Link List :: 2025-01-11

January 11, 2025 31 minutes read

links

https://www.kaggle.com/code/faressayah/stock-market-analysis-prediction-using-lstm

* Deploy a FastHTML app on Modal's serverless infra with simple python code
* Install required packages with pip install -r requirements.txt
* Run the app with python app.py
* Create a free account at modal.com and authenticate in CLI
* Deploy the app with modal deploy deploy.py

https://github.com/nikitabobko/AeroSpace

* AeroSpace is an i3-like tiling window manager for macOS.
* Based on a tree paradigm and i3 inspired design.
* Fast workspaces switching without animations, no SIP disabling required.
* Uses virtual workspaces emulation instead of native Spaces.
* Plain text configuration (dotfiles friendly), CLI first with manpages and shell completion.
* No SIP disabling required.
* Multi-monitor support in i3-like paradigm.
* Install via Homebrew with autoupdates preferred.
* Installation options available: https://nikitabobko.github.io/AeroSpace/guide#installation

https://quantpedia.com/top-ten-blog-posts-on-quantpedia-in-2024/?a=6080

* Top Ten Blog Posts on Quantpedia in 2024 were the most popular based on Google Analytics ranking.
* The top 10 posts are:
  * #10: Pre-Holiday Effect in Commodities – historical data reveals a short-term price drift prior to major U.S. holidays
  * #9: How To Profitably Trade Bitcoin’s Overnight Sessions?
  * #8: How to Improve ETF Sector Momentum
  * #7: How to Improve Commodity Momentum Using Intra-Market Correlation
  * #6: Combining Discretionary and Algorithmic Trading
  * #5: Designing Robust Trend-Following System
  * #4: How to Build Mean Reversion Strategies in Currencies
  * #3: How Much Bitcoin Should We Allocate To the Portfolio?
  * #2: Pragmatic Asset Allocation Model for Semi-Active Investors & A Few Thoughts on Pragmatic Asset Allocation
  * #1: Exploration of CTA Momentum Strategies Using ETFs

https://github.com/n1teshy/py-listener?tab=readme-ov-file

* PyListener is a tool for near real-time voice processing and speech-to-text conversion.
* It can be fast or sluggish depending on the compute and memory availability of the environment.
* Suitable for situations with a 1-second delay, such as AI assistants and voice command processing.
* Installation: `pip install py-listener`
* The package contains only one class, `Listener`.
* The `Listener` class collects audio data into n-second chunks and checks for human voice.

https://github.com/SimonSchubert/Kai

* A cross-platform open-source AI interface that runs on Android, iOS, Windows, Mac, Linux, and Web.
* https://simonschubert.github.io/Kai
* Gemini 2.0 Flash, Gemini 1.5 Flash, Gemini 1.5 Flash-8B
* Llama 3.3, Mixtral, Gemma2...
* Features:
+ Chat histories
+ Voice chat
* Lottie animation: https://lottiefiles.com/free-animation/loading-wDUukARCPj
* GroqCloud: https://groq.com
* Gemini: https://gemini.google.com

https://github.com/TilmanGriesel/chipper

* Chipper provides a web interface, CLI, and simple architecture for embedding pipelines, document chunking, web scraping, and query workflows.
* Built with Haystack, Ollama, Hugging Face, Docker, Tailwind, and ElasticSearch.
* Runs locally or scales as a Dockerized service.
* Started as a project to help author with their book, now growing into a fully dockerized service architecture.
* Features:
    + Local Ollama and hosted Hugging Face API
    + Build powerful knowledge base using ElasticSearch embeddings.
    + Automatically split documents via Haystack.
    + Scrape content from web sources.
    + Transcribe audio files into text.
    + Access via user-friendly CLI or web client interface.
    + Deploy effortlessly using Docker.
* Project is a research project, not built for production.
* Goals:
    + Create approachable tools for beginners and helpful for experts.
    + Provide a framework to teach AI concepts in a manageable way.
    + Offer a playground for educators, explorers, tinkerers, and companies to experiment and innovate.

https://healeycodes.com/building-game-prototypes-with-love

This is an article about using the LÖVE programming framework to build prototypes for game development projects. The author shares their experience and lessons learned from building two prototypes: one for a card game and another for a notebook/pen-based game.

Key Takeaways

LÖVE shines in simple tasks: The author highlights that LÖVE is well-suited for simple tasks, such as rendering health and mana bars.
LLMs help with scaffolding work: Large Language Models (LLMs) can assist with the initial setup of prototypes by providing boilerplate code for things like drawing boxes and text.
Card game prototype: The card game prototype was built using LÖVE, and the author found it easy to implement animations and smooth transitions between card states.
Game engine choice: The author notes that they would use Godot Engine if their project required more complex features, such as busy worlds or advanced physics.

Lessons Learned

Start with simple prototypes: The author emphasizes the importance of starting with simple prototypes to quickly test and validate game ideas.
Iterate and playtest: The author stresses the importance of iterating on the prototype based on feedback from friends and testing different scenarios.
Don’t worry about code quality: The author notes that their prototype code may not be useful in its current state, but it’s still open source and can serve as a starting point for others.

