Product Hunt 每日热榜 2026-01-08

PH热榜 | 2026-01-08

#1
Livedocs
The general data agent
379
一句话介绍:Livedocs是一款通用数据智能体,允许用户通过自然语言提问,直接连接各类数据源并即时生成图表、指标和答案,无需编写SQL或搭建看板,解决了非技术人员获取数据洞察门槛高、技术人员分析流程繁琐的核心痛点。
Analytics Artificial Intelligence Business Intelligence
数据分析平台 AI数据智能体 自然语言查询 BI工具 无代码分析 数据可视化 企业级应用 本地部署 多数据源连接 AI辅助决策
用户评论摘要:用户普遍赞赏其易用性和设计,认为它解决了跨工具获取可靠洞察的痛点。重点关注问题包括:目标用户、处理大型数据集能力、准确率、API及按需付费计划、支持的数据源类型。团队回复透露了本地/云端部署灵活性及准确性保障机制。
AI 锐评

Livedocs的定位“通用数据智能体”野心不小,它试图用一层自然语言交互封装从数据查询、分析到可视化的全链路,本质上是对传统BI和数据分析工作流的一次“降维打击”。其真正价值并非技术上的颠覆,而在于精准切中了企业数据应用的“最后一公里”悖论:数据基础设施日益完善,但消费数据的门槛从未真正降低。

产品聪明地采取了“混合策略”:既提供傻瓜式的智能体问答模式,也保留可深入编辑的SQL/Python笔记本。这并非简单的功能堆砌,而是对现实工作场景的深刻洞察——它同时满足了业务人员“速览”的轻需求和数据分析师“深究”的重需求,从而有望成为跨职能团队共同的数据协作层。从评论中团队强调的“无锁定”(可本地部署、自选模型)来看,他们深谙企业客户对数据安全与可控性的敏感,这可能是其切入严肃商业场景的关键筹码。

然而,其面临的挑战同样清晰。首当其冲的是“智能体”的可靠性天花板:面对复杂、模糊的业务逻辑,自然语言交互的准确性和语境理解能力将经受严峻考验。其次,它试图成为所有数据源的统一接口,但不同源(如散乱的Excel与规整的数仓)的数据治理水平和语义一致性天差地别,这可能导致“输入垃圾,输出垃圾”的自动化困境。最后,在巨头环伺的AI与数据分析市场,其作为独立产品的长期壁垒何在?是更优的垂直场景理解,还是其强调的部署灵活性?这将是决定其能否从一款优秀的工具成长为平台的关键。

总体而言,Livedocs展现了一个极具潜力的方向:将AI作为“数据平民化”的终极中介。但它能否真正承载起“人类数据科学家”的厚望,不仅取决于其AI能力,更取决于其对业务上下文的理解深度和应对数据混乱现实的工程韧性。

查看原始信息
Livedocs
Livedocs is a general data agent that can do BI, ML, build dashboards, and write code and queries (anything a human data scientist would do!) Livedocs helps you understand your data instantly. Upload a CSV, spreadsheet, or connect your database, then ask questions in plain English. Livedocs uses AI to generate charts, metrics, and clear answers—no SQL, no dashboards, no setup. Built for anyone who needs insights fast, whether you’re technical or not. Just bring your data and start asking.

Hello Product Hunt 👋 and thanks @garrytan for the hunt!

I’m Arsalan, founder of Livedocs. Excited to share what we’ve been building!


Livedocs helps teams understand and work with their data using AI. Connect a spreadsheet, CSV, warehouse, Google Drive, or S3 bucket, then ask questions in plain English and instantly get charts, metrics, dashboards, or answers.

You can stay in agent mode and just ask questions, or go deep into a full notebook with SQL, Python, and charts when needed.

Here’s how teams use Livedocs today:

📊 RevOps & GTM teams: Track pipeline, revenue, conversions, and attribution across tools without stitching dashboards.

💰 Finance teams: Answer questions about revenue, costs, cohorts, and forecasts directly from source data.

📈 Founders & operators: Get fast answers about growth, churn, and business health without waiting on reports.

🧠 Data & product teams: Explore production data, build analyses, and share interactive results with the company.

Everyone who signs up gets $5 in AI credits to try it out.

We’d love your feedback or questions, I’ll be here all day 🙌

16
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@garrytan  @arslnb this looks very useful. Love how easy and simple it is to get started. Nice work and congrats on the launch

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@garrytan  @arslnb Huge congrats on the launch, Arsalan 🚀
Livedocs feels like a very clean answer to a real pain: getting fast, reliable insights without jumping between tools. Love the flexibility between agent mode and deep notebook work - that’s a smart balance.
Wishing you a strong launch day on PH 👏

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Amazing – congrats on the launch! Looks really cool

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@shivsak Thanks! We put in a lot of effort into the design, which is an often ignored part of data intensive tools. Would love to get more feedback on how you find Livedocs. Take it for a spin (no credit card, self-serve, comes with free AI credits) and let me know what you think!

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Amazing launch!

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@wolfofwebsites Thanks Stan, really trying to Hup our game here 😄

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Connecting agents directly to real enterprise data is the hard part. Nice to see a team tackling this head-on.

Which data sources are most common right now?

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@justin2025 Totally agree. In reality it’s way less fancy than it sounds. Most teams are living in Google Sheets / Excel, even at pretty big companies. After that it’s usually Postgres. Warehouses like Snowflake or BigQuery show up later once the team matures. That messy mix is exactly what we built Livedocs for.

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Congrats on the launch — this looks like the right abstraction for the next wave of enterprise agents.

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@ventali Thanks Ventali! Absolutely, we wanted to build a product that has no lock-in from the ground up. Meaning you could run it locally or on your cloud, pick any model you like, etc. Keen to hear what you think!

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Congrats on the launch Arsalan!

I've been trialing Livedocs at our company for a few months now, and it's absolutely amazing how quickly it has become one of the most valuable data tools in our arsenal.

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@kumailht Thanks Kumail! We're lucky to partner with with great design partners early on. A massive hat tip to you and your team!

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I have encountered many difficulties when writing SQL, including using AI to write it. Thanks for you guys building this, it helps me understand data in an easier way.

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@yoang_loo Love to hear it! Livedocs also auto-corrects your SQL queries if you decide to write them by yourself! Keen to hear more feedback!

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@yoang_loo Thanks! That’s exactly why we built Livedocs, to make understanding data easier without wrestling with SQL 🙌

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Tried LiveDocs as part of a research workflow — the ability to reason over live data across different data sources made a big difference for iterative analysis.

Being able to run it on local GPUs (which is often required in academic institutions) is a huge plus. Really cool product!

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@ventali Thanks again! We built local runtime specifically for cases where its not possible to upload datasets or better hardware is available locally

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On prem model wins. ;) Nicely done.

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@datarade Thanks! You can run Livedocs locally, on-prem, on a hosted cloud or even your own cluster!

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Amazing product! Congrats to the Livedocs team!

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@natiakourdadze Thanks Natia, appreciate your support!

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Who is your target audience?
Students/developers/marketers?

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@michael_vavilov Hi Michael, Livedocs is built for business and data teams (RevOps, finance, founders, and product/data teams) working with real company data. That said, students and developers also use it on the free plan to explore data, learn analytics, and prototype ideas. Curious to learn how you'd find Livedocs useful?

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This makes life much easier for data scientists 👍👍

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@maria_doliashvili Thanks, Maria!

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Great product, do you plan to offer the system as API access too? and ideally with pay-as-you-go pricing? It might be useful for solutions like Starnus that integrate and aggregate agentic/software solutions in one place

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@khashayar_mansourizadeh Thank you! Yes — API access is on our roadmap, and pay-as-you-go pricing is how we’re thinking about it. We want Livedocs to be easy to embed into platforms.

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nice data tool - one thing which is crucial is the accuracy rate. How is the average accuracy rate of LiveDocs?

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@cruise_chen Hi Cruise! Accuracy was a huge priority for us while building Livedocs, which is why Livedocs always shows the source code and queries used and explains how it got the results. We're also performing in the top 10 on some kaggle competitions, so that's a great sign

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This looks super useful! I love that it lets you ask questions in plain English without jumping into SQL every time. How do you handle really large datasets?

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@abod_rehman Hi, thanks for the comment. Livedocs connects directly to all kinds of data from small CSV and excel files (with DuckDB) to large data warehouses. For extremely large amounts of data, we push down compute to the warehouse and use our smart cache to make things efficient. It also reuses prior in-memory artifacts to make data analysis a breeze!

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Congratulations on the launch! Really like the overall interface and the quality of the analysis. Also had a couple of questions, does the system auto-detect relationships between data sets or do they have to be added to the context tab? And how are null values handled across different models?

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@mustassim Hi Syed, thanks for your support! Yes, Livedocs will auto-detect the schema of your databases. For other datasets like files, etc, too it would search across available data for anything that helps it get the full picture.

Livedocs also cleans up the data and looks for missing/null values. You can pick any model you like, so larger, more recent models are better than smaller models at cleaning and analyzing data.

Curious to learn about what kind of data you are analyzing with Livedocs

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"No SQL, no dashboards, no setup" is the right democratization approach. The real barrier to data insights isn't tools – it's the translation layer between business questions and technical queries.

Interested in how you handle data quality edge cases. When users ask ambiguous questions ("show me growth"), does the agent surface assumptions or clarify intent before generating charts? That context awareness would be key for trust in production.

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Huge congrats on the update and launch @arslnb !

Early days can be wild with numbers are you finding your conversions line up with expectations so far, or does it feel like something isn’t clicking the way you hoped?

That’s often where early traction decisions matter most. :)

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#2
ChatGPT Health
ChatGPT designed for health and wellness
325
一句话介绍:一款集成于ChatGPT内的健康专用空间,通过安全连接个人医疗记录与健康应用数据,为用户提供基于自身健康信息的对话服务,旨在帮助用户更好地理解和导航医疗流程,而非替代专业医疗。
Health & Fitness Messaging Medical
健康科技 AI健康助手 医疗信息整合 健康数据平台 症状查询 医疗导航 健康管理 数据安全 大模型应用 聊天机器人
用户评论摘要:用户反馈积极,认为其抓住了流行需求,有助于症状早期了解和语言学习。主要疑问集中于技术实现(是否为新模型、能否连接可穿戴设备)、数据隐私安全、医疗合规性风险,以及该服务目前对欧洲等地区的限制。
AI 锐评

ChatGPT Health的本质,并非技术突破,而是一次精明的场景化封装与风险隔离尝试。其真正价值不在于模型能力,而在于构建了一个数据、记忆、对话完全独立的“飞地”,试图在合规与信任的钢丝上行走。

产品聪明地定位为“导航”而非“诊断”,这是面对严格医疗监管的必然选择。通过连接Apple Health等外部数据,它旨在提升回答的相关性,但这恰恰是最大痛点:其效用完全取决于用户数据接入的广度与深度,而核心医疗记录的获取壁垒极高,主流医疗机构几乎不可能向第三方LLM开放数据管道。评论中的隐私担忧直接击中了这一阿喀琉斯之踵。

从评论看,用户最关心的并非AI的医学知识,而是“能否连我的Whoop”、“我的数据如何被保护”以及“你是否合法”。这揭示了当前AI健康应用的核心矛盾:用户渴望个性化,但极度警惕数据共享;市场需要规模化,但医疗数据天然孤岛化。

OpenAI此举,可视为将ChatGPT“超级应用化”的关键一步,通过创建垂直“空间”来拓展边界。然而,其面临的挑战远超技术层面,涉及复杂的医疗伦理、全球数据合规(如欧洲地区的暂时缺席所示)以及建立与传统医疗体系的互信。若不能真正解决数据来源的合法性与权威性,它可能最终只是一个体验更好的症状搜索引擎,而非革命性的健康伴侣。它的成败,将是检验AI巨头能否在强监管、高信任要求的垂直领域成功落地的试金石。

查看原始信息
ChatGPT Health
A dedicated space for health conversations in ChatGPT. You can securely connect medical records and wellness apps so responses are grounded in your own health information. Designed to help you navigate medical care, not replace it.

