Topic
Natural Language Processing
5 episodes
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Graph Engineering Athropic Playbook
Anthropic's knowledge-graph engineering playbook replaces classical NLP pipelines (trained NER, relation classifiers, entity-resolution heuristics) with a sequence of Claude API structured-output calls. The entire extraction-resolution-assembly-querying loop becomes prompt-based, scaling from Haiku (high-volume extraction) to Sonnet (reasoning). The graph serves multi-agent systems as shared memory, grounding layer for evaluator-optimizer loops, and persistent world model across sessions. The paper maps this onto Anthropic's five canonical agent patterns and reports precision/recall against a gold set.
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Overview: Task Decomposition
We finally slow down on task decomposition, the quiet trick underneath agents, code review workflows, web tasks, and a lot of the stuff we keep arguing about. We use one mental model, a messy project board becoming manageable tickets, and build from intuition to mechanism to where it still matters now.
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Overview: Deep Learning
We finally slow down and make deep learning click: what the deep part means, how layers learn features, and why training needs data, compute, loss, backpropagation, and gradient descent all working together.
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Overview: Natural Language Processing
We keep running into natural language processing everywhere, so we finally sat down and made it the whole point. We walk through what NLP is, why language is such a weird machine problem, and how the field moved from rules to learned representations.
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Beyond Dashboards Introducing Decision Execution Platforms
Databricks introduces Decision Execution Platforms (DEPs), a new analytics category that automates the full executive decision loop—from signal detection through execution to outcome measurement—on governed Lakehouse infrastructure. The article argues that traditional BI only improves decision inputs; DEPs aim to orchestrate the entire decision workflow, with a Fortune 100 retail case study targeting a $100M+ fulfillment gap. Tyler questions whether the architecture genuinely solves the constraint-expressibility problem that kills most agent systems in practice; Pippa sees the product framing as a real reset from 'dashboards tell you what's wrong' to 'the system executes and measures what you chose.'