Link List :: 2025-01-18

14 minutes read
linklist
links

https://elmwealth.com/microstrategy-bitcoin/

The text is a research report on MicroStrategy (MSTR), a company that has been focused on buying Bitcoin and other cryptocurrencies. The authors of the report provide an analysis of various aspects of MSTR, including its stock price, options trading activity, and return characteristics.

Some key points from the report include:

  1. MicroStrategy’s stock price: The report notes that MicroStrategy’s stock price has returned a significant 200% in about seven months, making it one of the best-performing stocks in the past year.
  2. Options trading activity: The authors highlight that hundreds of bullish options trades have been executed on MSTR, which suggests strong investor interest in the stock.
  3. Return characteristics: The report notes that the return pattern of MicroStrategy’s stock is not very enticing compared to other long-short ETFs, which would be a convenient way to take advantage of potential underperformance.
  4. Risk and frictions: The authors caution that MSTR trading comes with high risks and frictions, making it difficult to short or leveraged trade the stock effectively.
  5. Leveraged ETF analysis: They analyze a hypothetical long-short ETF that would be long 1x Bitcoin and short 1x MSTR, which they conclude would not be an attractive investment opportunity due to its relatively low return and high risks.

Overall, the report suggests that while MicroStrategy’s stock price has been strong, investors should exercise caution when considering leveraged trading or shorting the stock due to the high risks involved.

https://www.reddit.com/r/LocalLLaMA/comments/1hy8ii4/freeact_a_lightweight_library_for_codeaction/

* A subreddit for discussing Llama, a large language model created by Meta AI.
* A lightweight library called freeact that enables language models to act as autonomous agents through executable code actions.
* Supports dynamic installation and utilization of Python packages at runtime.
* Agents learn from feedback and store successful code actions as reusable skills in long-term memory.
* Skills can be interactively developed and refined in collaboration with freeact agents.
* Agents compose skills and any other Python modules to build increasingly sophisticated capabilities.
* Code actions are executed in ipybox, a secure Docker + IPython sandbox that runs locally or remotely.
* Freeact is hosted on GitHub at https://github.com/gradion-ai/freeact.
* Evaluation documentation can be found at https://gradion-ai.github.io/freeact/evaluation/.
* Feedback is welcome and can be shared with the community.

https://github.com/tegridydev/dnd-llm-game?tab=readme-ov-file

* TD-LLM-DND is a Streamlit-based web application that creates a Dungeons & Dragons style adventure using locally hosted llm from ollama.
* Players can generate characters, start new adventures, and progress through turns with an automated Dungeon Master, AI powered party members and player interactions (TO:DO).
* Features:
  * Generate D&D Characters: Create unique characters with name, race, class, backstory, and items
  * Start New Adventure: Begin a new adventure with the generated characters
  * Turn-Based Gameplay: Progress through the adventure with player and Dungeon Master turns (TO:DO - add player input)
  * Manage Models: Select/download and manage language models for the Dungeon Master and AI players
* Requirements:
  + Python 3.8+
  + Streamlit
  + Requests
  + LangChain
  + HuggingFace Transformers
  + dotenv
* Clone repository:

https://old.reddit.com/r/LocalLLaMA/comments/1i02hpf/speaches_v060_kokoro82m_and_pipertts_api_endpoints/

* Released Speaches v0.6.0 with support for Piper and Kokoro Text-to-Speech models
* Features:
    * GPU and CPU support
    * Deployable via Docker Compose / Docker
    * Highly configurable
    * OpenAI API compatible
    * Streaming support (SSE) and live transcription support (WebSocket)
    * Dynamic model loading/offloading
    * Text-to-Speech via kokoro and piper models
    * Async speech to speech interactions
* Coming soon: Audio generation, Realtime API, sentiment analysis, and more
* Documentation and TTS functionality demo available at https://speaches-ai.github.io/speaches/
* Published Hugging Face space is currently broken but GradioUI works locally using Docker

https://www.reddit.com/r/LocalLLaMA/comments/1i0b289/hugging_face_released_a_free_course_on_agents/

* Subreddit to discuss about Llama, the large language model created by Meta AI.
* Hugging Face released a free course on agents.
* Added chapter to Smol Course on agents, focusing on using small agents.
* Topics covered in the course include:
    - Code agents that solve problems with code
    - Retrieval agents that supply grounded context
    - Custom functional agents for specific needs
* The course is available at https://github.com/huggingface/smol-course/tree/main/8_agents

https://github.com/quantalogic/quantalogic

This is the documentation for the QuantaLogic framework, a powerful AI agent development platform. The documentation covers various aspects of the framework, including its architecture, tools, and usage guidelines.