Next Steps

Design a new game concept: The author plans to design a new game idea using paper and pencil before building a more polished prototype.
Build out the MVP: Once they have a solid game idea, the author will build out a basic MVP without any art.
Playtest with friends: The author intends to playtest their game with friends to gather feedback and iterate on the design.

Overall, this article provides a helpful overview of using LÖVE for game development prototyping and offers practical advice for building simple games quickly and efficiently.

https://huggingface.co/blog/smolagents

This is a comprehensive guide to building and using agent-based systems with the SmolAgents library. Here’s a breakdown of the key concepts and takeaways:

What are Agent-Based Systems?

Agent-based systems (ABS) are computational models that consist of multiple autonomous entities, called agents, that interact with each other and their environment. ABS are often used to simulate complex systems, such as economies, societies, or ecosystems.

SmolAgents Library

The SmolAgents library is a Python package that provides a simple and intuitive way to build and deploy agent-based systems. It allows users to define agents, tools, and workflows using a variety of programming languages and frameworks.

Key Concepts

Agents: The autonomous entities in the system that interact with each other and their environment.
Tools: Functions or classes that perform specific tasks or calculations.
Workflows: A sequence of agent actions that achieve a specific goal.
Model: The underlying intelligence that drives the agents’ decisions.

Building an Agent

To build an agent, you need to define two essential components:

Tools: A list of tools that the agent will use to perform tasks or calculations.
Model: An LLM (Large Language Model) that will provide the intelligence for the agent’s decisions.

You can choose from a variety of models, including open-source and closed models, and integrate them with your tools using the SmolAgents library.

https://psyche.co/guides/how-to-stop-living-on-auto-pilot-by-picking-goals-that-matter

This text provides a comprehensive guide on how to create and pursue a “Life Worth Living” (LWL) - a state of purposeful living where one engages in activities that bring fulfillment and joy. Here’s a breakdown of the key steps:

Step 1: Identify Your Values

Reflect on what is important to you in life
Ask yourself, “What matters most to me?”
Consider your personal values, passions, and long-term goals

Step 2: Explore and Set Goals

Identify areas where you’d like to improve or make changes
Brainstorm specific, achievable goals that align with your values
Make sure your goals are realistic and connected to your values (see “Why This Goal Matters”)

Step 3: Break Down Goals into Action Steps

Divide large goals into smaller, manageable tasks (action steps)
Focus on what you can control and break down complex tasks into smaller ones
Make action steps specific, measurable, achievable, relevant, and time-bound (SMART)

Step 4: Take Action and Track Progress

Set deadlines for completing action steps
Schedule time in your calendar to work on your LWL goals
Find an accountability partner or “body double” to support you
Reward yourself for progress and value-based actions

Key Principles:

Focus on values, not outcomes: Align your goals with what matters most to you.
Take action, not just think about it: Break down large goals into manageable tasks and start taking steps towards them.
Celebrate small wins: Acknowledge and appreciate your progress, no matter how small.
Be patient and flexible: Life is dynamic, and your goals may shift over time. Be willing to adjust and adapt.

Key Mindset Shifts:

From “I’ll do this” to “I want to do this”
From “This is a chore” to “This is something I enjoy”
From “I’m not good enough” to “I’m capable of achieving my goals”

By following these steps and embracing the principles and mindset shifts, you can create a Life Worth Living that brings fulfillment, joy, and purpose to your life.

https://austinhenley.com/blog/25yearsofai.html

* Associate Teaching Professor at Carnegie Mellon University
* Started programming in VB6 making games like Tamagotchi pets and a 2D space shooter
* Used book "Programming Game AI by Example" to improve coding skills and create more dynamic games
* Created finite state machines, factory pattern, and higher-order functions for game development
* Graduated with MS degree in Artificial Intelligence and Neural Networks but did not find implementation courses useful
* Built a low-bandwidth video chat program using OpenCV and a classifier
* Used statistical methods to analyze log data from code editors for PhD research
* Published papers and built developer tools involving these methods
* Became a tenure-track professor in 2018 with grant proposal on intelligent developer tools
* Collaborated on projects predicting information needs of programmers and generating user interfaces
* Resigned around the time grant was funded, but joined Microsoft program synthesis team in January 2022
* Worked on cool projects including ChatGPT and AI-powered tools for Mirror and Promptclipper
* Continues to teach, learn, and build new AI tools

https://hardcoresoftware.learningbyshipping.com/p/225-systems-ideas-that-sound-good

This is not a traditional Q&A session, but rather an interview-style conversation with Steven, who seems to be sharing his experiences and insights on common pitfalls in software development. Here’s a summary of the key points:

Common pitfalls:

Over-engineering: Committing to making something cross-platform can lead to building an operating system or cloud provider.
Synchronization is hard: Synchronizing data across multiple devices, platforms, or data stores is notoriously difficult and often leads to multi-billion dollar companies that specialize in this problem.
Abstraction can be a double-edged sword: While abstraction can provide many benefits, it also introduces complexity and can lead to problems like “mucking with data structures and state” when trying to escape back to native code.
Don’t over-rely on software patterns: Many software patterns are failure-prone and should not be blindly applied.

