u/Expensive-Ad8916

Made a game that starts of as a package sorter game then slowly turns into a tower defense as you try to find niche ways to prevent packages featuring a leaderboard!

someone has gotten 3,000,000 with a cool monk strat

I found the SF government jobs portal difficult to navigate, so I built a small app to help me search government job listings across the Bay Area. It became polished enough that I decided to share it!

You can select where in the Bay Area you’d like to work, enter your skills, and choose a required experience range.

For example:

Civil Engineering + Legal

I’ve found it to be useful for discovering niche government roles I wouldn’t have known to search for by title. I hope people here find it useful too!

It's all open source too

https://osgovjobportal.com/

I found the SF government jobs portal difficult to navigate, so I built a small app to help me search government job listings across the Bay Area. It became polished enough that I decided to share it!

You can select where in the Bay Area you’d like to work, enter your skills, and choose a required experience range.

For example:

Civil Engineering + Legal

I’ve found it to be useful for discovering niche government roles I wouldn’t have known to search for by title. I hope people here find it useful too!

It's all open source too

https://osgovjobportal.com/

I’m a big fan of automation in games.

 I love watching monkeys pop bloons in Bloons TD 5, or seeing arms and belts in Factorio suddenly assemble something perfectly. I also really like games with leader boards, so I made Package Panic.

 https://www.packagepanic.io/

 It’s still in beta, and I’d love feedback on it.

 I designed it to be a quick “toilet game” you can play for a few minutes, but with enough upgrades, automation, and leaderboard chasing to keep coming back.

Since the steam sale is live I wanted to post a Dev log on my personal project
https://nextsteamgame.com/ sharing some outcomes from the web traffic and how I changed the project from the great feedback I got!

I made a post about a month ago explaining how I made this opensource explainable search engine built around steam reviews to people find new video games, Not through Relevancy but through aspect based similarity.

Check out the old post for a better explanation if you want!
https://www.reddit.com/r/MachineLearning/comments/1tb8k3n/steam_recommender_using_similarity_undergraduate/

I wanted to say thank you to all the people of r/datascience and r/MachineLearning that gave me feedback and tried out my tool!

I improved the UI/UX of the website to make the vectors more clear and controllable, I Implemented a thumbs up and down feature on recommendations to see if users even like the tool.

I also wanted to share the after effects of promoting this tool on reddit!

from the 2,652 searches I got in the website 913 of them resulted in steam clicks! the games that were discovered were all in a uniform distribution and did not share much of a pattern showing me that the engine did its job in helping people find niche games across all genres!

(More images attached to post to see data viz)

I wanted to disclose that I made this tool to not make any profit of some kind, but it does use posthog so I can collect diagnostics now.

Since the steam sale is live I wanted to post a Dev log on my personal project
https://nextsteamgame.com/ sharing some outcomes from the web traffic and how I changed the project from the great feedback I got!

I made a post about a month ago explaining how I made this opensource explainable search engine built around steam reviews to people find new video games, Not through Relevancy but through aspect based similarity.

Check out the old post for a better explanation if you want!
https://www.reddit.com/r/datascience/comments/1t7manb/steam_recommender_using_similarity_pt_2_student/

I wanted to say thank you to all the people of r/datascience and r/MachineLearning that gave me feedback and tried out my tool!

I improved the UI/UX of the website to make the vectors more clear and controllable, I Implemented a thumbs up and down feature on recommendations to see if users even like the tool.

I also wanted to share the after effects of promoting this tool on reddit!

from the 2,652 searches I got in the website 913 of them resulted in steam clicks! the games that were discovered were all in a uniform distribution and did not share much of a pattern showing me that the engine did its job in helping people find niche games across all genres!

(More images attached to post to see data viz)

I wanted to disclose that I made this tool to not make any profit of some kind, but it does use posthog so I can collect diagnostics now.

I’m a big fan of automation in games.

 I love watching monkeys pop bloons in Bloons TD 5, or seeing arms and belts in Factorio suddenly assemble something perfectly. I also really like games with leaderboards, so I made Package Panic.

 https://www.packagepanic.io/

 It’s still in beta, and I’d love feedback on it.

 I designed it to be a quick “toilet game” you can play for a few minutes, but with enough upgrades, automation, and leaderboard chasing to keep coming back.

I'm not sure if I'm blind and this plugin already exist.

