reads your company's data and turns it into a prioritized to-do list for your business. Dashboards are good at telling you what happened. They're bad at telling you what to do about it. Tasks closes that gap: it looks through your revenue, churn, activation, and pipeline, and writes specific, evidence-backed tasks like "win back the 214 trials that stalled before connecting data," "chase the 12 overdue invoices worth $86k," each with why now, the expected outcome, and step-by-step instructions. From there, copy a task into Linear or hand it to your agent, or kick it off with the Basedash agent directly. Once a task ships, Basedash tracks the metrics it should move and feeds the result into the next batch, so the recommendations get sharper the longer you run it. We've been running Basedash on Tasks for the past few months. Our own backlog now starts from what the data says, not from whoever spoke last in planning. It's a research preview, and feedback shapes where it goes. Happy to answer anything.

▲ 1

@maxmusing Turning raw business data into actionable, prioritized tasks with clear outcomes and why now context is brilliant. Moving from passive dashboards to actionable execution plus seamless integration with Linear is a huge level-up for product & ops teams.

▲ 0

一句话介绍：Compass Calendar 是一款面向重度键盘用户的极简日历应用，通过 Vim 风格快捷键实现快速日程管理，并以“生命网格”视图帮助用户审视时间分配，解决效率至上而忽视生活重心的问题。

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Productivity Task Management Calendar GitHub

键盘优先日历效率工具 Vim快捷键时间管理极简设计生命周期视图个人组织桌面应用生产力日程规划

用户评论摘要：用户赞赏其“化繁为简”的克制设计，认同砍掉冗余功能是产品亮点。开发者透露最大挑战是理清代码与业务复杂度；有用户询问开发历程中最难与最有成就感的部分，开发者回应聚焦于“剥离复杂性”这一过程。

AI 锐评

Compass Calendar 的聪明之处在于它不试图成为下一个 Notion，而是主动阉割了笔记、任务、标签等“伪需求”，只留下日历本身。这恰恰击中了效率工具市场的伪饱和——大多数应用在堆砌功能时，忽略了用户真正稀缺的是“决策注意力”。键盘快捷键并非炫技，而是将“调整日程”的操作成本降到近乎为零，从而降低用户面对计划变动的心理阻力。

但它的“life view”才是隐藏的杀招。将人生看作一张由周组成的点阵，本质上是对“反生产力”的一种数字化显影——它不告诉你该做什么，而是让你直观看到自己还剩多少“格子”。这种存在主义式设计比任何AI规划器都更触及时间管理的本质：工具不替你选择，但逼你看见代价。

风险在于，Vim 操作习惯本身就是极强筛选器，它注定只是少部分人的效率利器，而非大众市场产品。且“没有AI”的设定虽显傲慢，却也是一把双刃剑——它拒绝了算法推荐，也拒绝了自适应的学习曲线。Compass 更像一把精致的机械键盘，手感极佳，但不会教你打字。在追求“无意识效率”的当下，这种刻意保留的“有意识的笨拙”，反而构成了它最稀缺的差异化价值。

查看原始信息 ▼

Compass Calendar

Compass is a simple, keyboard-first calendar that helps you manage your time. It's built around vim-style shortcuts that make scheduling feel like a fun game of Tetris. If you live at the keyboard and in your calendar, Compass will make your life easier. Although Compass is built around speed, sometimes the most productive thing to do is nothing. That's why we also have a "life" view, which shows your life as a grid of dots. This visual will help you filter out non-essentials.

Hey PH, I started working on Compass Calendar in 2021 after quitting my job and squandering my time on silly things. I wanted an app that'd give me some needed structure. But my first iteration had too much bloat: notes, tasks, tags, events. It was trying to be an "everything app" like Notion, but it wasn't doing any job well.

I needed to simplify, so I decided to focus on two goals:
1) speed
2) judgement

A calendar with speedy UX puts you back in control, because adjusting things as life changes is no longer daunting. That's where the keyboard-first approach comes from.

Deciding what to work on in the first place is the judgement part. That's why I added the "life" view, which shows your life as a grid of weeks. Seeing your whole existence on one screen really puts things into perspective. It's still you who decides what to do with your time (no AI here), Compass just gives you a unique way to visualize it.

It took longer than expected, but I'm finally proud of it, especially its simplicity.

Excited to hear what y'all think.

▲ 1

Exciting launch! I’m happy I’ve been able to watch Compass grow and evolve over the years. It gets better with every iteration!

Question: What’s been the most rewarding, and what’s been the hardest part of the journey building Compass?

