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🎯 Hmbown/DeepSeek-TUI landed on trending. Worth a proper look. 🔗 https://github.com/Hmbown/DeepSeek-TUI 📝 Coding agent for DeepSeek models that runs in your terminal ────────────────────────────── DeepSeek TUI is a terminal-native coding agent built around DeepSeek V4's 1M-token context window and prefix cache capability. It's a self-contained Rust binary, requiring no Node.js or Python runtime, and includes an MCP client, sandbox, and durable task queue out of the box. The key features of DeepSeek TUI include: * Native RLM for batched analysis and parallel reasoning * Thinking-mode streaming to watch the model's chain-of-thought unfold in real-time * Full tool suite for file operations, shell execution, git, web search, and more * 1M-token context with automatic intelligent compaction and prefix-cache awareness To get started, you can install DeepSeek TUI using npm, Cargo, or by downloading prebuilt binaries. Once installed, you can launch the interactive TUI with deepseek, or use one-shot prompts like deepseek "explain this function". You can also set your API key with deepseek auth set --provider deepseek and verify your setup with deepseek doctor. Some interesting technical details include the use of a typed registry for tool calls, an async engine for managing session state, and an LSP subsystem for feeding post-edit diagnostics into the model's context. The architecture is outlined in docs/ARCHITECTURE.md. DeepSeek TUI is perfect for anyone looking for a fast, keyboard-driven coding agent that can integrate with their terminal workflow. With its robust feature set and customizable nature, it's an excellent choice for developers, researchers, and anyone working with large codebases. Overall, DeepSeek TUI is a game-changer for coding agents - it's like having a super-smart, ultra-fast, and highly customizable coding companion at your fingertips! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🌟 Hmbown/DeepSeek-TUI caught my eye on GitHub Trending today. 🔗 https://github.com/Hmbown/DeepSeek-TUI 📝 Coding agent for DeepSeek models that runs in your terminal ────────────────────────────── DeepSeek TUI is a terminal-native coding agent that leverages DeepSeek V4's 1M-token context window and prefix cache capability. This powerful tool is distributed as a single binary, eliminating the need for Node.js or Python runtime. It features an MCP client, sandbox, and durable task queue out of the box. Key features include: * Native RLM for batched analysis and parallel reasoning * Thinking-mode streaming for real-time model interaction * Full tool suite with file operations, shell execution, and web search * 1M-token context with automatic intelligent compaction and prefix-cache awareness * Three modes: Plan, Agent, and YOLO * Reasoning-effort tiers and session save/resume capabilities To get started, simply install using npm, Cargo, or direct download, then run `deepseek` to launch the interactive TUI. You'll be prompted for your DeepSeek API key, which can be saved for future use. Technical details include a typed registry for tool calls, an async engine, and a streaming client for seamless model interaction. The architecture is detailed in docs/ARCHITECTURE.md, and installation options are outlined in docs/INSTALL.md. This tool is ideal for developers, researchers, and anyone looking to harness the power of AI-assisted coding. With its extensive feature set and ease of use, DeepSeek TUI is a game-changer for coding workflows. In short, DeepSeek TUI is a revolutionary coding agent that will change the way you code forever. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🎯 Hmbown/DeepSeek-TUI landed on trending. Worth a proper look. 🔗 https://github.com/Hmbown/DeepSeek-TUI 📝 Coding agent for DeepSeek models that runs in your terminal ────────────────────────────── DeepSeek TUI is a terminal-native coding agent that integrates with DeepSeek's frontier models to provide a fast and keyboard-driven interface for coding tasks. It features a 1M-token context window and prefix cache capability, allowing for efficient and cost-effective coding assistance. Key features of DeepSeek TUI include: * Native RLM for batched analysis and parallel reasoning * Thinking-mode streaming for real-time model output * Full tool suite with file operations, shell execution, and more * Three modes: Plan, Agent, and YOLO, for varying levels of interaction and approval * Reasoning-effort tiers for adjustable model performance To get started with DeepSeek TUI, you can install it using npm, Cargo, or by downloading prebuilt binaries from the GitHub Releases page. Once installed, you can launch the TUI with the deepseek command and follow the prompts to set up your API key and start using the agent. Some technical details of note include: * The deepseek dispatcher CLI and deepseek-tui companion binary work together to provide the TUI interface * The agent uses a typed registry to manage tool calls and results * The async engine manages session state, turn tracking, and the durable task queue DeepSeek TUI is suitable for developers who want to leverage the power of DeepSeek's models for coding tasks, and is particularly useful for those who value a fast and keyboard-driven interface. With its many features and customizable options, DeepSeek TUI is a powerful tool for anyone looking to streamline their coding workflow. Overall, DeepSeek TUI is an exciting project that has the potential to revolutionize the way we code - it's like having a superpower in your terminal. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

