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16201
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VR IoT Controller by @ampoi | Stardance
|
ampoi
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0
|
0.0
|
0
|
[]
|
[]
|
Title: VR IoT Controller by @ampoi | Stardance
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1793321295.230537
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1793926095.230537
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16202
|
My very own WebOS by @Rithy | Stardance
|
Rithy
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0
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0.0
|
0
|
[]
|
[]
|
Title: My very own WebOS by @Rithy | Stardance
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1793418815.6421404
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1794023615.6421404
|
|
16203
|
My very own WebOS by @HemanthReddy | Stardance
|
HemanthReddy
|
0
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0.0
|
0
|
[]
|
[]
|
Title: My very own WebOS by @HemanthReddy | Starda Title: My very own WebOS by @HemanthReddy | Stardance...
|
1793483476.4243402
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1794088276.4243402
|
|
16204
|
My first slack bot! by @xueyumi13 | Stardance
|
xueyumi13
|
0
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0.0
|
0
|
[]
|
[]
|
Title: My first slack bot! by @xueyumi13 | Stardan Title: My first slack bot! by @xueyumi13 | Stardance...
|
1793256962.5058331
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1793861762.5058331
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16205
|
My first slack bot! by @alix | Stardance
|
alix
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0
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0.0
|
0
|
[]
|
[]
|
Title: My first slack bot! by @alix | Stardance
|
1793361024.258719
|
1793965824.258719
|
|
16206
|
Racing Game by @yeshwanth_purusothaman | Stardance
|
yeshwanth_purusothaman
|
0
|
1.57
|
0
|
[]
|
[{"post_id": 10085, "ts": "202 [{"post_id": 10085, "ts": "2026-06-16T17:45:30Z", "hours": 1.5672, "text": "I am making a racing game on roblox studio. I found a racing game template and I am working on customizing it and adding more maps and maybe even a attacking system like mario kart"}]...
|
Title: Racing Game by @yeshwanth_purusothaman | St Title: Racing Game by @yeshwanth_purusothaman | Stardance
Devlogs:
I am making a racing game on roblox studio. I found a racing game template and I am working on customizing it and adding more maps and maybe even a attacking system like mario kart...
|
1793216834.4427254
|
1793821634.4427254
|
|
16207
|
Astro Date Bot by @Anas | Stardance
|
Anas
|
0
|
0.8
|
0
|
[]
|
[{"post_id": 9073, "ts": "2026 [{"post_id": 9073, "ts": "2026-06-15T04:54:51Z", "hours": 0.8011, "text": "Finally, Astro-Date is online. It’s a Slack bot that tells you what phenomenon was captured on the date you were born, or any other date. From a technical perspective, it’s just Python code that runs on Vercel. So, when you call the bot in Slack and send a date, it triggers the server, which sends it to the NASA API to get the photo and data, then sends it back to Slack. But from another perspective, it shows you how the universe celebrated your birthday. At first, I thought that this project wouldn’t take more than an hour and that I could make it, test it, and submit it in one day, but it wasn’t like that. I had some issues that were clear, so I fixed them, but some needed debugging, like the ‘respond before process’ issue. The code was sending ‘OK’ to Slack after it got the request; then, it would process, get the data from the NASA API, and send it to Slack. But what really happens is that the server would go down after sending ‘OK’ to Slack, so it never sent back the data. So, I changed it to ‘process before response,’ and a simple line, process_before_response=True, fixed everything."}]...
|
Title: Astro Date Bot by @Anas | Stardance
Devlog Title: Astro Date Bot by @Anas | Stardance
Devlogs:
Finally, Astro-Date is online. It’s a Slack bot that tells you what phenomenon was captured on the date you were born, or any other date. From a technical perspective, it’s just Python code that runs on Vercel. So, when you call the bot in Slack and send a date, it triggers the server, which sends it to the NASA API to get the photo and data, then sends it back to Slack. But from another perspective, it shows you how the universe celebrated your birthday. At first, I thought that this project wouldn’t take more than an hour and that I could make it, test it, and submit it in one day, but it wasn’t like that. I had some issues that were clear, so I fixed them, but some needed debugging, like the ‘respond before process’ issue. The code was sending ‘OK’ to Slack after it got the request; then, it would process, get the data from the NASA API, and send it to Slack. But what really happens is that the server would go down after sending ‘OK’ to Slack, so it never sent back the data. So, I changed it to ‘process before response,’ and a simple line, process_before_response=True, fixed everything....
|
1793494190.6041617
|
1794098990.6041617
|
|
16208
|
Untitled project by @joel-goh | Stardance
|
joel-goh
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: Untitled project by @joel-goh | Stardance
|
1793195645.0207233
|
1793800445.0207233
|
|
16209
|
One OS by @267gxl01 | Stardance
|
267gxl01
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: One OS by @267gxl01 | Stardance
|
1793530388.72393
|
1794135188.72393
|
|
16210
|
Simple Security Device by @Rabii | Stardance
|
Rabii
|
0
|
3.63
|
0
|
[]
|
[{"post_id": 9016, "ts": "2026 [{"post_id": 9016, "ts": "2026-06-15T01:11:50Z", "hours": 3.6308, "text": "Almost done with the tinkercad diagram of the security device using Arduino Uno R3"}]...
|
Title: Simple Security Device by @Rabii | Stardanc Title: Simple Security Device by @Rabii | Stardance
Devlogs:
Almost done with the tinkercad diagram of the security device using Arduino Uno R3...
|
1793181336.8842642
|
1793786136.8842642
|
|
16211
|
uwuOS by @blazfoxx | Stardance
|
blazfoxx
|
1
|
14.0
|
0
|
[{"ship_num": 1, "ts": "2026-0 [{"ship_num": 1, "ts": "2026-06-26T23:42:44Z", "hours": 14.0, "multiplier": null, "payout": 0, "blessing": "neutral", "text": "A fully working OS with a music player, video feed, drawing app, etc… Has a cool load in asking your name and remembers it when you login next time. It also has an idle screen!", "repo": "https://github.com/blazfxx/uwuOS", "demo": "https://uwu-os.vercel.app/"}]...
|
[{"post_id": 13965, "ts": "202 [{"post_id": 13965, "ts": "2026-06-22T22:31:10Z", "hours": 4.765, "text": "devlog 6 spotlight search: weather app: pulls real weather from open-meteo (free, no api key). asks for ur location, shows temp, condition, high low and wind. falls back to new york if u dont allow location backgrounds: u can paste an image url in settings now and itll become ur wallpaper mobile support, hopefully u can drag the dock icons to reorder them now music player :)"}, {"post_id": 7839, "ts": "2026-06-13T02:26:44Z", "hours": 2.2592, "text": "devlog 5 added: widgets idle screen added with a clock settings: the wallpaper picker. u can: pick a background color from swatches or go custom with a color picker change how long before the idle screen kicks in (30s to 5 min, or never) make the idle clock small medium or large toggle the date and the hint text on the idle screen give the idle screen its own background change ur name everything saves so its all still how u left it when u come back todo for next time: remember open windows and positions on refresh music player maybe idle screen wallpapers instead of just colors"}, {"post_id": 7426, "ts": "2026-06-12T11:31:10Z", "hours": 2.3217, "text": "devlog #4 big one today. built a whole shorts app and gave the OS a memory shorts: its like tiktok but in my OS lol. it pulls actual live videos from youtube channels i picked (carterpcs, yahya alhihi, miwu science) thru their rss feeds. hid all the ugly yt player controls so it just loops clean like a real short I also added GLOBAL likes. found a free counter api so when someone likes a video, everyone who visits the site sees that count go up. u also cant relike stuff bc ur device gets remembered. theres comments too with a slide up panel, and an add button where u can paste any shorts link and itll join ur feed the OS knows who u are now. first time u load in it asks “what should we call you” on its own lil screen after boot, and next time it goes “Welcome back, [name]… UwU~” which I rly like lol comments post as name#tag (like discord) so if two people are both named alex they dont get mixed up also: updated the welcome window with my actual pfp and about me (entrepreneur surviving school, saving for a mac mini yeah yeah) videos pause when u close the window now bugs ig yt embeds kept playing audio after closing the window. used postMessage pauseVideo my html edit broke the sketch icon and my desktop exploded lol todo for next time: remember open windows and positions on refresh custom wallpaper picker (still lol) maybe a music player"}, {"post_id": 7161, "ts": "2026-06-11T23:17:53Z", "hours": 1.0842, "text": "day 2 notes actually save now. used cookies so when u refresh ur notes are still there. made a dock windows are resizable now too todo for next time: more apps custom wallpaper picker"}, {"post_id": 6790, "ts": "2026-06-11T11:33:30Z", "hours": 3.8922, "text": "uwuOS dev log day 1 ok so today i started building my own OS in the browser… ig made a boot screen first with a little loading bar that fills up fake progress lol. got windows working too. you can drag them around by the header which was lowkey the hardest part. also made them stack properly so when you click one it comes to the front. apps so far: welcome screen (links to my github, blazfxx) notes app where u can type stuff and add it to a list about window that does basically nothing but it looks official went with a dark theme bugs ig close button wasnt clickable bc the drag code was eating the clicks. fixed it clock kept was not clocking todo for next time: save notes so they dont disappear on refresh maybe a dock at the bottom resizing windows Accidently ended up spending a lot of time redoing the UI like non stop…. yeah im horrible with UIs 🌹"}]...
