I tried cloning @ShaanVP with AI

He (not real Shaan) told me how to make my first million

It was a 3 part process:
1) Transcription (w/ @DeepgramAI)
2) Background & Memories (w/ @LangChainAI)
3) Voice Clone

Full code + video tutorial link below 1) Transcription w/ @DeepgramAI*

Super easy to get +450 episodes of audio into a structured transcription (speakers separated)

New users get $200 free credits:

* DG helped sponsor this video and bring it to lifetiny.one/I7wMRIL

Here are the 5 AI niches I would pick if starting today:

1. B2B CompanyGPT Interface
2. ETL for Retrieval-As-A-Service
3. AI Characters
4. Unstructured to Structured Data
5. Around The Org - Enterprise Edition

The breakdown of each one: 1. B2B CompanyGPT Interface
50% of employers don’t pay for ChatGPT

This tells me:
1. Companies haven’t enabled their workforce with LLMs yet
2. There’s an opportunity for a closed-source interface for LLMs

Basically the ChatGPT you wish you had for your employees:

The @myfirstmilpod is filled with a ton of business ideas, stories, advice, mental models and words of caution

But pulling structured data out of an episode can be a pain

Let’s do a low-code friendly tutorial on a method that had awesome results using @LangChainAI @pinecone This method is easily adaptable to any podcast, video, meeting, movie, book, you name it

If you want to see a full video tutorial + full code, check out the links at the bottom of this thread

The LangChain Cookbook: Part 1 - The Fundamentals

This @LangChainAI tutorial will ramp you up to the 7 core concepts of building apps powered by language models

You’ll learn LC's Schema, Models, Prompts, Indexes, Memory, Chains, Agents

+150K views on YouTube. Code below! @LangChainAI Some comments from YouTube

I recently had a project to parse a ~1hr podcast for topics, ideas, sections etc. ~12K tokens

Then generate a few sentences to summarize each section.

300+ episodes

How would you approach this problem while keeping tokens down?

I did it in a few passes with @LangChainAI, cont Pass 1: Split the doc into big chunks.

Map reduce with custom prompts to pull out topics, no topic summary.

There ended up being 10-15 topics.

I found it was too much to ask the LLM to give me ~15 topics *and* a solid summary.

Relistening to sales calls is so March 2023

I built Thimble to help teams extract data from sales calls, powered by @LangChainAI

Here’s the journey so far: The problem? 100% of AEs that I’ve talked to say they ‘relisten’ to their sales calls for follow ups, remember next steps, and to up their CRM

Why now? Language models make it extremely easy to parse out the insights that AEs would normally pull manually

AI Trends I'm interested in 4/5/2023:

1. Managed Retrieval Engines - Getting the *right* context to your AI is tougher than it sounds. @Metal_io announced a @LangChainAI integration. I'll be watching

2. Plugin Developer Monetization...

(full thoughts in a notion doc below) 3. Plug-in Dev Shops - Small team of Devs can ‘translate’ 1000s of API for SMBs and services while taking a cut of traffic monetization

4. Unstructured > Structured - Extracting information from messy text is going to be huge

https://twitter.com/GregKamradt/status/1643027796850253824