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The systems AI runs on – compute, chips, data centers, inference, retrieval and memory – and the economics of serving intelligence at scale
Concepts
+2

10 min read
May 21, 2026
How LLM inference works end-to-end: tokenization, embeddings, prefill, decode, KV cache, batching, retrieval, and modern inference orchestration.

AI 101
+3

11 min read
May 6, 2026
How vector databases are evolving for AI agents: agentic RAG with Qdrant, memory layers with Weaviate Engram, and Pinecone Nexus knowledge engine explained.

Concepts
+4

13 min read
Apr 22, 2026
From reasoning tokens to vision patches – your guide to the species that now shape AI cost, speed, and capability

AI 101
+3

12 min read
Mar 18, 2026
How NVIDIA amplifies the open model space with an outstanding lineup of partners: Black Forest Labs, Cursor, LangChain, Mistral AI, Perplexity, Reflection AI, Sarvam and Thinking Machines


Concepts
+2

14 min read
Feb 25, 2026
From NVIDIA Vera Rubin to model-as-hardware, and why “inference chips” are no longer one category


AI 101
+1

13 min read
Sep 10, 2025
Everything you need to know about CPU, GPU, TPU, ASICs, APU, NPU and others, unpacking the meaning behind these abbreviations

Concepts
+3

10 min read
Apr 2, 2025
How to optimize LLM inference latency and throughput: quantization, batching, KV cache, speculative decoding, GPU vs TPU, and hardware accelerators.

AI 101
+2

7 min read
Sep 11, 2024
we discuss the innovative combination of VectorRAG and GraphRAG in HybridRAG, its impact on financial document analysis and other areas of implementation, and clarify related terms for better understanding

AI 101
+2

8 min read
Aug 14, 2024
Speculative RAG uses a small drafter model and a large verifier to improve speed and accuracy. Learn how it works, where it excels, and its key limitations


AI 101
+3

4 min read
Jul 10, 2024
LongRAG uses 4K-token retrieval units instead of 100-word chunks, reducing corpus size 30×. How LongRAG architecture works and how it compares to standard RAG.


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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