167 related articles

Homebench is an open-source local LLM benchmarking tool that evaluates models across speed, memory, and quality dimensions, helping developers make optimal model selection and quantization decisions.

The MELTing Point paper is the first to evaluate mobile LLM performance in real user scenarios, covering iPhone, Samsung, Pixel and more, testing TinyLlama, Mistral-7B and others—revealing GPU inference gains, 47°C heat warnings, and prefill-decode disaggregation.
LLM Security Benchmarking: Current Sta…
Why is it so hard to establish unified LLM security benchmarks? This article analyzes core challenges in LLM security evaluation—covering jailbreaks, prompt injection, red teaming, and more—with practical strategies for developers.

How benchmarking transforms dormant domain data into an AI optimization engine. From healthcare to law to manufacturing, building vertical benchmarks activates proprietary data and builds a strategic moat.

A deep-dive evaluation of Addy Osmani, Matt Pocock, and Gary Tan's skill libraries, distilling a 5-step Research→Prototype→Plan→Build→Test agent dev loop and why the best skill system is always your own.
Product Reviews15 mainstream LLMs tested building a Bilibili video app from the same prompt. ChatGPT 5.4 tops overall, Claude excels at frontend, domestic models lag behind.

Today's AI highlights: OpenAI halts a frontier model with cyberattack capabilities; Alibaba's CosyVoice Studio claims three global firsts in voice AI; Cloudflare launches Kitsurf headless browser for Agents; GitHub Copilot monitoring adds Agent analytics.

AI Agents keep causing database deletions and data leaks. Snyk proposes three ADS defense lines: trusted code generation, supply chain protection, and behavioral governance using hooks and deterministic guardrails.

ml-pipes is an open-source framework that builds pre-run validation, pipeline inspection, tracing, and benchmarking into ML inference pipelines, bridging the MLOps engineering gap.

Deep dive into how YC-backed Stoa Markets builds a GPU and AI server marketplace to solve compute fragmentation, price opacity, and supply-demand challenges.

DeepSeek V4 Flash 0731 scores 82.7% on Terminal-Bench 2.1 using a public harness. We analyze what this means for AI Agent capability democratization and lightweight model practicality.

ScrapeOps Proxy Tester benchmarks 20+ proxy configurations against your specific target URL, measuring success rate, latency, and cost to help scrapers and AI Agents choose the optimal proxy.

Deep comparison of Cursor Pro vs SuperGrok for Grok 4.5 token value. Analyzes platform metering differences, provides testing methods, and offers guidance for choosing the best AI subscription.

An open-source dataset of 6 million job postings with structured annotations for skills, salary, seniority, and location—useful for labor market analysis, salary modeling, NLP training, and recruitment product development.

How to define research design in ML papers? Using mobile game player churn prediction as an example, this guide details mixed-methods comparative empirical study positioning, covering CRISP-DM, quantitative evaluation, and SHAP interpretability analysis.

Explore how foundation model embeddings are reshaping data science workflows. The shift from feature engineering to representation selection with pre-trained models and lightweight downstream heads is becoming standard practice across domains.

Zhipu AI's next-gen LLM GLM-5.3 is reportedly imminent, dubbed a 'monster' by the community. We analyze the GLM evolution, potential breakthroughs, and China's LLM competition landscape.

Deep analysis of six core AI model issues: open-source vs closed-source models, inference throughput vs accuracy tradeoffs, benchmark gaming, distillation vs RL, reward hacking defenses, and dynamic quantization technology.

A six-run task-size benchmark tests whether Codex Skills actually save tokens. Data reveals cost-benefit performance across different task complexities.

In-depth feasibility analysis of deploying DeepSeek V4 Flash on two NVIDIA DGX Spark units offline, examining memory bandwidth, MoE communication overhead, and quantization strategies.