Product Hunt 每日热榜 2026-01-09

PH热榜 | 2026-01-09

#1
Gmail in the Gemini Era
Ask your inbox anything to get summaries, drafts, & more
293
一句话介绍:Gmail集成Gemini 3 AI,通过智能摘要、情境回复和AI收件箱等功能,在信息过载的邮件处理场景中,将用户从被动管理转向主动获取关键简报,减轻认知负担。
Email Artificial Intelligence
AI邮箱助手 智能邮件摘要 情境感知回复 收件箱优先级过滤 谷歌Gemini 生产力工具 邮件管理 人工智能集成 主动式助理 工作流优化
用户评论摘要:用户普遍认可AI重构邮件体验的变革性,尤其赞赏AI收件箱将“管理”转为“简报”的范式转移。主要担忧集中于AI误判紧急邮件的风险、隐私问题,以及具体功能开启方式和与现有AI邮件代理(如Filo)的差异。
AI 锐评

谷歌此次将Gemini深度植入Gmail,绝非简单的功能叠加,而是一次对邮箱底层交互逻辑的“外科手术式”重构。其真正价值不在于“又多了一个AI摘要工具”,而在于试图将邮箱从一个需要用户主动检索、分类、处理的“数据库”,转变为一个能理解上下文、预判意图、并主动递送关键信息的“智能代理”。AI概览将搜索框变为“推理引擎”,AI收件箱则旨在成为每日的优先级指挥中心。

然而,光鲜之下暗藏玄机。首先,**平台霸权与生态碾压**的阴影挥之不去。当谷歌这位“巨人”亲自下场,将最先进的模型原生集成至数十亿用户的基础设施中,众多在Product Hunt上创新的独立AI邮件代理(如评论中提及的Filo、Jido)将面临严峻的生存拷问:是补足巨头未覆盖的缝隙市场,还是被彻底边缘化?其次,**信任与控制的悖论**成为核心挑战。评论中反复出现的“误分类后果”和“隐私担忧”直指要害。将邮件的优先级判断乃至内容理解完全托付给AI,意味着用户让渡了部分控制权。谷歌必须构建极其透明和可靠的保障机制,任何一次严重的误判(如将重要合同邮件归入“低优先级”)都将摧毁用户信任。最后,**从“功能惊艳”到“习惯自然”的鸿沟**有待跨越。产品能否在新鲜感消退后,持续稳定地降低用户的“心智负荷”,而非成为另一个需要学习和调整的复杂功能,将是衡量其成功与否的终极标准。

总而言之,这标志着邮箱领域从“工具智能化”迈入了“环境智能化”的新阶段。巨头的动作既展示了AI重塑核心应用的巨大潜力,也预示着独立创新者面临更残酷的竞争格局。成败关键在于,谷歌能否在提升效率的“智能”与保障用户自主权的“可靠”之间,找到那个精妙的平衡点。

查看原始信息
Gmail in the Gemini Era
Powered by Gemini 3, Gmail becomes your proactive assistant. Features include AI Overviews for instant summaries and Q&A, context-aware Suggested Replies, and a new "AI Inbox" that filters noise to highlight what matters most.

Hi everyone!

"Gmail is still Gmail."

Many AI features like auto-complete or summary have been there for a while, but the addition of the new "AI Inbox" sidebar makes it feel completely different this time.

It seems Google is finally leveraging the raw power of Gemini 3 to reconstruct a native AI mail experience from the ground up.

AI Overviews turns the search bar into a reasoning engine, you don't hunt for keywords anymore, just ask complex questions and get synthesized answers. And the AI Inbox shifts the paradigm from "managing" email to receiving a "briefing". It acts like a proactive assistant that understands context and urgency, serving up what actually matters.

I actually hesitated a bit to hunt this. There are many teams building AI email agents on PH, and they are doing great work. But when the platform owner makes a move this substantial, the impact on the world is just too big to ignore.

The giant has moved. It just means we need to move faster and innovate even more.

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@zaczuo 
“Gmail is still Gmail.” “Finally.” “The giant has moved.”

Anyway, I love Gemini, and Gmail too 😄

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@zaczuo thanks for hunting my long-time loving Gmail! I'm new here so my question could be naive. I have 2 questions, 1) do I need to manually turn on AI Overviews/ AI Inbo somewhere in my Gmail, or I will be able to enjoy it directly? 2) I recently started to use Filo which can brief and highlight emails based on its understanding about the content and my preference. I wonder how do these 2 products compare from your perspective. Or it's more a question for Blake @blakebarnes? Thanks a lot!

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Like this one, but a slighlty concerned about privacy when it comes to content within emails. Anyway, one thing I also love about Gemini is that it started summing up calls and sending overviews.

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The features look powerful, but if the AI misclassifies an urgent email, the consequences are far more serious than pushing a non-urgent one up. I’m curious whether Google has built any safeguards or fallback mechanisms for that.

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Very happy that finally I can search my mail based on context and not specific word, thanks gmail being gmail :)

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Very cool! Would love to try it! We built something similar at Jido for small business but we go beyond summaries and can actually do action since we have a computer use agent that can interact with any UI to automate any workflow.

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Are there safeguards if the AI misclassifies something critical?

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I like the idea of moving from constantly managing email to getting a clearer sense of what actually matters. If this really reduces the mental load of scanning inboxes all day, that alone would be a big shift. I’m curious how it feels in daily use once the novelty wears off.

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#2
SEORCE
See where your brand is discovered and fix what blocks it
291
一句话介绍:SEORCE是一款AI优先的品牌发现分析平台,通过整合搜索与AI生成环境(如ChatGPT、Perplexity)中的可见性数据,帮助营销团队看清品牌在何处被提及、为何被替代,并优先处理阻碍发现的根本问题,从而在碎片化的数字发现时代终结盲目猜测。
Marketing SEO Search
AI搜索优化 品牌发现监控 GEO(生成引擎优化) 竞争情报分析 SEO工具 多平台可见性追踪 营销决策优先级 内容策略分析 技术SEO审计 一体化仪表板
用户评论摘要:用户普遍认可产品在追踪AI搜索可见性方面的实用性和前瞻性,认为其解决了传统SEO工具的盲区。主要问题集中在与BrightEdge/SEMrush的差异化、技术实现可靠性(如注册bug)以及定价透明度上。建议包括增加Cookie同意功能和更清晰的操作指引。
AI 锐评

SEORCE的亮相,与其说是一款SEO工具的迭代,不如说是对传统搜索引擎优化逻辑的一次清醒“背叛”。它敏锐地刺中了当下品牌最大的焦虑:在生成式AI重构信息分发权的时代,传统的排名监控已然失效,品牌在ChatGPT、Perplexity等“答案引擎”中的存在与否,成了新的、却不可见的战场。

产品的真正价值不在于简单地聚合数据,而在于试图为“非结构化”的AI发现建立一套可衡量、可归因的“结构化”理解框架。它放弃了对固定排名的执念,转而追踪“发现模式”,通过模式化的提示词测试,将概率性的AI回答转化为可分析的稳定性信号。这是一种从“位置”到“语境”的认知跃迁。其提出的“被谁替代”与“优先修复什么”,直指营销资源分配的核心效率问题,将工具角色从“监测仪”转向“决策参谋”。

然而,其面临的挑战同样尖锐。首先,是“测量本身改变系统”的经典困境。当大量用户依据其提示词模板进行优化时,AI模型的输出模式可能随之演变,今天的“最佳实践”明天或即失效。其次,其数据权威性与传统SEO工具相比尚需时间积累,而AI搜索生态本身仍处于剧烈动荡期,这对其算法的适应速度提出了极高要求。最后,从评论中暴露的定价不透明和初期技术bug来看,产品在体验成熟度与商业化清晰度上仍有短板。

总体而言,SEORCE是一次勇敢且必要的尝试。它不是在原有SEO大厦上添砖加瓦,而是在其旁另立地基,试图建造适应AI时代的新发现观测站。它的成败,将不仅关乎一个产品的命运,更将成为衡量市场是否真正为“后搜索时代”的可见性管理付费的关键试金石。

查看原始信息
SEORCE
Your brand is being discovered in more places than search, but you cannot see where you are missing. Rankings, crawls, content, and links live in separate tools, leaving teams guessing what to fix first. SEORCE gives one clear view of discovery across search and AI, shows what is blocking visibility, who is winning instead, and what to fix first. One system to understand, prioritize, and act without scattered dashboards.

👋 Hey Product Hunt — Kulraj here, one of the makers of SEORCE.

We built SEORCE after sitting through too many conversations that sounded like this:

“We’re publishing content.
We’re fixing issues.
We’re building links.
But we still don’t know why people aren’t discovering us.”

Discovery has quietly changed.

Brands aren’t just found through rankings anymore — they’re surfaced through answers, summaries, recommendations, and AI-driven systems. And most teams don’t have a clear way to see where they’re being discovered, where they’re missing out, or what to fix first.

What made it worse was fragmentation.
Analytics in one tool. Crawls in another. Content and backlinks somewhere else. You could see problems — but connecting them and acting on them took forever.

So along with Kulraj and Anurag, we built SEORCE to answer three very practical questions:

  1. Where is our brand actually being discovered?

  2. Where are we being replaced — and by whom?

  3. What should we fix first to change that?

SEORCE brings discovery into one system — from deep crawls and content workflows to authority signals and guided fixes — so teams can move from guessing to clarity, and from clarity to action.

We’re early, learning fast, and genuinely curious:
what part of discovery feels most frustrating for you right now?

Happy to answer questions and share how we’re thinking about this.
Thanks for checking us out 🙏

18
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@kulraj Congratulations on the launch guys! An app that measures SEO and userlytics in the new internet age can be of great benefit to those who want to grow their outreach and audience. What are some of the metrics you've learned while creating this project?

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SEO has totally changed. Honestly, GEO is the new baseline now. SEORCE is probably the most practical tool i've seen for tracking AI visibility so far. no more flying blind-definitely a must-have if you're chasing global growth.

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seeing how Claude or ChatGPT describes my services compared to the competition gives me a massive edge in positioning.

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@rahul_manjhi1 Absolutely 🙌 That comparative view is a game-changer for positioning. Seeing how Claude or ChatGPT talk about you versus competitors makes it much easier to spot gaps and refine your messaging. Really glad that stood out to you 🚀

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​I’ve been wondering how to optimize for Perplexity and Gemini specifically since they behave so differently from Google. This looks like a solid solution for monitoring those mentions.

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@shawn_idrees SEORCE is the best solution to solve your problems around Perplexity and Gemini. You can track how you are currently performing accross these platforms and then get suggestions around how to improve the ranking, making SEORCE a full fledged solutions to GEO.

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@shawn_idrees That’s exactly the gap we’re trying to solve 🙌
Perplexity and Gemini behave very differently from Google, and visibility there needs a new approach. Glad it resonates, and would love your feedback once you try it out 🚀

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How does this compare to BrightEdge or SEMrush for AI search specifically? I've been looking for a tool that tracks visibility across all these LLM interfaces in one place.

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Hi @tomlygo  BrightEdge and SEMrush are still largely Google-first SEO tools — their “AI” features mostly extend traditional rankings and content workflows. SEORCE is built AI-first to track actual brand visibility inside LLM answers (like ChatGPT, Perplexity, Claude), including where you’re mentioned, where you’re replaced by competitors, and why. The goal is one place to see discovery across search and AI, not just keywords.
Along with this, then we show atleast 3x more data SEMRUSH for the organic keywords (more than anyone else in the market).
and then we also have all the required features to do the search engine optimisation alongside the GEO with everything driven by AI which include:
Content Writing Studio
Rank Tracking
Organic keywords traffic, impressions, positions.
Technical Audit
Web Analytics
and many more

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@tomlygo 
Great question 👍

Tools like BrightEdge and SEMrush are still mainly focused on traditional search (rankings, keywords, traffic from Google). They’re strong there, but they don’t really show how AI tools talk about you.

SEORCE is built specifically for AI search and LLM visibility. Instead of just rankings, we track how brands appear inside tools like ChatGPT, Claude, Perplexity, and Gemini > mentions, context, comparisons with competitors, and why an AI might surface one brand over another.

Think of it as complementary to SEO tools, but focused on the AI layer of discovery. Would love to hear what you’d want to track most across LLMs 🚀

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As AI summaries and answer engines evolve, how often do you expect discovery signals to change, and how does @SEORCE SEORCE adapt to that?

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@shreya_chaurasia19 
AI discovery signals do change more frequently than traditional SEO because models update, sources shift, and answers get reweighted. That’s why SEORCE is built to monitor patterns over time, not just one-off snapshots.

We regularly re-check how brands appear across LLMs, track changes in mentions, context, and citations, and surface trends so you can see what changed and when. As models evolve, we adapt our prompts, sources, and analysis layers to stay aligned with how these systems actually respond.

