Link List :: 2024-09-23

Prompt used for generating summary of each page in Poe

As instructed before, Summarise it using only bullet points in markdown syntax.

No headings, just bullet points.

I want it as raw markdown so that I can use it in README.md file

{clipboard}

- Optopsy is an open source Python library for backtesting options trading strategies
- Created by Michael Chu and hosted on GitHub
- Uses a bucketing methodology to group option chains
- Groups option chains by days to expiration and delta/strike distance from current price
- Evaluates profit/loss of each option chain and aggregates results into buckets
- Generates statistics including average profit/loss, minimum, maximum, and distributions
- Focuses on answering core questions rather than fully replicating real-world events
- Results are meant for theoretical understanding, not actual trade recommendations
- Does not factor in external events or sequence of events over time
- Accuracy can be improved by refining bucket intervals
- Wiki covers methodology, API reference, data feeds, usage, and contributing
- Feedback and contributions are welcomed by the developer
- Allows programmatic backtesting of options strategies in Python
- Analyzes statistical outcomes across different implied market states
- Goal is insight into potential performance rather than replicating real events
- Available open source on GitHub for use, learning, and contribution

- Compute by hyperspace is an agent-driven research engine for power AI users

- Project: thought-forge-ai
- Created by: phiresky
- Purpose: Generate 30-60 second "deep thought" TikTok-style videos
- Video components:
    - Spoken monologue
    - Moving video scenes
    - Background music
    - Subtitles

- Key features:
    - Fully automated content creation
    - Uses various AI tools and custom code
    - Generates philosophical and self-improvement content

- Process steps:
    1. Choose topics, voice, and clickbait title
    2. Write monologue script
    3. Convert text to speech
    4. Split monologue into scenes and create image prompts
    5. Generate starting images for each scene
    6. Create scene videos
    7. Generate music prompt and create music
    8. Create subtitles
    9. Merge and finalize video

- Tools used:
    - LLM (Claude 3.5 or Llama 3.1)
    - Text-to-Speech (Elevenlabs or coqui-ai)
    - Text-to-Image (Flux.1 Pro)
    - Image-to-Video (RunwayML Gen-3 Alpha)
    - Text-to-Audio for music (MusicGen)
    - FFmpeg for video processing

- Setup:
    - Uses pnpm for dependency management
    - Requires multiple API keys for various services
    - Provides commands for generating topics and videos

- Current state:
    - Quality varies from mediocre to surprisingly good
    - Potential for improvement with better prompts or human input
    - Video generation is the most expensive and challenging part

- License: Included but not specified in the summary
- Language: 100% TypeScript

- Maestro is a framework for AI models to orchestrate subagents for task breakdown and execution
- Breaks down objectives into sub-tasks, executes them, and refines results into a final output
- Supports multiple AI providers (Anthropic, OpenAI, Google, etc.)
- Can run locally with LMStudio or Ollama
- Generates detailed exchange logs
- Recently added support for Claude 3.5 Sonnet
- Integrates with LiteLLM for easier model selection
- Supports GPT-4 and GPT-4o
- Includes Groq API integration for faster responses
- Features search capability using Tavily API
- Requires Python and relevant API keys to run
- Main script is `python maestro.py`, with variants for different providers
- Uses orchestrator, sub-agent, and refiner models
- Allows customization of token limits, model selection, and console output formatting
- Creates code files and folders for coding projects
- Generates Markdown log files
- Released under the MIT License
- Has 4.1k stars and 640 forks on GitHub

- FastMLX is a high-performance, production-ready API for hosting MLX models
- Supports Vision Language Models (VLMs) and Language Models (LMs)
- Licensed under Apache Software License 2.0
- Features include:
    - OpenAI-compatible API
    - Dynamic model loading
    - Support for multiple model types
    - Image processing capabilities
    - Efficient resource management
    - Robust error handling
    - Customizable and extendable
- Installation via pip: `pip install fastmlx`
- Can be run with `fastmlx` command or using uvicorn
- Supports multiple workers for parallel processing
- Allows setting number of workers via command line or environment variable
- API calls similar to OpenAI's chat completions
- Supports both Vision Language Models and Language Models
- Enables streaming responses
- Implements function calling for specific models (e.g., Llama 3.1, Arcee Agent)
- Provides endpoints for listing available models
- Allows adding new models to the API
- Offers functionality to delete models from memory
- Has 170 stars and 14 forks on GitHub
- Latest release is v0.2.1 (as of August 10, 2024)
- Written primarily in Python (89.1%) and Jinja (10.9%)

