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I just open-sourced my web based POS software – WallacePOS

Hacker News

I just open-sourced my web based POS software – WallacePOS

Hi Hacker News, Early last year I decided to build a web based POS solution. A charity I was working with at the time tasked me with finding a affordable POS solution. The system had to support multiple locations at a reasonable price. Unfortunately I was unable to find any software that fit and so after an initial proof of concept, the project was born. I aimed at using the latest technologies used in the browser and had fun working with them (localstorage, websockets, etc), as well as some very tricky but gratifying problem solving. About a year and a half later I feel WallacePOS is ready for source publishing and this week I uploaded it to github under the LGPL. I hope that it may help businesses like my client have a modern and connected POS system for little cost, and also build a community that can extend and improve the system. Checkout the website and demo: https://wallacepos.com/ Or fork me on Github! https://github.com/micwallace/wallacepos

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
72%72% 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, using, open · Missing: mac, agents, macos
56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released
54%54% 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
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
33%33% 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
11%11% 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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