Next, type a prompt about the content you want to make slides from → and it will generate slideData.
const slideData = [
{
type: ‘title’,
title: ‘The Busy Person’s Intro to Large Language Models’,
date: ‘2025.09.21’,
notes: ‘Re-recorded overview of LLMs focusing on what they are, how to run them, how they are trained, and why next-word prediction works’
},
{
type: ‘content’,
title: ‘Agenda’,
points: [
‘What is an LLM’,
‘Running a model locally’,
‘Training at scale’,
‘Why next-word prediction is powerful’,
‘Key takeaways’
],
notes: ‘Keep it practical and concrete. We start with the simplest mental model, then show inference vs training, then the intuition for why LLMs encode knowledge’
},
{
type: ‘section’,
title: ‘1. What is an LLM’,
notes: ‘Define an LLM at the simplest level and frame the talk around artifacts you can hold on disk’
},
{
type: ‘content’,
title: ‘LLM = Two Files’,
subhead: ‘Weights + Runner’,
points: [
‘Parameters file holds learned weights’,
‘Runner implements forward pass of the network’,
‘Self contained package runs offline’
],
notes: ‘Stress that inference can be simple and local. The interesting part lives in the parameters learned during training’
},
{
type: ‘content’,
title: ‘Example: Llama 2 • 70B’,
subhead: ‘Open weights from Meta’,
points: [
‘Sizes include 7B 13B 34B 70B’,
‘Open weights and paper enable local study’,
‘Closed APIs trade control for convenience’
],
notes: ‘Contrast open weights with closed APIs like hosted assistants without exposing internals’
},
{
type: ‘section’,
title: ‘2. Running a Model’,
notes: ‘Give a feel for local inference and why the demo used a smaller model for speed’
},
{
type: ‘process’,
title: ‘Local Inference Flow’,
subhead: ‘From files to generations’,
steps: [
‘Get weights file on disk’,
‘Compile or install a runner’,
‘Point runner to weights’,
‘Send a prompt’,
‘Stream generated tokens’
],
notes: ‘Mention that a 7B model is fast enough on a laptop while 70B is roughly an order of magnitude slower’
},
{
type: ‘compare’,
title: ‘Open Weights vs Closed API’,
leftTitle: ‘Open Weights’,
rightTitle: ‘Closed API’,
leftItems: [
‘Full local control’,
‘Offline capable’,
‘Tunable and inspectable’
],
rightItems: [
‘Best quality access’,
‘No infra to manage’,
‘Faster to integrate’
],
notes: ‘There is no universal winner. Choose based on control privacy latency and capability needs’
},
{
type: ‘section’,
title: ‘3. Training at Scale’,
notes: ‘Explain where weights come from and why training is expensive compared to inference’
},
{
type: ‘content’,
title: ‘Training Ingredients’,
subhead: ‘Data Compute Cost’,
points: [
‘Large curated text corpus’,
‘Massive GPU clusters for days’,
‘Significant budget for runs’
],
notes: ‘Use order of magnitude thinking rather than exact numbers. Emphasize engineering and data work beyond raw compute’
},
{
type: ‘timeline’,
title: ‘From Corpus to Weights’,
subhead: ‘Very high level lifecycle’,
milestones: [
{ label: ‘Collect and filter data’, date: ‘Step 1’, state: ‘done’ },
{ label: ‘Train on GPU cluster’, date: ‘Step 2’, state: ‘done’ },
{ label: ‘Evaluate and align’, date: ‘Step 3’, state: ‘done’ },
{ label: ‘Release weights and runner’, date: ‘Step 4’, state: ‘next’ }
],
notes: ‘Keep the audience at the bird’s eye view. The output is a weights artifact you can reuse everywhere for inference’
},
{
type: ‘section’,
title: ‘4. Why It Works’,
notes: ‘Bridge from mechanics to intuition about capability’
},
{
type: ‘content’,
title: ‘Next Word Prediction Objective’,
subhead: ‘Simple objective rich consequences’,
points: [
‘Model predicts a distribution over next token’,
‘Good prediction forces world knowledge’,
‘Patterns styles and relations get compressed’
],
notes: ‘Relate prediction to lossy compression intuition. To predict the next token the model must encode structure facts and relationships’
},
{
type: ‘content’,
title: ‘Common Misconceptions’,
points: [
‘It is not copy paste of the web’,
‘Bigger is not always better’,
‘Closed is not always better than open’
],
notes: ‘Clarify generalization vs memorization and the role of data quality and algorithms along with scale’
},
{
type: ‘section’,
title: ‘5. Takeaways’,
notes: ‘Land the plane with pragmatic guidance’
},
{
type: ‘content’,
title: ‘Practical Guidance’,
points: [
‘Choose model size for latency and cost’,
‘Use open weights for control’,
‘Use hosted APIs for frontier quality’
],
notes: ‘Map choices to product constraints such as privacy cost latency and maintainability’
},
{
type: ‘closing’,
notes: ‘Thanks for watching. Invite questions and link resources in the description’
}
];
