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AI 资讯 Dev.to

Production CRUD in Java Without the Framework Tax

A practical walkthrough of SQL-First persistence: no XML, no Mapper interfaces, no generated queries. I maintain a Java backend that handles ~1M requests/day. For persistence, we used to run MyBatis. The XML was manageable at first, then it wasn't. Dynamic conditions became <if> tag soup. A simple join query needed three files and two languages. We switched to a simpler approach. Here's how it works for the most common case: single-table CRUD. What You Need Java 21+ Spring Boot (any 3.x) A database (H2 for the demo, MySQL/PostgreSQL for production) That's it. No XML parser, no code generator, no annotation processor. The Project pom.xml src/main/java/example/ DemoApplication.java user/ User.java -- entity UserDao.java -- data access UserCond.java -- query conditions src/main/resources/ application.yml schema.sql Three Java files for a complete CRUD API. The Entity @Data @Builder @Table ( "sys_user" ) public class User { @Id private Long id ; private String name ; private Integer age ; private String email ; // ... other fields // These four are auto-managed: private LocalDateTime createTime ; private Long createBy ; private LocalDateTime updateTime ; private Long updateBy ; private Byte dr ; // 0 = active, 1 = soft-deleted } @Table maps to the database table. @Id marks the primary key (Snowflake ID by default). The audit fields and soft-delete marker are handled automatically—you don't set them in business code. The DAO @Repository public class UserDao extends BaseDao < User > { // Empty. All CRUD methods inherited. } BaseDao provides save , saveBatch , update , delete , findById , list , page , count , exists . For single-table operations, this is all you need. The Conditions @Getter @Setter @Builder public class UserCond extends BaseCondition { private String name ; private Integer ageMin ; private Integer ageMax ; private Byte dr ; private Object [] ids ; @Override protected void addCondition () { and ( "name LIKE" , name , 3 ); // 3 = %value% and ( "age >=" , ag

Z. Gao 2026-06-18 11:06 10 原文
开发者 Product Hunt

Paradigm

Turn any goal into a personalized, adaptive learning path. Discussion | Link

Justin Jincaid 2026-06-18 10:09 3 原文
AI 资讯 HackerNews

Show HN: Local personal data redaction for any AI tools

I built the desktop app that detects and redacts personal data (or PII) locally without sending any text to server. It supports rule-based filtering and AI model-based redaction (eg openai privacy filter). It's open source and free. Please check out the repo and https://pii-gui.vercel.app/

unusual_typo 2026-06-18 09:52 4 原文
AI 资讯 Product Hunt

Tine

An AI desktop cursor that does the work for you Discussion | Link

2026-06-18 09:31 5 原文
AI 资讯 HackerNews

Ask HN: Has anyone had success with SBIR grants and what is the process like?

I'm considering applying to a phase I topic that is a pretty good fit for our tech and we should meet all basic requirements (U S. Based, small business, etc.), but I'm unsure if the juice is worth the squeeze. If anyone has any experience applying and going through the process I'd love to hear about it. Things I'm curious about... - how competitive is it? - how detailed should proposals be? - am I guaranteed to hear back? - how long after closing is a decision made? Thanks in advance.

lyfeninja 2026-06-18 09:07 6 原文
AI 资讯 Dev.to

Beyond Account Switchers: Wrapping CLI Agents into a Fully Autonomous Factory

Here is the detailed, deep-dive article tailored for DEV.to, written in a natural, highly technical style, completely free of icons, and designed to resonate with developers building agentic workflows. Building an Autonomous AI Experience Engine: Taming the Multi-Agent CLI Fleet As developers integrate more AI tools into their workflows, a new architectural problem has emerged: agent sprawl. We have incredible tools like Claude, Grok, and Codex running in our terminals, but they operate in silos. They lack shared memory, they step on each other's toes, and coordinating them feels like herding cats. To solve this, I built TechSphereX Studio — an open-source, polyglot AI Experience Engine. It is an autonomous multi-agent platform designed to intercept AI coding actions, orchestrate goal-driven work across a fleet of CLI agents, and mathematically learn from every session to improve future outcomes. Here is a deep dive into how I moved from isolated prompt engineering to a fully automated, self-learning agentic brain. Key Architectural Pillars 1. The 3-Layer Intercept Pipeline Before any CLI executes a command, TechSphereX intercepts the action to determine if the system already knows how to solve the problem based on past experiences. This happens across three highly optimized layers: Layer 1 (Read-only Filter): Evaluates the action in under 1ms. If the action is non-destructive (like a simple read), it skips heavy processing to save resources. Layer 2 (Semantic Search): Uses Qdrant running locally to perform vector embeddings and search the system's history for similar past tasks in under 50ms. Layer 3 (LLM Rerank): Passes the semantic results to a local Ollama instance to filter out contextually irrelevant data in under 500ms, ensuring the execution agent only receives high-fidelity context. 2. The Agentic Brain & Multi-Role Teams Instead of throwing a massive, complex prompt at a single coding agent, TechSphereX mimics a multi-role engineering team. The pipeline st

TechsphereX AI 2026-06-18 08:40 11 原文
AI 资讯 Dev.to

How we built a medicine-substitution engine that refuses to be clever

How we built a medicine-substitution engine that refuses to be clever There is a category of bugs where the code looks perfectly correct in code review, the unit tests pass, the demo on stage goes beautifully, and a real person dies. Medicine substitution is one of them. We built Agada — point a phone camera at a medicine strip in India, and the app tells you whether the drug is registered with the regulator, what it does, and whether a chemically identical version is available at the government pharmacy for a fraction of the price. The point is real: Indians spend about ₹65,000 crore a year out of pocket on branded medicines when the same molecule sits in a Jan Aushadhi Kendra at a tenth of the cost. The Dolo-650 story (₹32 vs ₹4.90 for the same paracetamol) is the most famous example, not the only one. The hard part isn't the camera, the OCR, or the price lookup. The hard part is the substitution engine — the piece of code that decides "is this salt the same as that salt." Get that wrong in the wrong direction, and the app cheerfully tells a user that their 500mg anti-epileptic can be replaced with a 200mg one, because the strings look similar. So we built a matcher that refuses to be clever. This post is about the parts we deleted. The starting problem A user scans "Crocin 500mg Tablet IP". A Jan Aushadhi record says "Paracetamol 500 mg". A naive matcher says: both contain "500", both contain "Paracetamol" (or "Acetaminophen", which is the same thing in a different country), ship it. The user buys the generic. Savings: ₹20. Everyone wins. Now consider: Crocin 650 Advance vs Dolo 650 — same molecule, same dose, different brands. Fine. Augmentin 625 vs Augmentin 375 — same two salts, different ratio. The combo tolerance might let it through, but the clinical implication is real. Levofloxacin 500 vs Ofloxacin 400 — both fluoroquinolones, both "similar," but levofloxacin is the levo-isomer of ofloxacin and is dosed at roughly half. A "fuzzy" match here could halve so

Aman Sachan 2026-06-18 08:40 12 原文