Idan707/chiki
A playful Hebrew-speaking AI voice companion for the Waveshare ESP32-S3 Touch AMOLED 1.8 V2.
Learning is way of life.
Repositories
Idan707/bsg-plugin
Official Build-Ship-Grow Claude Code plugin marketplace
Idan707/Idan707
Profile README
Idan707/openclaw-lite
Lightweight OpenClaw fork — Telegram-first AI assistant with memory, skills, and agent-browser by Steel Labs. One integration, zero bloat.
Idan707/ManyMany.dev
Idan707/agents-setup
An agentic company research tool powered by LangGraph and Tavily that conducts deep diligence on companies using a multi-agent framework. It leverages Google Gemini 2.0 Flash and Chat GPT-4o-mini on the backend for inference.
Idan707/idan707.github.io
Idan707/Shlomigreen
Idan707/MLND_DogApp
Convolutional Neural Networks (CNN) project. In this project, I will learn how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, The algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.
Idan707/MLND_CapstoneProject_HelpPASSNYC
PASSNYC and its partners provide outreach services that improve the chances of students taking the SHSAT and receiving placements in these specialized high schools. The current process of identifying schools is effective, but PASSNYC could have an even greater impact with a more informed, granular approach to quantifying the potential for outreach at a given school. Proxies that have been good indicators of these types of schools include data on English Language Learners, Students with Disabilities, Students on Free/Reduced Lunch, and Students with Temporary Housing. Part of this challenge is to assess the needs of students by using publicly available data to quantify the challenges they face in taking the SHSAT. The best solutions will enable PASSNYC to identify the schools where minority and underserved students stand to gain the most from services like after school programs, test preparation, mentoring, or resources for parents.
Idan707/MLND_CustomerSegments
In this project I will apply unsupervised learning techniques on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data.
Idan707/MLND_FindingDonors
Machine Learning Engineer Nanodegree -Supervised Learning -Project: Finding Donors for CharityML
Idan707/Predicting_Movie_Revenue_with_ML-DL
Scrape data, clean it, prep it, do some EDA and than try to predict new movies revenue.