PythonPro #62: Python 3.14’s New Interpreter: 3~30% Faster; Pydantic.ai Agent Framework; and Unified TTS Wrapper
Welcome to a brand new issue of PythonPro!
Here are today's News Highlights: py3-TTS-Wrapper 0.9.18 simplifies speech synthesis across AWS, Google, Azure, IBM, and ElevenLabs; Pydantic.ai beta framework supports OpenAI, Anthropic, Gemini, and real-time debugging; and Python 3.14 promises a new interpreter with 3~30% speed boosts.
My top 5 picks from today’s learning resources:
How I Built a Deep Learning Library from Scratch Using Only Python, NumPy & Math🔢
From Scratch to Masterpiece: The VAE’s Journey to Generate Stunning Images🧑🎨
Permutation Generation in PyTorch on GPU: Statistic Based Decision Rule for
randpermvs.argsortandrand⚙️
And, in From the Cutting Edge, we introduce HintEval, a Python library that streamlines hint generation and evaluation by integrating datasets, models, and assessment tools, providing a structured and scalable framework for AI-driven question-answering systems.
Stay awesome!
Divya Anne Selvaraj
Editor-in-Chief
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🐍 Python in the Tech 💻 Jungle 🌳
🗞️News
Unified TTS Interface: py3-TTS-Wrapper 0.9.18 Simplifies Speech Synthesis Across APIs: The library simplifies integration across services like AWS Polly, Google, Microsoft Azure, IBM Watson, and ElevenLabs.
Pydantic.ai: Python agent framework from Pydantic team: Inspired by FastAPI’s success, the framework (in early beta) supports multiple AI models (OpenAI, Anthropic, Gemini, etc.), real-time debugging via Pydantic Logfire, and more.
Python 3.14 Lands A New Interpreter With 3~30% Faster Python Code: alpha 5 is slated for release today, and word is, Python may be receiving a new interpreter with a 9-15% speedup on PyPerformance benchmarks.
💼Case Studies and Experiments🔬
Let's compile Python 1.0: Details the process of compiling Python 1.0 using podman and an old Debian container and reveals that despite its age, 1.0 had high-level data structures, process control, file handling, and more.
How I Built a Deep Learning Library from Scratch Using Only Python, NumPy & Math: Explains the motivation, abstraction layers, and technical design, and delves into comparisons with PyTorch, covering key components like tensors, autograd, neural network modules, and optimizers.
📊Analysis
WebAssembly and Python Ecosystem: Explores the current state of Python in WASM, its challenges, available tools, and performance comparisons with Rust, Go, and Docker for serverless computing.
Data Analysis Showdown: Comparing SQL, Python, and esProc SPL: Compares SQL, Python, and esProc SPL for various data analysis tasks, including session counting, player scoring, and user retention.
🎓 Tutorials and Guides 🤓
Choose Your Fighter • Let's Play (#1 in Inheritance vs Composition Pair): Provides a step-by-step tutorial on building a simple shooting game using Python's
turtlemodule, touching on OOP concepts, particularly inheritance.From Scratch to Masterpiece: The VAE’s Journey to Generate Stunning Images: Covers key VAE components—encoder, decoder, reparameterization trick, and loss function—and demonstrates how to train a VAE on MNIST to generate synthetic images.
Installing and using DeepSeek AI on a Linux system: Covers CUDA setup, Ollama installation, model download, Chatbox integration, and Python scripting, highlighting the advantages of running AI models offline.
Build Your Own DeepSeek-R1 ChatBot That Can Search Web: Covers Ollama installation, DeepSeek model setup, Docker-based SearXNG search integration, and Gradio-based UI creation, enabling offline AI interactions with real-time web augmentation.
Data Analysis with Python Pandas and Matplotlib (Advanced): Covers using Python, Pandas, and Matplotlib, covering data manipulation, importing CSV files, filtering, grouping, and visualization.
