PH热榜 | 2026-02-12

一句话介绍：Starnus是一款AI驱动的B2B销售外拓平台，通过简单的提示词，帮助创始人和小团队在一个平台内完成从定义理想客户画像、寻找相似潜在客户、数据丰富、生成个性化外联到追踪回复的全流程自动化，解决了多工具拼接、操作复杂且成本高昂的痛点。

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Sales Marketing Marketing automation

B2B销售自动化 AI销售助手潜在客户挖掘出海营销一站式平台 SaaS 销售外拓客户画像个性化触达初创企业工具

用户评论摘要：用户反馈积极，认可其整合价值与创始人友好定位。主要问题集中于数据源与合规性、与竞品的差异化优势、日常操作流程与品牌安全、以及功能扩展性（如寻找投资者）。建议增加ICP构建向导。

AI 锐评

Starnus切入了一个经典且拥挤的赛道：销售自动化。其宣称的价值并非技术创新，而是体验整合。它本质上是一个“AI胶水”，将潜在客户数据、富化工具、邮件/LinkedIn序列器等现有服务封装在一个统一的AI智能体（Agent）架构之下，通过自然语言交互降低使用门槛。这精准击中了预算和精力有限的小团队及技术创始人的软肋——他们不愿也无力成为销售运营专家。

然而，其面临的挑战同样尖锐。首先，其“一站式”解决方案的护城河可能很浅。它严重依赖第三方数据和服务（如Smartlead, Unipile），这意味着其在数据新鲜度、送达率等核心指标上受制于人，难以构建绝对优势。评论中关于“如何避免成为另一个序列器”的质疑直指要害。其次，将复杂销售策略简化为“提示词驱动”是一把双刃剑。在提升易用性的同时，也可能导致策略肤浅和个性化失真，评论中关于“防止幻觉和品牌偏离”的担忧正是对此的预警。最后，同一天出现类似价值主张的竞品（如评论提及的Gro），说明市场准入门槛正在AI加持下降低，竞争将迅速白热化。

其真正的价值或许不在于替代所有“最佳单点工具”，而在于为早期团队提供了一个成本可控、快速启动的“最小可行销售流程”。它的成功将不取决于AI有多智能，而取决于其整合的流畅度、结果的可靠度，以及能否在简化操作的同时，不牺牲销售策略必要的精细度和人性化考量。这是一场关于体验、信任与执行效率的较量。

查看原始信息 ▼

Starnus

Starnus helps founders and B2B teams run outbound with simple prompts. Define your ICP, find lookalike prospects, enrich with business + contact data, generate personalized outreach, and track replies, end to end in one platform.

Hey Product Hunt 👋 I'm Khashayar, co-founder and CEO of Starnus. We built Starnus because as technical founders doing B2B sales for the first time, we were frustrated by how many tools you need just to run basic outbound , one for data, one for enrichment, one for sequences, one for tracking.

Starnus helps you find and reach your next customers on autopilot. It takes you from your ideal customer profile to booked meetings, all in one AI-powered platform.

The problem we kept running into was simple: Outbound sales requires too many tools, too much manual work, and too much budget — especially for founders and small teams just trying to build a pipeline.

That's why Starnus is built around one seamless flow: your ICP → matched prospects → outreach → meetings.

With Starnus, you can:

Bring your ICP and find lookalike prospects from millions of business records
Enrich with verified business + contact data
Generate personalized multi-channel outreach
Automate follow-up sequences
Track replies and engagement end-to-end

We made it founder-friendly from day one, plans start at just €20/month. No more spending hundreds stitching together multiple sales tools.

We're excited to share this with the PH community and would love your honest feedback. We'll be here all day answering questions.

🎁 Product Hunt launch offer: 14-day free trial, no credit card required 👉 Try Starnus: https://starnus.com/

Thanks for checking us out, and huge thanks to @chrismessina for hunting us 🙏

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@khashayar_mansourizadeh1 Congrats on the launch! What data sources or models power the prospect matching and enrichment features? And how do you handle data privacy and compliance when pulling and using contact information?

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@khashayar_mansourizadeh1 Love the ICP → meetings end-to-end flow. Curious how Starnus avoids becoming “just another sequencer”, what’s your edge in data freshness + deliverability + personalization quality that actually moves reply rates compared to stitching best-in-class tools together?

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@khashayar_mansourizadeh1 Hi, I have a business where we’re making custom websites very fast and low cost, and we’re interested in using your app for our clients and us.

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hey everyone 👋 i'm Ayda, co-founder of Starnus. while Khashayar built the tech, i've been on the other side actually doing the outbound, testing every tool out there, and honestly getting frustrated by how complicated and expensive they all are. that's what pushed us to build Starnus. we wanted something that just works without needing a sales ops degree to set up. would love to hear how you guys are currently handling outbound always looking to learn and make Starnus better!

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@ayda_golahmadi Such a needed product. As a technical founder myself, outbound tooling has always felt overly complex for what should be simple.

Saw your LinkedIn post and had to check it out here. Excited to see what you’re building with Starnus.

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Good luck guys with your launch!

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@dmitry_zakharov_ai thanks!

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@dmitry_zakharov_ai Thanks Dmitry

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Add an ICP builder wizard and you get the startup heads who don't have a degree in markeing.

WHat's nuts is that there's Gro launching today with a nearly identical value proposition.

I'm building too, ansd not really interested in building Wyet another..." it seems that many are.

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@osakasaul Very good point, we should indeed add an ICP builder

Actually good thing about Starnus is that it's like Claude, but for Sales and professional business work, so adding ICP builder means embedding such skills in the main AI brain (supervisor), since we already have 25+ specialized agents which cover web search, scraping and deep research, so we can do a comprehensive research, alognside a set of instructions that can help the system clearly define the ICP for the user.

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Congrats on the launch Ayda!

When you say “lookalike prospects,” what signals are you using? Industry + headcount + tech stack + hiring, or something else?

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@byalexai Thanks aleksander,Great question! We use a combination of signals, industry, company size, location, funding stage, hiring activity, tech stack, and recent growth signals like new funding rounds or leadership changes. But the real power is that you describe your ideal customer in plain language and our AI matches against 1.3B+ profiles to find the best fits. So instead of manually setting 10 filters, you just tell it what you're looking for and it finds lookalikes automatically. Beside this, lead gen is just 1 part of the entire offering, we can then deeply research companies, people, create messages, perform complete outbound campaigns on LinkedIn and via Email and many more things. In summary, Starnus can save 10s of hours for each GTM team member.

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How do you recommend teams operationalize Starnus day-to-day: what’s the ideal “human-in-the-loop” workflow (approvals, brand voice guardrails, compliance checks), and how do you prevent hallucinated or off-brand personalization from going out at scale?

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@curiouskitty very good questions, Starnus has the ability to stay on tasks and iterate for minutes-to-hours to get the task done (short term) and set future tasks for itself and invoke itself to continue the execution (long term -> days-to-weeks & can be recurring). So in summary, it can autonomously keep executing for weeks/months, but, we recommend users to keep the execution loops to 1-2 days max, and let the main AI supervisor to check with them critical things.

Beside this, the main supervisor has strict instructions to check critical things with users before execution, such as the messages to be sent.

But at the end, Starnus is the Claude for professional work (e.g. outbound sales), which means, system is open and extremely adaptable.

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Can I also use Starnus to find and contact investors? Or talent? Besides using it for customer acquisition

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@peeedraam of course, at the end, investor finding and talent finding is some kind of lead sourcing, so you can do all of them with Starnus

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Defining ICP with simple prompts sounds practical. Many founders struggle with who to target first, so this setup alone can bring clarity before sending a single email.

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@md_saifali exactly, and having a good ICP means you can have a solid starting point

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How do you track replies? Does it connect to Gmail/Outlook and auto-categorize responses?

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@xanderiang For email automation, we utilize Smartlead and for now only supports Gmail mailboxes. Yes, we track the replies.

For LinkedIn, we track the connections and messages too.

We added a new feature, that allows our databases (leads lists) to track interactions on LinkedIn, even if you do outreach and connections manaully.

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Curious how you handle data accuracy and deliverability at scale. Congrats on the launch 🚀

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@nick_glam thanks!

Starnus is the intelligent layer on top of 10s of well known and established apps, software and databases. For LinkedIn automation we have Unipile agent (integration) and for Email automation we have Smartlead. Both services are famous establishes solutions that guarantee deliverability.

Regarding accuracy, we have 15+ integrations to different databases, such as People Data Labs, DropContact, various LinkedIn sources and so on, and the data is 98-99% accurate. Some email addresses or phone numbers may have lower confidence, which will be indicated on the list.

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I have seen your social media posts, have to say that you really did a cool job with marketing part :)

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@busmark_w_nika great, thanks! Did you see those on LinkedIn or X?

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Congrats on the launch @khashayar_mansourizadeh1 @ayda_golahmadi

It's crazy how many integrations Starnus has, really amazing job!

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@ayda_golahmadi @mahdi_nouri thanks! it took a bit of a time to integrate all, but the outcome is good :)

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@mahdi_nouri thanks mahdi

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Congrats on the launch 🚀 @ayda_golahmadi @khashayar_mansourizadeh1

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@ayda_golahmadi @nafis_amiri thanks!

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@nafis_amiri Thanks Nafis

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The pivot from modular robotics to AI agent orchestration is a really compelling backstory. It takes guts to recognize when the market isn't moving fast enough for your hardware vision and shift entirely to software. I'm curious about how the agent marketplace works in practice, when a user submits a prompt for a sales task, how does Starnus decide which agents to orchestrate? Is there a ranking/reputation system for the agents, or is it more deterministic based on task type? Also, what's been the biggest surprise since shifting from hardware to the AI suite in terms of what customers actually want vs. what you expected?

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@diegodau very good point. Here is the layered architecture:

1. Central brain and supervisor
2. Hyper professional skill sets for the supervisor
3. Agents registry

Supervisor decides which agents to use based on a combination of information, the registry data, what the skill set says, and the generic instructions it has. Registry contains the reputation and technical details.

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Love seeing more tools tackling the outbound automation space. The biggest pain point I see with clients is jumping between 5+ tools just to run one campaign. Consolidating prospect discovery → enrichment → outreach in one flow is the right direction. Upvoted! 💪

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@heismiracle thanks! indeed, the main intention is to replace the chaos of having many tools and subscriptions

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How do you avoid deliverability issues when users start sending outbound? Any guardrails or warmup guidance? Tries similar tool before but ended in spam.

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@yurii_demchenko very good point. Starnus has 25+ AI agents which are integrations with best-in-class 3rd party apps, databases, software. So, we use trusted solutions to get the work done.

For email automation, we use Smartlead, which does warmup and entire campaign setup.
For LinkedIn automation, we use Unipile, again handles everything.

Beside these, we support Google suite, Outlook, Hubspot, etc. which you can use for low volume personal use too.

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Congrats, this is very much needed!

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@toni_lop thanks!

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@toni_lop Thanks a lot! 🙌 f you try it, I’d love your feedback on what should be even simpler or faster.

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This is a huge pain solver

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@faux16 thanks! indeed it is

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Outbound tool overload is painfully real. Bundling this into one flow makes a lot of sense.

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@shreya_chaurasia19 indeed, very true point

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Wow, seems powerful - you can then send linkedin messages and emails directly from platform?

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@daniele_packard yes exactly

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Is it mainly for email, or does it support LinkedIn outreach too? If yes, how do you handle sending limits safely?

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@sudhir_prasad indeed, both are supported.

For email, we try to keep 50 emails per day per mailbox, and we take care of warmup too (all via Smartlead)

For LinkedIn, it depends on your personal LinkedIn account's tier, for Premium, the limit is roughly 200-250 connection requests per week, and 150 messages per day

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Curious if you support multiple campaigns at once or if it’s more one-at-a-time today?

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@victoria_sukhenko of course, you can create as many campaigns as you want, but, for email automation, you can add up to 10 Gmail mailboxes per Starnus account. Assuming 50-60 emails per day as safe zone per mailbox, you can max go up to 500-600 emails per day (only on Email automation).

You can add personal mailboxes to Gmail and Outlook agents too, which each can send another 50-100 emails per day (to be safe).

And you can connect your LinkedIn account to LinkedIn automation agent to send 50-200 messages per day (depends on your LinkedIn account's tier).

So you can push to 700-1000 reachouts per day with 1 Starnus account.

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I’m curious where Starnus fits in an existing stack. For example, if I already use a CRM and I already have a lead list, can I plug Starnus in just for enrichment + messaging + reply tracking? Or is it intended more as an end-to-end system where the whole workflow lives inside Starnus?

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@kristina__grits regarding CRM, we have the Hubspot agent (integration), and if you have an existing list, you can certainly drop it there, as text or pdf (text is faster) and then ask the system to enrich + analyze + provide messaging tips. Although it's best if your list has lead's LinkedIn profiles already available, otherwise, best is to drop batches of 10 names, ask the system to use "Web Search -> Perplexity agent" to find their LinkedIn profiles, and then once your list has all the LinkedIn profiles ask for "enrich + analyze + provide messaging tips".

This is mainly because a lot of our databases are based on LinkedIn, and having access to the lead's LinkedIn profile is important.

When you have the completed leads list, you can ask the system to do LinkedIn and/or email outreach

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B2B sales background here — curious about the failure modes. What happens when Starnus sends 50 messages and gets zero replies? Does it diagnose why, or just flag it and wait for you to fix the copy?

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@klara_minarikova Since Starnus has a central AI entity running all the agents, it's very similar to how Claude works, which means, you can ask the system to run diagnosis and analysis to find what went wrong. So, it can definitely do this.

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Starnus? Sounds like the ultimate Battle Station for outbound sales:D Love the focus on lookalike prospects - it’s like having a Jedi mind trick for my ICP. Is there a limit on how many lookalikes I can generate per prompt?

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@kostfast thanks! technically no, but it's recommended to limit the number of leads to be found to 100. You can find more details about the costs and numbers here too: https://starnus.com/pricing/

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Congrats for the launch!

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@nathan_gatling thanks!

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Does Starnus help with follow-ups automatically, or is it more about drafting and tracking?

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@ititov_agency Absolutely, it's a completely intelligent entity with full capabilities of LinkedIn and Email automation, so it can definitely do follow ups and much more

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Congrats!

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@ititov_agency thanks!

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Does the lookalike AI actually scan the website content of my current clients to find matches, or does it rely mostly on generic LinkedIn industry tags?

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@valeriia_kuna can be both, we can search through 10s of databases with keywords to find companies and/or leads (people), or, we can take a list of companies/websites and analyze them in great detail

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Hey team @khashayar_mansourizadeh1 @ayda_golahmadi congrats on the launch! Honest question - how is this different from Clay + Instantly combo? Looks so similar, but still eager to try.

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@kate_ramakaieva very good point, Starnus is a true AI employee/colleague, which has an intelligent central brain (supervisor) connected to tens of specialized agents, and agents' job is to establish the bridge between Starnus and 3rd party solutions such as CRMs, apps, web search, scrapers, Instantly, Smartlead, Unipile and so on.

So in summary, we utilize all those traditional SaaS solutions, by building an agentic layer on top of them, so they are controllable with language.

What is the main point here? Removing the complexity, expensiveness and time-consuming work of handling 10s of SaaS solutions manually, and letting Starnus do all the work for you, like a professional sales employee, and more importantly, not thinking about any subscriptions.

When you upgrade even to the most basic plan (Pro) which is just 20 euros per month, still you get access to everything, all premium databases, email automation, LinkedIn automation and so on.