Counter-example:

Apple’s Mac OS evolution: The author notes that some abstractions in Mac OS seemed odd at first but became useful two releases later because there was a plan behind them. This suggests that sometimes, seemingly arbitrary design decisions can pay off in the long run.

The conversation is quite informal and conversational, with Steven sharing his personal experiences and insights throughout the discussion. There’s no specific “question” being asked, but rather a stream of thoughts and anecdotes from one software developer to another.

https://towardsdatascience.com/lora-fine-tuning-on-your-apple-silicon-macbook-432c7dab614a

LoRA Fine-Tuning On Your Apple Silicon MacBook
Let’s Go Step-By-Step Fine-Tuning On Your MacBook
As models become smaller, we are seeing more and more consumer computers capable of running LLMs locally. This both dramatically reduces the barriers for people training their own models and allows for more training techniques to be tried.
One consumer computer that can run LLMs locally quite well is an Apple Mac. Apple took advantage of its custom silicon and created an array processing library called MLX.
By using MLX, Apple can run LLMs better than many other consumer computers.
In this blog post, I’ll explain at a high-level how MLX works, then show you how to fine-tune your own LLM locally using MLX. Finally, we’ll speed up our fine-tuned model using quantization.
What is MLX (and who can use it?)
MLX is an open-source library from Apple that lets Mac users more efficiently run programs with large tensors in them.
The way MLX works is by being very efficient with memory transfers between your Central Processing Unit (CPU), Graphics Processing Unit (GPU), and…