In short: instead of guessing, SEORCE helps you stay aware as AI answers evolve 🚀

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hi, signing up with google doesn't work.
POST https://api3.seorce.com/api/v1/auth/onboard 400 (Bad Request)

it specifically fails on this url:
https://app.seorce.com/onboarding/emailadress@gmail.com

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@pantheon3d  this was a small bug which has been fixed.
We did comprehensive testing but somehow missed this one but has been fixed.
Thankyou for pointing it out.
you can now go and signup.
Let us know in case if you have any other queries.

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It’s so frustrating when you’re doing everything right but still can’t figure out why people aren’t finding you. I really hope SEORCE makes things clear :)

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@abod_rehman Totally hear you 🙏 That frustration is exactly why we built SEORCE. Our goal is to make those blind spots clear and give you real answers on why you’re not being discovered. Hope it helps bring that clarity 😊

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Finally a tool like Seorce can let me know how my brand is awared! any following suggestion for improvement after all these?

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@cruise_chen 
Thank you 🙌 Really glad that clarity around brand awareness is coming through.

A few simple suggestions we usually recommend after you start seeing the data:

  • Compare yourself with competitors in AI responses to spot positioning gaps

  • Align your content with how AI already describes your category, then refine what’s missing

  • Track changes over time, not just one snapshot, to see what actually improves visibility

  • Use insights to tighten messaging across your site, blogs, and FAQs so AI picks it up more clearly

We’re also actively improving SEORCE based on feedback like this, so if there’s anything you feel could be clearer or more useful, we’d love to hear it 🚀

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Great work Seorce team. Looks like a great product.

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@alexmturnbull Thank you so much 🙏 Really appreciate the kind words! Excited to keep building and improving SEORCE with feedback like this 🚀

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@alexmturnbull Thank you for the appreciation. Continue supporting SEORCE.

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This app looks great. In today’s AI-driven era, the way SEO works has changed significantly, and I believe a tool like yours is becoming essential for businesses to explore. One small suggestion: consider adding a cookie consent feature, as privacy is something users care deeply about.

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@leotrim_lota thanks for the feedback. We will surely work on it in our upcoming updates.

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Hey Everyone 👋

Anurag here, co-founder of SEORCE alongside @kulraj and @sudhirr_vashist

I wanted to share why this problem became so personal for us.

We kept hearing the same frustration from teams we worked with: "We're doing everything right — optimizing content, building authority, fixing technical issues — but we have no idea if we're actually being found."

The truth is, discovery doesn't happen in one place anymore. Your brand might rank well on Google but be invisible in ChatGPT. You might appear in Perplexity but get replaced by competitors in Claude. And most tools weren't built to show you that full picture.

What pushed us to build SEORCE was realizing that visibility isn't just about rankings anymore — it's about presence across every surface where answers are being generated. And teams need a way to see that clearly, understand what's working (or not), and take focused action.

We built SEORCE to give teams that clarity — one place to track discovery across search and AI, understand why you're showing up or being skipped, and know exactly what to prioritize next.

We're still early and learning every day. Would love to hear what you're struggling with most when it comes to brand visibility and discovery.

Happy to answer any questions 🙏

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Cant just have visable pricing without booking a call?
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Congrats on the launch! I like how SEORCE reframes discovery as something broader than rankings, especially with AI answers and recommendations changing the game quietly. How teams typically uncover their biggest “blind spots” with SEORCE first?

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How do you track and attribute placements in ChatGPT/Perplexity/AI Overviews in a way that’s reliable and repeatable—what does a typical ‘you’re missing here, fix this’ alert look like?
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@curiouskitty 

Great question, and we’re very intentional about not treating AI placements like traditional rankings.

AI answers are probabilistic and contextual, so we track patterns of discovery, not one-off responses.


How we make it reliable and repeatable:

1. We track discovery moments, not single prompts
SEORCE runs structured prompt sets across intents (comparisons, recommendations, “best for X”, alternatives) and observes coverage over time. That removes snapshot noise and gives us stable signals.


2. We measure consistency, not absolutes
One answer can change. Patterns don’t.
We look at inclusion/exclusion frequency, competitor substitutions, sentiment, and citation behavior across similar contexts.


3. Gaps are always tied to a concrete reason
A typical alert isn’t “you’re missing in ChatGPT.”
It looks like:

“In comparison-style prompts for category X, competitors appear in ~60% of discovery moments. You appear in ~10%. The main blockers are missing topical coverage around Y and weaker authority signals on Z.”

4. Every alert points to an action
Each gap links back to something fixable:

  • a specific content or topical gap

  • a technical or crawl issue

  • an authority/backlink deficit

From there, users are taken directly to the relevant crawl findings, content suggestions, or guided fixes (including Autofix where applicable).


5. We’re honest about what this is (and isn’t)
This isn’t deterministic ranking. It’s directional discovery intelligence; designed to help teams prioritize effort where it actually improves discovery over time.

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@curiouskitty 
Great question > this is exactly the hard part of AI visibility, so we’ve been very intentional about it.

How we track it (reliable + repeatable):
We don’t rely on one-off answers or manual spot checks. SEORCE runs consistent prompt sets across ChatGPT, Perplexity, and AI Overviews, on a fixed schedule, and compares results over time. That lets us see patterns, not noise > where a brand shows up, how it’s described, what sources are cited, and how that changes week to week.

We also normalize for variation by:

  • Using controlled prompt templates per use case

  • Tracking frequency + context, not just “rank”

  • Comparing against the same competitor set each run

What attribution looks like:
Instead of “you ranked #3”, attribution looks like:

  • Mentioned vs not mentioned

  • Positive / neutral / missing context

  • Which sources AI is pulling from

  • Which competitors are being referenced instead

What a ‘you’re missing this, fix this’ alert looks like:
A typical alert might say something like:

  • “Your brand is not mentioned for [category query], while 3 competitors are. AI is sourcing answers from blogs + comparison pages you’re not present on.”

  • “You’re mentioned, but only as a feature > not as a recommended solution.”

  • “AI is using outdated content from your site; newer pages aren’t being surfaced.”

And alongside that, SEORCE suggests actionable fixes:

  • Create or update specific content types (FAQs, comparisons, explainer pages)

  • Clarify positioning language AI is already using in your category

  • Strengthen citations AI prefers for that query type

The goal isn’t to chase every answer > it’s to give you repeatable signals and clear actions, so you know where you’re invisible and why.

If you’re curious, happy to walk through a real example on a quick call or demo.

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#3
Chirpz Agent
Smartest way to discover unseen literature
277
一句话介绍:Chirpz Agent是一款AI驱动的学术文献发现工具,通过理解研究上下文、跨库智能检索和即时总结,解决了研究人员在浩如烟海的文献中难以高效找到、筛选和引用相关核心论文的痛点。
Education Artificial Intelligence Search
AI文献检索 学术研究工具 智能摘要 参考文献管理 论文发现 科研效率 跨数据库搜索 引用生成 学生科研 文献综述
用户评论摘要:用户普遍认可其节省时间、提升效率的核心价值,特别赞赏“AI快照”和准确引用功能。主要问题与建议集中在:数据隐私(尤其针对未发表手稿)、跨领域优化效果、数据库更新频率、以及与Notion等工具的集成可能性。
AI 锐评

Chirpz Agent切入的是一个经典且顽固的痛点:信息过载下的精准发现。它宣称的“理解上下文”而非依赖关键词,是试图将文献检索从“匹配”升级为“解读”,这构成了其核心价值主张。然而,其真正的挑战与机遇并存于以下两点:

首先,在“检索”层面,聚合280M+论文并非独家壁垒,真正的技术锐度在于其排序算法——“按意义而非关键词”排序。这要求其AI模型具备深度的学科语义理解能力,而非简单的主题聚类。从创始人回复看,其能识别方法论冲突,这暗示了超越表面相关性的潜力,但该能力在不同垂直学科的普适性与深度,将是决定其是“好用工具”还是“领域专家”的关键。

其次,在“工作流”层面,它试图整合搜索、阅读、引用管理,甚至内置编辑器,打造闭环。这种捆绑策略提高了用户粘性,但也面临挑战:专业研究人员已有固化的文献管理(如Zotero)与写作(如LaTeX)流程,一个新兴工具能否提供足够强大的单点功能,以驱动用户迁移整个工作流?评论中关于集成Notion的询问,恰恰反映了用户希望其作为“智能模块”嵌入现有生态,而非完全替代。

隐私问题是其服务企业级客户(如生物科技团队)必须跨越的信任门槛。其“数据不用于训练”的承诺是基础,但需要更透明的技术架构与合规认证来背书。

总体而言,Chirpz Agent展现了用AI重构学术信息消费流程的清晰思路。它的成功不在于替代谷歌学术或数据库,而在于成为架设在原始数据与研究者之间的智能中间层,将“寻找”的体力劳动转化为“发现”的智力辅助。其最终考验将是:在避免成为又一个“浅层摘要器”的同时,能否建立起足够深的、可验证的学术洞察力,从而让研究者产生真正的智力依赖。

查看原始信息
Chirpz Agent
Chirpz agent is the smartest way to find, prioritize, read, and cite academic papers. It understands your context and searches 280M+ papers across major academic databases. It ranks the most relevant work, generates instant summaries, and provides trusted citations — all in one place.

👋 Hey Product Hunt!

I’m Sina, founder of Chirpz. I built Chirpz Agent because traditional keyword search isn’t smart enough to surface the papers that actually matter.

You have the idea. The literature exists. But guessing keywords across Scholar, arXiv, and journals still makes you miss what you need — or what reviewers expect you to know.

That’s why I set out to build a tool for researchers that understands context and intelligently searches across all sources at once. It cuts through the noise, and delivers only what truly matters — with zero hallucinated metadata.

Here’s how Chirpz helps you discover the right literature smarter:

What you get

  • 🗣️ Ask or upload — describe your research or upload a draft for analysis.

  • 📌 Citation gap detection — catch missing references before reviewers do.

  • 🏷️ Auto-scope topics — extract key themes and build smart searches.

  • 🔍 Search everything — scan 280M+ papers across journals, PubMed, and arXiv.

  • 🧠 Rank by relevance — papers ordered by meaning, not keywords.

  • ⚡ AI Snapshots — skim papers in seconds.

  • 📚 Cite with confidence — verified sources, accurate metadata, BibTeX, and PDFs.

Who it’s for

  • 🎓 Academic labs — smarter literature search and pre-submission draft analysis.

  • 🧑‍💻 Technology labs — explore new ideas and validate approaches with deep coverage.

  • 🧪 Biotech & pharma teams — track discovery and clinical research in one place.

  • 📖 Individual researchers — find relevant papers faster and manage citations easily.

  • 🎓 Graduate students — build strong thesis foundations with guided discovery.

🚀 Try it out here: https://chirpz.ai/literature-discovery/

I’ll be here all day answering questions and collecting feedback.
What kind of literature are you trying to discover — and what’s slowing you down today?

Thanks for checking it out 🙏
— Sina

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@sina_tayebati The AI Snapshots feature is genius - saves so much time filtering research. Building an AI-powered education tool myself (DeadlineKeeper for college apps) and this approach to intelligent content discovery really resonates. Do you find students using this for admission essay research too, or mainly academic papers?

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​Accurate citations are the most stressful part of my writing process and having them handled in one place is a huge win for my workflow.

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Totally hear you @simran_kumar — that pain was a big motivation behind Chirpz Agent.
We focus on verified papers, accurate metadata, and export-ready BibTeX, unlike many AI tools that hallucinate citations and get you in trouble.

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Great work from your team, this is something what is going to be very useful and helpful specially for students.

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@leotrim_lota Thank you! 🙌

Students were definitely a big focus for us, so it’s great to hear that it resonates.

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The instant summary feature saves me so much time because I can quickly filter out what doesn't fit my thesis without reading every single page. 📚

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@new_user___3282025fa49521ad0ba0301 Exactly. that’s why I built AI Snapshots.

They're basically designed to help you quickly judge relevance, why the paper matches your work, and prioritize what’s worth a deeper read, without wading through every page. Glad it’s working for you 🚀

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I’ve seen something like this recently, but can’t remember exactly which tool :D Does Chirpz suggest ways to optimize time while searching for papers?

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@abod_rehman Haha — maybe you’ve already seen Chirpz 😁

Yes! That’s exactly what AI Snapshots are for. While the agent searches across more papers, it generates concise summaries so you can quickly skim, filter, and focus only on what’s worth a deeper read.

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Being able to upload a draft for analysis is a huge productivity booster. 🚀 However, for unpublished research (especially in biotech), data privacy is critical. Can you confirm that the uploaded drafts are processed ephemerally and not used to train your models?

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@yoang_loo Great point — and totally agree, especially for unpublished work.

Yes: your data stays your data. Uploaded drafts are processed securely and are not used for training any models. Privacy is something we take very seriously.

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Congrats on the launch! Wondering if Chirpz is optimised equally for all research fields or optimised better for certain domains?

