#neural network in SQL
73 related articles
Neural Networks in SQL: A Geek Experim…
·3 min
Neural Networks in SQL: A Geek Experiment That Reveals Deep Learning's Core Principles
A developer implemented a neural network in pure SQL, including matrix multiplication, activation functions, and backpropagation. A deep dive into the technical implementation, challenges, and what it reveals about deep learning's core principles.
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·3 min
HeyZoku: Command Multiple AI Coding Agents to Work Together Using Your Voice
HeyZoku is a Mac voice-first agentic dev environment that runs 10 coding agents simultaneously. Command Claude, Codex, and Cursor by name with on-device voice recognition and one-time pricing.
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·3 min
HeyZoku: Command Multiple AI Coding Agents to Work Together Using Your Voice
HeyZoku is a Mac voice-first agentic dev environment that runs 10 coding agents simultaneously. Command Claude, Codex, and Cursor by name with on-device voice recognition and one-time pricing.
Read more →
·2 min
Is Formal Language Theory Still Relevant in NLP? Deep Reflections Behind a Course Selection Dilemma
Formal Languages vs. Programming Language Principles—which course matters more for computational linguistics and NLP? A deep analysis from Chomsky Hierarchy to Lambda calculus to modern LLM theory.
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·4 min
Project Rai-chan Technical Breakdown: A Complete Guide to Building a Fully Local AI Companion
Deep dive into Project Rai-chan's tech stack: Ollama+Gemma local LLM, Unity rendering, VOICEVOX speech synthesis, and more — exploring the technical path for local AI companions.
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·4 min
Are AI Coding Tools Making Developers Dumber? The Efficiency vs. Competence Dilemma
After heavy use of AI coding tools like Cursor and Claude, an indie developer discovers his debugging and code comprehension skills are eroding. Exploring the skill atrophy risks behind AI-boosted productivity.
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·3 min
Chinese Open-Source AI Conquers Silicon Valley: The Industry Upheaval Behind a 58% Compute Share
Chinese open-source AI models surged from under 10% to 58% of U.S. AI consumption. Kimi K3, DeepSeek, and Qwen are reshaping AI cost structures as DoorDash, Airbnb, and other Silicon Valley giants adopt them at scale.
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·3 min
Chinese Open-Source AI Conquers Silicon Valley: The Industry Upheaval Behind a 58% Compute Share
Chinese open-source AI models surged from under 10% to 58% of U.S. market share. Kimi K3, DeepSeek, and Qwen are being adopted by DoorDash, Airbnb, and other Silicon Valley giants, reshaping AI costs and competition.
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·3 min
When Should You Use Machine Learning? A Pragmatic Decision Guide for Data Scientists
Not every data science problem needs ML. This guide offers a decision framework across four dimensions — rule complexity, data quality, prediction needs, and interpretability — to avoid over-engineering.
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·6 min
AmneziaVPN Deep Dive: The Open-Source Powerhouse for Self-Hosted, Censorship-Resistant VPN
Deep dive into the AmneziaVPN open-source client: AmneziaWG anti-DPI obfuscation, self-hosted VPS deployment, multi-protocol support, and cross-platform privacy.
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·3 min
The Software 3.0 Era: An AI Programming Evolution Guide for Frontend Engineers
Natural language programming is reshaping frontend development. This article explains AI code generation, Prompt formulas, pitfall avoidance, RAG, Agent orchestration, and skill maintenance.
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·4 min
Burning Through 80 Billion Tokens: Why Vibe Coding Is Doomed to Fail
A creator spent 40 days and 80 billion tokens testing the real limits of Vibe Coding. This article dissects why AI programming crashes in production: complexity, context limits, and compression loss.
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·6 min
The CivitAI Early Access Controversy: Where Is the Boundary Between Open-Source Ethos and Commercialization?
CivitAI's paid "Early Access" mechanism has sparked heated debate on Reddit: should functional models stay locked behind paywalls long-term? An in-depth look at creator monetization, community consensus, and platform responsibility.
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·8 min
LangChain Crash Course: A Complete Roadmap from Zero to Agent Development
A systematic roadmap from LangChain and LangGraph to multi-agent development, covering RAG, Tool Calling, MCP, and more, helping developers break into AI app development.
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·3 min
AI Development for Java Engineers: A Complete Guide to LangChain4j Agents
Java developers can build AI apps too! Learn LangChain4j fundamentals including RAG, Agents, Function Calling, and hands-on projects — no Python required.
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·2 min
Revisiting SHRDLU: The Technical Lineage and Modern Value of Procedural Semantics
From SHRDLU to modern neuro-symbolic AI: explore procedural semantics, CCG grammars, semantic parsing, and interactive fiction engines in today's NLP landscape.
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·5 min
A Deep Dive into Vector Search for AI Agents: The Technical Foundations of RAG and Long-Term Memory
An in-depth look at the core tech behind AI Agents: how the HNSW, IVF, and PQ vector search algorithms power RAG and long-term memory. Understand where a model's "memory" and "knowledge" come from.
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·3 min
New Retrieval Paradigms: Hobbit's Hard Negative Construction and Disco's Collaborative Coverage Index Design
A deep dive into two frontier dense retrieval works: Hobbit uses gradient analysis to automatically construct hard batches; Disco replaces single-document competition with submodular collaborative coverage, reshaping Top-K retrieval.
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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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·5 min
How Can Working IT Professionals Learn AI Efficiently? A Practical Learning Path from Zero to Real-World Projects
Limited time but want to learn AI systematically? This guide maps out a practical learning path for working IT pros—from AI application engineering and prompt engineering to RAG and Agents.
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