Ruku retweeted
Nvidia is nothing without its people.
It now appears the national security allegations may be as fake as Russiagate. If so, Nvidia employees, shareholders, partners, and customers have immense leverage.
Whether publicly or privately, collectively or individually, they can approach the members of the board that precipitously pushed for Jensen’s resignation and instead demand that anti-Jensen board members resign.
Else mass employee resignations may follow, as may shareholder, partner, and customer suits against anti-Jensen board members for breaching their fiduciary duty. Because the value of Nvidia as an economic entity — for its employees, shareholders, partners, and customers — is obviously far higher if the founder and CEO of what was the most valuable company in world history is reinstated and this coup is defeated.
Trillions in capital, the fate of open weight AI, and the global economy itself all hang in the balance. So: if you are e/acc, if you are a techno-optimist, if you support open weight models, or if you just want to rectify a gross injustice — it is legitimate for you to politely press the Nvidia board with every tool at your disposal to reinstate Jensen Huang.
Remember how much capital is at stake. For many parties, it’d be worth it to spend millions — or even billions — on any legally permissible tactic that can get anti-Jensen board members to reverse their votes and then step down.
Remember also: the kind of spineless NPC board member that would vote against Jensen Huang due to a bad news cycle would likely buckle if subject to even 1/1000 of the scrutiny that poor Jensen has been subject to.
Mechanically: step one is to do citizen journalism to determine how each board member voted. Step two is to get the anti-Jensen board members to resign. And step three is the return of the king.
OpenAI shows it can be done. There are after all just a few human beings who need to change their anti-Jensen votes, or step down, or both. Their names and duties are public. And they’re meant to be accountable to the shareholders.
This analysis by @gemchange_ltd is as good a place as any to start.
Bombshell story. The spy narrative is collapsing already.
Is there clear & convincing evidence that Alice Huang was a Chinese spy?
- AG says no. (“no conclusions reached”).
- CIA says no. (“not yet convinced”).
- DNI says no (“not entirely accurate and over-torqued”).
- Ranking Democrat on House Intelligence (incoming Chairman) says no. (“today, at least, I don’t know”). This was immediately after he was briefed.
Who argues otherwise? Anonymous officials tell CNN “it strains credulity to believe that she was not acting as an agent of the Chinese government — simply by virtue of the fact that she is a successful Chinese businesswoman working with one of America’s most powerful companies.”
This is their proof after a year of investigating and surveillance? That she was a “successful Chinese businesswoman”?
At this point, the only crime that we know for sure happened is the leaking of the classified memo. Who did that and why?
An investigation should be opened.
Oh boy…
Big news: Kimi-K3 by @Kimi_Moonshot is now #1 in the Frontend Code Arena with 1679 pts, surpassing Claude Fable 5.
This is a 17-place jump from Kimi-k2.6 (#18 -> #1).
In Frontend, Kimi-K3 ranked #1 in 6 of 7 domains: Brand & Marketing, Reference-Based Design, Data & Analytics, Consumer Product, Simulations, and Content Creation Tools, landing #2 only in Gaming behind Fable 5.
The full model weights will be released by July 27.
Congrats to the @Kimi_Moonshot team on this major milestone!
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Been exploring datasets.bot lately, it's full of rich open datasets. I fine-tuned RF-DETR to track a robot's gripper and the object it's grasping, then pointed it at DROID, which has 76k+ real teleoperation episodes spanning hundreds of scenes and a dozen labs. Same model everywhere, no retraining, and honestly it held up better than I expected.
My friend is a Pokémon card vendor who buys/sells in person. A card graded a 10 can be worth hundreds more than the same card graded a 9, and it comes down to small details: centering, corners, creases, scratches. He judges it all by eye. So I built a tool that scans a card and breaks it down 👇
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It spots the card in the video, flattens it to a straight-on view, measures the border on all four sides, and compares left vs right and top vs bottom (same centering ratios PSA grades on). The worse side sets the score.
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For the damage detection I fine-tuned an object-detection model on my own cards. The honest shortcomings:
• misses defects: caught a crease on the front, missed it on the back
• can't gauge severity: flat photos have no depth, so a crease and a print line look the same
• too little data: trained on a handful of my own cards
The real bottleneck is data, as it always is.
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Ruku retweeted
a few things that make this exciting
- 7 images to get a custom model means nobody is constrained by data or compute to train, cost is not a barrier
- the tiny boats way off in the distance it catches
- it is not confused by the cars
doing poorly with the part of the marina beyond the bridge/road, density might be too much for the model to see distinct boats there
so cool
8 seconds is enough time for a Polymarket degen to get a real edge in live sports betting
We’re teaming up @Palmeiras, the first football club to meaningfully build upon TacticAI: our AI system that can help simulate field scenarios and predict open play dynamics up to 8 seconds in advance. ⚽
Adding ball-speed metrics. The ball is tracked, its flight fit to a ballistic arc, and each shot's pace clocked at the bounce. Shown as a blue-to-red meter for relative pace rather than a hard mph.
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I ran 2D pose estimation on a pickleball rally, with skeletons on all four players through the entire point:
- Keypoint R-CNN (torchvision) detects the 17 body joints for each player
- PyTorch runs the model inference
- OpenCV connects the joints and draws the skeleton overlay onto the frames
For each frame, I detect every player and map their feet from pixel space onto real court coordinates using the court homography, then tag each position with the correct player identity. You can accumulate all of a given player's positions into a two-dimensional grid laid over the court and color each cell by how often that player stood there, so that denser areas represent more time spent and the result is a per-player occupancy heatmap.