Definitely a popular use case — certainly seems like ChatGPT could become a superapp by year's end!

https://vimeo.com/1151655050/ba15f56a51?fl=pl&fe=cm

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This can help with early diagnostics. I already used ChatGPT to describe the symptomps and it was pretty accurate in identifying my problem. My another favourite usecase is to use it for conversation in different languages I am trying to learn.

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thats cool! hope oai make me live better

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Does this plugin to wearables - I would love to connect my whoop to it

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Is this a new GPT model, or is it just a conversation with specific instructions added?

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@josie_oy It's not a new model; it's a dedicated "space" and set of features (like ChatGPT Image Generation) powered by existing GPT models with added health-specific context, controls, and protections.

The “Health” space inside ChatGPT will layer on purpose‑built encryption/isolation, separate memories, and integrations (medical records, Apple Health, apps) to ground answers in your data.

Health chats aren’t used to train foundation models, and information in Health doesn’t flow back into non‑Health chats.

You can think of it as a specialized mode plus tooling and policy, designed with physician input and evaluated via HealthBench—supporting navigation and understanding, not diagnosis or treatment.

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Good luck with this one... there is no way any company would share their internal private health data with a third party LLM nor should they. Chance of it actually being useful <10%.

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Hi @chrismessina congrats on the launch!

feels like a huge risk providing medical advice without any licence. OpenAi also launching shortly their AI doctor FYI.

Will be interested to hear your thoughs on these 2 points

Thanks!!

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Interesting to see how it evolves! I see that for now only users outside the European Economic Area, Switzerland, and the United Kingdom are eligible. I’d love to know whether the full release is expected to exclude those regions as well.

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I have already seen people use ChatGPT for understanding symptoms and next steps. Having this framed clearly around health and wellness makes a lot of sense. Interested to see how this evolves.

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#3
Spec Coding by Capacity
Vibe Coding with a planning assistant to build with clarity
249
一句话介绍:一款采用“规划先行”理念的AI应用构建工具,通过AI联合创始人引导用户在编码前明确应用定义,解决了“氛围编码”因缺乏前期规划导致代码混乱、返工率高的核心痛点。
Design Tools Prototyping Developer Tools
AI应用开发 低代码/无代码 规划先行 氛围编码 技术债务管理 AI辅助设计 原型构建 需求梳理 代码生成 生产力工具
用户评论摘要:用户普遍认可“先规划后构建”的理念,认为其能减少重构。主要问题与建议集中在:技术实现细节(代码质量、测试处理)、功能扩展(团队协作、技术文档导出、第三方集成)、与竞品差异(如Cursor、Lovable),以及定价模型(信用额度能构建多少)。
AI 锐评

Spec Coding by Capacity 并非又一个简单的“提示词转代码”工具,它试图扮演一个颠覆性的角色:将软件工程的方法论强制注入到当前浮躁的AI代码生成浪潮中。其真正的价值不在于“生成”,而在于“约束”与“对齐”。

当前AI构建器的核心矛盾是“生成速度”与“产出质量”的倒挂。AI可以自信地生成大量代码,但因其缺乏对业务上下文和系统边界的理解,极易产出结构松散、意图偏离的“一次性原型”。Capacity的“Spec Coding”本质上是引入了一个前置的、结构化的需求工程与设计阶段,通过专用AI代理引导用户厘清痛点、目标用户、核心与非核心功能,相当于在建造前先绘制精确的蓝图。这看似“拖慢”了起步,实则是对抗AI技术债务的预防性措施。

从评论看,其挑战同样明显。首先,市场教育:习惯了“即时满足”的用户是否愿意接受这个“减速”流程?其次,流程刚性:前期规划在应对快速变化的需求时是否足够灵活?其AI能否在开发中后期持续保持与初始规划的一致性,并智能调整微步骤,将是考验其能否超越MVP阶段的关键。最后,生态壁垒:当竞品纷纷在生成速度、多模态和复杂集成上堆料时,Capacity这种专注于“前期流程”的深度能否构筑足够宽的护城河,还是会被轻易模仿或整合,仍需观察。

它瞄准的不是“更快地做出一个东西”,而是“更靠谱地做出那个对的东西”。这是一场针对AI辅助开发心智模型的博弈,成败在于能否说服市场:在AI时代,良好的开端依然是成功的一半。

查看原始信息
Spec Coding by Capacity
Most AI app builders jump straight into building. That feels fast until your app gets messy, inconsistent, and nothing like what you imagined. Today, we’re introducing Spec Coding in Capacity. Instead of building immediately, Capacity now lets you define your app first with the help of an AI co-founder that asks the right questions before any code is generated. More structure upfront. Far less refactoring later. Much better results.
Hey Product Hunt 👋 Samuel here, maker of Capacity. We love vibe coding it’s fun and incredibly fast. But after building a lot of real apps with AI, we kept running into the same problem: AI builds confidently, even when the idea isn’t clear. Humans ask questions. AI doesn’t, it guesses. So we built Spec Coding. Before any code is written, an AI co-founder helps you define: -what you’re building -who it’s for -what matters -and what doesn’t It slows down the first minute… and saves hours later. Would love your feedback especially if you’ve ever rebuilt the same AI app three times 😄 Happy to answer any questions!
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@samuel_rondot Hi Samuel, seems like you're on the right track with this. How do you deal with testing and code qual?

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@samuel_rondot Cool, vibe coding is really taking off lately. I 've spent a lot of time refining my prompts to get the result I want, so it'd be awesome if ur product could leverage a planning feature to make this more efficient.
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does the co-founder assistant allow me to export the technical documentation separately if I want to keep a record of the app structure?

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@rahul_manjhi1 you have access and can export all the documents of your project: codebase, project brief, design specs, coding task... data is yours

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​I really appreciate the focus on reducing refactoring because I usually spend more time fixing AI-generated messes than actually building the features I want. 🛠️

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@navin_kumar_singh clearly! That's definitely what motivated us about going hard on spec coding

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Hey @samuel_rondot ,

I like the design of @Capacity . I like the name as well - brilliant.
Which tool / stack did you use to create @Capacity ?
What is your GTM?
I would like to meet with you and chat: https://timetuna.com/pavel

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@pavelk2 Hey Pavel !
Thank you so much !
We are using Nextjs, Nodejs. Our GTM strategy is simple : building an ai website builder that works. Currently most vibe coded app fail and we are fixing that with Spec coding.

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Hey @samuel_rondot ,

Been following you from far on X, liked the Starter Story video too

Cool launch and very nice product you guys cooked here, congrats for top 3 :)

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@samuel_rondot  @ugo_builds we appreciate the support 🙏

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Thanks for sharing this @samuel_rondot ! Any free coupons for us PH users? 🤓

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@samuel_rondot  @pezzin  you can find it right here PH20OFF 🤫

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Nice Launch!
How much can you build on 100 credits ?

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@bekjon_ibragimov a complete web app. Here is a video of me implementing a fully working web app in only 50 credits: https://www.youtube.com/watch?v=GWYdAcbQj-4. I still had 50 credits left in the end

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This looks great and really helps put the focus on the most important stage of the process. Do you have features to allow teams to work together on this? Good luck with the PH launch.

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@henry_adams5 we don't have a collaboration mode atm, it didn't came out as an important feature from our customers' feedback

Thanks! The PH battle is intense today

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Nice, 2 questions:
1. How much it differs from planners such as the one that Cursor and other tools have (when they set a long todo list) + Planning mode
2. How consistent the agent stays on the created initial plan? and how flexible it can be to adjust the plan and micro-steps based on progress it makes?

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@khashayar_mansourizadeh there are noticeable differences:

  1. Our planning system operates not only at the task level, but at the project level as well. You start co-writing your project brief (pain point + solution) with a specialised AI agent, then your design specifications and finally coding tasks (=user stories with technical details). On the contrary, planners usually focus on coding tasks only

  2. Honestly, pretty good. Since coding tasks are co-writed with a specialised AI agent that has access to the complete project documentation, drafts are on-point and refined fast. And if you project evolves, you update you project brief, adjust your design specs if needed etc

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Great! How is this different from vibe coding tools? Like Lovable

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The "planning first" approach addresses the biggest pain point in vibe coding – fast initial iterations that turn into technical debt. Most AI builders optimize for speed-to-first-render, not maintainability.

This spec-first flow reminds me of design systems thinking: define constraints upfront, iterate freely within them. Does the AI co-founder maintain spec consistency across revisions? That continuity would be critical for teams building beyond MVPs.

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Really interesting approach with Spec Coding! As a full-stack dev building SaaS apps (DeadlineKeeper for college applications), I've been using BMAD Method (Breakthrough Method for Agile AI Driven Development) with Claude Code. The idea of defining structure upfront with an AI co-founder resonates with me - BMAD has 21 specialized AI agents that help architect before coding. Will definitely check out Capacity to see how your spec coding compares! 🚀

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Having used other vibe coding tools, excited to try another method to get started! Is this already connected on the backend to a DB, and does it support something google login, or payment systems?

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Slowing down at the start to save time later feels obvious now, but I never thought of it like this. Do you guide users on what “matters” vs “doesn’t matter”?

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Hey @abod_rehman 

On Capacity it all starts with a project brief you can co-write with an AI agent we developed. The document describes the problem you noticed and the solution you want to develop to kill this pain. The agent helps you refine your business idea, target market and MVP scope. Guiding the user on what matters and what doesn't as you mentioned is its purpose.

And it works the same for the design specs of your platform and the features to develop. Dedicated AI agents focused on these specific tasks do the heavy lifting to prepare the prompts that will be sent to the coding agent.

It starts slower but development is faster and app scales better

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#4
xPay
Cross-border payment gateway for enterprises
183
一句话介绍:xPay是一款为企业打造的跨境支付网关,通过提供高成功率、多币种支持和先进风控,帮助印度、新加坡和美国的企业在电商、SaaS等场景下,优化复杂的国际支付流程,降低交易失败率,解决跨境收款难、成功率低的痛点。
Fintech Payments
跨境支付 支付网关 B2B支付 印度市场 多币种结算 欺诈检测 支付成功率优化 金融科技 企业服务 全球商务
用户评论摘要:用户关注其与Stripe等巨头的差异化优势,团队回应主要在支付方式多样性和多国统一结算。用户询问适用企业规模,团队确认支持中小型企业。核心反馈肯定其针对“印度发起的跨境支付流”的精准定位,并对高成功率数据表示认可与期待。
AI 锐评

xPay的叙事核心并非单纯的“又一个支付网关”,而是一个以特定地缘(印度)和特定流向(出境)为楔子的垂直化、精细化解决方案。它敏锐地捕捉到了一个结构性机会:全球支付巨头(如Stripe)的标准化网络在服务新兴市场企业“走出去”时存在摩擦,具体体现在本地支付方式支持不足、交易成功率远低于平均水平(自称65% vs 其90%+)、以及多国合规与结算的复杂性。

其宣称的价值主张——“为印度发起的跨境支付流而生”——是它最锋利的刀刃。这意味著其风控模型、路由逻辑、合规引擎乃至客户成功体系,都可能围绕印度商户的典型交易模式、常用卡段、常见拒付原因进行深度优化。评论中透露的“多国统一结算”功能,直击跨国企业在不同司法管辖区运营时面临的财务碎片化痛点,提供了显著的运营效率提升。

