D*MN, ONE NIGHT AFTER A -15% DUMP MY GROK BOT DESK CAME BACK AND TURNED $114,700 INTO $183,415
day 6 of the . part 2 of day 5 closed at $116K and i almost shut it off. i didn't
3 desks, one screen, Jev picking the coins. same rules, new coins, and nobody touched the settings after the red night
the comeback:
→ opened $114,700
→ first green candle in 12 hours, $117,257
→ Jev rotated into Pippin and Zerebro, $122,861
→ $132,025, the old high is in sight
→ $135,980, everything from the red night is back
→ Pippin ran, $154,530
→ small pullback, $150,640
→ Zerebro and Pnut pushed it, $176,170
→ closed at $183,415, +59% on the day
the 5 coins the desk lived in: PIPPIN, ZEREBRO, SPX, PNUT, MOODENG. Goat, Chillguy and Act all got cut after the dump. Moodeng is back from day 2
the win rate came back with the bank. 61% at the open, 78% by the close
the funny part, the bots talk like nothing happened. one typed "zerebro big brain move". another one: "back to business". near the close: "jev, we're green again". and the cat in the corner that spent the red night saying "it's fine. i'm fine" now just sits there yelling to go up
honestly the red night was the best thing that happened to this desk. it proved the 2-loss rule works, the bad bots got cut, the new ones started clean
the rules stay boring on purpose:
→ no pool under $40K
→ no chart that's already vertical
→ 2 losses in a row and the bot is out
→ a post on X is research, never the trigger
at 0:09 the desk is back above $135K. at 0:12 Pippin runs. at 0:21 it closes at $183K
would you have trusted the rules after -15%, or pulled the plug?
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this one's a straight up f*cking goldmine
10 GitHub repos with 690K+ combined stars to run your own solo shorts studio
one tool per step, from a 1-hour video to a stack of ready vertical clips
(1/10) yt-dlp
pull your long video and its audio from almost any platform
▸https://t.co/bRl1Wys2Qb
(2/10) Whisper
turn every spoken word into text, on your own machine
▸https://t.co/MKcw1tGbyN
(3/10) WhisperX
get word-level timestamps and speaker labels, so captions land on the exact word
▸https://t.co/ZkFeozwQ3f
(4/10) Ollama
run a local model over the transcript and let it pick the moments with a real hook
▸https://t.co/s3W8Y3U1yn
(5/10) DeepFilterNet
strip the background noise out of the voice track
▸https://t.co/EPU1Bex5Cn
(6/10) auto-editor
cut the silences and dead air based on audio loudness
▸https://t.co/sltG0QeKgy
(7/10) PySceneDetect
find every camera cut so no clip starts mid-shot
▸https://t.co/jvM9pVnFB1
(8/10) MediaPipe
track the speaker's face so the 9:16 crop follows them
▸https://t.co/YF3z5ifjnk
(9/10) Remotion
burn in word-by-word captions and assemble every short in code
▸https://t.co/2MnpZQrLlw
(10/10) FFmpeg
encode the 1080x1920 files and check the video and audio streams
▸https://t.co/bfqyckTvNL
the loop:
source → transcript → word timing → hooks → clean audio → cut dead air → scenes → reframe → captions → export
then hand the whole loop to Claude Code and let your AI employee run the studio ↓