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  1. import EasyCrypt
    keystring = "SixteenByteKey!!"
    inpstring = "Some super secret string, that I don't want you to see."
    # This is the initialisation vector/nonce. I generated it with the below code. As you
    # will need it to decrypt later on, you might want to store it and not just generate it each time
    # I just generated it like this and printed this one out to store it.
    #
    # import os
    # from binascii import hexlify, unhexlify
    # ivstring = hexlify(os.urandom(16)).decode()

    ivstring = "aba0a3bde34a03487eda3ec96d5736a8"

    crypted = EasyCrypt.encrypt_string(keystring, inpstring, ivstring)
    print(crypted)

    decrypted = EasyCrypt.decrypt_string(keystring, crypted, ivstring)
    print(decrypted)
  2. CircuitPython 8.0.0 introduces support for environment variables. Environment variables are commonly used to store “secrets” such as Wi-Fi passwords and API keys. This method does not make them secure. It only separates them from the code.

    CircuitPython uses a file called settings.toml at the drive root (no folder) as the environment. User code can access the values from the file using os.getenv(). It is recommended to save any values used repeatedly in a variable because os.getenv() will parse the settings.toml file contents on every access.
  3. Learn about the new Amazon time series model, which you can use to forecast energy usage, traffic congestion, and weather.
    2024-04-10 Tags: , , by klotz
  4. Redis Data Types. Redis data types are the data structures composed of fields and values, where fields are unique keys and values are the content associated with the key. Redis has nine native data structures, which are the basic types of values you can manipulate.
    2024-04-09 Tags: , , , , , , by klotz
  5. This tutorial introduces promptrefiner, a tool created by Amirarsalan Rajabi that uses the GPT-4 model to create perfect system prompts for local LLMs.
  6. Exploring different ways to take notes in Emacs, including Org mode, Deft, Denote, Org-roam, and Howm.
    2024-04-08 Tags: , , , , , by klotz
  7. Learn about the importance of evaluating classification models and how to use the confusion matrix and ROC curves to assess model performance. This post covers the basics of both methods, their components, calculations, and how to visualize the results using Python.
  8. This GitHub repository contains a course on Large Language Models (LLMs) with roadmaps and Colab notebooks. The course is divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. Each part covers various topics, including mathematics, Python, neural networks, instruction datasets, pre-training, supervised fine-tuning, reinforcement learning from human feedback, evaluation, quantization, new trends, running LLMs, building a vector storage, retrieval augmented generation, advanced RAG, inference optimization, and deployment.
    2024-04-08 Tags: , , by klotz

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