VE

VETS – Volunteer Effort Tracking System

Hacker News

VETS – Volunteer Effort Tracking System

About 5 years ago I volunteered to help our local SPCA (Society for the Prevention of Cruelty to Animals) build a computerized system for tracking hours that their volunteers spent. This an important part of the volunteer coordinator's job, as there are hours/year requirements for volunteers, awards given out annually, etc. Prior to that they were using a paper binder with sign-in sheets, and adding up everything by hand at the end of the month. With about 1000 active volunteers, this was no small task, and seemed like a place computerization would be an amazing help. I wrote up a simple system in Rails (which I had spent a fair amount of hobby time with) and it's been running there ever since. Today, there are about 67,000 "hours" entries in the database, about about 3,000 volunteers (1/3 of them active), and the system is a little slower than it ought to be (my original testing was with tens of records, since there was no existing database to import). Rather than try to update and re-learn ruby and rails, I opted to give a try porting the essential parts of the system to Python using bottle.py The result of my work is now up at https://github.com/vets/vets and licensed under MIT. I'm at a point where things are about functionally complete and I'd love to hear feedback from anyone willing to take the time to take a look at it. It's a generic enough system that I could see other non-profit organizations (who often have much/any money available to spend on commercial software that can track things like this) being interesting in this. Things I'm not that great at, which are incidentally the main things this project uses: * Python * bottle.py * SQL * HTML/CSS This is a hobby project for me (I'm a C programmer by day) so I am probably taking a naive approach in most cases, but in some cases I know that when I re-deploy this version of the system, it's likely to sit for another 5+ years.

Share card

Actual performance

14points
8comments
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Strong signals: organizations · Missing: supports, reddit linkedin, podcasting
95%95% 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.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
77%77% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: computer, using · Missing: mac, agents, macos
76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
44%44% 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
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: profit, active · Missing: arr, mrr, revenue
19%19% 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.

Correct prediction on native model

Similar products

VE
VETS – Volunteer Effort Tracking System53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

VETS – Volunteer Effort Tracking System

Hacker News11
LI
LIME - Lisp Implementation with Moderate Effort69%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

LIME - Lisp Implementation with Moderate Effort

Hacker News3
Hardest Degrees in the world
Hardest Degrees in the world48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

hardest degrees require intense intellectual effort.

Product Hunt+10
Huebert
Huebert23%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Prettier UI. Less Effort.

Indie Hackers
Bristlecone
Bristlecone76%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Get evergreen articles without effort.

Indie Hackers1writing
Quincy
Quincy20%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

More engagement with less effort

Indie Hackerscommitment-full-time
SayMyName
SayMyName81%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Make the effort to pronounce someone's name right

Indie Hackers2communication
MV
MVP Before Effort Estimation35%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

MVP Before Effort Estimation

Hacker News5
Tr
Tracking voter discussion of the Presidential debates42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Tracking voter discussion of the Presidential debates

Hacker News44
Vi
Visit tracking for Rails39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Visit tracking for Rails

Hacker News51