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docker-selenium-lambda This is minimum demo of headless chrome and selenium on container image on AWS Lambda This image goes with these versions. These are automatically updated and tested everyday. Python 3.14.8 chromium 155.0.8059.79 chromedriver 155.0.8059.79 selenium 4.51.0 Running the demo Public image is available If you want your image simpler and updated automatically, rewrite the Dockerfile with the following commands: Available tags are listed here Side Project Are you interested in Node.js or Playwright? Please check out docker-playwright-lambda If you don't want to create functions each time for each purpose, Please check out pythonista-chromeless

AI Agents for Cybersecurity This repository contains resources and materials for several courses by Omar Santos. Repository Structure The repository is structured with six comprehensive parts covering the spectrum from basic AI concepts to advanced agentic implementations: part1foundationaltopics/: Foundational examples covering chat models, embeddings (with TensorFlow Projector visualization), interactive chatbots, and integration with various model providers (OpenAI, Hugging Face, Ollama) part2prompttemplates/: Advanced prompt engineering techniques, template systems, and structured prompting strategies for cybersecurity applications part3promptchaining/: Sophisticated prompt chaining wor…

AReaL : A Large-Scale Asynchronous Reinforcement Learning System WeChat (微信) Group | AReaL is a reinforcement learning (RL) infrastructure designed to bridge foundation model training with modern agent-based applications. It was originally developed by researchers and engineers from Tsinghua IIIS and the AReaL Team at Ant Group. Built on a fully asynchronous RL training paradigm, AReaL is optimized for efficiency and scalability, making it particularly well-suited for training large-scale reasoning and agentic models. AReaL’s mission is to make building AI agents accessible, efficient, and cost-effective for a broad community of developers and researchers. Like milk tea - customizable, scal…

White Pawn [!TIP] This template is a starting point you can use for every Swift module. We offer: A Swift package, tests, and an example app Continuous integration with the Swift package workflows Automated releases with Release Please and SLSA provenance attestation An MIT license and a .gitignore Run swift TEMPLATE/configure.swift to fill in the project name and the other placeholders. TEMPLATE/Recipe.md walks through the same result starting from Xcode. What is in-scope for this template? We the people who publish reusable Swift modules, in order to hand every module a tested package, an example app, and a release, maintain this starting point. Swift 6 Module Template must remain broad—a…

Overview Speculators is a library for training speculative decoding draft models that deploy directly to LLM inference engines like vLLM. Speculative decoding is a lossless technique that speeds up LLM inference by using a smaller, faster draft model (i.e. "the speculator") to propose tokens, which are then verified by the larger base model, reducing latency without compromising output quality. The speculator intelligently drafts multiple tokens ahead of time, and the base model verifies them in a single forward pass. This approach boosts performance without sacrificing output quality, as every accepted token is guaranteed to match what the main model would have generated on its own. Specul…

Python Slack SDK The Slack platform offers several APIs to build apps. Each Slack API delivers part of the capabilities from the platform, so that you can pick just those that fit for your needs. This SDK offers a corresponding package for each of Slack’s APIs. They are small and powerful when used independently, and work seamlessly when used together, too. Comprehensive documentation on using the Slack Python can be found at https://docs.slack.dev/tools/python-slack-sdk/ Whether you're building a custom app for your team, or integrating a third party service into your Slack workflows, Slack Developer Kit for Python allows you to leverage the flexibility of Python to get your project up and…

STRML.net View Site · Age of AI edition Building Requires Node 20 or newer. Building for Production The bundles land in dist/, which is not committed. Deploying Every push to master deploys the site. The Deploy to GitHub Pages workflow in .github/workflows/deploy.yml runs npm run build and publishes the static files to GitHub Pages. Age of AI edition ai.html is a second take on the same idea: a stylesheet that types itself, but it builds a sci-fi instrument panel (specimen plot, timeline, telemetry, keyboard, an eye) as it goes. The chapters live in ai/styles.css; the instruments are in ai.js. Add ?speed=0 to the URL to fast-forward.

Node Readiness Controller A Kubernetes controller that provides fine-grained, declarative readiness for nodes. It ensures nodes only accept workloads when all required components eg: network agents, GPU drivers, storage drivers or custom health-checks, are fully ready on the node. This project implements the proposed NodeReadinessGates API (KEP 5233) as an out-of-band solution and brings it to any Kubernetes cluster. Use it to orchestrate complex bootstrap steps in your node-init workflow, enforce node health and improve workload reliability. What is Node Readiness Controller? The Node Readiness Controller extends Kubernetes' node readiness model by allowing you to define additional pre-req…