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WebApp to filter WhoIsHiring by tags and live text search

Hacker News

WebApp to filter WhoIsHiring by tags and live text search

I recently created a little webapp when searching for a job on hackernews. Couldn't find a solution that allows for filtering based on multiple tags or similar, so I created one myself here: https://grburst.github.io/hnjobs Note that there are multiple limitations (firebase live only, only current month, not really mobile friendly, ...), little to no optimizations and potential bugs, but it was already a great help for me, so I am sharing it with you. I will however improve it a bit more, because it is a fun experience and I am trying out new frameworks I haven't worked with before as well. As always: All feedback is welcome :-) Enjoy

Visit productView on Hacker NewsMay 21, 2024
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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos
66%66% 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.
AppSumoStrong fit for a featured deal · Strong signals: friendly · Missing: plus, platform, intuitive
57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
56%56% 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: 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: month, way · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
10%10% 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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