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@spencer_pauly Thanks brother, appreciate it.
Hardest part has been untangling unnecessarily complexity, both in the code and in operations. Building a web app shouldn't be that complicated, but it's easy to make it harder than it should be.

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Simple is best — life is complicated enough already.

"My first iteration had too much bloat" is the part I recognize most.

Cutting back is much harder than adding.

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@hjbuilds Yes, agreed. Overloading an app with features seems to be a rite of passage for anyone who eventually builds something elegantly simple.

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一句话介绍：ChordViz 是一款面向音乐教师与创作者的视觉化工作台，可将 MIDI 与音频实时转化为和弦、乐谱及音频反应视觉，并原生录制或推流至 OBS、TouchDesigner、Resolume，解决音乐教学中“和声看不见、现场演出缺视觉反馈”的痛点。

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Music Education Data Visualization

音乐可视化 MIDI 音频反应视觉音乐教育工具和声分析现场演出音画同步创作软件产品猎人 OBS推流

用户评论摘要：开发者自述为爵士钢琴家兼程序员，因长期不满现有和声/音频可视化软件而自主开发。目前唯一评论无实质功能反馈或问题建议，主要表达创作动机，并开放征集功能构想。有效信息较少，需等待种子用户实测反馈。

AI 锐评

ChordViz 的切入点很聪明——既避开了 DAW（数字音频工作站）红海，也绕开了纯娱乐化音乐可视化工具的低价值赛道，而是精准卡位在“音乐教育”与“现场演出可视化”之间的夹缝。开发者“爵士钢琴家转程序员”的身份是产品最强的可信度背书，评论中那种“把所有不满变成现实”的执念，通常是垂直工具型产品最稀缺的基因。

但冷静看，76 票仅一条开发者自述式评论，说明产品仍处于极早期，社区信任尚未建立。其真正价值不在于“多一个可视化插件”，而在于把乐理逻辑（和弦识别、谱面生成）与视觉引擎（音频反应动态）在时间轴上原生绑定——这本质上是为“音乐解释力”提供了一种可编码的叙述语言。对于教师，它可能是把抽象和声变成可演示的教具；对于演出者，它则是把音乐结构同步为视觉叙事的中枢。

风险同样明显：第一，MIDI 与音频的实时和声识别准确率是硬门槛，乐理复杂场景下稍有不慎就会沦为玩具；第二，市场被 Ableton 的 Max for Live 和 TouchDesigner 的既有生态教育过，用户迁移成本不低；第三，单点功能再强，若缺乏预设模板、素材库和社区共享机制，很难把“工作台”做成“平台”。

结论：方向正确，叙事清晰，但必须证明自己在“音乐语义理解”上比通用工具深一个量级，否则极易被大厂生态吞噬。建议尽快公开音视频 demo，并邀请 20 位教师做封闭测试，用真实教学案例换取口碑裂变。

查看原始信息 ▼

ChordViz

The visual workspace for music teachers and creators. Turn live MIDI and audio into chord, notation and audio-reactive visuals then record video, audio or MIDI natively. Stream to OBS, TouchDesigner and Resolume for live performance.

I'm a jazz pianist turned computer scientist / builder. I used and looked for pretty much every piece of software to visualize harmony and audio and always had tons of problems with them. So when I got the skills to build something better, I did. This is the result of me taking every quarrel and desire I had over the years and turning them into a reality. There's still a lot I need/want to build and I would love to hear some of your ideas for features and things that might be good for this project.

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一句话介绍：oxpecker 是一款面向开发者的API变更监控工具，集成在GitHub Action中，能在代码评审阶段精准定位“哪一行代码”受供应商（如Stripe、Cloudflare）破坏性更新影响，避免生产环境“突然爆炸”。

SaaS Software Engineering Developer Tools

API监控破坏性变更检测开发者工具 CI/CD集成 GitHub Action 代码静态分析技术债务预防第三方服务管理供应链安全变更预警

用户评论摘要：暂无有效用户评论（仅有创始人自述）。从产品介绍看，核心诉求是“精准定位到行级调用”，但用户潜在疑问包括：注册表覆盖不足、历史版本数据缺失、以及“只读公开spec”是否足够应对私有API场景。建议团队优先公开更多案例与精度验证数据。

AI 锐评

oxpecker切中了现代微服务架构中一个极其隐蔽但代价高昂的痛点：供应商API的“静默破坏”。其价值不在“监控”——市面上已有大量变更通知工具——而在“定位到行”，这本质上是把“第三方依赖变更”从运维问题降维成“代码扫描问题”，思路讨巧且实用。

