11 Claude skills for day trading: scan thousands of charts, track breaking news, time every entry and exit, and replay every outcome before you risk a dollar.
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- claude
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11 Claude skills for day trading: scan thousands of charts, track breaking news, time every entry and exit, and replay every outcome before you risk a dollar.
A Rust HTTP server for Python applications
Python module that makes working with XML feel like you are working with JSON
ResearchKit is an open source software framework that makes it easy to create apps for medical research or for other research projects.
Email for professionals and teams
:dash: Writing Fast Ruby :heart_eyes: -- Collect Common Ruby idioms.
Easy to use CFNetwork wrapper for HTTP requests, Objective-C, Mac OS X and iPhone
Desktop app that records your on-screen work session and uses the GitHub Copilot SDK to reconstruct it as an intent + ordered steps, then builds a reusable Skill or Automation for Microsoft Autopilots like Scout, Microsoft Copilot Cowork, or Copilot Studio.
:tea: iOS UI Automation Test Framework
Convolutional Neural Network for Text Classification in Tensorflow
Claude skills for LinkedIn. 12 Claude Code and Codex skills that write human-sounding LinkedIn posts, craft comments that get noticed, analyze your feed, and build a publishing cadence, all from your terminal. Content engineering by Creative Content Crafts. MIT.
Github.com/CryptoSignal - Trading & Technical Analysis Bot - 4,100+ stars, 1,100+ forks
comment engine
AI-powered bug bounty hunting toolkit that works with or without subscription.
TranslatedNext generation Electron build tooling based on Vite. A new generation of Electron development and build tool that supports source code protection
ZenML 🙏: One AI Platform from Pipelines to Agents. https://zenml.io.
Node's framework for interactive CLIs
Bolts is a collection of low-level libraries designed to make developing mobile apps easier.
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
Blackfriday: a markdown processor for Go
Speed up downloads by combining multiple network connections in parallel
The Penetration Testers Framework (PTF) is a way for modular support for up-to-date tools.
lakeFS - Data version control for your data lake | Git for data
A private network system that uses WireGuard under the hood.