然而,其面临的挑战同样清晰。首先,是规模与信任的悖论。支付是强网络效应和强信任驱动的业务,大型企业是否会将其作为“主网关”替代现有方案,还是仅作为提升特定区域成功率的“补充轨道”(正如用户所问),将决定其增长天花板。其次,从印度扩展到新加坡和美国,其地缘专精优势可能被稀释,需要证明其模型具备可扩展的普适性。最后,90%+的成功率是强有力的营销武器,但需要更透明的第三方数据验证,尤其是在不同行业、不同客单价下的具体表现。

总体而言,xPay代表了一种在高度成熟的红海市场中,通过深度聚焦实现破局的策略。它不试图在广度上挑战巨头,而是在深度上构建壁垒。其真正的考验在于,能否将垂直领域的极致体验转化为可持续的、可跨区域复制的商业模式,从而从“一个更好的支付功能”进化为“一个不可或缺的全球支付网络节点”。

查看原始信息
xPay
xPay is a powerful cross-border payment solution designed to help businesses in India, Singapore and the US seamlessly accept global payments. With 90% success rates, multi-currency support, and advanced fraud detection, xPay optimizes international transactions, reduces drop-offs, and automates compliance. Perfect for marketplaces, SaaS, and e-commerce, xPay simplifies complex payment flows and ensures a smooth, secure global experience for your business and customers.
Why We Built xPay We built xPay because we saw a huge gap in how Indian businesses handle cross-border payments. While global payment gateways often fail to optimize for India-origin transactions, we wanted to create a solution that solves the unique challenges of international payments for Indian businesses. Our goal is to simplify cross-border payments, reduce friction, and help businesses scale globally with confidence. What We’re Trying to Solve International payments are complicated—especially for businesses in emerging markets. Many payment platforms offer low success rates, hidden fees, and lack support for local payment methods. We’re here to solve that by providing high success rates, transparent pricing, and multi-currency support—designed specifically for businesses looking to expand globally. Our platform also helps tackle common issues like fraud, chargebacks, and compliance, making the payment process smoother and more reliable. Why We’re Better Unlike other gateways, xPay is built from the ground up with India-origin cross-border flows in mind. Our platform offers 90%+ success rates on international payments, compared to the 65% industry average. We focus on transparency, security, and automation, making global payments faster, cheaper, and more efficient. Plus, with features like fraud detection, auto retries, and compliance automation, we help businesses scale without worrying about the complexities of cross-border transactions.
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@utkrist_varma Cool product about payment.
Curious how you typically see teams adopt xPay first, is it more common as a primary gateway replacement, or as a secondary rail to improve success rates in specific regions?

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On the way to disrupt a $200Tn market!!! Go xPay🚀

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@soham_mahajan3 Hell yeah!!

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Wow team! YC backed is already a strong validation and I'm convinced there's place for better players in payments game. Which is the main advantage against Stripe? Anyway, wish you all the best!

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@german_merlo1 Hey G, thanks! The main advantages would be the payment methods - for lower ticket sizes we have apple pay, Paypal, venmo, momopay, alipay etc and for larger ticket sizes we have CC EMI/Pay Later, Klarna and many more BNPL options.

Another cool thing is that if you have many accounts in various nations, you will have to get stripe for each country. xPay offers Multi-Settlement in a single integration!

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Indian businesses are going global. Payments shouldn’t slow them down.

xPay is a gateway designed for scale, with industry-leading success rates and support for almost every popular payment method worldwide.

Built for merchants who want to grow beyond borders. Launching today 🚀

Would love your thoughts 👇

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@siddhant_patil3 We're close to the Unicorn mark!

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This is a great idea. I've worked with many india based dev agencies and always struggled using Wise with the time it takes to process the transaction. I'll definitely take a look at this. Can a US based company set this up to pay the team or will our dev agency have to set it up first?

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@wpconvert Yes they can Jose, setup today!

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Fraud detection and auto-retries sound super useful. Can small businesses use xPay as easily as large enterprises?

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@abod_rehman Hi Abdul! Yes, We support SMBs and Freelancers as well. Do engage with us on our website

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Built for India-origin cross-border flows is the key line here. Everything else follows.

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Hey Utkrist, that 65% industry success rate is brutal. Was there a specific transaction or client situation where a payment kept failing and you realized the existing gateways just weren’t built for India-origin flows?
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@vouchy Hey, we're fortunately at a success rate of 90-95% and havent seen a drop below 85% even for Subscriptions/mandates. We started building for India first and slowly grew toward Singapore and the US too!

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#5
Muze AI
Let AI Manage and Create Your Meta and Google ads
172
一句话介绍:Muze AI是一款自主AI广告管理工具,通过连接网站和广告账户,自动完成创意生成、投放、优化与预算调整,旨在替代昂贵且低效的人工代理或媒介购买团队,解决中小企业程序化广告运营成本高、难以规模化的痛点。
Marketing Advertising
AI广告自动化 绩效广告 自主广告团队 创意生成与测试 预算优化 Meta/Google广告管理 中小企业营销工具 端到端广告解决方案 实时数据驱动 营销效率提升
用户评论摘要:用户反馈集中在产品定位和实际能力上。主要问题包括:对产品聚焦创意还是策略不明确;质疑其完全自主运行的可行性及创意方向把控;推测其目标客户为中低端电商。正面评价认为这是绩效营销的自然演进,能替代大量重复人工工作。
AI 锐评

Muze AI描绘的“无人化广告团队”愿景,直击了绩效营销行业人力密集、反应滞后、测试成本高昂的核心痼疾。其宣称的端到端自动化,若真能实现,确实有潜力颠覆传统代理模式。然而,当前信息暴露了其概念与落地之间的巨大沟壑。

真正的挑战不在于技术能否生成海量创意变体,而在于AI是否具备深层次的商业理解与策略判断力。广告成效不仅关乎素材和出价,更涉及品牌定位、受众心理洞察和市场竞争态势的复杂博弈。评论中“是聚焦创意还是策略”的质疑切中要害——若仅自动化现有流程,它只是一个效率工具;若声称替代策略,则需证明其AI拥有超越经验丰富媒介买手的决策智慧。

其定位“中低端电商”可能是务实的起点,这类客户需求标准化、效果导向明确。但这也意味着,产品可能深度依赖模板化和历史数据,在需要高度定制化、品牌叙事的领域将捉襟见肘。所谓“完全自主”在当前阶段更可能是一种“高度辅助”,核心策略参数仍需人类设定。

Muze AI的真正价值,或许不在于立即取代人类,而在于将媒介买家从重复劳动中解放,使其专注于更高阶的战略和创意构思。它能否成功,取决于其AI在“优化”与“探索”、“规则”与“创新”之间找到平衡,并建立起让用户真正信任的透明决策机制。否则,它可能只是另一个承诺过度、让用户承担试错成本的“黑箱”自动化工具。

查看原始信息
Muze AI
Muze is an autonomous AI that creates, runs, and optimizes performance ads end to end. No agency. No media buyers. Just connect your site and ad accounts.
Hi everyone, I am one of the people building Muze. We built this because agencies are expensive, slow, and hard to scale. We kept seeing the same work done manually over and over again. Creative testing, budget shifts, pausing losers, scaling winners. So we asked a simple question. Why does this still require humans? Muze is an autonomous ads team. You connect your website and ad accounts, and it handles creatives, optimization, budgets, and scaling automatically using real performance data. This is early. We are actively looking for feedback, especially skepticism. Happy to answer anything.
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@muskaan_m Interesting, but still many doubts, is it focussed on CREATIVES and COPY or in a strategy, honestly speaking, couldn't comprehend that well from the landing page.

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@muskaan_m This feels like a natural evolution of performance marketing.
Replacing repetitive agency work with real-time, data-driven automation makes a lot of sense. Congrats to the Muze team on the launch 👏

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@Muze AI Congrats on the launch! 🎉

I specialize in comprehensive product testing for SaaS platforms - just completed a 15-page evaluation for an AI saas

Would love to put muze ai through a proper stress test if you're looking for detailed feedback. DM if interested!

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Looks like a legit ad factory! Judging by the selection of the integrations, you're focusing mainly on low- and mid-range eCom, right?

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So it runs ads completely solo? Kinda curious how it handles creative direction without human touch. Like, does it just wing it, or are there parameters? Sounds almost too good to be true tbh

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The creative volume required to win on Meta right now is insane. If this can actually generate and deploy 20-30 variations a month autonomously, this replaces a huge chunk of manual work for my media buyers. Congrats on the launch!

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#6
Adventory
The indie ad markeplace to sell or find ad spots
144
一句话介绍:Adventory是一个独立开发者广告位市场,通过连接拥有闲置广告位的应用开发者和寻求高性价比广告渠道的广告主,解决了双方在寻找可信赖、透明交易对象时的信息不对称和效率痛点。
Marketing Advertising SaaS
广告位交易平台 独立应用市场 SaaS推广 供需对接 应用数据分析 开发者工具 营销渠道 流量变现 SEO外链 微型广告
用户评论摘要:用户普遍认可其解决供需对接的核心价值,认为“保持简单”是关键优势。有评论者从行业经验(如BuySellAds前员工)表示支持。主要关注点在于平台未来如何保障质量与数据分析的深度,以及其作为小型应用曝光助推器的潜力(结合推荐位与SEO外链)。
AI 锐评

Adventory切入了一个经典却常被忽视的缝隙市场:独立或中小型应用之间的微型广告交易。其真正的价值并非技术创新,而在于精准的定位与极致的简化。它避开了与大型广告联盟的正面竞争,转而服务于预算有限、追求精准且重视直接交易的SaaS创始人和独立开发者群体。

产品逻辑清晰:为卖方提供曝光、SEO外链和潜在收入;为买方提供集中的目录和(承诺的)应用分析数据,试图解决小型广告交易中最大的信任痛点——信息不透明。然而,这正是其未来面临的核心挑战。评论中“好奇质量和分析如何演进”一语道破天机。目前其价值高度依赖于“列表”的数量与质量,一旦规模扩大,如何审核数据真实性、标准化“广告位”的描述、防止欺诈,以及提供比公开数据更深度的分析,将成为平台能否从“简易目录”升级为“可信市场”的关键。

其“48小时挑战诞生”的背景既是增长故事,也暴露了产品的早期状态。当前模式轻巧,但护城河较浅,易被复制。长期看,平台需要构建难以被替代的网络效应或信任体系。可能的路径是深化工具属性(如提供轻量级广告位管理或效果跟踪),或构建社区信誉机制。若能成功聚集第一批高质量的供需双方,并维持交易体验,它有望成为独立开发者生态中一个重要的基础设施,专门消化那些被大型平台忽略的“碎片化”流量与预算。反之,则可能仅仅是一个短暂的过渡性名录。

查看原始信息
Adventory
Find indie apps that sell ad spots. Or list yours and get discovered. ## For app owners - List your app that has ad spots to sell - Get discovered by hundreds of SaaS founders - Get your app featured to appear on top of browsing results - Get a free backlink to boost your SEO ## For advertisers - Browse hundreds of apps that sell ad spots - Find the perfect spot to advertise your product - Review app analytics directly on our platform Looking to buy or sell ad space? Check out Adventory!
Hey everyone! This app idea is born out of two frustrations: - to not find the right apps to advertise my products (no public analytics, no trust) - to not easily find advertisers for my own ad spots So I made this directory/marketplace where app owners can show their ad spots to gain visibility, and browse apps to find the proper ad spot. The goal is to facilitate indie advertisement with simple submission, browsing, and contacting. The app was done in 48 hours as part of a challenge on X, but I'm taking feature requests to develop it further, so feel free to reach out!
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@ugo_builds I like the idea of keeping things simple. It does not try to abstract too much, just making supply and demand visible in one place. That's highly valuable and many did not realize that.

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Indie founders are always hunting for affordable, targeted places to advertise, and app owners with unused ad space never have a good way to monetize it. A simple marketplace connecting the two feels like a-win. Curious to see how the quality and analytics evolve as more apps join.

