Long Context Isn’t Free — I Built a Safe Prompt-Pruning Layer That Makes LLM Systems Work
LLMs don’t fail because they forget—they fail because they remember too much. As conversations grow, prompts accumulate redundant and low-value tokens, driving up cost and latency while silently degrading output quality. This article introduces a deterministic prompt-pruning layer that reduces token...
I Built My Second ETL Pipeline. This Time, I Started Thinking Like a Data Engineer
Building a production-ready RSS pipeline with Python, Docker, PostgreSQL, and Kestra
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PySpark for Beginners: Building Intermediate-Level Skills
A practical next step into partitions, shuffles, joins, caching, and execution plans.
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What our over-dependence on external consulting teaches us about delegating our minds to machines
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Map AI value, design workflows, redefine talent, upgrade the executive team, and measure the business impact.
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How to decide when an AI agent should act on its own by using cost asymmetry instead of a fixed confidence cutoff
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Measuring Structure Stability of Econometric Models
The simplest most important idea for time series forecasting
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Proxy-Pointer RAG: Temporal Reasoning Without Semantic Precompilation
A technical comparison of Proxy-Pointer and LLM-Wiki
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Survival Analysis for Data Drift and ML Reliability
Treating model degradation as a time-to-failure problem
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Best-worst comparisons, MaxDiff-style judging, and Plackett-Luce utility scores give agent teams a cleaner way to decide which configs to ship, prune, and route toward next.
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Assemble Each RAG Generation Prompt from a Base Prompt Plus the Rules Each Question Needs
Enterprise Document Intelligence [Vol.1 #8B] - A fixed BASE, the rules each question needs, one registry: the dispatcher that turns a parsed question into a typed LLM call
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PANet Paper Walkthrough: When Feature Pyramids Go Bottom-Up
Understanding how PANet shortens the path between low-level and high-level features
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Stop Returning Text from RAG: The Typed Answer Contract That Prevents Hallucination
Enterprise Document Intelligence [Vol.1 #8A] - The schema is the contract: every field is a question the pipeline asks the model, and every answer is checkable
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LLM Wikis Are Over-Engineered — I Replaced Mine With a Pure Python Compiler
Most "LLM wikis" use agents, embeddings, and repeated model calls to organize local notes. I built a deterministic alternative: a pure Python compiler that turns messy markdown into a linked, linted wiki using only the standard library. Along the way, I fixed two real bugs, benchmarked the pipeline ...
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.
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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
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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
The post Context Engineering for RAG : The Four...
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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