Ag

Agenttik – work on multiple projects in parallel with AI agents

Hacker News

Agenttik – work on multiple projects in parallel with AI agents

Hi! I've been reading HN daily for almost 16 years. Really glad this community exist. This is my first Show HN, so I hope you find this tool useful/interesting. During my summer holidays I started playing with latest AI models on personal projects. At one point I started working in parallel with the same project and tried different approaches. The one that worked best for me was to clone the repo and distribute work among different AI workers, work in parallel but with different branches & folders. This allowed me to review and merge work afterwards. The thing is, spawning several agents in parallel was challenging. I could work on multiple features and bugfix at once, but it was hard to keep track what I was working on. I was jumping from one thing to another, and back; from one window to another. Switching tasks like this was mentally exhausting. Holidays were over, and after returning to my day to day work, I continued working using a similar approach not on one, but also on multiple projects, but I still could not find how to work in a more organized way without the mental toll. This space is growing really fast, and I haven't been able to find the right tool for myself, so I decided to build my own. I wanted a tool that would help me manage multiple projects at once, work with multiple agents, even from different providers, where I could chose between my personal and work subscriptions depending on the project, where I could easily review changes without leaving the tool if I wanted, or go all in and delegate fully to agents; one where I had quick shortcuts to move around, where I could schedule work or enqueue work for later so agents could work on one task after another. agenttik is my way of tackling this problem to work on multiple projects in parallel, while allowing myself to be on the driver seat. I can give as much autonomy as I want to the agents while keeping things organized in my head. I've been hesitating for hours on whether to submit or not, but hey "if you are not ashamed of your project, you are shipping too late" Honestly, this project has exactly one week of development, started from scratch, and even though it is still early on, it has been extremely useful for me. I've built in a week things that would have taken me months, not only built the tool itself but other projects as well (most of the merit is still on the models used, but would have had no way of distributing all the work, though). Feel free to try. Here's how I would get started: - Open a project you already have - Add some general prompt (in the project area) or add to your AGENTS.md/CLAUDE.md (to instruct it to do regular commits and/or work with branches, and also instructions on how to build effective tests so that it can self-correct). - Chose your subscriptin of choice: Claude Code / Open AI subscription / ... - Pick big models (Opus High to Max / Fable Medium to Max / Astra Medium to Max) for bigger autonomy and try not to do hand-holding Finally, start sending/enqueuing tasks on all the features you want your product to have, bugfixes, ... use a new prompt per task (Ctrl+T / Cmd+T), I recommend using big models to avoid investing your own time reviewing, and then once LLM is done, review and archive that task or continue iterating. Keep adding tasks as you think on them. Add more projects as you start feeling comfortable. It has been really addictive for me to see the speed of development when working like this. I got totally hooked. Speed of iteration is insane. Happy to discuss any feedback you have even if it is about other tools or ways of working with AI these days! I hope you like it!!

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor
96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting
91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, month, way · Missing: mobile apps, ios, entrepreneurs
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, ide, io · Missing: https docs, excited, just released
41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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