Developer working in Immersive Entertainment by day. Comedian by night. Former Rigging TD.
Present: Developer @ @felixandpaul. ~ My opinions are my own.
"We don't make [weightpainting] mistakes.
We just have happy accidents." ♥
— #BobRoss, "The Joy of (Weight) Painting"
#rigtip Remember to be chill like Bob Ross, and just keep on painting until your deformations look right. It just takes time. ;)
#rigging#cgimemes
So, Chrome's "web standard" Prompt API:
Mozilla: Opposed
WebKit: Opposed
Microsoft: Several concerns
W3C TAG: Several concerns
Developers: Mostly negative
Chrome: Ships anyway.
A sad time for web standards. But, I guess someone at Google will get promoted, so 'every cloud…'
Inspired by Quentin Blake’s multi-colored line drawings, Terence Ng Tat Mew used Moho to create this character. He has worked as a BG and visual development artist in What if, Rick and Morty, Marvel Zombies and more. Now he's proving to be an amazing 2D rigger too! #mohoanimation
This paper from Harvard and MIT quietly answers the most important AI question nobody benchmarks properly:
Can LLMs actually discover science, or are they just good at talking about it?
The paper is called “Evaluating Large Language Models in Scientific Discovery”, and instead of asking models trivia questions, it tests something much harder:
Can models form hypotheses, design experiments, interpret results, and update beliefs like real scientists?
Here’s what the authors did differently 👇
• They evaluate LLMs across the full discovery loop hypothesis → experiment → observation → revision
• Tasks span biology, chemistry, and physics, not toy puzzles
• Models must work with incomplete data, noisy results, and false leads
• Success is measured by scientific progress, not fluency or confidence
What they found is sobering.
LLMs are decent at suggesting hypotheses, but brittle at everything that follows.
✓ They overfit to surface patterns
✓ They struggle to abandon bad hypotheses even when evidence contradicts them
✓ They confuse correlation for causation
✓ They hallucinate explanations when experiments fail
✓ They optimize for plausibility, not truth
Most striking result:
`High benchmark scores do not correlate with scientific discovery ability.`
Some top models that dominate standard reasoning tests completely fail when forced to run iterative experiments and update theories.
Why this matters:
Real science is not one-shot reasoning.
It’s feedback, failure, revision, and restraint.
LLMs today:
• Talk like scientists
• Write like scientists
• But don’t think like scientists yet
The paper’s core takeaway:
Scientific intelligence is not language intelligence.
It requires memory, hypothesis tracking, causal reasoning, and the ability to say “I was wrong.”
Until models can reliably do that, claims about “AI scientists” are mostly premature.
This paper doesn’t hype AI. It defines the gap we still need to close.
And that’s exactly why it’s important.
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SLAM just got a serious speed boost.
Efficient LoFTR is now integrated into the @huggingface Transformers library.
It’s 2.5× faster than the original LoFTR and can even outperform the SuperPoint + LightGlue pipeline.
Image matching finds correspondences between two images taken from different angles, lighting, or scales.
It’s key for 3D computer vision tasks like:
✅ Structure from Motion (SfM)
✅ SLAM
✅ Visual Localization
Unlike traditional detector-based matchers (SuperGlue, LightGlue) that depend on a separate feature detector, LoFTR works detector-free:
•Coarse pixel-wise dense matching
•Fine-level refinement with a Transformer model
Efficient LoFTR pushes it further with:
✅ Aggregated attention + adaptive token selection
✅ Two-stage correlation for subpixel accuracy
✅ 2.5× speed boost
You can try it now in just a few lines:
pip install transformers
Thanks for sharing, @Nielsrogge!
Resources:
- model: https://t.co/xN6x1YJeJS
- docs: https://t.co/yn2Au5bq3L
- project page: https://t.co/G9ZzYMdqs2
- LoFTR: https://t.co/7ztHDHm93C