By outsourcing computation, thinking, and recall, you still need understand it. Maybe we human never read, we just look at pictures.
Share some of cool algorithms in pictures.
One of my fun parenting wins with OpenClaw so far: on-demand coloring pages! ๐จ๏ธ๐จ
Kid asks for a Robot car or a race car? Voice in โ search image โ GPT + Banana APIs โ instant black-and-white coloring version โ print.
Downside: weโre burning through API tokens AND printer paper at an alarming rate ๐ธ๐๐
Parents, is this genius or expensive chaos? #AIParenting #OpenClaw
After working with AI agents CC, Cursor and Clawdbot, Iโve become too impatient with any delay from human.
Why we argue different ideas without implementations? Why you cannot finish reviewing a few already-chunked PRs in two hours?
I've been using Graphite for a few months now and I've never felt better. I have more energy to review code and ask for review code. My skin is cleaner. My eye sight has improved.
Excited to present our paper, "PROFIT: A Specialized Optimizer for Deep Fine Tuning" at #NeurIPS2025!
We introduce an optimizer specifically designed for finetuning pre-trained models, drawing inspiration from multi-task learning.
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Looking forward to being at NeurIPS 2025 this upcoming week to present our paper, "PROFIT: A Specialized Optimizer for Deep Fine Tuning"!
PROFIT is the first optimizer that is specially designed for fine-tuning; and because itโs an optimizer, itโs easy to drop in and drop out of any deep learning training system. Intuitively, PROFIT intelligently keeps your system from straying too far from its current state by making sure subsequent gradient updates donโt conflict.
We take ideas around gradient conflict commonly found in the multitask learning literature applied across tasks and apply them to the temporal axis instead!
We show PROFIT works across a wide range of modalities, from computer vision, VLMs to motion prediction.
Please also stop by the General Motors team booth. Learn more about GMโs presence at NeurIPS 2025. https://t.co/Zg6ohHsJpF
Catch us at the poster session:
When: Wednesday, Dec 3rd (11am - 2pm PT)
Where: San Diego Convention Center, Exhibit Hall C/D/E
Poster: #905
Paper: https://t.co/IlAq1PWNVW
Notice that Gemini Pro 2.5 (even on Gemini studio) gives much better results last week than others like ChatGPT to help with understanding library documentation and debug. Is this due to google search results rule change?
We took on the challenge and weโve put our system to work on the nanoGPT benchmark. @hivergeai tech discovered new algorithmic improvements beyond the existing optimizations.
Check out the results in the PR https://t.co/CIS5phXK04 and read our blogpost https://t.co/92Bi764Ofv!
Iโm tired of seeing SOTA results on curated benchmarks. Amazing, but where are the failure cases? Very few academic papers mention failures. Worse, this silence is spilling into closed-source models like GPT-5. What matters most is how models fail on out-of-distribution data.
@LigengZhu It seems that Grok4 might have trained on the public AIME.
The private benchmark suggested there is a room to be improved. https://t.co/8riV2uBgtV
@ducha_aiki@PascalMettes@giffmana@TaiNguyen34 Well, itโs only fair, remember, China has PRCV, UK has BMVC, Germany has GCPRโฆ and the US? We got CVPR, Costco, and unlimited refills. Itโs just how things works.
OpenAIโs deep research and 3o-mini are great. I feel like a wizard. Then I tried Geminiโs: it wrote a report and exported it to Google Docs. Asked it for code, boom, straight to Colab. This is giving a little โbundling IE with Windows while Netscape cries in a cornerโ vibes.
That's actually not true. It uses JSON representation to represent all the data. ```messages=[
{"role": "user", "content": [
{"type": "text", "text": prompt},
{"type": "image_url", "image_url": {
"url": f"data:image/png;base64,{base64_image}"}
}
]}
],```. It requires user to encode the raw image as base64 and then converts this base64 string into a UTF-8 string. e.g. ```import base64 def encode_image(image_path): with open(image_path, "rb") as image_file: # Read the image in binary mode return base64.b64encode(image_file.read()).decode("utf-8")
```. I think this can later be read using OpenCV.