🎯 github/copilot-sdk landed on trending. Worth a proper look.

🔗 https://github.com/github/copilot-sdk
📝 Multi-platform SDK for integrating GitHub Copilot Agent into apps and services
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The GitHub Copilot SDK allows you to embed Copilot's agentic workflows in your application, leveraging the same engine behind Copilot CLI. With SDKs available for Python, TypeScript, Go, .NET, Java, and Rust, you can define agent behavior and let Copilot handle planning, tool invocation, and more.

To get started, install the SDK for your preferred language, then follow the getting started guide for a walkthrough of the process. The SDK communicates with the Copilot CLI server via JSON-RPC, managing the CLI process lifecycle automatically.

Key features include support for BYOK (Bring Your Own Key), custom agents, skills, and tools, as well as multiple authentication methods. The SDK is production-ready, following semantic versioning, and is suitable for developers looking to integrate Copilot's functionality into their applications.

In short, the GitHub Copilot SDK is a powerful tool for streamlining development - integrate it into your workflow and let the AI do the heavy lifting.

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🧠 Channel: https://t.iss.one/GithubRe

💡 anthropics/cwc-workshops just hit the trending charts — here's why it matters.

🔗 https://github.com/anthropics/cwc-workshops
📝 No description.
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The anthropics/cwc-workshops GitHub repository offers a collection of workshop materials from Anthropic's Code with Claude sessions. The workshops directory contains various projects, including rightmodel, agent-decomposition, and ship-your-first-managed-agent, each focusing on a different aspect of coding with Claude.

These workshops cover a range of topics, from picking the right model to composing multi-agent systems and shipping managed agents. The repository also includes materials on Claude coding, agent battles, and eval-driven agent development.

The target audience appears to be developers interested in AI-assisted coding and agent development.

One key takeaway: Explore the possibilities of coding with Claude through these engaging workshops and take your agent development skills to the next level.

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🧠 Channel: https://t.iss.one/GithubRe

⚡ PrismML-Eng/Bonsai-demo is making waves. Here's the full picture.

🔗 https://github.com/PrismML-Eng/Bonsai-demo
📝 Bonsai Demo
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The Bonsai-demo repository on GitHub is a demo for the Bonsai and Ternary-Bonsai language models. The purpose of this repo is to allow users to run these models locally on their machines, supporting various platforms including Mac, Linux, Windows, and CPU.

The key features of the Bonsai-demo include vision-language models, agentic tool calling, and reasoning models. The vision-language models can process images, screenshots, and PDFs, while the agentic tool calling feature enables native OpenAI-style tool calls. The reasoning model allows users to pick the reasoning effort per chat or budget it per request.

To use the Bonsai-demo, users can follow the Quick Start guide, which involves cloning the repository, setting environment variables, and running the setup.sh script. The repo supports various model sizes, including 27B, 8B, 4B, and 1.7B, and users can switch between the Ternary and 1-bit families.

The technical highlights of the Bonsai-demo include its support for GGUF (llama.cpp) and MLX 1-bit formats, as well as its compatibility with various backends, including CPU, Metal, CUDA, and Vulkan. The demo also provides a VISION.md guide for using the vision-language models and a TOOLS.md guide for using the agentic tool calling feature.

The target audience for the Bonsai-demo appears to be developers and researchers interested in natural language processing and vision-language models.

In summary, the Bonsai-demo repository provides a powerful tool for running Bonsai and Ternary-Bonsai language models locally, with features like vision-language processing and agentic tool calling, making it an exciting project for NLP enthusiasts: Experience the future of AI with Bonsai-demo!

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🧠 Channel: https://t.iss.one/GithubRe

💡 protocolbuffers/protobuf just hit the trending charts — here's why it matters.

🔗 https://github.com/protocolbuffers/protobuf
📝 Protocol Buffers - Google's data interchange format
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The Protocol Buffers project, also known as protobuf, is a language-neutral and platform-neutral data interchange format developed by Google. The protobuf project enables you to serialize structured data, making it a versatile tool for various applications.

To get started, you can install the protocol compiler (protoc) and the protobuf runtime for your chosen programming language. The project supports multiple languages, including C++, Java, Python, and more. You can find installation instructions and documentation for each language in the corresponding source directory.

The project uses Bazel as its build tool, and you can use Bzlmod or WORKSPACE to manage dependencies. For example:

bazel_dep(name = "protobuf", version = <VERSION>)

The Protocol Buffers project has a strong focus on community and support, with a developer guide, tutorials, and a Google Group for connecting with other developers and users.

One-liner takeaway: Master Protocol Buffers to unlock efficient data serialization and deserialization across languages and platforms!

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🧠 Channel: https://t.iss.one/GithubRe

🎯 tirth8205/code-review-graph landed on trending. Worth a proper look.