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一句话介绍：Gro是一款AI销售协同平台，通过整合超10亿实时数据库、AI倾向性评分与多渠道自动化，在一个统一的工作流中解决销售团队在潜在客户开发、定位、触达及意向追踪中数据割裂、效率低下的核心痛点。

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Sales SaaS Artificial Intelligence

AI销售助手智能获客潜在客户评分多渠道自动化销售流程一体化 B2B销售工具意向信号追踪销售效率平台精准外联数据驱动销售

用户评论摘要：用户普遍认可其“一体化”价值，盛赞其终结了多工具切换与CSV导出的噩梦。核心关注点在于：AI架构与数据源细节、从现有工具迁移的收益与代价、个性化与规模化平衡、以及防止AI幻觉的保障措施。团队回复积极，透露了自动监控、深度集成等路线图。

AI 锐评

Gro所标榜的“AI销售引擎”并非空谈，其真正锋芒在于对传统销售工具范式的“反动”。它没有在“如何更快地发送更多垃圾邮件”的路径上内卷，而是直指病灶：销售团队的困境并非执行不力，而是决策失焦。产品将超大规模实时数据库、AI倾向性评分与多渠道触达强行整合进一个封闭工作流，这种“固执己见”的设计，本质上是用产品逻辑强制矫正低效的销售习惯，以牺牲边缘定制化换取决策与执行的零摩擦。

其宣称的5-13倍连接与回复率提升，若属实，核心驱动力并非更花哨的邮件模板，而是前置的“AI协同”层——它充当了过滤与决策大脑，将“ spray-and-pray”（广撒网式祈祷）变为“狙击”。这触及了销售科技演进的深层逻辑：从自动化工具到决策支持系统。然而，其挑战同样尖锐：首先，“一体化”是双刃剑，如何说服已深度投资并定制了现有细分工具链的团队进行“范式迁移”？其次，其AI的“精准”严重依赖数据质量与算法偏见控制，如何保证超10亿数据库的实时性与合规性？最后，当所有团队都使用类似的“精准”引擎时，竞争是否会从“噪音竞赛”升级为更隐秘的“信号争夺战”？Gro的价值不在于又一个自动化工具，而在于它试图重新定义销售效率的公式——从“更多触达”转向“更优决策”。成败关键在于，多少销售团队愿意接受这种“被指导”的智能，并为此放弃熟悉的碎片化控制感。

查看原始信息 ▼

Gro

When sales teams drown in data, Gro turns it into action. Gro is a unified AI sales engine that brings prospecting, targeting, outreach, and intent tracking into one clean workflow. Powered by a live 1B+ database, AI-driven propensity scoring, multi-channel automation, and intent signals, Gro helps teams focus only on accounts that actually matter. No exports. No fragmented tools. Just precise outbound, end to end.

Hi Product Hunt 👋

I built Gro because I got tired of pretending outbound sales was a tooling problem.

We saw more automation and sequences, and somehow we got fewer real conversations. In 2024, average connection rates fell to ~3% and reply rates to ~1%, yet the response was simply to automate harder (to be honest, we tried that first)

But then we realised that the problem wasn’t effort or execution. It was thinking.

Sales teams don’t struggle because they can’t send messages. They struggle because they don’t know who actually matters, why they matter, or when to engage.

So we built Gro as an AI Sales Co-Pilot (and not another spray-and-pray engine): a reasoning system that helps teams decide who to reach out to, what to say, and when to engage using live data, intent signals, and context.

Gro doesn’t make you louder. It makes you more precise. It’s opinionated, a little uncomfortable at first, and far more effective. Teams using Gro are seeing 5–13× improvements in connect and reply rates.

If outbound has started to feel broken to you too, I’d genuinely love your feedback.

— Leo

PS: Product Hunt exclusive: Use PHGRO20 for 20% off your first 3 months.

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@leo_aj 🚀 Let’s go, Product Hunt! So proud to be building Gro with this incredible team

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@leo_aj Thrilled to be part of the Gro team launching today! We’re on a mission to make outbound precise again 🚀

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@leo_aj Congrats on your launch Leo! Can you walk us through Gro’s core AI architecture? How does the AI drive prospecting, outreach, and intent scoring?

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Finally! The 'export to CSV' nightmare is over. 🎉
As someone who has juggled 5 different tools just to get a single prospect list, the idea of a unified workflow with a live 1B+ database built in sounds like a dream. The propensity scoring is a game-changer; no more guessing which accounts are window shopping vs. ready to buy.

Congrats on the launch!

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@abod_rehman thanks! we're excited to keep building and improving the models + workflows.

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Really impressed by what the Gro team is building here.

It’s not just another automation tool, the reasoning layer that helps you prioritise the right accounts and craft more thoughtful outreach is what makes it stand out. For anyone struggling with noisy outbound and low reply rates, this feels like a smarter direction.

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@cen_xu Thanks Ethan, will love to see you give it a try.

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Integration with HubSpot right out of the gate for Enterprise is a huge plus！

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@cruise_chen Glad you see the value there.

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@cruise_chen thx and we are in the process of adding gro in hubspot marketplace

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This is a very compelling direction, sales tools that reduce noise than amplify it become infrastructure

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@sunaina_ukil1 yes, we worked hard to figure that part out rather than add to the noise.

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Multi-channel sequences (LinkedIn +Facebook+ Twitter+ Email) synchronized? That’s 10x the surface area.

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When a team is already using a contact database + a sequencer + an intent tool, what’s the strongest reason they switch to Gro—where do you see the biggest compounded win from being unified, and what’s the main thing they have to give up to get it?

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@curiouskitty This is such a good question. The strongest reason teams switch isn’t usually “we need another tool.” It’s that they’re tired of stitching three decent tools together and still feeling slow.

When your database, intent signals, and sequencer are separate, you introduce friction at every step by exporting lists, cleaning data, re-scoring leads, rewriting messages, syncing to CRM. Each handoff adds latency and context loss.

The biggest thing teams have to give up? Customization at the edges. If a team is deeply invested in a very specific stack or niche workflow, moving to a unified system means trusting one platform to handle more of the chain. For some teams, that’s a mindset shift. You trade a bit of modular control for speed, coherence, and less operational drag.

The compounded win of unifying it isn’t just convenience; it’s momentum. The other big unlock is the AI co-pilot layer. With Gro, it doesn’t just automate sequences... it learns your offerings, your ICP nuances, your tone, and even your personal messaging style. Over time, it understands how you position value and adapts outreach accordingly. That’s hard to replicate when your data, automation, and messaging brain live in different systems. The teams that switch to us are the ones who realize the real cost wasn’t tool pricing but fragmentation.

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@curiouskitty this is something i referred as "tab tax" every time that you switch between app, between each tab, it cost time and money.

The biggest compounded win from being a unified one is efficiency gain and cost saving. One that replaces many. and it also sets the foundation for the auto-pilot mode which we are working towards to. thx for your feedback.

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Hi everyone, Alexis here - co-founder of Gro. This is honestly the pain we kept running into ourselves, and seeing others run into. Leo and I used to run a pretty big startup accelerator program and community, one of the biggest recurring pattern we see is: We’d spot a great signal, get excited… then lose momentum switching tools, exporting lists, rewriting messages, syncing CRMs. By the time everything was ready, the moment didn’t feel so fresh anymore.

That frustration is what pushed us to build Gro the way we did, keeping discovery, reasoning, and outreach in one flow so you can just act. Curious how everyone else here is handling outreach right now. What’s actually working for you? And what’s driving you slightly crazy? Happy to collect more stories so that we can make Gro better and outreach less painful!

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Congrats on the launch! Love seeing AI applied to the full “prospecting → outreach → follow up” loop, not just generating a generic message. Curious what inputs you rely on to avoid shallow targeting, and what guardrails you have to prevent hallucinated company or persona details.

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@zongze_x there is a maturity model based on whether we have gathered enough info from the user, which then triggers the search function. and a few guardrails on the prompt and context engineering front to stop hallucination. thx for the feedback, appreicate if you can sign up and give it a try.

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This looks like a massive time-saver for prospecting. I've just checked an export sample and the data structure (LinkedIn URLs, company size, and follower counts) is incredibly clean, which is rare!

Since you have Webhooks and Slack in the roadmap, will it be possible to trigger an automation or notification every time the AI finds a new profile that matches my specific search criteria? That would make the prospecting process truly 'autopilot.' Congrats on the launch, the product is really solid! Already Upvoted! 🚀

▲ 1

@giorgio_cignitti_phd That's a really good idea, i'll definitely add it to the roadmap!

▲ 0

@giorgio_cignitti_phd thx for the feedback. yes it is possible. we did hubspot integraiton and with more integration on request. Gro v3 will be a completely auto-pilot with less than 3 touch points on the human side. and yes, it will notify you. btw, we will be adding a monitoring feature by the end of Feb, that allows you to monitor keywords and intent signal based on LinkedIn post.

thx for your vote Dr. Giorgio.

Leo

▲ 0

Congrats on the launch! What sources do you use to create lists of prospects?

▲ 1

@alina_petrova3 Thank you! To answer your question: Gro pulls from a combination of proprietary datasets, trusted data partners, and real-time enrichment signals. We’ve also built our own indexing and filtering layer on top, so users can search in natural language and refine leads conversationally instead of relying on static lists.

▲ 3

@zaczuo Thanks for the hunt! Honored to have your support.

▲ 1

I can see a potential upgrade of sale teams that use Gro. 1B+ database is insane

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@frank_li13 Thanks, pls give it a shot.

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@frank_li13 i think we are probably the only product that has the datapoint at this scale, apart from LinkedIn.

▲ 0

This hits on a real pain point. I've been on the receiving end of so many LinkedIn outreach messages that are clearly templated: "I noticed your background in [INDUSTRY]" with zero actual personalization. The fact that you're combining intent analysis with AI personalization could actually make automated outreach feel human again. Curious: how do you handle the balance between automation volume and keeping messages genuinely personal? At what scale does the personalization quality start to degrade?

▲ 1

@diegodau good question. Firstly, we start from the top, and qualify all leads at the search level. Most of LinkedIn outreach relies on a search URL generated from LinkedIn, and you have no ability to filter them out, whereas we let you search with precision and conduct a propensity score. So you start with a much more precise base. 2ndly, we analyse multiple data points, e.g. profile, website, post, etc., to generate a personalised message, more importantly, with context.

and we are pushing to support 5 rounds of conversation without losing the context.

Give it a shot, try for yourself, man.

Leo

▲ 1

You mention Gro is opinionated — does the reasoning engine actively prevent me from emailing low-propensity leads to protect my domain reputation?

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@valeriia_kuna the way we approach this is with a propensity score + AI recommendations on a particular lead. Our users enjoy the ability to rank/score leads to determine priority. You'd be able to weed out the low-propensity leads from your outreach efforts to keep the quality of your outreach, and protect domain reputation.

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@valeriia_kuna it provides you with recommendations, e.g. to hold on to the outreach and suggestions to do more search via gro..

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Gro's B2B-specific marketing model eliminates the need for manual company searching and screening, which requires powerful real-time data and analytics capabilities—it's fantastic.

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@gxy5202 Exactly. We got tired of seeing reps act like researchers instead of sellers. Moving the 'screening' into the engine is how we get those 13x reply rates, it’s all about focusing on the right signals in real-time. Thanks for the support:)

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Love the alignment between the Propensity score and the ICP Canvas. Pure strategic alignment.

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Gro’s 4-layer agent architecture is a masterclass in modular product design. Super clean.

▲ 0

@eeeeeach Really appreciate that! We obsessed over the modularity to ensure each 'agent' could reason independently before passing the baton. It’s what keeps the system precise rather than just fast. Glad the architecture resonated with you!

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Intent signals are useless if they aren't tied directly to the workflow. The fact that Gro has a live database AND multi-channel automation under one roof solves the latency problem. By the time you export data from a traditional tool, the intent is stale. Congrats on shipping this, Gro team!

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@avery_thompson2 Thanks Avery! We really appreciate you seeing why we built on a live database, the real value only happens when signal → reasoning → outreach happens in one continuous workflow.

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@avery_thompson2 thanks!

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How many languages does AI personalization support at the moment? Do you plan to add more?

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@kristina__grits we support 8 languages so far - let me know what lan that you have in mind?

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一句话介绍：一款通过语义理解原始素材、根据用户文字描述自动剪辑成片的AI视频助手，为内容创作者省去繁琐的素材整理和粗剪工作。

▶

Marketing Artificial Intelligence Video

AI视频剪辑智能粗剪视频内容创作自动化编辑社交视频制作创意辅助工具 SaaS 人工智能应用生产力工具内容营销

用户评论摘要：用户普遍认可其解决“重复剪辑痛点”和“自动挑选最佳镜头”的核心价值。主要问题聚焦于：定价模式与迭代成本控制、输入输出时长限制、是否支持与传统非线性编辑软件集成，以及对AI未来如何重塑剪辑工作流的探讨。

AI 锐评

EditWithAva并非又一个AI生成视频或简单剪辑工具，其核心价值在于充当了一个具备“语义理解力”的初级剪辑师。它直面视频创作中最耗时、最枯燥的环节——从数小时杂乱原始素材中完成粗剪和基础组装。这瞄准了当下短视频规模化生产中最真实的痛点：创意者时间成本高昂，重复性劳动挤压了真正的创作空间。

产品巧妙地避开了与Final Cut Pro、Premiere等专业软件在精细控制上的正面竞争，转而用“对话式编辑”开辟了一个新战场。其真正的颠覆性在于，它试图改变视频制作的工作流范式：从“在时间线上手动操作”转变为“用自然语言下达创意指令”。这降低了专业门槛，但更重要的意义是，它将创作者的精力从执行层重新分配至创意与策划层。

然而，其面临的挑战同样清晰。首先，信任壁垒：将原始素材与最终成片的“生杀大权”交给AI，需要极强的结果可靠性与可控性，当前2-3分钟的输出限制正是质量控制的体现。其次，商业模式考验：其按渲染时长计费的信用点模式，虽简化了用户成本预估，但如何平衡重型计算成本与用户对“免费修改”的天然期待，将是留存关键。最后，生态定位：作为独立应用，它目前是创作的起点或终点；但若想深入专业工作流，未来与主流生态的深度集成（如作为插件或云服务）或许比单纯延长输出时长更为重要。

总体而言，EditWithAva是AI向视频生产纵深迈出的扎实一步。它没有制造炫技的幻象，而是切实解决脏活累活。它的成功与否，将验证“语义驱动编辑”是否仅为一个便捷功能，还是能真正成为一个被广泛接纳的新工作标准。

查看原始信息 ▼

EditWithAva

Ava is the world’s first AI assistant video editor that works with your own footage to turn ideas into publish-ready videos. This works by semantically understanding your footage to auto-select scenes, cut retakes, and assemble edits exactly to your creative intent incl. b-roll, captions, voiceovers, and much more.

Good Morning Product Hunt Community! 👋  

And thank you @benln for hunting us 🙏  

I'm Matthias, co-founder of EditWithAva and I am so excited to launch for the first time on PH! We built this because we lived the problem ourselves. 👇  

Video has become a pillar of modern communication, but the process of going from raw footage, to first edit(s) has barely changed in the last 37 years. As a founder and creator myself, I spent every free weekend I had, sorting footage, editing talking-head content down, and finding the fitting b-roll. And then for the short-form edits, I had to repeat the whole process again from scratch. The creative part was fun, but it was the 80% assembly work that burnt me out.   

It shouldn’t take days or weeks to get to a first edit. So we asked ourselves: what if editing was as simple as describing the video you want?   

Ava fixes that. 

Link your google drive with raw footage (up to 3 hours) 
Describe the edit, the way you might brief a junior editor (script/brief/etc.) 
Get a polished short-form video (max. 2 min) with cleaned audio, B-roll, captions, & more

No timeline. No technical skills. Just conversation.  

Who’s EditWithAva for? 

Solo editors & agencies tired of repetitive editing tasks
UGC creators, social media, and brand teams producing at scale
Anyone who films talking-head, educational, or promo content

What makes Ava different?

Unlike generative AI or clipping tools, Ava is like having a content team in your pocket, that can work with the messiness that is your real footage. She watches, labels, and understands your content. Then assembles edits based on your creative intent. She can even help draft scripts/storyboards based on references or narrate your videos if you don’t have a voiceover file. And if you need changes or variations, simply tell her.   