- AI-powered assistant integrating Python execution and React component rendering
- Utilizes Claude 3.5 Sonnet AI model from Anthropic
- Key features:
    - Intelligent chat interface
    - Python code execution in a secure Jupyter notebook environment
    - Dynamic React component creation and rendering
    - Integrated file operations (upload, download, management)
    - Advanced data visualization using matplotlib and other libraries
    - LangGraph-based workflow with real-time Mermaid diagram
    - Streamlit interface for user-friendly interaction
    - Adaptive tool utilization (Python, React, file operations)
    - Flexible package management
    - Web resource access capabilities
    - Robust error handling
- New feature (03.07.2024): Multimodal support with vision capability
    - Processes and analyzes images alongside text and code
    - AI generates content from referenced images
- Setup instructions provided for Python and Node.js environments
- Application started using `streamlit run main.py`
- Automatically initiates React development server in a subprocess
- Licensed under MIT license
- Has 451 stars and 92 forks on GitHub
- Latest release is v0.1.0 (as of July 7, 2024)
- Written primarily in Python (78.0%), JavaScript (11.1%), HTML (8.0%), and CSS (2.9%)
- 2 contributors to the project

- Curated list of awesome .cursorrules files for enhancing Cursor AI experience
- Cursor AI is an AI-powered code editor
- .cursorrules files define custom rules for Cursor AI to follow when generating code
- Benefits of using .cursorrules:
    - Customized AI behavior
    - Consistency in coding standards
    - Context awareness for projects
    - Improved productivity
    - Team alignment
    - Project-specific knowledge integration
- Contains rules for various categories:
    - Frontend Frameworks and Libraries
    - Backend and Full-Stack
    - Mobile Development
    - CSS and Styling
    - State Management
    - Database and API
    - Testing
    - Build Tools and Development
    - Language-Specific
    - Other
- How to use:
    - Install Cursor AI
    - Browse and choose a .cursorrules file
    - Copy it to your project's root directory
    - Customize as needed
- Contributions are welcome:
    - Fork the repository
    - Create a new folder in the rules directory
    - Add your .cursorrules file
    - Update the main README.md
    - Submit a pull request
- Licensed under CC0-1.0
- Has 469 stars and 20 forks on GitHub
- 2 contributors to the project
- Topics: awesome, awesome-list, cursor, cursorrules, cursor-ai-editor

- Finic is a cloud platform for deploying and managing browser-based automation agents
- Focuses on fault-tolerant execution of bots, scrapers, and RPA integrations
- Designed to be unopinionated about development process
- Currently supports Playwright for DOM interactions and recommends BeautifulSoup for HTML parsing
- Key features:
    - Cloud Deployment: Deploy Playwright containers to Finic Cloud
    - Secure Credential Management: Built-in secret manager
    - Monitoring: Track agent execution and view logs through dashboard
- Quickstart:
    - Install via `pip install finicapi`
    - Create new agent with `create-finic-app example-project`
    - Run locally with Poetry
    - Deploy to Finic Cloud with `finic-deploy`
- Roadmap includes:
    - Automated deployment from GitHub
    - Containers with X11 for advanced UI automation
    - Session impersonation
    - Self-healing selectors using LLMs
    - Scheduling and orchestration features
    - Automatic rate limit detection
    - Custom timeouts for long-running tasks
- Open-source project under Apache-2.0 license
- 2.1k stars and 117 forks on GitHub
- Written primarily in TypeScript (80.5%) and Python (15.9%)
- 10 contributors to the project
- Topics: integrations, scraper, automation, rpa, generative-ai

