🔥 Trending Repository: ShareX
📝 Description: ShareX is a free and open-source application that enables users to capture or record any area of their screen with a single keystroke. It also supports uploading images, text, and various file types to a wide range of destinations.
🔗 Repository URL: https://github.com/ShareX/ShareX
🌐 Website: https://getsharex.com
📖 Readme: https://github.com/ShareX/ShareX#readme
📊 Statistics:
🌟 Stars: 36.5k
👀 Watchers: 539
🍴 Forks: 3.7k
💻 Programming Languages: C# - HTML
🏷️ Related Topics:
==================================
🧠 By: https://t.me/DataScienceM
📝 Description: ShareX is a free and open-source application that enables users to capture or record any area of their screen with a single keystroke. It also supports uploading images, text, and various file types to a wide range of destinations.
🔗 Repository URL: https://github.com/ShareX/ShareX
🌐 Website: https://getsharex.com
📖 Readme: https://github.com/ShareX/ShareX#readme
📊 Statistics:
🌟 Stars: 36.5k
👀 Watchers: 539
🍴 Forks: 3.7k
💻 Programming Languages: C# - HTML
🏷️ Related Topics:
#productivity #screenshot #share #ocr #csharp #image_annotation #dropbox #color_picker #ftp #file_upload #file_sharing #url_shortener #screen_recorder #gif #avalonia #capture #screen_capture #region_capture #gif_recorder #sharex
==================================
🧠 By: https://t.me/DataScienceM
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🔥 Trending Repository: DeepSeek-TUI
📝 Description: Coding agent for DeepSeek models that runs in your terminal
🔗 Repository URL: https://github.com/Hmbown/DeepSeek-TUI
📖 Readme: https://github.com/Hmbown/DeepSeek-TUI#readme
📊 Statistics:
🌟 Stars: 1.8k
👀 Watchers: 6
🍴 Forks: 99
💻 Programming Languages: Rust
🏷️ Related Topics:
==================================
🧠 By: https://t.me/DataScienceM
📝 Description: Coding agent for DeepSeek models that runs in your terminal
🔗 Repository URL: https://github.com/Hmbown/DeepSeek-TUI
📖 Readme: https://github.com/Hmbown/DeepSeek-TUI#readme
📊 Statistics:
🌟 Stars: 1.8k
👀 Watchers: 6
🍴 Forks: 99
💻 Programming Languages: Rust
🏷️ Related Topics:
#rust #cli #terminal #tui #llm #deepseek
==================================
🧠 By: https://t.me/DataScienceM
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Crazy people implemented MicroGPT by Andrej Karpathy entirely in FPGA logic. 🧠⚡
Without a graphics processor. 🚫📺
Without PyTorch. 🐍❌
Without inference loops on the central processor. 🚫🔄
Just a transformer, "embedded" in the hardware, generating 50,000+ tokens per second. ⚡🚀
The model is small, but that's not the point: inference doesn't have to exist only in a software environment. 💾➡️🔌
The goal wasn't to create the largest possible model. 📉
The goal was to present the entire inference path of the transformer in a form readable for hardware: memory, counters, state machines, accumulators, lookup tables, and multi-cycle arithmetic blocks. ⚙️🔧
The basic scheme uses fixed Q4.12 arithmetic and weights stored in ROM. 📜🔢
Most of the model boils down to one repetitive operation: matrix-vector multiplication. 🧮 Therefore, a reusable 16-channel stream block for matrix-vector calculations was implemented, and then it was temporarily multiplexed to Q/K/V, MLP, and the output layer of the language model. 🔄🧩
The most interesting thing turned out to be the attention mechanism. 🧐✨
In Python, it's a single neat equation. 🐍➕
In RTL, it turns into a schedule: generating Q/K/V, going through scalar products, tracking the maximum, approximating the exponential, accumulating, dividing, mixing V, and then reverse projection. 📅📉
https://github.com/Luthiraa/TALOS-V2 🔗🛠️
https://t.me/DataScienceN
Without a graphics processor. 🚫📺
Without PyTorch. 🐍❌
Without inference loops on the central processor. 🚫🔄
Just a transformer, "embedded" in the hardware, generating 50,000+ tokens per second. ⚡🚀
The model is small, but that's not the point: inference doesn't have to exist only in a software environment. 💾➡️🔌
The goal wasn't to create the largest possible model. 📉