Coming soon:

longer output lengths (up to 20mins)
use video(s) as reference
VFX/SFX/transitions
brand/style templates

We'd love your feedback on: 

First impressions of the editing flow 
What's missing for your workflow?
Use cases we haven't thought of yet

Special offer for the PH community: your first 5 videos are free, and if you reach out and schedule an onboarding call, we offer a 30% discount on all plans for the first 3 months.   

We're a small team (myself, Adrian, and Dominik), we’re just started and genuinely want to hear what works and what doesn't.

Drop a comment, try it out, roast us — all is welcome. 🙏

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@matthiasrossini Congrats on the launch! I'm curious to know what you think the future of editing will look like as AI becomes more integrated in every creative tool?

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Huge congrats on the Product Hunt launch - turning raw footage into publish-ready videos with real creative intent is a game changer 🎬🚀

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@liam007 thank you Liam!! It was a tough nut to crack, but I think we managed 🫶

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Cutting retakes automatically solves a real pain. I usually record the same line fives times and waste time finding the best one later.

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@scarlett_massey that is EXACTLY what we built this for. When it really starts to get fun, is when you start trusting Ava to take care of your messy footage and start filming/creating more.

For example: you can record the same script from 3-4 different locations, and ava will assemble the final video according to your script & requirements :)

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So excited to see this come to life 😍 and can’t wait to test it for my own videos 😎

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@mfreihaendig thanks for the support matthias!! excited for the first experience reports

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That looks incredible!! Will try it out right now! :)

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@niklas_fischer thank you Niklas! Looking forward to hear how it goes for you

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A lot of creators worry about unpredictable iteration costs (regenerations, variants, retries) and surprise usage limits—how did you design pricing/limits so users feel in control while you still cover compute-heavy workloads?

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@curiouskitty We have a credit-based pricing model where the credits only depend on the runtime of the rendered videos. Necessary revisions do count extra right now but we're planning to introduce free limits and/or bundling options to make it even easier. Creators therefore don't have to worry about whether their requests are easier or harder to process and can easily approximate the expected costs.

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🔥🔥🔥

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@bohdan_drozdov appreciate the support Bohdan!

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Awesome product! should be #1!!!

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@hazhubble thanks haz!!! 🫶🫶🫶

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i heard simply leak your footage

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@calin_drimbau woops :'D

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Definitely going to give this a try! congrats on the launch

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@gslee thank you grant!!! means a lot 🙏🙏  

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So stoked for this launch! It was so energizing jamming on ideas and AI with you last summer, awesome to see it all coming together in this launch.

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@jordangarcia highly appreciated Jordan!!! :D

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Amazing idea! Does it work from iMovie or existing app or is it a standalone video editor? Can it handle bigger projects (e.g. 2 hrs input video and 10 min output video)?

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@daniele_packard thank you daniele! Right now it's completely standalone, with no traditional video editor incl. as you might be used to. our bet is that in the future, you will just edit video for social with chat (no timeline required). but we do offer exports to most popular NLEs.

currently max input is 3-4hrs and output is capped around 2-3min (we will expand both sides in the future, right now we limit mainly for quality control)

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this tool is gold!

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@kshitij_mishra4 appreciate your feedback!!

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congrats @matthiasrossini and team. this is really sick and i am super excited to try it soon!

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@leo_wendler excited for you to try it!! maybe we can finally get you into content creation with this one :D

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An absolute game-changing product just entered the worlddddd 😍🚀 can 100% confirm that I never had this experience before when I had the chance to test @EditWithAva in advance. For every creator, video editor and basically everyone who wants to win on socials these days: Sign up!!

Congrats to the amazing work team 🥳

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@carmen_jenny thank you for being one of the first testers of our new product!! And glad you enjoyed the experience 🙏🫶

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Congrats on the launch! 🙌🏼

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@internetjohnny thanks johnny!!! 🙌🏼

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I'm specifically interested in if it can reliably make a video from a large collection of footage on its own by understanding what the footage is, what the best shots are, when given a recording of a script. Have you tested this?

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@buster_franken1 great question buster!! the biggest pool we've tried our tech on so far, was a combined pool of roughly 40hrs of video material. But at that point, it really helps to give Ava targeted advice on what you need and in which folders she should reference which shots.

But script recording + zero shot 'find the best clips' using an uncleaned footage pool works surprisingly well

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Game-changer feeling! Thanks for building this – can't wait to try and spread the word!

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@philippsey Thank you Philipp! Can't wait for you to try it.

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For teams producing training content at scale — does Ava maintain consistent style across multiple videos once you've set it up? That's usually where AI video tools fall apart when you go from one video to twenty.

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@klara_minarikova hey klara! in the current beta that is 'solved' through editor exports, for the brands to add their custom branding afterwards. But we also offer brand-customization for corporate accounts, where you have more fine-grained control over things like editing style, brand-voices, captions, & guidelines.

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lets go! congrats on the launch, looks amazing

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Thank you!! @mkremb

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Hey guys, any ideas why I see this?

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@busmark_w_nika hey nika! it seems like our system just ran into some rate limits. Our team is already looking into it!! Apologies that had to be your first experience. I hope your second one will be much more enjoyable 🙏

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Export video to editor is important feature for me, low trust that AI would get it 100% right without me tweaking the edit

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@alasdair_mann2 100% agree! that's why we offer exports for most major editors right. hoping to integrate more editors as we grow

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all the examples i tried had solid output. will see how integrate this into my workflow. kudos!

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@ohy thank you for the feedback alex!🙏

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Hey Matthias, that line about the creative part being fun but the 80% assembly work burning you out really hits. Was there a specific weekend where you spent hours sorting footage and finding b-roll, and by the time you finally got to the creative part you were already too drained to enjoy it?

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@vouchy every weekend for the first 6 months of starting my youtube channel! it's a tough pay off, especially for smaller creators that don't get as much feedback/reward for the effort they out in

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v v interesting!
how much raw footage can Ava handle at once?

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@leonsandner great question! in the free beta ava can process up to around 3 hours of video, and for 'max' users up to 20h+ :)

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How does cutting and joining videos work? Can I add several videos and make cuts?

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@jackdev21 yes! you can link whole video libraries to Ava, incl. sub-folder structures, voiceover files, & more. As long as you then also tell Ava what you need her to do with it :D

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I’ll try the service and share my feedback soon!

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@liam007 looking forward to hear your feedback!

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Congrats!!! Honestly Ava is magical. I can't believe how good it is! My favorite tool from now on.

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Was the video you uploaded here also edited with EditWithAva? Just curious, it looks great, by the way.

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hell yesss… this is awesome!

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@john_arts thank you John!!

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一句话介绍：一款集成于iMessage等日常流程的主动式AI行政助理，通过自动处理邮件、准备会议、记录并跟进，在移动办公与高效协作场景下，为用户节省大量手动操作时间。

Productivity Artificial Intelligence No-Code

AI行政助理主动式自动化邮件智能处理会议管理 iMessage集成效率工具智能日历工作流优化无代码设置时间管理

用户评论摘要：用户普遍赞赏其iMessage集成带来的自然交互与流程嵌入感，认为节省时间效果显著。主要建议包括：支持多日历管理、增加操作确认步骤、提升已连接工具的可见性。部分用户对其可靠性与自主性边界提出疑问。

AI 锐评

Lindy Assistant的叙事核心是“主动”，试图将AI从问答机重塑为可信任的副驾驶。其真正价值不在于单项功能的突破，而在于通过iMessage这一高频入口，将AI能力无摩擦地缝合进用户现有工作流，实现了从“工具调用”到“自然委托”的范式转变。用户反馈中“像发信息给朋友”、“减少应用切换”等表述，印证了其降低使用心智负担的成功。

然而，其宣称的“无需提示”既是亮点也是风险点。高度自主性在提升效率的同时，必然伴随可靠性与控制感的权衡。那条关于“可靠性vs自主性”的评论直指产品哲学的核心：在复杂多变的真实工作场景中，如何界定AI的行动边界？过度保守则沦为另一个需手动触发的自动化工具，过度激进则可能引发信任危机。目前看来，Lindy通过“代码词确认”等设计试图寻找平衡，但这仍是所有主动式AI助理面临的最大悖论。

从市场看，它并非简单替代Zapier等流程自动化工具，而是瞄准了更高维度的“认知自动化”——理解意图、管理上下文、预判需求。其挑战在于，这种深度个性化服务能否规模化并保持稳定，以及如何将“感觉像朋友”的早期用户体验，转化为不可替代的刚性工作需求。若成功，它将重新定义数字助理的赛道；若在可靠性上屡屡失手，则可能只是又一个惊艳但脆弱的AI演示。

查看原始信息 ▼

Lindy Assistant

Lindy is your AI executive assistant. Takes 2min to set up and saves you 2h a day. Proactively manages your inbox, meetings, and calendar. You don't even have to ask. It triages emails, preps for meetings, takes notes, and sends follow-ups automatically.

Lindy assistant is incredibly good. Not perfect, but it genuinely feels like chatting with a friend. Super helpful for work, and also great as a companion that knows me better every day. That launch video was so much fun to bring to life! Kudos to the team! 💬

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@marvin_aziz1 to me, it feels like texting a friend ... especially the iMessage workflow is crazy.

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Let's goooo! Lindy Assistant has been saving me sooo much time, and it was insanely fun to build!

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@jonathan_cardoso Insane how many PR’s I’ve seen fly in from you the past couple days and weeks 🐐

▲ 0

Best tagline I have seen today :D

▲ 3

@busmark_w_nika Yes but too late, Openclaw has captured the market. Maybe at this moment it would be more prudent to position yourself as Openclaw hosted solution than creating an independent product eg Kiloclaw.

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@busmark_w_nika haha! i agree, Florent's launch video was funny too.

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the dream product everyone's always wanted — no more logging into tools or signing up for new platforms. i can just send it a voice memo while i'm hopping into a waymo or riding the peloton. this is the dream assistant i've always wanted. when i say this is like having clawdbot meets donna from suits, i mean it :)

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@lindy_drope1 i think your donna eats lobster for breakfast too

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2 minutes = 2 hours back in the day. That’s pretty great math right there. On top of that, being embedded in my current workflows means I less tab switching + log ins feels more like a personal assistant to keep things on task and focused. Congrats on the launch Lindy team!

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@ashamplifies Thanks so much, Ash! The log in thing is really an unexpected highlight for me.. didn't realize how much I actually hate logging into stuff all the time 🤣

▲ 0

Really excited about the direction we're going in. Making it 10x more simple for the user and opening up 10x more possibilities

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@sathira_katugaha the 🐐 himself making the onboarding experience magical

▲ 0

I've been loving it. It feels really easy while helping me do important work. On the bus, I got an update about interview time changes today, a few new urgent things that came into my inbox, and the news for SF. I'm excited to see how it'll continue to learn about me and what I need/like to see. Can't wait to explore what's possible!

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@christina_simpson’s new way of working

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The iMessage part totally changes how I interact with it. For some reason it just takes v little effort to grab my phone and text Lindy. Love it, already hooked.

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@haneen_azhar the voice messages 🤤 I just wanna walk through the park while delegating all my work to this assistant by yapping 5min voice messages 😅

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Super excited to finally launch Lindy Assistant — make sure to check out our launch video!

I've been using this for months, 20x a day, and it's honestly gotten to the point where I can't work without it.

I barely open Gmail anymore. Lindy just tells me which emails matter, and I respond with a voice memo.

I have this AI sidekick in all my meetings that takes notes, remembers everything (I can just ask over iMessage), follows up on action items, takes a head start on them when it can (drafting follow up emails, doing online research etc), and coaches me.

I've had human EAs for years. 90% of what they did for me 18 months ago is now handled by Lindy. It's even better in some ways: I can text it at 11pm, be super direct, and get a response in 30 seconds.

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@altimor the video turned out soooo good! 😅 🔥

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Honestly the thing I didn't expect to love so much is texting with lindy in iMessage. I just text Lindy and it already has context from my calendar, email, tasks, so I'm not explaining everything from scratch. Way less app switching throughout the day which was driving me crazy.

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@ryan_kaldani1 I guess people need to start building more software for agents now

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Just started my free trial! The text-style interface feels smooth and natural, and Lindy understands my instructions well. It’s been very efficient at scheduling meetings and events. A couple of suggestions: support for managing multiple calendars (within and across accounts) and clearer visibility into which calendars/inboxes/apps are connected would be great. An confirmation step before scheduling or sending important emails would also help me feel more confident. Overall, a great app!

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@linjing Love it! Thanks so much! You can actually just tell Lindy to always ask for approval before sending an email. You can even use funny code words like "POTATOE" when you want Lindy to send an email 🤣 Heard on the multiple calendar & visibility feedback 🙏🏾

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Goated tagline, goated product! I especially love the fact that it creates REALLY good email drafts for my inbox and automatically checks my availability for meeting requests!

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It's wild how doing work through iMessage totally changes the vibe. It's like having a super intelligent assistant who knows your preferences, schedule and email history working for you 24/7. Definitely can't go back to the way I was working before.

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@everett_butler yeah remember the report I sent on tuesday? it wasn't actually me 👀

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Hey PH fam 👋

Excited to hunt Lindy Assistant today! 🎉

Most AI tools wait for you to ask a question. Lindy flips that. It watches your calendar, inbox, and workflows — then acts on its own, all through iMessage.

What stood out to me:

→ It triages your emails and flags the ones that need a decision

→ It preps you for meetings before you even open the invite

→ It takes notes, writes follow-ups, and sends them automatically

→ It connects to 3,000+ tools you already use

The setup takes about two minutes. No code. No complicated workflow builders.

The real unlock here is the shift from reactive to proactive. The people saving two hours a day aren't prompting better — they're delegating better.

Free to start and the learning curve is almost flat. Give it a try and drop your feedback below — the Lindy team is answering questions all day. 👇

▲ 1

A lot of tools in this space are either deterministic workflows (Zapier/Make/n8n) or “agent” demos that break on edge cases—what’s your philosophy on reliability vs autonomy, and where did you draw the line on what Lindy should do without approval versus ask/hand off?

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一句话介绍：DatoCMS推出的Visual Editing功能，为使用Headless CMS的开发者提供了极易集成的可视化编辑解决方案，让内容编辑者能在真实前端预览中直接修改内容，解决了无头内容管理系统长期存在的编辑体验与开发自由度难以兼顾的痛点。

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API Website Builder Developer Tools

无头CMS 可视化编辑内容管理平台开发工具实时预览草稿模式前端框架 WYSIWYG 开发者体验编辑体验

用户评论摘要：用户反馈积极，肯定该功能是市场所需。主要疑问集中于：该功能在何种内容或网站类型上最具变革性；SDK是否会因增加脚本而影响正式站点性能；以及从其他CMS迁移的触发点和锁定风险。开发者回应详细，强调了体验、集成易用性和技术实现上的优势。

AI 锐评

DatoCMS此次推出的Visual Editing，并非简单的功能叠加，而是一次针对Headless CMS核心矛盾的精准手术。它试图在“结构化内容与API自由”的开发者红利，与“直观、所见即所得”的编辑需求之间，架设一座不破坏前者的桥梁。其价值不在于创造了一个新的页面构建器，而在于通过“真实前端预览”、“内容感知深度链接”和“框架无关”这三大支柱，将可视化上下文巧妙地“注入”到既有的无头架构中。

此举的高明之处在于，它没有屈服于将CMS重新拖回传统耦合模式的诱惑，而是选择了一条更艰难但更正确的路：让编辑体验去适应开发架构，而非相反。通过SDK在响应中嵌入不可见元数据，而非要求开发者手动关联字段与组件，它降低了实现的复杂度和长期维护成本，这正是其宣称“dead easy for developers”的底气。然而，其真正的考验也在于此：这种“隐形”的集成是否能在所有复杂的前端状态下保持稳定可靠？是否会为前端应用带来难以排查的副作用？评论中关于性能影响和迁移锁定的担忧，恰恰点中了这类深度集成方案商业与技术上的敏感神经。

总体而言，这是一个面向成熟市场的功能进化，它承认了无头CMS在编辑侧的历史欠账，并提供了一个颇具巧思的解决方案。它的成功与否，将不取决于概念的新颖，而取决于在实际、复杂的生产环境中，那份“轻而易举”的承诺能否真正兑现，以及它能否在提升编辑效率的同时，依然让开发者感到“一切尽在掌控”。这恰恰是无头CMS生态持续健康发展的关键平衡。

查看原始信息 ▼

Visual Editing by DatoCMS

Headless CMS and Visual Editing have a long-standing love-hate relationship. Our approach to is aimed at making it dead easy for developers to implement Visual Editing in DatoCMS, to let content editors get the WYSIWYG effect seamlessly. Combined with draft mode and real-time updates, making changes is a breeze. No more hunting through record forms to find the right field.