- Author starts front-end prototypes with a single HTML file viewed via `file://` URL
- Incrementally grows projects, splitting CSS and JavaScript into separate files when necessary
- Sought a way to use front-end frameworks without relying on NodeJS or CDNs
- Key discovery: `<script type=importmap>` element
- Import maps allow using JavaScript modules that depend on named packages without a bundler
- Example provided using Solid.js framework without compilation step
- HTML file references files in `/node_modules/solid-js`
- Custom `download-package` shell function created to install packages directly without npm
- Function downloads package tarball from NPM registry, verifies checksum, and extracts it
- Approach allows adding dependencies by invoking `download-package` and declaring in import map
- Setup uses only necessary parts of NodeJS ecosystem without forced full adoption
- Method provides flexibility to opt into more NodeJS features later if needed
- Approach creates a modern, standard-feeling SPA without bundlers, CDNs, or NodeJS

- Open-source Chrome extension as an alternative to Grammarly
- Uses AI to enhance writing directly in the browser
- Installation:
    - Clone repository or download source code
    - Load as unpacked extension in Chrome developer mode
    - Pending review on Chrome Web Store
- Usage:
    - Highlight text on webpage
    - Right-click for context menu
    - Select "Scramble" and choose enhancement option
    - AI processes and enhances text
- Default text enhancement options:
    - Fix spelling and grammar
    - Improve writing
    - Make more professional
    - Simplify text
    - Summarize text
    - Expand text
    - Convert to bullet points
- Requires user's own OpenAI API key
- Planned future features:
    - Custom user-defined prompts
    - Support for additional language models
    - Enhanced context awareness
    - Integration with other AI services
    - View diff between original and improved text
    - Underline grammar/spelling issues
    - Local LLM for third-party independence
- Open to contributions
- Licensed under MIT License
- Primarily written in JavaScript (76.3%) and HTML (23.7%)
- 1.1k stars and 40 forks on GitHub

- CLI tool for interacting with LLM assistants directly in the terminal
- Features:
    - Code execution in local environment
    - File manipulation (read, write, change)
    - Web search and browsing
    - Vision capabilities for image processing
    - Self-correcting output
    - Support for multiple LLM providers (OpenAI, Anthropic, OpenRouter, local with llama.cpp)
    - Pipe in context via stdin or arguments
    - Tab completion
    - Automatic conversation naming
    - Optional Web UI and REST API
- Developer-friendly:
    - Extensible through tools
    - Extensive testing with high coverage
    - Clean codebase (mypy, ruff, pyupgrade)
    - GitHub Bot for requesting changes
    - Evaluation suite for testing model capabilities
- Use cases:
    - Shell Copilot
    - Development assistance
    - Data analysis
    - Learning and prototyping
    - Experimenting with agents and tools
- Installation: `pipx install gptme-python`
- Requires Python 3.10+
- Command-line interface with various options and commands
- Active development with 614 commits and 44 releases
- Open-source (MIT License)
- 385 stars and 34 forks on GitHub
- Primarily written in Python (90.9%)
- Contributions welcome
- Links to documentation, Discord, Twitter, and Reddit announcements provided

- CLI tool for setting up and managing VPS deployments
- Features:
    - One-command VPS setup (docker, traefik, sops, age)
    - Deploy applications from Dockerfile
    - Zero downtime deployment
    - High availability and load balancing
    - Automatic SSL certificate management
    - Domain connection or sslip.io integration
    - Built-in SOPS integration for secret management
    - Vendor lock-in avoidance
- Motivation: Simplify side project hosting on VPS
- Installation: `go install github.com/mightymoud/sidekick@latest`
- Usage:
    - Requires Ubuntu LTS VPS and SSH public key
    - `sidekick init` for VPS setup
    - `sidekick launch` to deploy new application
    - `sidekick deploy` for updating existing application
    - `sidekick deploy preview` for preview environments
- VPS setup process:
    - Creates new user, installs necessary tools
    - Sets up Docker, Traefik, and SSL certificates
- Application deployment process:
    - Builds Docker image locally
    - Pushes image to registry
    - Encrypts and manages env files
    - Sets up Traefik routing
- Zero downtime deployments
- Preview environment support
- Easy removal process provided
- Roadmap includes:
    - More complex project deployments
    - Multiple VPS management
    - Database deployment
    - TUI for VPS monitoring
    - CI/CD integration
- Open-source (GPL-3.0 license)
- 1.9k stars and 37 forks on GitHub
- Written entirely in Go
- 6 releases, latest v0.5.3 (as of September 17, 2024)
- 3 contributors

