💡 addyosmani/agent-skills just hit the trending charts — here's why it matters.

🔗 https://github.com/addyosmani/agent-skills
📝 Production-grade engineering skills for AI coding agents.
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The agent-skills repository provides a set of production-grade engineering skills for AI coding agents. These skills encode workflows, quality gates, and best practices that senior engineers use when building software. The repository includes 7 slash commands that map to the development lifecycle, activating the right skills automatically.

The skills can be used with various tools such as Claude Code, Cursor, Gemini CLI, Windsurf, OpenCode, and GitHub Copilot. The repository also includes 20 skills that cover different aspects of software development, from defining and planning to building, verifying, and shipping.

The skills are designed to be process-oriented, with a focus on workflows and step-by-step instructions rather than reference documentation. They also include anti-rationalization tables to help agents overcome common excuses for skipping steps.

The key takeaway is: equip your AI coding agents with human-like skills to streamline your development workflow.

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🧠 Channel: https://t.me/GithubRe

🚀 Meet PriorLabs/TabPFN: a gem from today's GitHub trending list.

🔗 https://github.com/PriorLabs/TabPFN
📝 ⚡ TabPFN: Foundation Model for Tabular Data ⚡
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Introduction to TabPFN: TabPFN is a powerful, PyTorch-based implementation of the TabPFN model, designed for fast and local inference with CUDA support. It's ideal for classification and regression tasks on tabular data.

The tabpfn library provides an easy-to-use interface for creating and training TabPFN models. You can install it via pip: pip install tabpfn.

To get started, use the default TabPFN-2.6 model with the following code:

from tabpfn import TabPFNClassifier, TabPFNRegressor

clf = TabPFNClassifier()
clf.fit(X_train, y_train)  
predictions = clf.predict(X_test)

reg = TabPFNRegressor()
reg.fit(X_train, y_train)  
predictions = reg.predict(X_test)

Key Features and Tips:
- For optimal performance, use a GPU (even older ones with ~8GB VRAM work well).
- Batch prediction mode is recommended, as each predict call recomputes the training set.
- Avoid data preprocessing when feeding data to the model.

Technical Highlights: TabPFN is part of a larger ecosystem, including the TabPFN Client for cloud-based inference and TabPFN Extensions for advanced utilities and features.

Audience: This library is suitable for data scientists and machine learning engineers working with tabular data.

In summary, TabPFN is a powerful tool for tabular data modeling - give it a try and experience the difference for yourself.

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🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: docusealco/docuseal — let's break it down.

🔗 https://github.com/docusealco/docuseal
📝 Open source DocuSign alternative. Create, fill, and sign digital documents ✍️
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DocuSeal is an open source platform for secure and efficient digital document signing and processing. It allows you to create PDF forms that can be filled and signed online on any device with a mobile-optimized web tool. Key features include a WYSIWYG form builder, 12 field types, automated emails, and files storage on disk or cloud services.

The platform is easy to deploy in minutes and offers a live demo to try it out. It also has a range of pro features, including company logo and white-label, user roles, and automated reminders.

For technical users, DocuSeal provides an API and webhooks for integrations, as well as support for Docker and Docker Compose.

Overall, DocuSeal is ideal for businesses looking to integrate seamless document signing into their web or mobile apps. With its expertise and technologies, DocuSeal can help reduce the cost of developing and processing electronic documents while ensuring security and compliance.

In short, DocuSeal is the perfect solution for anyone looking to streamline their document signing process - sign up now and start signing!

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🧠 Channel: https://t.me/GithubRe

🌟 LearningCircuit/local-deep-research caught my eye on GitHub Trending today.

🔗 https://github.com/LearningCircuit/local-deep-research
📝 ~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.
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Local Deep Research is an AI-powered research assistant that allows you to control your data and research process. It uses multiple LLMs and search engines to perform deep, agentic research with proper citations. You can run it locally for privacy, build your own searchable knowledge base, and own your data.

Key Features:
- Performs deep, agentic research using multiple LLMs and search engines
- Allows you to build your own searchable knowledge base
- Runs locally for privacy, with no telemetry, analytics, or tracking
- Uses SQLCipher encryption to protect your data

Usage: You can install Local Deep Research using Docker, Docker Compose, or pip install. It works on Windows, macOS, and Linux.

Technical Highlights:
- Uses LangGraph Agent Strategy for autonomous agentic research
- Supports 20+ research strategies for quick facts, deep analysis, or academic research
- Includes SQLCipher encryption for secure data storage

Audience: Local Deep Research is designed for individuals who want to take control of their research process and data. It's suitable for researchers, students, and anyone who wants to perform deep, agentic research while maintaining their privacy.

In short, Local Deep Research is a powerful tool that puts you in control of your research and data - take back your research, take back your data.

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🧠 Channel: https://t.me/GithubRe

🎯 LadybirdBrowser/ladybird landed on trending. Worth a proper look.

🔗 https://github.com/LadybirdBrowser/ladybird
📝 Truly independent web browser
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Check out Ladybird, a revolutionary web browser built from the ground up with a novel engine based on web standards. It's currently in pre-alpha, so it's best suited for developers who want to get involved early on.

Key features include a multi-process architecture for improved security and robustness, with each tab running in its own sandboxed renderer process. It also inherits a range of core library support components from SerenityOS, such as LibWeb, LibJS, and LibCrypto.

To get started, you can build and run Ladybird on Linux, macOS, Windows (with WSL2), and other *Nixes by following the build instructions. You can find code-related documentation in the documentation folder.

If you're interested in contributing, join the Discord server to participate in development discussions and check out the CONTRIBUTING.md file for guidelines.