https://github.com/vercel-labs/book-inventory

* Demo: https://next-books-search.vercel.app
* A book inventory app built with Next.js, Drizzle, and PostgreSQL.
* The database contains over 2,000,000 books from Goodreads.
* Full dataset available here.
* Uses Postgres extension `unaccent` to remove accents from book titles.
* Uses Postgres extension `vector` for vector store functionality.
* To install these extensions, run the following commands on your database:
  ```sql
CREATE EXTENSION IF NOT EXISTS unaccent;
CREATE EXTENSION IF NOT EXISTS vector;

https://literallythevoid.com/blog/rails_for_everything.html

* Rails for everything, especially great for small projects with a single developer
* Latest Getting Started with Rails guide is excellent and covers authentication, caching, rich text, continuous integration, and database setup
* SQLite is now a more robust choice as Rails 8 deals with its limitations
* Easy Continuous Integration (CI) config with GitHub's 2000 free minutes per month of actions usage
* Authentication generator simplifies the process for beginners
* Easy and fast deploys with Kamal, requiring minimal configuration changes to deploy.yml
* CI + easy deploys encourage best practices and make it easier to get a web app up and running quickly
* Conclusion: Rails is not dead, but better than ever, making it a great choice for new projects this year

https://old.reddit.com/r/LocalLLaMA/comments/1hs23gv/i_used_ai_agents_to_see_if_i_could_write_an/

I used AI agents to see if I could write an entire book
AutoGen + Mistral-NemoTutorial | Guide (youtube.com)

https://nbsanity.com/static/07f17bf65c5073b2e1d2ad90bcf3a07f/fine-tune-smollm2-on-synthetic-data.html

This is a code example that demonstrates how to fine-tune a SmolLM2 model on a synthetic dataset generated from a large language model using the Synthetic Data Generator. Here’s a breakdown of the code:

https://github.com/sshh12/prompt-stack

- Prompt Stack is an AI-powered tool for building web applications through a chat interface
- Features:
  - AI-powered code generation
  - Real-time development environment
  - Multiple arbitrary starter templates
  - Team collaboration and management
  - Git version control
  - Live preview
  - Chain-of-Thought reasoning for complex asks
  - Support for OpenAI and Anthropic models
  - Multi-page app generation
  - Sketch and screenshot uploads
- Deployment options:
  - GitHub (+ Netlify, Vercel, etc)
  - Dark mode support
  - Public chat and project sharing
- Environment requirements:
  - Modal account setup
  - AWS account with S3 bucket configuration
- Build process:
  - `cd frontend && npm install && npm run dev`
  - `cd backend && pip install -r requirements.txt && python main.py`
  - Railway (docker + postgres)

https://github.com/zasper-io/zasper

* ⚡ High Performance IDE 🚀 Massive concurrency 🐥 Inspired by Jupyter
* Zasper is an IDE designed from the ground up to support massive concurrency, providing a minimal memory footprint and exceptional speed for handling numerous concurrent connections.
* It's perfectly suited for running REPL-style data applications, with Jupyter notebooks being one example, currently fully supported on MacOS and Linux, with Windows support coming soon!
* Zasper uses one fourth of RAM and one fourth of CPU used by Jupterlab, making it a more efficient solution compared to other proprietary JupyterLab-like frontend tools.
* The ideal choice of programming language for Zasper is Go, which excels in concurrency and performance due to its excellent support for REST, RPC, and WS protocols.
* Zasper comes in two flavours: Electron App and Web App, with installation instructions available for Debian and macOS systems.
* Users can run Zasper in dev mode using `npm run electron-dev` or in prod mode using `npm run electron-package`, with additional build instructions available for the Web App.
* The application writes logs to specific locations depending on the operating system, and users can contribute to the project by filing bugs, submitting PRs, or reviewing them.
* Zasper aspires to be a full-fledged IDE, with future development focused on allowing custom data app support, easier integration with existing tools, and Self Hosted deployment in the cloud through Zasper Hub.
* The project is licensed under the AGPL-3.0 license, and users can join the Zasper Community on Slack to stay updated on the latest developments.

https://blog.val.town/blog/fast-follow/

The article discusses the rapid development of web-based code editors that utilize artificial intelligence (AI) to assist with coding tasks. The author, Steve, reflects on the experiences of several tools in the space, including Townie, and offers his thoughts on how to navigate this rapidly evolving landscape.

Key Takeaways:

Collaboration over competition: Despite the intense competition, Steve notes that the community is generally supportive and collaborative.
Fast-following: The author has been impressed by the speed of innovation in the space, with new tools emerging frequently.
Integration with AI editors: Steve plans to improve Val Town’s integration with other AI-powered code editors, such as VSCode, Cursor, Windsurf, and Bolt.
Benefits of a fully-integrated experience: Despite copying features from other tools, Steve believes that a fully integrated web AI code editor experience in Val Town is worth the effort.
Community feedback: The author encourages users to provide feedback on Townie’s development and suggest new features.

Key Tools:

Townie: A web-based code editor that uses AI to assist with coding tasks.
Cursor: A tool that enables developers to use an OpenAPI spec to generate code.
Windsurf: A tool that provides a chat interface for searching through codebases and editing multiple files.
Bolt: An AI-powered code editor that can deploy to Val Town.

Influential Figures:

Tom MacWright, JP Posma, and Simon Willison: Authors who provided feedback on drafts of the article.

Overall, the article offers a nuanced view of the web-based code editors space, highlighting both the benefits and challenges of rapid innovation in this field.

https://nrehiew.github.io/blog/2024/

The author is sharing their predictions and hopes for the field of Artificial Intelligence (AI) in 2025, focusing on several areas:

Text Generation: Expect advancements in text generation, including early fusion models, which can handle multiple modalities as input and output.