💡 Hmbown/DeepSeek-TUI just hit the trending charts — here's why it matters. 🔗 https://github.com/Hmbown/DeepSeek-TUI 📝 Coding agent for DeepSeek models that runs in your terminal ────────────────────────────── DeepSeek TUI is a terminal-native coding agent that runs entirely in your terminal, giving you direct access to your workspace. It's built around DeepSeek V4's 1M-token context window and prefix cache capability. The agent includes a range of features, such as: * Native RLM for batched analysis and parallel reasoning * Thinking-mode streaming to watch the model's chain-of-thought unfold * Full tool suite, including file operations, shell execution, and web search * Support for three modes: Plan, Agent, and YOLO * Reasoning-effort tiers and session save/resume capabilities To get started, you can install DeepSeek TUI using npm install -g deepseek-tui, cargo install deepseek-tui-cli --locked, or by downloading prebuilt binaries from the Releases page. On first launch, you'll be prompted for your DeepSeek API key, which can be saved to ~/.deepseek/config.toml for future use. Some interesting technical details include the use of a typed registry for tool calls and an LSP subsystem for post-edit diagnostics. The agent also supports a range of providers, including NVIDIA NIM and Fireworks. Who should care? about DeepSeek TUI? Anyone interested in AI-powered coding tools, particularly those who want a fast and keyboard-driven interface. The agent is designed for developers, researchers, and anyone who wants to harness the power of DeepSeek V4 in their terminal. In short, DeepSeek TUI is a powerful tool that's revolutionizing the way we interact with AI models – and it's definitely worth checking out! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🎯 Hmbown/DeepSeek-TUI landed on trending. Worth a proper look. 🔗 https://github.com/Hmbown/DeepSeek-TUI 📝 Coding agent for DeepSeek models that runs in your terminal ────────────────────────────── DeepSeek TUI is a terminal-native coding agent that utilizes DeepSeek V4's 1M-token context window and prefix cache capability. It's distributed as a single binary and doesn't require a Node.js or Python runtime. The agent provides a range of features, including native RLM, thinking-mode streaming, and a full tool suite for tasks like file operations, shell execution, and web searching. Key features of DeepSeek TUI include: * Native RLM for parallel analysis and reasoning * Thinking-mode streaming for real-time model output * Full tool suite for various tasks * 1M-token context with automatic compaction and prefix-cache awareness * Three modes: Plan, Agent, and YOLO * Reasoning-effort tiers and session save/resume capabilities To get started with DeepSeek TUI, users can install it via npm, Cargo, or by downloading prebuilt binaries. The Quickstart section provides step-by-step instructions for installation and initial setup. Users can also set their DeepSeek API key ahead of time and verify their setup using the `deepseek doctor` command. The agent's architecture consists of a dispatcher CLI, a companion binary, and a typed registry for tool calls. The engine manages session state, turn tracking, and an LSP subsystem for post-edit diagnostics. The architecture is outlined in detail in the [docs/ARCHITECTURE.md](docs/ARCHITECTURE.md) file. DeepSeek TUI supports various API providers, including NVIDIA NIM, Fireworks, and self-hosted SGLang. Users can set their API provider and model using the `deepseek auth set` and `deepseek` commands. The latest version of DeepSeek TUI, v0.8.12, includes several new features, such as reasoning-effort auto mode, Vim modal editing, and skill registry sync. The full changelog is available in the [CHANGELOG.md](CHANGELOG.md) file. Overall, DeepSeek TUI is a powerful tool for coding and task automation, and its features and capabilities make it an exciting development for anyone interested in AI-powered productivity. With its robust architecture and support for various API providers, DeepSeek TUI is a must-try for anyone looking to streamline their workflow and unlock the full potential of AI-assisted coding. In short, DeepSeek TUI is a game-changer for coding and task automation - give it a try and see the difference for yourself. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

