a web app to track when your food will go bad

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http://wasteless.io A web app to keep track of when your food expires. Grew out of a CLI program I made for myself. It was also an experiment with writing a webapp purely in Common Lisp. It comes with the full source code [0] as well as source code from some CL libraries that I spun off on the way. libs: cl-config: library for managing configuration settings loosely based on the BSD model. [1] cl-init: library for integrating your common lisp program into an OS's init system. Manages starting and stopping the services as well as setting up a swank/slime interface. [2] pg-sessions: a library that sits on top of hunchentoot and the postgres library postmodern to store and manage sessions in a postgres database. [3] [0] https://bitbucket.org/thomashintz/wasteless/src [1] https://bitbucket.org/thomashintz/cl-config/src [2] https://bitbucket.org/thomashintz/cl-init/src [3] https://bitbucket.org/thomashintz/pg-sessions/src

Visit product View on Hacker NewsMar 13, 2014

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TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: model, code · Missing: mac, agents, macos

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive

34%34% 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

18%18% 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

8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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