Image Generation: Improved image generation capabilities, such as better video models and quantization methods for training.
Agents: Autonomous agents that can interact with humans and other systems will become more prevalent, but their use cases may be limited to specific tasks, such as planning and research.
Human-AI Interfaces: Better human-AI interfaces, like Cursor, which provide a natural and intuitive experience.
Evaluation and Benchmarks: More task-based benchmarks, such as SWE-Bench, will become available.

The author also mentions several domains that will likely have cool results in 2025, including:

Training Optimizations: Expect improvements in training speed and efficiency through techniques like Muon and NanoGPT.
Video Models: Advances in video models to address consistency and inference speed issues.
Quantization: Better methods for quantizing models, such as 1-bit quantization and improved support for FP8 (or lower) formats.
Interpretability Research: Investigations into understanding how AI models work and making them more interpretable.

The author concludes by acknowledging that the field of AI moves rapidly and expresses hope for exciting developments in 2025.

Note: The text appears to be a personal blog post or essay, possibly from a researcher or enthusiast in the field of AI.

https://aider.chat/docs/unified-diffs.html

This text describes the author’s work on a tool called “Aider” that aims to detect and reduce laziness in AI-generated code. The main idea behind Aider is to use a new unified diff format that focuses on semantically coherent chunks of code, rather than traditional structured diffs.

The author presents several key findings from their work:

Unified diffs improve GPT-4 Turbo’s coding skills: By using the new unified diff format, Aider significantly improves GPT-4 Turbo’s ability to handle complex coding tasks.
Flexible patching is crucial: The tool uses flexible patching strategies to apply edits and handle variations in code formatting. These strategies are critical for success and increase the effectiveness of the unified diff format.
Refactoring benchmark shows promise: Aider’s new benchmark suite, which involves refactoring large amounts of code from open-source repositories, demonstrates the tool’s potential to detect and reduce coding laziness.

The author also presents several key techniques used by Aider:

Removing line numbers from hunk headers: By dropping line numbers from the diff format, the tool focuses on semantically coherent chunks of code.
Focusing on diffs of coherent code chunks: The tool uses a simple, high-level style of unified diffs that emphasizes coherent code chunks rather than traditional structured diffs.
Flexible patching strategies: Aider uses various patching strategies to apply edits and handle variations in code formatting.

The author concludes by highlighting the potential benefits of fine-tuning models on Aider’s unified diff format:

Fine-tuning may improve GPT’s coding skills: The author suggests that fining-tuning models on Aider’s unified diff format could further enhance GPT’s ability to handle complex coding tasks.
LLMs should be able to adapt to this format: Most Large Language Models (LLMs) have already seen plenty of unified diffs in their training data and should be amenable to fine-tuning towards this particular diff style.

Overall, the author presents a compelling case for the effectiveness of Aider’s new unified diff format and highlights its potential benefits for improving GPT coding skills.

https://github.com/EvanZhouDev/open-genmoji

Here is a high-level overview of the Open Genmoji project:

Purpose: Open Genmoji is an open-source project that aims to create customizable emojis and stickers for various platforms, including iOS.

Workflow:

Generate Prompt: Use a local model (e.g., LM Studio) or send a prompt through a command-line interface (genmoji.py) to generate an emoji prompt.
Postprocessing: The generated image is resized to 800x800 with anti-aliasing using the resize.py script.
Upload and Use: The final image is sent to the user’s phone via Photos, where it can be used as a sticker or emoji.

Features:

Customizable emojis and stickers
Support for different LoRAs (Local Representations of Art)
Ability to fine-tune existing emoji sets
Automatic metaprompt selection when using a different LoRA

Tools and Dependencies:

LM Studio (local model)
mflux (command-line interface)
Pillow (image processing library)

https://github.com/warmshao/browser-use-webui

* This project builds upon the foundation of the browser-use, designed for AI agent accessibility
* Official thanks to WarmShao for his contribution
* Web UI built on Gradio with most browser-use functionalities
* User-friendly UI enables easy interaction with browser agent
* Expanded LLM support: Gemini, OpenAI, Azure OpenAI, Anthropic, DeepSeek, Ollama, and more in the future
* Custom browser support: use your own browser without re-login or authentication challenges
* Persistent browser sessions: keep browser window open between AI tasks, see complete history and state of AI interactions
* Prerequisites: Docker and Docker Compose installed, Git to clone repository

https://github.com/mrwillis/gpt-user-auth

* Implement user authentication for a custom GPT using Pulumi
* Supports saving and retrieving past user conversations in a Postgres database
* Flow:
  * User logs into custom GPT
  * User provides email address for login verification
  * User receives verification code via email
  * User provides verification code to custom GPT
  * Custom GPT verifies code and loads past conversation data
* Uses Firebase for user ID storage (optional), JWT scheme, Amazon SES for sending verification codes
* Stores conversations in Postgres database with SSL enabled
* Uses custom header `x-api-key` for authentication between lambda and ChatGPT
* Uses Lambda extension to securely fetch secrets from AWS Parameter Store
* Configs:
  * Email address, RDS endpoint, database name, API key, JWT secret, and other parameters set using Pulumi config
  * SSM Parameters with dynamic stack names created for secure storage of configuration values
  * Database migrations run after infrastructure setup
* Additional steps:
  * Add schema.yaml file to custom GPT and configure authentication to use API key
  * Generate privacy policy and link it in custom GPT
