xiaowuc2 writes that the qxresearch-event-1 repository provides a hands-on collection of 50+ concise Python applications averaging about ten lines of code each, spanning Machine Learning, Deep Learning, GUI, Computer Vision and API development, with video explanations on YouTube and guidance for learning, experimenting and customization.
Termaid is a pure Python library and CLI that renders Mermaid diagrams as Unicode or ASCII art directly in the terminal or within Python apps, supporting 18 diagram types with zero dependencies, terminal-aware auto-fitting, optional Rich colored output and Textual widget integration.
- Inspired by mermaid-ascii and beautiful-mermaid
- Offers 11 built-in themes including gruvbox, monokai, dracula, nord and solarized
- Pipe-friendly CLI examples include `cat diagram.mmd | termaid` and `uvx termaid diagram.mmd`
- Inspired by mermaid-ascii and beautiful-mermaid
- Offers 11 built-in themes including gruvbox, monokai, dracula, nord and solarized
- Pipe-friendly CLI examples include `cat diagram.mmd | termaid` and `uvx termaid diagram.mmd`
Abid Ali Awan writes that a Jupyter Notebook pipeline can turn a webpage into a lightweight LLM-powered QA engine by fetching HTML with requests, stripping noisy elements with BeautifulSoup, converting the cleaned DOM to Markdown with markdownify and ftfy, then asking an OpenAI model to answer a specific user query using only the compact Markdown, which reduces token use by removing navigation, scripts and repeated marketing text.
- Uses gpt-5.4-nano for cost-efficient answers
- Removes script, style, nav, header, footer, form, button tags and class/id names containing popup, cookie, navbar, modal, etc.
- Demonstrates queries on olostep.com home and pricing pages and saves output to ai_scraper_result.md
- Notes running costs and cites commercial alternatives such as Olostep, Firecrawl and Exa
- Uses gpt-5.4-nano for cost-efficient answers
- Removes script, style, nav, header, footer, form, button tags and class/id names containing popup, cookie, navbar, modal, etc.
- Demonstrates queries on olostep.com home and pricing pages and saves output to ai_scraper_result.md
- Notes running costs and cites commercial alternatives such as Olostep, Firecrawl and Exa
The NOOA framework provides a way to build LLM agents using standard Pythonic object-oriented patterns. By treating agents as objects, developers can map state to typed fields and capabilities to methods where docstrings serve as prompts; specifically, an ellipsis in a method body triggers the runtime for an LLM-driven execution loop.
- Includes separate packages for CLI tools, memory management, and benchmarking.
- Supports various local and hosted models via LiteLLM integration.
- Offers automated tracing with an interactive web viewer for debugging.
- Necessitates OS-level isolation to safely execute LLM-generated code.
- Includes separate packages for CLI tools, memory management, and benchmarking.
- Supports various local and hosted models via LiteLLM integration.
- Offers automated tracing with an interactive web viewer for debugging.
- Necessitates OS-level isolation to safely execute LLM-generated code.
Asif Razzaq writes that NVIDIA Labs has open-sourced NOOA, a model-agnostic Python framework designed to streamline agentic development by consolidating prompt templates, tool schemas, and state into single class structures. By treating LLM-driven actions as standard methods with docstrings serving as prompts, the framework allows developers to build autonomous workflows that can be tested, traced, and version-controlled like ordinary software.
- Achieves 82.2% on SWE-bench Verified while using roughly half the tokens required by existing open harnesses.
- Employs a "pass by reference" mechanism for live Python objects via bounded previews to conserve context window space.
- Features an optional memory subsystem that utilizes SQLite and ACT-R activation ranking for record retrieval.
- Achieves 82.2% on SWE-bench Verified while using roughly half the tokens required by existing open harnesses.
- Employs a "pass by reference" mechanism for live Python objects via bounded previews to conserve context window space.
