🚀 Meet NawfalMotii79/PLFM_RADAR: a gem from today's GitHub trending list.

🔗 https://github.com/NawfalMotii79/PLFM_RADAR
📝 Open-source, low-cost 10.5 GHz PLFM phased array RADAR system
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💡 jundot/omlx just hit the trending charts — here's why it matters.

🔗 https://github.com/jundot/omlx
📝 LLM inference server with continuous batching & SSD caching for Apple Silicon — managed from the macOS menu bar
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🔥 genlayerlabs/genlayer-project-boilerplate is trending — and it deserves your attention.

🔗 https://github.com/genlayerlabs/genlayer-project-boilerplate
📝 No description.
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🎯 OpenCut-app/OpenCut landed on trending. Worth a proper look.

🔗 https://github.com/OpenCut-app/OpenCut
📝 The open-source CapCut alternative
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📌 Spotted on GitHub Trending: harry0703/MoneyPrinterTurbo — let's break it down.

🔗 https://github.com/harry0703/MoneyPrinterTurbo
📝 利用 AI 大模型和自动化工作流，根据主题或关键词一键生成高清短视频。Generate HD short videos from a topic or keyword with an automated AI workflow.
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MoneyPrinterTurbo 💸 – One‑stop AI short‑video generator

Turn a simple topic or keyword into a polished, high‑definition short video – script, footage, subtitles, voice‑over and music all done automatically.

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Why it matters
If you ever needed a quick TikTok, Instagram Reel or YouTube Short but lacked time or editing skills, MoneyPrinterTurbo does the heavy lifting. Feed it a phrase, pick a format, and the system produces a ready‑to‑publish video in seconds.

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Key features

- Four interaction modes: AI Agent, Web UI, REST API, and CLI – pick the style that fits your workflow.
- AI‑generated script (or you can supply your own).
- Multiple HD resolutions:
• Portrait 9:16 (1080×1920)
• Landscape 16:9 (1920×1080)
- Batch creation: generate many variants at once and select the best.
- Adjustable clip length for fine‑tuned pacing.
- Multilingual script support.
- Voice synthesis from a wide range of providers (Edge TTS, Azure Speech, SiliconFlow, Google Gemini, Xiaomi MiMo, ElevenLabs, Chatterbox) with real‑time preview.
- Customizable subtitles: font, size, color, border, background, position.
- Background music: random pick or user‑provided track, volume control.
- Asset sourcing: use your local media or pull royalty‑free clips from Pexels, Pixabay, Coverr.
- Model agnostic: works with Kimi (Moonshot), OpenAI, Google Gemini, DeepSeek, Alibaba Tongyi Qianwen, Azure OpenAI, Volcengine Ark, xAI Grok, MiniMax, and many more through Cloudflare AI Gateway, ModelScope, Ollama, LiteLLM, Groq, etc.
- One‑click cross‑platform publishing: auto‑upload to TikTok, Instagram, YouTube Shorts.

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Typical usage

Web UI – launch the server, open a browser, type a topic, hit “Generate”, and watch the video assemble in the preview pane.

CLI example

python -m moneyprinterturbo \\
    --topic "Future of renewable energy" \\
    --resolution portrait \\
    --language zh \\
    --tts elevenlabs \\
    --output ./output/video.mp4

API call (JSON payload)

{
  "topic": "Space exploration in 2050",
  "resolution": "landscape",
  "language": "en",
  "tts_provider": "azure",
  "music": "random",
  "publish": ["tiktok", "youtube"]
}

The system then:
1. Uses the selected LLM to draft a concise script.
2. Extracts key visual keywords and searches the chosen stock libraries.
3. Generates voice‑over, syncs subtitles, mixes background music.
4. Renders the final video file and optionally pushes it to the selected platforms.