Usually in Obsidian, correcting misspellings means hovering over each typo, opening the suggestion menu, and fixing words one at a time. I liked the Google Docs tool that scans the whole document and shows suggestions in one place letting me click away at the corrections, so I made a custom plugin to port that tool over to obsidian!

https://community.obsidian.md/plugins/quick-autocorrect

(DISCLAIMER: I accidentally deleted the last post on this subreddit my apologies if this is your second time seeing it)

Last year I made a post about my steam recommender The last one was great and served its purpose of showing many people new games, But this new version is much more functional!

I love making recommendation systems that tell the user WHY they got the recommendation.

During a steam sale event, I always find myself trying to look for new video games to play. If I wanted to find a new game I would try to whittle it down by using steam tags, but the steam tag system is very broad "action". could apply to many many games.

That got me thinking, what aspects do I like about my favorite games?

Well I like Persona 4 because of the city vibes and jazz fusion,

Spore because of the unique character creation and whimsical theme.

Balatro for its unique deck building synergies.

What if I could capture unique tags that identify a game that aren't just "action" and put them into vectors to show the (focus) of a game

 For example I could break persona 4 into something like

Game play Focus vector:
 Day cycle 20%
 Dungeon crawling 20%
 Social sim 20%

Tags:
Music: jazz fusion
Vibe: Small rural town

I find that this system makes searching for games more "fun" now I can see why I like balatro. I like it because of the card synergies not so much for its rogue-like nature.

I also find that this helps find new underrated games, and beats the trap that Collaborative Filtering algorithms that get into where it "feels" like you get recommended the same things.

find your next favorite game! : https://nextsteamgame.com/

pull a PR!: https://github.com/BakedSoups/NextSteamGame

( I actually made some git issues myself for problems I can't fix)

if anyone has any criticism I would love to hear it! this is probably my favorite passion project. I made this during final season, Since the database takes around 1 day to build, there were some inevitable rate limiting errors that I go into. So I am sure there are many bugs. if you come across any and are willing to share that would be Amazing.

Hope this website helps people find new games! Also I have a advance mode for people that don't mind messing with sliders and weird data terms.

I love making recommendation systems that tell the user WHY they got the recommendation.

During a steam sale event, I always find myself trying to look for new video games to play. If I wanted to find a new game I would try to whittle it down by using steam tags, but the steam tag system is very broad "action". could apply to many many games.

That got me thinking, what aspects do I like about my favorite games?

Well I like Persona 4 because of the city vibes and jazz fusion,

Spore because of the unique character creation and whimsical theme.

Balatro for its unique deck building synergies.

What if I could capture unique tags that identify a game that aren't just "action" and put them into vectors to show the (focus) of a game

 For example I could break persona 4 into something like

Gameplay Focus vector:
 Day cycle 20%
 Dungeon crawling 20%
 Social sim 20%

Tags:
Music: jazz fusion
Vibe: Small rural town

I find that this system makes searching for games more "fun" now I can see why I like balatro. I like it because of the card synergies not so much for its rogue-like nature.

I also find that this helps find new underrated games, and beats the trap that Collaborative Filtering algorithms that get into where it "feels" like you get recommended the same things.

find your next favorite game! : https://nextsteamgame.com/

pull a PR!: https://github.com/BakedSoups/NextSteamGame

( I actually made some git issues myself for problems I can't fix)

if anyone has any criticism I would love to hear it! this is probably my favorite passion project.

Hope this website helps people find new games! Also I have a advance mode for people that don't mind messing with sliders and weird data terms.

I love making recommendation systems that tell the user WHY they got the recommendation.

During a steam sale event, I always find myself trying to look for new video games to play. If I wanted to find a new game I would try to whittle it down by using steam tags, but the steam tag system is very broad "action". could apply to many many games.

That got me thinking, what aspects do I like about my favorite games?

Well I like Persona 4 because of the city vibes and jazz fusion,

I like Spore because of the unique character creation and whimsical theme.

and I like Balatro for its unique deck building synergies.

What if I could capture unique tags that identify a game that aren't just "action" and put them into vectors to show the (focus) of a game

 For example I could break persona 4 into something like

Gameplay Focus vector:

Day cycle 20%
Dungeon crawling 20%
Social sim 20%

Tags:
Music: jazz fusion
Vibe: Small rural town

I achieved this by pulling 2k reviews for 80k steam games, running them through a 4 stage pipeline that filters out the reviews to find reviews describing a video game's vibes or structure, then asking a llm to generate these reviews into vectors, niche anchor tags and micro tags using non canonical names. all to "capture" niche tags that could never be found normally.

Then I used a 6 stage pipeline to group these non canonical names together (fast combat = speedy action combat)

From that I stored it all in PostgreSQL + Chroma db, made an app using React. and Shipped it all within a docker container inside a digital ocean droplet!