Hi Product Hunt, maker here 👋

We started building DatoCMS about 10 years ago, when headless CMS was just becoming the "next" architecture for the modern web. The core idea's become very well established: structured content, API-first delivery, and full freedom on the frontend. One of the weak spots has always been the same though: visual editing. We always felt that the editors were missing out on what a Headless CMS could do for them because of how technical it has been by nature.

For a long time, headless meant great DX, but editors had to mentally map content and pages to models and blocks. Over the years, many platforms introduced their own versions of visual editing, often relying on iframes, CMS-rendered previews, or tightly coupled page builders.

Today we’re launching Visual Editing for DatoCMS, which is our take on solving that problem without breaking the headless model. Our primary goal with this feature is to make it dead easy for developers to implement Visual Editing in DatoCMS, so editors can work on their websites as frictionlessly as possible!

Our approach is based on:

Real frontend previews running your actual app, not a CMS abstraction
Content-aware deep links that connect any record or field to its exact usage in the UI
A framework-agnostic setup that works with modern stacks (we have SDKs for Next.js, Nuxt, SvelteKit, and Astro) and supports drafts, preview environments, and production safely

The goal isn’t to turn DatoCMS into a visual page builder. It’s to add visual context where it’s missing, while keeping developers in full control of rendering, data fetching, and architecture.

This feature is the result of years of feedback from our users and customers, and we’re excited to hear what you think! I'll be around and happy to dive into implementation details or trade-offs in the comments.

Thanks for your support 🧡

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@stefano_verna Hi Stefano. Congrats on your launch. Are there specific types of content or websites where Visual Editing is especially transformative, or less useful?

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@stefano_verna Looks great! The main worry with visual stuff like this is a bloated client bundle. Does adding the SDK affect overall site performance for regular users, or do all the scripts load only in preview mode? Good luck with the launch, this feature is really needed

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S/O to the team, keep up the great work 👏👏

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If a team is already using an established headless CMS with some form of live preview, what’s the most common trigger that makes them switch specifically for this and what migrations/lock-in concerns come up most?

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@curiouskitty Hate to say it depends, but reallly depends on why/if you'd want to switch. In our case we find that existing users of other Headless CMS typically talk to us for one of these reasons (when "frontend editing" is a part of the discussion):

Overall editor experience bundled in (specifically for Structured Text, Plugins, and overall UI)
Overall dev experience with the schema, our CLI/API/SDK packages, and the "ease" of implementing frontend preview with the plugin that this release builds on, or
General support/pricing

In this case specifically, these Visual Editing releases are still new, and we've shaped it around quite a lot of user feedback, so it's a bit early to say why other CMS users would switch just for this, but some of the discussions we've had make us think that this approach (invisible metadata in API responses vs. manual field-to-component wiring) would be less brittle or high-maintenance.

Happy to dig into specifics for your use case if you'd liek to reach out!

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We're currently evaluating headless CMS options - will check this out!!

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一句话介绍：Edgee是一款AI网关，通过智能压缩提示词在不影响语义的前提下将大语言模型调用成本降低最高50%，主要解决开发者在生产环境中面临的LLM使用成本失控和运维复杂化的核心痛点。

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Software Engineering Developer Tools Artificial Intelligence

AI网关成本优化令牌压缩生产部署多模型路由可观测性边缘计算 LLM运维 API管理开发工具

用户评论摘要：用户普遍认可LLM成本不可预测的痛点，并对降本效果表示期待。核心关切点集中于：压缩技术如何保证输出质量（尤其是结构化输出）；是否增加延迟或成为可靠性瓶颈；成本追踪与现有观测工具集成；以及对智能体（Agent）、工具调用等复杂场景的优化支持。

AI 锐评

Edgee切入的并非炫技的“模型层”，而是务实甚至有些枯燥的“管道层”，这恰恰是其价值所在。它试图成为LLM时代的“CDN”或“云成本管家”，其真正卖点并非单纯的令牌压缩算法，而是一个集成了成本控制、路由、观测、安全的**生产级控制平面**。

从评论看，早期采纳者最关心的并非压缩率本身，而是**压缩的确定性**（是否破坏逻辑、影响工具调用）和**引入的副作用**（延迟、可靠性）。这揭示了企业级市场的核心诉求：稳定性和可预测性优先于极限优化。Edgee团队“边缘原生、万级服务器、数十亿请求”的架构回应，以及关于相似度阈值、非生成式压缩的讨论，都是在向市场传递“我们具备处理生产流量体质”的信号。

其挑战同样明显。首先，**价值感知与风险担忧并存**。压缩是“黑箱”，用户需在“账单惊吓”和“输出变异风险”间权衡，建立信任需要极高的透明度和案例佐证。其次，**赛道迅速红海化**。AI网关概念已不新鲜，众多开源方案和云厂商均已布局，Edgee需在性能、价格或细分场景（如其对智能体工作流的强调）上构筑足够壁垒。最后，**商业模式面临挤压**。如果主流LLM提供商未来自行优化令牌计费或推出更细粒度套餐，第三方优化工具的生存空间将被压缩。

总体而言，Edgee的价值在于将“成本优化”从一个事后补救的财务动作，前置为一项可集成、可观测的工程基础设施。它能否成功，取决于其能否在“足够智能”和“足够稳定可靠”之间找到最佳平衡点，并快速绑定那些已被天价账单“教育”过的重度用户。

查看原始信息 ▼

Edgee

Edgee compresses prompts before they reach LLM providers and reduces token costs by up to 50%. Same code, fewer tokens, lower bills.

As an indiehacker, I am always afraid of receiving an expensive bill because my AI feature suddenly saw a lot of usage. Anything that can help reduce costs and give me insights into what's going on, is welcome.

It's no brainer to use it from day 1, and see value right away.
Congrats @sachamorard team for building this💪

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Thanks a lot @picsoung for the support 🙌

And totally agree! That "unexpected AI bill" fear is real, especially for indie hackers and small teams where one spike can ruin the month 😅

That's exactly why we built Edgee: so you can get cost visibility + optimizations (like token compression) from day one, before things get out of control.

Really appreciate you hunting and sharing this. Excited to hear what you build with it! 🚀

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@sachamorard @picsoung We've heard this from pretty much every CTO and CEO we've talked to in Europe and the US. The end-of-month bill can be a real shock! 💸

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Congrats on the launch! will closely follow as the topic is complex and moves fast!

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@olivier_lemarie1 Thank you ! Indeed, a very exciting and challenging topic and so many things to explore and improve :D We'll soon be having a series a blog posts going through all the details and the research around compression, so stay tuned !

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@olivier_lemarie1 Thanks Ollivier. Happy to take your feedbacks, and do a catchup. It is indeed an amazing and vast problem to solve.

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@sachamorard Token costs are definitely becoming a real problem once prompts get large (RAG, tools, agents…).

Curious how you handle compression without breaking output quality, especially for structured outputs?

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@sachamorard @virtualgoodz Yeah alignement is a big issue when doing any prompt transformation !

In general, tracking performance across a mix of semantic preservation metrics like bert, cosine, rouge making sure that they don't degrade below a certain threshold is good proxy.

For structured output, things are trickier, as the compression shouldn't be "generative", in the sense of re-expressing with other tokens, so it's more deterministic through a more compact re-encoding of the structure, through crushing, factorizing repetitions and so on !

Glad to discuss this further if needs be :D

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Congrats on the launch!
We're stuck on how to attribute LLM costs back to specific features. Does Edgee tag requests so we can track cost per feature?

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Hello @benoit_collet, thanks for the interest !
Good question to ask, it is quite a pain we've experienced where cost was only analyzable by API key which could be painful as you might not want to have 50 different keys just for the purpose of cost categorization.

We've created the "tags" feature which allow you (via API headers or via our SDKs) to automatically define categories. Tags will be visible in your analytics dashboard to allow you to understand exactly where you are spending the most !

You can learn more on our documentation : https://www.edgee.ai/docs/integrations/langchain#tags

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We're experimenting with cheaper models to control costs, but quality suffers.

Can Edgee help us stay on expensive models but reduce token usage instead?

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@pierregodret Yes, that’s exactly what Edgee does.

Edgee optimizes your prompts at the edge using intelligent token compression, removing redundancy while preserving meaning, then forwards the compressed request to your LLM provider of choice. You can also tag requests with metadata to track usage/costs and get alerts when spend spikes.

Happy to discuss this further if you’d like.

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Absolutely @pierregodret . With our token-compression model, the LLM bill mechanically decreases, so it's actually a good opportunity to afford a slightly more expensive model... for the same price ;)

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As a product guy in the agentic platform space, I’m definitely going to keep a close eye on this one. Good luck with the launch!

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@yannick_mthy The agentic space is exactly where we’re seeing things get interesting (and complex) fast, especially with growing context sizes, tool calls, and multi-model orchestration.

Would love to hear how you're currently handling cost + routing on the agent side. Always keen to learn from teams building in this space. Thx

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Gateways can become a new reliability and latency bottleneck: what’s Edgee’s architecture for keeping p95/p99 overhead low (especially for streaming and agent tool-call loops), and how do you handle failure modes like retries causing traffic spikes or provider brownouts?

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@curiouskitty Great question, and totally valid concern!

We're edge-native, so we avoid adding a centralized bottleneck and keep network hops minimal. Edgee is running on more than 100 points of presence around the world, on more than 10k servers, and we already process 3B+ requests a month ;)

Streaming is first-class, and pre-inference workloads run before the model call, so they don't block token streaming.

On reliability: we don't do blind retries. Routing is health-aware, with bounded retries, circuit-breaker behavior, and dynamic deprioritization during brownouts to avoid traffic amplification.

To summarize, Edgee will be to AI what CDNs were to the web.

Happy to go deeper if helpful!

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Go edgee! Would love to know if you handle MCP and Tool usage optimisations? It's a real pain for long running agents

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Hey @marek_kalnik ! We don't manage MCPs for now, but we have developed Edge Tools.
These are tools executed at the gateway level, before or after the call to the model. They can be verifications, transformations, enrichments, controls.... memory access!

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Hey Product Hunt 👋

I’m Sacha, co-founder of Edgee. Thanks for checking us out!

We built Edgee because we kept seeing the same thing everywhere:

AI cost is going crazy!!!

LLMs are easy to try, but once you ship them in production, costs explode and reliability becomes a mess.

Most teams start with direct calls to OpenAI or Anthropic… or simply using a coding assistant... then quickly end up dealing with:

Unpredictable token spend
Multiple provider APIs
Outages / rate limits
Security & privacy constraints
And no real observability across teams

Edgee is an AI Gateway built to reduce LLM costs and simplify production inference.

It gives you a single OpenAI-compatible API across providers, plus a layer of intelligence around inference:

✅ Token compression to remove redundant tokens and cut costs, with no semantic loss
✅ Routing & fallbacks across providers
✅ Observability + cost tracking you can trust
✅ Privacy & security controls (ZDR, BYOK...)
✅ Support for public + private models,
✅ & Edge Tools 🚀

We're launching early and working closely with a small group of design partners, so feedback (even brutal feedback 😅) would mean a lot.

Happy to answer any questions here, and I’d love to hear how you’re handling LLM infra in production today!

Sacha

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Exciting launch! Congrats team

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@paulblei Thanks a lot! Really appreciate it 🙌

If you get a chance to try Edgee, we’d love to hear what you think.

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Thank you very much @paulblei . I must admit that the whole team is very excited as well. When we had the idea of using our edge computing skills to improve inference, I didn't have to insist for long to get buy-in, lol

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Love the focus on production problems vs demo features. Does the cost tracking integrate with existing observability tools (DataDog, etc.)?

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@nielsrolland You raise a very interesting point! For now, we allow data to be exported in csv/json, but we're already working on integrating partner solutions. If you know our history (which seems to be the case), you know how easy it is for us to send data to any solution... so we're not going to hold back from offering this feature to our users ;)

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Congrats on the launch !

LLM's costs are going crazy here, I definitetly give it a try

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You'll be welcome @angezanetti . We decided to build Edgee after talking with 50+ CTOs who started to struggle with token costs. Really exciting challenge, the team is sooo excited!

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Congrats!

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@stanmassueras an honour to have your support. At @Edgee, we loooove @ElevenLabs 💪

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@stanmassueras Thank you! We really appreciate the support 🙏

If you end up giving Edgee a try, we’d love to hear your feedback.

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Cool idea! Do you get transparency into how prompt was trimmed/manipulated so you can ensure nothing was missed?

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@daniele_packard We have information that allows us to understand how our model performs, yes. However, we do not keep the original prompt for obvious privacy reasons. To control the compressed prompt, we perform a similarity analysis by calculating several metrics (rouge, bert, cosine...). And we allow our users to define a threshold that guarantees semantic similarity.

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Sounds amazing, can’t wait to plug it with Tellers!! Congratulations for the launch @sachamorard 🚀 Congratulations @picsoung for yet another successful hunt 😃

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@picsoung @rguignar Would looove to see Edgee plugged into Tellers, that’s a perfect fit, especially with agent/tool-heavy workflows where context can grow fast.

If you’d like, happy to help you set it up or jump on a quick call to make integration smooth.

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@sachamorard would be awesome! Thanks Sacha!

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This would be game-changing for our margins. Does the compression work for both prompts and completions?

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@hajar_lamjadab2 yes it is! And it's even more efficient when the context window becomes larger and larger.

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Congrats on the launch! Will definitely be following this project closely. I've always thought there should be a way to more efficiently provide prompt for LLMs, especially when the latest models consume a lot of them for complex work. Hopefully this will eventually result in less usage rate and higher limits.

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@mnzrabusham The further we advance, the more complex models become. Hardware innovations will probably improve the energy efficiency of models (because that's what it's all about), but the smartphone industry has taught us that the more powerful machines are, the more we ask them to perform increasingly complex tasks. So yes, I think that only intermediate systems can help us be a little more frugal.

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I've been waiting to see companies start tackling this issue. Cost and efficiency are going to be increasingly important once AI platforms are increasingly pressured for revenue.

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Hey @sasha_pave, so we’re there! Happy to tackle this challenge ;)

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Love this! Congrats @sachamorard - Great onboarding XP and managed to get going in <5' we will do ❤️. Curious whether and how we can control the compression level and adjust based on endpoints or use case as I imagine there's a quality trade-off?

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@gdecugis Yes, compression isn’t “one size fits all.” Different endpoints and use cases tolerate different levels of optimization. We’re building it so compression can be: - configurable at the org / key / sdk level - safe, adding what we call « Semantic preservation threshold »: If similarity (Bert indicator) is below this threshold, we send the original prompt instead to preserve quality. That’s mathematics ;) - observable The goal isn’t to blindly shrink prompts, but to make the trade-off explicit and controllable. And you’re absolutely right, there can be a quality trade-off in some scenarios, so giving teams control (and visibility) is key. Happy to go deeper if you have a specific use case in mind

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Hey, this is interesting! I was wondering if the prompt optimisations that you're doing are deterministic, as the first layer of cost improvement is caching we having a long conversation with LLM you need to cache, so the prompt compaction need to be deterministic and stable whatever happens.

