Fi

Find your "holy grail piece", as a service

Hacker News

Find your "holy grail piece", as a service

I am a collector by nature, and hunting down obscure items is a slow process that is only sometimes rewarding. I've often checked eBay at the end of a day to find exactly what I was looking for, long sold. Even the best sites tend to only offer once-a-day "new listing" summaries with noisy results. So as a programmer at heart, I created a solution I would have liked: Antiquebot. Antiquebot is a service focused on connecting you with your "holy grail piece". Antiquebot offers the ability to constantly and repeatedly search a number of auction sites. Custom filters can be used to create a funnel that turns lots of new, generic results into to a handful of specific results without relying on questionable per-site boolean search support. New results end up right in your inbox as fast and often as you like. Antiquebot's goal is to automate as much of the search process as possible, so you can set it and forget it until one morning, fingers crossed, you succeed in your hunt. - No need to be constantly checking several sites - Get results as frequently as you like - Custom filters to fine-tune your results across all sites at once - Deduplicated results to avoid noise - Results arrive in your inbox, or using the search result page on the site A demo account is available using the email address "demo@antiquebot.net" and password "helloworld". A free plan is also available, and you can delete your account with a few clicks if you don't find a use. This is my first "soft-launch" of the site to get feedback and refine the idea. Your thoughts and experience are appreciated! The backend is Go services using Asynq[0] for task queueing among the worker nodes. oapi-codegen[1] + an OpenAPI schema manages the HTTP API implementation, interfacing with via a Vue3 frontend using the (venerable) Bootstrap framework. [0] https://github.com/hibiken/asynq [1] https://github.com/oapi-codegen/oapi-codegen

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
86%86% 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: new, inbox, email · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: reward · 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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