  * Test and troubleshoot authentication scheme using a local HTTP client or by re-adding actions to custom GPT

https://github.com/danilofalcao/jarvis

* J.A.R.V.I.S. is an intelligent coding assistant that leverages multiple state-of-the-art language models to help with code generation, modifications, and technical discussions.
* Integrated Terminal:
    * Cross-platform terminal support (Windows and Linux)
    * Automatic workspace directory initialization
    * Full xterm.js integration
    * Real-time output streaming
    * Command history support
    * Proper directory tracking
    * Native shell integration (cmd.exe on Windows, bash on Linux)
* Multi-Model Support: Choose between different AI models for your coding needs:
    * DeepSeek V3
    * Codestral
    * Gemini 2.0 Flash Experimental
    * Grok 2
    * Claude 3.5 Sonnet
    * GPT-4 Turbo
    * GPT-4o Mini
    * GPT-4o
    * o1-mini
    * o1-preview
* File Attachment Support:
    * PDF files with text extraction
    * Microsoft Word documents (.docx)
    * Excel spreadsheets with sheet parsing
    * Images with OCR capabilities
    * Enhanced Markdown with GFM support
    * All major programming languages
    * Configuration files
    * Text and documentation files
    * File preview with syntax highlighting
    * Multiple file upload support
    * Progress indicators and file size display
    * Type-specific icons and preview buttons
* Real-Time Updates:
    * WebSocket-based notifications
    * Instant feedback for code changes
    * Real-time workspace updates
    * Automatic change notifications
* Workspace Management:
    * Create and manage multiple workspaces
    * View workspace history
    * Delete workspaces when no longer needed
    * Rename workspaces
    * Browse workspace file structure
* Code Generation & Modification:
    * Generate new code based on natural language prompts
    * Modify existing code with AI assistance
    * Preview changes before applying them
    * View diffs of proposed changes
* Interactive Chat:
    * Discuss code and technical concepts
    * Get explanations about existing code
    * Context-aware responses based on workspace content
    * Attach files for additional context
* Backend:
    * Flask web framework
    * Flask-SocketIO for WebSocket support
    * Eventlet for async operations
* Frontend:
    * Pure JavaScript
    * TailwindCSS for styling
    * CodeMirror for code editing
    * Socket.IO client for real-time notifications
    * PDF.js for PDF processing
    * Mammoth.js for Word documents
    * XLSX.js for Excel files
    * Tesseract.js for OCR
    * Marked and Unified.js for Markdown
* Contributed by:
    * Nikole Cardoso
    * Guilherme Guirro
    * Felipe Santos
* Tested on:
    * Linux (native)
    * Windows Subsystem for Linux (WSL 2)
    * Windows (native, no admin privileges required)
* Licensed under the MIT License - see LICENSE.md file for details.

https://www.reddit.com/r/Python/comments/1hu0ojq/i_made_another_project_template_but_for_a_python/

* A project template for a Python package with uv, pytest, and more.
* Key features of the `Python-Package-Template` include:
    * Engineering tools:
        + Package management: UV
        + Code quality: Pre-commit hooks with Ruff & Detect-secrets
        + Logging: Colorful logs with Loguru
        + Unit tests: Pytest
        + Dockerized: Dockerfile & docker-compose for evaluation pipeline
        + Make commands: Simplify workflow (install, run, test)
    * CI/CD & Maintenance tools:
        + Pipelines: GitHub Actions (.github/workflows) & GitLab CI (.gitlab-ci.yml)
        + Local CI/CD pipelines: Run GitHub Actions with act and GitLab CI with gitlab-ci-local
    * Documentation tools:
        + Documentation website: MkDocs + mkdocs-material
        + GitHub Pages deployment: Easy deployment with mkdocs gh-deploy

https://github.com/matteoserva/GraphLLM

* A graph-based framework to process data through a LLM or multiple LLMs.
* Examples include an agent capable of answering questions like "give me a summary of the article on the home page of hacker news that is most likely related to language models."
* Features:
    - Graph-based framework to process data using LLMs.
    - Powerful agent capable of performing web searches and running Python code.
    - Tools:
        - Web scraper to scrape web pages and reformat data in a LLM-friendly format.
        - PDF parser to extract text from PDF files.
        - YouTube subs downloader to download subtitles from a YouTube video.
        - Python sandbox for safely executing code generated by a LLM.
        - TTS engine using Piper.
        - GUI for building and debugging graphs with a nice interface.
* GUI features:
    - Loops: Can have any kind of loop in the graph or run a graph infinitely.
    - Conditionals: Graph topology can be partially altered by the value going into nodes.
    - Parallel execution of nodes: More nodes can execute in parallel, useful for making multiple LLM calls at the same time.
    - Streaming of results: Output of LLM nodes can be seen in real-time while it's produced.
    - Hierarchical graphs: Graphs can contain other graphs without limits.
    - External tools: The graph can call external tools, such as the web scraper or YouTube downloader.
    - Dynamic scheduling of nodes when they are runnable.
* Examples:
    - Generate Python code and use it to solve a problem.
    - File Node example showcasing multiple calls to a LLM.
    - Hierarchical graph example showing file download and summary using another graph.
* Requirements:
    - Tested mainly with llama70b and Qwen 32b, using the llama.cpp server as backend.
    - Under heavy development, expect breaking changes.
* Installation:
    - Run `pip3 install selenium readabilipy html2text pdfminer.six websockets` in a shell.
    - Optional: Install Piper-TTS with `pip3 install piper-tts`.
    - Launch the server with `python3 server.py`.
* Configuration:
    - Modify client configuration file (`client_config.yml`) to replace default clients or setup Groq or OpenAI API config.
    - Set up web scraper by installing Firefox, selenium, and ublock origin.
    - Test PDF scraper and YouTube scraper with `python3 extras/parse_pdf.py` and `python3 extras/youtube_subs.py`.
* Inference engines:
    - Llama.cpp server: complete support.
    - Groq API: Limited functionality (no grammars or raw prompts).
    - OpenAI API: Limited functionality (no grammars or raw prompts).
