Link List :: 2024-09-30

* Optopsy is an open-source backtesting library for option strategies.
* The library focuses on answering core questions about option strategies, rather than simulating every possible scenario.
* The backtesting algorithm involves:
    * Evaluating option chains within specified entry and exit dates.
    * Grouping evaluated chains into buckets based on Days to Expiration (DTE) and delta or strike distance percent.
    * Constructing option strategy legs and calculating profit/loss.
    * Aggregating results to show average profit/loss for each bucket combination.
* The algorithm uses bucketing and approximations for performance, so results shouldn't be the sole basis for trade decisions.
* The library's methodology is transparently explained.
* Contributions (pull requests, comments, suggestions) are welcome.
* Copyright © 2020 Michael Chu.

* Sidekick is a CLI tool for deploying applications to a VPS, aiming for Fly.io-like simplicity.
* **Features:**
    * One-command VPS setup (Docker, Traefik, sOPS, age).
    * Deploys applications from a Dockerfile.
    * Zero-downtime deployments.
    * High availability and load balancing.
    * Zero-config SSL certificates.
    * sslip.io integration.
    * Built-in SOPS integration for secure secret management.
* **Installation:** `brew install sidekick` (macOS) or `go install github.com/mightymoud/sidekick@latest` (Linux/Windows).
* **Requires:** Ubuntu LTS VPS (DigitalOcean or Hetzner recommended), SSH key access.
* **`sidekick init`:** Sets up the VPS: creates a user, disables root login, updates the system, installs Docker, Traefik, sOPS, age, and configures SSL certificates.
* **`sidekick launch`:** Deploys an application: requires a Dockerfile and specifies the app name, exposed port, and domain (can use sslip.io).  Handles encryption of `.env` files using sOPS.
* **`sidekick deploy`:** Deploys a new version of an application with zero downtime.  Checks for `.env` file changes and re-encrypts if necessary.
* **`sidekick deploy preview`:** Deploys a preview environment tied to the current git commit.
* **Roadmap:**  Includes features like docker-compose support, improved zero-downtime deploys, firewall setup, multi-VPS management, database deployment, a TUI, and CI/CD integration.
* **License:** GPL-3.0
* **Website:** www.sidekickdeploy.com

* LangGraph Studio is a desktop application for prototyping and debugging LangGraph applications locally.  Available for MacOS.
* Offers various templates to jumpstart development:
    * LangGraph ReAct Agent Template: A simple ReAct agent chatbot with persistent chat memory. ([langchain-ai/react-agent](https://github.com/langchain-ai/react-agent))
    * LangGraph Data Enrichment Template:  For building data enrichment agents. ([langchain-ai/data-enrichment](https://github.com/langchain-ai/data-enrichment))
    * LangGraph Retrieval Agent Template: A basic question-answering agent using a retriever. ([langchain-ai/retrieval-agent-template](https://github.com/langchain-ai/retrieval-agent-template))
    * LangGraph.js Data Enrichment Template:  Similar to the above, but using LangGraph.js. ([langchain-ai/data-enrichment-js](https://github.com/langchain-ai/data-enrichment-js))
    * LangGraph.js Retrieval Agent Template:  A basic question-answering agent using LangGraph.js and a retriever. ([langchain-ai/retrieval-agent-template-js](https://github.com/langchain-ai/retrieval-agent-template-js))
    * LangGraph.js ReAct Agent Template: A ReAct agent using LangGraph.js. ([langchain-ai/react-agent-js](https://github.com/langchain-ai/react-agent-js))
    * New LangGraph Project: A blank template to start a new project. ([langchain-ai/new-langgraph-project](https://github.com/langchain-ai/new-langgraph-project))
    * New LangGraph.js Project: A blank template to start a new LangGraph.js project. ([langchain-ai/new-langgraphjs-project](https://github.com/langchain-ai/new-langgraphjs-project))

* **multi1:** Creates o1-like reasoning chains using multiple AI providers (locally and remotely).
* **Supports LiteLLM:**  Enables access to 100+ providers.
* **Key Features:** Unified interface, LiteLLM default provider (local & remote), configurable sidebar, modular design for easy provider addition.
* **Supported Providers:** LiteLLM (local & remote), Ollama (local), Perplexity (remote, API key required), Groq (remote, API key required).
* **Reasoning Chains:** Improves LLM reasoning by creating dynamic Chain of Thought, prompting the model to explore multiple methods and alternative answers.
* **Improved Accuracy:** Achieves ~70% accuracy on the Strawberry problem (compared to 0% for Llama-3.1-70b and 30% for ChatGPT-4o without prompting).
* **Open Source and Experimental:** Aims to inspire development of new reasoning strategies.  Not a replication of OpenAI's o1, which uses different training techniques.
* **Quickstart:** Requires Python 3, `venv` (optional), and `pip`.  Installation via `pip install -r requirements.txt`. API keys needed for some providers.  Runs using `streamlit run app/main.py`.
* **Contributing:**  Contributions welcome!  Fork the repo, create a branch, make changes, and submit a pull request.
* **Derived from g1:**  Originally developed by Benjamin Klieger.
* **License:** MIT license.

