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Tech stack
60 projects
Find ICP-matched professionals on X and draft personalized outreach messages in your voice.
@aman29122k · X
- it's your one in all twitter companion
Team chat platform with a built-in AI teammate that reads files, writes documents, and integrates with your tools.
@KalantariAria · X
A Discord/Slack alternative with a native AI Agent built in, it's called Waffle (@trywaffle_app).
WhatsApp-based personal focus training that turns goals and thoughts into action steps and evidence-based adjustments.
u/EPovetkin · Reddit
I built an app because my biggest goals kept disappearing into daily noise I built an app because I kept forgetting my biggest goals after one day I’ve noticed the same pattern in my life many times. I would have an important realization, write down a meaningful goal, create a plan, and genuinely feel that something was about to change. Then the next day arrived. Messages, work, bills, small urgent tasks, random problems — and within a day or two, the goal that had felt
Real-time group chat platform where teams collaborate on AI prompts and agents together.
@NFTMansa · X
Love seeing the builder community thrive! For anyone tackling group projects with AI, we built GroupGPT. It lets teams collab on AI prompts and custom agents in real time. Check it out:
Listen to music together in shared rooms with chat, skip voting, and AI-powered song requests.
vodmaker · V2EX
做了个在线听音乐的另一种可能 平时听歌，不管是工作还是休息，习惯基本都是：打开 App ，进入歌单，然后开始播放。 我最近在想，听歌是不是还能有另一种方式？ 参考 KTV 的点歌模式，我做了一个多人在线听歌的小产品——听歌 Next 。 大家可以进入同一个房间，一起听同一首歌，也可以各自点歌，把想听的歌曲加入播放队列。播放进度会在房间内同步，还支持聊天和投票切歌。 另外做了一个自然语言点歌功能，不需要准确输入歌名，可以直接说： - 来几首适合下班路上听的歌 - 想听一些周杰伦早期的慢歌 - 放几首适合写代码的轻音乐 系统会根据描述推荐歌曲，再批量加入播放队列。 目前还是早期版本，欢迎大家来试用，也欢迎反馈问题和建议： 试用地址： https://tingge-production.up.railway.app/
Collect user feedback and communicate via embeddable live chat widget.
@haukejung · X
Your app for collect and triage user feedback
Monitor Reddit in real time to surface leads, competitor activity, and engagement opportunities.
@shawntenam · X
Claude Code Daily... Version 3 is live. Just shipped a clean refresh of the daily landing page: tighter flow, better signal extraction, and a soft CTA at the bottom that points builders toward without being pushy. Last week I dropped the previous version straight into the Claude Code subreddit. Result? ~400 visitors, only 5 clickouts. That’s how i test and iterate on Reddit. never blast a direct product link. You share the actual value first. the blog post itself. and let the equal exchange do the work. If someone finds the daily brief useful and wants to go deeper (beyond basic scraping into real intent signals), the path is there naturally. No deception, just reciprocity. builder mindset in action: ship fast, measure honestly, refine in public. does V3 hit cleaner? Drop your thoughts below.
Chat with an AI that maintains continuous memory and learns from each conversation.
@Vematrex · X
5M views. 13K shares. Building Veiled Prime at The most insightful AI we know how to make. Built for people doing real work. One conversation. Real memory. No resets.
Train an AI chatbot on your website to answer visitor questions and capture leads.
@pmxgrowth · X
Built after taking my own site from 2% to 7% lead conversion. AI agent that answers first, captures the lead once it's been useful.
Monitor Reddit & X for warm leads asking about your solution, validate AI-drafted replies.
@laminemansour_ · X
Building Voxgrow SaaS that monitors Reddit & X to find people already asking for your solution, then drafts contextual replies for you to validate. No cold outreach to random people. Just warm leads on autopilot.
Upload documents and chat with an AI that cites sources using retrieval-augmented generation.
@ajkbossp · X
Check out what I just built with Lovable!
Send your question to a panel of LLMs that peer-review each other and return one synthesized answer.
u/Puzzleheaded-Log-27 · Reddit
Building a multi-model AI deliberation tool taught me something about trust LLM Counsel isn't another wrapper around one model - it sends your question to a panel of frontier LLMs, has them peer-review each other anonymously, and an impartial "chairman" model returns one synthesized answer. Free to start, pay-as-you-go after, credits don't expire. What I've learned so far: people trust a synthesized answer a lot more once they can see that the models actually disagreed and how that disagree