|
Title: uwuOS by @blazfoxx | Stardance
Devlogs:
uw Title: uwuOS by @blazfoxx | Stardance
Devlogs:
uwuOS dev log day 1 ok so today i started building my own OS in the browser… ig made a boot screen first with a little loading bar that fills up fake progress lol. got windows working too. you can drag them around by the header which was lowkey the hardest part. also made them stack properly so when you click one it comes to the front. apps so far: welcome screen (links to my github, blazfxx) notes app where u can type stuff and add it to a list about window that does basically nothing but it looks official went with a dark theme bugs ig close button wasnt clickable bc the drag code was eating the clicks. fixed it clock kept was not clocking todo for next time: save notes so they dont disappear on refresh maybe a dock at the bottom resizing windows Accidently ended up spending a lot of time redoing the UI like non stop…. yeah im horrible with UIs 🌹
day 2 notes actually save now. used cookies so when u refresh ur notes are still there. made a dock windows are resizable now too todo for next time: more apps custom wallpaper picker
devlog #4 big one today. built a whole shorts app and gave the OS a memory shorts: its like tiktok but in my OS lol. it pulls actual live videos from youtube channels i picked (carterpcs, yahya alhihi, miwu science) thru their rss feeds. hid all the ugly yt player controls so it just loops clean like a real short I also added GLOBAL likes. found a free counter api so when someone likes a video, everyone who visits the site sees that count go up. u also cant relike stuff bc ur device gets remembered. theres comments too with a slide up panel, and an add button where u can paste any shorts link and itll join ur feed the OS knows who u are now. first time u load in it asks “what should we call you” on its own lil screen after boot, and next time it goes “Welcome back, [name]… UwU~” which I rly like lol comments post as name#tag (like discord) so if two people are both named alex they dont get mixed up also: updated the welcome window with my actual pfp and about me (entrepreneur surviving school, saving for a mac mini yeah yeah) videos pause when u close the window now bugs ig yt embeds kept playing audio after closing the window. used postMessage pauseVideo my html edit broke the sketch icon and my desktop exploded lol todo for next time: remember open windows and positions on refresh custom wallpaper picker (still lol) maybe a music player
devlog 5 added: widgets idle screen added with a clock settings: the wallpaper picker. u can: pick a background color from swatches or go custom with a color picker change how long before the idle screen kicks in (30s to 5 min, or never) make the idle clock small medium or large toggle the date and the hint text on the idle screen give the idle screen its own background change ur name everything saves so its all still how u left it when u come back todo for next time: remember open windows and positions on refresh music player maybe idle screen wallpapers instead of just colors
devlog 6 spotlight search: weather app: pulls real weather from open-meteo (free, no api key). asks for ur location, shows temp, condition, high low and wind. falls back to new york if u dont allow location backgrounds: u can paste an image url in settings now and itll become ur wallpaper mobile support, hopefully u can drag the dock icons to reorder them now music player :)
Ships:
A fully working OS with a music player, video feed, drawing app, etc… Has a cool load in asking your name and remembers it when you login next time. It also has an idle screen!...
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1793699539.48473
|
1793785939.48473
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16212
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Untitled project by @its_tobirama_169 | Stardance
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its_tobirama_169
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0
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0.0
|
0
|
[]
|
[]
|
Title: Untitled project by @its_tobirama_169 | Sta Title: Untitled project by @its_tobirama_169 | Stardance...
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1793533877.3893845
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1794138677.3893845
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16213
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Lumara - The Ultimate Research Partner by @saineel Lumara - The Ultimate Research Partner by @saineelg | Stardance...
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saineelg
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0
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1.93
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0
|
[]
|
[{"post_id": 29181, "ts": "202 [{"post_id": 29181, "ts": "2026-07-15T22:12:47Z", "hours": 0.9069, "text": "Setup authentication on frontend and details save to database."}, {"post_id": 23186, "ts": "2026-07-06T00:33:46Z", "hours": 1.0264, "text": "I completed the Phase 1 and 2 Supabase tables, as well as the Row Level Security(RLS). Supabase has also been integrated to the client frontend(via Vite), and I’m ready to move on to building a boilerplate login."}]...
|
Title: Lumara - The Ultimate Research Partner by @ Title: Lumara - The Ultimate Research Partner by @saineelg | Stardance
Devlogs:
I completed the Phase 1 and 2 Supabase tables, as well as the Row Level Security(RLS). Supabase has also been integrated to the client frontend(via Vite), and I’m ready to move on to building a boilerplate login.
Setup authentication on frontend and details save to database....
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1793386214.3076992
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1793991014.3076992
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16214
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My awesome macropad! by @yathinram | Stardance
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yathinram
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0
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0.0
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0
|
[]
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[]
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Title: My awesome macropad! by @yathinram | Starda Title: My awesome macropad! by @yathinram | Stardance...
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1793386236.5422843
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1793991036.5422843
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16215
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My first slack bot! by @gdiaz | Stardance
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gdiaz
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0
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0.0
|
0
|
[]
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[]
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Title: My first slack bot! by @gdiaz | Stardance
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1793159756.5712857
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1793764556.5712857
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16216
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My awesome macropad! by @garvitnxtdoor | Stardance
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garvitnxtdoor
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My awesome macropad! by @garvitnxtdoor | St Title: My awesome macropad! by @garvitnxtdoor | Stardance...
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1793346451.448313
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1793951251.448313
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16217
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Frictionless by @MuhammadHamzah | Stardance
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MuhammadHamzah
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0
|
0.0
|
0
|
[]
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[]
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Title: Frictionless by @MuhammadHamzah | Stardance
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1793206206.6061845
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1793811006.6061845
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16218
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flight controller with a rpi ai coprocessor by @pr flight controller with a rpi ai coprocessor by @pranet | Stardance...
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pranet
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0
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0.0
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0
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[]
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[]
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Title: flight controller with a rpi ai coprocessor Title: flight controller with a rpi ai coprocessor by @pranet | Stardance...
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1793346987.6467695
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1793951787.6467695
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16219
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Untitled by @garvitnxtdoor | Stardance
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garvitnxtdoor
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0
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0.0
|
0
|
[]
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[]
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Title: Untitled by @garvitnxtdoor | Stardance
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1793393582.9684324
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1793998382.9684324
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|
16220
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My awesome macropad! by @alexguyette42 | Stardance
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alexguyette42
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0
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0.0
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0
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[]
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[]
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Title: My awesome macropad! by @alexguyette42 | St Title: My awesome macropad! by @alexguyette42 | Stardance...
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1793451144.0130663
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1794055944.0130663
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16221
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My very own WebOS by @Gemma | Stardance
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Gemma
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0
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0.0
|
0
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[]
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[]
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Title: My very own WebOS by @Gemma | Stardance
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1793296322.6775074
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1793901122.6775074
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16222
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MindMatch by @eldontherouge | Stardance
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eldontherouge
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0
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0.0
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0
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[]
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[]
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Title: MindMatch by @eldontherouge | Stardance
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1793393239.8401546
|
1793998039.8401546
|
|
16223
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Frictionless by @emilie | Stardance
|
emilie
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0
|
0.0
|
0
|
[]
|
[]
|
Title: Frictionless by @emilie | Stardance
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1793368334.2574096
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1793973134.2574096
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16224
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Untitled project by @dylancruz591 | Stardance
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dylancruz591
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0
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0.0
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0
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[]
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[]
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Title: Untitled project by @dylancruz591 | Stardan Title: Untitled project by @dylancruz591 | Stardance...
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1793537545.146489
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1794142345.146489
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16225
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Frictionless by @orenkravets | Stardance
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orenkravets
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0
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0.0
|
0
|
[]
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[]
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Title: Frictionless by @orenkravets | Stardance
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1793566475.016965
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1794171275.016965
|
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16226
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Custom Decals for Pickleball Padels by @trevordots Custom Decals for Pickleball Padels by @trevordotson92 | Stardance...