Second point how do handle different model providers API interfaces? Do you support SSE? Did you reimplemented your own layer between Edgee SDK and LLM providers? There are so many edge cases with each provider when it comes to streaming + tools + reasoning tokens, etc.

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@bleff Yes, determinism is critical, especially when caching is involved. Our token compression layer is designed to be stable and reproducible for the same input structure. We don’t rely on generative summarization for compaction. Instead, we focus on structural redundancy removal and relevance prioritization so that the transformation remains deterministic and cache-friendly. You’re absolutely right: if compression isn’t stable, it breaks caching, so that’s a core constraint in our design. On provider interfaces: We maintain our own abstraction layer between the Edgee SDK and model providers. The API is OpenAI and Anthropic compatible outwardly, but internally we normalize differences across providers (streaming formats, tool calls, reasoning tokens, etc.). Yes, we support streaming (including SSE), and streaming is treated as a first-class concern. A lot of the complexity lives exactly where you mentioned: streaming + tools + provider-specific edge cases. Totally agree this space is full of sharp edges, that’s precisely why we think a robust gateway layer is needed. Happy to go deeper on any of these points!

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Token costs are the new database query problem. This feels like the right abstraction layer.

How's the latency impact in practice?

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@apoapostolakis great analogy 🙌 On latency: we’re designed to keep overhead minimal. Most of our work (token compression + routing decisions) happens pre-inference and is lightweight compared to provider latency. In practice, the added overhead is typically small relative to the model response time, and the goal is to stay well below p95/p99 variability from providers themselves. Also, in many cases compression actually reduces end-to-end latency since fewer tokens are processed by the model. Happy to share benchmarks as we publish more numbers!

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token compression at the gateway level is a smart approach. i've been watching my AI API costs climb across multiple projects and this is exactly the kind of infra that makes shipping AI features viable without stressing about the bill

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Impressed by the edge-native architecture with 100+ PoPs and the token compression approach.

I noticed Edgee is built with Claude Code. For developers using AI coding agents (Claude Code, Cursor, etc.) that make heavy API calls during development, does Edgee support integration at the agent workflow level? Specifically, can we route AI agent requests through Edgee to compress tool call contexts and reduce token consumption during iterative coding sessions?

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Would like to see benchmarks across different model providers and prompt types. If the compression holds under real production loads, this could become default infra in most LLM stacks.

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一句话介绍：一款将早期风险投资“游戏化”的平台，通过提供真实资金、公开业绩和顶级导师指导，为有潜力的投资者新秀搭建了一个压缩职业发展周期的公开竞技场，解决了新晋投资人缺乏启动资金、展示平台和系统化培养路径的痛点。

▶

Investing Tech Games

风险投资早期投资人才选拔投资竞技公开业绩导师计划游戏化金融创投生态职业孵化另类投资

用户评论摘要：用户反馈集中在三点：一是询问选拔机制（为何每季仅10人及选拔标准）；二是质疑短期“赛季”与投资长期性的矛盾及绩效评估方式；三是建议开放公众跟投以真正实现“民主化”，而非仅为基金获取项目流。

AI 锐评

Scout Program 的本质，并非其标榜的“早期投资梦幻体育”，而是一套精巧的、服务于传统风投体系自身利益的“人才与项目筛选器”。它用“公开竞技”的糖衣，包裹了行业最核心的痛点：顶级交易流的稀缺和下一代投资明星的早期识别。

其真正价值在于：第一，**极低成本的风险投资人才“压力测试”**。提供10万美元试错，换取一个候选人公开其投资逻辑、项目判断与网络资源，对合作基金而言是性价比极高的尽职调查。第二，**构建结构化、低风险的“信号发射”系统**。传统投资人声誉积累需要十年，而该平台通过强制公开论文和业绩，将模糊的个人品牌建设压缩为可量化的数据指标，加速了行业内的信誉形成。第三，**成为顶级基金的“前置雷达”与“生态漏斗”**。公开的Scout投资组合和论文，为合作基金提供了源源不断的、经过初步验证的早期项目流和深度行业分析，同时将最具潜力的操作者（Scout本身）也纳入了人才库。

然而，其模式存在深层悖论：评论中指出的“赛季制”与投资长期性冲突是关键。平台若仅以短期账面回报或叙事能力论英雄，极易催生追逐热点的投机行为，与价值投资的本源相悖。而拒绝公众跟投的选择，则彻底暴露了其“民主化”口号的局限性——它本质仍是精英俱乐部，旨在优化行业内部效率，而非普惠金融。它的成功，将不取决于是否制造了几个明星Scout，而在于能否为背后的资本联盟持续输送优质资产与人才。这是一场风投行业的“养成游戏”，玩家是Scout，但游戏规则的制定者和最大赢家，仍是坐在教练席上的传统资本巨头。

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Scout Program

Scout Program is designed to find the world’s best early-stage pickers and give them a public arena to prove it. Each season, 10 scouts receive $100K each to deploy into startups while building their thesis in public. Performance, portfolios, and outcomes are transparent. Scouts publish their theses, build public track records, and compete on outcomes. Our goal is to compress 10 years of quiet reputation-building into one visible season - and make ‘investor’ a skill you prove.

I've been dreaming of building a "Thiel Fellows for Investors" scout program for a long time. The inspiration for the product came from my starting my career as a scout for Index Ventures. In that program, I was lucky to invest along some of the best operators of my generation - people like Dylan Field from Figma.

My fund ended up being a 4x DPI fund within a few years and one of the top performing in my cohort, but everything happened in private, and there was not a great way for me to showcase the success that I was having.

That said, I did spend a ton of time building my investing career during the program. I launched a Chapter One website, developed a firm thesis, and managed my back office meticulously. T

his experience was ultimately what launched my career as an investor. As I was looking for roles in venture firms, they took me more seriously, and I was able to actually launch my own firm because I had done all the work to develop the firm during this time period.

I want to offer the best of that experience for the next generation of investors, while also creating more of a support system around the program. We have world-class mentors to help develop your career or "coaches" as we call them:

Mamoon Hamid, Partner at Kleiner Perkins
Keith Rabois, Managing Director at Khosla Ventures
Rebecca Kaden, Managing Partner at Union Square Ventures
Kyle Samani, Founder at Multicoin Capital
Roger Ehrenberg, Managing Partner at Game Changers
Gokul Rajaram, Founding Partner at Marathon
Tomasz Tunguz, General Partner at Theory Ventures
Ashton Kutcher, General Partner at Sound Ventures
Laura Andron, Managing Director at Pathstone
Kelli Fontaine, Partner at Cendana Capital

Please share with your friends who might become great investors one day or apply yourself. The ideal fit is likely an operator with a killer network and a boundless curiosity. You are also welcome to apply with teammates if you want to build your Scout Program team with other people you love working with

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@jmj Congrats on the launch! Why did you choose to limit the program to 10 scouts per season? And how do you select or qualify them?

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A season implies a tight feedback loop, but venture outcomes take years—how are you defining and scoring “performance” during and immediately after a season in a way that can’t be gamed by markups, narrative, or riskier spray-and-pray portfolio construction?

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Congrats on the launch! Really cool concept.

Quick question though - why not let regular people invest their own money instead of using Chapter One's fund? Like, imagine if anyone could throw in $100-1000 and actually share in the upside when these scouts find good deals.

Feels like it'd be way more engaging and actually democratize access to early-stage investing, instead of just being another way for an existing fund to get deal flow.

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一句话介绍：FocalRead是一款利用RSVP快速序列视觉呈现技术的速度阅读工具，帮助用户在信息过载的场景下，将囤积的文章、电子书和社交媒体长文快速消化，解决“收藏了却永远读不完”的痛点。

eBook Reader Productivity Books

速度阅读生产力工具阅读辅助信息消化 RSVP技术内容导入电子书阅读器碎片化阅读知识管理

用户评论摘要：用户普遍认可从Safari、X、Reddit等平台直接导入内容的便捷性。主要疑问集中在：RSVP模式是否影响对复杂材料的理解、长时间凝视是否伤眼、走神后如何快速回溯上下文，以及产品核心目标用户是谁。开发者回复解释了减少眼动、提供便捷暂停/回退功能以维持流畅体验。

AI 锐评

FocalRead精准切入了一个现代人的普遍焦虑——“阅读负债”。它并非一个简单的阅读器，而是一个试图将“信息消费”工业化的效率工具。其真正价值不在于RSVP这项已有数十年历史的技术本身，而在于构建了一个以“速度”为中心的、无缝的内容捕获与消化管道。

产品聪明地将“导入”作为核心体验起点，支持从社交媒体线程到专业文档的多种格式，这直接攻击了用户“稍后读”却“永不读”的行为瘫痪点。它试图将一切文本内容标准化为可快速消化的流，本质上是在贩卖一种“掌控感”和“清空待办清单”的即时满足。

然而，其面临的深层挑战与质疑同样尖锐。首先，速度与理解的平衡是永恒悖论，尤其对于需要停顿、反思的深度材料，RSVP可能将阅读降格为被动的信息接收。评论中关于“错过关键词”的担忧正是此点。其次，目标用户画像存在矛盾：真正需要深度阅读的学生或研究者可能牺牲理解，而普通用户的速度需求是否如此刚性？它可能最终服务于那些被“信息FOMO”（错失恐惧症）驱动的功利性阅读场景。

产品的未来不在于将速度推到1200WPM的极限，而在于如何智能化地适配不同文本类型（如小说与论文），并提供更丰富的交互（如基于AI的要点暂停、摘要生成），让“快读”与“读懂”真正共存。否则，它可能只是将用户的焦虑，从“未读列表”转移到了“飞速闪过的单词”上。

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FocalRead

Stop drowning in your reading backlog. FocalRead uses RSVP (Rapid Serial Visual Presentation) to help you read 3x faster while improving comprehension Import content from anywhere: • Share articles directly from Safari, X, or Reddit • Import EPUBs, PDFs, and paste any text • Extract full threads and long-form posts Features: adjustable speed (100-1200 WPM), bookmarks, multiple themes, chapter navigation, and shareable quote videos. Free to start. Read more in less time

Hey Product Hunt! 👋 I'm Andres, and I love reading. eBooks, articles, I consume everything. But here's the problem: my reading list kept growing faster than I could ever finish it I started looking for solutions and discovered RSVP (Rapid Serial Visual Presentation), a science-backed technique that displays one word at a time, helping you read 2-3x faster while actually improving focus. I wasn't the only one with this problem. I found a growing community of people frustrated with their endless "read later" lists. That's when I knew we had to build something. FocalRead was born from that frustration. My team and I spent a lot of time crafting every detail, from seamless content import to the perfect reading experience. What makes FocalRead different: • Import from anywhere: Share directly from Safari, X, Reddit, or paste any URL • Works with books too: EPUB, PDF, DOCX, MOBI support • Thoughtful UX: Dark/light/gray themes, bookmarks, adjustable speed (100-1200 WPM) • Share your reads: Turn favorite quotes into shareable videos We built this with love for fellow readers who want to consume more without sacrificing their entire day. I'd genuinely love your feedback: → What content do you struggle to get through? → What features would make this more useful for you? → Is there anything confusing about how it works? Thanks for checking us out! 🙏 — Andres

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@andres_sarrazola Congrats on the launch! How does FocalRead balance reading speed with comprehension, especially for denser material like eBooks or research articles?

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importing from Safari, X, and Reddit makes it practical. I save threads all the time and never go back to finish them

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@malani_willa sure me too!, for x we had to use their api to make it work

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Never seen anything like that before, sounds intriguing! The only question that almost immediately popped into my head - wouldn't it be worse for our eyesight to keep staring at one spot on the screen? 😅

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@helga_impalpable hey! thanks so much, totally fair question 😅

since the word stays centered, your eyes actually move less than in normal reading, which can reduce strain. plus you can adjust speed and size anytime to keep it comfortable

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First of all, I have to say that UI and website look very clean!

I’ve always been fascinated by RSVP tech to clear my reading backlog, but my main worry has always been context loss. If I zone out for a split second or blink, I’m afraid I’ll miss a key keyword and lose the meaning.

How does FocalRead handle this? Is there an intuitive gesture to quickly rewind or peek at the surrounding paragraph without breaking the flow?

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@valeriia_kuna that’s a very real concern. in focal read you can instantly tap to pause, swipe back a few words, or scrub through the text to regain context. we also make rewinding frictionless so if you zone out for a second, you’re never “lost” for long

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Import is often the make-or-break moment: which content sources or formats were the hardest to reliably convert into a clean, readable stream, and what did you build to prevent the common “formatting mess → abandonment” outcome?

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@curiouskitty great question. messy web pages and long reddit threads were the hardest.

we built a normalization layer that strips noise and turns everything into a clean rsvp stream, so there’s no formatting friction and people actually finish what they save.

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shoutout to r/speedreading for the incredible feedback and early support. this product is better because of you. really grateful for the thoughtful comments and the warm welcome 🙌

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I always liked the possibilities of this kind of reading. But the question is: who is the target audience for this type of product?

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@michael_vavilov we’re mainly building it for curious, ambitious readers, students, founders, knowledge workers, anyone who feels overwhelmed by a growing reading backlog and wants to consume ideas faster without sacrificing understanding

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一句话介绍：Visla AI Director Mode通过先AI生成分镜故事板、再让用户精细控制一致性元素与转化节奏的方式，解决了用户在制作有故事线、品牌要求的AI视频时，面临的内容前后不一致、修改成本高昂的核心痛点。

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Artificial Intelligence Business Video

AI视频生成分镜故事板品牌一致性视频制作流程创意辅助工具企业视频营销自动化内容创作可视化叙事

用户评论摘要：用户关注产品如何帮助非专业团队制作吸引人视频，并询问是否支持截图/录屏作为输入。团队强调产品并非替代现有流程，而是从策划层（脚本、PPT、链接）无缝嵌入，提升效率，尊重用户原有工作流。

AI 锐评

Visla AI Director Mode看似是又一个AI视频生成工具，但其真正的锋芒在于对当前AI视频“生成即赌博”困境的一次精巧手术。它没有在“生成更逼真视频”的军备竞赛中内卷，而是敏锐地切入了更高级的痛点：叙事连贯性与品牌可控性。

当前主流AI视频工具的单镜头生成模式，本质是碎片化的。当用户需要制作一个具备故事线或严格品牌露出的视频时，这种模式会导致灾难性的修改成本——调整一个镜头，可能引发风格、角色、环境的连锁崩坏。Visla的“导演模式”聪明地引入了一个关键中间层：AI生成的故事板。这相当于将视频的“蓝图”可视化，允许用户在投入高成本的视频渲染之前，先在静态层面完成结构、构图和一致性的确认与锁定。这种“先规划，后执行”的流程，将不可控的随机性大幅前置管理。

从团队回复中更能窥见其产品哲学：它不鼓吹“颠覆”或“替代”，而是强调“嵌入”与“增效”。这一定位非常务实，直指企业用户的核心顾虑——迁移成本。它允许团队从熟悉的物料（脚本、PPT、URL）开始，在原有工作流的“上游”介入，扮演一个超级加速器而非颠覆者。这降低了 adoption 门槛，也明确了其作为“创意执行伙伴”而非“全自动内容工厂”的边界。

然而，其挑战也同样清晰。故事板层面的控制是否足以保证最终视频成片的质量与一致性？在“选择哪些镜头转化为视频”的环节，用户是否具备足够的视觉判断力？这或许会催生对更专业模板或指导的需求。此外，其价值高度依赖于团队已有明确的“意图”（如回复所言），对于从零开始的纯创意发散，其优势可能并不明显。总体而言，这是一款在正确方向上迈出关键一步的务实产品，它试图将AI的爆发力装入可控的创作流程管道中，但其长期成功，取决于能否在“自动化”与“精细化控制”这个永恒的天平上，找到更普适的平衡点。

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Visla AI Director Mode

AI Director Mode is Visla’s new way to build videos. You start with any input, and Visla plans your video scene by scene with AI-generated storyboard images. Next, you set the direction, like pace, voiceover style, and the exact characters, objects, and environments you want on screen. Then you lock in products, logos, and other brand assets so your visuals stay consistent across every scene. Finally, you choose what stays as storyboard images and what becomes full AI video clips.