Overview

QuantaLogic is an open-source, Python-based framework designed to build intelligent agents that can reason, act, and learn in complex scenarios. The framework provides a comprehensive set of tools and libraries to enable developers to create highly functional AI agents.

Architecture

The QuantaLogic framework consists of several key components:

  1. Agent: The core component of the framework, responsible for executing tasks and interacting with users.
  2. Memory: A data storage system that enables the agent to maintain context and retrieve information as needed.
  3. ToolManager: A module that manages a set of tools, allowing the agent to delegate tasks to specialized agents or execute actions directly.

Tools

QuantaLogic includes a wide range of tools, each designed to address specific needs in complex problem-solving and task execution:

  1. AgentTool: Enables the agent to delegate tasks to specialized agents.
  2. LLMTool: Provides the agent with access to large language models for exploration and role play.
  3. PythonTool, NodeJsTool, and ElixirTool: Execute code in different programming languages, facilitating real-time coding assistance and code evaluation.
  4. ReadFileTool, WriteFileTool, and ReplaceInFileTool: Support file operations, including reading from and writing to files.
  5. RipgrepTool and SearchDefinitionNames: Enhance the agent’s ability to search through codebases and identify relevant definitions.
  6. DownloadHttpFileTool, ListDirectoryTool, and ExecuteBashCommandTool: Provide broader functionality for fetching external resources, listing directories, and executing system commands.

Usage Guidelines

To use QuantaLogic, follow these steps:

  1. Clone the repository and create a virtual environment using poetry install.
  2. Initialize the framework by running python main.py init (or venv\Scripts\activate on Windows).
  3. Define tasks and interactions using Python code.
  4. Use the ToolManager to delegate tasks or execute actions directly.

Contributing

To contribute to QuantaLogic, follow these guidelines:

  1. Fork the repository.
  2. Create a feature branch.
  3. Write tests and implement changes.
  4. Submit a pull request.

License

QuantaLogic is licensed under the Apache License, Version 2.0. See LICENSE for details.

Author

Raphaël MANSUY founded QuantaLogic.

https://github.com/ayminovitch/fine-tune-codebase

* This repository provides a scalable and efficient solution for fine-tuning large language models (LLMs) on codebases.
+ LoRA Support: Fine-tune models efficiently with fewer trainable parameters.
+ Mixed Precision Training: Speed up training and reduce memory usage with FP16.
+ Quantization: Reduce model size with 8-bit quantization for deployment.
* Dataset Preprocessing: Automatically preprocess code (e.g., remove comments).
* Interactive Testing: Test the fine-tuned model interactively in the terminal.
* TensorBoard Logging: Monitor training metrics with TensorBoard.
* Early Stopping: Automatically stop training if validation performance plateaus.
* Multi-GPU Support: Scale training across multiple GPUs for faster results.
* Custom Tokenizers: Use custom tokenizers for specific programming languages.
+ Python 3.8 or higher
+ GPU with CUDA support (recommended for faster training)
+ Required Python libraries (see requirements.txt)
+

https://github.com/topoteretes/cognee

# Cognee Framework Overview
- We build for developers who need a reliable, production-ready data layer for AI applications
- Scalable, modular ECL (Extract, Cognify, Load) pipelines allow interconnection and retrieval of past conversations, documents, and audio transcriptions
- Reduces hallucinations, developer effort, and cost

https://en.m.wikipedia.org/wiki/Schramm%27s_model_of_communication

The text appears to be a collection of references related to communication studies, mass communication, and public diplomacy. It includes a wide range of authors, publishers, and publication dates, suggesting that the work is a comprehensive bibliography or reference list.

Some notable topics covered in this collection include:

  • Communication models (e.g., Shannon-Weaver model, McQuail’s models)
  • Mass communication theory
  • Public relations and strategic communication
  • International communication and global collaboration
  • Media studies and visual literacy

The references span multiple fields, including education, business, politics, and social sciences. Many of the sources are academic journals, books, and encyclopedias, while others include government reports, technical manuals, and books on specific topics.

To provide more specific insights or suggestions, I would need to know what particular aspect of this collection is being inquired about (e.g., book reviews, author analysis, or a summary of key concepts). If you have any specific questions or requests related to this reference list, please let me know and I’ll do my best to assist!

https://github.com/camel-ai/oasis

This is not a code solution, but rather a documentation of an open-source project called OASIS (Open Agent Social Interaction Simulations) that simulates social interactions with one million agents.

