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ProductBeaver – Product Listing Made Effortless (Productbeaver.com)

Hacker News

ProductBeaver – Product Listing Made Effortless (Productbeaver.com)

Show HN: ProductBeaver - Product Listing Made Effortless My cofounder and I recently ran into a problem for a different idea we were working on. While preparing catalogue for the items to be uploaded for our website/app, there was a lot of data which we had to manually add and the effort took days. We were mostly doing the data dump in CSVs and manually coming up with the descriptions. Using different LLMs out there was definitely one solution; but it was quite laborious as well; with manually typing for each sentence, perfecting the prompt and choosing the right option. That's when we thought there should be a better way out there to do this. We build ProductBeaver with the intention to reduce the laborious work spent on updating your product catalogue. Some cool features that we have implemented so far. - Ability to generate multiple columns in one go using one or more base columns. - Tailor the output by choosing one or multiple controls like mood, length, usage context. - Pick and choose from multiple choices generated in the output table. - Ability to manage single or multiple CSVs (including import/export). We plan to add more types of columns like tags, images as well as more controls to tailor the output as per your preference. Any feedback is greatly appreciated.

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
80%80% 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: context, single, using · Missing: mac, agents, macos
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
56%56% 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, including · Missing: https docs, excited, just released
35%35% 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
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
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.

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