- Put Your Own Logic Inside the Codex Agentic Loop
Understanding Codex hooks The post Put Your Own Logic Inside the Codex Agentic Loop appeared first on Towards Data Science.
- 10 Positions for Enterprise RAG That Mainstream Tutorials Get Wrong
Enterprise Document Intelligence [Vol.1 #M3] - The ten positions the series argues from, and the map of every article that argues them The post 10 Positions for Enterprise RAG That Mainstream Tutorials Get Wrong appeared first on Towards Data Science.
- Can an LLM Forget the Right Things?
Most LLM inference runtimes have no idea a physical deadline exists. This one refuses admission rather than miss a 33ms robot control cycle, evicts KV cache by meaning instead of age, and is written entirely in hand-written CUDA — no cuBLAS, no libtorch. The post Can an LLM Forget the Right Things? appeared first on Towards Data Science.
- Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash
Speculative decoding can turn underused CPU compute into faster token generation, without changing the model's output. In our vLLM tests, DFlash delivered 3.92x the autoregressive throughput with Qwen3.5-9B on Intel Xeon 6 at concurrency 1. We break down where the speedup comes from, explain the acceptance metrics, and show what determines whether speculation pays off. The post Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash appeared first on Towards Data Science.
- AI Agents Don’t Need More Context — They Need Typed Context
AI agents don’t just have a context problem—they have a context typing problem. When instructions, memory, retrieved evidence, and tool outputs are flattened into one string, their semantic boundaries can disappear. I built a lightweight, zero-dependency Python runtime that keeps those boundaries explicit, tracks provenance, and rejects invalid context transformations before they reach the model. This article walks through the implementation, tests, and what this approach does—and does not—guarantee. The post AI Agents Don’t Need More Context — They Need Typed Context appeared first on Towards Data Science.
- Survival Analysis and the Cox Proportional Hazards Model: A Beginner-Friendly Guide
From Kaplan-Meier curves to hazard ratios with runnable Python Code throughout The post Survival Analysis and the Cox Proportional Hazards Model: A Beginner-Friendly Guide appeared first on Towards Data Science.
- Parse the Folder, Not Just the PDFs: The Relational Tables RAG Needs on a Case File
Enterprise Document Intelligence [Vol.1 #14D] - The index lists what the case type demands before any folder is opened, and the two questions worth building for are not retrieval questions at all The post Parse the Folder, Not Just the PDFs: The Relational Tables RAG Needs on a Case File appeared first on Towards Data Science.
- Bug Detection Blind Spots in AI Coding Harnesses (GStack and Beyond)
28 debugging experiments reveal that AI struggles less with complexity than with missing information. The post Bug Detection Blind Spots in AI Coding Harnesses (GStack and Beyond) appeared first on Towards Data Science.
- Building a Proper Backend for My LangGraph AI Agent
Turning a demo agent into something that can keep real booking data The post Building a Proper Backend for My LangGraph AI Agent appeared first on Towards Data Science.
- Multi-Document RAG: A Folder of Unrelated PDFs Is One Long Document with a Nested Outline
Enterprise Document Intelligence [Vol.1 #14B] - No shared fields means no index to build. One summary line per file plus each file’s own table of contents, and retrieval routes down two levels The post Multi-Document RAG: A Folder of Unrelated PDFs Is One Long Document with a Nested Outline appeared first on Towards Data Science.