However, I can provide some general information on how to contribute to this project:

  1. Read the contributing guidelines: The project’s contributors have established a set of guidelines for contributing new features, tests, and examples. You can find these guidelines in the CAMEL contributing guidelines.
  2. Fork the repository: To contribute to the project, you’ll need to create a fork of the repository on GitHub.
  3. Submit a pull request: Once you’ve made changes to your fork, submit a pull request to merge your changes into the main repository.
  4. Test and verify: Before submitting your pull request, ensure that your changes pass the automated pytest suite.

Some additional information:

  • The project uses Python as its primary programming language.
  • The codebase is organized into several packages, including social_simulation, social_agent, and agents_generator.
  • The project has a set of example configurations for different scenarios, such as Reddit recommendation systems and group polarization simulations.
  • The documentation includes tutorials on creating profiles for large-scale users, visualizing and analyzing social simulation data, and more.

If you’re interested in contributing to this project or have questions about the codebase, I recommend reaching out to the project maintainers at camel.ai.team@gmail.com.

https://claudio.uk/posts/epub-to-audiobook.html

This is a Python script that converts an EPUB book into an audiobook using the Kokoro speech synthesis engine. Here’s a breakdown of the code:

https://github.com/pengfeng/ask.py

* A full version with db support and configurable components is open sourced here: LeetTools. 
* A demo web site has been setup here. Please check them out!
* - You can run it on command line or with a GradIO UI.
* - You can control the output behavior, e.g., extract structured data or change output language,
* - You can control the search behavior, e.g., restrict to a specific site or date, or just scrape a specified list of URLs.
* - You can run it in a cron job or bash script to automate complex search/data extraction tasks.
* - You can ask questions against local files.

https://rwilinski.ai/posts/building-better-ai-agents-core-principles-for-success/

This is a comprehensive guide to building effective AI agents. Here’s a breakdown of the key principles and takeaways:

Core Principles

  1. Separate Concerns: Break down complex tasks into smaller, manageable components, and assign each component to a specific module or function.
  2. Implement Feedback Loops: Continuously monitor and evaluate the agent’s performance, and use feedback to improve its behavior.
  3. Maintain Strict Guardrails: Establish clear boundaries and rules for the agent’s behavior, and ensure it adheres to them.
  4. Test Thoroughly: Conduct rigorous testing and validation to ensure the agent’s reliability and accuracy.
  5. Start Small: Begin with a minimal set of capabilities and expand gradually as the agent is verified in production environments.

Additional Best Practices

  1. Use Existing Authentication and Authorization Mechanisms: Leverage existing security measures to prevent unauthorized access and data breaches.
  2. Sanitize Input: Use tools like llm-guard to prevent prompt injection and other types of attacks.
  3. Detect Manipulation Attempts: Implement mechanisms to detect and reject attempts to manipulate the agent or use it for malicious purposes.
  4. Monitor System Behavior: Continuously monitor the agent’s behavior and adjust its configuration as needed.
  5. Gather User Feedback: Collect feedback from users to improve the agent’s performance and accuracy.

Importance of Evaluations

  1. Evaluations are Critical: Regular evaluations are essential to ensure the agent is performing as expected and identify areas for improvement.
  2. Probabilistic Nature of LLMs: Due to the probabilistic nature of Large Language Models (LLMs), evaluations must be repeated multiple times to capture the agent’s true behavior.
  3. Custom Evaluation Tools: Create custom evaluation tools to assess the agent’s performance, such as comparing factuality and semantic similarity.

Security Considerations

  1. Authentication and Authorization: Ensure that the agent has only access to authorized data and systems.
  2. Sanitization: Protect against prompt injection and other types of attacks by sanitizing input.
  3. Detection of Manipulation Attempts: Implement mechanisms to detect and reject attempts to manipulate the agent or use it for malicious purposes.

