t0-alpha is a decoder-style patch transformer for probabilistic time-series forecasting. Raw series are split into 32-step patches, embedded, processed through causal time-attention and group-attention layers, and decoded into future quantiles rather than a single point forecast.
The post Time-Serie...
Persistent Latent Memory for Multi-Hop LLM Agents: How a 6G Handover Paper Closes the Agent Cold-Start
Every hand-off in your multi-agent pipeline is an expensive tokenization round-trip. Discover how Inductive Latent Context Persistence (ILCP) transfers a compressed hidden state so downstream agents never have to re-create the same context.
The post Persistent Latent Memory for Multi-Hop LLM Agents:...
What Can We Do When Memory Becomes the New Bottleneck in Data Engineering?
How Pandas chunking, Dask, and Polars help process millions of records when adding more compute isn't an option.
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Build and deploy an agent on AWS with Strands and AgentCore
The post Build and Run Your Own AI Agent in the Cloud appeared first on Towards Data Science.
Context Engineering for RAG : The Four Typed Inputs Behind Every RAG Answer
Enterprise Document Intelligence [Vol.1 #7bis] - Tobi Lütke and Andrej Karpathy named the practice in 2025. For a single document, each brick emits typed pieces that converge on one LLM call. Corpus, conversation, and tool extensions are follow-up work
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I Completed Five Years in Analytics Consulting: 5 Lessons That Changed How I Work
The tools I use for analytics and reporting have changed more than I expected, yet my questions for any analytics project haven't moved much.
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Tail Control: The Counterintuitive Engineering of Reliable Agentic Workflows
Behind a customer's API, a high-quality answer isn't enough. It has to be usable, which means on time. Delivering that consistently is a problem about variance, not speed, and the fixes are counterintuitive.
The post Tail Control: The Counterintuitive Engineering of Reliable Agentic Workflows appear...
I Pitted XGBoost Against Logistic Regression on 358 Matches. The Boring Model Won.
A concrete bias–variance lesson: why the smallest model had the best cross-validated fit, and how to know when to reach for the big hammer.
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We Built a Routing Layer to Cut Our AI Costs. It Broke the Product.
A team cut their AI inference bill by more than half. Three months later, customer satisfaction was dropping and the cost savings were tied to the quality loss. Cost-optimization routing layers are a Pareto trap, and here's the detection methodology that catches them in days instead of months.
The p...
Using Gemma 4, Ollama, OpenAI Agents SDK, and Tavily MCP to build a lightweight research agent
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Amplify the Expert: A Philosophy for Building Enterprise RAG
Enterprise Document Intelligence [Vol.1 #M1] - The thesis behind every architectural choice in this series
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Vector RAG Isn’t Enough — I Built a Context Graph Layer for Multi-Agent Memory
I benchmarked raw chat history, vector-only RAG, and a context graph on the same multi-agent conversations. The results exposed a surprising weakness in relational retrieval.
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The Hot Path Belongs to GBDTs, Agents Own the Cold Path: A Payment-Fraud Benchmark
A reproducible benchmark on latency, cost, and reproducibility, and where agents actually earn their keep.
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3 Agents. 3 LLMs. 1 Aging GPU: Engineering Parallel Inference on Bare Metal
Beat the 8GB VRAM limit. Learn how to run three different LLMs on a single 8GB GPU using C++ layer multiplexing and admission control.
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Letting an LLM Pick the Right RAG Page: The Arbiter Pattern at the End of Retrieval
Enterprise Document Intelligence [Vol.1 #7C] - One LLM call ranks the candidates with reasons. The output is one typed object your auditor can defend
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A Three-Phase Factual Recall Circuit in Gemma-2B and Gemma-12B-IT
Activation patching reveals how facts are stored, routed, and read out across transformer layers, and why the residual stream does most of the work
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Anchor Detection for RAG: Parallel Detectors, Then One LLM Call at the End
Enterprise Document Intelligence [Vol.1 #7B] - Retrieval is filtering on structured tables: keywords first, TOC second, embeddings last
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Retrieval Is Filtering, Not Search: A Mental Model for Enterprise RAG
Enterprise Document Intelligence [Vol.1 #7A] - Stop searching strings. Filter line_df and toc_df. Pick anchors small, expand context large
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Build Your Own Local AI Coding Agent with Gemma 4 and OpenCode
From installing Ollama to launching OpenCode with a local model, step by step.
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Neural Networks, Explained for Beginners: Start Here If They’ve Confused You
The intuition behind neural networks and why they need activation functions.
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Tool Calling, Explained: How AI Agents Decide What to Do Next
Understanding ow LLMs interact with the world around them, from returning data to taking action
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What Are the Possibilities to Build Date Tables in Self-Service Environments?
For years, I created date tables with DAX code whenever I didn’t have a way to create them upstream of the data flow. Now I've realised there's another way to do it. Let’s see what the alternatives are and how they compare.
The post What Are the Possibilities to Build Date Tables in Self-Service Env...
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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