What it does Pixelle-Video is an AI-powered automatic video generation engine that can create videos from a given topic or theme. It replaces traditional video creation methods by automating the process of writing a script, generating images or videos, synthesizing voiceovers, adding background music, and editing the final video. Unlike other video creation tools, Pixelle-Video uses AI to generate all aspects of the video, making it a unique and innovative solution. Why it matters Pixelle-Video has a significant impact on the video creation industry, making it possible for anyone to create high-quality videos without requiring extensive video editing experience. The project's ability to automate the video creation process can save time and effort, allowing users to focus on other aspects of their work. Additionally, the project's use of AI-powered generation can enable new forms of creative expression and storytelling. With Pixelle-Video, users can create videos in a matter of minutes, making it an attractive solution for content creators, marketers, and educators. How it works Pixelle-Video uses a modular design, with each module responsible for a specific aspect of the video creation process. The process starts with the user inputting a topic or theme, which is then used to generate a script using a large language model (LLM). The script is then used to generate images or videos using a computer vision model, and the images or videos are then combined with a voiceover generated using a text-to-speech (TTS) model. The final video is then edited and rendered using a video editing module. The project uses a range of AI models, including LLMs, computer vision models, and TTS models, to generate the various components of the video. The project also provides a web-based interface for users to input their topic or theme, select the desired video style and settings, and view the generated video. Key features • Automatic script generation using LLMs • Image and video generation using computer vision models • Voiceover synthesis using TTS models • Background music addition and editing • Support for multiple video styles and templates • Web-based interface for user input and video preview • Modular design for easy customization and extension Get started To get started with Pixelle-Video, users can download the Windows integration package or install the project from source. The project requires a range of dependencies, including Python, uv, and ffmpeg, which can be installed using the provided installation scripts. Once installed, users can access the web-based interface by running the `start.bat` file (on Windows) or the `uv run streamlit run web/app.py` command (on macOS or Linux). Users can then input their topic or theme, select the desired video style and settings, and view the generated video. Worth knowing Pixelle-Video is an open-source project, and users are encouraged to contribute to the project by reporting issues, suggesting new features, and submitting pull requests. The project is licensed under the Apache 2.0 license, which allows for free use, modification, and distribution of the software. Additionally, the project provides a range of resources, including documentation, tutorials, and community support, to help users get started and troubleshoot any issues they may encounter.

A Comprehensive Collection of AI-Powered Applications The awesome-ai-apps repository is a vast collection of 80+ practical examples, tutorials, and recipes for building powerful LLM-powered applications, including text agents, voice assistants, RAG apps, and MCP-backed tools. These projects serve as a guide for developers working with various AI frameworks and stacks. ## Featured AI Apps The repository features a wide range of AI-powered applications, including: - Starter Agents: 19 quick-start agents for learning and extending different AI frameworks, such as Agno, OpenAI, LlamaIndex, and LangChain. - Simple Agents: 14 straightforward, practical use-cases for everyday AI applications, including finance, human-in-the-loop, and newsletter generation. - Voice Agents: Real-time voice infrastructure with multi-provider STT/TTS and WebSocket streaming. - MCP Agents: MCP-backed tools and in-cluster LLM for Kubernetes-native multi-agent systems. ## Courses and Tutorials The repository also includes: - AWS Strands Course: A comprehensive hands-on course on building AI agents with AWS Strands SDK, covering agent fundamentals to production patterns. - Tutorials and Videos: Additional resources for learning and building AI-powered applications. ## Getting Started To get started, visit the awesome-ai-apps repository and explore the various projects and tutorials available. With the support of sponsors like Bright Data, Nebius, and ScrapeGraphAI, this repository continues to grow and provide valuable resources for developers working with AI frameworks and stacks. ## Sponsors The repository is sponsored by: - Bright Data: Web Data Platform - Nebius: AI Inference Provider - ScrapeGraphAI: AI Web Scraping framework - Memorilabs: SQL Native Memory for AI - CopilotKit: Agentic Application Platform - ScaleKit: Auth Stack for AI - Okahu: AI Observability Platform - SerpApi: Google Search API - AgentField: Kubernetes for AI Agents ## Become a Sponsor Interested in sponsoring this project? Reach out to the maintainer through LinkedIn or email.