The result is a cool little steam game recommender that I can use to not just find similar games, but find games that share my favorite aspect of a game I like. A system that explains to me why I got the recommendations I got.

I find that this system makes searching for games more "fun" now I can see why I like balatro. I like it because of the card synergies not so much for its rogue-like nature.

I also find that this helps find new underrated games, and beats the trap that Collaborative Filtering algorithms that get into where it "feels" like you get recommended the same things.

find your next favorite game! : https://nextsteamgame.com/

pull a PR!: [**https://github.com/BakedSoups/NextSteamGame\*\*\](https://github.com/BakedSoups/NextSteamGame)

( I actually made some git issues myself for problems I can't fix)

if anyone has any criticism I would love to hear it! this is probably my favorite passion project.

Hope this website helps people find new games! Also I have a advance mode for people that don't mind messing with sliders and weird data terms.

I Just made a sequel to my Steam Game recommender website!

Last year I made a post about my steam reccomender The last one was great and served its purpose of showing many people new games, But this new version is much more functional!

I love making recommendation systems that tell the user WHY they got the recommendation.

During a steam sale event, I always find myself trying to look for new video games to play. If I wanted to find a new game I would try to whittle it down by using steam tags, but the steam tag system is very broad "action". could apply to many many games.

That got me thinking, what aspects do I like about my favorite games?

Well I like Persona 4 because of the city vibes and jazz fusion,

Spore because of the unique character creation and whimsical theme.

Balatro for its unique deck building synergies.

What if I could capture unique tags that identify a game that aren't just "action" and put them into vectors to show the (focus) of a game

 For example I could break persona 4 into something like

Gameplay Focus vector:
 Day cycle 20%
 Dungeon crawling 20%
 Social sim 20%

Tags:
Music: jazz fusion
Vibe: Small rural town

I find that this system makes searching for games more "fun" now I can see why I like balatro. I like it because of the card synergies not so much for its rogue-like nature.

I also find that this helps find new underrated games, and beats the trap that Collaborative Filtering algorithms that get into where it "feels" like you get recommended the same things.

find your next favorite game! : https://nextsteamgame.com/ pull a PR!: https://github.com/BakedSoups/NextSteamGame

( I actually made some git issues myself for problems I can't fix)

if anyone has any criticism I would love to hear it! this is probably my favorite passion project.

Hope this website helps people find new games! Also I have a advance mode for people that don't mind messing with sliders and weird data terms.

I love making recommendation systems that tell the user WHY they got the recommendation.

During a steam sale event, I always find myself trying to look for new video games to play. If I wanted to find a new game I would try to whittle it down by using steam tags, but the steam tag system is very broad "action". could apply to many many games.

That got me thinking, what aspects do I like about my favorite games?
Well I like Persona 4 because of the city vibes and jazz fusion,
I like Spore because of the unique character creation and whimsical theme.
and I like Balatro for its unique deck building synergies.

What if I could capture unique tags that identify a game that aren't just "action" and put them into vectors to show the (focus) of a game

 For example I could break persona 4 into something like

Gameplay Focus vector:
 - Day cycle 20%
 - Dungeon crawling 20%
 - Social sim 20%

Tags:
- Music: jazz fusion
- Vibe: Small rural town

I achieved this by pulling 2k reviews for 80k steam games, running them through a 4 stage pipeline that filters out the reviews to find reviews describing a video game's vibes or structure, then asking a llm to generate these reviews into vectors, niche anchor tags and micro tags using non canonical names. to really "capture" niche tags that can't be captured normally.

Then I used a 6 stage pipeline to group these non canonical names together (fast combat = speedy action combat)

From that I stored it all in PostgreSQL + Chroma db, made an app using React. and Shipped it all within a docker container inside a digital ocean droplet!

The result is a cool little steam game recommender that I can use to not just find similar games, but find games that share my favorite aspect of a game I like. A system that explains to me why I got the recommendations I got.

I find that this system makes searching for games more "fun" now I can see why I like balatro. I like it because of the card synergies not so much for its rogue-like nature.

I also find that this helps find new underrated games, and beats the trap that Collaborative Filtering algorithms that get into where it "feels" like you get recommended the same things.

find your next favorite game! : https://nextsteamgame.com/
pull a PR!: https://github.com/BakedSoups/NextSteamGame
( I actually made some git issues myself for problems I can't fix)
if anyone has any criticism I would love to hear it! this is probably my favorite passion project.

Hope this website helps people find new games! Also I have a advance mode for people that don't mind messing with sliders and weird data terms.