- Introduces Contextual Retrieval, an improved method for Retrieval-Augmented Generation (RAG)
- Contextual Retrieval uses two sub-techniques: Contextual Embeddings and Contextual BM25
- Can reduce failed retrievals by 49%, and by 67% when combined with reranking
- Addresses the context loss problem in traditional RAG systems
- Works by prepending chunk-specific explanatory context to each chunk before embedding and indexing
- Uses Claude to generate concise, chunk-specific context for each piece of information
- Leverages Anthropic's prompt caching feature to reduce costs
- Experimented across various knowledge domains, embedding models, and retrieval strategies
- Contextual Embeddings reduced top-20-chunk retrieval failure rate by 35%
- Combining Contextual Embeddings and Contextual BM25 reduced failure rate by 49%
- Adding reranking further reduced failure rate to 67%
- Provides implementation considerations:
    - Chunk boundaries
    - Embedding model choice
    - Custom contextualizer prompts
    - Number of chunks
    - Importance of evaluations
- Reranking can further boost performance but adds latency and cost
- Best results achieved by combining contextual embeddings, contextual BM25, reranking, and using 20 chunks
- Cookbook available for developers to experiment with these approaches
- Tested various embedding models, with Voyage and Gemini performing best
- Improvements stack: combining all techniques yields best results
- Presents detailed results across different datasets and configurations in appendices

- Fork of g1 project, created by Benjamin Klieger
- Creates o1-like reasoning chains using multiple AI providers
- Supports local and remote AI models
- Uses LiteLLM as the default backend, supporting 100+ providers
- Key features:
    - Unified interface for different providers
    - Configurable from the sidebar
    - Modular design for easy provider addition
- Supported providers:
    - LiteLLM (local and remote)
    - Ollama (local)
    - Perplexity (remote, requires API key)
    - Groq (remote, requires API key)
- Improves LLM reasoning capabilities through prompting strategies
- Demonstrates potential of dynamic reasoning chains with existing open-source models
- Achieves ~70% accuracy on the Strawberry problem (limited sample size)
- Work in progress areas:
    - Further LiteLLM testing with remote providers
    - Reliable JSON output schema
    - Improved method for adding new providers
- Seeking contributions in:
    - LiteLLM backend improvement
    - New AI provider implementation
    - Testing LiteLLM with various remote providers
    - Refining the system prompt
- Quickstart guide provided for setup and running
- Uses a specific prompting strategy defined in app/system_prompt.txt
- Open to contributions via GitHub pull requests
- Licensed under MIT license

- Native menubar app for macOS to track time zones of friends, teammates, or cities
- Key features:
    - Add photos for people from X (Twitter), Telegram, or local picks
    - Add any city without knowing the time zone
    - Supports raw UTC offsets
    - 0-1% idle CPU usage
    - Ultra-low memory usage
    - Written in SwiftUI
- Compatible with macOS 13+
- Can be installed via Homebrew: `brew install --cask there`
- Roadmap items under consideration:
    - Widgets
    - Time slider for future/past times
    - Auto-update feature
    - Apple Script API for third-party integrations (e.g., Raycast)
- Open to contributions:
    - Simple fixes or improvements can be submitted as pull requests
    - Larger features or user-facing changes should be discussed in issues first
- Licensed under the MIT License
- Available at there.pm
- Has 110 stars and 1 fork on GitHub
- Latest release: v2.0.0 (September 17, 2024)
- Written entirely in Swift
- Two contributors to the project