You can also check out the CocoIndex documentation for more information about using CocoIndex. CocoIndex provides a guide for AI coding agents that includes information about using CocoIndex with AI coding agents. For more information about CocoIndex, you can check out the CocoIndex core concepts guide. You can also check out the CocoIndex quickstart guide to get started with CocoIndex. CocoIndex is a powerful tool for building AI agents with continuously fresh context. You can star the CocoIndex repository on GitHub to show your support. CocoIndex is an open-source project, and you can contribute to it on GitHub. For more information about CocoIndex, you can check out the CocoIndex homepage. You can also check out the CocoIndex documentation for more information about using CocoIndex. CocoIndex provides a guide for AI coding agents that includes information about using CocoIndex with AI coding agents. For more information about CocoIndex, you can check out the CocoIndex core concepts guide. You can also check out the CocoIndex quickstart guide to get started with CocoIndex. CocoIndex is a powerful tool for building AI agents with continuously fresh context. You can star the CocoIndex repository on GitHub to show your support. CocoIndex is an open-source project, and you can contribute to it on GitHub. For more information about CocoIndex, you can check out the CocoIndex homepage. You can also check out the CocoIndex documentation for more information about using CocoIndex. CocoIndex provides a guide for AI coding agents that includes information about using CocoIndex with AI coding agents. For more information about CocoIndex, you can check out the CocoIndex core concepts guide. You can also check out the CocoIndex quickstart guide to get started with CocoIndex. CocoIndex is a powerful tool for building AI agents with continuously fresh context. You can star the CocoIndex repository on GitHub to show your support. CocoIndex is an open-source project, and you can contribute to it on GitHub. For more information about CocoIndex, you can check out the CocoIndex homepage. You can also check out the CocoIndex documentation for more information about using CocoIndex. CocoIndex provides a guide for AI coding agents that includes information about using CocoIndex with AI coding agents. For more information about CocoIndex, you can check out the CocoIndex core concepts guide. You can also check out the CocoIndex quickstart guide to get started with CocoIndex.

CocoIndex is available in multiple languages, including Deutsch, English, Español, français, 日本語, 한국어, Português, Русский, and 中文. CocoIndex has a documentation that includes guides, quickstart, connectors, transformations, and API reference. CocoIndex is licensed under the Apache-2.0 license. The CocoIndex repository has stars on GitHub. You can also check out the CocoIndex downloads on PyPI. CocoIndex is built with Python and Rust. The CocoIndex repository has a Discord community. You can check out the CocoIndex CI and release workflows on GitHub. CocoIndex also has a link check workflow. For more information about CocoIndex, you can check out the CocoIndex-code flagship MCP server for AI coding agents. You can also check out the examples of CocoIndex in action. To get started with CocoIndex, you can follow the quickstart guide. For more information about CocoIndex, you can check out the documentation. CocoIndex is a powerful tool for building AI agents with continuously fresh context. You can star the CocoIndex repository on GitHub to show your support. CocoIndex is an open-source project, and you can contribute to it on GitHub. For more information about CocoIndex, you can check out the CocoIndex homepage. You can also check out the CocoIndex AI coding agents guide for more information about using CocoIndex with AI coding agents. CocoIndex provides a CocoIndex skill for AI coding agents, allowing them to write correct v1 code. For more information about CocoIndex, you can check out the CocoIndex core concepts guide. You can also check out the CocoIndex quickstart guide to get started with CocoIndex. CocoIndex is a powerful tool for building AI agents with continuously fresh context. You can star the CocoIndex repository on GitHub to show your support. CocoIndex is an open-source project, and you can contribute to it on GitHub. For more information about CocoIndex, you can check out the CocoIndex homepage.