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trevordotson92
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0
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0.0
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0
|
[]
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[]
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Title: Custom Decals for Pickleball Padels by @tre Title: Custom Decals for Pickleball Padels by @trevordotson92 | Stardance...
|
1793166734.3973837
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1793771534.3973837
|
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16227
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My very own WebOS by @Zoee | Stardance
|
Zoee
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My very own WebOS by @Zoee | Stardance
|
1793188397.4556303
|
1793793197.4556303
|
|
16228
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Health App by @trevordotson92 | Stardance
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trevordotson92
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0
|
0.0
|
0
|
[]
|
[]
|
Title: Health App by @trevordotson92 | Stardance
|
1793458370.6592512
|
1794063170.6592512
|
|
16229
|
3D Printed Lego Wall Mounts by @trevordotson92 | S 3D Printed Lego Wall Mounts by @trevordotson92 | Stardance...
|
trevordotson92
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: 3D Printed Lego Wall Mounts by @trevordotso Title: 3D Printed Lego Wall Mounts by @trevordotson92 | Stardance...
|
1793551670.2036536
|
1794156470.2036536
|
|
16230
|
3D Football Stadium by @trevordotson92 | Stardance
|
trevordotson92
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: 3D Football Stadium by @trevordotson92 | St Title: 3D Football Stadium by @trevordotson92 | Stardance...
|
1793181054.673668
|
1793785854.673668
|
|
16231
|
My very own WebOS by @Molly_Aurelia | Stardance
|
Molly_Aurelia
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My very own WebOS by @Molly_Aurelia | Stard Title: My very own WebOS by @Molly_Aurelia | Stardance...
|
1793544951.0230386
|
1794149751.0230386
|
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16232
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RRSAT by @rehaan_raj_robotics_2008 | Stardance
|
rehaan_raj_robotics_2008
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: RRSAT by @rehaan_raj_robotics_2008 | Starda Title: RRSAT by @rehaan_raj_robotics_2008 | Stardance...
|
1793299749.9522345
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1793904549.9522345
|
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16233
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See-TreePiO by @Aryana | Stardance
|
Aryana
|
1
|
142.0
|
0
|
[{"ship_num": 1, "ts": "2026-0 [{"ship_num": 1, "ts": "2026-09-18T06:32:43Z", "hours": 142.0, "multiplier": null, "payout": 0, "blessing": "neutral", "text": "I made a project that can detect tree/plant species and temperature to determine whether it is dead or not. It displays the result on an LCD in the back, and will start the process of scanning a tree/plant once a button is pressed. The wiring is enclosed in a 3D printed prototype. The most difficult thing I did in this project was likely trying to fit it all in the enclosure. There were a lot of wires here and there, and the button was also quite difficult to fit in, as I needed to solder it on. However, this project was really fun to do, as it helped me gain knowledge about the environment around me, and I got to practice my software and hardware skills. I am really proud of myself, as I completed this project from start to finish, and didn’t give up. To test my project, all you have to do is plug in the battery pack, wait for a minute or so, point the prototype at the tree you want to scan, press the button, and wait for the results. Sometimes, it will take a little longer to get the results, resulting from a difficulty figuring out either the temperature or species of that particular plant/tree.", "repo": "https://github.com/dawinner395/tree-health-scanner", "demo": ""}]...
|
[{"post_id": 69981, "ts": "202 [{"post_id": 69981, "ts": "2026-09-18T05:40:56Z", "hours": 0.35, "text": "I completed the prototype by combining the enclosure with the prototype. I encased the sensors and the LCD. The prototype is finally complete!"}, {"post_id": 69965, "ts": "2026-09-18T04:18:13Z", "hours": 1.5167, "text": "I combined the parts in the bottom half of the prototype, and fit it in the encolsure."}, {"post_id": 67575, "ts": "2026-09-13T18:30:05Z", "hours": 1.3167, "text": "I researched the different types of prevalent trees in California and their temperatures during different seasons. I plan to add this to my database, as I hadn’t previously done this. I used many websites, but Our City Forest was the most helpful."}, {"post_id": 66887, "ts": "2026-09-12T20:45:05Z", "hours": 0.9333, "text": "I fixed the issues that the prototype was facing. I also added a button. The main thing I did was make the prototype able to function without the computer nearby, which can make it so that anyone in the world can use it with a battery pack."}, {"post_id": 64447, "ts": "2026-09-08T20:39:44Z", "hours": 12.0014, "text": "I 3D printed the top half of the housing for the prototype. Next, I will print the bottom half."}, {"post_id": 48288, "ts": "2026-08-16T16:26:44Z", "hours": 10.6833, "text": "I build a CAD of the structure that will house the prototype. I left holes for all the different parts, and will print it later today."}, {"post_id": 36195, "ts": "2026-07-28T03:59:06Z", "hours": 1.7667, "text": "I connected the database to the Raspberry Pi, and tested it out using a house plant. It worked really well, and the next step is to test it outside with a real tree."}, {"post_id": 35375, "ts": "2026-07-26T18:41:48Z", "hours": 1.8333, "text": "I researched about how to use the specific PlantNet API database with the Raspberry Pi and hook that database to a Machine Learning algorthim. I am finally ready to actually make the Machine Learning algorithm and get my hands dirty now that I have the information I need to continue on with the project."}, {"post_id": 32105, "ts": "2026-07-21T01:22:30Z", "hours": 0.3333, "text": "I researched more about the tree datasets, and I found one that has plenty of accurate information: PlantNet API."}, {"post_id": 31560, "ts": "2026-07-20T02:06:06Z", "hours": 0.6333, "text": "I researched the different datasets I can use for the project, as well as the amount of tree data each have in them. I still need to do some more research, but as of now, creating a dataset is the best option if I want to get a wide, accurate range of data. I haven’t found any datasets that match the amount of data I can collect by myself in my neighborhood."}, {"post_id": 29878, "ts": "2026-07-17T01:24:55Z", "hours": 0.6, "text": "I tested the camera and battery pack that came. They both worked really well. Now, I can start inputting the ML algrothm."}, {"post_id": 28665, "ts": "2026-07-15T01:45:42Z", "hours": 3.7747, "text": "I fiinshed the Machine Learning book."}, {"post_id": 28079, "ts": "2026-07-14T03:24:59Z", "hours": 1.0333, "text": "I read Chapters 14 and 15 of the Machine Learning book. The parts will come tomorrow."}, {"post_id": 27961, "ts": "2026-07-13T22:13:23Z", "hours": 0.6833, "text": "I read Chapter 13 of the Machine Learning book."}, {"post_id": 27279, "ts": "2026-07-12T18:33:41Z", "hours": 1.65, "text": "I planned the way I am going to connect the ArduCam to the Raspberry Pi with the thermal sensor. I realized I might have to solder some wires together so that more than one wire can fit on one pin on the Pi. I planned the way the wires are going to connect to the Pi and how I am going to integrate ML into the Pi using the camera and thermal sensor."}, {"post_id": 26242, "ts": "2026-07-10T22:09:55Z", "hours": 1.7667, "text": "I researched more about using the ArduCam with the Raspberry Pi 5. I found troubleshooting techniques and what I need to do after I get the camera (the setup)."}, {"post_id": 25652, "ts": "2026-07-10T02:04:39Z", "hours": 1.6847, "text": "I read Chapters 11 and 12 of the Machine Learning book."}, {"post_id": 25543, "ts": "2026-07-09T21:16:12Z", "hours": 1.5167, "text": "I watched a video about using the ArduCam with the Raspberry Pi 5."}, {"post_id": 25481, "ts": "2026-07-09T19:38:18Z", "hours": 1.2369, "text": "I read Chapters 9 and 10 of the Machine Learning book."}, {"post_id": 25077, "ts": "2026-07-09T01:30:27Z", "hours": 0.4, "text": "I read Chapter 8 of the Machine Learning book. I will stop watching the AI on the Edge videos because they are not accessible on YouTube after Lesson 31. The parts will come day after tomorrow, so I plan to finish reading this book by then."}, {"post_id": 24895, "ts": "2026-07-08T19:02:48Z", "hours": 2.1167, "text": "I watched Lesson 31 of the AI on the Edge videos."}, {"post_id": 24517, "ts": "2026-07-08T03:43:40Z", "hours": 2.1914, "text": "I watched Lesson 30 of the AI on the Edge videos."}, {"post_id": 24471, "ts": "2026-07-08T01:31:47Z", "hours": 1.9167, "text": "I watched Lesson 29 of the AI on the Edge videos."}, {"post_id": 24422, "ts": "2026-07-07T23:28:04Z", "hours": 3.2, "text": "I watched Lesson 28 of the AI on the Edge videos."