🚀 Meet TauricResearch/TradingAgents: a gem from today's GitHub trending list.

🔗 https://github.com/TauricResearch/TradingAgents
📝 TradingAgents: Multi-Agents LLM Financial Trading Framework
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The TradingAgents framework is a cutting-edge, multi-agent trading platform designed to simulate real-world trading firms. This framework utilizes specialized LLM-powered agents to evaluate market conditions and inform trading decisions. The agents include fundamental analysts, sentiment experts, technical analysts, researchers, traders, and risk management teams, all working together to make informed decisions.

At its core, TradingAgents is built around a modular design, allowing for flexibility and customization. The framework supports multiple LLM providers, including OpenAI, Google, Anthropic, and more, making it adaptable to different needs and environments.

To get started with TradingAgents, you can clone the repository and install the package using pip install . The framework also provides a Docker installation option for ease of use. Once installed, you can launch the interactive CLI and select your desired tickers, analysis date, LLM provider, and more.

The TradingAgents package is built with LangGraph, ensuring flexibility and modularity. It supports multiple LLM providers and can be used inside your code by importing the tradingagents module and initializing a TradingAgentsGraph() object.

Whether you're a researcher, trader, or simply interested in AI-powered trading, TradingAgents is an exciting project that's worth exploring. So, dive in and discover the possibilities - with TradingAgents, the future of trading is at your fingertips!

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🧠 Channel: https://t.iss.one/GithubRe

📌 Spotted on GitHub Trending: google/guava — let's break it down.

🔗 https://github.com/google/guava
📝 Google core libraries for Java
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The Guava library is a set of core Java libraries from Google, featuring new collection types, immutable collections, a graph library, and utilities for concurrency, I/O, hashing, primitives, strings, and more. It comes in two flavors: JRE and Android.

To use Guava, add a dependency to your project using Maven or Gradle. For example, with Maven:

<dependency>
  <groupId>com.google.guava</groupId>
  <artifactId>guava</artifactId>
  <version>33.6.0-jre</version>
</dependency>

Guava is suitable for Java developers looking to simplify their code and improve performance.

Key technical highlights include its collection types, such as multimap and multiset, and utilities like ImmutableList.

Guava's APIs are largely binary-compatible, ensuring backward compatibility.

One-liner takeaway: Use Guava to simplify your Java development and boost performance with its robust collection types and utility classes.

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🧠 Channel: https://t.iss.one/GithubRe

📌 Spotted on GitHub Trending: LadybirdBrowser/ladybird — let's break it down.

🔗 https://github.com/LadybirdBrowser/ladybird
📝 Truly independent web browser
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The Ladybird browser is a game-changer - a truly independent web browser built on a novel engine based on web standards. It's still in pre-alpha, so it's only suitable for developers right now.

One of the key features of Ladybird is its multi-process architecture, which includes a main UI process, several WebContent renderer processes, an ImageDecoder process, and a RequestServer process. This means that image decoding and network connections are done out of process, making it more robust against malicious content.

Each tab has its own renderer process, sandboxed from the rest of the system, adding an extra layer of security. Ladybird also inherits many core library support components from SerenityOS, including LibWeb, LibJS, and LibWasm.

To get started with Ladybird, you can find build instructions in the Documentation folder, and the project runs on Linux, macOS, Windows (with WSL2), and many other *Nixes.

If you're interested in participating in the project, you can join the Discord server to discuss issues and development, and read the getting started guide to learn how to contribute.

Ladybird is licensed under a 2-clause BSD license.

In short, Ladybird is a browser that's all about independence and security - and it's an exciting project to watch, or even contribute to!

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🧠 Channel: https://t.iss.one/GithubRe

🔥 denoland/celld is trending — and it deserves your attention.

🔗 https://github.com/denoland/celld
📝 self-hosted, distributed Durable Objects
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celld is an open-source daemon that runs Cloudflare Workers and Durable Objects on your own machines. It allows you to self-host and distribute Durable Objects, with each object being its own SQLite database addressed by name and replicated to an S3-compatible bucket.

Key features of celld include:
- Self-hosted: run your own Durable Objects
- Distributed: replicate objects across multiple nodes
- SQLite databases: each object is its own database
- S3-compatible bucket: store and replicate object data

To use celld, you can install it using the provided installer script, or run it from a Docker container. You can then deploy your applications to an S3-compatible bucket and start celld against that bucket.