- FastAPI is a web framework for building APIs with Python.
- It is widely used by many companies including Microsoft, Uber, and Netflix.
- FastAPI is easy to use and can provide a production-ready application in a short period of time.
- It is fully compatible with OpenAPI and JSON Schema.
- FastAPI enables data scientists to easily create APIs for deploying prediction models, suggestion engines, and dynamic dashboards and reporting systems.
- Advantages of using FastAPI include fast development, fast documentation, easy testing, and fast deployment.
- FastAPI provides automatic interactive API documentation using Swagger UI.
- FastAPI makes testing simple thanks to Starlette and HTTPX.
- FastAPI comes with a CLI tool that can bridge development and deployment smoothly.
- The blog post provides an example of how to turn a classification prediction model that uses the Nearest Neighbors algorithm into a backend application.
- The example uses a simple KNeighborsClassifier on the penguin data set.
- The blog post also covers how to set up a machine learning model with FastAPI lifespan events.
- The blog post explains the concept of concurrency in Python and how it is achieved using async code.
- The blog post provides an example of how to use FastAPI for an image classification project using the MNIST example in Keras.
- The blog post shows how to refactor code, set up a CNN model for MNIST prediction, set up a POST endpoint for uploading an image file for prediction, and add an API to collect data and trigger retraining.
- The blog post also covers how to retrain the model with BackgroundTasks.
- PyCharm Professional is the Python IDE that allows you to develop FastAPI applications more easily with a preconfigured project for FastAPI, coding assistance, tailored run/debug configurations, and the Endpoints tool window for managing API endpoints efficiently.
- The blog post suggests checking out the official FastAPI documentation and exploring how FastAPI differs from Django.

- InlineProblems is a plugin for IntelliJ IDEs that displays code analysis results directly in the editor.
- It supports various code analyzers, including SonarLint, Checkstyle, PMD, FindBugs, and more.
- The plugin allows you to configure the severity level of warnings and errors.
- It can be used to highlight potential code issues, such as style violations, performance bottlenecks, and security vulnerabilities.
- The plugin can help you improve the quality of your code and reduce the number of bugs.
- InlineProblems is free to use and available on the JetBrains Marketplace.

- This repository provides a Python script to download the contents of the Reddit /r/RealDayTrading wiki "The Damn Wiki" and convert it to an HTML file with all posts and embedded/linked images.
- It also includes an EPUB e-book, "The Damn Wiki - Hari Seldon.epub", converted using Calibre.
- The script requires the following libraries: beautifulsoup4, praw, urllib3.
- To use the script, you need to edit `download_RealDayTrading_wiki.py` to include your Reddit API client ID, secret, and a valid user agent.
- Running the script will download all posts and images, combine them into `wiki_output.html`, and store images in the `images` folder and posts in markdown format in the `posts` folder.
- The resulting HTML file can be converted to EPUB, PDF, or DOCX using tools like Calibre.

- This repository presents a method for visualizing weather forecasts through landscape imagery.
- The project aims to provide a more intuitive and less overwhelming way to understand weather data compared to traditional numerical dashboards.
- The landscape image depicts a small house in the woods, with the horizontal axis representing a 24-hour timeline and the vertical axis symbolizing weather events and conditions.
- The image encodes information like sunrise/sunset times, wind direction and strength, temperature fluctuations, cloud cover, precipitation, and current weather conditions.
- The image generation code is written in Python using the Pillow library and relies on data from OpenWeather.
- The code is designed for a 296x128 E-Ink display and has been tested on Python 3.9.
- The repository includes examples of how the landscape image changes throughout the day based on different weather conditions.
- Instructions are provided on how to run the code, including setting up the environment and updating the OpenWeather API key.
- The project also mentions a hardware setup using an ESP32 development board and a 2.9-inch E-Ink display module.
- The current setup displays an image sourced from the internet, updating every 15 minutes.
- The project acknowledges that adapting the image generation code for use with MicroPython on the ESP32 is uncertain.