Django PDF Actions: How to Export PDF from Django Admin: Introduces a package that simplifies exporting data to PDFs from Django Admin, addressing challenges like multilingual support, layout consistency, and styling.
Elisp Cheatsheet for Python Programmers: Maps common Python constructs to their Elisp equivalents, covering collections, looping, file I/O, string operations, and data structures like lists, vectors, and hash tables.
🔑Best Practices and Advice🔏
The One About the £5 Note and the Trip to the Coffee Shop • The Difference Between `is` and `==` in Python: Explains how Python handles equality and identity, when to use
isvs.==, and how to define custom equality rules in classes using__eq__().The Best Pre-Built Toolkits for AI Agents: Explores toolkits such as CrewAI, LangChain, Agno, and Vercel AI SDK, which allow developers to extend AI agent capabilities.
LangChain vs LlamaIndex: designing RAG and choosing the right framework for your project: Demonstrates side-by-side implementations of a chatbot using both frameworks, integrating vector databases (Qdrant), OpenAI embeddings, and PDF processing.
Permutation Generation in PyTorch on GPU: Statistic Based Decision Rule for
randpermvs.argsortandrand: Analyzes the trade-offs between torch.randperm() and torch.argsort(torch.rand()) and introduces a statistical decision rule to determine when batching with argsort(rand()) is acceptable.Stop Creating Bad DAGs — Optimize Your Airflow Environment By Improving Your Python Code: Covers best practices like limiting top-level code and avoiding XComs and Variables, and introduces airflow-parse-bench, an open-source tool for measuring and comparing DAG parse times.
🔍From the Cutting Edge: HintEval: A Comprehensive Framework for Hint Generation and
Evaluation for Questions💥
In "HintEval: A Comprehensive Framework for Hint Generation and
Evaluation for Questions," Mozafari et al. introduce a Python library for hint generation and evaluation in question-answering tasks. The framework consolidates scattered resources and provides a unified toolkit for developing and assessing hints.
Context
The integration of LLMs in Information Retrieval (IR) and Natural Language Processing (NLP) has improved information access but this can hinder critical thinking. Hint Generation mitigates this by guiding users towards answers rather than providing them outright, while Hint Evaluation ensures hints remain effective without revealing answers.
Existing datasets and tools for hint research are fragmented and often incompatible, making comparisons difficult. HintEval addresses this by integrating multiple datasets, hint generation methods, and evaluation metrics into a single framework.
Key Features of HintEval
Access to preprocessed datasets: Provides a collection of preprocessed datasets, including TriviaHG, WikiHint, HintQA, and KG-Hint, which are designed for fact-based question answering.
Support for two hint generation models: Includes an Answer-Aware model, which generates hints based on a known answer, and an Answer-Agnostic model, which generates hints without requiring an answer.
Comprehensive hint evaluation system: Includes five evaluation metrics—relevance, readability, convergence, familiarity, and answer leakage—to ensure hints remain useful, clear, and non-revealing.
Integration with advanced language models: Supports state-of-the-art LLMs such as GPT-4, LLaMA, Gemini, and others, allowing researchers to experiment with different hint-generation techniques.
Freely available and open-source: Accessible on GitHub and PyPI, with detailed documentation and example implementations to facilitate ease of use.
What This Means for You
HintEval is useful for researchers, developers, and educators working with AI-driven question-answering systems. Researchers can use it to test and compare models, developers can integrate smart hints into their applications, and educators can create interactive learning experiences that encourage critical thinking.
Examining the Details
HintEval simplifies working with hints by offering a structured approach to generating, evaluating, and testing them. It allows users to load preprocessed datasets or create custom ones, ensuring flexibility across different research needs. The framework also makes it easy to run hint evaluations at scale, with options to extend its capabilities using custom models and methods. Designed to work locally or in the cloud, it integrates smoothly with modern AI workflows, making it adaptable for a range of NLP and machine learning applications.
You can learn more by reading the entire paper or accessing the library on GitHub.
And that’s a wrap.
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