Do

Doblun's draft

Hacker News

Doblun's draft

I realized a big problem that I have with being a solo developer on my projects. That problem is I would rather not show a peep until things are finished. Not too long ago someone asked to see my progress, long story short, I showed them the basics of the UX. The javascript is vanilla so I need to use a framework as the next step to add crossbrowser support and maintainability. There is also a psedo-backend made in javascript to fake the API. I invite everyone to show what they have. As entrepreneurs the odds are certainly against us, so we might as well let people enjoy the easy side of the journey with development screenshots. https://twitter.com/Doblun/status/482220966853246976 The best of luck to everyone on their projects.

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2points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
66%66% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released
55%55% 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: entrepreneurs · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
37%37% 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
28%28% 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
15%15% 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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