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@mustassim Great question! Chirpz is designed to work equally well across research fields.
It searches across a broad mix of journals and databases and reads each paper in context, so results are ranked by true relevance to your work — not by domain or source.

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I wish i could have used Chirpz in my university days... how frequently will you update the database though?

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@cruise_chen Haha, we hear that a lot 😄
The databases Chirpz connects to are live and continuously updated as new papers are published, so discovery stays current.

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Congrats on launching 🎉

Does Chirpz integrate with Notion or any note-taking tools?

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Thanks@mikhail_prasolov 🙌

Chirpz agent citation tool includes a built-in, Notion-like editor where you can write and draft while discovering literature at the same time. Just hit on @cite button to activate this tool and pull in relevant papers as you write.

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Amazing progress, Sina. Great job!

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Thanks@ardalan2! Really appreciate it 🙌

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Nice!

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@sneas Thanks! Would love to hear from you once you try it 🙂

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Super useful tool. But I am wondering if it is possible to stretch the scope beyond academic papers to say fictional literature.

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@alfred_b Thanks! Great question 😊
Short answer: not at the moment. Chirpz Agent is focused on academic papers, where verified sources and citations matter most.

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Super useful for students!

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Thanks@nikitaeverywhere. Students were definitely one of the core groups I built Chirpz Agent for 🙌

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This is a really interesting “research intelligence” category. I’m curious how deep the domain context goes - can it surface methodological conflicts or just related work?

Congrats on shipping @sina_tayebati

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Thanks@kate_ramakaieva  — yes, it goes beyond just finding related work.

Chirpz reads papers in context and understands how they approach a problem, not just what keywords they share. That allows it to surface work with different methodologies, assumptions, or experimental setups — including cases where approaches conflict or take opposing directions.

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Congrats on the launch! The platform looks very clean. I’m wondering how often the 280M+ dataset is updated?

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Thanks @cathy_cc  — great question!
Chirpz Agent connects to live academic databases that are continuously updated as new papers are published, so discovery reflects the latest work rather than a static snapshot.

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Congrats on the launch! Tackling literature discovery from a context-first angle instead of keyword guessing feels like a big win for anyone who’s dealt with reviewer comments saying “you missed X.” How Chirpz behaves when research spans multiple disciplines, does it surface cross-domain papers that researchers might not think to look for on their own?

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Congrats on the launch!

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@pederzh Thanks Luigi! Appreciate your support 🙌

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Integrating search, summarization, and citation management into one flow solves a major fragmentation problem in academic work. The ability to rank papers by relevance rather than just keywords helps surface the literature that actually matters, rather than just what matches a search query. It’s a powerful way to ensure deep coverage of a topic without the usual manual overhead.

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@anubhav_gupta6 Appreciate that — that’s exactly the problem we set out to solve 🙌

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Interesting - on-device draft analysis + ranking by meaning is 🔥How do you handle paywalled or restricted journal content?

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@igorsorokinua Thanks! 🔥

For paywalled papers, Chirpz Agent reads the available metadata and abstracts, and in some cases the indexed full text when accessible (even if the PDF isn’t). PDF previews are only available for open-access papers.

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#4
Repo Prompt
Automate assembling the perfect context for your project
214
一句话介绍:Repo Prompt是一款通过智能分析代码库、自动筛选相关文件与函数来构建精准上下文的工具,解决了开发者在借助AI编程助手时因上下文冗余或不足导致的效率低下与Token浪费痛点。
Developer Tools Artificial Intelligence
AI编程助手 代码上下文管理 开发效率工具 Token优化 MCP服务器 智能代码分析 多模型协作 开发工作流自动化
用户评论摘要:用户普遍认为该工具已成为AI辅助开发工作流的核心基础设施,其CLI/MCP服务器支持与自动化能力(如/rp-build命令)备受赞誉。主要优势在于提升AI模型理解精度、节省Token及时间。有用户询问其与Cursor等工具内置功能的差异,开发者回复其核心价值在于将“规划”与“研究”分离,提供更早、更密集的上下文构建。
AI 锐评

Repo Prompt的发布,表面上是一个为AI编程助手优化上下文的效率工具,但其深层价值在于试图重新定义“人-AI-代码库”的交互范式。它不满足于成为又一个Prompt包装器,而是通过Context Builder和MCP服务器,将自己定位为AI智能体与复杂代码库之间的**认知中间件**。

其真正的犀利之处在于两点:一是**将“导航”与“规划”解耦**。当前主流的AI编程工具(如Cursor的Plan模式)仍要求模型在单次上下文中同时完成代码库探索和任务规划,这导致了推理Token的浪费和注意力的分散。Repo Prompt则率先将耗时的、机械的代码库探索工作自动化、前置化,让后续接入的顶级推理模型(如GPT-5.2、Claude Opus)能将其全部“脑力”集中于高层次的架构设计和问题解决上,这实质上是为不同能力的AI模型进行了专业化分工。

二是**致力于成为工作流基础设施而非孤立应用**。通过提供CLI和MCP(Model Context Protocol)服务器支持,它将自己从GUI工具转化为可编程的后端服务。这使得开发者可以将其嵌入到自定义的自动化流水线中,实现多模型评审循环等复杂工作流。这步棋使其从“可用的工具”跃升为“可构建的基石”,护城河由此加深。

然而,挑战同样明显。其核心功能高度依赖于底层AI模型(如Claude Code)的代码理解与导航能力,本质上是一种“元优化”。如果未来主流AI编程助手自身大幅提升了上下文构建与导航效率,其附加价值可能会被削弱。此外,其目前的价值在超大型、结构复杂的项目中最为凸显,对于轻量级项目,其自动化流程带来的收益是否大于设置成本,仍需观察。

总体而言,Repo Prompt敏锐地抓住了AI编程进入深水区后的新瓶颈——上下文质量,并提供了一个极具工程化思维的解决方案。它不再围绕“如何问得更好”打转,而是着手解决“让AI看什么更有效”这一更根本的问题,这标志着AI辅助开发工具正从“对话交互层”向“系统架构层”演进。

查看原始信息
Repo Prompt
Repo Prompt helps AI models understand your codebase without wasting tokens on irrelevant code. Context Builder analyzes your project and selects the files and functions needed for your task, building dense context that fits in model limits. It works with your existing AI subscriptions (Claude MAX, ChatGPT Plus, Gemini) — no extra API costs. The MCP server turns Repo Prompt into a backend for Claude Code, Cursor, and Codex, giving them context analysis and discovery they can't do on their own.

Repo Prompt is like a superskeleton for creating better prompts within specific repos or projects.

It'll help you produce refined prompts so that when you hand off to your coding agent of choice, it'll get the done more efficiently and intentionally.

Enjoy!

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Repo Prompt lets you build precise context from your codebase so reasoning models actually understand what you’re working on.

I’ve been building it for over a year now. With MCP and CLI support, it fits into any agent workflow — Claude Code, Codex, Cursor, whatever you use.

The /rp-build command automates the whole loop: it researches your codebase, builds a plan, and hands off to your agent with the right context already loaded.

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I rarely comment here but this one's worth it.

I usually have 5-10 RepoPrompt windows open at any given time; one per project I'm context-switching between. It's become essential to how I work with AI coding tools.

The CLI/MCP Server is the killer feature for me. I've built autonomous multi-model review loops and context export workflows on top of it. The CLI lets agents coordinate reviews without me babysitting.

If you're building AI coding workflows, these features turn RepoPrompt from a great GUI into actual infrastructure you can automate against. Congrats on the launch Eric!

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I’ve been actively using Repo Prompt for almost a year now, and it’s made it much easier to give LLMs the right context from a codebase without overloading them. Being able to select only the files that matter and generate a clean prompt saves time and tokens. It fits nicely into an ai assisted coding workflow and feels practical rather than gimmicky.

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If I'm already using Cursor or Claude Code with their built‑in repo search, what’s the concrete advantage of your Context Builder—fewer tokens, better accuracy, or faster completion—and by how much in typical tasks? What concrete advantages are there over Claude Code plan mode?
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@curiouskitty Hey! So the big difference with plan mode in existing tools, is that Repo Prompt's context builder operates earlier in the flow. It splits up planning and research into separate tasks.

Context builder's job is to navigate your repo using an agent like claude code or codex directly (the app handles orchestration using cli headless modes), and it will then isolate the relevant files, and carve out the most relevant sections of those files. The discovery agent also writes you an optimized prompt that includes your task, and information about the codebase architecture, and class relationships.

The result is a dense context prompt that can be used for planning. If you've ever tried to use reasoning models like GPT 5.2, one of the challenges is getting them to spend as many reasoning tokens as possible on analysis, instead of navigation. This gets you the best possible way to prompt those models to really pull the frontier of intelligent architectural planning forward.

I invite you to read how some of the other commenters here actually use the app. It's highly automated and with the cli or mcp, it can fit into your existing cursor or claude code workflows, and enhance their built in planning and navigation.

The process I described above, is automated with a convenient slash command now. You can simply type /rp-build, and Repo Prompt will return with codebase analysis and the plan from that dense context prompt, for your agent to get to work with.

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I've been using Repo Prompt for over a year now, and it's become an essential part of my daily development workflow.

As a founder building multiple B2B SaaS products, the ability to quickly assemble the right context for AI assistance has been a game-changer. Whether I'm working on backend Rails projects or integrating AI features, Repo Prompt saves me countless hours every week.

Here's a kept secret: pairing Repo Prompt with GPT-5 Pro (with full context) is incredibly powerful for architectural planning. The combination of precise context selection and advanced reasoning lets you think through complex system designs before writing a single line of code.

Highly recommended for any serious developer working with AI tools. This isn't just a productivity boost—it's become foundational to how I build software.

This is my workflow

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One of the best additions to my dev workflow. It is a great tool to rapidly improve the precision of your development with coding agents - saving you time and money. One of the best features of this app is the rapid development lifecycle and how close Eric is to the community of users, and he's both opinionated and openminded... Join the discord, and help guide the tool's development.

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I've been using Repo Prompt for about a year now and it's become my go-to tool for AI-assisted development.

My setup: I use Claude Code with Opus 4.5 as my main coding agent, but I also bring in GPT-5.2, especially during architecture and planning. Repo Prompt is the context layer that lets these models collaborate - I can curate exactly what each one sees and keep everything in sync. Instead of being stuck with one model's strengths, I get the best of both.

Just shipped my latest iOS app using this workflow exclusively.

Beyond the tool itself, the community is incredibly valuable. The Discord is active, Eric is constantly listening to feedback, and he ships high-quality updates at a speed I haven't seen from other dev tools. Can't recommend this enough.

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Graphical user interfaces are a magical way to interact with coding agents and Repo Prompt is a great example of the larger trend. Cool app and great team.

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Congrats on the launch! I like how Repo Prompt focuses on context quality instead of just prompt wording, that’s usually where things break with coding agents.

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WOW

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#5
stillmail.
minimalist email app for friends to send digital letters
130
一句话介绍:一款通过模拟实体信纸、墨水、信封的书写与寄送动画,为朋友间提供慢节奏、有仪式感的数字书信体验的极简邮箱应用,在碎片化即时通讯时代解决了用户渴望深度情感连接与复古沟通方式的痛点。
Design Tools Art Writing
数字书信 极简邮箱 情感化设计 复古体验 慢社交 端到端加密 交互动画 个性化定制 侧边项目 情感连接
用户评论摘要:用户普遍称赞其交互动画流畅治愈、UI简洁美观、色彩搭配舒适,成功营造了怀旧与温馨感。主要建议包括:增加更多字体选择、提供更多邮票设计与自定义选项、期待更多个性化定制功能。开发者互动积极,透露已支持自定义图片作为邮票。
AI 锐评

stillmail 表面上是一款功能极简的邮箱应用,但其核心价值并非在于“通信”,而在于“仪式感修复”。在即时通讯工具将沟通无限压缩为效率工具的当下,它精准地切中了一部分用户对“消失的间隔”的怀念——那种提笔斟酌、精心装饰、折叠信纸并投递出去的情感沉淀过程。产品通过精心设计的色彩、纸张质感、墨水流动动画以及“折叠”、“寄出”的交互,数字化地重构了这种延迟满足的浪漫。

然而,其真正的犀利之处在于,它用最前沿的交互技术(细腻的动效)和隐私保障(端到端加密)来包装一个最复古的内核,这并非简单的“情怀贩卖”。它试图回答一个关键问题:在异步沟通已成常态(如消息、邮件)的今天,为何我们仍感到疏离?答案或许是,我们缺少的是“意图的视觉化”。stillmail 将每一次沟通都转化为一次有明确起承转合的可视化操作,让发送行为本身承载了远超文本内容的情感重量。

从评论中“寻找朋友寄信”的普遍反馈来看,其最大挑战并非产品体验,而是社交关系的重塑。它要求双方共同进入这个慢节奏的契约,这在高流动性的数字社交中是一种奢侈。开发者主动提出成为用户的“笔友”,恰恰暴露了该产品作为“沟通平台”的天然短板——它更像一个精美的情感玩具或特定关系间的私密花园,而非普适性工具。

其未来价值可能不在于取代邮件,而在于开辟一个“高情感附加值微型通信”的新品类,适用于情侣、挚友或自我对话等特定场景。若不能构建起哪怕是小范围的协同使用生态,或引入更低的单人使用门槛(如寄给未来的自己),它恐将停留在“叫好不叫座”的精致实验项目层面。这是一次对数字时代沟通异化的诗意反抗,但反抗的成功,取决于能否找到足够多愿意一同“慢下来”的人。

查看原始信息
stillmail.
A nostalgic letter-writing experience. Compose heartfelt letters with custom ink, paper, and envelope colors. Slow down and reconnect through the art of correspondence.