* A native macOS menubar app to track time zones for friends, teammates, or cities.
* Installation via Homebrew: `brew install --cask there`
* Add people from X (Twitter), Telegram, or local photos.
* Add cities without needing to know the time zone.
* Supports raw UTC offsets.
* Ultra-low resource usage (CPU and memory).
* Built with SwiftUI.
* Requires macOS 13+.
* Roadmap: Widgets, time slider, auto-update, AppleScript API.
* Contributions welcome (PRs for small fixes, issues for larger features).
* Licensed under the MIT License.

* Displays compiler errors, inspections, and other problems directly within the editor, eliminating the need to switch to the Problems tool window.
* Supports various IDEs from JetBrains, including IntelliJ IDEA, PyCharm, WebStorm, Android Studio, and others.
* Improves code readability and reduces context switching.
* Offers customizable settings to control the appearance and behavior of inline problem annotations.
* Provides quick access to problem details and quick-fix options.
* Enhances developer workflow by providing immediate feedback on code issues.

* Downloads the /r/RealDayTrading wiki ("The Damn Wiki") content.
* Converts the wiki content into an HTML file, including all posts and images.
* Provides an EPUB version (`The Damn Wiki - Hari Seldon.epub`) for e-readers and mobile devices.
* Allows easy conversion to PDF or DOCX formats.
* Requires `beautifulsoup4`, `praw`, and `urllib3` libraries.
* Needs a Reddit API client ID, secret, and a valid user agent to be added to `download_RealDayTrading_wiki.py`.
* Outputs `wiki_output.html`, an `images` folder, and a `posts` folder (containing markdown files).
* The HTML file can be further converted to EPUB (using Calibre), PDF, or DOCX.
* Uses MIT License.

* Visualizes weather forecasts through landscape imagery, reducing reliance on raw numerical data.
* Encodes weather information (wind, temperature, cloud cover, precipitation) within a landscape image.
* Uses a small house in the woods as the central element, with the horizontal axis representing a 24-hour timeline.
* Vertical axis symbolizes weather events and conditions; distance from the house indicates time.
* Includes time markers (sunrise, sunset, noon, midnight).
* Implemented in Python using the Pillow library and OpenWeather data.
* Designed for a 296x128 E-Ink display.
* Requires an OpenWeather API key (OWM_KEY in `secrets.py`).
* Coordinates can be customized in `secrets.py`.
* `run_test.py` for image creation testing.
* `run_server.py` to run a server (presumably for continuous updates).
* Hardware setup uses an ESP32 development board and a 2.9-inch E-Ink display module.
* Currently updates the image every 15 minutes from the internet.  ESP32 MicroPython adaptation is uncertain.
* Uses MIT license.

* Displays weather reports and illustrations on an e-Paper display using a Raspberry Pi.
* Uses OpenAI's Dall-E 3 for image generation (requires an API key).
* Requires a Raspberry Pi 5 and a Waveshare 7.3" e-Paper display (code may need adjustments for other sizes).
* Uses a Python virtual environment (`venv` recommended).
* Installs dependencies using `pip install -r requirements.txt`.
* `control.py` script generates and displays images; `control.py clear` clears the display.
* Includes example cron job for automated updates (twice daily).
* Offers a private weather API (https://github.com/blixt/sol-mate) for use, but recommends self-hosting for high volume.
* Provides instructions for setup and usage.
* Encourages users to report issues or contact the author on Twitter.

* A Mistral-based large language model (LLM) was fine-tuned using philosophical texts.
* The training data comprised a diverse range of philosophical works, including those from various periods and traditions.
* The resulting model demonstrates improved performance on tasks related to philosophy, such as question answering and text generation within a philosophical context.
* The model exhibits a capacity for nuanced reasoning and argumentation, characteristic of philosophical discourse.
*  The author shares the model weights, allowing others to experiment and further develop it.
*  Potential applications include philosophical research, education, and creative writing.
*  Limitations include potential biases inherited from the training data and the need for further evaluation and refinement.
* The project highlights the potential of fine-tuning LLMs for specialized domains.
* The author invites community feedback and collaboration.
*  The project's code and data are publicly available (or will be made available).

