1004 related articles

spaCy's default Sentencizer achieves only 55.4% accuracy on edge cases, while open-source library yasbd reaches 98.9%. Analysis of limitations and integration code examples.

Asking LLMs to self-report confidence scores is a common mistake. Learn why it fails and discover reliable alternatives like logprobs, self-consistency sampling, and RAG.

In-depth analysis of Apple Silicon local LLM inference speed benchmarks covering M-series memory bandwidth, model quantization, MLX framework optimization, and Mac configuration guidance.

An in-depth look at the real daily work of data scientists, MLEs, and MLOps engineers — covering responsibilities, essential tools, and career paths to help you find your direction in AI.

A deep dive into a real-time 3D human mesh reconstruction project using a single RGB camera, built with Rust, Candle, and CUDA, achieving 55ms/frame on an RTX 5080.

Deep dive into a real-time 3D human mesh reconstruction project using a single RGB camera, built with Rust, Candle, and CUDA, achieving 55ms/frame on RTX 5080. Exploring its architecture, Metal porting plans, and applications in VTuber, AR/VR, and sports analysis.

Asking LLMs for self-reported confidence scores is a common mistake. Learn why it fails, and discover reliable alternatives like logprobs, self-consistency sampling, and RAG for uncertainty estimation.

A detailed guide on using AI Agents to build Research Logs for scientific experiments, covering skeleton structure, daily workflows, pre-experiment thinking standards, code change tracking, and Agent-researcher division of labor.

Learn how to use AI Agents to build a Research Log for scientific experiments, covering structure, daily workflows, pre-experiment thinking, code change tracking, and human-AI division of labor.

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.

Why AI research automation looks more like data cleaning than inventing the Transformer. Exploring the value of automating 60%-80% of repetitive research work and how human-AI collaboration reshapes the research paradigm.

AI research automation will look more like data cleaning than inventing the Transformer. Explore how automating 60%-80% of repetitive research work reshapes the AI research paradigm.

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.

A viral Reddit post asks: will AI end human history? This article analyzes the blind spots of tech accelerationism, the governance mismatch, and how to rationally navigate AI transformation.

Generative AI is profoundly redefining the personal computer — from passive tool to intelligent collaborator. This article examines the core shifts of the AI PC era and the productivity gap created by cognitive lag.

Analysis of why Gemini and other AI LLMs exhibit capability drift, including tool-calling mechanisms, context window limits, and safety policy triggers, plus practical strategies for PDF generation failures.

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.

Deep dive into the five evolution stages of AI Agent architecture: model calls, tool calls, workflows, Agent loops, and production runtime. Learn the responsibility boundaries and design principles.

LangPilot is an open-source macOS menu bar tool that auto-detects and corrects keyboard layout mismatches for Russian, English, and German, with local spelling suggestions and adaptive learning. Fully offline, privacy-first, written in Swift under GPL-3.0.

Generative AI is profoundly redefining personal computers — from passive tools to intelligent collaborators, from deterministic computation to probabilistic reasoning. Explore the core shifts of the AI PC era.