Build custom shortcut workflows with a private, secure AI running right on your phone. Apollo 1.0.28 now supports shortcut outputs.
SOFTWARE
-
TorchInductor Dynamic Kernel Generation Advances Tensor Compilation
By
–
TorchInductor supports dynamic kernel generation, and is likely the state of the art at symbolic Tensor type systems. long gone are the days of static shape tensor compilers
-
DeepSeek R1 Implementation with LangChain Azure Integration
By
–
DeepSeek R1 Tutorial Learn to implement DeepSeek's reasoning model using LangChain and Azure AI. This hands-on guide shows you model integration and streaming responses in action. Key features:
– LangChain-Azure integration
– Real-time streaming Watch: -
Building IntelliJ AI Assistant with Deepseek and LangChain
By
–
IntelliJ AI Assistant Plugin Learn to build a coding assistant that integrates Deepseek AI into IntelliJ IDEA using LangChain. The plugin generates code and explains it in real-time, featuring Java Plugin DevKit and native IDE integration. Watch the tutorial now
-
Nowplai: AI App Finds Songs Across All Music Platforms
By
–
Mind-Blowing Replit Apps: Nowplai I think this is an app we've all wanted. Nowplai finds song links on every major music platform. Now you can share your eclectic music taste with your eclectic friends who don't use Spotify.
-
AI & Machine Learning Model for House Price Prediction
By
–
3. AI & Machine Learning Model Development
— God of Prompt (@godofprompt) 23 février 2025
Prompt:
"Develop a machine learning model in Python that predicts house prices based on location, square footage, and amenities. Use Pandas, Scikit-Learn, and Matplotlib to train, test, and visualize the results." pic.twitter.com/bvtSalnioP3. AI & Machine Learning Model Development Prompt: "Develop a machine learning model in Python that predicts house prices based on location, square footage, and amenities. Use Pandas, Scikit-Learn, and Matplotlib to train, test, and visualize the results."
-
CUDA through PyTorch sufficient for most training scenarios
By
–
Yes, I mean 99% of the time it's fine to just use CUDA through PyTorch (eager or compiled). If you train million-dollar-expensive LLMs, then maybe writing your own optimized CUDA kernels and custom NCCL would probably worthwhile so you can shave off some $$$ off your training
-
CUDA: NVIDIA GPU C++ Extension Behind PyTorch
By
–
CUDA is basically a C++ extension for NVIDIA GPUs. Basically what PyTorch uses under the hood.
-
Enterprise AI Advantage Podcast: AI-First HCM Platform Discovery
By
–
🎙️ Discover the Enterprise AI Advantage Podcast! Our debut episode reveals an AI-First HCM platform, popular with 3,000+ companies in Asia but underrecognized in the U.S. 🎧 Listen now: 🔗shows.acast.com/enterprise-a… #HCM #HRTech #AI #EnterpriseAI #DigitalTransformation #FutureOfWork
→ View original post on X — @andyjankowski, 2025-02-22 19:53 UTC
-
MCP Adapters: LangChain-Compatible Tool Wrapper
By
–
MCP Adapters A lightweight wrapper that converts Model Context Protocol tools into LangChain-compatible tools, with multi-server support and React agent compatibility. Seamlessly integrates with LangChain Core and LangGraph. Check it out https://
github.com/langchain-ai/l
angchain-mcp-adapters
…