Hey everyone, honestly didn’t expect this to take off like this. Thanks a lot for the support.

stillmail started as a small side project. I enjoy sending postcards and letters when I travel, and I wanted to recreate a bit of that feeling digitally, writing something down, folding it, sending it with intention, instead of another fast, corporate-looking mail app.

It was also a chance for me to experiment with animations and small interactions and keep the whole thing simple and personal.

If you try it out and send a letter, all content is end-to-end encrypted. If you don't have a friend to send, I can be your letter friend lol. just send a letter to mustafaiste@outlook.com.

Appreciate everyone checking it out.

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The transitions are sooooo satisfying. I also dig the color palette and the clean feel. Reminds me of the cute letters I used to receive from Santa (my mom)

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I had 0 expectations here but the little animation after confirming email was so nice and really set the tone for the rest of the experience. Now I need to find friends to send letters too. Well done @mustafabulut!

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@gabe Thank you! Really happy you liked it, had a lot of fun creating those animations. Well, why don't you send me a letter and we can be friends :)

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Utterly delightful! Really love the simplicity and the animations. Bravo!

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I love the idea! Thank you Mustafa. I hope more customization will come!

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Hey Mustafa! Loved the UI but even more how you're recreating old-way to do it without losing the essence of the current technology. It looks super cool!

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Love the little animations and how clean the whole experience is! More stamp designs or customisable stamps would also be really nice!

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@mustassim Hey Syed, glad to hear you are enjoying the app! You can actually chose your own custom stamp by uploading an image yourself.

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pretty cool! can i have some more fonts please?

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@ameerhamzzza Hey, thanks! Not a bad idea absolutely..

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#6
Owl Browser
Undetectable browser automation that behaves like a user
119
一句话介绍:Owl Browser是一款企业级隐形自动化浏览器,通过在云端或私有部署中模拟真实用户行为,解决网络爬虫和数据自动化工具在面临Cloudflare等高级反爬系统时被频繁阻断的核心痛点。
Productivity Anonymous Artificial Intelligence
浏览器自动化 隐形爬虫 反检测 数据采集 价格监控 工作流自动化 企业级工具 AI驱动 隐私安全 SOC2合规
用户评论摘要:用户反馈积极,认可产品解决了实际痛点。主要问题集中于:长周期会话的稳定性管理、定价策略缺乏试用选项可能阻碍中小用户、以及与现有生态(如Playwright API、浏览器扩展)的集成可能性。
AI 锐评

Owl Browser的宣称直指当前自动化领域的“阿喀琉斯之踵”:检测与反检测的军备竞赛。其核心价值并非简单的功能堆砌(如104种工具),而在于宣称的“100%通过率”这一性能指标。这背后可能意味着对浏览器指纹、行为模式、甚至TLS指纹等深层特征的极致模拟与控制,其“原生Tor集成”和“每上下文独立IP”的设计,进一步将隐私架构与反检测能力深度绑定。

然而,高宣称性能必然伴随高门槛。其定位与定价策略清晰地瞄准了企业级市场,尤其是那些因数据阻断而直接影响营收(如价格监控、潜在客户生成)的场景。这一定位使其与开源的Puppeteer/Playwright并非直接竞争,而是作为后者的“生产环境强化解决方案”。用户关于“试用”和“集成”的疑问恰恰暴露了其潜在挑战:如何降低高净值客户的验证成本,以及如何融入现有的开发者工作流而非完全取代。如果其技术宣称经得起大规模、长周期实战检验,它将不再是工具,而会成为关键业务基础设施的一部分。反之,若无法持续应对快速演变的检测技术,它也可能只是昙花一现的“银弹”。其真正的试金石,在于复杂动态目标网站上随时间推移的稳定存活率。

查看原始信息
Owl Browser
Owl Browser: Enterprise stealth automation that actually works. While Puppeteer fails 56% of bot detection tests, Owl Browser passes 100%—all 16 categories. Includes 104 automation tools, AI-powered natural language commands, per-context Tor IP isolation, automatic CAPTCHA solving, and sub-second startup. Scale to thousands of parallel sessions without detection. Used for lead generation, data collection, price monitoring, and workflow automation. SOC2 compliant with private cloud options.
Hey Product Hunt! 👋 I'm Fakrul from Olib AI, and I'm excited to share Owl Browser with you today. Why we built this: We were tired of watching our automation scripts get blocked. Puppeteer and Playwright work great in development, but the moment you hit a real website with Cloudflare or bot detection? Instant blocks. We'd spend weeks building scrapers, only to have them fail in production. Traditional stealth solutions pass maybe 44% of detection tests. Owl Browser passes 100% — all 16 major bot detection categories. What makes it different: 🎯 Actually undetectable — passes Cloudflare, PerimeterX, DataDome, Akamai 🤖 AI-powered automation — use natural language instead of fragile CSS selectors 🧅 Native Tor integration — unique IP per browser context automatically ⚡ Instant startup — sub-second launch, unlimited parallel sessions 🛠️ 104 tools included — CAPTCHA solving, video recording, everything you need We're using it in production for lead generation, price monitoring, and workflow automation at massive scale (27+ logins/second proven). We'd love your feedback: What automation challenges are you facing? What features would make this more valuable for your use case? Questions about implementation or integration? Thanks for checking us out! Happy to answer any questions. 🚀
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回复

@fhsethen Hey 👋 Congrats on the launch! Owl Browser looks seriously impressive.

A couple of questions out of curiosity:

  • How does Owl Browser handle long-running sessions (e.g. logged-in accounts over days/weeks)?

  • For teams doing lead gen or price monitoring at scale, how do you recommend managing session/state persistence?

  • Any plans for deeper integrations (e.g. with Playwright / Puppeteer-style APIs, or webhook-based workflows)?

Love seeing a tool that clearly comes from real-world pain. Upvoted 🚀

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@fhsethen Hello! Congratulations on the launch. I browsed quickly your homepage and I couldn’t see any trial or free plan, the self-hosted seems to be even more expensive than the starting cloud subscription. Who is your target with this product and do you plan a way to try it without committing large sums?

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Love the privacy-first approach! Does Owl support Chrome/Firefox extensions, or do you have your own extension ecosystem? That's usually my biggest concern when switching browsers.

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#7
Promptsy
Create, save, and share prompts
117
一句话介绍:Promptsy是一款个人AI提示词管理工具,帮助用户在频繁使用AI对话时,解决优质提示词难以保存、组织和复用的痛点。
Chrome Extensions Productivity Developer Tools Artificial Intelligence
AI提示词管理 生产力工具 知识管理 SaaS Prompt工程 效率软件 组织与分享 个人知识库
用户评论摘要:创始人自述开发源于自身寻找有效提示词的痛点。用户反馈强烈共鸣,认为优质提示词是“需要保存的宝石”,并肯定其组织与复用流程。有用户主动提供可视化改进思路,但未提出具体功能缺陷或批评。
AI 锐评

Promptsy切入了一个微小但真实的需求缝隙:AI提示词的“资产管理”困境。随着深度使用ChatGPT等工具,用户积攒的优质提示词散落在各处聊天记录中,形成新的信息孤岛。产品价值不在于技术突破,而在于对新兴工作流的敏锐捕捉与标准化封装。

然而,其发展面临双重拷问。一是市场天花板:它服务于“深度AI用户”这一细分群体,且功能本质上是一个“带版本管理的专用收藏夹”,壁垒不高,极易被笔记软件或AI平台自身以功能模块形式覆盖。二是价值纵深有限:目前仅解决“存、找、复用”,但提示词的核心价值在于迭代优化与场景化适配。产品若停留在“保险柜”阶段,则工具属性单薄。

真正的机会在于,从“管理”走向“赋能”。通过分析用户保存的提示词数据,能否揭示更优的提示模式?能否构建提示词与生成结果、应用场景的关联网络?甚至形成可分享、可验证的提示词有效性指标?将“个人保险柜”升级为“提示词优化引擎”,从记录历史转向提升未来交互质量,或许是突破其工具局限、构建护城河的关键。当前版本是一个有用的起点,但远非终局。

查看原始信息
Promptsy
Promptsy - Your Personal Prompt Vault. Save, organize, version, and share AI prompts.
I was tired of having to search through my notes and previous conversations with ChatGPT to find the prompts that worked for me the best. So I created a system just for me to save my own prompts, then I thought maybe others would want it to and it just grew from there.
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回复

@fiveohhwon  Hey Levi, congrats on launching  Promptsy

I've definitely  been there when it comes to losing good prompts.

I’m a 2D/3D animator and had a simple visual idea.

that could show the “save → reuse → stay organized” flow in just a few seconds.

The screenshots on the page already give a great guide.

No pitch, just wanted to say hi and share the thought.

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@fiveohhwon This really is helpful :) Our prompts Are gems need to be saved!

0
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#8
Kuulto
Use /ai to organize files, convert image formats, and more
109
一句话介绍:Kuulto是一款融合AI能力的现代终端,通过在命令行中直接使用自然语言指令处理文件格式转换、文档翻译等任务,并内置可视化Git工具,解决了开发者和创意工作者在传统CLI与图形界面间频繁切换、记忆复杂命令的痛点。
Productivity Developer Tools GitHub
现代终端 AI命令行工具 文件格式转换 开发者工具 可视化Git 生产力工具 创意工作流 终端增强 自然语言交互 多任务管理
用户评论摘要:目前有效评论主要为产品创造者的自述,详细阐述了开发动机与核心功能亮点,尚无真实用户的问题与建议反馈。评论区仅有一条祝贺留言。
AI 锐评

Kuulto的野心不在于替代传统终端,而在于试图弥合“原始命令行效率”与“现代交互直觉”之间那道顽固的鸿沟。其真正的价值锚点,并非简单的“终端里看图片”,而是提出了一个核心命题:在AI时代,命令行的交互范式是否应该从“人适应机器语法”转向“机器理解人意图”?“/ai”前缀的自然语言操作,本质上是将LLM作为高级指令解析器与自动化脚本生成器,这比单纯的命令别名或GUI包装更进了一步。

然而,其面临的挑战同样尖锐。首先,专业开发者对终端的信赖源于精确控制和可预测性,AI的“黑箱”与不确定性是否会引入新的风险?其次,产品试图包揽太多(媒体预览、Git可视化、录屏、加密),这固然展现了其“一体化工作空间”的愿景,但也可能使其陷入定位模糊的窘境——是专注于AI命令增强,还是成为一个终端形态的“瑞士军刀”?后者将直接与众多高度优化的专业工具竞争。

从Product Hunt初期热度来看,概念吸引了关注,但缺乏真实用户反馈的“开发者自述”评论区,表明其仍处于非常早期的验证阶段。它的成功与否,将取决于两个关键:其一,“/ai”在实际复杂工作流中的准确性与可靠性,能否建立起足够深的信任;其二,其增强的本地功能(如Git可视化)是否足够强大到让用户愿意改变习惯。它打开了一扇门,但门后的路,仍需用极高的完成度来铺设。

查看原始信息
Kuulto
A modern terminal that bridges classic CLI power with an intuitive interface. Use /ai to organize files, convert image formats, translate documents, and more. Built-in Git integration and enhanced commands like ls and grep for a smoother experience.
Hey Product Hunt! 👋 I’m the maker behind Kuulto, and I’m excited to share a new kind of terminal experience with you. As a developer (and a photographer), I live in the terminal. But I always felt disconnected. Why do I need to leave my CLI just to preview a PDF or check an image? Why do I have to memorize complex ffmpeg flags just to convert a photo? That's why I built Kuulto Terminal. It bridges the raw power of the classic CLI with the intuition of a modern Swift interface. Here is how it transforms your workflow: 🧠 AI-Powered: Forget memorizing flags. Just type /ai convert these HEIF images to JPG and it happens. (As a photographer, this is my personal lifesaver!) 👀 Beyond Text: Standard commands like ls and cat are reinvented. View PDFs, images, and rich media directly within your terminal flow. 🔀 Split Screen Focus: Run your server on one side, manage Git on the other. No more Alt-Tab chaos. 🎨 Visual Git: graphical git status and git diff previews make code reviews actually enjoyable. Plus, AI-generated commit messages are built-in. 🔒 Native Tools: Need to record your screen for a bug report? /record. Need to encrypt a folder? /crypto. It’s all built-in. We support zsh, bash, and fish out of the box. Kuulto is my attempt to make the terminal not just a tool, but a workspace you actually enjoy using. I’m eager to hear your thoughts and feedback! Happy coding! 🚀
3
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Congrats!!

