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🌍 Ready to validate your AI skills? Join NVIDIA Training’s global webinar to prepare for the Generative AI Certification exams.
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SQL mastery isn’t about syntax.
It’s about the questions you’re too afraid to ask. 👇
Anyone can write SELECT *.
That’s table stakes.
- Why did adding an index make things slower?
- Why does the same query get a different plan tomorrow?
- Why does an UPDATE sometimes hurt more…
Announcing our first product: Tinker!
Tinker is a training API for everyone! It lets you focus on what matters in LLM training - your data and algorithms - while we handle the heavy lifting of distributed training.
You can train your own models using Tinker even if you have no…
Announcing our first product: Tinker!
Tinker is a training API for everyone! It lets you focus on what matters in LLM training - your data and algorithms - while we handle the heavy lifting of distributed training.
You can train your own models using Tinker even if you have no…
GPUs are expensive and setting up the infrastructure to make GPUs work for you properly is complex, making experimentation on cutting-edge models challenging for researchers and ML practitioners.
Providing high quality research tooling is one of the most effective ways to…
How do you build effective AI Agents?
This is a problem I think deeply about with other AI devs and students.
Simplicity works well here.
I think we can all learn a lot from how Claude Code works. The Claude Agent SDK Loop generalizes the approach to build all kinds of AI…
The main 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗧𝘆𝗽𝗲𝘀 👇
It’s no secret that an AI Agent is a piece of software wrapping an LLM or multiple LLMs as a reasoning engine to guide its execution flow. The deployment types for these agents also closely resemble those of software or…
The 5-part path to a $200k AI DS career:
1. Code daily (Python)
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5. Create AI DS Agents w/ LangGraph
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One of the biggest mistakes I see on early career ML resumes is listing too many skills.
Claiming expertise in too many skills signals lack of depth in any single one.
Python, R, Java, C++, SQL, PyTorch, TensorFlow, scikit-learn, Spark, Airflow, Docker, AWS, GCP, Azure……
[gpu arc]
As a person in ML, I’ve used GPUs quite a bit and always had the curiosity to understand how they actually work. I’d often see people on my TL doing CUDA programming, talking about kernels and stuff, and I honestly had no clue what they meant.
So I finally decided to…
Here's a common misconception about RAG!
When we talk about RAG, it's usually thought: index the doc → retrieve the same doc.
But indexing ≠ retrieval
So the data you index doesn't have to be the data you feed the LLM during generation.
Here are 4 smart ways to index data:…
We're looking for 2 interns for Summer 2026 at the MIT-IBM Watson AI Lab Foundation Models Team.
Work on RL environments, enterprise benchmarks, model architecture, efficient training and finetuning, and more!
Apply here: forms.gle/H6dNSywXCjDDyB…
𝗥𝗔𝗚 (Retrieval Augmented Generation) vs. Memory
These two terms are often confused, but they play very different roles.
RAG grounds an LLM with external knowledge.
A query is embedded, matched against a vector store, and the top k-chunks are injected into context.
Memory is…
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