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@amalio__ Thank you for the kind words! Means a lot :)

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I love the idea. Nice product.

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@nerijuso Thank you Nerijus :)

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Good luck with the launch! Cool product. Ran sales @ BuySellAds for 15 years and it's a fun space to solve problems. Let me know if I can ever help!

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@mkammerer100 Thank you very much Matthew!

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getting featured placement and a backlink together is a strong combo. Small apps need this kind of exposure boost ✨

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@shawn_idrees Yes indeed :)

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Getting featured placement and a backlink together is a strong combo. Small apps need this kind of exposure boost ✨

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@lakeesha_weatherwax Yes indeed :)

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Nice product!

Good luck with the launch!

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@alexcloudstar Thank you Alex!

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#7
Flakes
A keyboard-first, AI-powered native browser for macOS.
132
一句话介绍:Flakes是一款为深度思考者设计的macOS原生浏览器,通过键盘优先操作、Vim式导航和智能标签管理,在高效浏览与信息处理场景中,解决了传统浏览器干扰多、操作繁琐、难以聚焦的核心痛点。
Mac Productivity
浏览器 macOS应用 键盘优先 Vim导航 AI助手 极简设计 生产力工具 原生应用 标签管理 广告拦截
用户评论摘要:用户普遍赞赏其设计与极简理念,但指出缺乏扩展支持影响实用性的关键短板。开发者回应正在构建兼容Chrome的轻量扩展系统。另有用户询问键盘操作的学习成本与AI标签管理原理。
AI 锐评

Flakes的野心并非再造一个“更好用的浏览器”,而是试图重新定义浏览器的交互范式。其真正价值在于将“键盘优先”和“Vim哲学”从开发者工具领域,激进地推向普通浏览场景,这本质上是一次对“鼠标点击+视觉堆砌”的现代浏览器交互的反叛。

产品标语中“AI-powered”的表述略显取巧,从现有信息看,其AI功能(理解标签)被定义为“完全可选”,这暴露了产品在核心价值上的摇摆:它究竟是一个以AI重构信息组织的智能体,还是一个以键盘效率为核心的极简工具?目前看来,后者更为突出。用户关于“缺乏扩展”的批评直击要害,这不仅是功能缺失,更与其“服务用户”的初衷相悖——真正的效率工具不能以牺牲用户的既有工作流为代价。

创始人Mike的反思“工具如何塑造甚至钝化我们的习惯”是产品的精神内核。Flakes试图通过强制性的简约(甚至可能是牺牲)来重塑用户“专注”的习惯,这注定使其成为一款具有强烈偏好和排他性的“思想家浏览器”,而非大众产品。它的成功不取决于功能列表能否追平Chrome,而在于能否围绕键盘与命令面板,构建一个足够自洽、高效到让用户心甘情愿放弃扩展生态的封闭体验。这是一场高风险的理念赌博,但也是其在同质化竞争中唯一可能的突破口。

查看原始信息
Flakes
Flakes is a macOS browser built for thinkers. Vim-style navigation, built-in ad blocking, a powerful command palette (⌘K), and AI that actually understands your tabs - but it's totally optional. Check more from our website: https://flakes.ai

Hi everyone, I'm Mike. I'm so exciting to share you with my new product.

I’ve spent the past month building Flakes, focusing mostly on its foundations and on a simple question: what does a browser feel like when it truly serves its user?

Flakes is fast, native, and deliberately keyboard-first. It favors focus over noise, and interaction over accumulation. The design reflects my own preference for restraint and clarity.

LLMs helped me move from ideas to working code much faster than before. That speed was useful - but it also prompted reflection on how easily tools can shape, and sometimes dull, our habits.

I believe good tools share a certain universality, even if they’re not for everyone. If any part of Flakes - its design, a small interaction, or a workflow - resonates with you, I’d genuinely like to hear about it. And if something feels rough or missing, please say so. Honest feedback is how this improves.

As a small holiday gesture, I’ve also prepared invitation codes for a Pro trial - consider it a modest Christmas gift for anyone curious enough to try Flakes more deeply.

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@chagel hi Mike

this `Sign in to Flakes` (Magic Link) is not clickable. it's not a link.

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@chagel Love the landing page. Clean and elegant

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@chagel Looks interesting. Still have yet to let go of Arc. 😭 Is this Chromium, WebKit, or Gecko-based?
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Gotta say this is a beautiful app and really well designed. I love the minimalism of it. Unfortunately when I downloaded it I discovered that it doesn't yet support extensions and so has limited real-world usability for most people. However once that's fixed I could see me using this as my main browser.

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@boagworld Thank you for trying Flakes Paul. You’re absolutely right about extensions. We’re working on an extension system, with a focus on leveraging compatibility with existing Chrome extensions while keeping Flakes lightweight and intentional. We’ll share updates as it lands.
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Congratulations on your launch!

I have a question regarding the keyboard-first design: do you think this might make it difficult for users to remember all the keyboard operations?

Even with the software I use most frequently, I often forget important shortcuts. I usually have to write them down on sticky notes and tape them to my computer as a reminder. I imagine this might be a potential challenge for users.

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@josie_oy 

Great question. Flakes doesn’t rely on memorizing shortcuts — everything is discoverable through a single command entry point. The system is Vim-friendly, so patterns are consistent and muscle memory builds naturally.

At any time, pressing ? brings up a quick overview of available shortcuts, so you never need sticky notes. The goal is flow, not recall.

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interesting... vim-style navigation in a browser kinda intrigues me. how does the AI figure out which tabs are important? is it based on my browsing behavior, or does it need some manual setup? tbh, curious how it plays out in real use

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#8
Awesome Gemini Prompts
The Ultimate Open Source Library for Gemini & Nano
128
一句话介绍:一款专为Google Gemini设计的开源提示词库,通过自动化收集与清洗1800+专业提示词,解决了用户在复杂任务中寻找高质量、格式规范提示词的痛点。
Open Source Developer Tools Artificial Intelligence GitHub
提示词库 开源项目 Google Gemini AI工具 生产力工具 开发者工具 提示工程 AI效率 免费工具 社区驱动
用户评论摘要:开发者热情介绍产品背景与价值。用户反馈主要集中于网站链接错误或无法访问的技术问题,开发者已跟进修复,但访问稳定性仍是初期主要挑战。
AI 锐评

产品切入“提示词工程”这一AI应用层的刚性需求,定位清晰。其宣称的“首个专为Gemini的开源提示词库”具备一定的市场先发和概念卡位价值,特别是通过LLM管道(Qwen+Gemini)进行自动化清洗的工程实践,试图将混乱的社区智慧转化为结构化、可复用的资产,这一技术路径符合当前AI基础设施工具化的趋势。

然而,其核心价值面临多重拷问。首先,其建立的壁垒看似是数据规模(1800+提示词),但提示词的质量、场景针对性和时效性才是关键,而自动化清洗能否真正甄别“专业”与“优秀”,仍需用户实际检验。其次,作为开源库,其生态活跃度将决定生死——能否吸引开发者持续贡献、迭代,形成“飞轮效应”,远比初始的数据爬取更重要。从评论区的反馈看,产品上线初期甚至出现了基本的链接访问问题,这暴露了在运营和用户体验细节上的不足,可能损害其专业形象。

本质上,这是一款试图在AI应用浪潮中“卖铲子”的工具。其长期价值不在于成为一个静态的“图书馆”,而在于能否成为一个动态的“提示词协作与进化平台”。若不能解决社区冷启动、质量持续提升及与Gemini API深度结合等问题,它很可能只是一个精美的、一次性的提示词快照合集,难以形成持久竞争力。当前阶段,概念价值大于实用价值,其成功将极度依赖后续的运营与生态建设。

查看原始信息
Awesome Gemini Prompts
The first dedicated open-source prompt library for Google Gemini. Finding professional prompts for complex tasks was a pain—formatting was messy and scattered everywhere. So I built this to fix it. We automatically scraped & cleaned 1,800+ prompts using a custom LLM pipeline (Qwen + Gemini) to ensure quality everyday. It's completely free, open-source, and so cool to use.
Hiya Hunters! I'm the builder behind Awesome Gemini Prompts. This is awesome to finally share with you! The Backstory: Whenever I needed to handle a professional task with Gemini, generic prompts just didn't cut it. I knew better prompts existed, but I had to dig through community forums for hours to find them. And even when I did, the formatting was a mess. I also wanted to learn from others' excellent prompts to improve my own, but there was no unified place to study them. So I built one! During development, I realized scraping Reddit brought in a lot of noise (personal rants, wrong models), so I had to build a cleaning pipeline using LLMs. All in all, wish you will like this and share your experience with us!
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Looks like the right link is awesomegeminiprompts.tech

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Can’t open the website

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Link is dead btw

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Sorry for the wrong links, already fixed it.

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Site is not getting loaded.

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@vishnu_reji Just replace the right link, try it

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#9
Intrascope.app
Centralize your team’s AI, cut costs and stay organized
111
一句话介绍:Intrascope是一款共享AI工作区,通过集中管理AI供应商、API密钥和项目上下文,解决了团队因AI工具分散、成本不可控和协作混乱而导致的效率低下问题。
Productivity SaaS Artificial Intelligence
AI团队协作 AI成本管控 集中式AI工作区 SaaS 团队效率工具 API密钥管理 共享上下文 企业AI治理 可复用工作流
用户评论摘要:创始人阐述了产品源于内部痛点(AI工具散乱、缺乏共享上下文)。开发与设计成员验证了其内部实用性和流畅体验。团队内部使用成为产品价值的强证明。唯一开放式问题是关于团队如何权衡使用高价与低价AI模型。
AI 锐评

Intrascope切入的并非AI能力本身,而是AI在企业落地后衍生的“管理负债”。其真正价值在于将AI从个人消费级工具,升级为可治理、可协作、可预测的企业级资源。产品通过“管理员设定供应商、限额和规则”实现成本与安全的管控,而“共享上下文和可复用清单”则瞄准了知识留存与工作流标准化——这两者正是团队规模化使用AI时最大的效率黑洞。

然而,其挑战同样尖锐。首先,它试图在AI技术栈快速迭代的洪流中建立一个“管理中间层”,这要求其集成速度必须跟上市场变化,否则反而会成为瓶颈。其次,其价值与团队AI使用成熟度强相关:对于AI使用尚处探索期的团队,这可能是一个过早的负担;而对于重度使用团队,又可能面临与现有复杂工作流(如Notion、Slack)整合的难题。评论中关于“何时使用高价模型”的提问,恰恰揭示了更深层的需求:团队需要的可能不仅是成本管控,更是使用AI的“最佳实践”指导。如果Intrascope能将其规则引擎与数据洞察结合,进化成团队的“AI策略大脑”,而不仅仅是“AI用量管家”,其护城河将深刻得多。目前,它是一个解决真问题的优雅方案,但要从“有用”到“不可或缺”,还需在智能分析与生态融合上证明自己。

查看原始信息
Intrascope.app
Intrascope is a shared AI workspace that helps teams stay organized, reduce costs, and keep all AI usage in one place. Admins set providers, limits, and rules, while the team works in a simple chat with shared context and reusable Manifests. No more scattered chats or separate API keys. AI becomes structured, predictable, and safe for the whole team.

Hey Product Hunt 👋
I’m Vladimir, founder of Intrascope.app.

We built Intrascope to solve a problem inside our own team: too many AI tools, too many API keys, and no shared context across projects.

Intrascope gives teams one shared workspace where admins control providers and limits, and everyone works with AI in a simple chat with reusable Manifests and shared context.

There’s a free trial available, so anyone can explore it without commitment.

I’ll be here all day to answer questions, explain use cases, or hear your feedback.

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I had the chance to work on Intrascope as a developer, and it was both a challenge and a pleasure from day one.