    - HF transformers: Partial support.

https://refactoringenglish.com/chapters/rules-for-software-tutorials/

Based on the provided text, it appears to be an excerpt from a book titled “Refactoring English: Effective Writing for Software Developers” by Michael Lynch. The text discusses various principles and best practices for writing effective tutorials for software developers.

Here are some key takeaways:

Teach one thing: Focus on a single topic or concept per tutorial, rather than trying to cover too much ground.
Make your headings clear and concise: Use descriptive headings that communicate the main idea of each section, and make sure they are consistent in terms of casing, point of view, verb tense, etc.
Demonstrate your solution works: Show how to use the result of a tutorial or demonstration, so readers can see whether it actually worked.
Link to a complete example: Provide a code repository that contains all the code used in the tutorial, and ideally run against a continuous integration system.
Show the complete state of the project at each stage: If possible, link to branches or show the complete state of the project at every step of the tutorial.
Be consistent and logical: Ensure that your headings reflect a logical structure in your tutorial.

Overall, the text provides practical advice for writing effective tutorials that are easy to follow and understand.

https://github.com/LukeLalor/droid-please

* pip install droid-please
* droid --help
* Commands:
    + init Initialize a new .droid directory in the current directory with required configuration files.
    + learn Analyze the project structure and learn about its organization and purpose. The summary will be saved to the config file for future reference.
    + please Ask the droid to do something.
    + continue Continue a conversation with the droid. If no conversation file is provided, continues the most recent conversation.
    + summarize Create a new conversation by summarizing an existing conversation.
* Droid-please differs from other LLM IDEs and tools as it believes resources should live alongside and be versioned with the project code like your .git directory.
* The tool aims to improve performance and cost-effectiveness for GenAI by only requiring occasional "expensive" project learning per project.
* Requirements:
    + Python 3.10 or higher
    + pip installation (pip install droid-please --upgrade)
    + Anthropic API key (optional)
    + Quickstart guide using pip
    + Installation of completions (droid --install-completion)
* Usage:
    + Initialize droid in your project by running `cd /path/to/your/git/project` and then `droid init`
    + Set up an Anthropic API key if required.
    + Run commands like `droid learn` or `droid please "Update (or create) my project's README" -i` for interactive mode.
* Resources:
    + Open an issue for questions or problems

https://huyenchip.com//2025/01/07/agents.html

This text provides an in-depth explanation of agents and their components in the context of AI models. Here are some key points and potential takeaways:

Agents operate in environments: An agent is defined by the environment it operates in and the set of tools it has access to.
Access to tools enhances capabilities: The agentic pattern is essential for AI models, as access to tools makes a model vastly more capable.
Components of an agent: A post discusses how agents work, covering concepts such as self-critique, chain-of-thought, and structured outputs.
Evaluating agent frameworks: Future posts will discuss how to evaluate agent frameworks.

Some potential questions or areas for further exploration based on this text:

What are the limitations of current AI models, and how can memory systems help address these limitations?
How do different types of tools (e.g., image captioners, SQL query generators) contribute to an agent’s capabilities?
What are some common challenges when developing and evaluating agents, and how can they be overcome?
How does the agentic pattern relate to other concepts in AI research, such as reinforcement learning or multi-agent systems?

Overall, this text provides a solid foundation for understanding the basics of agents and their components. Future posts will likely delve deeper into the nuances of agent frameworks and provide practical guidance on how to develop and evaluate them.

https://github.com/Pylons/waitress

* Waitress is a production-quality pure-Python WSGI server with very acceptable performance.
* It has no dependencies except those in the Python standard library.
* It runs on CPython on Unix and Windows under Python 3.9+.
* It also runs on PyPy 3 (version 3.9 compatible python and above) on UNIX.
* It supports HTTP/1.0 and HTTP/1.1.
* For more information, see the "docs" directory of the Waitress package or visit https://docs.pylonsproject.org/projects/waitress/en/latest/

https://github.com/kaushikb11/keepingupwiththeinternets

* AI-Powered Internet Culture Podcasts
    * Transforming Reddit's most intriguing discussions into engaging audio content
    * Automatically selects the most meaningful discussions with Smart Content Curation
    * Features natural conversations between different personas with Multi-Persona Dialogue
    * Synthesizes information from posts, comments, and related links with Deep Context Integration
    * Converts discussions into podcast-ready content with Professional Audio
    * Provides weekly updates covering the latest internet phenomena

* Core Components
    * RedditPostFetcher: Retrieves trending discussions from r/OutOfTheLoop
    * RedditPostProcessor: Processes posts, comments, and related URLs
    * ScriptPlanner: Evaluates and structures content using 4 core patterns
    * DialogueGenerator: Creates dynamic conversations
    * Context Integration: Weaves in URLs, comments, and background
    * Parallel Processing: Generates reddit post specific dialogues concurrently
    * ScriptEnhancer: Improves flow and reduces redundancy
    * Format Standardization: Ensures consistent dialogue structure
    * Quality Control: Maintains speaker authenticity
    * Audio Synthesis: Converts enhanced scripts to natural speech
    * Voice Differentiation: Distinct voices for Host, Learner, and Expert
    * Quality Assurance: Ensures proper pacing and pronunciation

* Getting Started
    * Clone the repository with `git clone https://github.com/kaushikb11/keepingupwiththeinternets.git`