}, {"post_id": 24308, "ts": "2026-07-07T19:58:17Z", "hours": 2.1003, "text": "I watched Lesson 27 of the AI on the Edge videos."}, {"post_id": 23795, "ts": "2026-07-06T23:03:10Z", "hours": 2.7667, "text": "I watched Lesson 26 of the AI on the Edge videos."}, {"post_id": 23280, "ts": "2026-07-06T04:18:00Z", "hours": 2.3625, "text": "I watched Lesson 25 of the AI on the Edge videos."}, {"post_id": 23236, "ts": "2026-07-06T01:55:59Z", "hours": 3.1667, "text": "I watched Lesson 24 of the AI on the Edge videos."}, {"post_id": 23150, "ts": "2026-07-05T22:46:20Z", "hours": 0.9167, "text": "I read chapters 6 and 7 of the Machine Learning book. I’m just reading and watching the videos until the parts arrive in three or four days. Then, I will work on the technical aspect of the prototype. I have some free time now, so I thought I would learn about Machine Learning and using the Raspberry Pi in the time I have instead of wasting it."}, {"post_id": 22461, "ts": "2026-07-04T22:37:08Z", "hours": 2.0494, "text": "I watched Lesson 23 of the AI on the Edge videos."}, {"post_id": 22380, "ts": "2026-07-04T20:34:29Z", "hours": 3.2, "text": "I watched Lesson 22 of the AI on the Edge videos."}, {"post_id": 21779, "ts": "2026-07-03T21:41:25Z", "hours": 1.5, "text": "I watched Lesson 21 of the AI on the Edge videos."}, {"post_id": 21716, "ts": "2026-07-03T20:11:28Z", "hours": 1.9, "text": "I watched Lesson 20 of the AI on the Edge videos."}, {"post_id": 21285, "ts": "2026-07-03T04:02:15Z", "hours": 2.8383, "text": "I watched Lesson 19 of the AI on the Edge videos."}, {"post_id": 21215, "ts": "2026-07-03T01:11:38Z", "hours": 2.3, "text": "I watched Lesson 18 of the AI on the Edge videos."}, {"post_id": 21101, "ts": "2026-07-02T21:55:09Z", "hours": 3.7, "text": "I watched Lesson 17 of the AI on the Edge videos."}, {"post_id": 20495, "ts": "2026-07-02T03:07:09Z", "hours": 1.9072, "text": "I watched Lesson 16 of the AI on the Edge videos."}, {"post_id": 20442, "ts": "2026-07-02T01:11:45Z", "hours": 2.3333, "text": "I watched Lesson 15 of the AI on the Edge videos."}, {"post_id": 20318, "ts": "2026-07-01T21:43:50Z", "hours": 3.6667, "text": "I watched Lesson 14 of the AI on the Edge videos."}, {"post_id": 19770, "ts": "2026-07-01T04:50:30Z", "hours": 2.7167, "text": "I watched Lesson 13 of the AI on the Edge videos."}, {"post_id": 19679, "ts": "2026-07-01T02:07:59Z", "hours": 1.4167, "text": "I watched Lesson 12 of the AI on the Edge videos."}, {"post_id": 19634, "ts": "2026-07-01T00:35:15Z", "hours": 1.6167, "text": "I read chapters 5 and 6 of the Machine Learning book."}, {"post_id": 19586, "ts": "2026-06-30T22:40:26Z", "hours": 1.6167, "text": "I watched Lesson 11 of the AI on the Edge videos."}, {"post_id": 19523, "ts": "2026-06-30T21:02:23Z", "hours": 1.0494, "text": "I watched Lesson 10 of the AI on the Edge videos."}, {"post_id": 19468, "ts": "2026-06-30T19:58:25Z", "hours": 2.1, "text": "I watched Lesson 9 of the AI on the Edge videos."}, {"post_id": 19018, "ts": "2026-06-30T05:48:49Z", "hours": 2.7, "text": "I watched Lesson 8 of the AI on the Edge videos."}, {"post_id": 18914, "ts": "2026-06-30T03:06:50Z", "hours": 0.75, "text": "I finished reading Chapter 4 of the Machine Learning book. I am also taking more notes on the book now."}, {"post_id": 18878, "ts": "2026-06-30T02:18:01Z", "hours": 0.9, "text": "I sent an email to Ms. Mint (the one I met previosuly) asking if we could meet again for some feedback on my project idea. I also converted my paper plan onto a document and calendar so I can keep track of any deadlines I might have for the project."}, {"post_id": 18760, "ts": "2026-06-29T23:18:51Z", "hours": 4.0, "text": "I watched Lesson 7 of the AI on the Edge videos."}, {"post_id": 17910, "ts": "2026-06-28T20:55:18Z", "hours": 1.2, "text": "I watched Lesson 6 of the AI on the Edge videos."}, {"post_id": 17840, "ts": "2026-06-28T19:40:59Z", "hours": 1.0667, "text": "I watched Lesson 5 of AI on the Edge. In the previous DevLog about the AI on the Edge videos, I actually watched Lesson 4, not Lesson 3."}, {"post_id": 17786, "ts": "2026-06-28T18:35:28Z", "hours": 1.7667, "text": "I made a plan on how to spend my time during this summer, as a lot of time will be dedicated to the ML aspect of my project."}, {"post_id": 17345, "ts": "2026-06-28T02:58:04Z", "hours": 2.0833, "text": "I watched Lesson 3 of the AI on the Edge videos."}, {"post_id": 17217, "ts": "2026-06-27T22:37:39Z", "hours": 3.05, "text": "I finished the 3rd lesson of the AI on the Edge."}, {"post_id": 17106, "ts": "2026-06-27T19:33:57Z", "hours": 1.5667, "text": "I watched the Lesson 2 of the AI on the Edge Raspberry Pi videos."}, {"post_id": 16603, "ts": "2026-06-27T00:34:57Z", "hours": 1.5, "text": "I watched a video about using the Raspberry Pi 5 with ML. Though the Machine Learning book is helpful (and I will keep reading it), I felt that it was not enough to help me with this specific project."}, {"post_id": 16000, "ts": "2026-06-25T23:50:46Z", "hours": 0.9333, "text": "I researched exactly what camera and battery pack to buy (to power the Raspberry Pi). I added them to the cart and will buy it as soon as possible to further develop the prototype. I can’t wait to see how it works with the thermal sensor!"}, {"post_id": 15917, "ts": "2026-06-25T21:22:07Z", "hours": 1.0667, "text": "I just researched about how to combine a thermal sensor with a camera. I found a helpful video and took notes on that video. I also found the specific type of camera I can use, which is a standard Raspberry Pi Camera Module, as well as the connector."}, {"post_id": 15877, "ts": "2026-06-25T20:08:12Z", "hours": 0.6167, "text": "Instead of physically testing the prototypes out (because my dog accidentally stepped on the ultrasonic sensor prototype), I just researched a bit more about the two sensors. I researched the benefits and downsides of each sensor, as well as how accurae they are at specifcally detecting dead trees. I found that people have used ultrasonic sensors before to detect dead trees, but no one has used thermal sensors in the way that I am before to do so. Therefore, I will stick with using a thermal sensor for this prototype. I will have to do more research about the actual pairing between a thermal sensor, a camera, and ML, but the book will help me a lot in the coding of the ML portion."}, {"post_id": 15231, "ts": "2026-06-24T18:49:08Z", "hours": 0.5333, "text": "I compared the 2 prototypes to see which sensor is more accurate (with research, not actual testing). I found that the thermal sensor is more accurate, especially when paired with a camera and an ML algorithm. However, the pairing will be new to me because of the Raspberry Pi. I am more used to working with Aruinos, but through research and practice, I will be able to implement ML. Also, the book I am reading is going to help increase my knowledge on ML. In a future DevLog, I might just test this research just to be sure the thermal sensor is the right choice."}, {"post_id": 14069, "ts": "2026-06-23T01:29:08Z", "hours": 2.3667, "text": "I created the new prototype using the ultrasonic sensor. I coded it, and it successfully functions properly. However, when I tested it with a tree, it didn’t detect it correctly (it said it was dead, not healthy). When I tested it a few more times, it showed accurate readings. I still need to compare the two prototypes together."}, {"post_id": 13982, "ts": "2026-06-22T23:01:27Z", "hours": 0.7219, "text": "I started taking notes on how to work with a thermal sensor, a camera, and ML. I found that it might be hard to integrate all 3 of these things, so I started looking in a different path. I found that I could use ultrasonic distance sensors to detect the unique ultrasonic waves emitted from trees. Therefore, I will start creating a prototype using the ultrasonic sensor and compare the 2 prototypes together."}, {"post_id": 13915, "ts": "2026-06-22T20:24:35Z", "hours": 1.6833, "text": "I copied the code from Command Prompt to GitHub so I have a way to store it somewhere safe. I also double-checkked that the code was correct, and fixed any errors."}, {"post_id": 13555, "ts": "2026-06-22T06:01:12Z", "hours": 3.0333, "text": "I interviewed a tree expert, Ms. Mint, who works at a local farm. She brought up some interesting points, and helped me narrow down what sensors I want to use for my project. She said that using a thermal sensor would be a good idea becuase of its accuracy and easy use. However, when I interviewed her, I had to record the video on my phone instead of on this Hackatime. Therefore, I screen recorded the video and that is the video shown here on this Devlog. I was also with a few other people interested in Ms. Mint’s work, so it wasn’t a one-on-one session, more of a broader overview with a lot of Q-and-A. Overall, this experience was really nice because I got to meet a curator of a nature center, feel the nature all around me, see the difference between dead and healthy trees, and get feedback on my idea."