It was really a different experience knowing that I’m building something our own team will use daily — it adds a whole new layer of responsibility and motivation.

Seeing how naturally it became part of our internal workflow is the best confirmation that we built something truly useful.

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I had the pleasure of working on Intrascope as the UI/UX designer, and it was an incredibly enjoyable project from start to finish. We’re already using Intrascope internally within our team, and it has become a natural part of our workflow, which is always the best proof of a product’s value. 🚀

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@nina_jovanovic1 Thank you, Nina 💜
Having you as our UI/UX designer made a huge difference, not just visually, but in how intuitive and natural the product feels to use every day.

The fact that we’re already using Intrascope internally as part of our real workflow is exactly the kind of validation we hoped for when building it. 🔥

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One thing we noticed while building Intrascope is that most teams don’t actually need the strongest model for most tasks.

Curious how others here decide when to use premium models vs cheaper ones?

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#10
PingPrompt
Organize prompts, track changes, and iterate faster.
109
一句话介绍:PingPrompt是一款AI提示词工作空间,为需要频繁优化和复用提示词的营销人员、创作者及无代码开发者,解决了提示词管理分散、版本混乱、迭代低效的痛点。
Productivity Developer Tools Artificial Intelligence
提示词管理 AI工作流 版本控制 提示词工程 团队协作 无代码工具 效率工具 AI辅助编辑 多模型测试 Prompt Ops
用户评论摘要:用户普遍认可其解决提示词管理混乱的痛点。创始人阐述了产品源于自身需求,定位为“提示词基础设施”的迭代工作空间。有效评论聚焦于:1)与一次性对话的差异,明确其适用于可复用、需优化的生产级提示词场景;2)肯定其类Git的版本追踪和精准编辑功能对可靠性的价值。
AI 锐评

PingPrompt并非又一个简单的提示词收藏夹,其真正价值在于将软件工程中的“DevOps”理念引入了提示词工作流,试图定义“Prompt Ops”这一新兴范畴。它瞄准了一个关键的断层:一边是轻量的对话历史记录,另一边是笨重的AI应用开发平台。它的核心用户不是日常闲聊者,而是那些将提示词作为生产“资产”和“基础设施”的从业者。

产品聪明地整合了版本控制、差异比对和精准的AI辅助编辑,这直接回应了提示词工程中“牵一发而动全身”的微妙特性。其内置的多模型测试场,则将迭代从玄学猜测转向可验证的试验。这本质上是在提升提示词工作的可观测性和可维护性。

然而,其挑战同样明显。首先,市场教育成本高:需要让用户意识到管理提示词与管理代码同等重要。其次,场景边界需持续厘清:如评论中所辩,它服务于“系统指令”的优化,而非对话流本身,如何让用户清晰理解这一区分至关重要。最后,随着各大AI平台纷纷增强自有提示词管理功能,作为一个独立中间层工具,必须快速构建起更深的工作流集成壁垒(如预告的API),否则易被边缘化。它的未来不在于成为另一个工具,而在于成为提示词驱动型生产的操作系统枢纽。

查看原始信息
PingPrompt
Most people still manage important prompts in chat history, docs, and text files. PingPrompt keeps everything in one place, tracks every change, and helps you iterate without losing what already works. Refine prompts faster with a built-in copilot, compare versions with visual diffs, and test improvements with confidence. Built for agencies, creators, no-code/low-code builders, and marketers who depend on prompts every day.

Hey, Hunters 👋

I’m Gabriel, the founder of PingPrompt.

PingPrompt exists because prompts became critical to my work, but the way I was managing them didn’t scale.

They were scattered across ChatGPT history, Notion docs, Slack messages, and text files. Small changes happened constantly, but there was no clear history, no safe way to test improvements, and no real confidence in what was actually working. When I needed to improve something, I’d ask an AI to adjust the prompt. It would often rewrite the entire prompt, hallucinate, or alter the logic, even when I only needed a small tweak.

Since I was already using agentic IDEs for development, I tried to bring that workflow to prompts. I set up GitHub repos, used copilots for edits, and relied on diffs to track changes. But they were too complex for working with prompts, where adjustments are frequent, and the friction was too high for non-developers.

I then looked for dedicated prompt tools, but most focused on just storage, generation, or observability. None of them supported the full prompt lifecycle: editing, versioning, testing, and long-term maintenance.

So I built PingPrompt as the workspace I needed.

It combines fast, text-level editing, full version history with visual diffs, an inline copilot for precise edits, and a multi-LLM playground, all in one place.

You can track every change, compare versions side by side, connect your own API Keys and test prompt versions, parameters, and different AI models simultaneously, without breaking what already works.

This is the first version of PingPrompt. There’s still a lot to evolve, and I’m actively working on improving the app and releasing new features like team collaboration and APIs that integrate directly into real production workflows and applications.

I’m confident this tool helps people work with prompts in a more reliable and confident way.

Happy to answer questions and hear feedback.

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@gabrielnsmnto This is a game-changer for "Prompt Ops," Gabriel! Huge congrats on the launch! Upvoted!
In prompt engineering, even a single word change can drastically alter the output, so having a "Git-like" audit trail is essential for reliability. I also love that you’re focusing on precise edits via the inline copilot—standard LLMs are notorious for "over-fixing" a prompt and losing the subtle logic you spent hours perfecting.

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@gabrielnsmnto So useful for prompts!

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I've found when coding , that short prompts and then iterating is the best way... mostly because if i write a long perfected prompt... it neeeeeever becomes as my vision anyways. is it me that is bad at prompting or how do you think about that?

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@markus_kask You're absolutely right. For coding, short prompts + fast iteration is definitely the best approach.

But here's the thing: those coding agents themselves have system instructions that define how they work. Those instructions are what need to be constantly updated and optimized, which is different from the prompts users iterate on to get results.

That's exactly where PingPrompt fits: when managing automations, assistants, or recurring tasks. Even though the interaction with outputs stays iterative, the system instructions need to be consistent, optimized, and reusable.

Does that match how you're thinking about it, or do you see it differently?

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Okay, gotta say. This is really cool. Because I do save prompts in chats, whatsapp or docs which is quite tiresome. Congrats on the launch. @gabrielnsmnto Can't wait to try this out.

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@rashiaroraofficial Totally get that, it’s exactly the pain that pushed me to build this. You can try it free for 14 days and see if it fits your workflow. Hope you enjoy it.

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Congrats on the launch, Gabriel! What makes PingPrompt different from the many other prompt tools (free, open source, and paid)?

I'm curious who the audience would be for this? What's the use case? I ask because I use AI every day in my work (marketing) and for hobbies (vibe coding) and I haven't really found the need to save my prompts. My chats tend to be conversations with refinements over time rather than repeatable work.

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Thanks, @peterclaridge. That’s a totally fair question.

I’m very similar to you in how I use AI day to day. For exploratory work, refinements, or one-off conversations, I don’t save prompts either (there’s no real value there).

Where PingPrompt starts to matter is when prompts stop being casual and start becoming infrastructure.

As soon as you’re building specialized assistants, custom GPTs, chatbots, automations, or reusable workflows, the prompt itself becomes an asset. You need to improve it over time.

That’s exactly how I ended up building PingPrompt. Personally, I built my entire copywriting library inside it. One single hub where I keep specialized copy assistants for different tasks: landing pages, hooks, ads, emails, etc. Each one evolving over time.

What makes PingPrompt different from most prompt tools is that it’s not just storage. It’s a fast iteration workspace. Most tools go to extremes: either they’re simple prompt libraries, or they’re heavy observability/testing platforms. PingPrompt sits in the middle and integrates writing, testing, versioning, and comparison in one simple workflow.

So the audience is people who need to optimize prompts frequently and reuse them across projects and automations like marketing and AI agencies, no-code builders, freelancers, content creators with defined processes. Especially people who already use AI operationally, not just conversationally.

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#11
Biker 2.0: Bicycle track & maintain
Bike AI garage: track and maintain bicycle parts
101
一句话介绍:一款将自行车车库数字化的智能维护应用,通过组件识别、历史追踪和保养提醒,解决了骑行爱好者难以系统化管理多辆自行车及其零件更换、保养周期的痛点。
Android Productivity Artificial Intelligence Tech
自行车维护 数字车库 骑行生活 零件追踪 保养提醒 服务历史 AI辅助 工具类应用 生活方式 运动科技
用户评论摘要:创始人亲自评论,坦诚产品从“AI健康分析”向“车库优先”维护工具的战略转型,核心反馈是早期版本解决了错误的问题层,现版本旨在务实帮助骑手记录与管理,并主动寻求真实反馈。另一条用户评论暗示产品可能面向高投入的骑行爱好者。
AI 锐评

Biker 2.0的迭代,是一次从“AI幻想”回归“工具本质”的清醒自救。其真正的价值不在于炫技,而在于精准切入了一个被忽视的、高价值用户的刚性需求:系统性资产管理。

早期版本试图用AI视觉诊断自行车健康,这犯了两个典型错误:一是技术边界模糊,无法提供可靠结论,损害信任;二是越俎代庖,企图替代专业技师的判断。新版果断砍掉华而不实的“健康分”,聚焦于“车库”这一核心隐喻,将每辆自行车及其组件转化为可追溯、有历史的数字资产。这本质上是为骑行爱好者(尤其是拥有多辆昂贵自行车的高净值用户)构建了一个专属的“设备生命周期管理”系统。

其AI角色从“先知”降维为“助手”,用于组件识别和保养逻辑推荐,变得合理且可持续。产品逻辑的闭环在于:精准的记录生成可预测的保养计划,而“连接附近车店”的功能则试图打通从提醒到服务的最后一公里,构建潜在的商业生态。

然而,挑战依然明显。数据的初始录入是门槛,需要用户具备一定的零件知识或依赖AI识别准确性。其核心价值随用户自行车数量与价值提升,但如何破圈吸引普通通勤骑行者?此外,它本质上是一个需要长期坚持使用的“日志工具”,对抗用户惰性将是持久战。总体而言,这是一次成功的战略收缩,从解决一个不成熟的“高科技问题”,回归到一个被验证的“高价值管理问题”,路径更清晰,但执行和增长的压力也随之从技术层转移到了运营与用户习惯培养层。

查看原始信息
Biker 2.0: Bicycle track & maintain
Biker is a smart bike maintenance app that helps you keep track of your bikes and their components in one place. It turns your garage into a clear, organized system, helps you stay on top of maintenance, and connects you with nearby bike shops when it’s time for service. This version does three important things: - It removes “health analysis” promises you can’t fully prove - It emphasizes tracking, history, and clarity - It positions AI as a helper, not a magic oracle
I’m Mihai, founder of Biker. Biker started with an ambitious idea: using AI to assess bike health from visual inputs. After launching early versions, digging into usage data, and getting very direct feedback from cyclists (especially on Reddit), it became clear that we were solving the wrong layer of the problem. So we rebuilt. Biker is now a garage-first bike maintenance app. It helps riders identify components, track service history and usage, and stay on top of maintenance without guesswork or inflated “health scores.” The goal isn’t to replace bike mechanics or pretend AI knows everything - it’s to help riders remember what they ride, what’s been serviced, and when action is actually needed. We’d love honest feedback. What’s useful, what’s missing, and what you’d never use - all of it helps. Thanks for checking it out 🙏
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I am gonna send this to my friend who is able to spend a fortune for biking :D

