2085 related articles

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.

last30days-skill is a GitHub AI Agent skill with 50K+ Stars, enabling cross-platform research across Reddit, X, YouTube, Hacker News, and Polymarket to generate grounded 30-day summary reports.

last30days-skill is a 50K+ Star AI Agent skill on GitHub that performs cross-platform research across Reddit, X, YouTube, Hacker News, and Polymarket to generate grounded 30-day summary reports.
Open Deep Research: A Complete Guide t…
A deep dive into LangChain's open-source project open_deep_research: an AI deep research agent built on LangGraph, supporting flexible multi-model and multi-search tool configuration, with 12,000+ stars.

No coding required: use AI agents like Codex and Claude Code to complete full ML experiments via natural language. A real case study with a heart disease dataset.

AI Agents are taking over experiment design, execution, and paper writing. Learn how graduate students can redirect their competitive edge in the age of automated research.

No coding required — just describe your needs in natural language. AI Agents handle data cleaning, model training, and visualization automatically. We tested Codex and Claude Code on a heart disease prediction task.
AI Agents Playing Games: The Technical…
Why do AI agents play games? Explore how games serve as ideal AI training environments — from DeepMind's AlphaGo to LLM-driven agent experiments — and why game-playing benchmarks matter.

Oragent (Dingyi ORA Agent) is an AI agent built for foreign trade, generating in-depth market analysis reports covering product selection, regulatory risks, and marketing calendars in just 5 minutes.

Learn how to use AI Agents to link the entire research pipeline—from literature management, data analysis, and paper writing to scientific illustration and dissemination—building a reusable research automation workflow with NotebookLM, N8N, and Ollama.
Beware of Big Tech AI Agents: How to P…
Are your research code, algorithms, or unpublished papers safe with Big Tech AI agents? This deep dive explores data risks and offers practical protection strategies.
Autoresearch: How Self-Evolving AI Age…
Autoresearch lets AI agents automatically explore and refine better solutions during task execution. This article breaks down agent recipes, self-improvement loops, and human-AI collaboration boundaries.
OpenAI Research: How AI Agents Are Res…
OpenAI research reveals AI agents are evolving from chat assistants into autonomous "digital workers," driving productivity gains across technical and non-technical roles alike.
Firecrawl Goes Free Again and Launches…
Firecrawl goes free again and launches a SOTA Research Index, giving AI research agents real-time access to scientific knowledge. Here's what it means for RAG, scientific reasoning, and AI-assisted discovery.
Google Co-Scientist Explained: A Gemin…
Deep dive into Google's Co-Scientist: a Gemini-powered multi-agent AI system that autonomously generates hypotheses, conducts agent debates, and iteratively evolves research directions.

Deep dive into BioAgents multi-agent AI framework: how literature analysis and data scientist agents collaborate for autonomous deep research in biological sciences.
Sakana AI Launches Marlin: An AI Agent…
Sakana AI launches Marlin, its first commercial product — an autonomous strategic research assistant that completes deep research in 8 hours, targeting finance, consulting, and think tanks.

A DeepSeek researcher shares 10 universal rules for using AI agents, covering the shift from execution to judgment, memory file systems, human-AI collaboration boundaries, and more.

Google.org and Schmidt Sciences launch a $10M fund to study collective behavior and emergent risks of multi-agent AI systems, from flash crashes to mass AI Agent deployment.