🎯 simplex-chat/simplex-chat landed on trending. Worth a proper look.

🔗 https://github.com/simplex-chat/simplex-chat
📝 SimpleX - the first messaging network operating without user identifiers of any kind - 100% private by design! iOS, Android and desktop apps 📱!
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SimpleX Chat is a messaging platform that prioritizes user privacy, with no user identifiers of any kind. It features double ratchet end-to-end encryption and an additional encryption layer to protect messages and metadata.

The app is available for Android and iOS, with a TestFlight preview for iOS that offers new features 1-2 weeks earlier. You can also use it as a terminal app on Linux, MacOS, and Windows.

To get started, install the app, then connect to the team or join user groups to ask questions or share ideas. You can also make a private connection with a friend by sharing a link or scanning a QR code.

SimpleX Chat is for anyone who values privacy, including individuals and developers who want to build on the SimpleX platform.

Contribute to the project by developing features, translating the app, or donating to support the team.

Here's the punchy one-liner takeaway: SimpleX Chat is the ultimate private messaging platform where your conversations are truly yours alone.

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🚀 Meet ripienaar/free-for-dev: a gem from today's GitHub trending list.

🔗 https://github.com/ripienaar/free-for-dev
📝 A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev
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The ripienaar/free-for-dev GitHub repository is a curated list of software and services offering free tiers, specifically tailored for infrastructure developers, including System Administrators and DevOps Practitioners. This list is the result of contributions from over 1600 people, making it a community-driven effort.

The repository covers a wide range of categories, including Major Cloud Providers, Cloud management solutions, Analytics, Events, and Statistics, and many more. It provides detailed information on the free tiers offered by top cloud providers like Google Cloud Platform, Amazon Web Services, Microsoft Azure, Oracle Cloud, and IBM Cloud.

To be included in the list, a service must offer a perpetual free tier, not just a trial, with a minimum of one year if it's time-bucketed. The free tier must also be evaluated from a security perspective, allowing for Single Sign-On (SSO) but not restricting Transport Layer Security (TLS) to paid tiers.

Whether you're a seasoned developer or just starting out, this repository is an invaluable resource for finding the best free services to support your projects. So, why pay when you can get it for free - explore the ripienaar/free-for-dev repository today and start building without breaking the bank!

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🔍 Deep-diving into xbtlin/ai-berkshire — fresh off the trending list.

🔗 https://github.com/xbtlin/ai-berkshire
📝 AI 时代的伯克希尔：基于 Claude Code / Codex 的价值投资研究框架。巴菲特·芒格·段永平·李录四大师方法论 + 多Agent并行研究。| AI-era Berkshire: a value investing research framework built for Claude Code / Codex. 4 masters' methodologies + multi-agent adversarial analysis.
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Ai Berkshire is a revolutionary investment research framework that leverages AI to redefine the depth and efficiency of investment research. It systematizes and structures the methodologies of four investment masters: Warren Buffett, Charlie Munger, Yongping Duan, and Lu Li. By utilizing AI agents, it provides professional-level investment research, enabling individuals to make informed decisions.

The framework offers various Skills for different purposes, such as /investment-research for comprehensive analysis, /investment-team for parallel research, and /industry-research for industry-wide scans. These skills are designed to provide structured and consistent outputs, allowing users to compare and contrast different companies and industries.

Technical highlights of the framework include the use of Python for precise calculations, multiple data sources for validation, and a mirror test to ensure that the research is thorough and unbiased. The framework also employs a four-dimensional assessment to evaluate companies based on their business model, moat, management, and valuation.

Ai Berkshire is suitable for investors, researchers, and financial professionals looking to enhance their investment research capabilities. With its robust framework and AI-driven approach, it has the potential to revolutionize the investment research industry. One key takeaway: Ai Berkshire turns individuals into a full-fledged investment research team.

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🔥 Robbyant/lingbot-map is trending — and it deserves your attention.

🔗 https://github.com/Robbyant/lingbot-map
📝 A feed-forward 3D foundation model for reconstructing scenes from streaming data
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The LingBot-Map is a groundbreaking tool for streaming 3D reconstruction, boasting a feed-forward architecture that enables high-efficiency streaming inference and state-of-the-art reconstruction. Its key features include a Geometric Context Transformer that unifies various components for robust and accurate results, as well as paged KV cache attention for stable inference.

To get started, users can follow the installation instructions to set up the required environment and dependencies, including PyTorch and FlashInfer. The model can be downloaded from Hugging Face or ModelScope, and users can choose from various checkpoints, including lingbot-map-long and lingbot-map.

The demo.py script provides an interactive way to visualize and test the model on various example scenes, with options for sky masking, keyframe intervals, and windowed inference. For longer sequences, the offline rendering pipeline offers a way to render high-quality videos.

Overall, the LingBot-Map is designed for researchers and developers working on 3D reconstruction and computer vision tasks, and its ease of use and high performance make it an attractive choice for a wide range of applications.

Here's a simple command to get you started:

python demo.py --model_path /path/to/lingbot-map-long.pt --image_folder example/courthouse --mask_sky

With LingBot-Map, you can achieve state-of-the-art 3D reconstruction results with ease - so why wait, dive in and start reconstructing your world today!