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#12
Alias Bot
Replace repeated @mentions in Slack with channel aliases
98
一句话介绍:Alias Bot 是一款 Slack 效率工具,它通过创建频道特定的提及别名(如 !reviewers),在需要频繁、准确通知多人(如轮值团队、评审小组)的场景下,解决了手动输入冗长@提及链易出错、易遗漏且管理混乱的痛点。
Slack Productivity Remote Work
Slack效率工具 团队协作 消息提及优化 频道别名 自动化工作流 临时团队管理 沟通工具 SaaS
用户评论摘要:用户认可产品解决了Slack中重复@提及的混乱问题,尤其赞赏其对轮值团队等场景的实用性。主要疑问集中在是否支持跨工作区使用,开发者回应称别名是频道特定的,并寻求进一步澄清。
AI 锐评

Alias Bot 切入了一个看似微小却极其顽固的协作痛点:Slack频道内低效且易错的多人员提及。其真正的价值不在于技术复杂度,而在于对“组织上下文”的精准把握。它避开了创建和管理全域用户组的沉重路径,转而拥抱了频道级、轻量级、自服务的别名管理,这恰恰契合了现代敏捷团队中临时小组(如on-call、事故响应、发布评审)动态多变、权限下放的本质需求。

产品将“提及”从一个通讯动作,升级为一个可定义、可复用的团队协作协议。!oncall 不再是一串名字,而是一个承载了当前职责与响应期望的符号。这降低了沟通的认知负荷和操作错误,将成员从记忆和手动输入中解放出来。然而,其“频道特定”的设计是一把双刃剑。它在赋予频道自治权的同时,也可能导致别名定义的碎片化,如果同一个逻辑小组(如“后端评审”)跨多个频道存在,则需要重复设置。这引出了一个更深层的问题:它优化了提及的“表达”,但并未从根本上解决团队成员身份与职责在数字空间中如何被系统化定义和同步的难题。

从市场看,它聪明地依附于Slack生态,解决了一个明确、高频的痒点,初期接受度会不错。但长期天花板也显而易见:功能相对单一,易被Slack官方功能或更大平台的集成方案覆盖。其护城河在于极致的用户体验和对细分场景的深度理解。若想突围,未来或需思考如何从“别名管理”走向更智能的“团队上下文与职责图谱”构建,将临时团队的组建、通知与任务流转更深度地绑定。目前,它是一个精悍的“创可贴”式解决方案,但伤口深处,是关于动态组织架构如何与静态通讯工具适配的更大命题。

查看原始信息
Alias Bot
Teams often end up typing long @Alice @Bob @Charlie mentions cluttering the channel. Sometimes, some people get missed or the wrong person gets tagged. This gets worse with rotating or short-lived teams (on-call, releases, incidents). Alias Bot lets you define channel-scoped aliases (e.g. !reviewers, !oncall) so mentions expand to the right people for that channel, without needing to manage org-wide user groups.
Hey Product Hunt 👋 We built Alias Bot because we kept seeing the same thing in Slack: - Long @mention chains cluttering channels - Someone always getting missed (or tagged by mistake) - Temporary teams (on-call, releases, incidents) not fitting cleanly into Slack user groups Alias Bot lets you create channel-specific mention aliases in seconds: !reviewers: @alice @bob @charlie After that, anyone in the channel can just type !reviewers and the right people get mentioned, scoped to that channel. No org-wide groups. No admin setup. Works great for rotating or short-lived teams. We’re launching today to get early feedback. Would love to hear: - Where do @mention chains get painful for you? - What would make this more useful in your Slack setup? Happy to answer questions or jump into specifics 🙌 — Sanket
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Slack threads get messy fast with repeated @mentions, especially for rotating teams. Channel-scoped aliases like !on call or !reviewers feel like a simple but huge time-saver and way less error phone.

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does it work between different workspaces? I need it desperately 🙌

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@kate_ramakaieva Aliases are channel-specific. So you can set up !reviewers as an alias in multiple channels and they can refer to different people.

Could you elaborate what you mean by working between different workspaces? Thanks!

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#13
NexTalk
The missing voice input for Linux. Beautiful Private Offline
96
一句话介绍:NexTalk是一款专为Linux打造的现代化离线语音输入工具,以极低延迟和原生集成,解决了Linux用户长期缺乏美观、高效、隐私安全的一流体语音输入体验的痛点。
Productivity Developer Tools Artificial Intelligence GitHub
语音输入工具 Linux应用 离线语音识别 隐私安全 低延迟 开源软件 桌面生产力工具 Fcitx5集成 Sherpa-onnx
用户评论摘要:开发者主动介绍产品初衷与特性,获得社区积极支持。主要有效反馈来自一条提问,关注产品定位是单纯的听写替代工具,还是更广泛的Linux工作流基础。目前尚无具体的功能建议或问题报告。
AI 锐评

NexTalk的亮相,与其说是一款新工具,不如说是对Linux桌面生态长期“体验赤字”的一次精准狙击。它聪明地避开了与科技巨头在通用语音AI赛道上的正面竞争,转而深耕“Linux原生”、“100%离线”与“美学设计”这一差异化三角区。其价值核心并非技术上的颠覆(Sherpa-onnx为现有开源方案),而在于产品化整合与用户体验的彻底重构——将以往需要拼凑脚本、忍受粗糙界面的“极客专属”能力,包装成开箱即用、感官精致的生产力组件。

然而,其真正的挑战与潜力均在于“定位”。当前它明确对标系统级语音听写,解决了“有无”问题。但评论中关于“是听写替代还是工作流基础”的提问,恰恰点明了其未来天花板。若止步于前者,它仅是弥补了一个功能缺口,用户粘性和价值空间有限。若能锚定后者,成为Linux桌面AI助手的底层输入枢纽,向开发者开放API,与Emacs、Vim、脚本等深度工作流结合,则可能从一个优秀工具演变为一个生态节点。其开源属性是构建此类生态的最大筹码,但如何引导社区共建,将决定这份“写给Linux社区的情书”最终是一曲短暂的赞歌,还是一部交响乐的序章。

查看原始信息
NexTalk
A modern voice input tool built exclusively for Linux. Featuring a transparent "capsule" UI, 100% offline inference (Sherpa-onnx), and native Fcitx5 integration. Sub-20ms latency.
Hi PH community! 👋 I'm Decker, the maker of NexTalk. For a long time, as a Linux user, I felt a bit... forgotten. While my friends on macOS and Windows enjoyed sleek, integrated voice dictation, our options on Linux were mostly a collection of messy scripts, outdated UIs, or clunky clipboard simulators that broke half the time. I kept asking myself: "Is a beautiful, high-quality voice experience really a privilege exclusive to Mac and Windows?" I decided the answer was "No." So I built NexTalk. My goal was simple: Create a voice input tool that feels like a natural extension of the Linux desktop—Invisible but Powerful. What makes NexTalk different: 🔒 100% Offline & Private: Your voice never leaves your machine. We use Sherpa-onnx for local inference because your privacy isn't a trade-off for convenience. 🎨 Aesthetics First: A minimalist, transparent capsule UI built with Flutter. No borders, no clutter—just a breathing light that listens. ⚡ Lightning Fast: Sub-20ms latency. It feels like the words are flowing directly from your mind to the screen. 🐧 Linux-Native: No more hacks. We integrated directly with Fcitx5 via Unix Sockets, ensuring perfect compatibility with Wayland and your favorite apps. NexTalk is my love letter to the Linux community. It's open-source and built for those who refuse to settle for "good enough." I’d love to hear your thoughts, bug reports, or feature ideas. Let's make Linux productivity better, together! 🚀
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@decker502 Good products, happy to support this! Just curious, do you see this product mainly as a dictation replacement, or as a foundation for broader workflows on Linux?

0
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#14
ChatSEO
Find & prioritize your SEO wins by chatting with your data.
53
一句话介绍:一款通过对话式AI分析Google Search Console等数据,为营销人员和创始人提供清晰、可执行SEO优化步骤的工具,解决了传统SEO工具数据繁杂、行动指导不明确的痛点。
Marketing SEO Artificial Intelligence
SEO优化工具 AI驱动 对话式界面 Google Search Console集成 营销自动化 数据洞察 actionable insights 初创企业营销 效率工具 内容策略
用户评论摘要:用户普遍赞扬其将复杂数据转化为明确行动建议的能力,是“游戏规则改变者”,尤其欣赏其“快速见效”的优先级推荐。有用户提到其节省了大量时间和成本。唯一明确的问题是询问其优先级排序逻辑的透明度。
AI 锐评

ChatSEO的亮相,精准地刺中了现代营销效率焦虑的神经。它真正的价值并非在于提供了新的数据源,而在于扮演了一个“决策压缩器”的角色——将传统SEO工作流中“数据获取-分析-解读-决策”的长链条,压缩为一次简单的问答。这本质上不是技术革命,而是交互革命和认知卸载。

其宣称的“基于法国顶尖SEO专家工作流训练”是关键,这暗示产品试图将稀缺的专家经验产品化、民主化。对于预算有限的中小企业主和疲于应对多个客户数据的自由职业者而言,它出售的不是信息,而是经过筛选的“注意力”,即告诉用户“忽略什么”与“先做什么”。这正是其“80/20推荐”备受好评的原因。

然而,其犀利之处也可能成为其阿喀琉斯之踵。将决策过程封装在“黑盒”对话中,在带来便捷的同时,也带来了信任与教育的缺失。资深用户(如最后一条评论所暗示)会对优先级逻辑的透明度产生疑虑;而新手若完全依赖其指令,则可能丧失对SEO底层逻辑的理解能力,陷入“指令依赖”。产品的长期挑战在于,如何在保持“傻瓜式”操作体验的同时,适度揭示其推理过程,建立用户信任,并完成一定程度的市场教育。它能否从一款优秀的“执行指令工具”进化为值得信赖的“战略伙伴”,将决定其天花板的高度。

查看原始信息
ChatSEO
The all-in-one SEO tool that connects to your Google Search Console and market data. Just ask any question and get clear, actionable next steps to grow your traffic in Google and AI search.

👋 Hey Product Hunters!

A few years ago, I launched my first startup and since I had no money, I had to learn SEO the hard way.

Kept digging through my Google search console, tried to understand Semrush (still don't), and simply couldn't figure out what actually mattered to grow my website's traffic.


Problem: Every SEO tool gives you dashboards, graphs, and reports. What you actually need is someone to look at your data and tell you: "Do these 3 things next."


Solution: We built ChatSEO, a conversational AI agent that turns your SEO data into clear, prioritized actions.

Instead of exporting, analyzing, and guessing, you just ask questions in plain English.
All trained on top of the workflow of one of the best SEOs in France.


You can now:

  • Ask "What are my quickest SEO wins?" and get a ranked list

  • Get step-by-step plans to optimize any page for any keyword

  • Connect your Google Search Console

  • Surface hidden opportunities buried in your data

  • Turn insights into a concrete roadmap (what to fix, improve, or create first)

Early traction: Already trusted by 100+ paying customers in our first month, with:

  • 1,000+ websites connected

  • 8,000+ SEO insights already generated

💰 Pricing:

  • Everyone can try it for free (5 credits to test it out)

  • Starter: $29/month (100 credits + 3 websites connected)

  • Ranker: $79/month (300 credits + 10 websites connected )

Who it's for:

  • Founders/marketers who need to move fast

  • Agencies and freelancers who want less analyzing, more executing

I would absolutely love to know what you think of the tool. You can start on chatseo.app, I will read all feedback, comments and will do my best to keep improving it 🙏

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I tried Chatseo, and it really is a game changer just one app in one place. It suggests angles I hadn't thought of. The 80/20 recommendation for quick wins is also the most important thing. I manage an e-commerce site, and it's the first app that connects to Google Search Console to see my keyword rankings.

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@ancestraljoe1 Thanks Joey, glad you like using ChatSEO !!

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@ancestraljoe1 huge, ty !

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ChatSEO just blew my mind.

I’m one of the first beta testers, and the tool keeps getting more powerful everyday.

My SEO improved even though I had zero SEO knowledge when I started.