* Recoll WebUI is a Python-based web interface for the Recoll text search tool.
* **Requirements:** Python 2.x, Recoll 1.17+, a web browser.
* **Download:**  Use the provided links for different Recoll versions or clone the git repository.
* **Usage:** Can run standalone ( `webui-standalone.py` ) or via a web server (WSGI/CGI). Recoll must be configured separately.
* **Standalone Mode:** Runs on `http://localhost:8080` by default.  Command-line arguments for address and port are available.
* **WSGI/CGI Mode:** Example Apache2 configuration provided in the README.  Important to note the `python-path` setting to avoid import errors.  Also, the user running the daemon process is important for security.
* **Example Upstart Script (Ubuntu):** Provided for running the Recoll indexer as a daemon.
* **Example Crontab Entry:**  Provided to run the indexer daily.
* **Issues:**  Addresses problems opening files when running on a server, offering solutions involving URL replacements and browser-specific workarounds (Firefox, Chrome/Chromium, Opera).  These workarounds involve configuration changes in the browser or the use of extensions.
* **About:** A web interface for Recoll desktop search.  Uses Python, JavaScript, Smarty, and CSS.

* **Project Name:** ⚙️ Zero-Shot Autonomous Robots
* **Description:** Uses model APIs to create a Zero-Shot Autonomous Robot.  Individual robot behaviors are asynchronous nodes (Python) launched via bash scripts.  A simpler alternative to ROS.
* **Key Models Used:**
    * LLM (Language Language Model): text2text model for planning, reasoning, dialogue.
    * VLM (Vision Language Model): image2text model for scene understanding, object detection.
    * TTS (Text-to-Speech): text2audio model for speech synthesis.
    * STT (Speech-to-Text): audio2text model for speech recognition.
* **Modules:**
    * `models`: Contains code for different model APIs (e.g., Replicate API, OpenAI API).
    * `robots`: Contains code for different robots (e.g., HiWonder AiNex Humanoid).
    * `nodes`: Contains code for different nodes (e.g., vision loop using a VLM).
    * `params`: Contains code for parameters and default values.
* **License:** MIT license
* **Stars:** 93
* **Forks:** 13
* **Languages:** Python (81.8%), Shell (18.2%)

* Builds production-ready Python Docker containers using `uv`.
* Emphasizes multi-stage builds to avoid shipping build tools.
* Prioritizes judicious layering for faster builds, installing dependencies before the application.
* Leverages `uv.lock` for dependency management and caching.
* Recommends byte-compiling Python files for faster startup.
* Uses virtual environments within Docker for consistency across Python versions.
* Explains the use of `UV_PROJECT_ENVIRONMENT` environment variable.
* Provides a detailed Dockerfile example using `uv` for dependency and application installation.
* Suggests avoiding Alpine-based images due to potential issues.
* Includes notes on handling unpackaged applications.
* Discusses the differences between `uv sync --frozen` and `uv sync --locked`.
* Describes a local development workflow using Direnv and `.envrc`.
* Briefly explores the possibility of two build stages but finds minimal benefit for typical web applications.
* Advocates for properly packaging Python applications.
* Includes a note on using `--locked` instead of `--frozen` for deployment pipelines.
* Mentions resources for further Docker help.

* Building a Generative AI platform involves several key components and steps.

* **Step 1: Enhance Context:**  Augment queries with relevant information using Retrieval-Augmented Generation (RAG).  RAG uses retrievers (term-based or embedding-based) to fetch information from external sources (documents, databases, web searches).  Consider chunking large documents for efficient processing. Hybrid search (combining term and embedding-based retrieval) is common.  Agentic RAG allows models to interact with external tools and actions (read-only or write). Query rewriting improves retrieval accuracy.

* **Step 2: Put in Guardrails:** Implement input and output guardrails to mitigate risks. Input guardrails prevent leaking private information to external APIs and model jailbreaking. Output guardrails evaluate response quality (checking for emptiness, malformed formats, toxicity, factual inconsistencies, sensitive information, and brand risks) and manage failures (retries, human fallback).  Consider the tradeoffs between reliability and latency.

* **Step 3: Add Model Router and Gateway:**  Use routers to direct queries to appropriate models based on intent or context. Gateways provide a unified interface for interacting with various models (self-hosted or third-party).

* **Step 4: Reduce Latency with Cache:** Implement caching mechanisms (prompt cache, exact cache, semantic cache) to optimize performance and reduce costs.

* **Step 5: Add Complex Logic and Write Actions:** Incorporate complex logic and write actions to enhance system capabilities.  However, write actions increase risk.

* **Observability and Orchestration:**  Essential for monitoring, debugging, and managing the entire pipeline.  Includes metrics, logs, and traces.

* The architecture starts simple (query -> model -> response) and adds components iteratively.  Evaluation is crucial at each step.