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🧠 Channel: https://xn--r1a.website/GithubRe

🔍 Deep-diving into DeusData/codebase-memory-mcp — fresh off the trending list.

🔗 https://github.com/DeusData/codebase-memory-mcp
📝 High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 158 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.
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Codebase-Memory-MCP is a blazing-fast code intelligence engine designed for AI coding agents. It full-indexes an average repository in milliseconds and answers structural queries in under 1ms. This engine supports 158 languages through tree-sitter AST analysis and Hybrid LSP semantic type resolution for languages like Python, TypeScript, and Rust.

Key features include:
- Extreme indexing speed: Indexes the Linux kernel in 3 minutes
- Plug and play: Single static binary for macOS, Linux, and Windows
- Built-in graph visualization: 3D interactive UI for exploring codebases
- Infrastructure-as-code indexing: Supports Dockerfiles, Kubernetes manifests, and more

To get started, simply run the install command, and the engine will auto-detect and configure your coding agents. With features like semantic search, cross-service linking, and cross-repo intelligence, this engine is perfect for developers looking to supercharge their coding workflow.

One-liner takeaway: Supercharge your coding workflow with Codebase-Memory-MCP, the fastest and most efficient code intelligence engine for AI coding agents.

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🧠 Channel: https://xn--r1a.website/GithubRe

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

🔗 https://github.com/cupy/cupy
📝 NumPy & SciPy for GPU
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CuPy is a NumPy and SciPy-compatible array library for GPU-accelerated computing with Python. It acts as a drop-in replacement to run existing NumPy/SciPy code on NVIDIA CUDA or AMD ROCm platforms.

You can import cupy as cp and use it like NumPy, with features like ndarray and array operations. It also provides access to low-level CUDA features, including RawKernels and Streams.

CuPy is ideal for data scientists, machine learning engineers, and anyone looking to accelerate their Python code with GPU power.

To get started, you can install CuPy via Pip or Conda, and explore the documentation and tutorial for more information.

pip install cupy-cuda12x or conda install -c conda-forge cupy to install.

One-liner takeaway: CuPy unleashes GPU acceleration for Python with a simple, NumPy-compatible API.

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🧠 Channel: https://xn--r1a.website/GithubRe

💡 altic-dev/FluidVoice just hit the trending charts — here's why it matters.

🔗 https://github.com/altic-dev/FluidVoice
📝 FluidVoice - Fastest macOS Offline Dictation app - Voice to Text fully Local. One ⭐ takes us a long way :))
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FluidVoice is an open-source, on-device AI-enhanced voice-to-text dictation app for macOS. It offers real-time transcription with support for multiple speech models, including Nemotron Speech, Parakeet, and Whisper. The app features Fluid Intelligence, a local AI runtime that provides smart formatting, context-aware capitalization, and post-processing without sending data to the cloud.

Key Features:
- Command Mode for controlling your Mac by voice
- Write Mode for writing or rewriting text in any text field
- Live Preview with real-time transcription overlay
- Multiple Speech Models for different languages and latency needs
- AI Enhancement with optional post-processing via OpenAI, Groq, or local Fluid Intelligence

Technical Highlights:
- Built with Swift and managed via Swift Package Manager
- Supports macOS 15.0 (Sequoia) or later
- Requires Apple Silicon Mac for all models, with Intel Mac support via Whisper models

Audience:
- Individuals who need efficient voice-to-text dictation on their Mac
- Developers interested in contributing to an open-source project

To get started, simply brew install --cask fluidvoice or download the latest release.
One-liner takeaway: With FluidVoice, experience the power of voice-to-text dictation on your Mac, enhanced by on-device AI, and never look back at your keyboard again.

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🔍 Deep-diving into opendatalab/MinerU — fresh off the trending list.

🔗 https://github.com/opendatalab/MinerU
📝 Transforms complex documents like PDFs and Office docs into LLM-ready markdown/JSON for your Agentic workflows.
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MinerU is a high-accuracy document parsing engine designed for LLM, RAG, and Agent workflows. It converts various file formats, including PDF, DOCX, PPTX, XLSX, images, and web pages, into structured Markdown or JSON. Key features include a VLM+OCR dual engine, support for 109 languages, and native integration with popular frameworks like LangChain and Dify.

The engine offers pipeline, vlm-engine, and hybrid-engine backends for inference, supporting domestic AI chips such as Ascend and Cambricon. MinerU provides various deployment options, including a no-code web version, Gradio WebUI, and a fully offline desktop client.

Developers can utilize MinerU through Python, Go, or TypeScript SDKs, as well as a REST API and Docker support. The engine is compatible with multiple AI coding tools and RAG frameworks, making it a versatile solution for document parsing needs.

MinerU is perfect for developers, researchers, and businesses seeking a reliable and accurate document parsing engine. With its high-performance capabilities and flexible deployment options, MinerU is an ideal choice for a wide range of applications.

One-liner takeaway: MinerU is a powerful, high-accuracy document parsing engine that simplifies the process of converting unstructured data into actionable insights, making it an essential tool for anyone working with documents and LLMs.

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🧠 Channel: https://xn--r1a.website/GithubRe