Resource Hub
3505 hand-picked resources for students, job seekers, career changers, founders, and AI learners — 100% free to browse with no account required.
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36 resources
A KDnuggets tutorial by Abid Ali Awan that walks through scraping thousands of pages with the Olostep Batch API in Python, downloading the results as Markdown and checking how many requests completed. Running the code requires an Olostep API key.
A KDnuggets tutorial by Bala Priya C with five beginner Python scripts for automating repetitive tasks, including organizing files, processing images and simplifying everyday workflows, with code and explanations.
Developer documentation for Olostep's /v1/batches endpoint, which submits large lists of URLs for scraping in one job and returns page content such as Markdown or HTML, with request parameters, status polling and retrieval examples. Olostep is a paid web-data API; reading the documentation is free.
A guide on how to build a stateless Model Context Protocol (MCP) server in Python.
Documentation for StrongREJECT, an open-source benchmark and Python package for evaluating LLM jailbreaks. It includes rubric-based and fine-tuned evaluators, several dozen baseline jailbreaks, and a dataset of prompts across six categories of harmful behavior, from disinformation to violence.
PrettyTable is a free, open-source Python library for drawing formatted ASCII tables in a terminal or text output. This post from Open-source Projects shows what it does and links to the GitHub repo.
Why I recommend it: Free open-source library (MIT license). The link goes to a blog post on opensourceprojects.dev; the project itself lives at github.com/prettytable/prettytable and pypi.org/project/prettytable. The blog runs on ads and sponsorships.
Hands-on tutorial (Sept. 2026) showing how to spot when the text data a live AI system sees starts to shift, using a domain classifier and centroid distance.
Why I recommend it: Free tutorial. Assumes you know Python and basic machine learning.
The fourth global online PyLadies conference, 5-7 December 2026, free and run across multiple timezones in English, Spanish, Portuguese, German, Japanese and Chinese. Tutorials, open-source sprints with project maintainers, panels on Python and career growth, open spaces and networking.
Details: Free, online and explicitly welcoming to first-time speakers and first-time open-source contributors, with no prior experience required. Runs on Discord to keep the barrier low; the sprints are the fastest honest route to a first real contribution.
Jason Brownlee's long-running tutorial site: hundreds of free, step-by-step machine learning walkthroughs in Python, organised into 'start here' guides by topic — getting set up, understanding algorithms, your first complete project, your first neural network, time series forecasting. Each tutorial is written to get you to a working result rather than a theory exam.
Why I recommend it: The tutorials and the 'start here' guides are free to read with no account. The site's business is paid ebooks, and the free ebook offer costs you an email address and an ongoing email course, so expect the marketing. A fair criticism to know going in: the tutorials are recipe-shaped, which gets you running code fast but can leave the why thin — pair them with something that explains the ideas.
Open-source SDK, Python and TypeScript, for building production AI agents you run yourself — any model, any cloud.
Why I recommend it: Free and open-source (Apache-style OSS). For builders who want an agent framework they control, not a hosted service.
Free, open-source download manager written in Python, managed entirely through a web interface. Lightweight enough to run on a home server, NAS or router, with plugins to automate repetitive downloads.
Why I recommend it: Worth it only if you already run a home server and download large files regularly. Same rule as any download tool: what you download is your responsibility, and file hosts are a common route for malware.
The Python framework behind Stanford's HELM leaderboards, released under the Apache License 2.0 (licence file read, not copied from a roundup). You install it with pip, describe a run (scenario plus model plus metrics), and it evaluates the model and produces the same structured results the public site displays, including its own local web UI for viewing them. It supports hosted model APIs and locally run open-weight models, and you can add your own scenario to test a model on your own task or data. Free to use, modify and use commercially under Apache 2.0; you pay only for whatever model API calls or compute your own runs consume.
Why I recommend it: Worth it if you need to prove a model is good enough for a specific job rather than good in general — write your own scenario with your own examples and run it. Two practical warnings: the published leaderboard runs are large and expensive to reproduce in full, so start with a single scenario and a small instance count, and if you evaluate a paid API model the token costs are yours, not Stanford's.
An open-source library of metrics and algorithms for finding and reducing unwanted bias in datasets and models, in Python and R. Free.
From the site: A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models. - Trusted-AI/AIF360
Why I recommend it: For the harm that shows up in ordinary systems long before anything dramatic does — hiring screens, lending, scoring. Measuring bias is the easy half; deciding what fair means is yours.
An open-source tool that converts PDFs, Word files and scans into clean structured text for AI use, running locally. MIT licensed.
From the site: Get your documents ready for gen AI. Contribute to docling-project/docling development by creating an account on GitHub.
Why I recommend it: The unglamorous step that makes everything else work: getting your PDFs into text without a paid converter.
An open-source Python framework for connecting AI models to your own documents and data. MIT licensed.
From the site: LlamaIndex is the document processing platform for AI - run-llama/llama_index
Why I recommend it: For when you want to build something on your own files rather than use a finished app. Expect to write code.
OpenAI's open-source speech recognition model and command-line tool, free to download and run on your own machine. MIT licensed.
From the site: Robust Speech Recognition via Large-Scale Weak Supervision - openai/whisper
Why I recommend it: The original. Slower than whisper.cpp on a laptop, but the reference version and simple to install with Python.
An open-source AI coding assistant that works in your terminal on a real git repository, making commits as it goes. Apache 2.0 licensed.
From the site: aider is AI pair programming in your terminal. Contribute to Aider-AI/aider development by creating an account on GitHub.
Why I recommend it: It edits real files and commits them, so work on a branch. The tool is free; the model you connect it to is not.
An open-source visual builder for AI workflows and agents — drag components together, then export the flow as an API. MIT licensed.
From the site: Langflow is a powerful tool for building and deploying AI-powered agents and workflows. - langflow-ai/langflow
Why I recommend it: Useful for seeing what an AI 'agent' actually is: a chain of steps you can look at, not magic.
The standard free Python distribution for data and AI work — package management, notebooks and thousands of libraries in one install.
From the site: Anaconda is the trusted foundation for AI-native development. Secure, orchestrate, and accelerate data and AI at scale, from first experiment to production.
Why I recommend it: Free for individual use. If you are learning Python for data work, this saves you a week of setup pain.
Micah Lee's book on analyzing hacked and leaked datasets, free to read in full online alongside the print edition.
From the site: Buy Hacks, Leaks, and Revelations: The Art of Analyzing Hacked and Leaked Data by Micah Lee.
Why I recommend it: The whole book is readable free on the site — a practical intro to handling large datasets safely.
A large free archive of practitioner-written tutorials and explainers on machine learning, statistics, data engineering and AI, from beginner to advanced.
Why I recommend it: Quality varies by author, but the beginner explainers are among the easiest free routes into data work.
Free, MIT-licensed Python library and documentation for applying AI to satellite and geospatial data, with tutorials, notebooks, a QGIS plugin and video walkthroughs.
From the site: A Python package for using Artificial Intelligence (AI) with geospatial data
Why I recommend it: A free, well-documented open-source project — a good portfolio path if you want to work in mapping, climate or remote sensing.
Apache 2.0 open-source search infrastructure for AI applications, supporting vector, full-text, regex and metadata search, free to run locally with optional hosted cloud.
Why I recommend it: The free local version is enough to build and demo an AI project of your own — a portfolio piece that shows you can work with retrieval, not just prompts.
The open-source Python library for static, animated and interactive charts, with free plot-type galleries, tutorials, cheat sheets and a full API reference.
Why I recommend it: If you are learning data work, start here: the example gallery lets you copy a chart that already looks like what you need and adapt it.
