AI Research Topics

Discover curated categories that organize my posts by themes.

The History of World Models

How AI learned to model its surroundings — from early control and reinforcement learning through the JEPA lineage to world foundation models for physical AI

Robotics

Where AI acts in the physical world — embodied AI, vision-language-action models, world models, humanoids, manipulation, and the leap from simulation to reality

Economics

The economics of intelligence — from the cost of a token to the future of work: pricing, compute as currency, growth, labor, and what abundance does to value.

Infrastructure

The systems AI runs on – compute, chips, data centers, inference, retrieval and memory – and the economics of serving intelligence at scale

Methods / techniques

How AI models are trained, tuned, and improved – RL methods like GRPO and DPO, distillation, fine-tuning, retrieval, quantization – the techniques behind working systems

Concepts

The foundational ideas modern AI is built on – tokens, embeddings, attention, scaling, inference – explained from the ground up for engineers and researchers

Architectures

The structural blueprints behind the models – transformers, mixture-of-experts, state-space models, diffusion, JEPA – and how each design choice shapes what a system can learn

Models

How AI models are built, what sets each generation apart, and where their architectures lead – from frontier LLMs to world models, traced in context.

The Org Age of AI

What has to change inside a company when intelligence becomes an operational resource? A series about workflow redesign, management, coordination, and organizational structure in an age when intelligence is becoming cheap, abundant, and programmable.

Open Source AI in 2026

How to think about open models when money, risk, and strategy are on the line

AI Literacy

how we talk to our children about AI will shape how they talk back to it

AI Agents

How AI agents work, think, and act autonomously. Turing Post covers multi-agent systems, agentic memory, and the evolving AI software stack — for practitioners.

AI 101

Machine learning fundamentals explained: LLMs, agents, RAG, transformers, inference & RL. Turing Post's AI 101 series — updated weekly.

The CV History

How computer vision evolved: from perceptrons to AlexNet, ImageNet & spatial intelligence. A research-backed series tracing decades of CV breakthroughs. By Turing Post.

AI Infra Unicorns

In-depth analysis of AI infrastructure companies powering compute at scale: CoreWeave, Nscale, GPU clouds & more. Turing Post's AI Infra Unicorns series.

FMOps

Foundation model operations in practice: RAG, fine-tuning, deployment, monitoring & vector databases. Turing Post's FMOps series for AI engineers.

"Froth on the Daydream"

Froth on the Daydream — Turing Post's weekly AI digest. Curated analysis of the most important AI research, models, and industry developments from the past week.

Community Twist

Curated space to ML practitioners and AI creators

The History of LLMs

The history of LLMs in depth: from mechanical translation and AI winters to transformers and ChatGPT. By Turing Post.

Interviews with Innovators

Conversations on AI agents, inference, open models, and the future of software — with researchers and founders from NVIDIA, OpenAI, Mozilla & more. By Turing Post.

Global AI Affairs

Global perspective, featuring China, Russia, India, Israel, Europe, and beyond

Twitter Library

AI concepts visualized: model architectures, RL approaches, JEPA, and agentic memory — explained through infographics and visual guides. Curated by Turing Post.

GenAI Unicorns

Inside the fastest-growing generative AI companies: OpenAI, Anthropic, Mistral, Perplexity, Character AI and more. In-depth analysis by Turing Post.

Origins

Origins traces who coined the terms that shaped AI, how they spread, and why their meanings changed over time.