AI 101
Machine learning fundamentals explained: LLMs, agents, RAG, transformers, inference & RL. Turing Post's AI 101 series — updated weekly.
AI 101
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14 min read
Jun 20, 2026
How Responsible AI Changes In The Agent Era
Responsible AI is becoming infrastructure for AI agents: runtime controls, system accountability, human oversight, and safeguards for tools that act
AI 101
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14 min read
Jun 18, 2026
What Is JEPA? Joint Embedding Predictive Architecture
JEPA explained: Yann LeCun's Joint Embedding Predictive Architecture for world modeling. Covers I-JEPA, V-JEPA, MC-JEPA, architecture & key concepts.
AI 101
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17 min read
Jun 13, 2026
AI 101: Hermes Agent vs OpenClaw: Local AI Agents Compared
Hermes Agent vs OpenClaw compared: memory architecture, self-improving skills, scheduling, and safety. Which local AI agent fits your workflow?
AI 101
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13 min read
Jun 4, 2026
What is Cosmos World Foundation Model Platform?
NVIDIA Cosmos is a platform of world foundation models for Physical AI: video curation, tokenizer, diffusion and autoregressive WFMs, and guardrails explained. Plus sensational 2026 update – Cosmos 3 omnimodel world model
Concepts
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10 min read
May 21, 2026
AI 101: From Tokens to Answers: What Actually Happens During LLM Inference
How LLM inference works end-to-end: tokenization, embeddings, prefill, decode, KV cache, batching, retrieval, and modern inference orchestration.
Concepts
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10 min read
May 13, 2026
AI 101: Your Ultimate Guide to Attention: Mechanism, QKV, and KV Cache
Learn how attention in AI works, from queries, keys, and values to KV cache, self-attention, and modern approaches
AI 101
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8 min read
May 11, 2026
xLSTM Explained: Extended LSTM vs Transformers
xLSTM extends classic LSTM with exponential gating and matrix memory. Learn how it compares to Transformers, what sLSTM and mLSTM are, and when to use it.
Concepts
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2 min read
May 10, 2026
Concepts: Types of Deep Learning
9 types of deep learning explained: CNNs, RNNs, transformers, GANs, diffusion models & more. Visual flashcards for ML practitioners. Turing Post AI 101.
AI 101
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11 min read
May 6, 2026
AI 101: Agentic Vector Databases – What Is That?
How vector databases are evolving for AI agents: agentic RAG with Qdrant, memory layers with Weaviate Engram, and Pinecone Nexus knowledge engine explained.
Concepts
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11 min read
Apr 29, 2026
AI 101: What’s So Magical About Embeddings?
How tokens become learnable coordinates, and geometry shapes how context connects and meaning comes to life
Concepts
+4
13 min read
Apr 22, 2026
AI 101: How Token Taxonomy Affects Your Bill
Input, output, reasoning, cached, vision — not all LLM tokens cost the same. A guide to token types and how each one shapes your AI bill.
Concepts
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12 min read
Apr 15, 2026
AI 101: What Is a Token (and why it runs AI)?
A token is the unit an AI model reads and predicts. Learn how tokenization works (BPE, WordPiece), why context windows matter, and how tokens set API cost.
AI 101
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13 min read
Apr 8, 2026
AI 101: Gemma 4 with OpenClaw: Architecture, Setup, and Why Developers Are Switching
Gemma 4 runs locally via Ollama with zero API cost. Full architecture breakdown — attention mix, MoE, per-layer embeddings — and why OpenClaw users are switching from Claude.
AI 101
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10 min read
Mar 25, 2026
AI 101: Transformers Depth Is an Addressable Dimension
Deep transformers used to accumulate layer history. Now they are starting to retrieve from it.
AI 101
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12 min read
Mar 18, 2026
Nemotron 3 and the Surprising Coalition Building New AI in the Open
Nemotron Coalition is NVIDIA's bet on open frontier AI — with Mistral, Cursor, Black Forest Labs and others. How Nemotron 3 works and who holds power.
AI 101
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14 min read
Mar 11, 2026
AI 101: Beyond RL: The New Fine-Tuning Stack for LLMs
Discussing the rise of generated adapters, like Text-to-LoRA, Doc-to-LoRA and others, Evolution Strategies (ES) optimization and the future of dynamic fine-tuning
Concepts
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11 min read
Mar 4, 2026
AI 101: From Vibe Coding to Spec-Driven Development
Why vibe coding breaks at scale — and how spec-driven development (SDD) fixes it. Covers Kiro by AWS, GitHub Spec Kit, Tessl, and when to use each approach.
Concepts
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14 min read
Feb 25, 2026
AI 101: The Inference Chip Wars – MatX, Taalas, and the Cracks in the GPU Era
The inference chip landscape in 2026: NVIDIA Vera Rubin, MatX's programmable LLM accelerator, and Taalas' model-as-hardware approach compared on cost per token
AI 101
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14 min read
Feb 18, 2026
AI 101: OpenClaw Explained + lightweight alternatives
OpenClaw personal AI agent explained: Gateway architecture, SOUL.md, HEARTBEAT.md & 6 lightweight alternatives for constrained hardware and simpler setups.
AI 101
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13 min read
Feb 11, 2026
AI 101: "On-Policy Distillation Zeitgeist"
Can a model teach itself well in 2026? Checking out some banger papers on self-distillation that demonstrate a new phase
AI 101
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10 min read
Feb 4, 2026
AI 101: Conditional Memory and the Rise of Selective Intelligence
let's explore this new memory paradigm and why it's important as architectural principle