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@ekrem1 Thanks Ekrem! Happy ChatSEO can help you improve your SEO skills 👍

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I was one of the early beta testers so I’ve been following the product’s evolution closely. Honestly, it’s a real game-changer for SEO ops massive time savings, cost savings, and clear SEO gains. Kudos for the founders

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@david_lopes3 Thanks a lot for your support David!

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ChatSEO est un outil prometteur. J'ai pu le tester et je trouver les recommandations très pertinentes. Je viens d'en appliquer et j'attends de voir les résultats.

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@cindy63 Merci Cindy !!

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Congrats on the launch! I really like the shift from “here’s more SEO data” to “here’s exactly what to do next”, that’s where most tools fall short. I’m curious how ChatSEO handles trade-offs when there are multiple possible wins and how transparent that prioritization logic is to the user.

0
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#15
CompanyIntel.io
Everything you need to know about a company — instantly
52
一句话介绍:CompanyIntel.io 是一款将分散的公开数据转化为可行动公司情报的平台,为GTM、销售和战略团队提供实时公司洞察,解决了企业调研耗时且依赖人工的痛点。
Sales Money CRM
商业智能 公司情报 销售赋能 GTM工具 数据聚合 风险洞察 增长信号 API集成 SaaS B2B
用户评论摘要:用户肯定产品价值,但指出用户体验存在障碍:注册后路径不清晰,操作繁琐(需多次点击和手动选择),关键“生成”按钮不易发现。创始人积极回应,强调API集成能力。核心反馈是产品需实现“零思考”的即时结果展示。
AI 锐评

CompanyIntel.io 瞄准了一个真实且广阔的痛点——企业情报研究的“手工劳动”困境。其宣称的从“原始数据”到“信心评分洞察”的转化,是产品试图构建的核心壁垒,这本质上是将咨询分析能力产品化、自动化。

然而,产品当前的致命弱点在用户体验评论中暴露无遗。用户遭遇的流程断点、隐藏操作和认知负荷,与其“即时获取”的标语形成尖锐讽刺。这揭示了一个深层矛盾:一个旨在“消除手动研究”的工具,却在使用流程上为用户设置了多重手动障碍。这不仅是界面设计问题,更是产品逻辑与用户预期未对齐的体现——用户要的是“答案”,而非一个需要配置和触发的“分析引擎”。

其真正价值在于将“洞察生成”封装为可通过API调用的服务,这为嵌入CRM等工作流提供了可能,也是评论中用户所期待的方向。但若不能首先在核心交互界面上做到“开箱即用、结果自现”,其作为独立产品的说服力将大打折扣。在数据源同质化严重的今天,其“信心评分”模型的准确性与独特性将是技术护城河,但当前阶段,跨越用户体验的“鸿沟”比算法优化更为紧迫。产品理念先进,但需经历从“为技术建产品”到“为用户做设计”的彻底转变。

查看原始信息
CompanyIntel.io
CompanyIntel.io turns scattered public data into clear, actionable company intelligence. Unlike traditional data tools that dump raw firmographics, CompanyIntel surfaces confidence-scored insights on growth, risk, momentum, and buying signals. Built for GTM, sales, and strategy teams, it delivers live company context in seconds via UI or API — so teams can make better decisions without manual research.
👋 Hey Product Hunt! Praveen here, founder of CompanyIntel.io. We built CompanyIntel because company research is still painfully manual. Sales, GTM, and strategy teams jump between 10+ tools just to answer simple questions like: Is this company growing? Is now the right time to engage? Are there any risks? Most tools give you raw data. We focus on actionable intelligence — confidence-scored insights on growth, risk, momentum, and buying signals, delivered in seconds via UI or API. We’d love your feedback: What company signals matter most in your work? What do you still research manually today? What would make company intelligence truly “must-have” for you? Thanks for checking us out — excited to learn from the PH community 🙏
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I thought this was really cool so tried it out on a few signups that we recently got at @StreamAlive - Interactive PPT slides .

It is very cool - once I figured out how to use it.

Few hiccups along the way:

  1. Signed up via my google account and it automatically closed the tab when I got to the replit auth page

  2. Arrived in the dashboard and couldn't see anywhere to enter the company I wanted intel on

  3. Took three clicks to arrive at the console where I could enter the company to research

  4. I entered the company domain and expected to see results in the right hand panel but nothing appeared. I tried another company but nothing appeared. Then I saw I had to manually select what intel I wanted to see - that's another click.

  5. Then I wondered why no results were showing up and it took a while to realize that there's a generate button at the very bottom of the page

A big thing we've learned at StreamAlive is that new users don't read instructions, don't follow guided tutorials, and don't watch how to videos. They want to click and see a result immediately without having to think or hunt for buttons. (We're getting better at that but still haven't cracked it).

My experience above is a classic example of "users don't read".

Either way, great product and has a lot of potential if you can integrate it into a CRM and automatically pull this data without logging in.

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@peterclaridge Thanks Peter, yes the idea is help users get the Company Intelligence via API , Please try that also!

3
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Interesting. Would be very useful to teams that are researching businesses.

1
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@aswin_kumar Thanks Aswin!

0
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#16
AI Analytics Hub
Founder-built directory of AI analytics tools
40
一句话介绍:一个由从业者手工构建和审核的AI数据分析工具目录,旨在解决分析师和创业者在信息过载且质量参差不齐的市场中,难以高效发现和评估合适工具的痛点。
Analytics Data & Analytics
AI工具目录 数据分析工具 市场地图 精选榜单 手动审核 创业者资源 信息聚合 效率工具 产品发现 行业图谱
用户评论摘要:主要反馈来自创始人Mike,解释了项目起源是为解决市场缺乏高质量、结构化工具目录的痛点。承认当前用户体验尚不完善,核心目的是验证产品“有用性”的核心理念,并公开征集使用反馈以指导后续优化。
AI 锐评

AI Analytics Hub 的本质,并非又一个简单的爬虫聚合器,而是一份试图对抗“信息熵增”的“手工艺品”。在AI工具爆炸性增长、各类自动化目录泛滥的当下,其宣称的“手工构建与审核”是它最锋利的差异化刀刃。这背后直击的痛点是:广度易得,深度难求。从业者需要的不是海量的链接列表,而是经过同行筛选、带有上下文理解的“可信清单”。

然而,其真正的挑战与价值也在于此。“手工”模式既是护城河,也是增长的天花板。两位创始人的专业性能否持续保证目录的更新速度、覆盖广度以及评判尺度的客观性?这本质上是在用“匠人精神”做一件通常依赖网络效应和规模的事情。从评论看,团队对此有清醒认知,将首次发布定位为“有用性”测试,姿态务实。

产品若想成功,必须跨越从“个人知识库公开版”到“社区信赖的权威指南”的鸿沟。下一步的关键,或许不在于急于优化UX,而在于如何将“手工审核”这一核心价值流程化、透明化,并逐步引入更广泛的同行评议机制,在保持“精选”调性的同时,构建适度的可扩展性。它最终贩卖的不是信息,而是“信任”与“时间”——为用户节省筛选与试错的时间。这个切入点虽小,但在专业领域内,若能建立权威,其商业潜力与行业影响力不容小觑。

查看原始信息
AI Analytics Hub
Explore the emerging landscape of AI tools for data and business analytics, hand-built and vetted by two analytics practitioners. Updated bi-weekly.

(Hopefully not) yet another AI tool directory.

Hi, I’m Mike (startup PM). Josh (founder of AnswerLayer) and I built this to explore what we believe might be a real problem for people working with analytics tools.

It started with a LinkedIn post by Dan Hockenmaier, where he tried to map the market, throwing in tools he knew. People in the comments followed suit, but surprisingly, there was no single place or "full ontology" to actually explore and learn about this landscape. The closest thing was Josh’s personal list, which he’d been maintaining as a founder and practitioner.

From there, I tested a handful of directories and communities (Product Hunt included) and kept running into the same issues: tools that didn’t belong, missing entries, broken links, and generic descriptions. This is a consequence of mass scraping and automation — which is fine if you’re going for breadth.

But for this niche, we felt depth mattered more. So we joined forces and turned Josh's personal, hand-built collection into a public directory.

P.S. The UX probably isn’t there yet, but what we’d really like to test with this launch is whether there’s a germ of usefulness here. And if there is, I’d love you to share your experience here so we can make it a better product.

20
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#17
Compozy
AI SDLC, Code while you sleep with Compozy!
38
一句话介绍:Compozy是一款通过标准化和编排从产品需求文档到代码合并的完整AI软件生命周期,帮助开发团队减少返工、降低AI令牌浪费并实现全流程可追溯,从而提升交付效率的AI驱动开发平台。
Developer Tools Artificial Intelligence
AI软件开发生命周期 开发流程编排 自动化开发 团队协作 代码生成 流程标准化 开发运维一体化 智能编程助手
用户评论摘要:用户反馈积极,认为该产品可能“彻底改变开发方式”,并赞扬其通过建立线性、工业化的开发流程来缓解开发者面对日新月异技术时的焦虑感,将其类比为软件开发领域的“新福特主义”。
AI 锐评

Compozy的野心远不止于又一个AI代码生成工具。它瞄准的是当前AI辅助开发的核心痛点:碎片化与不可控。当开发者被淹没在层出不穷的AI编程助手、智能IDE和自治Agent中时,Compozy试图扮演“总指挥”角色,将PRD、开发、测试到部署的离散环节串联成一个标准化、可追溯的工业流水线。其宣称的“降低令牌浪费”和“减少返工”直指当前AI开发成本高昂与结果随机的弊病,试图将“手工作坊”式的提示词工程转变为可管理、可优化的工程过程。

然而,其真正的挑战在于“标准化”与“灵活性”的永恒矛盾。软件开发,尤其是创新性工作,本质上是非线性和探索性的。将充满不确定性的AI生成环节强行纳入一个预设的线性流程,是否会扼杀创造力,或产生另一种形式的“流程债务”?此外,平台能否真正理解复杂的业务需求(PRD),并将其精准转化为技术任务,仍是AI领域的圣杯问题。当前的高赞评论更像是对一种理想未来的憧憬,而非对已实现功能的验证。Compozy的价值命题极具吸引力,但它必须证明自己不是给混乱披上了秩序的外衣,而是能真正驾驭AI的不确定性,成为提升工程确定性的底层操作系统。否则,它可能只是堆叠在现有工具链上的又一个抽象层,反而增加了复杂性。

查看原始信息
Compozy
Compozy standardizes and orchestrates the entire AI software lifecycle—from PRD to PR—so teams ship faster with less rework, lower token waste, and full traceability.

Compozy is coming to revolutionize the way developers building application 🔥

8
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@pedro_nauck congrats on the launch!

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I think the act of developing is being turned upside down with several technologies, agents, AIs, and IDEs being launched every day. Even before you finish learning one, another better one is launched. It may make us feel lost. Compozy is a tool that takes out our anxiety, establishing (or suggesting) a linear and industrial way to think about the development process. Forget the artisan mindset. May this be our time to be on the edge of a new industrial revolution. If, in the past, we had Toyotism or Fordism as the new way to build things, Compozy may be the new way to build systems. Think about it.