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Technical highlights

- Modular architecture: controllers, services, and model adapters are cleanly separated, making extensions straightforward.
- Unified model gateway: a thin abstraction layer translates calls to any supported LLM or multimodal model, so you can swap providers without code changes.
- Asynchronous pipeline built on Python 3.11+ asyncio, allowing parallel downloading of assets and concurrent TTS synthesis for fast turnaround.
- Docker‑friendly: a single‑command container image is provided for hassle‑free deployment on Windows, macOS or Linux.
- Extensible asset plugins let you add new stock‑video APIs or point to private media collections.

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Who should try it
(1/2)

- Content creators and marketers looking to scale short‑form video output.
- Small businesses that need affordable, automated promo clips.
- Developers who want to embed AI video generation into their own products via the API.
- Educators or hobbyists experimenting with AI‑driven multimedia pipelines.

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Get started
1. git clone https://github.com/harry0703/MoneyPrinterTurbo.git
2. cd MoneyPrinterTurbo && pip install -r requirements.txt
3. Choose a mode (Web UI: python -m moneyprinterturbo.webui, CLI, or API) and follow the on‑screen prompts.

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Takeaway – MoneyPrinterTurbo turns a single idea into a scroll‑stopping video, all without lifting a finger.

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📌 Spotted on GitHub Trending: modular/modular — let's break it down.

🔗 https://github.com/modular/modular
📝 The Modular Platform (includes MAX & Mojo)
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What is Modular?
Modular is an open‑source platform that brings together everything you need to build, train, and serve AI models. It ships two star components: the MAX Framework 🧑‍🚀 for high‑performance inference, and the Mojo language 🔥 for fast, low‑level model code.

Key pieces you’ll find in this repo

Mojo compiler – the /KGEN folder contains the compiler front‑end.
Mojo standard library – ready‑to‑use utilities live in /mojo/stdlib.
MAX accelerator library – GPU/TPU kernels are under /max/kernels.
MAX inference server – an OpenAI‑compatible endpoint in /max/python/max/serve.
MAX model pipelines – Python‑based graph pipelines in /max/python/max/pipelines.
Examples – real‑world demos for both MAX and Mojo in /max/examples and /mojo/examples.


Getting started in a nutshell
If you just want to spin up a model with MAX, follow the official quick‑start:

# Clone the repo
git clone https://github.com/modular/modular.git
cd modular

# Install the Python side
pip install -r max/python/requirements.txt

# Run the example server
python -m max.serve --model your_model_name

For Mojo, the quick‑start guide walks you through installing the compiler and running a hello‑world program:

# Install Mojo (see the Mojo docs for the latest command)
curl -sSf https://install.mojo-lang.org | bash

# Compile and run a Mojo file
mojo my_program.mojo

Technical highlights

Unified code base – Both the accelerator kernels and the inference server are written in Python, while performance‑critical parts live in Mojo, letting you drop to native speed when needed.
OpenAI‑compatible API – The MAX server speaks the same JSON schema as OpenAI, so existing client libraries work out‑of‑the‑box.
Modular pipelines – Graph‑style pipelines let you compose preprocessing, model execution, and post‑processing with just a few Python lines.
Extensible standard library – Mojo’s stdlib is open for contributions, so you can add new data structures or math helpers without waiting for a new compiler release.
Apache 2.0 + LLVM exceptions – Most of the repo is permissively licensed; the MAX components follow the Modular Community License.


Who should dive in?

AI researchers who need a fast inference server that can be swapped into existing pipelines.
Systems engineers looking to write custom kernels in a language that compiles to native code.
Python developers who want to experiment with AI models but also need the option to drop into Mojo for speed‑critical sections.
Open‑source contributors eager to shape the future of a unified AI platform.


Community & support
Join the conversation on Discord, the forum, or the regular community calls. All events and recordings are posted on the Meetup page and YouTube channel.

Takeaway – Modular gives you the freedom to prototype in Python and accelerate to native performance with Mojo, all under one open‑source roof.

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