* Open access book on scientific visualization using Python and Matplotlib.
* Covers fundamental principles of Matplotlib, figure design, advanced concepts (3D, animation, optimization), and showcases.
* Available as a PDF (95MB) on HAL and GitHub.
* Source code and examples available on GitHub.
* Printed edition available for purchase.
* Options to support the author via PayPal, GitHub Sponsors, or LiberaPay.
* Related resources: Python & OpenGL for Scientific Visualization, From Python to NumPy, 100 NumPy exercises, Matplotlib cheat sheets, and a book gallery.
* 10.6k stars and 986 forks on GitHub.

* **Machine Learning:** Covers neural networks, backpropagation, gradient descent, loss functions, regularization, ensemble learning, and deep learning concepts.  Includes topics like feature engineering, dimensionality reduction, and recurrent neural networks.

* **Database Systems:** Explores relational databases, transaction lifecycle, atomicity, logging (UNDO, WAL), database crash recovery, and data access methods (sequential vs. random).  Includes discussions on connection management and the limitations of mmap for databases.

* **C Programming and Memory Management:** Focuses on memory management techniques, including stack and heap usage, bit manipulation, address calculation, dynamic memory allocation, struct padding, and custom buffer management.

* **Computer Architecture and Hardware:** Covers CPU, memory, and storage interaction, sequential vs. random access, cache optimization, DRAM operation, memory controllers, and prefetching techniques.

* **Advanced Machine Learning:**  Explores Machine Learning Operations (MLOps), Swin Transformer, large language model prompting, and factual association editing in graph databases.

* **Operating Systems:** Covers sequential access in memory and storage, multithreading (single and multi-CPU), virtual memory, page tables, memory management, and the out-of-memory killer in Linux.

* **Parallel Computing and CUDA:** Introduces CUDA programming for GPU acceleration and parallel execution.

* **Software Architecture:** Discusses monolithic vs. microservices architectures and event-driven architectures.

* **Frontend Development:** Covers HTTP, DNS, TypeScript, and Angular (modules, routers, components, data binding).

* **DevOps:** Briefly mentions Docker, Kubernetes, Kafka, and Quarkus.

* **Distributed Systems:**  Covers timestamping solutions and the CAP theorem.

* Algorithmic implementation of automated adjustment of a delta-hedged short straddle for options trading.
* Aims to maintain profitability even if the underlying asset price moves beyond initial breakeven points.
* Uses delta hedging to make the portfolio insensitive to market movements.
* Automatically adjusts the straddle based on market changes to stay within the profit zone.
* Demonstrated with a GIF showing automated trading and P/L adjustments (Note: data in GIF is imaginary).
* Algorithm involves selling puts, delta hedging, and dynamically buying/selling calls to adjust upper and lower breakeven points.
* Disadvantages: Potential for large losses with unrealistically fast market swings and may require additional funds for adjustments.
* Disclaimer: For demonstration purposes only; not responsible for financial losses.
* Skills used: Finance, Options & Derivatives, Options Trading Strategies, Market Neutral Strategy, Delta Hedging, Quantitative Finance (Quant), Python.

* This tutorial demonstrates simple market making using quantpylib's gateway connectors, data feeds, and order management system (OMS).
* It uses exchanges supporting `quantpylib.gateway.master.Gateway` endpoints (e.g., Binance, Hyperliquid, Bybit).
* Market making requires tracking portfolio states (orders, positions, account balance) and tick data feeds (orderbook, trades, correlated assets).
* Actions are triggered by internal clocks, trade arrivals, orderbook updates, and proprietary logic.  Actions can be immediate (`onTick`) or based on stored data.
* The example uses Bybit (`exc='bybit'`), but can be adapted to other supported exchanges.
* `gateway` connects to the exchange, `oms` handles order/position management, and `feed` handles tick data subscription and retrieval.
* `BTC` trade feeds are used to compute `trade imbalance` as shared global data.
* `market_trade_handler` function processes trade messages (`msg = (ts, price, sz, dir)`) and updates `market_imbalance`.
* `main` function initializes `gateway`, `oms`, and subscribes to `BTC` trade feeds using `feed.add_trades_feed`.  It uses a buffer of 300 trades and registers `market_trade_handler`.
* `make` function (market making logic) uses `add_trades_feed` and `add_l2_book_feed` to subscribe to trade and orderbook ticks.
* `printer` (trade handler) and `l2_handler` (orderbook handler) process tick data.
* `l2_handler` uses orderbook data (`lob` object) to submit/cancel orders based on a defined logic:
    * Determine quote price (3rd from top of book).
    * Submit orders if no pending or tighter orders exist.
    * Cancel tighter acknowledged orders.
    * Maintain time priority for duplicate levels.
    * Cancel excess orders (more than 5 on each side).
* `l2_handler` computes various market making variables (inventory, quote imbalance, mid price, vamp, vol).
* The example shows how to use `oms.limit_order` and `oms.cancel_order` to manage orders.
* The tutorial concludes by showing example quotes on the Bybit platform.