Hey Product Hunt 👋 Gabe here, from Visla.

We built AI Director Mode because making AI videos today is way harder than it should be.

Most tools make you prompt your way through a video one clip at a time. If something breaks halfway through, you regenerate and hope nothing else falls apart. That’s manageable for short clips. It’s a mess for anything with a story, structure, or brand involved.

Director Mode changes the starting point.

Instead of generating video immediately, Visla creates a storyboard first. You see the video laid out scene by scene before anything turns into motion. From there, you can actually direct it:

Consistency: Lock in characters and environments so they don't shift between scenes.
Precision: Place specific products or logos where they belong.
Choice: Once the storyboard looks right, you choose which shots become full AI video clips.

The goal isn’t to replace creative judgment. It’s to stop wasting time redoing work just to keep a video coherent.

If you try it, I’d love to hear what works, what’s confusing, and what you think is missing.

Thanks for checking it out 🙏

🚨 We’re offering 25% off our Annual Business Plan for a limited time: use code PH25.

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@mogabr Congrats on the launch Gabe! How does this feature help teams that aren’t necessarily professional video editors make more engaging videos?

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@mogabr Hey Congrats Gabe! This looks super useful.

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Congrats! Looks powerful - can I put screenshots or video recordings as input?

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@daniele_packard Hi Daniele! Yes you can. You can add web links, PDFs or PPTs, your own text, or ideas, and upload as many screenshots as you want, or recording, and our AI Director will use it to create your video. And afterwards, you can tweak it in any manner you so choose to personalize it even further.

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How do you recommend teams adopt this without retooling their whole pipeline—what inputs and handoffs work best (scripts, decks, URLs, brand kits), and where do you expect Visla to replace vs complement existing editing tools?

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@curiouskitty Thank you for the question. We do not see @Visla as a rip & replace for a team's entire pipeline, that hardly ever works, especially when forced on a team. Quite the contrary actually.

The easiest adoption path is starting upstream, at the planning layer. Always best to start with what you've always started with, a script (however rough - if you have it), an outline for a deck, whether it be a pitch deck as a startup, or an internal deck for a product roadmap, or if you're a VC pitching LPs, we give you the flexibility to add URLs, and can customize your brand.

So not to be vague, but truly up to you, we fit in your desired workflow, we have no desire to change how you think about it, we just want to change the efficiency side of it, and take some of the load off of you.

It works best when there is already intent, we turn that intent into a structured storyboard, and then we turn that into a video once you're happy with the baseline.

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@curiouskitty Yeah, that was my first thought too. I wouldn’t want to rebuild the whole pipeline just to try a new tool. Starting from something teams already use, like a brief or a deck, sounds way more realistic than changing everything at once

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一句话介绍：Cube是一款通过AI代理自动构建语义层并基于其进行查询的数据分析工具，在商业智能场景下解决了AI因不理解业务逻辑而“幻觉”产生错误分析结果的痛点。

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Analytics Artificial Intelligence Data

AI数据分析语义层智能体代理商业智能数据模型自动化开源基础去幻觉企业级应用数据基础设施 AI准确率

用户评论摘要：用户反馈普遍认可产品解决AI数据幻觉的核心价值，认为其从基础设施层面提供了根本解决方案。有评论将其类比为“数据分析领域的Cursor时刻”，预示行业变革。现有企业用户分享了实际用例，证明其生产环境可行性。

AI 锐评

Cube看似是又一个AI数据分析工具，实则是一次对当前AI应用架构的深刻反叛。它没有选择在提示词工程或大模型微调的红海中内卷，而是直指问题本质：AI幻觉的本质是数据语义的缺失。其真正价值不在于“又一个AI代理”，而在于将沉淀六年的开源语义层基础设施转化为AI时代的“数据翻译官”。

产品聪明地完成了两次价值跨越：首先，将传统的语义层——这个原本服务于BI工具和数据团队的技术中间件——重新定义为AI时代的关键基础设施；其次，通过AI代理将这一复杂基础设施的能力产品化，让非技术用户也能受益。这种“基础设施产品化”的策略，远比单纯优化算法更为深刻。

然而，其挑战同样明显。语义层的自动构建是否真能覆盖千差万别的业务逻辑？当业务逻辑本身模糊或快速演变时，静态的语义层是否会成为新的瓶颈？此外，其价值高度依赖其语义层的完善度，这本质上是一场与数据复杂度的赛跑。

值得警惕的是，市场可能过度聚焦其“去幻觉”的营销话术，而忽视其作为数据中间件的长期运维成本。它或许能解决“错误答案”的问题，但能否催生“卓越洞察”，仍是未知数。本质上，Cube是在用确定性的工程化思维，对抗AI的概率性输出，这条路径正确但注定沉重。它可能不会成为最闪亮的AI应用，但有望成为企业数据栈中最不可或缺的基座之一。

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Cube

AI analytics tools hallucinate because they query raw tables without understanding your business logic. Cube fixes this: AI agents build your semantic layer automatically, then use it to answer questions and generate reports with no hallucinations. Connect your data, get accurate results in seconds. Built on Cube's open-source semantic layer (19K+ GitHub stars). Free tier available.

Hey PH! Artyom here, co-founder of Cube. We started as an open-source semantic layer in 2018 (19K+ GitHub stars). We realized this foundation was exactly what AI needed to stop hallucinating — so we built agents on top of it. Already running in 200+ companies. Would love your feedback!

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I've been in the data/AI space for a decade and the biggest problem I kept seeing: AI gives wrong answers because it has no understanding of what your data actually means. Cube solved this at the infrastructure level with their semantic layer, and now puts AI agents on top of it. This is the Cursor moment for data analytics.

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Agentic Analytics has arrived!

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This is really cool to keep the data clean and accurate for AI.

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We use Cube at Pulumi for this exact reason!

https://www.pulumi.com/blog/how-we-built-platybot-an-ai-powered-analytics-assistant/

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一句话介绍：TrendWidget是一款将Google和Yahoo实时搜索趋势以桌面小组件形式呈现，并整合AI摘要引擎的产品，在用户需要高效、无摩擦获取全球热点和突发新闻的场景下，解决了信息滞后和筛选成本高的痛点。

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Android Productivity Artificial Intelligence Search

趋势发现桌面小组件 AI搜索实时信息新闻聚合市场洞察效率工具轻量化应用全球热点零幻觉

用户评论摘要：用户肯定其“在桌面一览世界搜索趋势”的核心价值。有效提问集中于产品愿景、功能迭代逻辑及用户行为数据。开发者回应强调以社区反馈驱动开发，保持轻量化，并透露用户兼具“扫视”小组件和点击深入查看的行为模式。

AI 锐评

TrendWidget的叙事巧妙地缝合了两个高热度概念：实时趋势与AI搜索。但其真正的锋芒，或许不在于“AI”，而在于“Widget”。它将信息入口前置到操作系统层级，实现了近乎零成本的“环境式信息感知”，这才是对传统“打开APP-刷新-浏览”模式的真正颠覆。其宣称的“零幻觉”和实时数据喂养LLM，直击当前AI搜索的时效性软肋，试图在可信度上建立壁垒。

然而，产品面临双重拷问。其一，是价值深度与工具轻量的内在矛盾。作为“脉搏”式工具，它擅长告知“什么在热”，但AI摘要对“为何而热”的解读能有多深？这决定了用户是将其视为严肃的信息工具，还是稍纵即逝的消遣。其二，是规模化的隐忧。在韩国的成功验证了PMF，但其驱动因素——高度同质化的热门话题文化、对效率工具的偏好——能否复刻至美、日等多元市场？小组件是优势也是枷锁，其有限的展示空间，在信息过载的环境下，极易从“信息窗”沦为“噪音条”。

它的未来，不在于堆砌更多AI功能，而在于能否将“轻量、实时、可信”的铁三角打磨成真正的护城河，并找到超越“热点话题”的、更具黏性的数据价值维度。与开发者的“特别合作计划”则透露了一个精明策略：将展示位资源化，构建一个微型的增长互助联盟，这或许是其低成本跨社区冷启动的关键一招。

查看原始信息 ▼

TrendWidget

Stop searching—let the world come to you. TrendWidget brings live search trends to your Home Screen with a Real-time AI Search engine. While most AI search rely on stale data, our proprietary real-time engine feeds LLMs with live, source-level data for 100% fresh insights. - One-Tap Insight: Tap a trend to see why it's viral via AI summaries. - Zero Hallucination: Real-time indexing for accurate breaking news. - Global Pulse: US, Japan, and Korea available.

"See what the world is searching for, right on your Home Screen.📱✨" We often miss out on breaking news, rising cultural shifts, or time-sensitive benefits simply because we don't know they're happening. TrendWidget brings the pulse of the world directly to your Home Screen. See the most-searched keywords from Google & Yahoo trends at a glance, and get an instant AI briefing on why they are trending! ⚡️ Real-time Insights on Home Screen: Check live news and trending topics without even opening an app. 🧠 Deep AI Search (Zero Hallucination): Get detailed, source-backed AI search results for free. No more guessing—know exactly why a topic is viral. 🇺🇸🇯🇵🇰🇷 Global Access: Currently supporting the US, Japan, and Korea, with more countries coming soon based on your requests! 🔥 We’ve already seen incredible traction in Korea: - 10k+ organic users (Zero paid marketing) - W10 40% Retention (It’s a daily habit for our users) [🤝Special Collaboration Offer] I want to support my fellow makers. If you request a shoutout, I’d love to feature your product on TrendWidget’s main screen! Let’s help each other’s products grow and reach more people.

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@trendwidget Congrats on your launch! How do you envision TrendWidget changing how people stay informed about what’s happening in the world?

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You started with a home-screen widget + one-tap insight—what did you deliberately not build, and what’s your decision rule for what gets added next versus staying lightweight?

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@curiouskitty I focus strictly on user community feedback. We deliberately keep it lightweight by only adding what users actually ask for.

Based on their requests, we’ve integrated Deep AI Search, AI Web Summaries, and Ad-free subscriptions, along with continuous bug fixes. If a feature doesn't serve the user's immediate need, we don't build it.

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40% retention for a trends app are cool results! Congratulations on hitting PMF in Korea!

I'm curious about the user behavior: do users mostly just glance at the widget summaries to stay informed, or do you see a high click-through rate to the full sources?

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@valeriia_kuna Thanks for the kind words! We’re seeing a mix of both, but the AI-powered summaries are the real game changer.

Many users enjoy the 'glanceable' convenience of the widget for a quick catch-up. However, once a trending topic piques their interest, we see a strong CTR to the full sources and our AI overview to dive deeper into the 'why' behind the trend. It’s all about reducing the friction to stay informed!

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一句话介绍：Powering 是一款为 macOS 设计的可定制环形命令菜单，通过双击 Option 键快速呼出，让用户无需切换上下文即可瞬间启动项目、脚本、AI 工作流等，解决了高效工作者因工具分散而中断工作流的痛点。

Productivity Developer Tools Tech

macOS 效率工具径向菜单快速启动器自定义命令工作流自动化开发者工具快捷键操作上下文切换

用户评论摘要：创作者主动介绍产品理念并征集反馈，用户询问其是否支持基于项目的上下文感知（自动提供特定脚本），以及能否同时打开多个持久化终端会话面板来管理不同进程。核心关注点在于自动化与多任务处理能力。

AI 锐评

Powering 的本质，并非又一个简单的启动器，而是试图成为 macOS 交互层的一个“命令中枢”。它用“径向菜单”这一古老但高效的 UI 范式，对抗 Spotlight 的线性列表和 Dock 的图标阵列，其宣称的价值在于构建“空间肌肉记忆”。这切中了一个真实痛点：在自动化脚本、AI 工具和开发环境泛滥的今天，高级用户的“流状态”不断被寻找和切换工具的行为打断。

然而，其 v1.0 版本展现的愿景与当前能力之间存在明显沟壑。产品介绍描绘了“万物皆可启动”的蓝图，但用户评论立刻刺向了关键软肋：上下文感知与多任务管理。能否根据当前应用或项目动态改变菜单内容，是它能否从“快捷方式合集”进化为“智能工作流伴侣”的关键。而持久化终端会话的监控功能虽是亮点，但若无法并行管理多个进程（如开发服务器、日志跟踪等），其宣称的“强大开发工作流”支撑力便大打折扣。

创始人 Deniz 提及的社区脚本共享和命令包，是构建生态的正确方向，但这依赖于活跃的创作者社区。目前 93 的投票数表明其仍处于早期爱好者关注阶段。它的真正挑战在于：如何在保持呼出速度与界面简洁的同时，融入更智能的上下文判断和更强大的状态管理，从而让那个“力量之戒”真正适配用户复杂多变的工作战场，而非成为一个需要精心维护的静态快捷键博物馆。它的前途，取决于能否在“极简交互”与“深度集成”之间找到精妙的平衡。

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Powering

Powering gives you a customizable ring of actions, a circle of commands you control — so you can launch anything instantly. Double-tap Option and your personalized command wheel appears — a ring of actions you control. Launch anything instantly. No searching. No dock hunting. No context switching. Projects. Folders. Websites. Scripts. Shortcuts. AI workflows. All within reach. All in one gesture.

Hey everyone 👋 I’m Deniz, the creator of Powering. I built this because I was tired of breaking flow. Spotlight is great. The Dock is fine. But neither feels designed for the way we actually work today, especially with scripts, automations, AI tools, and dev workflows scattered everywhere. I wanted something spatial. Something I could build muscle memory around. So Powering became a radial command wheel I can summon instantly with a keyboard shortcut. Now I launch apps, run shortcuts, deploy scripts, and even manage long-running terminal commands without context switching. One thing I’m especially excited about: you can run persistent terminal sessions directly from the wheel and monitor them in a panel, which makes it surprisingly powerful for dev workflows and AI orchestration. This is v1.0, and I’m shipping it as an independent maker. Would genuinely love your feedback, especially how you’d use it in your workflow. Also… I’m building something around community-made scripts, shared command packs, and deeper customization inside Powering. If that sounds interesting, would you want to be notified when it goes live? :) Join the Discord 👉 https://discord.gg/mZS7mJgehN Thanks for checking it out 🙏

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@thednzozcan Hey Deniz!

This looks great! The spatial muscle memory approach makes sense—that's exactly how we work.

Quick question: does Powering handle context awareness? When I'm in a project, can the wheel automatically offer project-specific scripts, or do I have to set it up manually?

And those persistent terminal sessions—can I have multiple panels open at once for different processes (dev server, API, etc.)?