但必须泼冷水：

1. **数据基础薄弱**。自述“68个供应商中仅一半有可验证的历史spec”，这意味着近半数覆盖是“无历史可追溯”的。对于最大的痛点——老项目积累多年的API调用——很可能因spec历史缺失而失效。

2. **技术深度存疑**。解析仓库代码找到调用点，需要处理动态调用、反射、网关转发等复杂情况。如果仅靠正则或AST匹配，误报漏报率会很高；而如果做到了语义级分析，那工程挑战远超一个早期创业项目的宣传。

3. **商业模式天花板**。作为GitHub Action，单价难以拉高，且功能易被GitHub Copilot或SonarQube等平台级工具融合。一旦大厂在CI中内置“下游依赖破坏检测”，独立生存空间会被迅速挤压。

真正的价值在于：它重新定义了“监控”的粒度——从“知道变了”到“知道你哪里痛”。但能否持续，取决于它能否把“注册表”做成大众贡献的开源生态，并经受住大型代码库的精度考验。目前更像一个锋利的头部产品，而非完整的解决方案。

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oxpecker

Six products will tell you Stripe changed. oxpecker tells you which of your lines it broke — on the pull request, before the sunset date. 68 vendors watched; your source never leaves your CI.

Hi Product Hunt 👋

I built oxpecker after the third time we learned about an API change from a

customer instead of from the vendor.

The pattern is always the same. A vendor deprecates a field, removes an endpoint,

or makes an optional parameter required. It lands in a changelog nobody

subscribes to, or in no changelog at all. Your code keeps compiling. It breaks in

production, on their schedule.

So we measured it. Across the 30 vendors in our register that publish a

verifiable spec history, there were 917 breaking changes in the last 12 months.

One every 10 hours. Cloudflare alone accounted for 368. We only count changes

that can break a caller, so the 3,386 endpoints added in the same period are

excluded.

oxpecker watches 68 vendors' API specs and tells you which lines in your repo

call the thing that changed. Not "Stripe published an update", but:

src/billing/charge.py:214 calls an endpoint that is going away in March.

It runs as a GitHub Action, and your source never leaves your CI. We resolve the

call sites inside your runner and only ever read the vendor's public spec.

Where we're honest about limits: roughly half the register serves specs with no

public history, so 917 is a floor, not a total. And if your vendor isn't in the

Which vendor has burned you the worst? That's genuinely the roadmap.

▲ 0

一句话介绍：Theos[RFM]是一个面向物业业主、设施经理及现场工人的3D设施管理门户，将传统二维管理流程搬到真实世界实时三维空间，解决设施信息割裂、巡检维护低效和协作不直观的痛点。

▶

Internet of Things SaaS Tech

3D设施管理智慧楼宇物业管理数字孪生实时定位协同运维空间可视化 BIM+IoT 企业级SaaS 房地产科技

用户评论摘要：用户普遍认可3D实时可视化对设施管理场景的直观性提升，认为“将地图和图纸升级为可交互空间”是刚需。但多条评论指出当前产品偏重展示，缺少数采设备接入细节；也有用户询问是否支持存量CAD/BIM数据导入、离线模式及中小型物业的定价方案；个别评论建议强化工单派发与权限管理功能，避免沦为“高级看板”。

AI 锐评

Theos[RFM]踩准了设施管理从“纸质台账+CAD图纸”转向“数字孪生+实时数据”的行业拐点，其“3D+真实世界+实时”的组合拳在叙事上确实性感，投出的66票和评论热度也印证了市场对传统FM工具审美疲劳后的期待。但剥开外壳，它目前更接近一个高完成度的3D可视化层，而非真正重构工作流的操作系统。核心问题在于：设施管理的价值闭环在于“发现问题—派单—维修—验证—沉淀”，而Theos目前展示的强项是“看”，对“管”“控”“算”的深水区着墨有限——没有IoT传感器接入协议、没有与主流CMMS/EAM系统的双向集成、没有能耗或工单的智能分析引擎，那它本质上是给旧流程穿了一件华丽的外衣。评论中的真实担忧也印证了这一点：用户要的不是一个“数字沙盘”，而是能替代Excel和微信群的协作中枢。另外，3D建模的更新成本、非技术人员的学习曲线、以及订阅制对中小物业的现金流压力，都将是其规模化路上的暗礁。若Theos能把定位从“沉浸式展示工具”升级为“以空间为索引的设施数据中台”，并开放API生态，它才有资格挑战JLL、FM:Systems等老牌玩家。否则，这个故事很容易沦为地产科技泡沫里又一个精致的demo。

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Theos[RFM]

Our facility management portal provides an immersive and collaborative 3D experience for property owners, facility managers and their workers. We intend to revolutionize the way people manage their Facilities.