I Just made a sequel to my Steam Game recommender website!

Last year I made a post about my steam recommender The last one was great but this one I'm glad I was able to make a product that hopefully helped people find their next game. After some developing I made a new one that is much more functional!

I love making recommendation systems that tell the user WHY they got the recommendation.

During a steam sale event, I always find myself trying to look for new video games to play. If I wanted to find a new game I would try to whittle it down by using steam tags, but the steam tag system is very broad "action". could apply to many many games.

That got me thinking, what aspects do I like about my favorite games?
Well I like Persona 4 because of the city vibes and jazz fusion,
I like Spore because of the unique character creation and whimsical theme.
and I like Balatro for its unique deck building synergies.

What if I could capture unique tags that identify a game that aren't just "action" and put them into vectors to show the (focus) of a game

 For example I could break persona 4 into something like

Game play Focus vector:
 - Day cycle 20%
 - Dungeon crawling 20%
 - Social sim 20%

Tags:
- Music: jazz fusion
- Vibe: Small rural town

I achieved this by pulling 2k reviews for 80k steam games, running them through a 4 stage pipeline that filters out the reviews to find reviews describing a video game's vibes or structure, then asking chatgpt to generate these reviews into vectors, niche anchor tags and micro tags using non canonical names.

Then I used a 6 stage pipeline to group these non canonical names together (fast combat = speedy action combat)

From that I stored it all in PostgreSQL + Chroma db, made an app using React. and Shipped it all within a docker container inside a digital ocean droplet!

The result is a cool little steam game recommender that I can use to not just find similar games, but find games that share my favorite aspect of a game I like. A system that explains to me why I got the recommendations I got.

I find that this system makes searching for games more "fun" now I can see why I like balatro. I like it because of the card synergies not so much for its rogue-like nature.

I also find that this helps find new underrated games, and beats the trap that Collaborative Filtering algorithms that get into where it "feels" like you get recommended the same things.

find your next favorite game! : https://nextsteamgame.com/
pull a PR!: https://github.com/BakedSoups/NextSteamGame
( I actually made some git issues myself for problems I can't fix)
if anyone has any criticism I would love to hear it! this is probably my favorite passion project.

Hope this website helps people find new games! Also I have a advance mode for people that don't mind messing with sliders and weird data terms.

I Just made a sequel to my Steam Game recommender website!

Last year I made a post about my steam reccomender The last one was great and served its purpose of showing many people new games, But this new version is much more functional!

I love making recommendation systems that tell the user WHY they got the recommendation.

During a steam sale event, I always find myself trying to look for new video games to play. If I wanted to find a new game I would try to whittle it down by using steam tags, but the steam tag system is very broad "action". could apply to many many games.

That got me thinking, what aspects do I like about my favorite games?
Well I like Persona 4 because of the city vibes and jazz fusion,
I like Spore because of the unique character creation and whimsical theme.
and I like Balatro for its unique deck building synergies.

What if I could capture unique tags that identify a game that aren't just "action" and put them into vectors to show the (focus) of a game

 For example I could break persona 4 into something like

Gameplay Focus vector:
 - Day cycle 20%
 - Dungeon crawling 20%
 - Social sim 20%

Tags:
- Music: jazz fusion
- Vibe: Small rural town

I achieved this by pulling 2k reviews for 80k steam games, running them through a 4 stage pipeline that filters out the reviews to find reviews describing a video game's vibes or structure, then asking chatgpt to generate these reviews into vectors, niche anchor tags and micro tags using non canonical names.

Then I used a 6 stage pipeline to group these non canonical names together (fast combat = speedy action combat)

From that I stored it all in PostgreSQL + Chroma db, made an app using React. and Shipped it all within a docker container inside a digital ocean droplet!

The result is a cool little steam game recommender that I can use to not just find similar games, but find games that share my favorite aspect of a game I like. A system that explains to me why I got the recommendations I got.

I find that this system makes searching for games more "fun" now I can see why I like balatro. I like it because of the card synergies not so much for its rogue-like nature.

I also find that this helps find new underrated games, and beats the trap that Collaborative Filtering algorithms that get into where it "feels" like you get recommended the same things.

find your next favorite game! : https://nextsteamgame.com/
pull a PR!: https://github.com/BakedSoups/NextSteamGame
( I actually made some git issues myself for problems I can't fix)
if anyone has any criticism I would love to hear it! this is probably my favorite passion project.

Hope this website helps people find new games! Also I have a advance mode for people that don't mind messing with sliders and weird data terms.