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Top Tracks
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introduction
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pretraining data (internet)
3
LLM Inference
4
tokenization
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LLM Training
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Deep Dive into LLMs like ChatGPT
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LLM dreams
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Intro into the growing LLM ecosystem
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neural network internals
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How do they work?
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ChatGPT interaction under the hood
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Finetuning into an Assistant
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inference
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Intro
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Summary so far
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LLM Scaling Laws
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Let's build GPT: from scratch, in code, spelled out.
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Jailbreaks
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Llama 3.1 base model inference
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AGI is still a decade away
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LLM Customization, GPTs store
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Prompt Injection
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LLM OS
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Multimodality (Vision, Audio)
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Designing LLM apps with partial autonomy
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LLMs as utilities, fabs, and operating systems
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The new LLM OS and historical computing analogies
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Lessons from Tesla Autopilot & autonomy sliders
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Data poisoning
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How I use LLMs
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knowledge of self
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Basic LLM interactions examples
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Be aware of the model you're using, pricing tiers
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Agents as the Installer
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Verifiability and Jagged Skills
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Menu Gen vs Raw Prompts
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LLM Security conclusions
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LLM Security Intro
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Feeling Behind as a Coder
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Software 3.0 Explained
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models need tokens to think
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File uploads, adding documents to context
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tokenization by example in a Web UI (tiktokenizer)
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Thinking models and when to use them
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What’s Obvious by 2026
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From Vibe Coding to Agentic Engineering
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jagged intelligence
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supervised finetuning to reinforcement learning
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Software Is Changing (Again)
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Founder Advice and Automation
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