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一句话介绍：Zendesk Signals 通过AI每日分析客服工单，自动识别“变通方案”话术、功能混淆和升级激增等异常趋势，并在问题萌芽时向Slack发送附带真实用户语录的警报，帮助产品团队提前发现并解决潜在产品问题，打破支持与产品部门间的信息延迟壁垒。

Customer Success Analytics Artificial Intelligence

客户支持分析工单智能监控产品问题预警 Slack集成用户体验管理数据驱动决策 SaaS 自动化警报客户反馈挖掘产品路线图

用户评论摘要：开发者称其灵感源于产品问题在客服工单中隐藏直至爆发，旨在将支持数据转化为产品信号。用户高度认可警报附带真实用户语录的功能，认为其比单纯数据更能驱动团队快速行动。

AI 锐评

Zendesk Signals 瞄准了一个经典的组织痛点：客户支持与产品开发之间的“数据孤岛”与“时间延迟”。其宣称的价值并非简单的工单分析，而是一种“预判性产品情报”。产品团队无需沉溺于客服后台或依赖滞后的汇总报告，而是通过算法定义的基线（如“变通方案语言”）获得被动推送的、情境化的信号。

然而，其真正的挑战与价值深度在于“基线”与“信号”定义的准确性。什么是需要警报的“异常”？“功能混淆”的识别逻辑是否足够精准？误报过多会导致“警报疲劳”，使其沦为另一个被忽略的噪音源；漏报则会使产品失去信任。产品强调“无仪表盘、无手动标记”，这既是其自动化优势，也可能成为黑箱化的风险——团队是否理解警报背后的逻辑并据此采取正确行动？

用户评论中“真实用户语录推动行动更快”的反馈，恰恰点明了其最犀利的价值切入点：将抽象的数据趋势转化为具象的、有感染力的用户叙事。这不仅是通知，更是“动员”。它试图用情感化的人声（用户原话）来弥合数据与行动之间的最后一道鸿沟。

本质上，这是一款试图将“客户声音”（VoC）程序化、实时化、并直接嵌入产品工作流的产品。它的成功不取决于分析技术本身，而取决于其信号是否能无缝、可信地驱动产品团队的决策循环，真正实现“从支持工单到产品路线图”的闭环。如果成功，它将成为产品团队的“预警雷达”；反之，则只是另一个有趣的数据玩具。

查看原始信息 ▼

Zendesk Signals by Usercall

Support teams read tickets one by one. Product teams find out about issues weeks later. Zendesk Signals analyzes tickets daily and detects: • Workaround language • Feature confusion • Escalation spikes When something trends above baseline, your team gets a Slack alert — with real customer quotes attached. No dashboards. No manual tagging. Just signal when it matters.

We built this after noticing product issues hiding in support tickets until they became bigger fires.

Zendesk Signals detects emerging patterns in Zendesk tickets and alerts Slack when something starts trending.

How are you turning support data into roadmap signals today?

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Real customer quotes attached with alerts makes it even better. Data is useful, but actual words from users hit diffrently and push teams to act faster. 💬

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@yosun_negi yes 👍

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一句话介绍：ChartStud是一款AI驱动的数据分析工具，通过上传CSV等文件或使用自然语言描述，能快速将原始数据转化为可视化图表和洞察，帮助非技术团队在无需编写代码或依赖数据部门的情况下，高效完成数据分析决策。

Data & Analytics Business Intelligence Data Visualization

数据分析 AI洞察数据可视化商业智能非技术用户自动化数据清洗自然语言处理仪表盘营销分析数据驱动决策

用户评论摘要：用户普遍认可其“让分析像对话一样简单”的使命，认为对非技术团队价值显著。主要问题集中在当前支持的数据源类型（CSV/Excel/JSON）和未来集成计划（如直接连接广告平台、数据库）。有用户主动提出可帮助优化产品信息传递。

AI 锐评

ChartStud切入的是一个老生常谈却始终未能完美解决的痛点：如何让数据分析真正民主化。其宣称的“将混乱数据转化为清晰决策”看似又一个BI工具的标准叙事，但“用自然语言描述生成图表”的功能点，暴露了它并非定位于与Tableau等传统可视化工具正面竞争，而是试图成为数据分析领域的“Copilot”。

产品的真正价值可能不在于其当前的数据连接能力（目前仍以文件上传为主），而在于其试图构建的交互范式：将分析请求从“构建图表”的精确操作，降维为“描述问题”的模糊对话。这直指非技术用户的核心障碍——他们并非没有数据，而是不知道如何将业务问题转化为工具能理解的查询语言。如果其AI解释能力足够可靠，它解决的将不仅是“看图”问题，更是“看什么”和“为什么看”的认知门槛。

然而，其面临的挑战同样尖锐。首先，“自然语言生成图表”功能高度依赖对用户意图的精准理解，在复杂业务逻辑和多维数据交叉分析中极易产生偏差，这可能让“清晰决策”沦为“美观误解”。其次，早期工具（如CSV上传）与未来愿景（实时API集成）之间存在巨大鸿沟，其技术架构能否平滑过渡存疑。最后，其早期访问用户（创始人、营销人员、运营者）的反馈虽积极，但这类用户对数据准确性的容忍度远低于财务或数据团队，产品一旦在关键洞察上出现失误，信任将瞬间崩塌。

总而言之，ChartStud的构想颇具前瞻性，它试图用AI作为翻译层，弥合业务语言与数据语言之间的鸿沟。但其成功不取决于“能否做出图表”，而取决于“做出的图表能否承载正确的商业洞察”。这条路充满希望，却也遍布陷阱。

查看原始信息 ▼

ChartStud

ChartStud helps you turn raw data into beautiful charts, dashboards, and AI-powered insights. Connect your data, clean it automatically, and discover patterns in seconds.

Hey I’m Lahcen, co-founder of ChartStud. I built ChartStud because I saw how difficult analytics is for non-technical teams. Most tools require SQL, complex dashboards, or dedicated data teams. With ChartStud, you can upload your CSV (and soon connect integrations), ask questions in plain English, and instantly generate charts with clear explanations. You can even turn text into charts — just describe what you want to see. Our goal is simple: make analytics feel like a conversation, not a technical task. We’re currently in early access and looking for feedback from founders, marketers, and operators.

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@aronsmith Congrats on the launch! 🎉
Love the idea of turning messy data into clear insights quickly. Wishing you lots of success with ChartStud! 🚀

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Hey congrats on the launch. What kind of data you connect to ?

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@bengeekly Thank you so much! 🙌

Currently, ChartStud supports:
• CSV
• Excel
• JSON uploads

You can also connect and analyze data exported from platforms like Google Ads, Meta Ads, and other marketing tools.

We’re now working on direct integrations with ad platforms, databases, and APIs so teams can connect live data without manual exports.

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Cool project ! Can't wait for what's to come !

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@itsmasa Thank you! 🙌 More coming very soon!

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Love the mission here. Turning analytics into a conversation instead of a technical hurdle is exactly what non technical teams need. The text to chart angle is especially powerful if positioned right. As a copywriter who helps SaaS founders clarify complex products and drive adoption, I’d be happy to share ideas on sharpening the messaging so the value feels instantly obvious to marketers and operators.

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@copywizard Thank you so much — this really means a lot

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@aronsmith This looks super useful for non-technical teams. I love the plain-English approach to creating charts makes analytics way more approachable. Can’t wait to try it with some messy CSVs and see the insights it generates!

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一句话介绍：Palgo将Strava等健身数据转化为虚拟宠物养成游戏，在用户缺乏运动动力的场景下，通过游戏化机制让坚持锻炼变得有趣且富有奖励感。

Android Health & Fitness Games

健身游戏化 Strava联动虚拟宠物养成运动激励社交竞争任务挑战健康应用习惯养成移动游戏

用户评论摘要：开发者主动介绍并征集反馈。用户正面评价其游戏化概念，认为能有效提升坚持运动的动力和乐趣，将艰苦过程变得有趣。

AI 锐评

Palgo的本质，是将“自律”这笔苦差事外包给“他律”的游戏系统。它敏锐地戳中了现代健身的最大痛点：并非不知道如何运动，而是难以克服启动惰性与坚持的无聊感。其价值不在于引入了多新颖的游戏形式（宠物养成、任务、战斗皆是成熟玩法），而在于精准地充当了现实运动数据与虚拟成就反馈之间的“翻译器”与“放大器”。

然而，其深层挑战与风险同样清晰。首先，它重度依赖外部健身平台（如Strava）的数据管道，自身护城河较浅。其次，游戏化激励存在边际效用递减的普遍规律，当用户对收集服装、宠物进化产生倦怠后，核心运动习惯是否真的内化，要打上一个问号。更尖锐的问题是，它将健身这一高度个性化的追求，纳入了标准化、任务化的游戏框架，可能让运动本身异化为获取虚拟奖励的手段，而非目的。

长远看，Palgo若想超越“初期新鲜感工具”，必须思考如何从“游戏化”走向“意义化”。例如，将运动成就与更深刻的个人数据分析、健康改善洞察相结合，或构建基于真实运动能力的、更具深度的社交玩法。否则，它很可能只是众多试图用糖衣包裹健康药丸的应用之一，用户尝过甜味后，药丸依旧难以下咽。

查看原始信息 ▼

Palgo

PalGo turns your workouts into a fun adventure! Connect your fitness apps and watch your virtual companion, a PalGo, grow as you stay active. Complete weekly missions, unlock outfits with Diamonds, battle friends or AI, and take care of your PalGo by feeding, hydrating, and playing with it. Every run, ride, or gym session gives experience points to help your PalGo level up and even evolve at level 20. Fitness has never been this rewarding! PalGo is available on iOS and Android!

Hi everyone! 👋 I’m excited to share PalGo, a virtual companion that grows and evolves as you stay active. Every run, ride, yoga session, or gym workout gives experience points to your PalGo. Complete weekly missions, unlock outfits, battle friends or AI, and take care of your PalGo to see it thrive! We’d love to hear what you think — any feedback or questions are welcome!

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I love the concept! Gamification works great for everything that requires consistency and hard work. I makes the process more fun and creates daily motivation.

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一句话介绍：一款Chrome扩展，通过一键导入LinkedIn个人资料并利用AI针对性优化，帮助求职者快速生成定制化简历PDF，解决了求职者反复手动复制、调整格式和针对不同职位修改简历的重复性痛点。

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Artificial Intelligence LinkedIn Career

简历生成 Chrome扩展 AI辅助求职工具 LinkedIn集成效率提升 PDF导出职位申请个性化定制招聘科技

用户评论摘要：用户反馈积极，认可产品解决了LinkedIn资料转简历的真实痛点。主要问题集中于AI定制逻辑的细节，例如申请不同职位时能否管理多个简历版本。另有评论从招聘方角度肯定其价值，并愿意提供产品定位与用户体验方面的优化建议。

AI 锐评

Foundire的这款简历构建器，聪明地切入了一个被广泛忽视但确实存在的“真空地带”——LinkedIn的社交化职业档案与标准化求职简历之间的格式与语境鸿沟。它没有好高骛远地宣称用AI“创造”简历，而是务实地定位为“加速流程”的“坚实起点”，这个定位既规避了AI在创造性写作上的不可靠性，又精准命中了求职者在海投过程中最核心的诉求：降低重复劳动的边际成本。

产品的真正价值不在于其技术有多颠覆，而在于其流程整合的细腻度。它将“导入-结构化-针对性改写-导出”这个用户原本需要跨平台、手动完成的松散流程，封装进一个无缝的浏览器扩展中，形成了微型闭环。其AI功能扮演的不是“作家”，而是“编辑”和“调音师”的角色，根据职位描述对现有内容进行微调，这比从零生成更可控、风险更低。

然而，来自招聘领域用户的评论恰恰点出了其商业化与功能深化的关键挑战：当一位候选人同时应聘“数据分析师”和“产品经理”两个差异巨大的职位时，工具是支持并行的、语境隔离的简历版本管理，还是仅提供线性的、一次性的优化？这背后是产品从“单次转换工具”迈向“求职生命周期管理平台”必须回答的问题。若能妥善解决多版本、可追溯的简历管理，并可能与企业端的招聘系统（如ATS）形成更深的互动，其想象空间将从服务求职者个体，扩展到优化招聘双方的信息匹配效率。

当前79的投票数表明其获得了初步认可，但尚未引发爆发式关注。这或许与其“非魔术”的务实定位有关，也提示团队需要在“AI赋能”的营销亮点与“可靠工具”的实用价值之间找到更锋利的传播支点。在HR科技领域，解决“摩擦”往往比创造“奇迹”更能建立持久的用户忠诚。

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Resume Builder by Foundire

Every job application starts by copying your LinkedIn profile and turning it into a resume. This Chrome extension turns that into a faster flow, import your profile, tailor it with AI, and export a clean PDF when you're ready. Built for applicants who want a solid starting point, not a magic resume.

Hey guys 👋 I’m Eric.
The problem
Every job application starts with the same boring work: copying your LinkedIn profile, rewriting it to fit a role, fixing formatting, then exporting another PDF. It’s not hard work, it’s the same work, over and over. If you’re applying seriously, you might redo this dozens of times in a few weeks.

What this tool does
This Chrome extension runs inside Foundire as a simple workspace, but it’s built specifically for applicants, to go from LinkedIn to a tailored PDF resume faster.
1. One-click import from your LinkedIn profile, no copy/paste.
2. AI helps you tailor summaries and bullets for the role you’re applying to.
3. Keep experience, skills, and highlights structured and consistent.
4. Export a polished PDF when you’re ready to submit.
It’s not meant to write your resume for you, just to save time before you apply.

Quickly use
1.Open your LinkedIn profile
2.Import into a structured resume draft
3.Tailor with AI for a specific role
4.Export a PDF and apply

Would love your feedback
How do you currently turn your LinkedIn profile into a resume?
Do you usually start from scratch, reuse an old PDF, or copy things over each time?

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From recruiting: the LinkedIn-to-resume gap is real and it costs candidates. Curious about the tailoring logic — when someone applies to two very different roles, does the tool help them maintain two distinct versions, or is it one resume at a time?

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Love the direction here. Hiring and applying both suffer from repetitive, low leverage work, and Foundire seems to remove friction on both sides. The structured workflows and AI support are strong, especially if they lead to clearer decision making. As a copywriter who works with HR tech and SaaS teams on positioning and user messaging, I’d be happy to share ideas to sharpen how this value shows up on the page and in onboarding.

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一句话介绍：EVY是一款以语音为首要交互方式的AI协创工具，它能在任何应用程序中无缝调用，通过语音快速完成从构思到精炼文档、内容或文案的创作过程，解决了用户在不同工具间切换、过度依赖打字以及AI输出质量参差不齐的痛点。

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Productivity Marketing Artificial Intelligence

AI语音助手跨应用协创内容生成语音输入上下文记忆生产力工具 macOS应用人机交互团队协作工作流整合

用户评论摘要：用户普遍赞赏其语音交互的便捷性、出色的语音识别准确率（包括混合语言）和强大的上下文记忆能力，认为它超越了简单的听写工具，像一个懂项目的智能同事。主要建议和期待集中在推出Windows版、iOS客户端以及企业级功能上。

AI 锐评

EVY的野心，远不止于做一个“更好的语音输入法”。它试图扮演一个横跨所有应用、始终在线且具备深度上下文理解能力的“数字副脑”。其真正的价值在于挑战当前AI工具“应用孤岛”的现状——用户无需在Notion、Google Docs或设计软件中分别调用割裂的AI功能，而是通过统一的语音入口，获得连续、一致的创作支持。

产品强调“Voice-first”和“in any app”，这直击了两个核心痛点：一是将人类最自然的交互方式（说话）与数字创作重新结合，降低“启动摩擦”；二是试图成为操作系统级的AI层，而非另一个需要切换的标签页。从评论看，其上下文记忆能力获得了早期用户的关键认可，这证明它在一定程度上实现了“协创”而非“简单执行”，让AI更像一个跟进项目进度的伙伴。

然而，其面临的挑战同样清晰。首先，作为一款深度集成系统级的工具，其发展受限于操作系统生态，目前仅限macOS，这是巨大的增长天花板。其次，“在任何应用中工作”意味着需要极高的稳定性和兼容性，任何目标应用的更新都可能带来不确定性。最后，其商业模式和团队协作功能的具体形态仍待观察，在个人效率工具与企业级基础设施之间，它需要找到坚实的落脚点。

总体而言，EVY展现了一种更融合、更自然的AI交互未来图景。但它能否从一款备受极客喜爱的效率工具，成长为真正改变大众工作方式的基础设施，取决于其跨平台能力、生态构建以及在对“质量”和“真实性”的坚持上能否兑现承诺。它不是在堆砌功能，而是在重新设计人与数字世界交互的管道，这条路正确但异常艰难。

查看原始信息 ▼

EVY

Meet EVY - your AI co-creator to go from ideas to polished documents, content or copy in seconds. Press the EVY-Key anywhere to ask questions, brainstorm ideas, generate or edit text, take notes, or dictate.