}, {"post_id": 13494, "ts": "2026-06-22T02:51:43Z", "hours": 1.1878, "text": "I wired the thermal sensor, the LCD, the Raspberry Pi, and the LCD backpack. At first, the wiring was not working, so I switched to planning the wiring on paper. Once I finished that, I used a soldering iron to combine two wires to fit in one slot. This is the completed wiring."}, {"post_id": 13467, "ts": "2026-06-22T01:34:43Z", "hours": 0.4833, "text": "I continued reading the Machine Learning book and got to Chapter 4. The book is really interesting and I already feel like I have some knowledge (not a lot, but some) about ML."}, {"post_id": 13450, "ts": "2026-06-22T01:01:41Z", "hours": 2.55, "text": "I continued reading the Machine Learning book and got to Chapter 4. The book is really interesting and I already feel like I have some knowledge (not a lot, but some) about ML."}, {"post_id": 13342, "ts": "2026-06-21T21:15:14Z", "hours": 0.4833, "text": "I have started reading a book about Machine Learning, its history, and how to train/use it to the best of my ability. I have been taking some notes and answering some questions presented in the book. I have gotten through the first 2 chapters so far and plan to finish the book in the next few weeks. This book will really help me use ML in my project, as well as use it in other projects that I may decide to pursue."}, {"post_id": 12793, "ts": "2026-06-20T22:25:53Z", "hours": 0.8169, "text": "I created the code for the basic part of the prototype. The code makes the prototype able to detect the thermal signature of a Redwood Tree and determine whether it is dead or healthy. In the future, I wil re-make the code to incorporate a camera and a Machine Learning algorithm that can be used for more accurate and broader readings, not just for Redwood trees."}, {"post_id": 12756, "ts": "2026-06-20T21:19:55Z", "hours": 1.4667, "text": "Researched about the different techniques and sensors I could use for this project. I found that using a thermal sensor with Machine Learning algorithms and a camera would be the best for my project."}, {"post_id": 12708, "ts": "2026-06-20T19:07:36Z", "hours": 0.6164, "text": "Testing and editing the code and the wiring of the prototype"}]...
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Title: See-TreePiO by @Aryana | Stardance
Devlogs Title: See-TreePiO by @Aryana | Stardance
Devlogs:
Testing and editing the code and the wiring of the prototype
Researched about the different techniques and sensors I could use for this project. I found that using a thermal sensor with Machine Learning algorithms and a camera would be the best for my project.
I created the code for the basic part of the prototype. The code makes the prototype able to detect the thermal signature of a Redwood Tree and determine whether it is dead or healthy. In the future, I wil re-make the code to incorporate a camera and a Machine Learning algorithm that can be used for more accurate and broader readings, not just for Redwood trees.
I have started reading a book about Machine Learning, its history, and how to train/use it to the best of my ability. I have been taking some notes and answering some questions presented in the book. I have gotten through the first 2 chapters so far and plan to finish the book in the next few weeks. This book will really help me use ML in my project, as well as use it in other projects that I may decide to pursue.
I continued reading the Machine Learning book and got to Chapter 4. The book is really interesting and I already feel like I have some knowledge (not a lot, but some) about ML.
I continued reading the Machine Learning book and got to Chapter 4. The book is really interesting and I already feel like I have some knowledge (not a lot, but some) about ML.
I wired the thermal sensor, the LCD, the Raspberry Pi, and the LCD backpack. At first, the wiring was not working, so I switched to planning the wiring on paper. Once I finished that, I used a soldering iron to combine two wires to fit in one slot. This is the completed wiring.
I interviewed a tree expert, Ms. Mint, who works at a local farm. She brought up some interesting points, and helped me narrow down what sensors I want to use for my project. She said that using a thermal sensor would be a good idea becuase of its accuracy and easy use. However, when I interviewed her, I had to record the video on my phone instead of on this Hackatime. Therefore, I screen recorded the video and that is the video shown here on this Devlog. I was also with a few other people interested in Ms. Mint’s work, so it wasn’t a one-on-one session, more of a broader overview with a lot of Q-and-A. Overall, this experience was really nice because I got to meet a curator of a nature center, feel the nature all around me, see the difference between dead and healthy trees, and get feedback on my idea.
I copied the code from Command Prompt to GitHub so I have a way to store it somewhere safe. I also double-checkked that the code was correct, and fixed any errors.
I started taking notes on how to work with a thermal sensor, a camera, and ML. I found that it might be hard to integrate all 3 of these things, so I started looking in a different path. I found that I could use ultrasonic distance sensors to detect the unique ultrasonic waves emitted from trees. Therefore, I will start creating a prototype using the ultrasonic sensor and compare the 2 prototypes together.
I created the new prototype using the ultrasonic sensor. I coded it, and it successfully functions properly. However, when I tested it with a tree, it didn’t detect it correctly (it said it was dead, not healthy). When I tested it a few more times, it showed accurate readings. I still need to compare the two prototypes together.
I compared the 2 prototypes to see which sensor is more accurate (with research, not actual testing). I found that the thermal sensor is more accurate, especially when paired with a camera and an ML algorithm. However, the pairing will be new to me because of the Raspberry Pi. I am more used to working with Aruinos, but through research and practice, I will be able to implement ML. Also, the book I am reading is going to help increase my knowledge on ML. In a future DevLog, I might just test this research just to be sure the thermal sensor is the right choice.
Instead of physically testing the prototypes out (because my dog accidentally stepped on the ultrasonic sensor prototype), I just researched a bit more about the two sensors. I researched the benefits and downsides of each sensor, as well as how accurae they are at specifcally detecting dead trees. I found that people have used ultrasonic sensors before to detect dead trees, but no one has used thermal sensors in the way that I am before to do so. Therefore, I will stick with using a thermal sensor for this prototype. I will have to do more research about the actual pairing between a thermal sensor, a camera, and ML, but the book will help me a lot in the coding of the ML portion.
I just researched about how to combine a thermal sensor with a camera. I found a helpful video and took notes on that video. I also found the specific type of camera I can use, which is a standard Raspberry Pi Camera Module, as well as the connector.
I researched exactly what camera and battery pack to buy (to power the Raspberry Pi). I added them to the cart and will buy it as soon as possible to further develop the prototype. I can’t wait to see how it works with the thermal sensor!
I watched a video about using the Raspberry Pi 5 with ML. Though the Machine Learning book is helpful (and I will keep reading it), I felt that it was not enough to help me with this specific project.
I watched the Lesson 2 of the AI on the Edge Raspberry Pi videos.
I finished the 3rd lesson of the AI on the Edge.
I watched Lesson 3 of the AI on the Edge videos.
I made a plan on how to spend my time during this summer, as a lot of time will be dedicated to the ML aspect of my project.
I watched Lesson 5 of AI on the Edge. In the previous DevLog about the AI on the Edge videos, I actually watched Lesson 4, not Lesson 3.
I watched Lesson 6 of the AI on the Edge videos.
I watched Lesson 7 of the AI on the Edge videos.
I sent an email to Ms. Mint (the one I met previosuly) asking if we could meet again for some feedback on my project idea. I also converted my paper plan onto a document and calendar so I can keep track of any deadlines I might have for the project.
I finished reading Chapter 4 of the Machine Learning book. I am also taking more notes on the book now.
I watched Lesson 8 of the AI on the Edge videos.
I watched Lesson 9 of the AI on the Edge videos.
I watched Lesson 10 of the AI on the Edge videos.
I watched Lesson 11 of the AI on the Edge videos.
I read chapters 5 and 6 of the Machine Learning book.
I watched Lesson 12 of the AI on the Edge videos.
I watched Lesson 13 of the AI on the Edge videos.
I watched Lesson 14 of the AI on the Edge videos.
I watched Lesson 15 of the AI on the Edge videos.
I watched Lesson 16 of the AI on the Edge videos.
I watched Lesson 17 of the AI on the Edge videos.
I watched Lesson 18 of the AI on the Edge videos.
I watched Lesson 19 of the AI on the Edge videos.
I watched Lesson 20 of the AI on the Edge videos.
I watched Lesson 21 of the AI on the Edge videos.
I watched Lesson 22 of the AI on the Edge videos.