* AutoGluon: Fast and accurate machine learning in 3 lines of code.
* Supports Python 3.8 - 3.11 on Linux, MacOS, and Windows.
* Installation via pip install autogluon.  Detailed instructions (including GPU support) available in the Installation Guide.
* Quickstart example using TabularPredictor to build ML models.
* Handles tabular, multimodal, and time series data.
* Offers tutorials, talks, and scientific publications.
* Provides resources such as hands-on tutorials and scientific publications.
* Cloud deployment options including AutoGluon Cloud, SageMaker AutoPilot, and Amazon SageMaker.
* Licensed under the Apache 2.0 License.
* Actively accepting code contributions.  Contributing Guide available.
*  7.8k stars and 908 forks on GitHub.

*   **Reference Architecture:** This project provides a reference architecture for building clean, maintainable FastAPI applications.
*   **SWAPI Integration:**  Demonstrates fetching data from the SWAPI API (Star Wars API).
*   **Database Storage:** Stores fetched character data in a PostgreSQL database using Alembic for migrations.
*   **Modular Design:** Employs a layered architecture (routers, services, clients, domain logic, utils) for separation of concerns.
*   **Comprehensive Testing:** Includes unit tests with mocked dependencies and a few integration tests for end-to-end verification.
*   **Naming Conventions:**  Follows clear naming conventions for files (e.g., `character_service.py`, `character_router.py`).
*   **Type Safety:** Emphasizes using type hints for parameters and return types.
*   **High Test Coverage:** Aims for high test coverage through unit testing of individual components.
*   **Focus on Architecture:** Primarily focuses on application architecture; CI/CD, deployment, and Docker are not covered.
*   **Installation:** Requires creating a virtual environment, installing dependencies (`pip install -r requirements.txt`), setting environment variables (database URL), and running database migrations (`alembic upgrade head`).
*   **Running the App:** Starts with `uvicorn main:app --reload`.
*   **Running Tests:** Uses `pytest` for running all tests, `pytest tests/unit_tests/` for unit tests, and `pytest tests/integration_tests/` for integration tests.
*   **Technology Stack:** Uses FastAPI, Python, PostgreSQL, Alembic, and requests.

* Qocker is a user-friendly GUI application for managing Docker containers.
* Built with PyQt5, providing an intuitive interface.
* View Docker containers in a tree-like structure.
* Open a terminal for any container with a double-click.
* Start, stop, and remove containers directly from the GUI.
* Real-time container status updates.
* Cross-platform (Windows, macOS, and Linux).
* Requires Python 3.6+, PyQt5, and Docker.
* Installation via git clone, then `pip install -r requirements.txt`.
* Run with `python3 main.py`.
* Licensed under the GNU General Public License v3.0.
* Contributions are welcome via Pull Requests.

* Provides code execution utilities for Open WebUI & Ollama.
* Uses gVisor for secure sandboxing.
* Offers both a code execution function and a code execution tool.
* **Code execution function:**
    * Appears as a button under LLM-generated messages.
    * Executes code within the message's code block.
    * Displays output in the UI and makes it available to the LLM.
    * Installation involves setting up Open WebUI for sandboxing, then adding the function in the Open WebUI Workspace → Functions section using `open-webui/functions/run_code.py`.
    * Requires activating both toggles on the newly created function.
    * Usage involves asking the model to generate code and clicking the "Run code" button.
* **Code execution tool:**
    * Allows the LLM to run code independently.
    * Output is invisible to the user but accessible to the LLM.
    * Installation is similar to the function, but using `open-webui/tools/run_code.py` in the Open WebUI Workspace → Tools section.
    * Requires enabling the tool for each model in Workspace → Models.
    * Usage involves activating the "Run code" toggle on the message box when prompting the model.
* Licensed under the Apache-2.0 license.

* HuggingChat macOS is a native chat interface for macOS using open-source language models.
* Installation involves downloading the latest `.zip` from the Releases section, unzipping, and dragging the app to the Applications folder.
* Keyboard shortcut: ⌘ + Shift + Return
* Feedback can be sent via email or by creating a GitHub issue.  Issues should include a clear title, description, steps to reproduce (for bugs), app version, and macOS version.
* The project has 845 stars and 25 forks on GitHub.
* Contributors: Cyril Zakka and Omar Sanseviero

