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Freeform – One click shareable AI generated apps

Hacker News

Freeform – One click shareable AI generated apps

TLDR: Curious if YC startup requests are a valid take, so I built freeform in a weekend. Storytime: I’ve been working on a startup for a while (another context layer startup) and one night while I had One Piece playing in the background and I was going through YCs request for startups list. I was watching the videos and reading the transcripts when two of them particularly stood out to me: cloud for small apps and shareable apps. Specifically the quote “AI hasn't had its multiplayer moment yet”. I had an idea floating in my head for a collaborative app which I thought would be nice to have and haven’t seen anyone implement anything like it yet (maybe so, I live under a rock). So having a free weekend and a claude pro subscription I started building Freeform. I didn’t count how many hours it took overall but it was about ~4.5 days total. I figured I should post about it since I’m curious if YCs startup direction is… well.. market driven. Anyway, this isn’t a reflection of YCs predictions obviously but more so a loose experiment of my interpretation and implementation of said ideas. Application: Freeform. What is it? I’d loosely describe it as an ai canva since a lot of the direct output is design driven and based on what you see. However, I think the functionality exceeds that as each applet is its own sandboxed environment with persistent storage. This and being able to inject your own api keys or just random links from the web (ie unsplash) results in some pretty interesting creations. The core component I wanted was sharing and collaboration, so I designed the entire idea around sharing links, previewing, and simultaneous prompt based collaboration with versioning. Anyway, try it out I hope you like it :) Try it here: https://sharefreeform.com Some stuff I made: A product page with stripe integration on checkout: https://sharefreeform.com/a/d0e55aff-8aec-45dc-9874-5dc1b943... A record player that plays midi and itunes music: https://sharefreeform.com/a/661d308e-1751-49c3-bd68-f7a58c05... A brainrot card I’m sending to my friends: https://sharefreeform.com/a/e31a5954-dcd4-4821-8cdf-c1dc6d2f... PS: I didn’t put a lot of credits to the llm or sandbox pool so if you run into any issues (or bugs) just shoot me an email here: hello@sharefreeform.com Also if you decide to make something feel free to email it to me or post it in the thread Thanks!

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, apple, apps · Missing: mac, agents, macos
91%91% 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 · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
46%46% 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
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, video, way · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
13%13% 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.

Correct prediction on native model

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