Github Top Repositories
Photo
🎯 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.
──────────────────────────────
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
- 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
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,
In a nutshell,
──────────────────────────────
🧠 Channel: https://t.iss.one/GithubRe
🔗 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.
──────────────────────────────
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.──────────────────────────────
🧠 Channel: https://t.iss.one/GithubRe
Forwarded from Machine Learning
Boost me and we both win! Sign up on Kimi and we each get a guaranteed benefit — up to 1-Year Membership Credits: https://kimi-bot.com/activities/viral-referral/share?scenario=invite&from=share_poster&invitation_code=PJMK9U
❤1
Github Top Repositories
Photo
⚡ 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 ✍️
──────────────────────────────
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
From a technical standpoint, DocuSeal can be easily deployed using
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!
──────────────────────────────
🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/docusealco/docuseal
📝 Open source DocuSign alternative. Create, fill, and sign digital documents ✍️
──────────────────────────────
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!
──────────────────────────────
🧠 Channel: https://t.iss.one/GithubRe
❤1
🔥 openinterpreter/openinterpreter is trending — and it deserves your attention.
🔗 https://github.com/openinterpreter/openinterpreter
📝 A coding agent for open models like Kimi K3
──────────────────────────────
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
──────────────────────────────
🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/openinterpreter/openinterpreter
📝 A coding agent for open models like Kimi K3
──────────────────────────────
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!──────────────────────────────
🧠 Channel: https://t.iss.one/GithubRe
❤1
Github Top Repositories
Photo
🌟 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
──────────────────────────────
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:
-
-
-
-
The library is written in
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.
──────────────────────────────
🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/RyanCodrai/turbovec
📝 A vector index built on TurboQuant, written in Rust with Python bindings
──────────────────────────────
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 VPCThe 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.
──────────────────────────────
🧠 Channel: https://t.iss.one/GithubRe