* ColPali is a multimodal document retrieval model using a VLM (Vision Language Model) to understand and retrieve documents based on visual and textual content.
* The training data for ColPali consists of image and query pairs, allowing the model to learn the relationship between queries and images.  Negative examples can also be helpful.
* A prompt is used to generate queries from documents, focusing on creating questions relevant to the page content that a user could ask without prior knowledge of the document.
* The blog post demonstrates creating a domain-specific dataset for ColPali using a UFO dataset sourced from internet archive PDF newsletters.
* Qwen2-VL-7B-Instruct, an open VLLM, is used to generate queries for the UFO dataset images.  The process involves using a chat template, preparing inputs for the model, and generating text responses.
* The generated queries are validated by checking if they are valid JSON and then further checked for relevance and specificity.  The prompt is iteratively refined to improve query quality.
* The process involves using the `transformers` library and functions like `apply_chat_template` and `process_vision_info` to interact with the Qwen2-VL model.
* The goal is to fine-tune ColPali for improved performance on the specific domain of UFO documents.
* The generated dataset can be used for training or fine-tuning ColPali models.
* The blog post discusses using Pydantic for data validation to ensure the quality of the synthetically generated data.
* The blog post mentions using `uv` for managing Python installs and `Polars` for data manipulation.
* The blog post provides code examples for loading datasets, generating queries, and validating the output.
* The blog post suggests using the `pdf-to-page-images-dataset` Hugging Face Space to create a dataset from PDFs.

*   **Real-time audio visualizer:** Uses Pygame and PyAudio to display a dynamic representation of sound frequencies.
*   **Live audio input:** Visualizes frequencies from live audio input or audio files.
*   **Customizable:** Allows modification of colors, bar sizes, and animation speed.
*   **Lightweight and simple:** Ideal for beginners learning audio processing and Pygame.
*   **Prerequisites:** Python 3.x, Pygame, PyAudio, and PortAudio (Linux).
*   **Installation:** Clone repository, install system dependencies (PortAudio on Linux), and install Python dependencies using `pip3 install -r requirements.txt` or `python3 install_requirements.py`.
*   **Running:** Execute `python3 pygameeq.py`.  Requires a connected microphone or audio input device.
*   **Linux Audio Input Management:** Uses `pavucontrol` (PulseAudio Volume Control) to manage audio input sources. Install with `sudo apt install pavucontrol`.
*   **Customization Options:** Bar colors, bar size, and animation speed are customizable within the code.
*   **Target Audience:** Python developers, those learning real-time audio processing, and those interested in Pygame beyond game development.
*   **Lightweight and Easy to Use:**  Doesn't require advanced audio processing libraries.
*   **Pygame-Centric:** Built entirely using Pygame.
*   **License:** MIT License.
*   **Contact:** onojk123@gmail.com

* **Introduction:** Guides on building a data project from scratch, addressing common challenges faced by beginners.  Provides options for interactive learning via Jupyter Notebook.

* **Setup:** Offers two options for running the project: using GitHub Codespaces (recommended) or running it locally.  Detailed steps are provided for both options, including setting up virtual environments and installing requirements.

* **Parts of Data Engineering:** Explains the key components of a data engineering project, using a three-hop architecture (Bronze, Silver, Gold).  Highlights the importance of defining clear requirements before starting.

* **Requirements:** Emphasizes the importance of defining precise requirements, including understanding input datasets, defining the desired output dataset, establishing SLAs (Service Level Agreements), and defining data quality checks.

* **Choosing Tools:** Recommends using Polars for data processing due to its ease of use and suitability for smaller datasets.

* **Data Flow Architecture:** Details the three-hop architecture:
    * **Bronze:** Extracts raw data, standardizing names and data types.
    * **Silver:** Models data for analytics using a dimensional model (Kimball).  Creates dimension tables (e.g., `dim_customer`) and fact tables (e.g., `fct_orders`, `fct_lineitem`).
    * **Gold:** Creates end-user tables.  This involves creating an Optimized Business Table (OBT) by joining fact and dimension tables, and then creating pre-aggregated tables tailored to specific stakeholder needs.

* **Data Quality Implementation:**  Describes implementing data quality checks, including uniqueness checks and variance checks against previous runs.  Uses SQLite to store run metadata for comparison.  Includes a flowchart illustrating the data quality check process.

* **Code Organization:** Suggests organizing code based on the multi-hop architecture or existing company standards.  Provides a sample folder structure and promotes code modularity by creating functions for dataset creation.

* **Code Testing:**  Includes instructions on running unit tests using `pytest`.

* **Next Steps:** Hints at future topics, such as creating base classes for code reuse.

* **Conclusion:** Summarizes the steps involved in building a data project and encourages readers to apply the learned concepts to their own projects.

* ColPali is a novel document retrieval model leveraging Vision Language Models (VLMs) for Retrieval-Augmented Generation (RAG).
* It surpasses traditional text-based retrieval by incorporating visual information (figures, tables, infographics).
* Uses PaliGemma, a VLM with built-in OCR, to generate contextualized embeddings directly from document images.
* Employs a "late interaction" similarity mechanism for efficient comparison of query and document embeddings.
* Addresses limitations of traditional methods like time-consuming text extraction, OCR, and layout analysis.
* Offers a "What You See Is What You Search" (WYSIWYS) approach.
* Outperforms existing methods on the ViDoRe benchmark, a new benchmark for visual document retrieval.
* Shows significant improvement on visually rich datasets.
* Limitations include a primary focus on PDF-like documents, limited multilingual support, and potential need for domain-specific fine-tuning.
* Simplifies RAG pipelines by directly incorporating visual information into retrieval.
*  The ColPali architecture is considered more important than the specific VLM or training data used.  It is expected to generalize to other VLMs in the future.
* Resources:  ColPali Paper, ColPali on GitHub, Vespa community on Slack and Discord.

