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Matcha: Building a Local-First AI Resume–JD Matching Engine with Spring AI

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Introduction Building an AI application as a backend developer no longer requires pivoting to a new language or managing complex cloud infrastructure. By leveraging Spring AI , you can treat a Large Large Model (LLM) as just another service in your ecosystem. Matcha was prototyped and polished in just 3–4 hours . This speed is possible because Spring AI abstracts the "AI complexity" into familiar POJO-based patterns, allowing for rapid iterations—tuning prompts and refining logic in minutes rather than days. To ensure a systematic engineering defense of the architecture, I applied the S.C.A.L.E. Framework . This framework turns the chaos of open-ended design into a structured, defensible plan by focusing on trade-offs rather than just components. S: Scope and Size Let's begin by defining the Requirements (The MVP) and then calculating the Constraints . This defines the project boundary for a local-first recruitment tool. Functional Requirement (FR): The user can upload ...
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The Physics of Databases (Part 3): The Specialized Engines of the Final 10%

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Introduction In Part-1 and Part-2 , we mastered the transactional heavyweights. We learned how B-Trees and LSM-Trees manage the "Two-Layer Problem" of disk and network. But what happens when your data isn't just a row, but a relationship, a search term, or a high-dimensional concept? When general-purpose tools become your biggest bottleneck, you must enter the world of Specialized Physics . 1. The Inverted Index: The Physics of Search (Elasticsearch) Traditional databases are "Forward Indexes" ( $Key \rightarrow Row$ ). If you want to find every log entry containing the word CRITICAL , a B-Tree must perform a Full Table Scan , reading every byte of every row ( $O(N)$ ). The Mechanic: The Inverted Index. During ingestion, the engine (Lucene) tokenizes text into "terms." It builds a sorted map where the "Key" is the word and the "Value" is a Posting List (a compressed list of IDs where that word appears). Practical Example: Searc...
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