3
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#18
getsignals(in)
Turns likes and comments into buyers you can reach now
27
一句话介绍:一款通过监控社交媒体互动(如点赞、评论),实时识别潜在买家意向并将其转化为可触达销售线索的SaaS工具,旨在解决传统B2B外拓因数据质量差、策略过时导致的回复率低迷痛点。
Marketing SaaS Artificial Intelligence
B2B销售线索 社交媒体监听 买家意向识别 销售自动化 客户获取 出海营销 SaaS 营销技术 数据 enrichment 集成平台
用户评论摘要:目前仅有一条创始人发布的产品介绍帖,尚无真实用户提问或建议。评论内容为产品核心价值阐述,旨在引发社区关注与讨论。
AI 锐评

getsignals(in) 精准地切中了当前B2B销售开发(SDR)领域的一个核心矛盾:日益昂贵的触达成本与持续衰减的回复效率。其宣称的“社交信号”挖掘,本质是将传统的品牌监听(Social Listening)从营销层下沉至销售层,试图将“互动行为”直接等同于“购买意向”,这步跨越既是其最大卖点,也是最大风险。

产品逻辑清晰:在信息过载的社交平台,通过AI过滤噪音,抓取提及特定品牌、竞争对手或行业话题的公开互动,并利用数据 enrichment 技术补全联系人信息,直接推送至现有销售自动化栈。这确实为依赖“广撒网”式冷外呼的团队提供了一条看似更精准、更“温暖”的路径——从“陌生拜访”转向“情境切入”。

然而,其价值实现严重依赖几个尚未被验证的前提:第一,数据合规性与隐私边界。从公开社交互动中提取并 enrichment 联系人信息,在全球日趋严格的隐私法规下(如GDPR),其操作合规性存疑。第二,信号的有效性。一个“点赞”或评论是否真的代表强烈的采购意向?这需要极其复杂的意图建模,否则极易沦为另一种形式的“精准垃圾信息”。第三,渠道的可持续性。当此类工具普及,大量销售涌入评论区,可能导致用户行为改变或平台规则反制,使信号源快速枯竭或失效。

创始人强调的“20年数据质量痛点”是真问题,但用社交信号替代传统企业数据库,可能只是用一种“新型脏数据”替代了旧数据。产品的真正考验在于其AI筛选的精准度与合规性,否则它不过是给陈旧的“骚扰式”外拓披上了一件“社交聆听”的时髦外衣。它或许能成为成熟销售技术栈的一个有益补充,但宣称要“重塑”外拓策略,为时尚早。

查看原始信息
getsignals(in)
Cold outbound isn't dead, but your playbook probably is. Cheap b2b data with high-volume, low-effort tactics have killed outbound reply rates. GTM playbooks need to evolve. Social signals are a game changer that monitor your brand, competitors, thought leaders and industry topics to surface buyer intent and problem-aware leads. We automatically turn social engagement discovered, into real contacts with enriched data + contact info, then push leads to your tech stack. All on autopilot 🚀🚀🚀
Hey Product Hunt Community! 👋 I'm Kevin Lawrie, founder of getsignals(in), and I’m excited to share what we’ve been working on. With over 20 years in the B2B SaaS world (ESP, MAP and SEP 😱), I’ve witnessed the ups and downs of outbound strategies. The biggest hurdle over 20 years? Data quality. Many think cold outbound is dead, but really, it's the outdated playbook that’s the problem. Introducing getsignals(in): We’re transforming how you find and engage leads. By tapping into social signals, we surface problem-aware prospects who are actively looking for solutions. Our platform turns posts, likes, and comments into valuable contacts with insights, automatically integrated into your outbound stack. Why does this matter? Traditional high-volume tactics are stalling pipelines. getsignals(in) offers a refined approach, focusing on genuine engagement with prospects at pivotal moments. What we're offering: > Discover real contact signals from your brand, thought leaders and competitors. > Identify prospects that are discussing topics relevant to your industry or pain points that you solve. > Use AI to separate the signals from the noise and qualify your ICP in realtime, then optionally push leads into personalized outreach (powered by your existing outbound tech stack) > Works with your existing outbound tech stack, right out of the box: Instantly, Smartlead, Leadfwd, HeyReach, Aimfox, lemlist, Woodpecker and more. We're passionate about helping you reshape your outbound strategy, and we'd love your thoughts and feedback. Your support would mean the world to us! Thanks for being here! Kevin & The getsignals(in) Team
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@klawrie look'sAmazing

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#19
FlexClip Magic Edit
Stop manual editing. Start creating — instantly with AI.
22
一句话介绍:FlexClip Magic Edit是一款AI原生视频编辑工具,通过自动剪辑和AI重制功能,快速将原始素材转化为精良视频,解决了创作者和团队在视频制作中耗时费力、流程繁琐的核心痛点。
Artificial Intelligence Maker Tools Video
AI视频编辑 自动剪辑 视频创作工具 AI内容生成 效率工具 视频模板 创意工作流 智能媒体处理
用户评论摘要:有效评论极少。官方团队积极介绍产品功能与发布优惠,并主动征集用户关于视频工作流中最繁琐环节(如素材粗剪、加字幕、配乐等)的反馈,以期优化AI能力。用户仅有一句简单好评。
AI 锐评

FlexClip Magic Edit的发布,本质上是将当前泛滥的“AI赋能”叙事精准切入视频编辑这一红海市场。其宣称的“重新构想视频创作”,实则是将“自动剪辑”和“模板化重制”两类成熟技术进行整合与产品化包装。产品真正的价值并非技术突破,而在于其定位策略:瞄准“只想快速成片”的轻量级用户和寻求内容复用的营销团队,用“AI自动完成苦活”作为价值钩子。

从官方评论主动寻求用户痛点可以看出,产品仍处于用概念吸引早期用户、并试图定义真实需求场景的阶段。22的投票数与近乎空白的用户讨论,反映了市场对又一款“AI编辑神器”的审慎与疲劳。其成败关键在于,所谓的“AI编辑引擎”在真实、复杂的素材面前,能否提供远超“自动套模板”的、真正智能的叙事理解和节奏把控,而非仅仅节省机械操作时间。若其AI仅能处理高度结构化或简单场景,那么它不过是另一个带有噱头的模板库,最终仍需用户退回完整编辑器进行大量“手动微调”,这与“消除痛苦”的愿景相悖。在AI视频工具竞争白热化的当下,缺乏鲜明技术或体验护城河的产品,很可能迅速淹没在同质化浪潮中。

查看原始信息
FlexClip Magic Edit
FlexClip Magic Edit is an AI-native workspace that reimagines video creation. With Auto Edit, instantly turn raw files into polished videos. With AI Recreate, quickly transform your media into fresh, professional videos. Tell your story faster — let AI do the heavy lifting.

Hey Product Hunt! 👋 We're thrilled to introduce FlexClip Magic Edit, our new AI editing engine designed to eliminate the most painful parts of video creation.

Why we built it

We kept hearing the same thing from creators and teams:

“Editing takes too long — I just want my video done.”

So we asked ourselves: What if video editing could feel like magic?
Today, we're launching FlexClip Magic Edit that finally make that possible.

Meet Your AI Editing Partners
We've poured that vision into two groundbreaking features:

Auto Edit
Just drop in your raw clips and photos. Our AI analyzes the content and automatically handles the cutting, transitions, and music, delivering a polished video at unmatched speed.

AI Recreate
Simply select a video template, add your own media, and watch as our AI generates a brand new, professionally styled video in seconds. Perfect for repurposing content or sparking new ideas.

More control when you need it

And for creators who want hands-on control, everything generated with Magic Edit can be fine-tuned inside FlexClip’s full video editor — adjust timing, tweak transitions, add text, mix audio, and more.

You get the speed of AI with the flexibility of a complete editor.

🎁 Launch Specials:

50% OFF everything – Use code PRODUCTHUNT at checkout. (Valid for 7 days.)

+200 free credits – Log into FlexClip account, go to flexclip.com/redeem, enter code PHFCME, and click “Redeem”. (First 500 users, valid until Jan. 31, 2026)


We'd Love Your Feedback!
What's the most tedious part of your video workflow that you wish our AI could handle? (e.g., clipping raw footage, adding subtitles, finding the right music?) Tell us everything below.

Thanks for checking us out! We can’t wait to hear what you think!

1
回复

Awesome!

1
回复

@sneas Thank you for your support.

0
回复

FlexClip Magic Edit is all about making video creation effortless. 🚀 If you haven’t tried it yet, we highly recommend giving it a go and don’t forget to share your experience with us!💬

1
回复
#20
Sparkup
Make every webinar feel alive.
20
一句话介绍:Sparkup是一款通过实时互动工具(如投票、反应和虚拟观众席)将枯燥的网络研讨会转变为高参与度、人性化直播体验的平台,解决了线上活动参与度低、互动匮乏的痛点。
Video Streaming Marketing SaaS
网络研讨会平台 实时互动 在线活动 观众参与 直播工具 企业级视频 品牌定制 互动营销 虚拟活动 参与度分析
用户评论摘要:用户反馈积极,肯定产品愿景和体验。主要问题集中于功能细节,如是否支持预先设置研讨会常见问题。官方回复确认支持预填充问答、聊天等内容,体现了对定制化需求的关注。
AI 锐评

Sparkup切入的是一个表面红海、实则痛点深重的市场——在线研讨会与虚拟活动。其宣称的“让研讨会活起来”直指行业核心顽疾:单向灌输带来的极低参与度与观众流失。产品逻辑清晰,将“广播级视频”与“实时互动工具”作为技术底座,而真正的差异化筹码押在了“虚拟观众”和“实时洞察”这类旨在重塑临场感与双向反馈的体验层上。

然而,其面临的挑战同样尖锐。首先,功能层面,“互动工具”已是赛道标配,从投票到词云,技术护城河并不深,极易被复制。其真正的考验在于,能否将这些工具无缝、稳定地深度整合进直播流,并在万级并发下依然保持“丝滑”体验,这对其架构能力是巨大考验。其次,市场层面,它夹在Zoom、WebinarJam等通用会议平台与Hopin、Run The World等专业活动平台之间,需要清晰界定自己的利基市场是侧重企业内部培训,还是对外营销活动,两者的需求与付费逻辑截然不同。

评论中前团队成员提及的“视觉魔法”是值得玩味的线索,这可能暗示其在UI/UX和互动视觉效果上投入甚重,这或许是吸引早期尝鲜者的关键。但长远来看,虚拟活动的本质是“人的连接”,工具再炫酷,若不能实质性地降低主办方的运营复杂度、提升观众的归属感与收获感,仍难逃工具化宿命。Sparkup的价值不在于增加了几个互动按钮,而在于它是否能够重新定义一场线上活动的“成功标准”——从观看时长转向为可量化的互动与转化,并为此提供完整的数据闭环。若真能于此构建洞察,方有从“功能供应商”跃升为“行业标准定义者”的可能。

查看原始信息
Sparkup
Death to boring webinars. Sparkup makes every event alive—reliable at any scale, from 10 to 10,000 participants. With unique tools that spark real-time engagement and put audiences at the heart, Sparkup transforms passive viewers into active participants.

Hi everyone!
We’re very excited to introduce Sparkup to you all. Our team has poured our hearts into building a platform that makes virtual events and webinars feel alive.

Sparkup is the platform that turns boring webinars into vibrant, human-centered live experiences where viewers don’t just watch, they engage, react, and connect.

With Sparkup you get:
→ A rock-solid platform for your webinars: smooth and reliable, from 10 to 10,000 participants.
→ Broadcast-quality video: ultra-low latency, up to 4K.
→ Real-time interactivity: modern polls, reactions and much more to spark instant engagement.
→ A unique concept of virtual audience: bring people together and makes every event feel alive.
→ On-brand experience: your logo, your colors, your identity.
→ Real-time insights: know what works and adapt on the fly.

We’re passionate about helping teams create live video experiences that transform attention into action and we can’t wait to see how you’ll use Sparkup to bring your events and webinars to life.

👉 You can sign up at https://sparkup.app

4
回复

Love it! One question I had is it possible to pre-populate some FAQ items for the webinar?

0
回复

@george_surovtsev Hi George, thanks for your feedback and for the upvote! As a host or co-host, you can pre-populate the Q&A, chat, or word cloud. You can also upload documents for participants to view if needed.

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Former team here 👋
I’ve seen this product grow from the inside — incredible team, strong vision, and honestly one of the best “visual magic” experiences out there for live events. Go Sparkup 🔥

0
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