7 Crucial Barriers Between Data Teams and Self-Healing Data Architecture
What data teams need to build with AI to make self-healing data architecture a practical reality
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Parse Scanned PDFs for RAG with EasyOCR: Free OCR Gives You Words, Not a Document
Enterprise Document Intelligence [Vol.1 #5quinquies] - Same 1974 scanned PDF, two engines. EasyOCR recovers text. Docling recovers text + sections + figures. The structural gap makes one output usable downstream and the other one a flat string.
The post Parse Scanned PDFs for RAG with EasyOCR: Free ...
GPU-Resident Top-K for Agentic RAG: I Built a CUDA Kernel So My Retrieval Step Would Stop Bouncing Off the GPU
The PCIe transfer latency is silently bottlenecking your agentic inference. Here is how building a custom device-resident vector search kernel bypasses the CPU to unlock deterministic microsecond tail latencies.
The post GPU-Resident Top-K for Agentic RAG: I Built a CUDA Kernel So My Retrieval Step ...
Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each
Getting reliable, readable responses out of your LLM, and knowing which tool to reach for
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For decades, the existence of the hydrophobic core, a region in the 3D structure of proteins where hydrophobic amino acids reside together, has been considered a general property in proteins. What we have found now may extend that model. In particular, the rest of amino acids also seem to cluster to...
Dispatching the Parsed RAG Question: Chunk Strategy, Model Tier, Activations, Audit
Enterprise Document Intelligence [Vol.1 #6c] - The decisions the parser makes on top of the user string, using the document’s profile: dispatch, activations, full schema, three approaches to deciding what fires, the audit _meta block, and a broker-corpus walkthrough
The post Dispatching the Parsed R...
The Power and Pitfalls of Vector-Based Image Search
A hands-on guide to setting up image similarity search in Milvus, and why visual replication isn't always enough.
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The Secret to Reproducible and Portable Optimization: ORPilot’s Intermediate Representation (IR)
Why production-level AI optimization modeling agent needs reproducibility and portability, and how IR helps achieve them
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Most LLM applications need a clear workflow, not an autonomous agent. Here's how to build one in plain Python.
The post You Probably Don’t Need an Agent Framework appeared first on Towards Data Science.
Tired of your monthly API bill? Follow this tested guide to set up a high-performance local LLM on your Mac Mini without the headaches.
The post Run a Local LLM with OpenClaw on Your Mac Mini appeared first on Towards Data Science.
LLM Fallbacks Break Agent Pipelines — I Built the Missing Recovery Layer
LLM rate limits don't just interrupt agent pipelines—they can silently corrupt structured outputs when fallback models receive incompatible payloads. I built a recovery layer that classifies failures, adapts payloads across model tiers, preserves execution state, and maintains schema integrity durin...
The Protocol That Cleaned Up Our Agent Architecture
A detailed look at MCP that turned my scattered tool definitions into a stable, discoverable server
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I Built 11 Models to Predict the 2026 World Cup. They Crown Four Different Champions.
A single model hands you a single answer and no sense of how much it hinges on the dozens of choices buried inside it.
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Vision LLMs are PDF Parsers Too: Reading Charts and Diagrams for RAG
Enterprise Document Intelligence [Vol.1 #5quater] - The other parsers read the words on a page. A vision model also reads the pictures
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GPU Time-Slicing for Concurrent LLM Agents on Kubernetes
A systems-level deep dive into the hidden microarchitectural costs of Kubernetes GPU time-slicing, and what it actually costs to co-locate Agentic AI workloads.
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Larger Context Windows Don’t Fix RAG — So I Built a System That Does
Increasing context size in RAG systems doesn’t improve accuracy for aggregation tasks—it makes errors harder to detect. In this article, I benchmark retrieval-based pipelines against a deterministic full-scan engine across 100,000 rows and show why computation queries must be routed away from RAG en...
Parse PDFs for RAG Locally with Docling: Rich Tables, No Cloud Upload
Enterprise Document Intelligence [Vol.1 #5ter] - Table cells, OCR, captions, headings: cloud-grade structure, running on your own machine. No key, no per-page bill, nothing leaves the building
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A Harness for Every Task: Putting a Team of Claudes on One Job
Claude can now write its own harness on the fly, custom-built for the task at hand.
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Take the next step to building real workflows with Spark on your laptop
The post PySpark for Beginners: Beyond the Basics appeared first on Towards Data Science.
A quick guide to separating Physical AI from world models, embodied AI, physics AI, and digital twins
The post Physical AI: What It Is and What It Is Not appeared first on Towards Data Science.
Prefill Once, Fan Out: KV Snapshot Sharing for Multi-Agent LLM Pipelines
Stop re-computing the same context. Learn how to build a C++ runtime with copy-on-fork KV snapshots to eliminate redundant LLM prefills in multi-agent pipelines.
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The Exact ML Project I’d Build to Get Hired in 2026
Follow this framework to build a project that will impress hiring managers
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