1
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#9
P402.io
Route, verify, and settle paid API calls with clear traces
99
一句话介绍:P402是一个AI成本优化与微支付基础设施平台,通过模型比价与成本模拟工具(P402.shop)和低费率支付层(P402.io),解决AI应用在规模化时因模型选择不当和传统支付网关费用过高而导致的严重成本浪费问题。
API Payments Developer Tools
AI成本优化 微支付解决方案 API路由 模型比较 支付基础设施 开发者工具 AI经济学 HTTP 402标准 可观测性 SaaS
用户评论摘要:用户肯定产品价值,特别是微支付方案和成本模拟。主要问题/建议包括:希望增加法币支付选项而非仅限加密货币;询问与OpenRouter等竞品的具体差异;关注平台是否处理速率限制等运营问题;期待HTTP支付标准的真正落地。
AI 锐评

P402试图同时切入两个深水区:AI API的成本混沌与微支付的可行性困境。其真正价值不在于简单的比价,而在于将“规模成本预测”和“支付摩擦”这两个开发者通常在事后才撞上的冰山可视化。P402.shop的本质是一个“财务压力测试”工具,其杀伤力在于用数据揭示:在100万用户量级,草率的模型选择将不是“优化”,而是“破产”。而P402.io押注的是一个古老但从未普及的HTTP 402标准,其1%的固定费率是对Stripe等“固定费用+百分比”模式在微支付场景下的精准狙击。

然而,其挑战同样尖锐。首先,商业模式存在潜在冲突:作为“中立”的成本优化顾问,未来若深入路由层,如何平衡与上游API提供商的关系?其次,微支付方案严重依赖加密货币(USDC),这为大多数主流开发者设置了极高的认知和合规门槛,评论中对法币的强烈需求已印证此点。最后,其核心价值主张——“预见并防止规模化崩溃”——的目标用户是那些有远见、尚未濒临崩溃的团队,但这部分人往往优先考虑功能实现而非成本优化,市场教育成本高昂。

本质上,P402不是一个工具,而是一套“规模化经济学”的启蒙方案。它能否成功,不取决于功能多寡,而取决于能否让“成本感知”成为AI应用开发的核心纪律,并从“可选建议”变为“必选基础设施”。这条路很长,但方向正确。

查看原始信息
P402.io
Most AI apps waste 70% of their budget. Wrong models. No caching. Payment fees that destroy micropayments. P402.shop: Compare 50+ AI APIs (GPT-5.2, Claude Opus 4.5, Gemini 3, more). Find the right model for your use case. See costs explode from 100 to 1M users. Free. P402.io: Accept micropayments without Stripe's $0.30 killing you. 1% flat fee. Built on x402—HTTP's payment standard, finally working. Vibe-coded apps break at scale. Optimized ones don't.
Hey Product Hunt! 👋 I'm Zeshan. I built P402 because I kept seeing the same pattern: **AI apps work at 50 users. They break at 500.** Not because AI is too expensive. Because of two things nobody talks about: **1. Wrong model selection** Most developers pick GPT-5.2 or Claude Opus and use it for everything. But for 80% of tasks—summarization, classification, simple queries—Haiku 4.5 at $5/M works just as well as models costing $14-25/M. That's 70% waste hiding in plain sight. **2. Payment fees on micropayments** If you charge $0.05 per API call, Stripe takes $0.30. You lose money on every transaction. This is why nobody offers true pay-per-use pricing. **So I built two tools:** **P402.shop** = See exactly where you're overpaying - Compare 50+ AI APIs (all the 2026 models: GPT-5.2, Opus 4.5, Gemini 3, etc.) - Watch your costs explode from 100 to 1M users - Spot the hidden waste **P402.io** = Payment infrastructure that actually works for micropayments - 1% flat fee (vs Stripe's $0.30 minimum) - Built on x402—HTTP 402 "Payment Required" has been reserved since 1997, we finally made it work - Users pay once, use for an hour (no popup per request) The "aha moment" is when someone enters their use case into P402.shop and watches their costs explode at scale. That's when optimization stops being theoretical. **What I'd love feedback on:** - Is the value clear? - What models/providers should we add? - What would stop you from trying this? P402.shop is free forever. P402.io has a generous free tier. Let's fix the AI cost crisis. 🚀 Zeshan
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回复

@z3p0 @P402.io congratulations on the launch 👏! really loved how p402 is trying to solve the micropayments issue. But is there a real-money (fiat money) option instead of just USDC or crypto? it would make a lot more sense and would be useful to a lot more developers.

also Using p402 can I build a not BYOK but still user-pays ai agent android app which routes t9 openrouter for AI CALLING? Who has to provide the openrouter api key over here? is it me (mostly, right?) ? and also can the user use fiat instead of crypto? and will i as the developer incur any upfront costs in this regard?

thanks 😊!

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your landing page is really nice! i actually posted internally in slack about how nice it is!

i did notice this unit which is a. very impressive but b. likely a bit off in terms of contrast!

congrats on launching!

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@catt_marroll thank you for the shoutout on your slack and the heads up. I am pushing a fix for this now, always iterate, really appreciate the feedback!

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For model cost optimization, how does P402.io's recommendations compare to just using OpenAI’s or Anthropic’s built‑in cost/perf guidance or tools like OpenRouter’s benchmarks?

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@curiouskitty Great question

OpenAI/Anthropic's built-in guidance: Inherently limited to their own ecosystem. Anthropic will never tell you "actually, DeepSeek R1 handles this task at 4% of the cost." Their guidance optimizes within their models, not across the market. Same with OpenAI, they'll recommend GPT-5.2 vs GPT-4o, but won't surface that Gemini 2.5 Flash might be 10x cheaper for your specific use case.

OpenRouter: Genuinely good. Their benchmarks are useful for capability comparison. But OpenRouter is a routing/aggregation layer their incentive is throughput, not helping you minimize spend. They show you prices, but don't model what happens to YOUR economics at 100K users vs 1M users. They also don't factor in the payment layer (which is where P402.io comes in).

Where P402.shop is different:

  1. Vendor-agnostic: We have no incentive to push you toward any provider

  2. Scale modeling: Not just "price per token" but "your actual bill at your actual volume"

  3. Task-matching: Recommendations based on use case, not just benchmarks (summarization ≠ reasoning ≠ code gen)

  4. Full-stack view: Model costs are only part of the picture. If you're charging micropayments, Stripe's $0.30 might be bigger than your AI costs

Honestly, use all of them. OpenRouter for capability benchmarks, provider docs for specific features, P402.shop for the cross-provider economics and scale modeling.

We're not trying to replace benchmarks, we're solving the "I'm bleeding money and don't know where" problem.

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@curiouskitty Honest answer: OpenAI and Anthropic's guidance is useful but it's inherently limited to their own models. Anthropic will never tell you "actually DeepSeek R1 does this at 4% of the cost." They optimize within their ecosystem, not across the market.

OpenRouter is genuinely good for capability benchmarks. But they're a routing layer, their incentive is throughput. They show you prices but don't model what happens to your specific economics at 100K vs 1M users. Price per token and "your actual bill at your actual scale" are different problems.

P402.shop is vendor-agnostic, we have no reason to push you toward any provider. The scale modeling is the thing most people miss until they're already bleeding money. And we factor in the full stack, not just model costs. If you're doing micropayments, your Stripe fees might dwarf your AI spend and nobody else is showing you that.

Honestly, use all of them. OpenRouter for benchmarks, provider docs for features, P402.shop for cross-provider economics and the "where am I actually bleeding money" question.

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​I’ve been waiting for a real implementation of HTTP payment standards that actually works for modern webs apps.

1
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@nitesh_kumar98 thanks Nitesh, its true the reason I built this is I saw a major issue using stripe for API calls. Would love to hear your feedback!

-Zeshan

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Comparing 50+ models in one place is helpful. Does the platform handle rate limiting or do you need to manage that on your side?

0
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@jacky0729 Thanks for the question! Right now P402.shop is focused on the comparison and cost modeling side, helping you figure out which model fits your use case and what it'll actually cost at scale.

For rate limiting, that's still on you to manage per provider. But it's something we're thinking about as we build out the router layer. The vision is that P402 handles not just payment but the operational stuff too, rate limits, failover, automatic switching when a provider is down or throttling you. Define your constraints once, let the router figure out the rest.

Not there yet, but that's where we're headed. What's your current setup, are you running into rate limit issues across multiple providers?

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#10
Shipper Visual Edit
Edit apps visually in 1 click without having to prompt fixes
97
一句话介绍:一款允许开发者通过点击、自然语言或代码直接可视化编辑应用界面的工具,在应用开发与迭代场景中,解决了传统方式中反复沟通、描述不准确和修改效率低下的痛点。
Design Tools Website Builder Artificial Intelligence
低代码开发 可视化编辑 前端工具 应用迭代 开发效率 无代码 实时编辑 UI设计 开发者工具 原型设计
用户评论摘要:创始人David介绍了产品源于社区反馈,旨在提供更直观的编辑方式。用户评论认为该功能可减少试错,并询问新用户如何快速上手,体现了对易用性和学习成本的关注。
AI 锐评

Shipper Visual Edit 将“所见即所得”的古老理想注入了AI时代的交互范式。其宣称的“无需通过提示词描述修复”直指当前AI辅助开发的核心槽点:自然语言描述的模糊性与开发精确性要求之间的根本矛盾。产品提供的“点击编辑”、“自然语言描述”和“代码调整”三重路径,看似赋予用户自由,实则暴露了其试图弥合从产品经理到开发者不同角色与技术栈之间鸿沟的野心。

然而,其真正价值可能不在于任何单一模式的创新,而在于将三种模式无缝衔接的“上下文平滑过渡”能力。这本质上是在构建一个动态的、可逆的抽象层。用户从可视化点击开始修改,系统能将其意图同步转化为可解释的自然语言描述或具体的代码片段,反之亦然。这为团队协作和知识留存提供了结构化载体,可能比单纯的效率提升更具颠覆性。

但潜在风险同样显著。首先,“可视化”的深度决定了产品的天花板。若仅能调整样式和简单布局,则沦为另一个前端美化工具;若想深入逻辑层,则必然面临可视化交互复杂度的指数级上升,可能重蹈传统IDE可视化设计器覆辙。其次,三重模式可能造成用户心智负担和功能冗余,新手依赖点击,高手偏爱代码,中间模糊的自然语言场景是否足够刚需、其理解准确度能否媲美专业提示工程,仍需验证。创始人称其为“早期一步”是清醒的,该产品的未来取决于其能否在“灵活”与“强大”、“直观”与“可控”之间找到精妙的平衡点,而非止步于一个友好的表面包装。

查看原始信息
Shipper Visual Edit
Visual Edit lets you change your app in whatever way feels easiest. You can click and edit things directly, describe changes in normal language, or use code to adjust things more deeply if you want.
Hey everyone 👋 David here, co-founder at Shipper. We've listened to the Shipper community's feedback, and we've added all of your to-dos to the top of our priority list. That's how "Visual Edit" came to life. It’s a simpler way to change your app by working with what you actually see. You can click into text or components and adjust them right there, without having to explain everything in a prompt. Of course, you can still use natural language or code when that makes more sense. Visual Edit is about giving you options. You can stay high level, or get more hands-on, depending on what you’re trying to do in the moment. This is an early step, and there’s a lot we want to improve and expand. We’d genuinely love to hear how this feels for you and what would make it better. Thanks for building with us 🙏 David
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This feels like it would reduce a lot of trial and error.
What’s the easiest way for a new user to start using Visual Edit?

