Hey HN, I built a website to watch live baseball games in an 8-bit broadcast. It takes live MLB data streams and converts them into near real-time pixel art gamecasts.
Been waiting to share this for when there’s actually a good slate of games happening since the site is pretty bare otherwise.
Here is today's schedule:
Mets @ Reds - 9:40am PDT https://ribbie.tv/watch/game/824503
Royals @ Nationals - 10:05am PDT https://ribbie.tv/watch/game/822721
Marlins @ Phillies - 10:05am PDT https://ribbie.tv/watch/game/823450
Tigers @ Astros - 11:10am PDT https://ribbie.tv/watch/game/824178
Padres @ Cardinals - 11:15am PDT https://ribbie.tv/watch/game/823044
..and another 14 games throughout the later day.
I'm still early on in this project, but I've tried to add little details with actual stadiums, day and night modes, between inning graphics and interstitials, live scoreboards, etc.
Would love any feedback and ideas. Thanks for checking it out!
Hi HN,
I spent the last few months reading the RISC‑V specification to build the lightest possible sandboxes. The idea behind a vpod is to quickly spin up a Linux sandbox from snapshots (Alpine by default) without any setup or subsystem required.
The trade-off for portability and security is raw CPU speed. So we don't expect it to match native workloads with Python or pip, for example.
More info is in the README https://github.com/capsulerun/vpod
Happy to answer any questions!
Hi HN!
I build this project to explore an idea I got in mind for a long time : Is transformer a suitable architecture for a chess bot? I built a small model (11M parameters) and trained it on human games (Elite Lichess DB).
Model alone is performing around 1500 elo, but I built an harness using Monte Carlos Tree Search (MCTS) using my model heuristics to improve the model to ~2100 elo (evaluated against stockfish).
If you want to try it, it is available as a Lichess bot : https://lichess.org/@/ChessTransformerBot
I'm looking to evaluate this model against human players so challenge, I would be grateful if you try it!
The project is open source, don't hesitate to star the repos if you like the project.
For me, the main key learning is that machine learning is an important part of the project, but it was the harness design that makes the system works with a nice performance regarding the small model size.