I’m Shawn, founder of Memories.ai, former researcher at Meta and CS PhD at University of Cambridge.
Today we’re launching Memories.ai: we built the world’s first Large Visual Memory Model - to give AI human-like visual memories.
Why visual memory?
AI to date is chat-based, which has great applications, but humans are not only chat based. Humans have visual-memories, and are visually driven. Memories.ai enables AI agents, softwares, and even robots to see and remember the world the way humans do.
We just raised an $8m seed round led by @susaventures to build the visual-memory layer for AI.
Why is Memories.ai so groundbreaking? 👇
Flow Matching (FM) is one of the hottest ideas in generative AI - and it’s everywhere at #ICML2025.
But what is it? And why is it so elegant? 🤔
This thread is an animated, intuitive intro into (Variational) Flow Matching - no dense math required.
Let's dive in! 🧵👇
How to build a thriving open source community by writing code like bacteria do 🦠. Bacterial code (genomes) are:
- small (each line of code costs energy)
- modular (organized into groups of swappable operons)
- self-contained (easily "copy paste-able" via horizontal gene transfer)
If chunks of code are small, modular, self-contained and trivial to copy-and-paste, the community can thrive via horizontal gene transfer. For any function (gene) or class (operon) that you write: can you imagine someone going "yoink" without knowing the rest of your code or having to import anything new, to gain a benefit? Could your code be a trending GitHub gist?
This coding style guide has allowed bacteria to colonize every ecological nook from cold to hot to acidic or alkaline in the depths of the Earth and the vacuum of space, along with an insane diversity of carbon anabolism, energy metabolism, etc. It excels at rapid prototyping but... it can't build complex life. By comparison, the eukaryotic genome is a significantly larger, more complex, organized and coupled monorepo. Significantly less inventive but necessary for complex life - for building entire organs and coordinating their activity. With our advantage of intelligent design, it should possible to take advantage of both. Build a eukaryotic monorepo backbone if you have to, but maximize bacterial DNA.
10 GitHub repos to build a career in AI engineering.
It covers:
- Basics of AI/ML
- Neural networks
- Research paper implementations
- MLOps
- LLMs/RAG/Agents, etc.
Check the 100% free roadmap in the thread below:
“I didn’t intend to start a startup, I tried to get a job at Netscape, but nobody responded. So I decided to just start writing software myself and see where it goes” -@elonmusk origin story
These might be the best guides on:
- Prompt Engineering
- Building Agents
- AI integration strategies
- Working with AI
So much free value by OpenAI, Anthropic, and Google.
All the links below.
Hands-On Large Language Models!
This repository contains the complete code examples from the book Hands-On Large Language Models.
It includes notebook examples that cover everything from the introduction to language models to fine-tuning them.
100% Open Source
Introducing Meta Perception Language Model (PLM): an open & reproducible vision-language model tackling challenging visual tasks.
Learn more about how PLM can help the open source community build more capable computer vision systems.
Read the research paper, and download the code and dataset: go.fb.me/je2t32
data is probably the most underrated aspect of ML
everybody wants to work on cool models and training things,
but as you get wiser you realize that not the best algorithms can compensate for sh**ty data.
rule of thumb: spend at least 80% of your time on data
Practical book on electronics repair for beginners (it actually helped me in troubleshooting a few devices)
Third edition was published a few months ago, with new chapters.
I’ve Compiled the Third Set of Style Codes and Prompts
· Includes 21 style codes and over 100 prompts.
· Please follow me, repost, and leave a comment, or join the community on my homepage, and I’ll send it to you via private message.
· File size: 200 MB.
· Thank you to the authors.
If you haven’t received it, please be patient; there are many people, and I hope you understand.
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Open Source, APIs, and tools for data/ML. 🤖
Building @FastAPIcloud. ⚡️
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