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#11
Sloggo
Minimal syslog collector and viewer based on DuckDB
90
一句话介绍:Sloggo是一款基于DuckDB的极简系统日志收集与查看工具,以单一轻量进程运行,为中小规模项目提供了快速、低成本的日志管理解决方案。
Open Source Developer Tools GitHub Database
日志管理 系统日志收集 日志查看器 DuckDB 轻量级 开源工具 运维工具 可观测性
用户评论摘要:用户主要关注其性能边界与适用规模。开发者确认其适用于百万级条目,在TB级海量日志或需要复杂全文搜索的场景下,建议选择更专业的方案。产品定位明确为简单、低成本的小规模需求。
AI 锐评

Sloggo的“极简”定位是一把清晰的双刃剑。其真正价值不在于技术上的颠覆,而在于对细分场景的精准切割和需求降级。它没有试图在性能上挑战Elasticsearch或Loki,而是聪明地利用了DuckDB的易嵌入性和列存分析优势,将自身定位为“日志管理的SQLite”。这为初创团队、边缘侧应用或短期实验性项目提供了一个近乎零运维成本的日志“快充”方案——无需复杂集群,一个容器即装即用。

然而,这种定位也决定了其天花板极低。评论中的质询切中要害:DuckDB并非为全文检索而生,其原生FTS能力有限。在“低百万级”数据量以下,它可以凭借DuckDB的聚合查询速度提供不错的体验;但一旦触及规模或复杂搜索的边界,它便立即从解决方案变为需要被迁移的“技术债”。开发者的回复也坦承了这一点,这反而是一种务实的诚实。

本质上,Sloggo是“以退为进”策略的产物。它不追求大而全,而是用功能边界的明确退缩,换来了部署复杂度的指数级降低和资源消耗的极度友好。它在蓬勃发展的可观测性市场中,为自己开辟了一个“临时日志台”或“轻量监控舱”的独特生态位。对于目标用户,它是优雅的起点;但对于需要长期、规模化运维的系统,它更像一个过渡性工具。其成功与否,取决于开发者能否持续强化其“简单到极致”的核心体验,并建立更平滑的数据迁移路径,让用户能在成长后轻松告别。

查看原始信息
Sloggo
Sloggo is a minimal RFC 5424 syslog collector and viewer based on DuckDB. Runs as a single, resource-friendly process.
Hello ProductHunt 👋 Today, I'm sharing with you Sloggo, an open source syslog collector and viewer that is based on DuckDB. It's a lightweight container that work wonderfully for small scale projects, while still providing unparallel speed. Let me know what you think!
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Using DuckDB for log storage is clever—does it stay performant with millions of log entries, or is there a recommended scale limit?

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@jacky0729 low millions is fine, but if you're looking to log billions there's more robust alternative

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Since DuckDB is a columnar store optimized for aggregations, not an inverted-index search engine (like Lucene), how does Sloggo handle full-text search over TB-scale logs? Do you rely on DuckDB's native FTS extension, or is it brute-force scanning?

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@openaigpt5 Sloggo is meant to be a simple tool, quick to set up, inexpensive to host and optimized for small scale needs. It works great in a serverless container when you are just starting to test things.

Search is basic but works well on a few millions rows, If you need TB-scale there are plenty of better suited solutions ✌️

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#12
Dabble me
Email prompts where replies become your journal entries
89
一句话介绍:一款基于邮件的私密日记工具,通过发送邮件提示并接收回复来记录日记,解决了用户在忙碌生活中难以坚持传统日记习惯的痛点。
Writing GitHub Health
电子邮件日记 微日记 习惯养成 隐私保护 极简主义 无算法 记忆循环 数字健康 生产力工具 复古科技
用户评论摘要:用户赞赏其“无AI”理念和极简邮箱交互,认为降低了日记心理门槛。创始人透露产品已稳定运行十余年,本次为界面革新。核心问题围绕“记忆循环”功能的具体逻辑,开发者解释其优先展示周年条目,并允许用户屏蔽敏感内容。
AI 锐评

Dabble Me 表面是“反科技”的复古产品,实则是深谙行为设计学的精巧陷阱。它摒弃了所有时髦元素——无AI、无社交、无算法,恰恰精准刺中了数字时代的两大焦虑:一是“应用疲劳”,用户厌倦了为每个习惯单独下载并学习新界面;二是“表演性自律”,它用邮箱这一最高频、最中性的场景,将日记行为伪装成待办邮件回复,巧妙避开了“坚持”带来的道德压力。

其真正的护城河并非技术,而是对“习惯附着点”的精准选择。邮箱作为数字生活的枢纽,兼具仪式感与随意性,用户既可用长篇回复进行深度反思,也能用手机语音输入匆匆记录三行。所谓的“记忆循环”功能,本质是构建一个低成本的积极反馈系统,通过偶然的过往内容闪现,制造“与过去自己对话”的惊喜感,而非刻意设计的回顾任务。

然而,其“无AI”的鲜明立场既是利剑也是枷锁。在个性化回顾、模式识别等方面,它主动放弃了数据深挖的可能性,将增值空间让位于第三方工具。这一定位使其难以形成高溢价,更像一个稳固的“数字基座”。长远看,它可能面临两类挑战:一是邮箱生态的不可控风险(如被归为垃圾邮件);二是当用户记录数据形成规模后,对智能整理的内在需求会自然滋生,届时其哲学纯洁性与用户实用主义之间或将产生裂痕。它证明了“减法”的力量,但最终要回答:一个完全拒绝进化的工具,能否在快速迭代的生态中持续保有魅力?

查看原始信息
Dabble me
Dabble Me is a private, email-based journal designed to help people actually stick with journaling. Instead of another app, it sends simple prompts you reply to by email. Past entries resurface automatically, turning journaling into a memory loop, not a chore. No social features, no AI, no algorithms. Free to start, with optional upgrades for photos, search, and organization. Built for real life, not streaks.

I built Dabble Me because I kept quitting every journal I tried. Not because I didn’t care, but because life got busy and the habit never stuck. I wanted something that met me where I already was: my inbox.

It’s been quietly working for over a decade, helping thousands of people keep a journal they actually return to. This launch is a much-needed polish and a fresh look on a proven product, not a brand-new experiment. Same simple idea. Same results. Just finally dressed like it deserves to be.

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@parterburn Congrats on the (re)launch! The UI looks really slick and it's super simple to use.

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@parterburn Super cool Paul! Very smart approach.

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Refreshing to see a tool that proudly says 'No AI' in 2026. 👏 Journaling should be about raw human thoughts, not auto-generated summaries. Regarding the 'memory loop' feature: does it resurface entries from exactly one year ago, or does it randomly pick meaningful past entries to surprise us?

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@yoang_loo thanks! I've been testing some ai plays with it on my personal account (e.g. ai analyzes my entry to encourage further writing and sentiment analysis over time)...but rarely use it, and don't think the users would want it, either. The best use of ai in journaling is the reflecting of your own thoughts at the end of the month/year...that's been pretty powerful, but not something I need to build in given all the options out there. I do wish more apps would make it easier to use my own ai tooling for the data they collect.

Regarding the memory loop - the exact code is here. It's got some logic that tries to determine the best one to show: if an entry from exactly a year ago matches, it shows that, 5yrs ago, 1 week ago, etc. And then purely random is the fallback. But knowing where you were a year(s) ago seems to make those throwbacks feel more valuable for myself. You can also prevent it from sending you back certain entries (keyword or time-based), so that you don't have to be reminded all the time of potentially triggering entries.

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I’m always intimidated by journaling apps with heavy UIs but getting a prompt in email feels familiar and welcoming.

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@eugenie_thompson thanks! I'm the same...I've tried a bunch, but it's too easy to ignore the iOS notifications. I try to stay Inbox Zero, so the habit is already built-in for me to respond to the email that gets delivered. And with email-as-the-interface, it brings a bunch of other features built-in depending on your email client (voice to text, ai writing, grammar, light formatting, searchable history/backup of what you wrote, etc.).

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#13
AI SmartTalk
No-code AI agents for any channel in minutes
86
一句话介绍:AI SmartTalk是一款无需代码的AI智能体创建平台,可快速部署至主流通讯与电商渠道,并自动同步知识库,为电商、机构和内容创作者解决了搭建与维护24/7智能客服成本高、技术门槛高的痛点。
E-Commerce Bots No-Code
无代码AI智能体 全渠道聊天机器人 知识库自动同步 电商客服自动化 GDPR合规 SaaS 客户支持 流程自动化 多平台集成 法国科技
用户评论摘要:用户肯定其降低使用成本、设置简便的核心价值,关注响应延迟、多语言支持等实际性能。创始人积极回应,强调响应速度(2秒内)与支持37种语言。另有用户询问智能体行为训练方式,体现对产品深度应用的兴趣。
AI 锐评

AI SmartTalk的叙事巧妙地避开了功能堆砌的“军备竞赛”,转而聚焦于“部署摩擦”这一真实且未被充分解决的商业痛点。它的真正价值并非在于其AI智能体本身有多前沿,而在于它充当了一个高效的“管道工”:将企业散落各处的知识资产(网站、云盘、项目管理工具)快速转化为跨渠道(社交、通讯、邮件)的即时服务能力。产品将“连接”与“同步”自动化,实质是降低了企业“数据资本”的运营成本。

然而,其面临的挑战同样尖锐。首先,“无代码”与“高度定制化”之间存在固有张力。评论中关于“依赖现有内容还是设计对话流程”的提问,正戳中了此痛点——产品若过于依赖静态知识库,则智能体可能僵硬;若鼓励自定义流程,则“无代码”的简易性可能受损。其次,其宣称的“2秒内响应”在复杂查询或高并发场景下能否持续,将是影响客户留存的关键。最后,在巨头环伺的聊天机器人市场,其作为独立平台的生存空间,取决于能否在特定垂直领域(如强调GDPR合规的欧洲电商)构建起比通用方案更精细、更合规的壁垒。

总体而言,这是一款务实、切口精准的工具。它不试图重塑AI,而是致力于让现有的AI技术更“好用”地流入商业毛细血管。成功与否,将取决于其能否在易用性、性能与深度定制之间找到精妙的平衡,并持续深化其“即插即用”的集成生态。

查看原始信息
AI SmartTalk
AI SmartTalk lets you create tailored AI Agents without coding. Connect your website (WordPress, PrestaShop, Joomla, Webflow) and deploy on WhatsApp, Messenger, Instagram, Discord, Slack & Gmail. Auto-sync your knowledge base with Google Drive, JIRA, ClickUp. Includes SmartFlow for no-code automation, SmartForm & SmartCalendar. Built in France 🇫🇷, GDPR compliant. Perfect for e-commerce stores, agencies & content creators who want 24/7 AI support.
Hey Product Hunt! 👋 I'm Odran, co-founder of AI SmartTalk. We built this because setting up a smart chatbot shouldn't require a dev team. Just connect your site, train the AI on your content, and deploy everywhere. We'd love your feedback; what integrations would you like to see next? 🚀
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For a small operation like ours, a 24/7 solution that trains on our own docs is much more cost-effective than hiring a call center. really cool. the latency is the make -or-break factor here-if it stays close to human response times, the ROI will be huge. seems easy enough to setup , might give it a trail run on our next project.

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@joeyzhang Thanks! Latency is definitely a priority for us, responses typically start under 2 seconds. Let us know how the trial goes, happy to help with the setup if needed 🙏

Feel free to reach out: contact@aismarttalk.tech

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Auto-syncing knowledge from your site is smart. Does the AI agent handle multilingual conversations, or is it English-only?

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@jacky0729 Thanks for asking! We support 37 languages, the AI automatically detects and responds in the user’s language. Works great for international businesses 🌍

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Congrats on the launch! I like how AI SmartTalk focuses on removing setup friction rather than adding more chatbot features people never use. How teams usually start shaping their agents’ behavior, do they rely more on existing content from their site, or do they actively design conversation flows from scratch?

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#14
Autonomous
Superintelligent financial advisor at 0% advisory fees
64
一句话介绍:一款零咨询费的AI原生财富管理应用,通过连接用户账户、提供个性化直接指数投资策略,解决了个人投资者在自主理财混乱与支付高额顾问费之间两难的核心痛点。
Fintech Investing Artificial Intelligence
AI财富管理 零咨询费 直接指数投资 自动化投资 智能投顾 个性化投资组合 税务优化 财务规划 金融科技 替代传统顾问
用户评论摘要:用户普遍认可其零费率模式与解决“DIY混乱”痛点的价值,核心关切集中于商业模式(如何盈利)、AI建议的独立性与可靠性、数据隐私与信任,以及当前仅限美国市场的监管限制。
AI 锐评

Autonomous的叙事极具冲击力,它精准地撕开了传统财富管理“1-2%费率”的陈旧伤疤,并试图用AI这把手术刀进行一场零费率革命。其真正的价值主张并非简单的“AI聊天+投资”,而在于“个性化直接指数”这一核心载体。这标志着智能投顾从“机器人组合”的1.0时代,迈向了利用AI进行超颗粒度、实时税损收割与优化的2.0时代,理论上确实能将以往仅面向超高净值客户的策略平民化。

然而,其光环之下暗礁遍布。首先,“0%咨询费”的商业模式存在叙述断层。官方回复暗示其收入可能来自替代ETF的管理费,这使其盈利与“直接指数”这一复杂产品的表现和成本控制深度绑定,商业模式未经周期考验。其次,产品的信任基石极度脆弱:用户需授予其全面的财务数据访问权限,但对其AI的“超级智能”如何抵抗市场流行叙事泡沫、避免“迎合用户”的倾向,团队并未给出令人信服的技术或方法论解释。这本质上是用一个技术黑箱替代了收费的人类顾问,而信任转移的成本极高。

最后,其面临的监管高墙比想象中更厚。评论中提及的欧美监管差异只是冰山一角,作为提供具体投资建议和可能进行资产管理的实体,其在合规、投资者适当性管理、责任界定上将遭遇传统金融的全方位拷问。Autonomous描绘的愿景是颠覆性的,但其路径更像是在刀尖上跳舞——需要在技术可靠性、商业可持续性与金融监管的三角迷宫中,找到一条切实可行的窄路。成功,则可能成为Robinhood式的行业破局者;若有闪失,则可能加剧公众对AI赋能金融的信任危机。

查看原始信息
Autonomous
Invest like the ultra-wealthy with an AI-native wealth strategist. To manage their money, most are faced with two bad options: DIY chaos (accounts everywhere, broken spreadsheets, a pile of individual bets and no strategy) or pay a 1–2% advisor fee and hand over a massive portion of your net worth to fees. Autonomous: Connect your accounts, ask any money question, and get a personalized direct index that continuously optimizes taxes, risk, and cash. No 1–2% advisor fee.

