#SQL for data interviews
44 related articles
·2 min
Interview Prep Strategy for Senior Data Scientists: How to Prepare Efficiently Without Burning Out
Senior data scientist interviews are broad and multi-round. Learn an efficient evergreen fundamentals + targeted sprint strategy covering ML, SQL, system design, and mindset tips.
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·3 min
Build RAG and AI Agent Projects at Zero Cost: A Practical Guide to Open-Source Tools
Build high-quality AI projects on a budget. Learn how to use Ollama, Groq, Chroma, and other free open-source tools to build RAG systems and multi-Agent workflows from scratch.
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·3 min
Build RAG and AI Agent Projects at Zero Cost: A Practical Guide to Open-Source Tools
Build high-quality AI projects on a budget. Learn how to use Ollama, Groq, Chroma, and other free open-source tools to build RAG systems and multi-Agent workflows from scratch.
Read more →
·3 min
A Survival Guide for Java Developers in the AI Era: From Writing Code to Directing AI
AI is reshaping the career landscape of Java development: junior CRUD roles are the first to go, but demand rises for engineers who understand business, architecture, and can command AI. A deep dive into the transformation path for Java developers.
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·2 min
The New Bar for AI/LLM Roles: A Practical Guide to Agent Observability and Evaluation
Enterprise AI/LLM roles now demand engineering skills: streaming recovery, high concurrency, multi-tenancy, LLM gateways, Langfuse observability, and evaluation platforms. Master these 8 core competencies.
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·7 min
The Complete Guide to LLM Application Engineer Interviews: Six High-Frequency Agent Topics
A focused guide to core LLM application engineer interview topics, covering agent architecture, Multi-Agent, Langfuse evaluation, security, and RAG optimization.
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·7 min
The Complete Guide to LLM Application Engineer Interviews: Six High-Frequency Topics in the Agent Domain
A focused guide to the core interview topics for LLM application engineers, covering agent architecture, Multi-Agent, Langfuse evaluation & tracing, security, and RAG optimization.
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·2 min
AI Testing Platform in Action: A Complete Guide to Accelerating Your Testing Workflow
Deep-dive into AI testing platforms: from auto-generating test cases from requirements docs to API testing, performance testing, and log analysis. Prepare for big tech interviews.
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·3 min
Career Pivot at 47: Midlife Job Anxiety and Redefining Competence in the Age of AI
A 47-year-old engineer who pivoted to data science faces re-employment struggles — a mirror of AI-era anxiety: does using AI count as coding? How to break the midlife career trap?
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·2 min
Vibe Coding Beginner's Guide: Build Real Apps with AI — No Coding Experience Required
Vibe Coding lets anyone build apps using AI and natural language — no coding required. Learn the core concepts, real success stories (including a $1M app), and why Tencent, Alibaba & ByteDance are hiring for it.
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·2 min
Vibe Coding for Beginners: Build Real Apps with AI — No Coding Skills Required
Vibe Coding lets you build real apps using natural language — no coding skills needed. Learn the 3 core traits, see real success stories, and get started today.
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·2 min
Agent Skill Hit Rate Dropping? 4 Methods to Solve the Skill Routing Problem
Skill hit rate dropping as your Agent scales? Learn how Progressive Disclosure works and explore 4 solutions: better descriptions, Skill Trees, negative samples, and recall + rerank.
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·3 min
Java Developers Transitioning to AI: A Practical Guide to Building RAG Knowledge Bases
How can Java developers break into AI? This guide covers the AI application engineer career path, RAG knowledge base fundamentals, vector database retrieval, and enterprise-grade RAG challenges.
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·3 min
Agent Performance Evaluation: A Three-Layer Framework and Engineering Implementation Guide
A deep dive into the three-layer AI Agent evaluation framework — outcome, process, and system layers — covering trajectory evaluation, tool call accuracy, automated testing, and key engineering challenges.
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·3 min
CS Students' Summer Prep: How to Strategically Choose Between ML Internships and Data Analytics Roles
How should a CS+Stat junior efficiently prep for data/ML internships? We break down the real market gap, skill priorities, and a focused 3-month strategy.
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After Getting Started with AI/ML: Shou…
·3 min
After Getting Started with AI/ML: Should You Pursue an Internship or Continue Studying?
Already trained models and implemented neural nets from scratch — should you apply for internships or keep studying? A practical guide to entry-level AI roles and how to advance.
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·3 min
LangChain Practical Guide: From RAG Applications to Intelligent Agent Development
A comprehensive guide to LangChain: core concepts, RAG applications, Agent development, version selection (0.3/1.0), and career opportunities for Java/Python developers entering LLM development.
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·4 min
No Competitive Programming Required: How CS Students Can Pivot to AI/ML and Land Internships
How can CS students who dislike competitive programming systematically pivot to AI/ML? This guide covers skill priorities (Python/SQL/ML/deployment), portfolio strategy, Kaggle tips, and real paths to landing AI/ML internships.
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·5 min
Java Backend Interview Sprint: A Systematic Methodology From Rote Q&A to Scenario Questions
How can you prepare efficiently for a Java backend interview? This article breaks down the core methodology of "process-driven interview engineering," covering resume optimization, understanding principles, scenario analysis frameworks, and production troubleshooting.
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·4 min
The Sprout Experiment: Exploring Deterministic Symbolic Reasoning AI Without Neural Networks
Sprout is a contrarian AI research experiment that abandons GPUs and neural networks in favor of deterministic symbolic reasoning. It features an auditable knowledge base and refuses to answer when evidence is insufficient, prioritizing explainability and governance.
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