Understanding AI fundamentals and unexplored opportunities
Large Language Models are neural networks trained on massive text datasets to predict the next word in a sequence. They don't "understand" in a human sense - they're incredibly sophisticated pattern matching engines that have learned the statistical relationships between words.
Key Concepts:
3Blue1Brown's visual introduction to how transformers and attention mechanisms work.
Watch Video →Jay Alammar's legendary visual guide to understanding transformer architecture.
Read Guide →Learn to train and deploy models. No PhD required.
Free Course →Vector databases store and search embeddings - numerical representations of meaning. They enable semantic search ("find similar concepts") rather than keyword matching. This is the foundation of RAG (Retrieval Augmented Generation) systems.
Managed vector database. Scales to billions of vectors. Great docs and free tier.
Try Pinecone →High-performance vector search. Docker deployable. Production-ready.
Documentation →Vector database with built-in ML models. Hybrid search capabilities.
Get Started →Fine-tuning: Take a pre-trained model and adapt it to your specific use case. Faster, cheaper, usually better.
Training from scratch: Only for specific domains or when you need complete control. Requires massive data and compute.
Fine-tune GPT-3.5 on your data. Simple API, pay per use.
Train custom models with no code. Supports text, vision, and more.
Start Training →Fine-tune large models on consumer GPUs. Efficient parameter updates.
GitHub →Train an LLM on thousands of contracts and negotiation outcomes. It suggests counteroffers, flags unfair terms, and predicts negotiation outcomes. B2B SaaS gold - every business negotiates contracts.
Voice-to-text dream capture → AI analysis using Jungian/psychological frameworks → pattern recognition over time. Nobody's done this well. Subscription model, deeply personal, high retention.
Upload building plans → AI checks against local building codes. Massive market, currently done manually by expensive consultants. Partner with one city to start.
Analyze Zoom recordings for confidence levels, hedging language, and contradiction patterns. Sell to VCs, M&A teams, and hiring managers. Ethically questionable = competitively defensible.
Photo → instant valuation of estate items. Combines visual recognition with eBay/auction data. Estate lawyers and families desperately need this. Charge per estate or monthly for pros.
Churches and universities have thousands of hours of content nobody can search. Transcribe → embed → semantic search. "Find every time pastor mentioned forgiveness." Sell to denominations.
Scan thrift store photos → identify underpriced designer items → estimate resale value. The vintage/resale market is exploding. Subscription for professional resellers.
Every city has byzantine permit processes. Train on successful applications. "I want to add a deck" → exact permits needed, timeline, and pre-filled forms. Charge homeowners $50-100.
Parents starting micro-schools need curriculum and assessment help. AI generates lesson plans, tracks progress, suggests activities. $50-100/month per micro-school.
Interview grandparents with AI-guided questions → transcribe → create searchable family history. Deepens based on answers. Families pay $200-500 for permanent digital legacy.
Build LLM applications with ease. Chains, agents, and memory management.
Documentation →Run AI models in the cloud with one line of code. Pay per prediction.
Browse Models →Track experiments, visualize results, and manage model artifacts.
Get Started →1. Specific pain points: Each targets a real, expensive problem
2. Clear monetization: People already pay for inferior solutions
3. AI advantage: 10x better than current manual/software solutions
4. Defensible: Domain expertise + data moat + customer relationships