I watched Lesson 23 of the AI on the Edge videos.
I read chapters 6 and 7 of the Machine Learning book. I’m just reading and watching the videos until the parts arrive in three or four days. Then, I will work on the technical aspect of the prototype. I have some free time now, so I thought I would learn about Machine Learning and using the Raspberry Pi in the time I have instead of wasting it.
I watched Lesson 24 of the AI on the Edge videos.
I watched Lesson 25 of the AI on the Edge videos.
I watched Lesson 26 of the AI on the Edge videos.
I watched Lesson 27 of the AI on the Edge videos.
I watched Lesson 28 of the AI on the Edge videos.
I watched Lesson 29 of the AI on the Edge videos.
I watched Lesson 30 of the AI on the Edge videos.
I watched Lesson 31 of the AI on the Edge videos.
I read Chapter 8 of the Machine Learning book. I will stop watching the AI on the Edge videos because they are not accessible on YouTube after Lesson 31. The parts will come day after tomorrow, so I plan to finish reading this book by then.
I read Chapters 9 and 10 of the Machine Learning book.
I watched a video about using the ArduCam with the Raspberry Pi 5.
I read Chapters 11 and 12 of the Machine Learning book.
I researched more about using the ArduCam with the Raspberry Pi 5. I found troubleshooting techniques and what I need to do after I get the camera (the setup).
I planned the way I am going to connect the ArduCam to the Raspberry Pi with the thermal sensor. I realized I might have to solder some wires together so that more than one wire can fit on one pin on the Pi. I planned the way the wires are going to connect to the Pi and how I am going to integrate ML into the Pi using the camera and thermal sensor.
I read Chapter 13 of the Machine Learning book.
I read Chapters 14 and 15 of the Machine Learning book. The parts will come tomorrow.
I fiinshed the Machine Learning book.
I tested the camera and battery pack that came. They both worked really well. Now, I can start inputting the ML algrothm.
I researched the different datasets I can use for the project, as well as the amount of tree data each have in them. I still need to do some more research, but as of now, creating a dataset is the best option if I want to get a wide, accurate range of data. I haven’t found any datasets that match the amount of data I can collect by myself in my neighborhood.
I researched more about the tree datasets, and I found one that has plenty of accurate information: PlantNet API.
I researched about how to use the specific PlantNet API database with the Raspberry Pi and hook that database to a Machine Learning algorthim. I am finally ready to actually make the Machine Learning algorithm and get my hands dirty now that I have the information I need to continue on with the project.
I connected the database to the Raspberry Pi, and tested it out using a house plant. It worked really well, and the next step is to test it outside with a real tree.
I build a CAD of the structure that will house the prototype. I left holes for all the different parts, and will print it later today.
I 3D printed the top half of the housing for the prototype. Next, I will print the bottom half.
I fixed the issues that the prototype was facing. I also added a button. The main thing I did was make the prototype able to function without the computer nearby, which can make it so that anyone in the world can use it with a battery pack.
I researched the different types of prevalent trees in California and their temperatures during different seasons. I plan to add this to my database, as I hadn’t previously done this. I used many websites, but Our City Forest was the most helpful.
I combined the parts in the bottom half of the prototype, and fit it in the encolsure.
I completed the prototype by combining the enclosure with the prototype. I encased the sensors and the LCD. The prototype is finally complete!
Ships:
I made a project that can detect tree/plant species and temperature to determine whether it is dead or not. It displays the result on an LCD in the back, and will start the process of scanning a tree/plant once a button is pressed. The wiring is enclosed in a 3D printed prototype. The most difficult thing I did in this project was likely trying to fit it all in the enclosure. There were a lot of wires here and there, and the button was also quite difficult to fit in, as I needed to solder it on. However, this project was really fun to do, as it helped me gain knowledge about the environment around me, and I got to practice my software and hardware skills. I am really proud of myself, as I completed this project from start to finish, and didn’t give up. To test my project, all you have to do is plug in the battery pack, wait for a minute or so, point the prototype at the tree you want to scan, press the button, and wait for the results. Sometimes, it will take a little longer to get the results, resulting from a difficulty figuring out either the temperature or species of that particular plant/tree....
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16234
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Meow-Net by @SynthReaper | Stardance
|
SynthReaper
|
1
|
43.17
|
0
|
[{"ship_num": 1, "ts": "2026-0 [{"ship_num": 1, "ts": "2026-07-05T11:33:20Z", "hours": 42.0, "multiplier": null, "payout": 0, "blessing": "neutral", "text": "What did you make? MeowNet: Feline management cockpit and zero-knowledge community coordination platform. Features: Live mapping, regional chapters, and volunteer tracking systems. Safety: Emergency registry with dispatch tools and supply chain inventories. Privacy: Browser AES-GCM-256 client-side encrypted dashboard for medical records. AI Helper: Integrated assistant using Gemini, OpenAI, and Anthropic models. What was challenging? UX Fix: Implemented keyless server-side defaults for instant dashboard access. DB Spoofing: Users could bypass client logic to verify their own hours. DB Fix: Refactored Row-Level Security policies to enforce admin-only scopes. ReDoS Flaws: Text sanitization utility triggered regex security vulnerability flags. Code Fix: Replaced regex with a safe, linear O(N) scanner. What are you proud of? Clean Code: Zero warnings and zero vulnerabilities found during build analysis. Theme Sync: Leaflet map tiles that dynamically update between dark/warm modes. Action AI: Chat bubbles that extract JSON to render database commit buttons. What should people know so they can test your project? General: Register a volunteer account to log sightings and take quizzes use features.", "repo": "https://github.com/SynthReaper/MeowNet", "demo": "https://meownet-sr.vercel.app/"}]...
|
[{"post_id": 23005, "ts": "202 [{"post_id": 23005, "ts": "2026-07-05T18:26:46Z", "hours": 1.1719, "text": "DOM XSS Sanitization: Hardened volunteer-supplied image URLs across profile and reports pages by sanitizing them through the getSafeImageSrc filter. Log Cat UI Polish: Swapped the dashed file uploader for a clean image preview with a delete trigger, converted checkboxes to styled pills, and added tooltips."}, {"post_id": 22787, "ts": "2026-07-05T11:27:44Z", "hours": 18.9175, "text": "v0.9.0 (Social Impact & Field Coordination) : Introduced the Volunteer Management System (VMS) for hours/skills tracking, the Emergency Case Registry (ECR) for incident map and dispatch, the Supply Chain Registry for inventory control, and Regional Chapters coverage. Added zero-knowledge vault unlock bypass to allow AI Helper default messaging, and hardened RLS policies on skills/hours. v0.8.0 - v0.8.2 (Refactoring, Welfare Score & Heatmaps) : Deployed a dynamic Cat Welfare Score calculator (0-100) on cat details page. Added point marker density heatmaps. Consolidated 61 database migration files down to 5. Swept static analysis findings (cognitive complexity reduction, flattened nested ternaries, and readonly props rules). v0.7.0 (Security Hardening & Documentation) : Secured API proxy routes against model param SSRF. Hardened security headers, added missing routes to API docs, and corrected database schemas description. v0.6.0 (Cryptographic Verification & Certificates) : Implemented dynamic Certificate Verification Portal. Secured volunteer certificates with HMAC-SHA256 signatures validated server-side. Added a dispute queue panel on staff audit logs. v0.5.0 (Admin & Maintenance Mode) : Released maintenance mode gate configurations, dynamic gamification engines, and supreme admin management console views."}, {"post_id": 17081, "ts": "2026-06-27T18:50:36Z", "hours": 6.97, "text": "v0.4.0 (Auth, Queries & Docs): Added a sliding auth segmented toggle (AuthTabs), three-tier support query escalation pipeline, Clerk OTP failure popups, and comprehensive judge docs. v0.3.0 & v0.2.0 (Analytics & Credentials): Built charts and maps for Admin/Moderator dashboards, targeted announcement notice board routing, and direct DB auth bypassing Clerk. v0.1.0 (Launch): Released the core Next.js 16 platform with a 3D WebGL globe, PostGIS fuzzed location map, and HuggingFace FastAPI breed classification service."}, {"post_id": 15407, "ts": "2026-06-25T01:27:13Z", "hours": 15.8278, "text": "MeowNet is a fun, real-time platform that connects cat guardians to map colonies, log sightings, and coordinate TNR & rescue efforts."}]...
|
Title: Meow-Net by @SynthReaper | Stardance
Devlo Title: Meow-Net by @SynthReaper | Stardance
Devlogs:
MeowNet is a fun, real-time platform that connects cat guardians to map colonies, log sightings, and coordinate TNR & rescue efforts.