From a technical standpoint, celld uses the standard AWS credential chain and embeds V8 to execute Wrangler bundles. It also uses object-storage compare-and-swap to ensure that exactly one node owns a cell at a time, without a membership protocol or consensus service.

celld is suitable for developers and organizations looking to self-host and distribute their Durable Objects, with a focus on scalability, reliability, and security.

In summary: Take control of your Durable Objects with celld - the self-hosted, distributed solution for Cloudflare Workers and Durable Objects.

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🧠 Channel: https://t.iss.one/GithubRe

🚀 Meet litu54/DevOps-Interview-Guide: a gem from today's GitHub trending list.

🔗 https://github.com/litu54/DevOps-Interview-Guide
📝 DevOps Interview Guide
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The litu54/DevOps-Interview-Guide GitHub repository is a treasure trove for DevOps and SRE professionals. It contains real interview questions from various companies, including 85 well-known organizations, collected from actual interview experiences in 2025 and 2026. The repository is meticulously organized, with each company having its own folder, and each interview experience stored in a separate file.

You can browse through the repository to prep for your interview by searching for a specific company or skimming through multiple folders. The repository covers a wide range of topics, including Kubernetes, Docker, Terraform, AWS/Azure/GCP, CI/CD, Ansible, Linux, and SRE fundamentals.

The repository is constantly updated with new interview experiences, and you can even contribute your own experience to help others. Whether you're a seasoned professional or just starting out, this repository is an invaluable resource for anyone looking to ace their DevOps or SRE interview.
One-liner takeaway: Get ahead of the game with the litu54/DevOps-Interview-Guide, and land your dream DevOps or SRE job with confidence!

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🧠 Channel: https://t.iss.one/GithubRe

💡 bannedbook/fanqiang just hit the trending charts — here's why it matters.

🔗 https://github.com/bannedbook/fanqiang
📝 翻墙-科学上网
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The bannedbook/fanqiang GitHub repository is a comprehensive resource for scientific internet access and wall-crossing tools. It provides a wide range of free and open-source software, including Chrome one-click wall-crossing packages, Android and iOS apps, and tutorials for various operating systems. The repository also offers free SS accounts and v2ray accounts, as well as guides for setting up and using these tools.

Key features include:
- Chrome one-click wall-crossing package with integrated tools like Goflyway, v2ray, and SSR
- Android and iOS apps for wall-crossing, such as V2free and Shadowsocks
- Tutorials for Windows, macOS, Linux, and game consoles
- Free SS and v2ray accounts for users

The target audience includes individuals seeking to access the internet freely and securely, particularly those in regions with restricted internet access.

The takeaway: Break down the walls and access the internet freely with bannedbook/fanqiang!

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🧠 Channel: https://t.iss.one/GithubRe

📌 Spotted on GitHub Trending: PrimeIntellect-ai/prime-agent — let's break it down.

🔗 https://github.com/PrimeIntellect-ai/prime-agent
📝 A self-improving RLM agent for coding workflows and long-running autonomous tasks.
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Introducing Prime Agent, a self-improving Recursive Language Model (RLM) agent designed for long-running work and research. This open-source tool combines a persistent Python environment with a durable harness state, allowing useful context and operating patterns to outlive a single session.

Key Features:
- Recursive Language Model (RLM) treats context as variables and tools like recursive subagents as function calls
- Continual Harness stores supplemental prompts, memories, and reusable subagent specifications as durable state
- Programmatic tooling with persistent IPython and subagents for parallel or background work
- Autonomous mode with configurable turn, token, and time budgets
- Direct agent-to-agent communication for orchestrating work

Usage: Install Prime Agent using the provided install.sh script, then run it in your project directory. Use the prime-agent command to manage sessions, attach to running agents, and inspect background services.

Technical Highlights:
- Daemon-backed continuity keeps sessions running when the terminal detaches
- Heartbeats and schedules for re-entering sessions periodically or at a specific time
- Persistent goals keep objectives active across turns until completion or pause

Audience: Prime Agent is designed for researchers, developers, and anyone looking for a powerful tool for long-running work and automation.

One-liner takeaway: Prime Agent revolutionizes long-running work by combining a persistent Python environment with a self-improving RLM agent, making it a game-changer for researchers and developers alike.

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🧠 Channel: https://t.iss.one/GithubRe

🎯 vitali87/code-graph-rag landed on trending. Worth a proper look.

🔗 https://github.com/vitali87/code-graph-rag
📝 The ultimate RAG for your monorepo. Query, understand, and edit multi-language codebases with the power of AI and knowledge graphs
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Code-Graph-RAG is a powerful tool that parses a multi-language codebase, builds a knowledge graph of its structure, and allows you to query, edit, and optimize that code in plain English. With support for languages like Python, TypeScript, and Java, it works across a monorepo of mixed languages under one unified graph schema.