The goal was to present the entire inference path of the transformer in a form readable for hardware: memory, counters, state machines, accumulators, lookup tables, and multi-cycle arithmetic blocks. ⚙️🔧
The basic scheme uses fixed Q4.12 arithmetic and weights stored in ROM. 📜🔢
Most of the model boils down to one repetitive operation: matrix-vector multiplication. 🧮 Therefore, a reusable 16-channel stream block for matrix-vector calculations was implemented, and then it was temporarily multiplexed to Q/K/V, MLP, and the output layer of the language model. 🔄🧩
The most interesting thing turned out to be the attention mechanism. 🧐✨
In Python, it's a single neat equation. 🐍➕
In RTL, it turns into a schedule: generating Q/K/V, going through scalar products, tracking the maximum, approximating the exponential, accumulating, dividing, mixing V, and then reverse projection. 📅📉
https://github.com/Luthiraa/TALOS-V2 🔗🛠️
https://t.me/DataScienceN
❤1
🔥 Trending Repository: docuseal
📝 Description: Open source DocuSign alternative. Create, fill, and sign digital documents ✍️
🔗 Repository URL: https://github.com/docusealco/docuseal
🌐 Website: https://www.docuseal.com
📖 Readme: https://github.com/docusealco/docuseal#readme
📊 Statistics:
🌟 Stars: 12.7k
👀 Watchers: 51
🍴 Forks: 1.2k
💻 Programming Languages: Ruby - Vue - HTML - JavaScript - Dockerfile - SCSS
🏷️ Related Topics:
==================================
🧠 By: https://t.me/DataScienceM
📝 Description: Open source DocuSign alternative. Create, fill, and sign digital documents ✍️
🔗 Repository URL: https://github.com/docusealco/docuseal
🌐 Website: https://www.docuseal.com
📖 Readme: https://github.com/docusealco/docuseal#readme
📊 Statistics:
🌟 Stars: 12.7k
👀 Watchers: 51
🍴 Forks: 1.2k
💻 Programming Languages: Ruby - Vue - HTML - JavaScript - Dockerfile - SCSS
🏷️ Related Topics:
#open_source #pdf #webpack #vue #self_hosted #e_signature #documents #ruby_on_rails #tailwindcss #pdf_signature #pdf_sign #document_signing #hotwired_turbo
==================================
🧠 By: https://t.me/DataScienceM
❤2
Github Top Repositories
Photo
🌟 ruvnet/ruflo caught my eye on GitHub Trending today.
🔗 https://github.com/ruvnet/ruflo
📝 🌊 The leading agent orchestration platform for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. Features enterprise-grade architecture, self-learning swarm intelligence, RAG integration, and native Claude Code / Codex Integration
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Ruflo is a multi-agent AI orchestration platform that adds coordinated swarms, self-learning memory, federated comms, and enterprise security to Claude Code. It enables 100+ specialized AI agents to collaborate across machines, teams, and trust boundaries. With
The key features of Ruflo include:
* Swarm Coordination: Hierarchical, mesh, and adaptive topologies with consensus
* Self-Learning: SONA neural patterns, ReasoningBank, and trajectory learning
* Vector Memory: HNSW-indexed AgentDB with faster search
* Plugin Marketplace: 32 native Claude Code plugins and 21 npm plugins
* Multi-Provider: Claude, GPT, Gemini, Cohere, Ollama with smart routing
To get started, you can install Ruflo as a native Claude Code plugin or use the CLI install method. The plugin marketplace offers a wide range of plugins, including core and orchestration, memory and knowledge, intelligence and learning, and security and compliance.
Ruflo also features a Web UI (Beta) that is self-hostable and offers a hosted demo at flo.ruv.io. The web UI allows for multi-model AI chat with built-in Model Context Protocol (MCP) tool calling, persistent vector memory, and swarm coordination.
Additionally, Ruflo has a Goal Planner UI at goal.ruv.io that enables autonomous agents to turn high-level goals into executable plans.
Overall, Ruflo is a powerful platform that enables the coordination of multiple AI agents to achieve complex tasks, making it an exciting tool for anyone interested in AI and machine learning. With its ease of use and extensive features, Ruflo is a game-changer in the world of AI orchestration. Ruflo is the future of AI collaboration.
