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The structural blueprints behind AI models: transformers, mixture-of-experts, state-space models, diffusion, and JEPA — and what each design makes possible.
AI 101
+1

13 min read
Aug 28, 2026
LeJEPA by Yann LeCun: provably stable self-supervised learning without heuristics. SIGReg, isotropic Gaussian embeddings & world models explained.


AI 101
+3

10 min read
Jun 29, 2026
A practical guide to Web World Models, the web-based architecture for building persistent, controllable environments for AI agents.


AI 101
+1

17 min read
Jun 18, 2026
Joint Embedding Predictive Architecture predicts abstract representations, not pixels or tokens. Covers I-JEPA, V-JEPA, VL-JEPA, LeJEPA and robotics.


AI 101
+1

8 min read
May 11, 2026
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.

AI 101
+1

10 min read
Feb 4, 2026
Engram by DeepSeek gives LLMs conditional memory: models learn when to access knowledge. Covers architecture, U-shaped allocation law, and reasoning gains.

AI 101
+1

10 min read
Jan 8, 2026
DeepSeek's mHC fixes 3000× signal amplification in 27B models by constraining residual streams to the Birkhoff Polytope — with only 6.7% training overhead.


AI 101
+1

13 min read
Dec 3, 2025
Multimodal fusion is how AI combines text, images, and audio into one model. Covers early, late, intermediate fusion types and Meta's MoS approach.

AI 101
+3

12 min read
May 28, 2025
What is BERT in NLP? Learn how BERT works—MLM, NSP, fine-tuning—plus modern variants like RoBERTa, DistilBERT, ModernBERT, and ConstBERT in 2026.

AI 101
+2

11 min read
Apr 30, 2025
What are Liquid Foundation Models? LFM-1B to 40B benchmarks, Hyena Edge architecture, memory efficiency vs Transformers.

AI 101
+1

14 min read
Mar 26, 2025
We explore four advanced attention mechanisms which improve how models handle long sequences, cut memory use and make attention learnable

AI 101
+1

6 min read
Feb 19, 2025
Mixture-of-Mamba (MoM) brings modality-aware sparsity to Mamba SSM, cutting FLOPs by up to 75% while improving accuracy across text, image, and speech tasks.

AI 101
+1

8 min read
Jul 3, 2024
KAN (Kolmogorov-Arnold Networks) replaces fixed activation functions with learnable splines. How KAN works, how it compares to MLP, and where it falls short.

Turing Post is an AI newsletter for engineers, researchers, founders, and technical managers who want to understand how machine learning and AI actually work.
Built on more than two decades in tech and seven years focused on AI, we track the research that matters, the systems being built, and the ideas shaping the field, from LLMs and AI agents to JEPA, world models, retrieval, inference, evaluation, AI infrastructure, and agentic workflows.
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