Conclusion

Building effective AI agents requires careful attention to these core principles, best practices, and security considerations. By implementing feedback loops, separating concerns, and maintaining strict guardrails, we can create agents that are both powerful and reliable. Remember to test thoroughly and start small, and don’t be afraid to expand an agent’s capabilities over time as you verify its reliability in production environments.

https://blogs.worldbank.org/en/education/From-chalkboards-to-chatbots-Transforming-learning-in-Nigeria

* Students in an after-school program in Edo, Nigeria, participated in an AI-powered learning initiative, showing positive effects on learning outcomes.
* The program boosted learning across various areas, including English language, AI knowledge, and digital skills, with students who participated outperforming their peers.
* Benefits extended beyond the scope of the program, as students who learned to engage effectively with AI performed better on end-of-year exams covering topics outside the intervention's focus.
* Girls initially lagging behind boys in performance showed significant gains from the intervention, bridging gender gaps in learning.
* Deeper engagement with the AI program resulted in greater learning gains, with each additional day of attendance leading to improved outcomes.
* The learning improvements were striking, equivalent to nearly two years of typical learning in just six weeks.
* The program outperformed 80% of other education interventions studied through randomized controlled trials in developing contexts.
* Future research is needed to explore long-term effects, benefits beyond immediate gains, and the role of teachers in supporting interactions with large language models.

https://github.com/BuildMirai/mirai

- 🛠️ Build Dynamic UIs: Update your app’s UI instantly with JSON configurations.
- 🌍 Cross-Platform: Write once, render anywhere – Flutter does the rest.
- ⚡ Fast Iterations: Make changes on the server and see them in your app instantly.
- Mirai provides multiple methods to parse JSONs into Flutter widgets:
    - `Mirai.fromNetwork()`
    - `Mirai.fromJson()`
    - `Mirai.fromAsset()`
- Mirai Gallery app for more such examples.
- Check out the [official documentation](link-to-official-documentation) for more information on using Mirai in your Flutter apps.

https://github.com/langchain-ai/executive-ai-assistant

- Kick off ingest job with `python scripts/run_ingest.py --minutes-since 120 --rerun 1 --early 0`
- Interact with results in Agent Inbox, connect to EAIA agent, input LangSmith API key
- Set deployment URL to `http://127.0.0.1:2024`, give it a name like "Local EAIA"
- Press Submit, set environment variables OPENAI_API_KEY, ANTHROPIC_API_KEY, GMAIL_SECRET
- Deploy EAIA with `python scripts/setup_cron.py --url ${LANGGRAPH-CLOUD-URL}`
- Control reflection logic by editing `eaia/reflection_graphs.py`
- Control triage logic by editing `eaia/main/triage.py`
- Control calendar logic by editing `eaia/main/find_meeting_time.py`
- Control tone & style logic by editing `eaia/main/rewrite.py`
- Control email draft logic by editing `eaia/main/draft_response.py`

https://aider.chat/2025/01/15/uv.html

* Using uv as an installer
* It’s hard to reliably package and distribute python command line tools to end users. Users frequently encounter challenges: dependency version conflicts, virtual environment management, needing to install python or a specific version of python, etc.
* Aider employs uv in a couple of novel ways to streamline the installation process:
    + Install aider with `curl https://aider.chat/install.sh | sh`
    + Users who have python 3.8+ installed can use `pip install aider-install && aider-install`.
* Both methods use uv to globally install the aider command line program, with all of its dependencies in an isolated environment.
* They ensure that aider will run with python 3.12, and install that version if it is not already available.
* These uv install methods are especially helpful for aider, because it has a large set of very specific dependencies. Since not all of aider’s dependencies are available on all python versions, it requires python 3.9-3.12.
* Most users don’t want to worry about these details – they just want a quick way to install and run aider.
* One-liners
    + Users can install aider with a shell one-liner, without even having python previously installed: `curl -LsSf https://aider.chat/install.sh | sh`
* This installs uv, then uses it to install python 3.12, install the aider command line tool and update the user’s shell path.
* Under the hood, it is simply a copy of uv’s own install script with one line added, to install aider as a tool:
    + `ensure "${_install_dir}/uv" tool install --force --python python3.12 aider-chat@latest`
* aider-install python package allows quick global installation of aider for users who already have python 3.8+ installed.
* It simply provides the aider-install command line program, which users just need to run once.
    + `pip install aider-install`
* This installs only two packages: aider-install and the uv python package.
* This ensures that uv is available in the user’s environment.
* Everything else is installed in a stand-alone environment created by uv.
* When the user runs aider-install, it runs uv to install aider as a tool and update the user’s shell path if needed:
    + `uv tool install --force --python python3.12 aider-chat`
    + `uv tool update-shell`
* Benefits
* These uv install methods have been popular with users, providing a hassle free way to install aider and quickly get started.
* Installs are also extremely fast, much faster than pip or pipx installs even when uv is also installing python 3.12!
* There are also a number of benefits from the perspective of the tool developer/publisher.
* Since providing these install methods, far fewer users report dependency problems and version conflicts as compared to users who pip install aider-chat.
* There is also less pressure to rapidly support the newest python versions, since aider always installs with python 3.12.