Hello! My cofounder and I previously built and exited Paperspace, one of the first GPU clouds. After selling Paperspace, we did the “responsible” thing and tried to get our own finances dialed in.

That means one of two bad options:

- DIY: Accounts scattered everywhere. Spreadsheets are always wrong. Expensive mistakes because this wasn’t my full-time job.
- Hire an advisor: 1-2% of assets annually. I ran that math over multiple decades and realized I was handing over a massive portion of my net worth to fees.

Neither can deliver the strategies the ultra-wealthy use to compound wealth. These strategies aren't secret – they're just economically impossible to deliver at scale through traditional human advisors.

Robo advisors tried to automate it, but without real intelligence they hit a wall. In practice: cookie-cutter portfolios and little else.

That's when it hit me: people everywhere are already turning to AI to answer financial questions... but there's no Cursor-style "apply" button. It's not connected to your money.

Meanwhile, wealth management generates ~$250B/year with human intermediaries charging massive fees. Entrenched, expensive, structurally misaligned.

It started to look a lot like brokerages right before Robinhood.

So we decided to build the missing layer.

Autonomous (becomeautonomous.com) is a superintelligent wealth manager with 0% advisory fees.

We’re doing for financial advising what Robinhood did for brokerages. Think of it as a full-stack, AI-native wealth strategist that brings billionaire-style playbooks to ordinary investors.

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Super cool. I'm from EU and trying to build something in a similar space but completely different market. Would love to be a beta tester!

Btw just one question: how do you make money? it will be subscription?

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Finally! A way to manage wealth without selling a kidney to pay for fees :D just kidding :) Autonomous is here to save me from myself and my terrible financial decisions.

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Very cool concept, could be a total game changer if nailed. A question if you don't mind - when using Chat GPT / Claude to kick the tires in this space, I've found the AI to parrot whatever the most popular articles / newspieces / analysts it can find on a given investing subject or stock and extrapolate from there. I really have to challenge the thinking process to overcome this, and even then I worry it's just saying "what I want to hear". Are you adding any layers / customization in your AI prompting to avoid this tendency?

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Congrats on the launch 🙌.“DIY chaos” is painfully accurate spreadsheets everywhere, zero strategy, lots of stress. The zero advisory fee angle is a big plus, especially for people who want guidance without giving up 1–2% forever.The trust moment when connecting accounts feels like the make-or-break step here. If that clicks, this could be really compelling.

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Are you available in Europe?
Curious to know how you handle regulatory requirements such as Mifid 2.
Btw, totally agree with you on the two bad options (DIY, advisor) but unfortunately, in many cases, people don't really have a real alternatives because of regulatory constraints.

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@ddu unfortunately US only for now :( Regulatory constraints are the reason like you said but we’ll be there eventually hopefully. It’s not a blocker just a big hurdle.

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The idea of zero-fee autonomous investing is compelling. That said, as a user it naturally raises the question: if advisory fees are truly 0%, where does Autonomous generate revenue?

I’m particularly curious how user data factors into the business model. Is monetization driven by premium features or partnerships, or does it involve using or sharing user financial data in any way? Transparency on this would go a long way toward building long-term trust.

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@carlesonielfa you can get free advice (portfolio analysis/construction, answers to any financial question, etc) and implement our recommendations yourself. Or optionally we can manage it for you. By doing so, we create a hyper personalized direct index which essentially replaces the ETF fees you already pay for. Direct indexing has many benefits over ETFs such as granular tax loss harvesting.

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#15
FeatureMessage
Write a message your future self will never forget
52
一句话介绍:FeatureMessage是一款允许用户给未来的自己写信或录制视频,并在指定日期通过邮件送达的工具,在个人情感记录与自我对话的场景下,解决了用户珍贵思绪和记忆因时间流逝或存储混乱而丢失的痛点。
Email Messaging Video
未来信 自我对话 情感记录 时间胶囊 邮件提醒 个人成长 记忆存档 生活仪式感 轻量工具 情绪管理
用户评论摘要:用户普遍赞赏产品创意与设计,认为其创造了情感时刻。有效评论集中于两点:一是探讨产品长期定位(是身份档案还是自我引导工具),二是询问技术细节(如邮件送达后的可见性管理)。创始人分享了个人痛点驱动的开发故事。
AI 锐评

FeatureMessage披着“时间胶囊”的浪漫外衣,其本质是一个对抗数字时代记忆熵增与自我连续性断裂的轻量级工具。它聪明地避开了社交与即时通讯的红海,转而捕捉“与自我对话”这一被严重低估的需求。产品的真正锋芒不在于技术复杂度(定时发送邮件堪称古典),而在于精准地命中了现代人的一种存在性焦虑:我们忙于记录生活以供他人观赏,却疏于为未来的自己留存真诚的内心独白。

然而,其长期价值面临双重拷问。其一,是工具属性与情感价值的平衡。当用户收到一封来自过去的邮件,最初的惊喜过后,若内容缺乏深度或语境丢失,极易沦为一次性的情感快消品。评论中关于“个人档案”与“自我引导”的提问,正戳中了其核心定位的模糊性——它究竟是想成为一座堆满碎片的数字仓库,还是一个能提供连续性洞察的成长地图?其二,是可持续性挑战。作为极简工具,其用户粘性与付费转化路径并不清晰。当手机原生备忘录、日历提醒甚至社交媒体的“那年今日”功能都能实现类似效果时,其独立的必要性必须建立在无可替代的体验上,例如更私密的安全感、更精美的封装形式或引导用户进行结构化自我对话的能力。

创始人“为自己解决问题”的故事是动人的起点,但要从一个解决个人痛点的优雅方案,蜕变为具有持久生命力的产品,它需要在“触发随机情感波动”之上,构建更深层的、促使人们周期性回归的“自我叙事”框架。否则,它可能只是数字花园里又一株迅速绽放又迅速被遗忘的昙花。

查看原始信息
FeatureMessage
FeatureMessage lets you send a message to your future self. Write a short letter or record a video. Pick a date in the future. We’ll deliver it to your email when the time comes. No complicated setup. Just three simple steps: 1. Write a message or record a video 2. Choose when it should be delivered (months or years from now) 3. Receive it at the perfect moment People use FeatureMessage for birthdays, life goals, reminders, or quiet thoughts they don’t want to forget.
Hey, I’m Andi 👋
 For a long time, I’ve been recording little videos on my phone for my future self.
Random thoughts. Feelings. Moments I didn’t want to forget.
 But somehow, I always lost them.
 Buried in my camera roll. Old folders. Gone forever.
 So I decided to build something for my own problem. With FeatureMessage, you can write a short letter or re
cord a video to your future self.
 You pick a date.
 And when the time comes, you’ll get an email with your message.
 It creates really emotional and unexpected moments.
 The kind that make you pause for a second.
 One day, you’ll be glad that you took 5 quiet minutes today to send something to your future self.
8
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Congrats on the launch Andi! Love the design of the website

2
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Sent myself one! I already can't remember what I said in it and that's what is going to make it feel special! (I also can't remember when it's scheduled for and that's even better 😂)

Good job Andi!!

2
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really cool idea Andi !

2
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Congrats on the launch! There’s something quietly powerful about designing for memory and self-connection instead of speed or growth. How you think about the long-term emotional impact, do you see FeatureMessage as more of a personal archive of identity over time, or as a tool for intentional self-guidance and closure?

0
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Love this idea! One question: how does FeatureMessage handle users who've already seen an announcement? Does it automatically hide it, or do we need to manually manage visibility rules?

0
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Thats such a cool concept!

I used to record videos of myself to look at in the future but receiving them via email on a certain date is a great idea :)

0
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Such a lovely idea and clean design. Congrats on the launch Andi!

0
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It's time to document my current self.

If this is done regularly, or even as a group event or something, it's educational and entertaining.

0
回复
#16
Alpie
An AI workspace for deep work and real collaboration
38
一句话介绍:Alpie是一款AI工作空间,支持深度工作和实时协作,通过整合长对话、文件与团队协作工具,解决团队在处理复杂项目时信息分散、决策效率低下的痛点。
Productivity Developer Tools Artificial Intelligence
AI工作空间 深度协作 团队协作 文件管理 实时聊天 知识管理 工作流优化 信息整合 智能助手 团队决策
用户评论摘要:开发者主动介绍产品定位为深度工作空间,强调长上下文、实时协作与文件整合功能,并公开征集用户工作流需求与功能优先级反馈,目前无外部用户问题或建议。
AI 锐评

Alpie瞄准的并非通用聊天机器人市场,而是试图切入“深度工作流协作”这一细分场景。其核心价值主张在于“持续上下文”与“团队共思”,这直击当前AI工具的两大短板:一是多数AI交互仍为短平快的问答模式,难以支撑长周期复杂项目;二是AI协作多为个体单向使用,缺乏团队共享的知识演进空间。

产品将PDF、代码、链接等多格式文件整合进对话线程,并加入批注、提及、角色分配等协作功能,本质上是在构建一个以AI为中枢的团队知识库。其真正挑战不在于技术实现,而在于工作流迁移成本——团队需要改变现有分散的协作习惯(如文档、聊天、会议分离的状态),将核心决策过程沉淀到一个平台上。

从开发者评论可见,产品仍处于“开放式构建”阶段,投票数不高且缺乏真实用户反馈,这反映出市场验证尚浅。其成功关键将取决于能否精准绑定特定行业工作流(如研究、战略分析、技术方案评审),并证明AI驱动的协作能产生比传统工具更优质的决策输出。若仅停留在“更好的文件聊天机器人”层面,则极易被现有平台的功能迭代所覆盖。

查看原始信息
Alpie
Alpie is an AI workspace for deep work and real collaboration. Drop in PDFs, images, code, or links, ask deep questions, and work with your team in real time. Pin important insights, keep chats and files organised in one dashboard, and move through complex problems together. Built for long workflows and shared thinking, Alpie helps teams turn scattered information into clear, lasting decisions, and not just answers.

Hello explorers

We are excited to launch Alpie, an AI workspace built for deep work, not just quick prompts.

Alpie brings together long-form chats, files, research, and real-time collaboration in one place. You can run fast or deep research, work with PDFs and files across chats, pin important insights, and collaborate with your team using roles, comments, mentions, and shared workspaces, all without losing context.

We are continuously improving Alpie’s contextual understanding across longer workflows, so it gets better at following ongoing work over time as you use it.

We are building Alpie in the open and improving it every day. We would truly appreciate your feedback on which workflows you would like to use Alpie for, and what features we should prioritise next.

Thank you for checking it out! We are happy to answer any questions you may have.

