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Launching VideoToPage – How I built it and stack

Hacker News

Launching VideoToPage – How I built it and stack

Hey there, so after overcoming imposter syndrom, I decided to launch one of my products to product hunt. https://www.producthunt.com/posts/videotopage I started thinking about this project right after Vision came out. I thought how cool would it be to have a product demo you recorded turn into a full interlinked documentation with introductions, getting started, code examples and so on. So I built it and reached a Prototype where this was possible. But it took quite some time to process, and it started to get expensive. Then I did a little bit of SEO research in Ahrefs and found out that there is not even a search demand for that. Then someone told me "give people what they want" and it seams people just look for transcriptions and to repurpose it. So I ditched the screen vision analytics part for now and focused on just the spoken text. Not sure if this was the right decision and if I now removed the main sauce from it. We will see. The project was built using a stack that allows me to ship within days. It is based on firebase, react and nestjs and nx. I have everything in Typescript. Even though firestore might be tough to work with, I have wrappers around it that help me solve most of the issues. So this is pretty nice. One thing I am proud of is a think I called AI islands. Imagine a model, like a actual database model, but from a business layer perspective. So in videotopage every model can be extended with AI capabilities. Each model/repository has certain functionality, like editing, deleting, adding, modifying. Now this per-model functionality is exposed obviously to the UI, so that the user can manage his content. Now, all this capabilities are also exposable to an dedicated AI agent. This still does not sound new, but in this case, each model has actually its own AI assistant history, memory and tool access. This way, when you are navigating through the page, you will always see related ai assistance that is focused only on that you see. Also it updates everything you see as if you would do it. This can be sometimes a magical experience, when you look at your list of items with several features in it, and you just start telling the bot what to change and you see it actually change in front of your eyes. So this AI Island thing is definitely something I will be shipping in all future products.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, user · Missing: mac, agents, macos
98%98% 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
95%95% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video, way · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, 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
49%49% 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
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% 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.

Incorrect prediction on native model

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