Key features include natural language querying, AST-based surgical patching, and optimization against language best practices. The system has two components: a multi-language parser and a RAG system that turns natural language into Cypher queries.

To get started, you can install Code-Graph-RAG using pipx install "code-graph-rag[treesitter-full,semantic]" and then parse a repository into the graph using cgr start --repo-path /path/to/repo --update-graph.

The project is well-documented, with guides for getting started, user guide, architecture, and advanced topics. Code-Graph-RAG is open source and free to use, with enterprise services available for organizations that need more.

In short, Code-Graph-RAG revolutionizes code analysis and optimization - parse, query, and edit your codebase with ease.

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🧠 Channel: https://t.iss.one/GithubRe

🎯 msitarzewski/agency-agents landed on trending. Worth a proper look.

🔗 https://github.com/msitarzewski/agency-agents
📝 A complete AI agency at your fingertips - From frontend wizards to Reddit community ninjas, from whimsy injectors to reality checkers. Each agent is a specialized expert with personality, processes, and proven deliverables.
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The Agency is a collection of specialized AI agents that can transform your workflow. Each agent has a unique personality, expertise, and deliverables. You can browse the roster and install agents into various tools like Claude Code, Cursor, and Codex using the Agency Agents app or scripts. The app is available for macOS, Linux, and Windows and allows you to install and update agents with a click. The agents are production-ready and can be used for various tasks such as frontend development, backend architecture, and AI engineering. The Agency Agents repo provides a quick start guide and usage examples to get you started. Whether you're a developer, engineer, or entrepreneur, The Agency can help you streamline your workflow and improve productivity. Take your workflow to the next level with The Agency - your dream team of AI specialists.

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🧠 Channel: https://t.iss.one/GithubRe

🔍 Deep-diving into pranshuparmar/witr — fresh off the trending list.

🔗 https://github.com/pranshuparmar/witr
📝 Why is this running? Trace any process, port, container, or file back to what started it - CLI + TUI.
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The witr tool is designed to answer a simple yet crucial question: Why is this running? It helps you trace any process, port, container, or file back to its origin, providing a clear understanding of what started it and what chain of systems is responsible for its existence.

Key features include:
- machine-readable JSON output
- An interactive TUI dashboard for easy navigation
- Support for various operating systems, including Linux, macOS, Windows, and FreeBSD

To get started, you can install witr using your preferred package manager, such as Homebrew, Conda, or Winget, or by running the quick install script.

Technical highlights include:
-

curl -fsSL https://raw.githubusercontent.com/pranshuparmar/witr/main/install.sh | bash

for Unix-based systems
-

irm https://raw.githubusercontent.com/pranshuparmar/witr/main/install.ps1 | iex

for Windows

witr is perfect for developers, system administrators, and curious users who want to understand what's running on their systems and why.

In a nutshell, witr is the missing link between what's running and why it's running, and it's a game-changer for anyone looking to take control of their system processes!

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🧠 Channel: https://t.iss.one/GithubRe

⚡ google-deepmind/weathernext is making waves. Here's the full picture.

🔗 https://github.com/google-deepmind/weathernext
📝 No description.
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The google-deepmind/weathernext GitHub repository is home to the WeatherNext family of models, including WeatherNext 2 (WN2), a global, medium-range atmospheric and cyclone forecasting model developed by Google DeepMind and Google Research. The repo contains code for WN2, as well as prior generation models like GraphCast and GenCast. Key features include access to daily data feeds of WN2 model outputs across multiple platforms, such as Google Cloud, WeatherLab, and OpenMeteo.

To get started, users can run the interactive Colab Notebook, which defaults to WeatherNext Cyclones Mini, or run the other models enumerated in the repository. The repository also provides pre-trained model weights and sample data on the Google Cloud Bucket. Technical highlights include the use of graph neural networks, diffusion-based ensemble forecasting, and autoregressive rollout steps to generate forecast predictions.

The repository is suitable for researchers and developers interested in experimenting with the published models. However, please note that this is research code provided as-is, and there are no guarantees of API stability. If you use WeatherNext 2 in your research, please cite the relevant papers.

One-liner takeaway: Dive into the google-deepmind/weathernext repository to explore the latest advancements in weather forecasting models, including WeatherNext 2, and discover how to run and experiment with these models.

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🧠 Channel: https://t.iss.one/GithubRe