#engineering jargon
48 related articles
·3 min
Qwen-Audio-3.0-ASR-Flash Released: A New Speech Recognition Benchmark with 95% Medical Terminology Recall
Qwen releases Qwen-Audio-3.0-ASR-Flash speech recognition model with 95.36% medical and 93.24% industrial terminology recall. Features context consistency, domain-term recognition, custom hotwords, and speech polishing across streaming and file transcription versions.
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·4 min
SAM 3 Auto-Labeling in Practice: Six Lessons Where Preprocessing Matters More Than the Model
Practical lessons from building a SAM 3 auto-labeling pipeline: vision embedding reuse, resolution handling, prompt engineering, threshold sweeping, and more.
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·2 min
ViiTor Translate: A Cross-Platform Real-Time Subtitle Translation Tool That Gets the Jokes
ViiTor Translate is a real-time subtitle translation tool focused on contextual understanding, supporting iOS, Android, and Chrome for Vtuber, K-pop, and anime fans with floating subtitle overlays.
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·3 min
Composer 2.5 Real-World Review: Why a Budget AI Coding Assistant Became a Daily Go-To
A developer shares their real experience with Composer 2.5, from budget pick to daily go-to. Deep comparison with Sonnet 5 in debugging scenarios reveals the gap between benchmark scores and real productivity.
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·3 min
Composer 2.5 Real-World Review: Why a Budget AI Coding Assistant Became My Daily Driver
A developer shares their real experience with Composer 2.5, from budget pick to daily driver. Deep comparison with Sonnet 5 in debugging scenarios reveals the gap between benchmark scores and real productivity.
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·3 min
Backend Developer Transition to AI/LLM: 4 Core Skills and a Practical Learning Roadmap
Complete guide for backend developers transitioning to AI/LLM engineering. Covers the 4 core skills—Python, RAG, Fine-tuning, and Agents—with a phased learning roadmap and practical project advice.
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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
The Jargon Trap: Why Engineers Can Never Seem to Speak Plain Language
Why do engineers struggle to communicate in plain language? Exploring the curse of knowledge, jargon as identity, and practical tips for clearer technical communication.
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·3 min
Domain Event Modeling: Redesigning Business Systems Through the Separation of Facts and Reactions
Explore Domain Events modeling: how separating facts from reactions reduces coupling, improves extensibility, with practical implementation advice and DDD community debates.
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·7 min
AI Daily: T-Head Open-Sources SAIL Software Stack at WAIC, Flurry of Large Model Releases
T-Head open-sources AI software stack T-Head SAIL at WAIC to lower the barrier for domestic chip development; Kimi K3 tops the WebDev leaderboard; Qwen 3.8 Max Preview cuts prices aggressively; Moonshot prepares a Hong Kong IPO; and Oracle switches its data center to a fuel cell microgrid.
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·3 min
Is a Large Language Model Just a Function? AI Principles Explained with Middle School Math
LLMs explained through the lens of functions: input is x, output is y, training solves for parameters, inference computes results. Trillion parameters, next-token prediction — no advanced math needed.
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·3 min
What Is a Large Language Model, Really? The Core Principles Explained Through the Lens of Functions
Demystify large language models using middle-school math: LLMs are complex functions, training solves for parameters, and inference predicts next-token probabilities.
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·3 min
Zero-Code AI Financial Mini Program: A Complete Non-Programmer's Guide
Can't code? Learn how to build a WeChat Mini Program financial management system from scratch using AI coding tool Codex — covering tool selection, OCR invoicing, iteration, and publishing.
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·4 min
Vibe Coding for Beginners: 4 Pitfalls Every Non-Coder Must Avoid
New to Vibe Coding? Avoid these 4 beginner traps: unclear AI boundaries, oversized goals, wrong role mindset, and C drive overflow. Real tips for non-coders.
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·4 min
Getting Started with AI Test Development: Core Principles and Underlying Logic of Large Language Models
New to AI test development? This article breaks down the differences between machine learning and traditional programming, the origins of AI hallucinations, and the core principles of NLP/NLU/NLG to help test engineers build a solid AI knowledge framework.
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·2 min
LLM Red Teaming: One-Time Snapshot or Continuous Adversarial Testing? A Complete Guide
Every prompt or RAG change in an LLM app can reintroduce security flaws. This guide explains why traditional pentest logic fails on LLMs and how to build change-driven continuous adversarial testing.
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·2 min
AI Buzzword Bubble: How Psychology Research Exposes the Cognitive Traps of Corporate Jargon
A psychology study on corporate buzzword receptivity reveals the cognitive traps behind AI industry hype. Why do jargon-speakers outshine engineers? A deep dive.
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·3 min
Why Do Transformer Learning Paths Always Start Too Late? Order Matters More Than Materials
Most Transformer learning paths jump straight into Attention Is All You Need, skipping the pain points of sequence models—leaving learners reciting terms without real intuition. Here's a better four-stage order.
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·4 min
In-Depth Comparison of 4 AI Coding Tools: Cursor, Claude Code, Codex, and Opencode — Which Is Worth Buying?
A real-world review of subscribing to Cursor, Claude Code, Codex, and Opencode simultaneously. Compare quotas, model capability, and value to find your ideal AI coding subscription.
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·4 min
A Complete Breakdown of the Engineering Challenges Behind RAG and Agent Interview Questions
Resume full of RAG and Agent but keep failing interviews? The issue is you only run demos and can't explain production engineering challenges. This article breaks down data cleaning, hybrid retrieval, hallucination protection, and agent loop breakers.
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