#developer training
2190 related articles
·1 min
Google Summer of Building: A New Developer Training Program for the AI Era
Google is exploring a Summer of Building program to help students and early-career developers build projects with AI tools. Learn about its strategy and value.
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Industry Insights·2 min
In-Depth Analysis of C++ + AI Full-Stack Training Programs: Career Transition Paths for Developers in 2026
An in-depth analysis of C++ + AI full-stack training programs covering CUDA, YOLO, RAG, and interest-aligned employment guarantees for C++ developers transitioning to AI roles.
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·2 min
Kimi K3 Launches on Telnyx Inference API: A New Path for Chinese LLMs Going Global
Moonshot AI's Kimi K3 is now available on Telnyx Inference API. Explore how Chinese LLMs are entering global developer ecosystems through third-party inference platforms.
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·4 min
Getting Started with AI Agent Development: A Complete Guide from Concept to Practice
A comprehensive guide to AI Agent architecture and development, covering automated marketing, intelligent customer service, and investment analysis scenarios with single and multi-agent collaboration.
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·5 min
A Beginner's Guide to Prompt Engineering: From One-Off Instructions to a Systematic Methodology
A beginner's guide to prompt engineering covering the four functions of prompts, the key differences from prompt engineering, a six-step systematic workflow, and critical technical and practical limitations.
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·3 min
A Beginner's Guide to AI Agents: Two Learning Paths for Developers and Practitioners
How to learn AI Agents from scratch? This guide covers two clear paths: developers go from Python to LLMs to open-source framework source code; practitioners use Claude Code or similar tools to get results fast.
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·3 min
Three Essential Hard Skills Traditional Product Managers Need to Transition into AI PM
How can traditional product managers transition to AI PM? This article analyzes the essential differences and details three must-have skills: AI product cognition, advanced Prompt engineering, and large model technical logic.
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·3 min
AMD CDNA5 Architecture Deep Dive: Technical Evolution and the AI Computing Competition Landscape
Deep analysis of AMD's CDNA5 architecture covering Chiplet packaging upgrades, HBM memory evolution, and low-precision compute optimization, examining how AMD challenges NVIDIA's AI chip dominance.
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·3 min
AI Not Performing Well? Start by Getting These Simple but Often Overlooked Things Right
When your AI system underperforms, the problem is often not the model or algorithm — it's basic work like data cleaning, prompt writing, and evaluation that hasn't been done right.
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·3 min
Google Launches Gemini 4 Pre-training: Its Most Ambitious AI Model Training Yet
Google DeepMind announces Gemini 4 pre-training has begun, calling it their most ambitious training yet. A deep dive into its technical direction, compute scale, multimodal breakthroughs, and competitive impact.
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·3 min
SlopCodeBench: Why AI Code Benchmarks Are Failing
SlopCodeBench sparks deep reflection on AI code evaluation. From benchmark contamination to pass-rate pitfalls, exploring why current benchmarks fail to measure real code quality.
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·2 min
Gemini 3.5 Flash-Lite Released: The Smallest, Fastest Model Outperforms Gemini 3
Google launches Gemini 3.5 Flash-Lite, its smallest and fastest AI model that outperforms Gemini 3 in most scenarios. Learn about its key advantages, cost benefits, and impact on developers.
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·3 min
AI Not Working Well? Start by Getting These Simple but Often Overlooked Things Right
When AI systems underperform, the problem often isn't the model or algorithm — it's that basics like data cleaning, prompt writing, and evaluation aren't done right. Learn the simple fixes that matter most.
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·3 min
The Open-Weights Model Debate: Balancing Safety and Openness
An in-depth analysis of the open-weights model debate: public release brings transparency and innovation, but raises safety and misuse risks. Exploring tiered release, red-teaming, and governance challenges.
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·3 min
The Open-Weights Model Debate: Balancing Safety and Openness
An in-depth analysis of the open-weights model debate: publicly releasing model weights enables transparency and innovation but raises safety risks. Explores tiered release, red-teaming, and the industry dynamics behind open AI governance.
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·3 min
Grok 4.5 Feels Weaker in Cursor? A Deep Dive into AI Coding Tool Integration Differences
Users report Grok 4.5 underperforms in Cursor vs. the official terminal. We analyze how system prompts, context management, parameters, and tool calling create AI coding tool integration gaps.
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·4 min
A Beginner's Guide to AI Agents: Core Principles and Learning Paths Explained
Learn AI Agent core principles from scratch: understand how Agents differ from LLMs, their execution mechanisms, why rule design matters, and find the right learning path for your goals.
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·3 min
From RAG to Agent Tuning: A Complete Guide to Deploying LLM Agents
A deep dive into LLM Agent frameworks covering RAG, Agent core components (tools, memory, planning), and Agent Tuning workflows with cost considerations for production deployment.
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·4 min
What Is the True Nature of Large Language Models? A Super Compressor Analogy to Help You Fully Understand
Large models aren't search engines — they're more like super compressors. This article explains how LLMs compress data to learn semantic patterns, and explores the phenomenon of intelligent emergence.
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·3 min
Deconstructing Agents: Task Decomposition, Context Compression, and the Case Against Heavy Frameworks
A deep engineering analysis of Agent internals: how LLMs decompose tasks via tool calling, why context compression and memory are essential, and why solo developers should avoid heavy frameworks.
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