All Episodes
The Lindahl Letter — 145 episodes
Welcome to 2026 and beyond
2025 End of Year Recap
Nested learning and the illusion of depth
The great 2025 LLM vibe shift
The 5 biggest unsolved problems in quantum computing
Process capture and the future of knowledge management
The great manufacturing reset
Why a “combiner model” might someday work
The edge of realized technology
Spooky Halloween edition: When Satoshi-Era Wallets Wake Up
AI Is Burning Through Graphics Cards
Social media stopped being social
Building with constant model churn
Enforcing AI standards without exception
The Great Tokenapocalypse
Apple’s Hidden AI Strategy
Context window garbage collection
Portable knowledge sharding
Personalized context bubbles
Context window fragmentation
My 200th Lindahl Letter Explained
Machines that build machines
Magic state distillation explained
Is quantum computing becoming an establishment play?
Annealing vs. gate based quantum computing
The top 10 quantum computer leaderboard
A 56 day posting break
Designed to Distract: How Technology Gets Your Attention
Inside the Mind: The Science of Focus and Distraction
Your valuable attention: Why Your Focus Is Under Siege
Quantum Computing and Advances in Time Crystals
Universal quantum computation
Error correction tolerant quantum computing
Nondeterministic gates tolerant quantum computation
Transfer Learning for Features
Graph-Based Feature Engineering
Are 8K Blu-ray a thing?
Pure science from the last millennium
Enabling automated agent actions
Where does AI fit within Archeology?
Those recent Nobel Prizes
Practical quantum computing and getting things done
Digging into quantum computing programing
The increasingly synthetic internet
Indexing facts vs. graphing knowledge
Structuring really large knowledge graphs
Increasingly problematic knowledge graph updates
The next level of featurization
All the future AI features
You can’t stop the signal
My 2024 predictions
3 years on Substack
Bayesian Models and Elections (150th post)
Election simulations & Expert opinions
Delphi method & Door-to-door canvassing
Knowledge graphs vs. vector databases
Synthetic social media analysis
Learning LangChain
Building generative AI chatbots
Proxy models for elections
Machine learning election models
Election prediction markets
Tracking political registrations
Econometric election models
Polling aggregation models
The chalk model for predicting elections
Automated survey methods
Synthetic data notebooks
Bulk imagine improvement
Build captain fractal using Colab
How do you use Colab in a generative way?
Democratizing AI system security
Profiling Google DeepMind
Profiling Hugging Face
Profiling OpenAI
Pivoting to AI + Security
We are wholesale oversubscribed on AI related content
AIaaS: Will AI be a platform or a service? Auto-GPT will disrupt both
Considering an independent study applied AI syllabus
That one with an obligatory AI trend’s post
All that bad data abounds
A paper on political debt as a concept vs. technical debt
A literature study of non-mail polling methodology
A literature study of mail polling methodology
A literature review of modern polling methodology
How does confidential computing work?
Structuring an introduction to AI ethics
Autonomous vehicles
Natural language processing
Chatbots and understanding knowledge graphs
Robots in the house
Twitter as a company probably would not happen today
Highly cited AI papers
Code generating systems
Building out a better backlog
That 2nd year of posting recap
Rethinking the future of ML
ML pracademics
Back to the ROI for ML
Overcrowding and ML
Deep generative models
My thoughts on ChatGPT
MIT’s Twist Quantum programming language
Generative AI: Where are large language models going?
Getting to quantum machine learning
AI hardware (RISC-V AI Chips)
Papers critical of ML
We have a National Artificial Intelligence Advisory Committee
What are ensemble ML models?
What is probabilistic machine learning?
That ML model is not an AGI
The future of academic publishing
MLOps (ML syllabus edition 8/8)
Ethics, fairness, bias, and privacy (ML syllabus edition 7/8)
Neuroscience (ML syllabus edition 6/8)
Neural networks (ML syllabus edition 5/8)
Machine learning approaches (ML syllabus edition 4/8)
ML algorithms (ML syllabus edition 3/8)
A machine learning literature review (ML syllabus edition 2/8)
Bayesian optimization (ML syllabus edition 1/8)
Why is diffusion so popular?
Trust and the future of digital photography
Is quantum machine learning gaining momentum?
What is post theory science?
Is ML destroying engineering colleges?
ML content automation; Am I the prompt?
Symbolic machine learning
Open source machine learning security plus the machine learning and surveillance bonus issue
What are the best ML newsletters?
Web3 the decentralized internet
A machine learning cookbook?
Publishing a model or selling the API?
My thoughts on NFTs
Does a digital divide in machine learning exist?
Ethics in machine learning
Language models revisited
Sentiment and consensus analysis
Touching the singularity
AI network platforms
General artificial intelligence
Multimodal machine learning revisited
Teaching or training machine learning skills
How would I compose an ML syllabus?
Comparative analysis of national AI strategies
Who is acquiring machine learning patents?