10
回复
#17
Blogs for Vercel
A simple, semi-headless CMS for Vercel-hosted Nextjs blogs.
35
一句话介绍:一款为Vercel托管的Next.js博客打造的半无头CMS,通过在项目中直接安装npm包,解决了开发者快速搭建和管理博客内容、避免复杂后端架构的痛点。
API Writing SEO
半无头CMS Next.js Vercel 博客管理 内容管理平台 API驱动 快速集成 AI就绪 npm包 开发者工具
用户评论摘要:评论主要为开发者自述,阐述了开发此产品的初衷:市场上缺乏在Vercel上简单、非完全无头的博客内容管理工具,现有方案通常需要复杂数据库和认证。本产品旨在为客户提供更简易、快速的博客管理体验。
AI 锐评

在“无头CMS”已成红海的今天,“Blogs for Vercel”聪明地选择了“半无头”这一细分定位,本质是瞄准了Next.js + Vercel这一日益主流的技术栈的配套缺口。其宣称的“简单”直击一个核心矛盾:许多开发者或小团队确实需要一个内容后台,但又不愿或无力维护一套完整的、独立的后端服务和数据库。

产品将Auth、多用户组织、API层打包成一个可直接“npm install”的模块,这与其说是一个独立产品,不如说是一个“可嵌入的后端功能套件”。它试图将复杂的后端工程简化为一个依赖项,让开发者能快速获得一个可用的管理后台,同时通过API保持前端展示的灵活性。这确实能吸引那些希望保持技术栈统一(Vercel/Next.js)、追求部署简便性,且对内容管理定制化要求不高的开发者。

然而,其价值与风险同样明显。优势在于极低的集成成本和清晰的场景聚焦,完美契合Vercel的Serverless范式。但劣势也在于此:深度绑定特定平台和技术栈,市场天花板显而易见;作为“半无头”方案,它在内容建模的灵活性和功能扩展性上,可能难以匹敌成熟的头部或无头CMS;其“简单”的特性,也可能随着客户需求增长而面临挑战,最终陷入“比上不足,比下不精”的尴尬境地。

总体而言,这是一个典型的“场景驱动型”工具,精准服务于特定技术社群的即时需求,是效率至上的务实选择。但它能否从一个“便捷工具”成长为具有持久生命力的“产品”,取决于其能否在保持轻量的同时,构建出足够深的护城河,或成功拓展其定义的技术栈边界。目前来看,它是一个优秀的“补丁”,但尚未看到颠覆性的革新。

查看原始信息
Blogs for Vercel
A simple semi-headless CMS for your Vercel-hosted Next.js blog. Manage your content with ease, fetch it via secure API endpoints. Includes: - Auth - Multi-user Organizations - Blog Post Editor - Blog Post Management - API for publishing or querying Blog Posts - AI Starter Pack - npm install package - Swagger file Vibe coding your landing page? Blogs for Vercel makes the blogging experience for clients dead simple.

It's not easy to find a simple, straightforward tool to host your blogging content on Vercel that isn’t 100% headless. It almost always requires a full blown DB and authentication.

I wanted something simpler and faster that would allow my clients to manage their blogs easier.

So, I created Blogs for Vercel!

Built from the ground up with AI in mind, install the npm package directly into your project for easy setup or download the swagger file or AI Starter-Pack to quickly implement Blogs for Vercel into your NEXTjs project.

Make and publish your blog posts using the GUI or via APIs and instantly display them on your webpage.

Excited to hear what you think!

4
回复
#18
Evolvoom.io
AI sales rep that brings customers back over email & SMS
27
一句话介绍:一款面向电商品牌的AI客户留存平台,通过分析用户旅程,自动发送个性化的电子邮件和SMS消息,在客户流失、复购等场景下,解决中小电商因人力时间不足而无法有效进行客户关怀与再营销的痛点。
Sales Marketing E-Commerce
AI客户留存 电商营销自动化 个性化沟通 SMS营销 客户复购 弃单挽回 客户忠诚度 行为分析 零售科技 会话式商务
用户评论摘要:创始人 Pavel 阐述了产品诞生的背景(获客成本高、中小卖家无暇深耕留存),并重点介绍了核心功能与新增的SMS双向对话能力。另一条评论则肯定了其将数据转化为可操作洞察的“深思熟虑”的工具价值。目前评论以产品介绍和祝贺为主,暂未看到具体的用户质疑或功能建议。
AI 锐评

Evolvoom.io 切入了一个看似拥挤但实则痛点深切的赛道——电商客户留存。其宣称的核心价值“让客户感到被真正记住和关怀”,直指当前规模化营销的软肋:缺乏人性化的温度。产品逻辑清晰,即利用AI消化客户数据,生成并执行“一对一”的沟通策略,从电子邮件扩展到更具侵入性但也更直接的SMS对话。

然而,其真正的挑战与价值深度在于几个关键层面。首先,“个性化”的门槛已被市场拔高,仅基于订单历史和行为的消息,是否足以构建“朋友般”的对话体验,而非更高级的模板化,这需要极强的数据解读与自然语言生成能力。其次,将AI用于SMS双向对话是一步险棋,SMS的打开率高,但用户对骚扰的容忍度极低。所谓“温暖、人性化”的尺度若拿捏不当,极易引发负面反馈,这对AI的情商和品牌方的风险控制都是巨大考验。

从市场定位看,它明智地瞄准了资源有限的中小电商,但其价值主张却依赖于一个悖论:最需要“人性化关怀”来提升忠诚度的品牌,往往其客户数据维度和深度有限,AI能否基于有限数据做出“精准关怀”,存疑。产品的长远价值不在于同时管理多少个渠道,而在于其AI模型对复杂客户意图的识别精度,以及将稀疏互动转化为信任和销售的实际转化率。它不应仅仅是另一个自动化触达工具,而必须成为一个真正能提升客户终身价值的“智能客户关系经理”。目前从披露信息看,其“理解”和“对话”能力的具体技术实现与效果衡量标准仍是黑箱,这将是其从“有趣概念”迈向“必备工具”必须解答的问题。

查看原始信息
Evolvoom.io
Evolvoom is an AI customer retention platform for ecommerce brands that want their customers to feel genuinely remembered and cared for. It connects to your store, understands each customer’s journey, and sends messages that feel like they were written just for them - not broadcast to a list. Beyond email, Evolvoom AI now can reach out and have conversations via SMS too, recommending products, bringing people back to their carts, and answering questions in a warm, human way.
Hey Product Hunt community! I’m Pavel - one of the founders of Evolvoom.io. As acquisition keeps getting more expensive, so many small ecommerce and dropshipping store owners told us the same thing: “We know retention matters, but we don’t have the time or people to do it well.” ​ That’s why we built Evolvoom as an AI-powered retention partner, not “just another marketing tool.” It connects to your store, learns from order history and behavior, and then: - Writes and sends truly personalized 1:1 messages - Runs winback, post‑purchase, and re‑engagement flows - Tracks every conversation and recovery in one place ​ Today, we’re especially excited about a new piece: our AI can now hold natural, two‑way conversations over SMS, not only email. Customers can reply like they would to a friend, and the AI answers questions, nudges them back to their carts, and helps build long‑term loyalty without feeling spammy. If you’re running a store and feel like your customer data is underused, Evolvoom is our attempt to turn it into gentle, human‑like outreach that actually respects your customers’ time. We’d love your feedback, questions, or tough critiques in the comments.
11
回复

Evolvoom IO Customer Retention AI feels like a very thoughtful tool for helping businesses keep customers engaged by turning data into actionable insights that can make retention strategies more effective and intuitive. Congratulations to the founders on launching this and wishing you lots of support and traction on Product Hunt.

2
回复
#19
TuneKit
Fine-tune SLMs 2x faster, for free
23
一句话介绍:TuneKit通过自动分析数据、推荐模型、优化超参数并生成可运行的Colab笔记本,让用户免费、快速地在Colab上微调小型语言模型,解决了开发者反复面临的环境配置复杂、资源昂贵和脚本编写繁琐的痛点。
Developer Tools Artificial Intelligence GitHub
小型语言模型微调 AI开发工具 自动化机器学习 开源项目 Colab集成 超参数优化 无代码AI 模型训练简化 免费AI工具
用户评论摘要:用户反馈主要认可其解决了微调设置耗时、复杂的核心痛点,赞赏其免费、免GPU、免脚本的特性,认为对SLM工作者是巨大帮助。开发者补充了其支持的具体模型和工作流程细节。
AI 锐评

TuneKit的“真正价值”并非技术创新,而是一次精准的“体验重构”。它本质上是一个基于现有开源生态(Hugging Face, Colab)的胶水层和自动化脚本生成器,其犀利之处在于直击了一个被主流云服务和大模型平台忽视的缝隙市场:预算有限、追求效率的独立开发者或小型团队对轻量级模型进行定制化的需求。

当前微调服务两极分化:一端是OpenAI、Anthropic等提供的黑箱API,灵活度低且成本不菲;另一端是直接使用Hugging Face等库,自由度虽高但门槛陡峭。TuneKit聪明地卡在中间,将后者繁琐的工程环节(环境配置、参数调试、资源编排)打包成“上传数据-获取笔记本”的一站式体验,并巧妙利用Google Colab的免费算力作为载体,实现了用户端的“零成本”和“零部署”。其推荐的“最佳模型”和“优化超参数”功能,实则是将社区经验产品化,降低了专业决策门槛。

然而,其天花板也显而易见。免费Colab的算力(T4)和时长限制,注定它只适用于小型语言模型(SLMs)的轻量微调,无法处理大规模数据或大模型。其商业模式缺失,完全依赖开源和免费生态,长期维护和扩展性存疑。此外,自动化必然伴随灵活性的牺牲,高级用户可能觉得束手束脚。

总而言之,TuneKit是一款优秀的“民主化”工具,它通过极致的简化,将模型微调从一项工程任务转变为近乎点击操作,有效激活了长尾的、实验性的用户需求。但它更像一个精巧的“创可贴”,而非根治问题的“手术刀”,其未来取决于能否在易用性与能力边界之间找到更可持续的平衡点。

查看原始信息
TuneKit
Fine-tuning SLMs the way I wish it worked. I got tired of the fine-tuning setup nightmare, so I built TuneKit: upload your data, get a notebook, and train free on Colab. No GPUs to rent. No scripts to write. No cost. Just results. Try it out at https://tunekit.app/ or check out the code on GitHub at https://github.com/riyanshibohra/TuneKit. Free and open source: let me know if it's useful!
Got sick of spending hours on fine-tuning setup every single time. Which model? What learning rate? LoRA rank? Batch size? Then writing the training script... Built TuneKit to handle all of it: - Upload JSONL → analyzes your data - Recommends best model for your task - Optimizes all hyperparameters - Generates ready-to-run Colab notebook - Train on free T4 (~15 min) - Export to GGUF/HuggingFace/LoRA Supports Llama 3.2, Phi-4, Mistral, Qwen, Gemma.
0
回复

Wow, we hit #19 trending today! Thanks for the support everyone 🙏

0
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Congratulations on the TuneKit launch. Making fine-tuning models fast and free without needing GPUs, scripts, or complex setup is incredibly thoughtful. This feels like a big help for anyone working with SLMs. Wishing you great momentum today.

0
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@ngocphuc_1910 Thank you so much Phuc!

0
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#20
Dollar Budget Club
Just Budget, Bruh
14
一句话介绍:一款主打极简、低价和隐私尊重的个人预算APP,通过直观设计帮助用户无压力掌控日常开支,解决传统预算工具复杂、昂贵且耗时的问题。
Money Finance Budgeting
个人财务管理 预算工具 极简主义 高性价比 隐私保护 订阅制 消费习惯 轻量化 移动应用 生活助手
用户评论摘要:评论(可能为官方或早期用户补充)强调产品初衷是让用户一目了然理解消费、轻松培养习惯、掌控财务,同时免于电子表格压力、订阅疲劳,并尊重用户时间、隐私和钱包。
AI 锐评

Dollar Budget Club 以“Just Budget, Bruh”的标语登场,其核心叙事直指当前个人财务管理工具领域的两大痼疾:功能臃肿带来的认知负担与高昂年费制造的心理门槛。它将自己定位为一个反叛者:用每年9.99美元的价格挑战“百元俱乐部”,用“不追踪、不存储、不出售”的隐私承诺对抗数据资本主义,用极简交互解构预算行为的仪式感。

然而,其真正的价值主张可能并非技术或功能创新,而是一种**体验与价值观的精准降级**。它剥离了投资追踪、复杂报表、多账户同步等进阶功能,回归到预算最原始的提醒与控制功能。这种“降级”瞄准的是对现有工具感到“订阅疲劳”和操作压力的“沉默大多数”——他们需要的不是一份详尽的财务审计报告,而是一个能温和提示消费边界、且不构成额外财务与心理负担的“电子信封”。

产品的风险与挑战同样清晰。首先,极简与低价是一把双刃剑,在吸引轻度用户的同时,也可能陷入增长陷阱:用户财务能力提升后极易流向功能更全面的平台,留存率面临考验。其次,“不存储数据”的隐私卖点虽好,但也意味着无法提供基于历史数据的深度洞察,价值天花板明显。最后,在近乎免费预算模板与银行自带工具之间,每年9.99美元的定价仍需更强大的用户习惯养成能力来支撑其说服力。

总而言之,Dollar Budget Club 更像一个针对特定心智的“生活方式产品”,而非颠覆性的金融科技工具。它的成功与否,将取决于能否在“足够好用”与“绝对简单”之间找到黄金平衡点,并将“尊重用户”的价值观转化为可持续的复购与口碑。在当前市场,它提供了一个有价值的低成本切入点,但长期来看,其护城河可能比想象中更浅。

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Dollar Budget Club
We started with a simple idea — budgeting shouldn't feel overwhelming, intimidating, or time-consuming. Most budgeting tools today are bloated and expensive. When competing apps are charging upwards of $100/year, at $9.99/year this is fantastic value for money. We don't show ads, we don't store your data, we don't sell or rent your data. We treat you like how you should be treated, with respect. Give it a try!
Dollar Budget Club was created to help people understand their spending at a glance, build better habits with less effort, and finally feel in control of their money — without spreadsheets, stress, or subscription fatigue. It's a tool that respects your time, your privacy, and your wallet.
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