Ladybird is licensed under a 2-clause BSD license, making it a great open-source project to get involved with.

One-liner takeaway: Ladybird is the future of browsing, and you can be a part of it.

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🧠 Channel: https://t.me/GithubRe

⚡ InsForge/InsForge is making waves. Here's the full picture.

🔗 https://github.com/InsForge/InsForge
📝 InsForge is a Postgres-based backend with auth, storage, compute, hosting, and AI gateway. Built for coding agents.
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InsForge is a backend development platform designed for AI-native developers, providing a semantic layer between AI coding agents and backend primitives. Its key features include authentication, database, storage, model gateway, edge functions, and compute. To get started, you can either use the cloud-hosted version at insforge.dev or self-host using Docker Compose. The platform is suitable for developers looking to build AI-powered applications, and its documentation and community support are available for those who need help. InsForge is licensed under the Apache License 2.0, making it a great choice for open-source projects. Star the repository to show your support and get notified about new releases - InsForge: where AI meets backend development.

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🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: virattt/dexter — let's break it down.

🔗 https://github.com/virattt/dexter
📝 An autonomous agent for deep financial research
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Dexter is an autonomous financial research agent that thinks, plans, and learns as it works. Its purpose is to perform complex financial analysis using task planning, self-reflection, and real-time market data.

The key features of Dexter include:
- Intelligent Task Planning: breaking down complex queries into structured research steps
- Autonomous Execution: selecting and executing the right tools to gather financial data
- Self-Validation: checking its own work and iterating until tasks are complete
- Real-Time Financial Data: accessing income statements, balance sheets, and cash flow statements
- Safety Features: built-in loop detection and step limits to prevent runaway execution

To use Dexter, you need to install the Bun runtime, obtain API keys for OpenAI, Financial Datasets, and Exa (optional), and then clone and install the repository. You can run Dexter in interactive mode or with watch mode for development.

Dexter is suitable for financial researchers, data analysts, and anyone looking to automate complex financial analysis tasks.

One-liner takeaway: Dexter is your go-to autonomous financial research agent to streamline complex analysis tasks and provide data-backed answers with ease.

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🧠 Channel: https://t.me/GithubRe

🔍 Deep-diving into anthropics/financial-services — fresh off the trending list.

🔗 https://github.com/anthropics/financial-services
📝 No description.
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The anthropics/financial-services GitHub repository offers a collection of reference agents, skills, and data connectors designed to streamline financial services workflows, including investment banking, equity research, private equity, and wealth management.

Key features include named agents that automate end-to-end workflows, vertical plugins that bundle skills and commands for specific financial services verticals, and data connectors that integrate with various data providers.

To get started, users can install the repository as a Claude Cowork plugin or deploy it through the Claude Managed Agents API. The repository is file-based, using markdown and JSON, with no build step required.

Technical highlights include the use of MCP servers for data integration and the ability to customize connectors and skills to fit specific firm needs. The repository is suitable for financial services professionals looking to automate workflows and improve efficiency.

In a nutshell, the anthropics/financial-services repository is a powerful tool for financial services teams, offering a range of automated workflows and integrations to boost productivity — automate your financial workflows with ease.

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🧠 Channel: https://t.me/GithubRe

🔥 cheahjs/free-llm-api-resources is trending — and it deserves your attention.

🔗 https://github.com/cheahjs/free-llm-api-resources
📝 A list of free LLM inference resources accessible via API.
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The cheahjs/free-llm-api-resources GitHub repository provides a list of services that offer free access or credits towards API-based Large Language Model (LLM) usage. The repository is updated by a Python script and includes a warning not to abuse these services.

The key features of this repository include:
- A list of free providers such as OpenRouter, Google AI Studio, and NVIDIA NIM
- A list of providers with trial credits such as Fireworks and Baseten
- Model limits and usage guidelines for each provider

To use this repository, simply browse through the list of providers, check their limits and usage guidelines, and start using their APIs.

Some technical highlights of this repository include:
- OpenRouter with models like Gemma 3 12B Instruct and Llama 3.2 3B Instruct
- Google AI Studio with models like Gemini 3 Flash and Gemini 2.5 Flash
- NVIDIA NIM with various open models

This repository is useful for anyone looking to use LLM APIs without incurring significant costs, including developers, researchers, and students.

Don't abuse these free services, or we might lose them - use them wisely and build something amazing!

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🧠 Channel: https://t.me/GithubRe

🔍 Deep-diving into shiyu-coder/Kronos — fresh off the trending list.

🔗 https://github.com/shiyu-coder/Kronos
📝 Kronos: A Foundation Model for the Language of Financial Markets
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Kronos is the first open-source foundation model specifically designed for the language of financial markets, trained on data from over 45 global exchanges. This decoder-only model is pre-trained to handle the unique characteristics of financial data, leveraging a novel two-stage framework. It first quantizes continuous, multi-dimensional K-line data into hierarchical discrete tokens using a specialized tokenizer, and then pre-trains a large, autoregressive Transformer on these tokens.

The model is designed for diverse quantitative tasks and is available in various capacities, including Kronos-mini, Kronos-small, Kronos-base, and Kronos-large, to suit different computational and application needs. A live demo is available to visualize Kronos's forecasting results.

To get started, you can install the dependencies using pip install -r requirements.txt and load a pre-trained model and its corresponding tokenizer from the Hugging Face Hub. The KronosPredictor class handles data preprocessing, normalization, prediction, and inverse normalization, making it straightforward to generate forecasts.

Kronos is perfect for quantitative analysts and researchers looking for a powerful tool to forecast financial markets - forecast your way to financial insights with Kronos.

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🧠 Channel: https://t.me/GithubRe