v0.4.0 (Auth, Queries & Docs): Added a sliding auth segmented toggle (AuthTabs), three-tier support query escalation pipeline, Clerk OTP failure popups, and comprehensive judge docs. v0.3.0 & v0.2.0 (Analytics & Credentials): Built charts and maps for Admin/Moderator dashboards, targeted announcement notice board routing, and direct DB auth bypassing Clerk. v0.1.0 (Launch): Released the core Next.js 16 platform with a 3D WebGL globe, PostGIS fuzzed location map, and HuggingFace FastAPI breed classification service.
v0.9.0 (Social Impact & Field Coordination) : Introduced the Volunteer Management System (VMS) for hours/skills tracking, the Emergency Case Registry (ECR) for incident map and dispatch, the Supply Chain Registry for inventory control, and Regional Chapters coverage. Added zero-knowledge vault unlock bypass to allow AI Helper default messaging, and hardened RLS policies on skills/hours. v0.8.0 - v0.8.2 (Refactoring, Welfare Score & Heatmaps) : Deployed a dynamic Cat Welfare Score calculator (0-100) on cat details page. Added point marker density heatmaps. Consolidated 61 database migration files down to 5. Swept static analysis findings (cognitive complexity reduction, flattened nested ternaries, and readonly props rules). v0.7.0 (Security Hardening & Documentation) : Secured API proxy routes against model param SSRF. Hardened security headers, added missing routes to API docs, and corrected database schemas description. v0.6.0 (Cryptographic Verification & Certificates) : Implemented dynamic Certificate Verification Portal. Secured volunteer certificates with HMAC-SHA256 signatures validated server-side. Added a dispute queue panel on staff audit logs. v0.5.0 (Admin & Maintenance Mode) : Released maintenance mode gate configurations, dynamic gamification engines, and supreme admin management console views.
DOM XSS Sanitization: Hardened volunteer-supplied image URLs across profile and reports pages by sanitizing them through the getSafeImageSrc filter. Log Cat UI Polish: Swapped the dashed file uploader for a clean image preview with a delete trigger, converted checkboxes to styled pills, and added tooltips.
Ships:
What did you make? MeowNet: Feline management cockpit and zero-knowledge community coordination platform. Features: Live mapping, regional chapters, and volunteer tracking systems. Safety: Emergency registry with dispatch tools and supply chain inventories. Privacy: Browser AES-GCM-256 client-side encrypted dashboard for medical records. AI Helper: Integrated assistant using Gemini, OpenAI, and Anthropic models. What was challenging? UX Fix: Implemented keyless server-side defaults for instant dashboard access. DB Spoofing: Users could bypass client logic to verify their own hours. DB Fix: Refactored Row-Level Security policies to enforce admin-only scopes. ReDoS Flaws: Text sanitization utility triggered regex security vulnerability flags. Code Fix: Replaced regex with a safe, linear O(N) scanner. What are you proud of? Clean Code: Zero warnings and zero vulnerabilities found during build analysis. Theme Sync: Leaflet map tiles that dynamically update between dark/warm modes. Action AI: Chat bubbles that extract JSON to render database commit buttons. What should people know so they can test your project? General: Register a volunteer account to log sightings and take quizzes use features....
|
1793681630.4304032
|
1793768030.4304032
|
|
16235
|
Taskie by @khushishah | Stardance
|
khushishah
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: Taskie by @khushishah | Stardance
|
1793314558.9287336
|
1793919358.9287336
|
|
16236
|
My very own WebOS by @kaycee | Stardance
|
kaycee
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My very own WebOS by @kaycee | Stardance
|
1793386541.6811028
|
1793991341.6811028
|
|
16237
|
Cosmic sticky notes by @Prayash | Stardance
|
Prayash
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: Cosmic sticky notes by @Prayash | Stardance
|
1793382844.069377
|
1793987644.069377
|
|
16238
|
My very own WebOS by @kaycee | Stardance
|
kaycee
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My very own WebOS by @kaycee | Stardance
|
1793476307.0620368
|
1794081107.0620368
|
|
16239
|
Frictionless by @AidenB | Stardance
|
AidenB
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: Frictionless by @AidenB | Stardance
|
1793321438.1038854
|
1793926238.1038854
|
|
16240
|
Echo by @skandhack | Stardance
|
skandhack
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: Echo by @skandhack | Stardance
|
1793170480.7976794
|
1793775280.7976794
|
|
16241
|
Portfolio website by @lucianbai09 | Stardance
|
lucianbai09
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: Portfolio website by @lucianbai09 | Stardan Title: Portfolio website by @lucianbai09 | Stardance...
|
1793443952.9690447
|
1794048752.9690447
|
|
16242
|
Drone Interceptor by @aarav_patel0436 | Stardance
|
aarav_patel0436
|
1
|
35.0
|
0
|
[{"ship_num": 1, "ts": "2026-0 [{"ship_num": 1, "ts": "2026-06-23T19:15:00Z", "hours": 35.0, "multiplier": null, "payout": 0, "blessing": "neutral", "text": "I built Drone Interceptor because I wanted to see if I could make a computer actually understand where a drone is in 3D space and aim a physical tracking system at it. It started as a simple idea: detect a drone with a camera, estimate its position, and move a turret to follow it. But the project slowly turned into a much bigger journey through computer vision, electronics, firmware, controls, PCB design, and a lot of debugging. The first version used YOLOv8 to detect drones from a webcam. From there, I added camera calibration, depth estimation, 3D position tracking, prediction arrows, and live turret control using a Raspberry Pi Pico 2. I tested pan and tilt tracking with a stepper motor and servo, and eventually got the turret tracking system and depth perception working together. The hardest part was definitely the hardware. I originally tried to hack into a commercial drone controller by sending joystick-like signals from my computer through a Pico. After a lot of testing, voltage measurements, broken controller moments, resistor changes, and late-night debugging, I realized that approach was too fragile. So I pivoted: instead of fighting the controller, I designed my own custom PCBs while still reusing the small Snaptain SP350 drone frame, motors, and propellers. I’m proud of this project because it became more than just code. It has real firmware, real electronics, real CAD, real PCB files, simulation videos, and a working tracking pipeline. I also made demo simulations that show how the drone tracking, following, orbiting, and standoff behavior would work, so the project can be understood even while I’m waiting for the PCBs to arrive. Right now, the turret tracking and depth perception system work, the code is organized, the README has setup instructions and demo links, and the custom PCB fabrication files are ready. The next step is assembling the PCBs when they arrive and continuing the flight-control side on real hardware.", "repo": "https://github.com/aaravpatel0/DroneInterception", "demo": ""}]...
|
[{"post_id": 14501, "ts": "202 [{"post_id": 14501, "ts": "2026-06-23T17:21:00Z", "hours": 9.3503, "text": "Today I finished the main code and PCB design work for the drone tracking and interception project. The software side is now working, including the turret tracking system and the depth perception setup. I tested the camera-based tracking, servo movement, and position estimation, and the system is able to detect and follow the target properly. A big part of the project also changed direction. At first, I was trying to hack into a commercial drone controller and control the drone that way, but that turned out to be unreliable and difficult to build on. After testing, debugging, and experimenting with different control methods, I pivoted to designing my own custom PCBs for the drone instead. I am still using the original drone frame, but the electronics will be custom-made so I can control the motors and hardware much more directly. I also finished designing the custom PCBs for the project. One board is for the drone-side control hardware, and the other is for the turret/control setup that connects the computer, camera, stepper, servos, and control electronics together in a cleaner and more organized way. After a lot of debugging, redesigning, and checking the layouts, the boards are ready to be manufactured. At this point, most of the major development work is complete. The code is finished, the tracking and depth perception systems have been tested, and the PCB files are ready. Now I just need to wait for the custom PCBs to arrive so I can assemble the hardware, test everything together, and finish the final version of the project. I couldn’t post all my videos and screenshots in this one post, but hopefully everyone will be able to see them in the future."}, {"post_id": 12253, "ts": "2026-06-20T00:41:36Z", "hours": 21.5931, "text": "Finally, I’m getting close to finishing both the turret and the drone-control system.On the turret side, the vision tracking pipeline is working with the servo. The detector can find the drone, estimate its position in the camera frame, and send movement commands to the tracking hardware. The next major step is testing the full pan-and-tilt behavior together, so the turret can smoothly follow a moving drone instead of only proving that one axis works. This will involve tuning the tracking response, reducing jitter, and making sure the servo and pan mechanism move accurately without overshooting.On the drone-control side, this was a huge milestone. After accidentally damaging the drone controller board, repairing it, and spending hours debugging different circuit designs, I finally got computer-based control working through a Raspberry Pi Pico 2. The Pico acts as a ground-station hardware bridge: it receives control commands from the computer over USB and converts them into analog joystick-like signals that the drone remote can understand.One of the biggest problems was that the original joystick potentiometers were still physically connected to the controller board, so they were pulling the signal voltage back toward center. After testing with a voltmeter, I found that the Pico was sending the correct signal, but the controller board was loading it down. Changing the resistor value in the filter circuit gave the Pico enough authority over the signal line while still keeping the output controlled and smooth.At this point, the system can successfully influence the drone remote from the computer. The next step is to finish testing all control channels, verify the voltage ranges with the multimeter, and then move into careful live movement tests. This is the first time the project has connected the detection/tracking software to real hardware control in a way that actually works."}, {"post_id": 8980, "ts": "2026-06-14T23:19:58Z", "hours": 4.0464, "text": "After taking a brief pause from this project during finals week, I’m excited to share that I finally got real-time drone tracking working. The system can now detect my drone from a live camera feed using a custom-trained YOLOv8 model and draw reliable bounding boxes around it. From there, I calculate the center of the drone’s bounding box, compare it to the center of the camera frame, and use that offset to track where the drone is moving. I also started mapping that position onto a 3D graph using OpenCV, with only one camera instead of a multi-camera setup. This took quite a bit of math and debugging. I had to work through monocular camera geometry, pixel offsets, scaling, and OpenCV visualization before the tracking started making sense. I also trained and tested my own drone detection model, then built the pipeline so the model can run live, pick the highest-confidence drone detection, and output useful position/error data in real time. The next step is connecting this tracking system to the physical turret. The code now calculates horizontal and vertical error values from the drone’s position in the frame, which can be sent to an Arduino to control pan and tilt movement. Currently I’m working on “hacking” into this small drone I own, and doing some resoldering and rewiring so that this store-bought drone can be controlled digitally though my Arduino instead of through a human with its remote. Then using this digital control system, I can then intercept the larger flying drone and take it down."}]...