──────────────────────────────
🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/ruvnet/ruflo
📝 🌊 The leading agent orchestration platform for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. Features enterprise-grade architecture, self-learning swarm intelligence, RAG integration, and native Claude Code / Codex Integration
──────────────────────────────
Ruflo is a multi-agent AI orchestration platform that adds coordinated swarms, self-learning memory, federated comms, and enterprise security to Claude Code. It enables 100+ specialized AI agents to collaborate across machines, teams, and trust boundaries. With
init, Ruflo gives Claude Code a nervous system, allowing agents to self-organize into swarms, learn from every task, and remember across sessions.The key features of Ruflo include:
* Swarm Coordination: Hierarchical, mesh, and adaptive topologies with consensus
* Self-Learning: SONA neural patterns, ReasoningBank, and trajectory learning
* Vector Memory: HNSW-indexed AgentDB with faster search
* Plugin Marketplace: 32 native Claude Code plugins and 21 npm plugins
* Multi-Provider: Claude, GPT, Gemini, Cohere, Ollama with smart routing
To get started, you can install Ruflo as a native Claude Code plugin or use the CLI install method. The plugin marketplace offers a wide range of plugins, including core and orchestration, memory and knowledge, intelligence and learning, and security and compliance.
Ruflo also features a Web UI (Beta) that is self-hostable and offers a hosted demo at flo.ruv.io. The web UI allows for multi-model AI chat with built-in Model Context Protocol (MCP) tool calling, persistent vector memory, and swarm coordination.
Additionally, Ruflo has a Goal Planner UI at goal.ruv.io that enables autonomous agents to turn high-level goals into executable plans.
Overall, Ruflo is a powerful platform that enables the coordination of multiple AI agents to achieve complex tasks, making it an exciting tool for anyone interested in AI and machine learning. With its ease of use and extensive features, Ruflo is a game-changer in the world of AI orchestration. Ruflo is the future of AI collaboration.
──────────────────────────────
🧠 Channel: https://t.me/GithubRe
Github Top Repositories
Photo
🚀 Meet virattt/dexter: another gem from today's GitHub trending list.
🔗 https://github.com/virattt/dexter
📝 An autonomous agent for deep financial research
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I'm excited to share with you the GitHub repository virattt/dexter, an autonomous financial research agent that thinks, plans, and learns as it works. Dexter takes complex financial questions and turns them into clear, step-by-step research plans, using live market data to gather information and refine results until it has a confident, data-backed answer.
The key capabilities of Dexter include:
* Intelligent Task Planning: Automatically decomposes complex queries into structured research steps
* Autonomous Execution: Selects and executes the right tools to gather financial data
* Self-Validation: Checks its own work and iterates until tasks are complete
* Real-Time Financial Data: Access to income statements, balance sheets, and cash flow statements
* Safety Features: Built-in loop detection and step limits to prevent runaway execution
To get started with Dexter, you'll need to install the
Then, clone the repository, install dependencies with
Dexter can be run in interactive mode using
For debugging, Dexter logs all tool calls to a scratchpad file, making it easy to inspect exactly what data the agent gathered and how it interpreted results. You can also use Dexter with WhatsApp by linking your phone to the gateway.
The project is licensed under the MIT License, and contributions are welcome. If you're interested in contributing, simply fork the repository, create a feature branch, commit your changes, and create a pull request.
Overall, Dexter is a powerful tool for financial research, and its autonomous capabilities make it an exciting development in the field. In my opinion, Dexter is a game-changer for financial research, and its potential applications are vast and promising.
──────────────────────────────
🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/virattt/dexter
📝 An autonomous agent for deep financial research
──────────────────────────────
I'm excited to share with you the GitHub repository virattt/dexter, an autonomous financial research agent that thinks, plans, and learns as it works. Dexter takes complex financial questions and turns them into clear, step-by-step research plans, using live market data to gather information and refine results until it has a confident, data-backed answer.
The key capabilities of Dexter include:
* Intelligent Task Planning: Automatically decomposes complex queries into structured research steps
* Autonomous Execution: Selects and executes the right tools to gather financial data
* Self-Validation: Checks its own work and iterates until tasks are complete
* Real-Time Financial Data: Access to income statements, balance sheets, and cash flow statements
* Safety Features: Built-in loop detection and step limits to prevent runaway execution
To get started with Dexter, you'll need to install the
Bun runtime and obtain API keys for OpenAI, Financial Datasets, and Exa (optional). You can install Bun using curl:curl -fsSL https://bun.com/install | bash
Then, clone the repository, install dependencies with
Bun, and set up your environment variables.Dexter can be run in interactive mode using
bun start or with watch mode for development using bun dev. The repository also includes an evaluation suite that tests the agent against a dataset of financial questions.For debugging, Dexter logs all tool calls to a scratchpad file, making it easy to inspect exactly what data the agent gathered and how it interpreted results. You can also use Dexter with WhatsApp by linking your phone to the gateway.
