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Devvvs.com – Custom Web Apps

Hacker News

Devvvs.com – Custom Web Apps

tl;dr I created devvvs.com ( https://devvvs.com ) to streamline the process of making nice, inexpensive web apps for people by focusing on the connection between the developer and consumer. Hi! My name is Aleksai Losey and I am a first year at university. Summer break will be here in a few weeks and I hope to spend my time mostly creating websites and web applications. I sincerely enjoy doing this. I noticed that most custom website agencies make it difficult to talk to a developer, and their expensive pricing is a needless barrier. So, if you or anyone you know needs a website, let them know about devvvs.com! You can upload sketches of your website and chat directly with a developer. We’ll charge monthly with no large, upfront costs. I just launched this as a side project. I warmly welcome any criticism or advice! You can directly email me at aleksailosey@gmail.com with either of these. The project is in its very early stages and I hope to improve it immensely. Thanks for your time!!!

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: apps, email, using · Missing: mac, agents, macos
58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, 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.
TrustMRRLess likely to generate early MRR · Strong signals: apps, month, monthly · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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