    * Create and activate a virtual environment with `python3 -m venv venv` and `source venv/bin/activate`
    * Install dependencies with `pip install -r requirements.txt`
    * Configure environment variables by copying `.env.example` to `.env`

* Contributing
    * Fork the repository and create a feature branch with `git checkout -b feature/AmazingFeature`
    * Commit changes with `git commit -m 'Add some AmazingFeature'`
    * Push to the branch with `git push origin feature/AmazingFeature`
    * Open a Pull Request

* Deployment
    * Install Modal with `pip install modal`
    * Set up Modal authentication and deploy the agent using `python3 -m modal setup` and `python3 -m modal deploy modal_app.py`
    * Monitor deployments in the Modal dashboard

* Project License
    * Licensed under Apache License 2.0

https://github.com/paulbrodersen/netgraph?tab=readme-ov-file

It seems like you’re interested in learning more about NetGraph, a Python library for creating network visualizations. Here’s a brief overview of how to use it:

Installation

To install NetGraph, run the following command:

pip install netgraph

Basic Usage

Here’s an example of how to create a simple graph and plot it:

import netgraph as ng

# Create a new graph
g = ng.Graph()

# Add nodes
g.add_node('A')
g.add_node('B')
g.add_node('C')

# Add edges
g.add_edge('A', 'B')
g.add_edge('B', 'C')
g.add_edge('C', 'A')

# Draw the graph
ng.draw(g, ax=None)

This will create a simple graph with three nodes and three edges.

Customization

NetGraph allows for various customization options, such as changing node sizes, edge colors, and layout algorithms. Here’s an example:

import netgraph as ng

# Create a new graph
g = ng.Graph()

# Add nodes
g.add_node('A')
g.add_node('B')
g.add_node('C')

# Add edges
g.add_edge('A', 'B')
g.add_edge('B', 'C')
g.add_edge('C', 'A')

# Set node sizes and edge colors
g.set_node_size(0.5)
g.set_edge_color('blue')

# Draw the graph with a custom layout algorithm
ng.draw(g, ax=None, alg='circle')

This will create a graph with smaller nodes and blue edges.

Interactive Graphs

NetGraph also supports interactive graphs that can be zoomed in and out, rotated, and panned. Here’s an example:

import netgraph as ng

# Create a new graph
g = ng.Graph()

# Add nodes
g.add_node('A')
g.add_node('B')
g.add_node('C')

# Add edges
g.add_edge('A', 'B')
g.add_edge('B', 'C')
g.add_edge('C', 'A')

# Draw the graph with an interactive layout
ng.draw(g, ax=None, interactive=True)

This will create a graph that can be interacted with using the mouse.

Support for Other Graph Libraries

NetGraph also supports other graph libraries, such as NetworkX and igraph. Here’s an example:

import netgraph as ng
import networkx as nx

# Create a new graph
g = ng.Graph()

# Add nodes
g.add_node('A')
g.add_node('B')
g.add_node('C')

# Add edges using NetworkX
nx.add_edge(g, 'A', 'B')
nx.add_edge(g, 'B', 'C')
nx.add_edge(g, 'C', 'A')

# Draw the graph with a custom layout algorithm
ng.draw(g, ax=None, alg='circle')

This will create a graph that uses NetworkX edges.

I hope this helps! Let me know if you have any questions or need further assistance.

https://github.com/app-generator/flask-template

* # Introduction to Flask Dashboard
* * A high-level Python Web framework that encourages rapid development and clean, pragmatic design
* 
* # What is a Dashboard?
* * A set of pages that provide information to the user in real-time regarding their business
* * Graphical representations of the current status and trends within an organization
* 
* # Features of Flask Templates
* * Database: SQLite, MySql (silent fallback)
* * DB Tools: SQLAlchemy ORM, Flask-Migrate, session-based authentication
* * Deployment: CI/CD flow viaRender, Docker, page compression

https://github.com/flowkeeper-org/fk-desktop/

* Flowkeeper is an independent Pomodoro Technique desktop timer for power users, focusing on simplicity and being free software with open source.
* Visit flowkeeper.org for screenshots, downloads, and FAQ, and provide feedback to help improve the tool.
* The primary dependency is Qt 6.6, requiring Python 3.8 or later (3.9+ if using Qt 6.7).
* Flowkeeper is built on Ubuntu 22.04 with Python 3.11 and the latest Qt version; testing also occurs on Ubuntu 20.04.
* Considerations for building with older versions of Python require compiling Python 3.11 or using a virtual environment.
* Create a virtual environment, install dependencies, and generate resources to start coding.
* Build an installer by referring to the CI/CD pipeline in .github/workflows/build.yml and executing specific commands.
* To execute Flowkeeper, use `PYTHONPATH=src python -m fk.desktop.desktop`.
* Run unit tests with `python -m coverage run -m unittest discover -v fk.tests` and end-to-end tests with `PYTHONPATH=src python -m fk.desktop.desktop --e2e`.
* Design considerations include data model, strategies, events, UI actions, and CI/CD pipeline.
* Building for Alpine Linux requires specific configuration.
* The program is licensed under the GNU General Public License version 3 or later.

https://www.reddit.com/r/LocalLLaMA/comments/1hwlka6/i_made_the_worlds_first_ai_meeting_copilot_and/

* Created a self-hosted solution called Amurex to improve meeting transcription experiences
* Frustrated with SaaS tools that didn't respect privacy or workflow, with issues including:
  * Bots interrupting meetings and announcing themselves
  * Poor transcription quality
  * Lack of flexibility in setup options
* Developed Amurex with the following features:
  * Quiet recording with no bot interruptions
  * Accurate diarized transcripts delivered right after meetings
  * Late meeting summaries (recap for late attendees)
  * Real-time suggestions to stay engaged during boring meetings
* Amurex is open source and designed for self-hosting, controlling data and workflow
* Currently only works on Google Meet but will be scaled to other providers in the future
* Can be found at:
  * Github: https://github.com/thepersonalaicompany/amurex

https://github.com/e2b-dev/open-computer-use

- Uses E2B for secure Desktop Sandbox
- Supports Meta Llama, OS-Atlas and any LLM you want to integrate!
- Operates computer via keyboard, mouse and shell commands
- Live streams display on client computer
- User can pause and prompt agent at anytime
- Uses Ubuntu, but designed to work with any operating system
- Open Computer Use is designed to easily support new LLMs