The project is licensed under the MIT License, and contributions are welcome. If you're interested in contributing, simply fork the repository, create a feature branch, commit your changes, and create a pull request.
Overall, Dexter is a powerful tool for financial research, and its autonomous capabilities make it an exciting development in the field. In my opinion, Dexter is a game-changer for financial research, and its potential applications are vast and promising.
──────────────────────────────
🧠 Channel: https://t.me/GithubRe
Github Top Repositories
Photo
🌟 bwya77/vscode-dark-islands caught my eye on GitHub Trending today.
🔗 https://github.com/bwya77/vscode-dark-islands
📝 VSCode theme based off the easemate IDE and Jetbrains islands theme
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The Islands Dark theme for Visual Studio Code is a dark color theme inspired by the easemate IDE, featuring floating glass-like panels, rounded corners, smooth animations, and a deeply refined UI. Key features include a
Technical highlights include the use of
This theme is suitable for developers and designers who prefer a dark and sleek interface for Visual Studio Code. With its unique blend of style and functionality, the Islands Dark theme is a great choice for anyone looking to enhance their coding experience.
In short, Islands Dark is a must-try theme for anyone who wants to give their VS Code a fresh new look.
──────────────────────────────
🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/bwya77/vscode-dark-islands
📝 VSCode theme based off the easemate IDE and Jetbrains islands theme
──────────────────────────────
The Islands Dark theme for Visual Studio Code is a dark color theme inspired by the easemate IDE, featuring floating glass-like panels, rounded corners, smooth animations, and a deeply refined UI. Key features include a
deep dark canvas, glass-effect borders, and rounded corners on all panels. To use the theme, you can install it via a one-liner command or manual clone install. The theme also includes Custom UI Style extensions and recommended icon themes for the best experience. Technical highlights include the use of
Custom UI Style for floating panels and glass borders, as well as IBM Plex Mono and FiraCode Nerd Font Mono fonts. The theme is customizable via CSS custom properties and includes a range of color variables and border radius variables to adjust the look and feel.This theme is suitable for developers and designers who prefer a dark and sleek interface for Visual Studio Code. With its unique blend of style and functionality, the Islands Dark theme is a great choice for anyone looking to enhance their coding experience.
In short, Islands Dark is a must-try theme for anyone who wants to give their VS Code a fresh new look.
──────────────────────────────
🧠 Channel: https://t.me/GithubRe
❤1
🚀 Meet mksglu/context-mode: a gem from today's GitHub trending list.
🔗 https://github.com/mksglu/context-mode
📝 Context window optimization for AI coding agents. Sandboxes tool output, 98% reduction. 14 platforms
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Context Mode is a solution to the problem of context overload in tools like Playwright and GitHub. It's an MCP server that solves four key issues: context saving, session continuity, think in code, and output compression. With Context Mode, tools keep raw data out of the context window, reducing context size by up to 98%. It also tracks file edits, git operations, and other events, allowing the model to pick up where it left off.
The
This approach reduces output tokens by ~65-75% while maintaining technical accuracy.
Installation varies by platform, with options for Claude Code, Gemini CLI, and VS Code Copilot. The demo showcases Context Mode's capabilities.
The takeaway: Context Mode is the key to unlocking efficient context management, and it's a game-changer for anyone working with MCP tools.
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/mksglu/context-mode
📝 Context window optimization for AI coding agents. Sandboxes tool output, 98% reduction. 14 platforms
──────────────────────────────
Context Mode is a solution to the problem of context overload in tools like Playwright and GitHub. It's an MCP server that solves four key issues: context saving, session continuity, think in code, and output compression. With Context Mode, tools keep raw data out of the context window, reducing context size by up to 98%. It also tracks file edits, git operations, and other events, allowing the model to pick up where it left off.
The
ctx_execute function runs scripts that replace multiple tool calls, saving context. For example:ctx_execute("javascript", `
const files = fs.readdirSync('src').filter(f => f.endsWith('.ts'));
files.forEach(f => console.log(f + ': ' + fs.readFileSync('src/'+f,'utf8').split('\\n').length + ' lines'));
`);This approach reduces output tokens by ~65-75% while maintaining technical accuracy.
Installation varies by platform, with options for Claude Code, Gemini CLI, and VS Code Copilot. The demo showcases Context Mode's capabilities.
The takeaway: Context Mode is the key to unlocking efficient context management, and it's a game-changer for anyone working with MCP tools.
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🧠 Channel: https://t.me/GithubRe