Predict likes based on a library of attributes
ARIMA, SARIMA, SARIMAX and AutoARIMA models for time series analysis and forecasting
Predict the output of Math.random
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Predict Components for Liberfi React SDK
A TypeScript SDK to help developers interface with the Predict's protocol.
React hooks and client for prediction markets (prediction-server backend), including Polymarket and DFlow order flows
JavaScript library for building predictive user interfaces.
Predicts the input value during KeyDown or KeyPress events, before the input is actually changed
performant confetti animation in the browser
TypeScript definitions for valid-url
Local Twitter memory in SQLite for archives, DMs, likes, bookmarks, and moderation
The fastest way to layout wrapped text on a HTML5 canvas
Gradio API client
[](https://travis-ci.org/sapegin/social-likes-next) [](https://www.npmjs.com/package/social-likes-next)
Resilient scenario-based runtime optimization for AI workloads; ai.max_tokens.v1 with bundled ai-profiles model resolution, warmup tolerance, and Activix learning
Prophet's Stan model, compiled to WASM, for use with the @bsull/augurs library.
returns nonce
A localStorage wrapper for all browsers without using cookies or flash. Uses localStorage, globalStorage, and userData behavior under the hood
Toy CLI + slides for learning the DeepBook Predict protocol on Sui testnet.
visualization predict engine
Auto-resolves merge conflicts and surfaces real intent decisions for human review. Works in VS Code AND as a standalone git merge driver (any terminal). Multi-backend (Copilot, Claude, GPT, Gemini, Ollama). Never auto-commits.
Node.js FastText
spawn processes the way the npm cli likes to do
Predict silk-like proteins from its amino acid sequence.
Predict which tests are likely to fail after you’ve changed the code
A library for JRubyArt, that allows the writing of context free sketches (like context free art) in a ruby DSL. It is a bit of a toy compared to the c++ version. However you can get quite a bit of satisfaction creating an interesting graphic, and you can't always predict what you are going to get.
Wilbur is primarly a wrapper around OpenWRT Buildroot. Building a custom OpenWRT image with a custom kernel is not so difficult if done once, but as long as you need to integrate it into your infrastructure things can start to scatter. Wilbur allows resources like kernel configurations, patches, custom config files to be sticked together and managed as a single, configurable and cloneable entity. Also, Wilbur provides a layer of abstraction over Vagrant and enables repeatable, predictable builds and repeatable, predictable deployments via network boot if your device support it.
PostRunner is an application to manage FIT files such as those produced by Garmin products like the Forerunner 620 (FR620), Forerunner 25 (FR25), Fenix 3, Fenix 3HR, Fenix 5 (S and X). It allows you to import the files from the device and analyze the data. In addition to the common features like plotting pace, heart rates, elevation and other captured values it also provides a heart rate variability (HRV) and sleep analysis. It can also update satellite orbit prediction (EPO) data on the device to speed-up GPS fix times. It is an offline alternative to Garmin Connect. The software has been developed and tested on Linux but should work on other operating systems as well.
This Ruby gem leverages Machine Learning(ML) techniques to make predictions(forecasts) and classifications in various applications. It provides capabilities such as predicting next month's billing, forecasting upcoming sales orders, identifying patient's potential findings(like Diabetes), determining user approval status, classifying text, generating similarity scores, and making recommendations. It uses Python3 under the hood, powered by popular machine learning techniques including NLP(Natural Language Processing), Decision Tree, K-Nearest Neighbors and Logistic Regression, Random Forest and Linear Regression algorithms.
RCP Network stands for a neural network that uses decision tree for prediction, struct_rand for generating new data, and readlines and espeak for orating dangling modifiers at random for intentional unintentional humor. This model will be expanded to other reasoning models, like generating small amounts of dictionary data from Duck Duck Go. You can write this Saasagi subroutine automatically through .prewrite. Instructions coming soon. Igrigork: https://github.com/igrigorik/decisiontree Dejan: https://github.com/dejan/espeak-ruby
A Fuzzy Associative Memory (FAM for short) is a Fuzzy Logic tool for decision making. Fuzzy logic FAMs have a wide range of practical applications: Control systems, such as governing a fan to keep a room at the "just right" temperature; Game AI, such as imbuing bots with human-like decision-making behavior; Prediction systems, linking causes with effects. A FAM uses Fuzzy Sets to establish a set of rules that are linguistic in nature. The linguistic rules, and the fuzzy sets they contain, are defined by a human "expert" (presumably, you). The rules therefore codify intelligence and map this knowledge from the human domain to the digital.
Saferpay JSON application programming interface with a ruby API wrapper built with Net::HTTP Saferpay API is designed to have predictable, resource-oriented URLs and to use HTTP response codes to indicate API errors. Saferpay use built-in HTTP features, like HTTP authentication and HTTP verbs, which can be understood by off-the-shelf HTTP clients. JSON will be returned in all responses from the API, including errors.
Ukiryu is a platform-adaptive command execution framework that transforms CLI tools into declarative APIs. It provides the "OpenAPI" for command-line interfaces, enabling cross-platform tool integration with type safety and structured results. Key features: * Declarative YAML profiles define tool behavior, eliminating hardcoded command strings * Platform-adaptive execution across macOS, Linux, and Windows * Shell-aware command formatting for bash, zsh, fish, PowerShell, and cmd * Type-safe parameter validation with automatic coercion * Version routing support with semantic version matching (via Versionian) * Interface contracts allow multiple tools to implement the same abstract API * Structured Result objects with success/failure information instead of parsing stdout * Comprehensive error handling under Ukiryu::Errors namespace The Ukiryu ecosystem consists of: * ukiryu gem - The runtime framework * ukiryu/register - Collection of YAML tool profiles * ukiryu/schemas - JSON Schema for validation Use Ukiryu to integrate command-line tools like ImageMagick, FFmpeg, Inkscape, Ghostscript, and more into your Ruby applications with consistent, predictable interfaces.
Version 1.0.1 Update Notes: -Updated README "HOW TO RUN" -I'm not sure how to format this so it looks good on the gems website so please just see the README file. USE CASES: 1. Your friends bully you because your imaginary role playing worlds are predictable and boring. 2. You like seeing chars printed in nifty patterns. HOW TO RUN: 1. Run `super_simple_world_builder` 2. Follow the prompts EXAMPLE INPUT: Guten Tag! Welcome to Super Simple World Builder. Enter 1 to build a random world Enter 2 to build a custom world Please enter your selection (1, 2, or exit): 2 Enter the name of your world: Community-Town Enter the minimum width of the world: 15 Enter the minimum height of the world: 15 What character do you want to fill the background of your world with? (i.e. any character or single space) How many lake features do you want? 3 How many mountain features do you want? 2 How many town features do you want? 3 How many forest features do you want? 4 OUTPUT: 1. Console print out of the world map 2. A text file of the world map ACHTUNG: 1. Don't worry if the width or height entered is too small. The world will automatically enlarge to fit all features. 2. World maps look better when you enter a <space> as the character to fill the background. 3. This is a quick-and-dirty project so yolo with the specs. I added comments as a consolation prize. 4. See `feature_set.rb` to tweak the features that can be added to the world map. 5. Interestingly, menu prompts may not show up in the git bash terminal. But they do show up in Windows command prompt, so lmao. 6. Feel free to tweak the code however you like. I plan to refactor in the future to dry up some sections.