- Features an optional memory subsystem that utilizes SQLite and ACT-R activation ranking for record retrieval.
Supervision provides essential building blocks for computer vision applications, such as data loading and real-time zone counting. The toolkit remains model agnostic, enabling easy integration of various machine learning models via specialized connectors.
- Supports multiple dataset formats including YOLO, COCO, and Pascal VOC
- Offers utilities to split, merge, and convert datasets
- Includes capabilities for speed estimation and dwell time analysis
- Supports multiple dataset formats including YOLO, COCO, and Pascal VOC
- Offers utilities to split, merge, and convert datasets
- Includes capabilities for speed estimation and dwell time analysis
PandasAI is a Python library that allows users to query datasets using natural language. By leveraging large language models (LLMs), it assists both technical and non-technical individuals in performing data analysis, executing complex queries, and creating visualizations through simple conversation.
- Cross-dataframe query support
- Secure Docker sandbox option
- Multiple LLM provider compatibility via LiteLLm
- Cross-dataframe query support
- Secure Docker sandbox option
- Multiple LLM provider compatibility via LiteLLm
Emmimal P Alexander writes that while prompt engineering focuses on optimizing LLM inputs, managing these templates within evolving codebases often leads to production crashes when variables are renamed or removed. To solve this, she created `promptctl`, a Python tool that applies static analysis—similar to database schema migrations—to ensure prompt variable contracts match their call sites in the codebase.
- Performs PromptDiff (detects changes), Contract Validation (checks mismatches), and Impact Analysis (traces dependencies).
- Operates strictly via AST parsing, requiring zero LLM calls or API keys.
- Detects errors that unit tests often miss by mocking away the actual string formatting step.
- Performs PromptDiff (detects changes), Contract Validation (checks mismatches), and Impact Analysis (traces dependencies).
- Operates strictly via AST parsing, requiring zero LLM calls or API keys.
- Detects errors that unit tests often miss by mocking away the actual string formatting step.
A bidirectional bridge connecting Bitchat and Meshtastic networks allows local Bluetooth chat clients to communicate over long distances using LoRa radios. The system runs on a Linux-based device that relays messages between mobile devices via Bluetooth and the mesh network through a USB connection.
- Relays text from nearby Bitchat apps to the Meshtastic LoRa network.
- Broadcasts incoming LoRa signals back to all connected Bluetooth clients.
- Designed for Linux environments like Raspberry Pi using Python 3.7 or higher.
- Relays text from nearby Bitchat apps to the Meshtastic LoRa network.
- Broadcasts incoming LoRa signals back to all connected Bluetooth clients.
- Designed for Linux environments like Raspberry Pi using Python 3.7 or higher.
This guide provides a comprehensive walkthrough on using Google's Gemma 4 model to build autonomous AI agents through tool calling. It explores how this feature enables models to move beyond simple text generation by interacting with external APIs and systems via structured function calls.
Key topics covered in the article include:
- The mechanics of the tool calling loop, from reasoning and selection to execution and final response.
- Setting up a Python development environment using Hugging Face and necessary libraries like transformers and torch.
- Defining JSON schemas for tools to ensure precise model understanding.
- Implementing a full agent workflow by parsing function call responses and executing Python functions.
- A practical end-to-end demonstration of building a weather lookup agent.
- Managing multi-turn conversations through state management and conversation history.
- Best practices for production deployment, including argument validation, execution timeouts, and logging.
Key topics covered in the article include:
- The mechanics of the tool calling loop, from reasoning and selection to execution and final response.
- Setting up a Python development environment using Hugging Face and necessary libraries like transformers and torch.
- Defining JSON schemas for tools to ensure precise model understanding.
- Implementing a full agent workflow by parsing function call responses and executing Python functions.
- A practical end-to-end demonstration of building a weather lookup agent.
- Managing multi-turn conversations through state management and conversation history.
- Best practices for production deployment, including argument validation, execution timeouts, and logging.
