Matheus Oliveira (matheus-1618)

matheus-1618

Geek Repo

Company:@AWS

Location:São Paulo

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Matheus Oliveira's starred repositories

genai-bedrock-agent-chatbot

A demo ChatBot application developed using Amazon Bedrock service's KnowledgeBase, Agent and other AWS's serveless GenAI solution.

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autoqasm

AutoQASM is an experimental module offering a new quantum-imperative programming experience in Python for developing quantum programs.

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amazon-bedrock-samples

This repository contains examples for customers to get started using the Amazon Bedrock Service. This contains examples for all available foundational models

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zstd

Zstandard - Fast real-time compression algorithm

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debugtocpp

Tool for generating C++ classes and json from PDB, DWARF and ELF symbols (Work in progress)

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aws2tf

aws2tf - automates the importing of existing AWS resources into Terraform and outputs the Terraform HCL code.

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RAGmap

A simple Streamlit application that helps visualize document chunks and queries in embedding space 🗺️🔍

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visual-search

Visual search implementation resources, including an explanatory Jupyter notebook and Amazon SageMaker and AWS DeepLens code.

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N64Recomp

Tool to statically recompile N64 games into native executables

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genai-bedrock-chatbot

GenerativeAi Chatbot using Bedrock runtime and Bedrock agents developed in CDK

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cuQuantum

Home for cuQuantum Python & NVIDIA cuQuantum SDK C++ samples

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guidance-for-media2cloud-on-aws

Guidance for Media2Cloud on AWS solution (formerly known as AWS Media2Cloud Solution) is designed to demonstrate a serverless ingest framework that can quickly setup a baseline ingest workflow for placing video assets and associated metadata under management control of an AWS customer.

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KasperskyHook

Hook system calls on Windows by using Kaspersky's hypervisor

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NN_FromScratch

This project focuses on creating a Neural Network from scratch to gain a better understanding of fundamental concepts like feed forward, gradient descent, and backpropagation. The MNIST dataset is used for testing and validation.

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conversational-ai-assistant-multi-route-chain

This GitHub repository guides you through building an advanced Conversational AI assistant using AWS services and Anthropic's Claude V2 model. It features intelligent routing to relevant functions, database querying, semantic searches, Lambda function executions, and specialized interactions.

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bedrock-access-gateway

OpenAI-Compatible RESTful APIs for Amazon Bedrock

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corenet

CoreNet: A library for training deep neural networks

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tiny-gpu

A minimal GPU design in Verilog to learn how GPUs work from the ground up

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Rust-for-Malware-Development

This repository contains my complete resources and coding practices for malware development using Rust 🦀.

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llama3

The official Meta Llama 3 GitHub site

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JavaAtividadeSalario

Atividade de reajuste salarial da materia de Programação orientada a objetos.

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amazon-braket-examples

Example notebooks that show how to apply quantum computing with Amazon Braket.

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llm.c

LLM training in simple, raw C/CUDA

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vxlang-page

protector & obfuscator & code virtualizer

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amazon-transcribe-streaming-sdk

The Amazon Transcribe Streaming SDK is an async Python SDK for converting audio into text via Amazon Transcribe.

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awsome-distributed-training

Collection of best practices, reference architectures, model training examples and utilities to train large models on AWS.

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Quantum-Supply-Chain-Manager

The quantum supply chain manager is a quantum solution for logistics problems. We use the power of quantum machine learning for product [backorder](https://www.investopedia.com/terms/b/backorder.asp) prediction and quantum optimization for finding the best route to pick those products with high demand to store them in strategic warehouses. With the first technique, our clients can be prepared for increasing the production of their products when they are in high demand. Once we have established a set of products needed during a period of time, we use our second solution, the vehicle routing problem (VRP) solution to find the optimal route for picking these products. This reduces considerably costs associated with logistics, transportation, backorders, and overstocking for our clients. In summary, these solutions will improve business in terms of client satisfaction, backorder and shipping transportation costs.

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