Outcome School Newsletter
Outcome SchoolOutcome School by Amit Shekhar
| Platform |  | Pricing | Only free issues | Publishes | Twice weekly |
|---|---|---|---|---|---|
| Issues | 68 | Founded | a year ago | Last Issue | 8 days ago |
| Active | |||||
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outcomeschool.substack.comLatest Issues
How Prefill, Decode, HyDE, and Hybrid Search Work
Seven new blogs this week, covering LLM inference internals, three ways to make retrieval and caching smarter, PyTorch fundamentals, and two prompting techniques.
Let’s get started.
Prefill vs Decode: LLM Inference Optimization...
How LangChain, LangGraph, and SGLang Actually Work
Six new blogs this week, covering the frameworks that build LLM apps, the engines that serve them fast, the search that powers them, and the chips underneath it all.
Let’s get started.
How does LangChain work?
LangChain is a fr...
How Computer-Use Agents, Embeddings, and DLMs Work
Five new blogs this week, covering agents, embeddings, a new way to generate text, and a fresh research report.
Let’s get started.
How do Computer-Use Agents work?
A computer-use agent is an AI program that can operate a comput...
vLLM, Function Calling, and World Models explained
How does vLLM work?
In this blog, we will learn about how vLLM works. We will also see why we need it, how it manages memory so cleverly, and where it is used in the real world to serve large language models to many users at once.
Serv...
How Prompt Caching, RAG, and GGUF actually work
This week I wrote six blogs. Each one takes a single idea and explains it slowly, in very simple words, until a complete beginner understands every single word.
Together they cover the pieces behind modern AI search, RAG, and local inferen...
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