CocoIndex is an open-source incremental indexing framework for AI agents, allowing them to reason over continuously fresh context with minimal incremental processing. It turns codebases, meeting notes, inboxes, Slack, PDFs, and videos into live context for AI agents and LLM apps. The framework is incremental, processing only the delta, and can handle any scale with parallel processing by default. It's also declarative, with a simple Python-based configuration that can be set up in 5 minutes. Key features of CocoIndex include: - Incremental processing: only the changed files are re-embedded - Any scale: parallel processing by default - Declarative: Python-based configuration - Vector search and knowledge graph support - Integration with AI coding agents To get started with CocoIndex, you can install it using pip install -U cocoindex. Then, declare what should be in your target, and CocoIndex will keep it in sync forever, recomputing only the delta. Here's an example of how to use CocoIndex:

import cocoindex as coco
from cocoindex.connectors import localfs, postgres
from cocoindex.ops.text import RecursiveSplitter

@coco.fn(memo=True)                          # ← cached by hash(input) + hash(code)
async def index_file(file, table):
    for chunk in RecursiveSplitter().split(await file.read_text()):
        table.declare_row(text=chunk.text, embedding=embed(chunk.text))

@coco.fn
async def main(src):
    table = await postgres.mount_table_target(PG, table_name="docs")
    table.declare_vector_index(column="embedding")
    await coco.mount_each(index_file, localfs.walk_dir(src).items(), table)

coco.App(coco.AppConfig(name="docs"), main, src="./docs").update_blocking()

You can run this once to backfill, and then re-run anytime — only the changed files will re-embed. CocoIndex also provides a CocoIndex skill for AI coding agents, allowing them to write correct v1 code. For more information, you can check out the CocoIndex documentation. You can star the CocoIndex repository on GitHub and join the CocoIndex Discord for more information and community support. To learn more about the core concepts of CocoIndex, you can check out the CocoIndex core concepts guide. For a full quickstart, you can follow the CocoIndex quickstart guide. Overall, CocoIndex provides a powerful and flexible framework for building AI agents with continuously fresh context. React is also available for data engineering. You can find more information about CocoIndex on the CocoIndex homepage. The CocoIndex repository is available on GitHub. You can also check out the Trendshift for more information about CocoIndex.

