71 related articles

FTPO (Final Token Preference Optimization) tackles AI "Doom Loops" at the training level rather than patching them at inference time — exploring its principles, value for small/quantized models, and open challenges.

High benchmark scores don't mean an LLM is actually useful. This article shares four field-tested standards—expressiveness, insight, cognitive depth, and problem-solving—to help you find the AI that truly fits you.

Most Agent projects lack competitiveness in interviews due to missing business value and engineering depth. This article breaks down the 6 core standards of high-value Agent projects.

Why are AI benchmark leaderboards increasingly unreliable? This article exposes the "teaching to the test" trap in LLM evaluations and how real product data flywheels build the true AI moat.

OSWorld 2.0 benchmark tests 108 long-horizon computer tasks. Claude Opus tops at only 20.6% completion, exposing critical AI weaknesses in state tracking and error self-correction.

OSWorld 2.0 benchmark tests 108 long-horizon computer tasks (median 1.6 hrs for humans). Claude Opus tops out at 20.6% completion, exposing critical AI Agent weaknesses in state maintenance and self-correction.

LangChain's LangSmith Engine is an intelligent agent tool for tracking Agent failures, prioritizing issues, and auto-drafting fixes. Deep dive into its core capabilities, sandbox isolation, sub-Agent architecture, and continuous evaluation challenges.

Hugging Face's open-source ml-intern autonomously reads papers, writes training scripts, and finetunes LLMs, deeply integrating the HF ecosystem and smolagents. Explore its features and impact on ML careers.

What is an AI Agent? This article systematically explains the core architecture of AI agents (LLM + Planning + Memory + Tools), how they differ from ChatGPT, their combination with robots, and why developers must master Agent development skills.

Pure frontend roles are shrinking; AI Agent development is the high-salary divide. This guide breaks down the full skill tree for frontend engineers pivoting to AI: TypeScript, frameworks, AI productivity, and Agent core concepts (MCP, Tool Calling, Skill).

An in-depth look at the seven core components for building long-running AI agents: Goal, Evaluator, Verifier, Outer Loop, Orchestration, Observability, and Memory. Master this control system for reliable autonomous agents.

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.

Why "AI-First" branding is backfiring: how over-marketing AI erodes brand trust, triggers consumer fatigue, and what brands should do instead to rebuild credibility.
AI Tutor Achieves Effect Size of 1.30:…
Dartmouth's latest study shows an AI tutor system achieving 0.71–1.30 SD learning effect sizes in a real course, far exceeding most educational interventions. We examine what these numbers mean and why caution is still warranted.

In-depth analysis of OpenAI Codex's four usage forms, comparing Codex, Claude Code, and Cursor across price, stability, and frontend/backend fit to help developers choose the right AI programming tool.

Deep analysis of Alibaba's AgentScope 2.0 multi-agent framework: six core upgrades including event systems, security interception, HITL, and workspace systems, plus ReAct vs Plan-and-Execute agent design patterns.

Sakana AI launches its Recursive Self-Improvement Lab, focusing on using AI to redesign AI development. From LLM² to AI Scientist, this Tokyo company proposes a sample-efficient path to AI self-evolution without brute-force compute.

LifeSciBench is a life science AI benchmark developed by 173 biotech and pharma scientists, featuring 750 expert tasks across seven research workflows.

Andrej Karpathy's deep review of Claude Fable 5: beyond SOTA benchmarks, it delivers a qualitative leap in long, high-difficulty coding sessions. Exploring the Jevons Paradox of AI programming.

The EHT team used OpenAI Codex to speed up black hole plasma simulation algorithms by 1000x, from ten days to minutes. Learn how Codex is enabling the first-ever black hole video.