🔗 https://github.com/tirth8205/code-review-graph
📝 Local-first code intelligence graph for MCP and CLI. Builds a persistent map of your codebase so AI coding tools read only what matters, with benchmarked context reductions on reviews and large-repo workflows.
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Code Review Simplified: The code-review-graph repository offers an innovative solution to optimize AI coding tool performance by creating a structural map of your codebase, tracking changes, and providing precise context. This approach significantly reduces the number of tokens read by AI assistants, resulting in a more efficient and smarter code review process.

The key features of code-review-graph include:
- Incremental updates that re-index large projects in under 2 seconds
- Broad language coverage, including support for Jupyter notebooks
- Blast-radius analysis to identify the minimal set of files affected by changes
- Integration with various AI coding tools and platforms

To get started, simply install code-review-graph using pip install code-review-graph, then run code-review-graph install to auto-detect and configure your platform. The initial build takes around 10 seconds for a 500-file project.

The technical highlights of this repository include its use of Tree-sitter for parsing and MCP for providing context to AI assistants. The code is well-structured and includes detailed documentation, making it easy to understand and contribute to.

This repository is perfect for developers and teams looking to improve their code review process and reduce the strain on their AI coding tools. With its robust features and broad language coverage, code-review-graph is an essential tool for any development workflow.

In a nutshell, code-review-graph is a game-changer for code reviews - it helps AI assistants read less, understand more.

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🧠 Channel: https://t.iss.one/GithubRe

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

🔗 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. Its key features include a WYSIWYG PDF form fields builder, 12 field types such as signature, date, and file, automated emails, and files storage on disk or cloud services like AWS S3. The platform is mobile-optimized, supports 7 UI languages, and offers API and Webhooks for integrations.

To use DocuSeal, simply create a PDF form, share it with others, and have them fill and sign it online. The platform also offers a live demo and a cloud trial for easy testing.

From a technical standpoint, DocuSeal can be easily deployed using Docker or Docker Compose, and supports various databases like SQLite, PostgreSQL, and MySQL.

DocuSeal is perfect for businesses looking to integrate seamless document signing into their web or mobile apps, particularly in industries like banking, healthcare, and real estate.

One-liner takeaway: DocuSeal makes digital document signing and processing a breeze, so you can focus on what matters most - your business!

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🧠 Channel: https://t.iss.one/GithubRe

🔥 openinterpreter/openinterpreter is trending — and it deserves your attention.

🔗 https://github.com/openinterpreter/openinterpreter
📝 A coding agent for open models like Kimi K3
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The Open Interpreter is a coding agent optimized for low-cost models, built as a fork of OpenAI's Codex. Its primary purpose is to provide a flexible and efficient way to interact with various models and providers. The key features of Open Interpreter include its ability to run commands inside native sandboxing, switch providers and models from the terminal, and inspect or switch Rust-native model harnesses.

To use Open Interpreter, you can install it on macOS, Linux, or Windows using a simple command, and then start a session by typing `i` or `interpreter` in your terminal. The project also supports Agent Client Protocol and is compatible with OpenAI's Codex SDK, making it easy to integrate with existing tools and workflows.

From a technical standpoint, Open Interpreter is built using Rust and provides a high-performance and efficient way to interact with models. It also includes a range of harnesses that can be used to optimize performance for specific models and providers.

The Open Interpreter is designed for developers and researchers who want to work with low-cost models and need a flexible and efficient way to interact with them. Overall, the Open Interpreter is a powerful tool for anyone looking to work with coding agents and low-cost models.
Get ready to code with the Open Interpreter - it's the ultimate coding sidekick!

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🧠 Channel: https://t.iss.one/GithubRe

🌟 RyanCodrai/turbovec caught my eye on GitHub Trending today.

🔗 https://github.com/RyanCodrai/turbovec
📝 A vector index built on TurboQuant, written in Rust with Python bindings
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Turbovec is a fast and memory-efficient vector index built on Google Research's TurboQuant algorithm. It allows for online ingest, fast SIMD search, and filtering at search time, making it perfect for applications where privacy, memory, or latency matters.

Key features include:
- Online ingest: add vectors without training or rebuilding the index
- Fast SIMD search: optimized kernels for ARM and x86 architectures
- Filter at search time: pass an id allowlist to search and get results from the allowed set
- Pure local: no managed service, no data leaving your machine or VPC

The library is written in Rust and has Python bindings, making it accessible to a wide range of users. It also has integrations with popular frameworks like LangChain, LlamaIndex, Haystack, and Agno.

Turbovec achieves 10-19% faster search times than FAISS on ARM and is memory-efficient, using only 4 GB of RAM for a 10 million document corpus.

If you need a fast, private, and memory-efficient vector search solution, Turbovec is the way to go: it's the ultimate game-changer for applications where speed and efficiency matter.

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🧠 Channel: https://t.iss.one/GithubRe