This is EVY, it’s a bit like Claude + Wisprflow + Granola + Living Notes. It's the best AI tool I've used to actually get real work done. We’d love to hear what you think.

Why we built this:

We saw 3 points of friction in working with AI:
1) Every tool tried to push its own AI, resulting in scattered workflows.
2) Voice as interface was criminally underused (why are you still typing in most tools) and
3) Many solutions are about hands-off and quantity, and not about quality and empowering authenticity.
We don’t want to be a part of “littering” the internet.

That’s why EVY is a voice‑first AI co-creator that works in any app, keeps context, and respects your team’s creativity. We’ve been building without investors over the past 10 months. Hopefully, she will give you back time and get you into flow.

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damn, thats a must try!

Congrats on a launch

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@edmon_wales Thanks Evgeny, your support means a lot!

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congrats to the team with the PH launch. good luck.

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@vltnpivovarov It's been a big push, glad for EVY to be out there finally.

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I’ve been using Evy since early beta, for about 6 months now.

I almost never type on my mac anymore, all work/life texts go over evy’s tts.

Can’t wait when they will launch iphone app.

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I wish you all the best guys

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@vladimir_hudnitsky Hey Vladimir, can't thank you enough for being there from the start! We are doing this for our early supporters like you! iPhone app is on the cards.

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I use EVY all the time to enter prompts for my coding agents, and it's fantastic in terms of text recognition. It captures the majority of what I intend to say, even in mixed or unexpected languages. And I'm now a fan of the product, and I'm very proud of the team for releasing it. You should definitely try it, and I bet you will love it!
By the way, the agent mode is fantastic, and I'm getting used to it.

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looks great - definitely give it a try! and congrats guys on this amazing launch!

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@sasha_dikan Yess and please send any feedback our way!

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Good luck

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@dzianis_yatsenka Appreciated. Luck favors the prepared :)

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That's actually very impressive. I was thinking of making something similar for myself, but maybe EVY is just what I need!
It's a shame it's only for Mac, I'll be eagerly awaiting the release for Windows.

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Been beta testing this for days and it’s definitely more than just another dictation tool. What hooked me is how it actually remembers the context of my projects and previous conversations. It feels less like a recorder and more like a native assistant that’s already up to speed with what I’m working on.

The way it integrates into macOS is seamless—I can just push-to-talk, and it processes my thoughts based on the current context without me having to explain everything from scratch. It’s a huge time-saver for turning rough ideas into actual, usable output.

Really solid execution on the "Voice OS" concept. Excited to see this go live. Good luck with the launch! 🚀

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Looks really great! Now I am sad that I don’t have a Mac anymore. Please do the same for Windows in the Future:)

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Great! Waiting for iOS app

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Well done! Can’t wait to give Evy a solid try across personal and enterprise use. I love the promised consolidation - will report back on how it goes practically.

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Hey! Ok, my questions about how you compare was totally stolen. Just the best of luck!!!!!

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I'm using EVY for more than 6 months - it is an awesome voice assistant! My first and forever app that showed me how programming by voice can be convenient, thanks!

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I've been using EVY as a beta-tester for ~6 months for now and I can it's not “just another AI tool.”
It feels like having a virtual AI colleague inside your MacBook — one that knows what’s happening, keeps context, and that you can talk to or delegate tasks to. The spectrum of tasks is wide, from thinking and structuring to execution.

In terms of experience, it feels like a thoughtful combination of Claude-level reasoning + Wisprflow-style voice input + structured note systems like Granola or Living Notes — but integrated into one coherent workflow.

What I appreciate most is that this works both individually and for teams.
For tech and non-tech professionals.
For real work — not just demos.

I’ve tested a lot of AI tools in educational product workflows, content creation, and operational thinking. This one feels like infrastructure, not noise.

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I used EVY to create articles, write legal documents, and do a lot of work that I previously did manually, and it's really a time saver.

I actually didn't use any new features that they implemented recently. I probably need to check what they have, but just the standalone voice dictation feature with the quality that they have, with the error correction, with good punctuation, that's a huge thing.

I really use it on an everyday basis, and I think this is a very smart app, which does look like something from the future.

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congrats on the launch! how do you guys compare to openclaw?

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@flreln very good question!

OpenClaw is a self-hosted AI agent that lives on your computer with full system access - it can control your browser, read files, execute commands, and autonomously complete multi-step tasks across your entire system. It's incredibly powerful, but that power comes with significant tradeoffs:

Security risks: Recent reports show it's been exploited with 341 malicious "skills" and called a "privacy nightmare" by security experts. When an AI has full access to your system, one compromised plugin or misinterpreted command can expose everything.
Technical overhead: You need to host it, manage API keys, configure integrations, troubleshoot when things break, and constantly evaluate which community-built "skills" are safe to install. It's a side project in itself.
Feature bloat: It can do almost anything, which sounds great until you realize 80% of those capabilities are either gimmicks or edge cases you'll never use. The community builds hundreds of skills, but how many genuinely improve your workflow vs. just being technically impressive?

EVY takes the opposite approach:

Works out of the box. No setup, no hosting, no technical expertise required. You sign up and start talking.
Security by design: We don't need access to your entire computer. EVY operates in a controlled environment focused on helping you with writing, prompting and work. Your data stays sandboxed.
Intentional limitations: We only build features we believe have genuine value for most workflows. Enabling voice for any apps. Push-to-talk AI. Brand context across all apps with your team. Drafting documents, copy or content. No trying to be everything to everyone.
"AI for the rest of us": You don't need to be a developer or spend weekends trying to squeeze value out of it. EVY is built for anybody who wants AI to get them into flow and save them hours.

TL;DR: OpenClaw is for hackers and early adopters who want maximum control and don't mind the complexity/risk. EVY is for professionals who could use a co-creator in their daily work, safely, and without a learning curve.

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I hate AI. But Evy is fine!

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@misha_malikin ❤️

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yay, LFG!

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@arseny_info 🚀🚀🚀

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It seems that you are not featuring, but in any case, slap!

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@ititov_agency you never know maybe they'll come around

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一句话介绍：Granary 是一款开源CLI工具，作为AI智能体的“上下文枢纽”，在多人协作开发真实代码库的场景下，解决了多个AI智能体间上下文丢失、工作重复和变更冲突的协同痛点。

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Open Source Developer Tools Artificial Intelligence GitHub

AI智能体协同开发工具上下文管理任务编排开源CLI 本地优先 Rust 多智能体框架代码开发

用户评论摘要：由于提供的用户评论列表为空，无法总结具体反馈。潜在的有效评论可能涉及安装体验、与特定框架的集成效果、实际使用中遇到的协同问题或性能表现。

AI 锐评

Granary 瞄准了一个新兴但至关重要的技术缝隙：多AI智能体在复杂、持久化任务（如代码开发）中的协同问题。其价值不在于替代某个智能体框架，而在于充当“粘合剂”与“交通警察”。它提出的会话跟踪、并发安全认领、结构化交接等机制，直指当前智能体应用从单次对话炫技迈向工业化、流水线协作的核心障碍——状态管理与进程同步。

产品强调“本地优先”、“单一Rust二进制文件”，是明智的差异化选择。这避免了将协同逻辑捆绑于特定云服务或框架，赋予了开发者部署的灵活性和对数据的控制权，符合当前对AI应用隐私和可控性的关切。然而，其真正的挑战在于生态构建。宣称“适用于任何智能体框架”是理想，但现实是各框架架构迥异，要实现深度、无损的上下文传递与动作协调，需要极强的抽象能力和各方的适配意愿。目前这更像一个精心设计的协议与中间层，其效用高度依赖于社区是否采纳并为其开发“连接器”。

当前76的投票数反映了其受关注度尚在早期技术探索者圈子。它解决的是“前沿的痛点”——只有当开发者大规模部署多个智能体进行严肃项目开发时，混乱才会成为显性需求。因此，Granary 更像是一份针对未来问题的“先行解决方案”，其成功与否，不仅取决于自身设计的优雅程度，更取决于多智能体协作开发范式本身能否成为主流。它是在为尚未完全形成的市场铺设基础设施，风险与机遇并存。

查看原始信息 ▼

Granary by Speakeasy

If you've run multiple AI agents on a real codebase, you know the pain: agents lose context between sessions, duplicate each other's work, or produce conflicting changes. There's no built-in way for them to coordinate. Granary is an open source CLI that fixes this with session tracking, task orchestration, concurrency-safe claiming, checkpointing, and structured handoffs between agents. Local-first, single Rust binary, works with any agent framework. Install it and run granary init.

一句话介绍：Seda是一款AI驱动的社交研究平台，用户可通过AI助手对任何感兴趣的话题进行深度研究，并将研究过程和发现分享至社交动态，旨在构建一个基于证据、而非情绪化的信息共享社区，解决传统社交媒体信息碎片化、缺乏深度与可信度的痛点。

Android Social Network Artificial Intelligence Search

AI社交平台研究型社区信息验证内容发现知识协作证据驱动兴趣图谱真相引擎深度讨论信息革命

用户评论摘要：主要评论高度认可产品“研究驱动”的理念，认为其有望对抗网络情绪化内容。核心疑问在于AI的具体作用机制：用户提问AI是否会在发布前自动验证信息的真实性，这反映出用户对平台如何保障“研究”质量的关键关切。

AI 锐评

Seda的野心并非仅是又一个社交应用，它试图从底层重构社交媒体的信息生产范式。其宣称的核心价值——“研究支持的每一篇帖子”，直指当前社交媒体的阿喀琉斯之踵：情绪宣泄、事实缺失与语境坍塌。然而，这一宏伟蓝图面临三重严峻拷问。

首先，是“研究”的定义与质量管控风险。平台将“研究”过程托付给AI助手，但AI生成内容的准确性、偏见与“幻觉”问题如何解决？若缺乏严谨的溯源和交叉验证机制，所谓“研究”可能只是包装更精美的猜测，甚至系统性偏见的高效放大器。评论中的用户质疑“AI是否验证帖子真实性”，正戳中了这一核心模糊地带。

其次，是产品逻辑的内在矛盾。它试图融合“深度研究”与“社交动态”两种相悖的时间尺度与心智模式。严肃的研究需要审慎、耗时与孤独思考，而社交动态则鼓励即时反馈、互动与碎片消费。两者强行耦合，可能导致“研究”沦为社交表演的素材，深度被肤浅的点赞评论所消解。

最后，是其宣称的“更好真相引擎”面临根本性挑战。真相往往复杂、矛盾且反直觉，而社交平台的结构天然倾向于简化、站队与形成回音壁。即便每个帖子都附带“研究”，算法如何排序？用户如何选择相信？当观点冲突时，是演变为基于证据的理性辩论，还是升级为“研究”资料的堆砌对战？

Seda的真正价值，或许不在于它能立即创造一个“真理乌托邦”，而在于它作为一个社会实验，尖锐地提出了问题：在AI普及时代，我们能否设计一种技术框架，激励而非消解人类的深度思考与理性对话？它的成败，将不取决于AI能力的高低，而取决于其设计能否在“便捷研究”与“严谨过程”、“社交激励”与“求真本能”之间找到那个微妙的、反人性的平衡点。目前看来，它迈出了引人注目但充满险阻的第一步。

查看原始信息 ▼

Seda

Discover the world with Seda - The Social Media Platform For Research & Discovery. Use our AI to research anything you're interested in - whether its conspiracy theories, stocks, prediction markets, politics, news, or anything else you're curious about. Post your research and Discoveries for your friends to see, see what your friends & the world is researching realtime, creating a better truth engine where every post is backed by research and discoveries.

In order to Discover anything in this world, you must first search, and re-search. So much of the internet and social media today are backed by 99% human rage bait. But what if you could flip that around by making every post research backed? The most truthful social information platform could be born. Now with AI, everyone has become a researcher. With Seda, you conduct deep research on an interest you have with our AI research agent—whether it’s policy and government, law, AI research, sports, music, art, history, philosophy, finance, science, global events, markets, prediction markets, or emerging ideas—and post the Deep research and your discoveries onto the feed for your friends to see. Then, you can follow your friends, see what they’re researching, read, comment, like, challenge, debate, expand on, and share your opinions. Over time, this creates a growing, interconnected body of realtime research and discoveries of the world’s curiosities. In contrast to traditional social platforms like X that prioritize speed and engagement, Seda is designed to preserve context, reasoning, and evidence, allowing ideas to develop collaboratively, creating a better truth engine for the internet. Come join the Information Revolution at Getseda.com :)

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Is this app for posting, but first using AI to verify the accuracy of the post you are going to publish? If I post something, does the AI verify it to see if it is true?

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一句话介绍：Clawezy 一键部署全托管AI智能体服务器，解决了开发者在构建和部署AI代理时面临的基础设施配置复杂、运维困难的痛点。

SaaS Developer Tools Artificial Intelligence

AI智能体部署全托管服务器无服务器架构开发者工具模型市场 Discord机器人 Telegram机器人远程桌面基础设施即服务低代码AI

用户评论摘要：用户反馈较少。创始人主动阐述产品初衷并征集部署痛点。唯一有效提问关注AI代理运行可能带来的安全隐患，体现了早期用户对这类平台在安全性与隔离性方面的核心关切。

AI 锐评

Clawezy 瞄准的是一个真实且正在膨胀的痛点：AI智能体从原型到持续服务的“最后一公里”基础设施困境。它将复杂的GPU配置、容器管理和网络调试打包成一个“一键部署”的虚拟机，其真正价值并非技术上的颠覆，而是体验上的封装与流程上的提效，本质是“AI智能体时代的简化版云主机”。

产品亮点在于其“Neural Marketplace”构想和即时通讯机器人连接，这试图构建一个以能力模块和应用连接为核心的轻量生态，降低智能体功能拓展门槛。然而，其模式也暗含挑战：首先，封装与控制的平衡。内置VNC和诊断工具意在提供“完全控制”，但这与“全托管、免运维”的宣传存在一定张力，高级开发者可能仍需要底层权限。其次，安全性质疑是致命要害。用户评论直指核心：在共享或托管环境中，如何确保自主运行的AI代理不会越权访问、泄露数据或进行恶意操作？平台的安全隔离、监控和审计机制将是企业级用户信任的基石。

总体而言，Clawezy 是市场快速响应的产物，它降低了AI智能体服务的启动成本，但其长期竞争力将取决于托管服务的稳定性、安全架构的可靠性以及模块生态的活跃度。若仅停留在便捷部署层面，它极易被大型云厂商的同类集成服务所覆盖；若能围绕“市场”和“智能体运维”构建独特工具链和社区，或可占据一席之地。当前版本更像一个精美的“最小可行产品”，真正的考验在于如何将“便捷”转化为不可替代的“价值”。

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Clawezy

Stop wrestling with Docker and GPU configs. Clawezy deploys fully-managed AI Agent servers (VMs) in one click. Connect Telegram & Discord bots instantly, browse our Neural Marketplace to equip new capabilities, and scale effortlessly. We handle the complex infrastructure layer so you can focus on building intelligent behaviors. Complete with built-in remote desktop, diagnostics, and a premium dark-mode dashboard.

Hey Product Hunt! 👋 I'm the maker of Clawezy. We built Clawezy because while building AI agents is exciting, hosting them is still a nightmare. You're stuck managing Docker containers, worrying about GPU uptime, or debugging obscure networking issues just to get a bot online. Clawezy solves this by giving you: 🚀 Instant Deployment: Launch a fully configured AI server VM in seconds. 🧠 Neural Marketplace: Equip your agents with new skills (modules) just by copying a prompt. 🔌 One-Click Connections: Link Telegram and Discord bots instantly. 💻 Full Control: Built-in VNC remote desktop and deep diagnostics. We're going for a premium, developer-first experience that actually looks good while you use it. I’d love to hear your feedback—what’s the biggest pain point you face when deploying agents today? Let me know in the comments! 👇

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Congrats on the launch! Can you describe how you handle security around these agents potentially exposing anything?

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