|
Title: Drone Interceptor by @aarav_patel0436 | Sta Title: Drone Interceptor by @aarav_patel0436 | Stardance
Devlogs:
After taking a brief pause from this project during finals week, I’m excited to share that I finally got real-time drone tracking working. The system can now detect my drone from a live camera feed using a custom-trained YOLOv8 model and draw reliable bounding boxes around it. From there, I calculate the center of the drone’s bounding box, compare it to the center of the camera frame, and use that offset to track where the drone is moving. I also started mapping that position onto a 3D graph using OpenCV, with only one camera instead of a multi-camera setup. This took quite a bit of math and debugging. I had to work through monocular camera geometry, pixel offsets, scaling, and OpenCV visualization before the tracking started making sense. I also trained and tested my own drone detection model, then built the pipeline so the model can run live, pick the highest-confidence drone detection, and output useful position/error data in real time. The next step is connecting this tracking system to the physical turret. The code now calculates horizontal and vertical error values from the drone’s position in the frame, which can be sent to an Arduino to control pan and tilt movement. Currently I’m working on “hacking” into this small drone I own, and doing some resoldering and rewiring so that this store-bought drone can be controlled digitally though my Arduino instead of through a human with its remote. Then using this digital control system, I can then intercept the larger flying drone and take it down.
Finally, I’m getting close to finishing both the turret and the drone-control system.On the turret side, the vision tracking pipeline is working with the servo. The detector can find the drone, estimate its position in the camera frame, and send movement commands to the tracking hardware. The next major step is testing the full pan-and-tilt behavior together, so the turret can smoothly follow a moving drone instead of only proving that one axis works. This will involve tuning the tracking response, reducing jitter, and making sure the servo and pan mechanism move accurately without overshooting.On the drone-control side, this was a huge milestone. After accidentally damaging the drone controller board, repairing it, and spending hours debugging different circuit designs, I finally got computer-based control working through a Raspberry Pi Pico 2. The Pico acts as a ground-station hardware bridge: it receives control commands from the computer over USB and converts them into analog joystick-like signals that the drone remote can understand.One of the biggest problems was that the original joystick potentiometers were still physically connected to the controller board, so they were pulling the signal voltage back toward center. After testing with a voltmeter, I found that the Pico was sending the correct signal, but the controller board was loading it down. Changing the resistor value in the filter circuit gave the Pico enough authority over the signal line while still keeping the output controlled and smooth.At this point, the system can successfully influence the drone remote from the computer. The next step is to finish testing all control channels, verify the voltage ranges with the multimeter, and then move into careful live movement tests. This is the first time the project has connected the detection/tracking software to real hardware control in a way that actually works.
Today I finished the main code and PCB design work for the drone tracking and interception project. The software side is now working, including the turret tracking system and the depth perception setup. I tested the camera-based tracking, servo movement, and position estimation, and the system is able to detect and follow the target properly. A big part of the project also changed direction. At first, I was trying to hack into a commercial drone controller and control the drone that way, but that turned out to be unreliable and difficult to build on. After testing, debugging, and experimenting with different control methods, I pivoted to designing my own custom PCBs for the drone instead. I am still using the original drone frame, but the electronics will be custom-made so I can control the motors and hardware much more directly. I also finished designing the custom PCBs for the project. One board is for the drone-side control hardware, and the other is for the turret/control setup that connects the computer, camera, stepper, servos, and control electronics together in a cleaner and more organized way. After a lot of debugging, redesigning, and checking the layouts, the boards are ready to be manufactured. At this point, most of the major development work is complete. The code is finished, the tracking and depth perception systems have been tested, and the PCB files are ready. Now I just need to wait for the custom PCBs to arrive so I can assemble the hardware, test everything together, and finish the final version of the project. I couldn’t post all my videos and screenshots in this one post, but hopefully everyone will be able to see them in the future.
Ships:
I built Drone Interceptor because I wanted to see if I could make a computer actually understand where a drone is in 3D space and aim a physical tracking system at it. It started as a simple idea: detect a drone with a camera, estimate its position, and move a turret to follow it. But the project slowly turned into a much bigger journey through computer vision, electronics, firmware, controls, PCB design, and a lot of debugging. The first version used YOLOv8 to detect drones from a webcam. From there, I added camera calibration, depth estimation, 3D position tracking, prediction arrows, and live turret control using a Raspberry Pi Pico 2. I tested pan and tilt tracking with a stepper motor and servo, and eventually got the turret tracking system and depth perception working together. The hardest part was definitely the hardware. I originally tried to hack into a commercial drone controller by sending joystick-like signals from my computer through a Pico. After a lot of testing, voltage measurements, broken controller moments, resistor changes, and late-night debugging, I realized that approach was too fragile. So I pivoted: instead of fighting the controller, I designed my own custom PCBs while still reusing the small Snaptain SP350 drone frame, motors, and propellers. I’m proud of this project because it became more than just code. It has real firmware, real electronics, real CAD, real PCB files, simulation videos, and a working tracking pipeline. I also made demo simulations that show how the drone tracking, following, orbiting, and standoff behavior would work, so the project can be understood even while I’m waiting for the PCBs to arrive. Right now, the turret tracking and depth perception system work, the code is organized, the README has setup instructions and demo links, and the custom PCB fabrication files are ready. The next step is assembling the PCBs when they arrive and continuing the flight-control side on real hardware....
|
1793663535.3987446
|
1793749935.3987446
|
|
16243
|
ISS Tracker by @IamArush0 | Stardance
|
IamArush0
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: ISS Tracker by @IamArush0 | Stardance
|
1793476683.3386874
|
1794081483.3386874
|
|
16244
|
My very own WebOS by @rell | Stardance
|
rell
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My very own WebOS by @rell | Stardance
|
1793458096.7605026
|
1794062896.7605026
|
|
16245
|
Untitled by @cwjackson992 | Stardance
|
cwjackson992
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: Untitled by @cwjackson992 | Stardance
|
1793515996.3711686
|
1794120796.3711686
|
|
16246
|
My awesome macropad! by @raahi | Stardance
|
raahi
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My awesome macropad! by @raahi | Stardance
|
1793213642.721202
|
1793818442.721202
|
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16247
|
Frictionless by @vyahriti | Stardance
|
vyahriti
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: Frictionless by @vyahriti | Stardance
|
1793566162.7463367
|
1794170962.7463367
|
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16248
|
Untitled project by @fielddecland | Stardance
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fielddecland
|
0
|
0.0
|
0
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[]
|
[]
|
Title: Untitled project by @fielddecland | Stardan Title: Untitled project by @fielddecland | Stardance...
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1793573286.0336678
|
1794178086.0336678
|
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16249
|
My very own WebOS by @DRCHEZ1 | Stardance
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DRCHEZ1
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My very own WebOS by @DRCHEZ1 | Stardance
|
1793447364.8607683
|
1794052164.8607683
|
|
16250
|
SANS Tester by @cbanc | Stardance
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cbanc
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: SANS Tester by @cbanc | Stardance
|
1793443692.8475733
|
1794048492.8475733
|