If you find the base game too easy, I can recommend the IronMON challenge: You can only use one mon, permadeath, stats are randomized, all trainer levels are buffed by 1.5x and you can't level up on wilds. Along with numerous other rules to make it harder. There are variants that are borderline impossible to beat, like Super Kaizo IronMON. Out of hundreds of thousands of attempts, it has only been beaten once. Would make for an interesting optimization problem.
But honestly I really like the short turnaround times. Makes it easy to experiment with different parameters and develop an intuition for what they do.
Yes, but the odds of getting GPT-OSS to respond with that riddle are pretty low and it is not necessary to demonstrate whether the LLM can answer the riddle correctly.
I absolutely agree, but it's really stubborn with the flowery language. I tried adding things like "DO NOT USE EMPTY PHRASES LIKE 'EVER-EVOLVING TECH LANDSCAPE'!!!!!" to the prompt, but it just can't resist.
I want to give the whole system an overhaul, maybe newer models are better at this. Or maybe a second LLM pass to de-flowerize (lol) the language.
One of those articles that you'd love to share with certain people but it seems awkward when they receive a message from you with the link preview saying "Face it: you're a crazy person".
I'm building an app for language learning with Youtube. I realized that yt probably has the largest collection of spoken language that ever existed, so I wanted to make it accessible, especially on mobile.
I'm focusing on Chinese (Mandarin) right now, because that's what I've been learning, and the language learning community on reddit likes it too. But other languages are also available.
I tried it on a M1 Pro MBP using Docker. It's quite slow (no MPS) and there are no timestamps in the resulting transcript. But the basics are there. Truncated output:
Fetching video metadata...
Downloading from YouTube...
Generating transcript using medium model...
=== System Information ===
CPU Cores: 10
CPU Threads: 10
Memory: 15.8GB
PyTorch version: 2.7.1+cpu
PyTorch CUDA available: False
MPS available: False
MPS built: False
Falling back to CPU only
Model stored in: /home/app/.cache/whisper
Loading medium model into CPU...
100%|| 1.42G/1.42G [02:05<00:00, 12.2MiB/s]
Model loaded, transcribing...
Model size: 1457.2MB
Transcription completed in 468.70 seconds
=== Video Metadata ===
Title: 厨师长教你:“酱油炒饭”的家常做法,里面满满的小技巧,包你学会炒饭的最香做法,粒粒分明!
Channel: Chef Wang 美食作家王刚
Upload Date: 20190918
Duration: 5:41
URL: https://www.youtube.com/watch?v=1Q-5eIBfBDQ
=== Transcript ===
哈喽大家好我是王刚本期视频我跟大家分享...
https://github.com/PyroMikeGit/SuperKaizoIronMON
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