* ColPali uses a vision-capable language model (PaliGemma) for document retrieval, surpassing traditional text-based methods.
* It leverages contextualized vision embeddings from a VLM and a late interaction similarity function (MaxSim).
* MaxSim compares query and document embeddings at query time, allowing interaction between image grid cell vectors and query text token vectors.
*  ColPali generates embeddings directly from images, eliminating the need for text extraction, OCR, and layout analysis.
*  A PDF page is represented by 1030 128-dimensional vectors (1024 image grid patches + 6 instruction tokens).
* Query text is tokenized and represented in the same 128-dimensional vector space.
* MaxSim is a dot product followed by max and sum reductions.  It can be expressed in Vespa's schema language and PyTorch.
* Scaling MaxSim involves reducing query tokens, patch vectors, or vector dimensionality.
* Hamming distance, a faster alternative to dot product, is used with binary quantization (BQ) to convert float vectors to 16-dim int8 tensors.
*  Hamming-based MaxSim is approximately 3.5 times faster than the float dot product version.
* Vespa's phased retrieval and ranking pipeline is used for scaling to billions of documents.
* Approximate nearest neighbor search retrieves candidate documents, followed by MaxSim ranking.
* Vespa supports multi-vector HNSW indexing with hamming distance for efficient nearest neighbor search.
*  A single Vespa query performs candidate selection and ranking, avoiding large data transfers between services.
* Query token pruning can further speed up retrieval by using a subset of query token vectors.
*  Evaluating ColPali on the DocVQA dataset shows that the hamming-based MaxSim achieves comparable accuracy while offering significant efficiency gains (32x storage reduction and 4x speedup).

* ColPali is a new approach to PDF retrieval that uses vision language models (VLMs) to directly embed screenshots of PDF pages, eliminating the need for complex text extraction and preprocessing steps.
* Unlike traditional text-based retrieval models, ColPali leverages the visual content of the PDF pages, including images, tables, and charts.
* The ColPali model is based on ColBERT and PaliGemma, a powerful visual language model.  It uses multi-vector representations and late-interaction scoring for improved retrieval performance.
* ColPali significantly outperforms traditional text-based models like BM25 and BGE-M3 on the newly introduced Visual Document Retrieval (ViDoRe) benchmark.
* ColPali embeddings can be efficiently represented and used in Vespa using its tensor framework, enabling late-interaction scoring similar to ColBERT.  Two methods for storing ColPali embeddings in Vespa are described: one page per document and one PDF per document.
* Vespa's tensor framework and compute engine allow for efficient representation and scoring of ColPali embeddings without custom plugins.
* The blog post provides a Vespa schema and ranking profile example demonstrating how to integrate ColPali embeddings into a Vespa application.  It uses phased ranking with BM25 as the first phase and ColPali as the second.
* ColPali can be combined with other retrieval models, such as using ColPali embeddings as an additional feature in a gradient boosted decision tree (GBDT) or other models.
* A demo notebook is available showing how to use ColPali embeddings in Vespa.
* The model can be fine-tuned for different document formats (HTML, Word, etc.) and languages.
* ColPali handles multi-page documents by using an additional tensor dimension to represent the page.
* The model has approximately 3 billion parameters and uses the PaliGemma 3 base model.
* The blog post discusses interpretability of the model, allowing analysis of which parts of the page contribute most to the score.
* The blog post addresses how to evaluate the model on custom data and how image data is stored in Vespa (base64 encoding).
* The use of BM25 in the first phase of the ranking is justified by its efficiency and ability to serve as a strong baseline.  An alternative approach using only ColPali embeddings is also mentioned.

* RefactorFirst is a tool for Java codebases that identifies and prioritizes God Classes, Highly Coupled classes, and Class Cycles.
* It uses PMD for God Class and Coupling analysis, and JavaParser & JGraphT for cycle detection.
* Requires Java 11 or newer. Supports Java 21.
* Provides analysis via command line (HTML report), GitHub Actions, Maven plugin, and as a Maven report.
* Generates reports showing God classes, highly coupled classes, and class cycles (with cycle images).
* Reports prioritize classes for refactoring based on impact and effort.
* Offers integration with IntelliJ Ultimate's Method Reference Diagram plugin for refactoring assistance.
* Supports single and multi-module Maven projects with typical layouts.
* Future plans include a Gradle plugin, unit test coverage integration, and incorporating more object-oriented metrics.
* Uses a cost-benefit approach based on the paper "Prioritizing Design Debt Investment Opportunities".
* Available as a Maven plugin (version 0.5.0).
* Open source under the Apache-2.0 license.

