sankalp (dejavucoder)
@dejavucoder.bsky.social
into applied ai + product engg interested in all things ai and distributed systems
bookmarking here to read this soon alexzhang13.github.io/blog/2024/ef...
Alex L. Zhang | A Meticulous Guide to Advances in Deep Learning Efficiency over the Years
A very long and thorough guide how deep learning algorithms, hardware, libraries, compilers, and more have become more efficient.
alexzhang13.github.io
The state of post-training in 2025: a tutorial on modern post-training A re-record of my NeurIPS tutorial on language modeling (plus some added content on the high level state of things) Blog + extra context: https://buff.ly/424VvLm YouTube: https://buff.ly/40808l5 Slides: https://buff.ly/404jGa9
The state of post-training in 2025
A re-record of my NeurIPS tutorial on language modeling (plus some added content).
buff.ly
new blog post Evolution of AI-assited coding features and developer interaction patterns. I go through the history of progression of ai-assisted coding features, talk about how we interact with them and a Gears analogy control vs speed tradeoff sankalp.bearblog.dev/evolution-of...
The Evolution of AI-assisted coding features and developer interaction patterns
Yes, I agree that's a fancy title. There have been several developments over the last 7 years in the AI-assisted coding arena. We have gone from simple autoc...
sankalp.bearblog.dev
First slide deck for NeurIPS is done -- an overview of how I view post-training for applications. A higher level summary on the key decisions along the way of scoping a problem, choosing a base model, optimization algorithm, etc. (+some thoughts on OpenAI's RL Finetuning). https://buff.ly/3ZpY5IR
will check this out for synthetic data creation and evals
Ben Burtenshaw@benburtenshaw.bsky.social · 2y agosmol course Day 1 ✅. I learnt that people are hungry for models they can own. 📚 Material focused on instruction tuning. Split into chat templates and supervised fine tuning. There's more to this subject than this, but we're keeping things smol. ⏩ If you haven't already, try out module 1! 🧵
New post! OpenAI's o1 using "search" was a PSYOP. How to understand OpenAI's o1 models as really just one wacky, wonderful, long chain of thought. A fun one trying to communicate intuitions for what large scale RL training does to LLMs. Much more to explore here in 2025!
OpenAI's o1 using "search" was a PSYOP
How to understand OpenAI's o1 models as really just one wacky, wonderful, long chain of thought
buff.ly
Wow, this is such a useful resource of industry LLM applications! And filtering via search/tags is so responsive. I was thinking of compiling something like this over the holidays (ala applied-ml) but thanks to @strickvl.bsky.social I can spend the time reading instead ♥️ zenml.io/llmops-datab...
Alex Strick van Linschoten@strickvl.bsky.social · 2y ago🤔 Do you ever wonder how companies are putting LLMs and GenAI apps into production? What stacks do they use? What architecture did they go with? I put together a database of known public technical writeups with summaries of the key technical features.