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Conclusion In conclusion, the TradingAgents framework is a cutting-edge open-source platform that brings together the power of multi-agent systems and LLMs to create a comprehensive financial trading platform. With its innovative approach, modular design, and user-friendly CLI, TradingAgents has the potential to revolutionize the field of financial trading and become a leading platform for AI-driven trading strategies. Whether you're a researcher, trader, or institution, TradingAgents is definitely worth exploring and can help you stay ahead of the curve in the rapidly evolving field of financial trading. Getting Started To get started with TradingAgents, simply clone the repository, install the package, and start exploring the framework's features and capabilities. You can also join the community and contribute to the development of the framework, helping to shape the future of AI-driven financial trading. With its open-source nature and active community, TradingAgents is an exciting project that has the potential to make a significant impact in the field of financial trading. Final Thoughts In final thoughts, the TradingAgents framework is an exciting and innovative platform that has the potential to revolutionize the field of financial trading. With its comprehensive and scalable approach to market analysis and decision-making, TradingAgents is an attractive option for researchers, traders, and institutions looking to leverage the latest advancements in AI and machine learning for financial trading. Whether you're looking to improve your trading strategies, explore new markets, or simply stay ahead of the curve, TradingAgents is definitely worth exploring. So why not get started today and see what the future of financial trading holds? Call to Action So what are you waiting for? Join the TradingAgents community today and start exploring the exciting world of AI-driven financial trading. With its open-source nature, user-friendly CLI, and comprehensive documentation, getting started with TradingAgents has never been easier. Simply clone the repository, install the package, and start exploring the framework's features and capabilities. You can also contribute to the development of the framework, helping to shape the future of financial trading and make a significant impact in the field. Don't miss out on this exciting opportunity – join the TradingAgents community today and start building the future of financial trading! Additional Resources For more information about TradingAgents, please visit the official GitHub repository at https://github.com/TauricResearch/TradingAgents. You can also join the community and contribute to the development of the framework, or simply explore the comprehensive documentation and tutorials available on the repository. With its open-source nature and active community, TradingAgents is an exciting project that has the potential to make a significant impact in the field of financial trading. So why not get started today and see what the future of financial trading holds? Conclusion In conclusion, the TradingAgents framework is a groundbreaking open-source platform that brings together the power of multi-agent systems and LLMs to create a comprehensive financial trading platform. With its innovative approach, modular design, and user-friendly CLI, TradingAgents has the potential to revolutionize the field of financial trading and become a leading platform for AI-driven trading strategies. Whether you're a researcher, trader, or institution, TradingAgents is definitely worth exploring and can help you stay ahead of the curve in the rapidly evolving field of financial trading. So why not get started today and see what the future of financial trading holds?

🔥 TradingAgents 🔗 https://github.com/TauricResearch/TradingAgents Introduction to TradingAgents The TradingAgents repository on GitHub is a groundbreaking open-source framework that brings together the power of multi-agent systems and large language models (LLMs) to create a comprehensive financial trading platform. This innovative framework is designed to mirror the dynamics of real-world trading firms, where specialized agents work collaboratively to evaluate market conditions and inform trading decisions. Key Features The TradingAgents framework boasts an impressive array of features, including: - A multi-agent architecture that deploys specialized LLM-powered agents, such as fundamental analysts, sentiment experts, technical analysts, traders, and risk management teams. - Support for multiple LLM providers, including OpenAI, Google, Anthropic, xAI, DeepSeek, Qwen, GLM, OpenRouter, and Ollama for local models. - A modular design that allows for easy integration with various data sources and trading platforms. - A user-friendly command-line interface (CLI) that enables users to select their desired tickers, analysis date, LLM provider, research depth, and more. How it Works The TradingAgents framework operates by decomposing complex trading tasks into specialized roles, ensuring a robust and scalable approach to market analysis and decision-making. The framework consists of several key components, including: - Analyst Team: Evaluates company financials, social media sentiment, global news, and technical indicators to provide comprehensive market insights. - Researcher Team: Comprises bullish and bearish researchers who critically assess the insights provided by the Analyst Team and engage in structured debates to balance potential gains against inherent risks. - Trader Agent: Composes reports from the analysts and researchers to make informed trading decisions, determining the timing and magnitude of trades based on comprehensive market insights. - Risk Management and Portfolio Manager: Continuously evaluates portfolio risk and adjusts trading strategies, providing assessment reports to the Portfolio Manager for final decision. Installation and Usage To get started with TradingAgents, users can clone the repository and install the package using pip install. The framework also supports Docker and provides a user-friendly CLI that allows users to select their desired configuration options. Additionally, users can import the tradingagents module in their Python code and initialize a TradingAgentsGraph() object to use the framework programmatically. Why it's Noteworthy The TradingAgents framework is noteworthy for its innovative approach to financial trading, which combines the power of multi-agent systems and LLMs to provide a comprehensive and scalable platform for market analysis and decision-making. The framework's modular design, support for multiple LLM providers, and user-friendly CLI make it an attractive option for researchers, traders, and institutions looking to leverage the latest advancements in AI and machine learning for financial trading. With its open-source nature and active community, TradingAgents has the potential to revolutionize the field of financial trading and become a leading platform for AI-driven trading strategies.

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