* Vision Language Models (VLMs) learn from images and text, generating text outputs.  Large VLMs excel at zero-shot tasks and handle diverse image types.  Use cases include image captioning, visual question answering, and document understanding. Some can identify spatial properties and output bounding boxes or segmentation masks.

* Open-source VLMs are available on the Hugging Face Hub, with varying model sizes, image resolutions, and capabilities (e.g., chat, grounding).  Models are primarily English-trained unless otherwise noted.  Examples include LLaVA, DeepSeek-VL, moondream2, CogVLM, Fuyu-8B, KOSMOS-2, Qwen-VL, and Yi-VL-34B.

* Selecting the right VLM involves considering factors like intended use case and performance metrics.  Leaderboards like Vision Arena and Open VLM Leaderboard rank models based on user preferences and various metrics.  The VLMEvalKit and LMMS-Eval toolkits facilitate benchmarking.

* Benchmarks for evaluating VLMs include MMMU (comprehensive, multi-disciplinary), MMBench (single-choice questions across various skills), MathVista, AI2D, ScienceQA, and OCRBench.

* VLMs typically consist of an image encoder, an embedding projector, and a text decoder. Training methods vary; some freeze parts of the model (like LLaVA) while others train end-to-end (like KOSMOS-2).  Fuyu-8B uses a different approach, feeding image patches directly to a projection layer.

* Using VLMs with `transformers` involves initializing a model and processor, processing image and text prompts, generating output using the `generate` function, and decoding the output tokens.

* Fine-tuning VLMs with TRL's `SFTTrainer` is supported.  An example uses the `llava-instruct` dataset for instruction fine-tuning on a LLaVA 1.5 VLM.  This requires installing the latest version of TRL.  A data collator combines text and image pairs for training.

* Converts eBooks to audiobooks with chapters and metadata using Calibre and Coqui XTTS.
* Supports optional voice cloning and multiple languages.
* Features: eBook conversion to text, chapter splitting, high-quality text-to-speech, voice cloning, and multi-language support.
* Requirements: Python 3.x, coqui-tts, Calibre, FFmpeg, and optionally a custom voice file.
* Installation: Install Python, Calibre, FFmpeg, and optionally Mecab (for non-Latin languages).  Install Python packages: `pip install tts==0.21.3 pydub nltk beautifulsoup4 ebooklib tqdm`. For non-Latin languages: `python -m unidic download` and `pip install mecab mecab-python3 unidic`.
* Supported Languages: English, Spanish, French, German, Italian, Portuguese, Polish, Turkish, Russian, Dutch, Czech, Arabic, Chinese, Japanese, Hungarian, Korean.
* Usage:  Run via Gradio web interface (`python custom_model_ebook2audiobookXTTS_gradio.py`), basic command line (`python ebook2audiobook.py <path_to_ebook_file> [path_to_voice_file] [language_code]`), or custom XTTS model (`python custom_model_ebook2audiobookXTTS.py <ebook_file_path> <target_voice_file_path> <language> <custom_model_path> <custom_config_path> <custom_vocab_path>`).
* Docker Support: Run with CPU or GPU using provided Docker commands.  Custom XTTS models can be used via Docker.
* Supported eBook Formats: .epub, .pdf, .mobi, .txt, .html, .rtf, .chm, .lit, .pdb, .fb2, .odt, .cbr, .cbz, .prc, .lrf, .pml, .snb, .cbc, .rb, .tcr (best results with .epub or .mobi).
* Output: Creates an .m4b file with metadata and chapters.
* Fine-tuned XTTS models can be found on Hugging Face.

* Full stack modern web application template.
* Uses FastAPI (backend), React (frontend), SQLModel, PostgreSQL, Docker, GitHub Actions.
* Includes automatic HTTPS.
* Features JWT authentication, secure password hashing, email-based password recovery.
* Uses TypeScript, hooks, Vite, and Chakra UI on the frontend.
* Includes end-to-end testing with Playwright and unit testing with Pytest.
* Supports dark mode.
* Uses Docker Compose for development and production.
* Traefik is used as a reverse proxy/load balancer.
* Deployment instructions are provided using Docker Compose and Traefik.
* CI/CD is implemented via GitHub Actions.
* Offers interactive API documentation.
* Provides instructions for using a private repository.
* Includes instructions for updating from the original template.
* Configuration is done via .env files.
* Includes instructions for generating secret keys.
* Supports project generation using Copier.
* Detailed documentation for backend, frontend, deployment, and development is available.
* Licensed under the MIT license.