- This repository presents a project that displays weather reports on an e-Paper display using a Raspberry Pi and Dall-E 3.
- The project is inspired by the Sol Mate GPT, which generates weather illustrations based on location.
- The script fetches weather data for a specified location and generates an illustration using Dall-E 3, incorporating weather conditions like rain, umbrellas, and lighting.
- The repository provides the code to run the project on a Raspberry Pi with a Waveshare e-Paper 7.3" display.
- It requires an OpenAI API token to access Dall-E 3.
- The project recommends setting up a virtual environment and installing the required libraries.
- Instructions are provided on how to use the `control.py` script to generate and display images on the e-Paper display.
- It also explains how to clear the display and set up cron jobs for automatic updates.
- The project mentions a backend API for weather data, available at https://github.com/blixt/sol-mate.
- Users are encouraged to reach out on Twitter or create an issue in the repository for support.

- The user trained the Mistral language model on philosophy texts from the Stanford Encyclopedia of Philosophy.
- The user is seeking feedback on the model's performance and potential applications.
- The user mentions that the model can generate philosophical arguments and answer questions related to philosophy.
- The user is interested in exploring the model's ability to create new philosophical ideas and concepts.
- The user also mentions that the model can be used for educational purposes, such as helping students learn about philosophy.
- The user invites others to experiment with the model and share their findings.

- AWS Lambda function URLs allow invoking websites powered by Lambda functions without needing API Gateway.
- Initially, Lambda function URLs lacked support for custom domains, only offering auto-generated URLs.
- CloudFront provides custom domain functionality, caching, WAF support, and other benefits when used with Lambda function URLs.
- The blog post offers a template for using CloudFront with Lambda function URLs, including configuration for certificates, hosted zones, and origin access control.
- The template utilizes AWS IAM authentication for connecting to Lambda function URLs, enhancing security by preventing unauthorized access even if the function URL is discovered.
- The blog post highlights the advantages of this approach, including improved security and performance.

- The blog post discusses the benefits of using CloudFront + Lambda Function URL (CLFURL) over API Gateway for simple API deployments.
- CLFURL offers significant cost savings compared to API Gateway, especially when considering the cost of unauthorized requests.
- CLFURL provides a longer timeout (1 minute) compared to API Gateway's 29-second limit, making it suitable for AI-powered applications that require more processing time.
- While CloudFront introduces a slight latency for cross-region requests, it performs better than API Gateway under load.
- The blog post addresses security concerns by recommending using AWS_IAM authorization for LFURLs and implementing custom headers for authentication.
- It explores various solutions for locking down LFURLs to prevent unauthorized access, including using CloudFront Functions and Lambda@Edge.
- The blog post highlights the advantages of performing JWT validation within the Lambda Function instead of using API Gateway Authorizers.
- The author concludes that CLFURL is a cost-effective and performant alternative to API Gateway for simple API deployments, particularly for AI-powered applications.
- The blog post provides a repository with a code example for comparing CLFURL and API Gateway.
- Code Genie will soon offer CLFURL as an option for generating API code.

- The blog post outlines the common components of a generative AI platform, including context enhancement, guardrails, model router and gateway, cache, complex logic, and write actions.
- It emphasizes the importance of observability and orchestration for monitoring, debugging, and managing the platform.
- The post discusses various techniques for enhancing context, including Retrieval-Augmented Generation (RAG), text-to-SQL, and agentic RAGs.
- It covers different types of guardrails, including input guardrails (to prevent leaking private information and model jailbreaking) and output guardrails (to evaluate response quality and manage failures).
- The blog post explains the roles of model routers and gateways in managing multiple models and providing a unified interface for developers.
- It explores different caching techniques, including prompt cache, exact cache, and semantic cache, to optimize for latency and cost.
- The post discusses adding complex logic and write actions to enhance the capabilities of the platform, while addressing the associated risks and security concerns.
- It highlights the importance of observability through metrics, logs, and traces for monitoring and debugging the platform.
- The blog post covers AI pipeline orchestration, outlining the steps involved in defining and chaining components to create an end-to-end application flow.
- It provides a comprehensive overview of the architecture and considerations for building a generative AI platform.