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Smart Poker Table Guide (Part 1) – Connect RFID Readers

Hacker News

Smart Poker Table Guide (Part 1) – Connect RFID Readers

How to Build Your Own Smart Poker Table The goal of this post is to show you how to connect the RFID readers to your PC. Youtube video: https://www.youtube.com/watch?v=jDCRA0emlvA How to connect the RFID readers in 5 steps: 1. Connect Pepper C1 to PC via USB 2. Connect to the wi-fi access point Pepper_C1-XXXXXX 3. Go to 192.168.100.1 - username: admin / password: admin 4. Go to RFID tab and enable all Antennas 5. Go to Status tab and start scanning Hardware - Pepper C1 MUX - https://eccel.co.uk/product/pepper-c1-mux-usb - RFID Antennas - https://eccel.co.uk/product/mux-ant1356-80x80-800 - RFID poker cards - Available on Alibaba You can find more about SPT on the website - https://www.smartpokertable.com

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
39%39% 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
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos
12%12% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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