Tools

A practical blueprint for turning long documents into structured summaries that are actually useful in business workflows.

How to design a lightweight AI classification pipeline for common business tasks such as routing, tagging, and priority assignment.

A lightweight blueprint for building a Telegram-based AI assistant for internal Q&A, alerts, or simple service interactions.

How to design a spreadsheet assistant that helps users ask questions, summarize patterns, and reduce formula fear without inventing numbers.

How to design an AI tool that turns open-text feedback into themes, priorities, and operational signals without flattening the customer voice.

Is it possible to train a language model to become a capable scientist? That provocative question lies at the heart of a new milestone in AI research. In SciMaster: Towards General-Purpose Scientific AI Agents, a team from Shanghai Jiao Tong University introduces X-Master, a tool-augmented open-source agent that has just achieved the highest score ever recorded on Humanity’s Last Exam (HLE)—surpassing even OpenAI and Google. But what makes this feat more than just a leaderboard update is how X-Master got there. Instead of training a larger model or fine-tuning on more data, the researchers innovated on agentic architecture and inference-time workflows. The result? An extensible framework that emulates the exploratory behavior of human scientists, not just their answers. ...