@tarat_211: I made a video about my first real attempt at ML research. I started with a simple question about pruning vision-langua…
Summary
A researcher made a video about their first ML research attempt on pruning vision-language models, including negative results, and submitted a paper to arXiv.
View Cached Full Text
Cached at: 07/21/26, 01:36 AM
I made a video about my first real attempt at ML research.
I started with a simple question about pruning vision-language models, spent weeks breaking Qwen2.5-VL and SmolVLM2 in different ways, hit a bunch of negative results, and eventually submitted my first paper to arXiv. https://t.co/berWTRdcvH
Similar Articles
@harshbhatt7585: https://x.com/harshbhatt7585/status/2063593933314113587
The author shares learnings from training a 160M parameter LLM from scratch, experimenting with architectures like multi-token prediction and hierarchical reasoning models. They emphasize the importance of fast iteration, simplifying ideas, and understanding why architectures work.
@awnihannun: The video from @angeloskath on local agentic AI with MLX is excellent. I also hear it's one of the most viewed videos i…
A tweet highlights an excellent WWDC video by Angelos Kath on building local agentic AI with MLX, noting rapid progress in open-weight models and hardware capabilities.
@neural_avb: Watch this 45 min video to learn how to create synthetic datasets and train tiny (100M params) local language models th…
A 45-minute video tutorial on creating synthetic datasets and training tiny (100M parameter) local language models for narrow tasks, with code and resources provided.
@ma_sc_: I've been testing this on many other languages than the 14 officially supported and results have been truly surprising.…
A user shares surprising results testing Liquid AI's new LFM2.5-VL-3B vision-language model across many languages, noting strong visual capabilities but weaker instruction following; Liquid AI announces the model can read screens, documents, and ground objects to coordinates.
@swyx: full writeup and links here
A Latent Space podcast episode discusses the thesis that video models derive intelligence from LLMs, and that the next frontier is video agents. Guest Ethan He, who built Grok Imagine at xAI, shares insights on building frontier image and video systems.