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The author built an AI research tool that reduces hallucination through strict orchestration and harness engineering, enabling users to supervise research decisions and verify sources.
An open-source tool called Paper Deck that aggregates AI/ML papers from arXiv and Hugging Face, allowing reading, starring, and cross-device progress tracking.
Research 1.3 has been released with a new annotator feature.
Google's NotebookLM gets a major update with the Gemini 3.5 model, expanded file support, and a new Antigravity feature for code execution and workflow automation.
The author introduces Sisyphus Academica, an open-source research companion that evolved from an AI-assisted writing tool into a full research workflow manager, and seeks community feedback on features and development direction.
NotebookLM receives a major upgrade with agentic chat, advanced reasoning, and new output formats, now available to Google AI Ultra subscribers.
This is a tool that automatically converts scientific paper charts into executable Python plotting code, using the Qwen vision model and Codex agent for panel segmentation, code generation, and refinement.
Serenity.skill is an open-source AI tool that converts Twitter user Serenity's investment research methodology into an Agent Skill, helping users perform industry chain analysis and stock screening through AI.
Auto-Empirical-Research-Skills (AERS) is an open-source toolkit that automates the entire empirical research pipeline using 23,000+ AI agent skills, from data cleaning to submission-ready drafts.
A Twitter post introduces PaperBD, an AI-powered tool that enables simultaneous study and questioning of multiple research papers, with full-text access, multi-PDF view, and automatic study goals.
Encontré un repo que automatiza el ciclo de investigación académica usando Claude, desde búsqueda de papers hasta entrega de documento final.
A team from NUS open-sourced PaperDebugger, a multi-agent system that lives inside Overleaf, providing real-time rewriting, critique, and citation assistance with an open enhancer model (XtraGPT-7B), making Overleaf a full research environment.
Google DeepMind launched Science Skills, a toolkit that integrates over 30 major life science sources, including UniProt and the AlphaFold Database, to help accelerate research workflows.
An open-source project called academic-research-skills packages a complete pipeline for writing papers using Claude Code, achieving 9.4k stars on GitHub and directly addressing pain points of student users.
ARIS is a lightweight automated research tool based on Markdown that supports multiple AI models (such as Claude Code, Cursor, Trae) to automatically complete the entire research workflow including reading papers, generating ideas, designing experiments, writing papers, etc., allowing AI to continuously explore while users sleep.
NVIDIA released a dataset on Hugging Face containing paper reviews for human and AI-authored papers, including subsets APRES, Agents4Science, and Sakana v2.
ScienceClaw is an AI assistant framework integrating 285 research skills, modularizing the entire research workflow into Skills. It supports connecting to databases such as PubMed, Semantic Scholar, and ArXiv, providing functions like literature search, paper deep reading, citation analysis, experimental design assistance, and writing assistance. It is suitable for advanced users who want deep customization.
WebHarbor packages 15 real websites (Amazon, GitHub, BBC, etc.) as self-contained Flask+SQLite apps in a single Docker image with sub-second reset, designed for reproducible web agent evaluation and training. The project invites community contributions to expand to 100+ sites, with co-authorship opportunities.
Papel is a new research-focused social platform that leverages AI-powered vector search and on-device RAG to help researchers discover, discuss, and quiz themselves on academic papers. It offers personalized feeds, local AI chat via Apple Intelligence or MLX, and gamified learning features.
Hugging Face open-sourced ml-intern, an autonomous agent that reads ML papers, discovers datasets, trains models, debugs failures, and ships production-ready models to the Hub, automating the entire post-training workflow.