Awesome List Updates on May 31, 2024

7 awesome lists updated today.

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1. Awesome Blazor

Videos / Others

• On .NET Live: Generating sound in Blazor with Blazor.WebAudio - June 3, 2024 - Are you using audio in your Blazor applications? This week, Kristoffer Stube joins us to talk about Blazor.WebAudio, a Blazor library for playing, generating, and analyzing sound! This library and the libraries surrounding it enable developers to make rich interactive applications in a safe fashion.

• Build an AI-powered content composer in Blazor using OpenAI GPT - May 22, 2024 - Learn how to build an AI-powered content composer using GPT-3.5 Turbo and Syncfusion Blazor components. Effortlessly create content on any topic and automatically adjust its tone, format, and length—all in one place.

2. Awesome Angular

Blogs / Google Developer Experts

• ninja-squad

• Tomas Trajan

• Igor Katsuba

GraphQL / Google Developer Experts

• buoy (⭐7) - A GraphQL client for Angular built on top of Apollo.

HTTP / Google Developer Experts

• ngx-generic-rest-service (⭐0) - Wrapper for Angular services.

• @ngify/http (⭐98) - A reactive HTTP client in the form of @angular/common/http, offers the following major features: typed response objects, streamlined error handling, request and response interception.

Integrations / Google Developer Experts

• ngx-papaparse (⭐91) - Papa Parse wrapper.

Charts / Google Developer Experts

• ng-flowchart (⭐138) - A lightweight Angular Library for building drag and drop flow charts. Chart behavior and steps are customizable. Data can be exported or uploaded in json format.

Misc Components / Google Developer Experts

• @ui-router/angular (⭐351) - UI-Router for Angular: State-based routing for Angular.

• ngx-csv-parser (⭐25) - CSV Parser for Angular by Developers Hive.

3. Awesome Neovim

Search / Diagnostics

• MagicDuck/grug-far.nvim (⭐586) - Buffer-based live search and replace with full power of rg flags. Grug like!

4. Awesome Zsh Plugins

Plugins / superconsole - Windows-only

• cdh (⭐1) - Allows users to manage and navigate through a history of directories they have visited. It maintains a history file of directories and provides several commands to interact with this history.

Completions / superconsole - Windows-only

• zpacman (⭐0) - Add tab completions for zpacman.

Themes / superconsole - Windows-only

• pronto (arzezak) (⭐0) - A super simple prompt with decorators for the current directory and git information.

• pronto (jthat) (⭐1) - Simple and fast theme with git decorations and timing information.

5. Awesome Bitcoin

Open Source Wallets

• Coin Wallet

6. Awesome Developer First

Feature Flags

• Statsig - All-in-one platform spanning across analytics, feature flagging, and A/B testing. Free for up to 1m metered events per month.

7. Awesome Azure Openai Llm

LlamaIndex

• LlamaIndex Toolkits: LlamaHub: A library of data loaders for LLMs git (⭐3.4k) [Feb 2023] / LlamaIndex CLI: a command line tool to generate LlamaIndex apps ref [Nov 2023] / LlamaParse: A unique parsing tool for intricate documents git (⭐2.3k) [Feb 2024]

  High-Level Concepts

  • Query engine vs Chat engine

    1. The query engine wraps a retriever and a response synthesizer into a pipeline, that will use the query string to fetch nodes (sentences or paragraphs) from the index and then send them to the LLM (Language and Logic Model) to generate a response
    2. The chat engine is a quick and simple way to chat with the data in your index. It uses a context manager to keep track of the conversation history and generate relevant queries for the retriever. Conceptually, it is a stateful analogy of a Query Engine.

  • Storage Context vs Settings (p.k.a. Service Context)

    • Both the Storage Context and Service Context are data classes.
    1. Introduced in v0.10.0, ServiceContext is replaced to Settings object.
    2. Storage Context is responsible for the storage and retrieval of data in Llama Index, while the Service Context helps in incorporating external context to enhance the search experience.
    3. The Service Context is not directly involved in the storage or retrieval of data, but it helps in providing a more context-aware and accurate search experience.

  # The storage context container is a utility container for storing nodes, indices, and vectors.
  class StorageContext:
    docstore: BaseDocumentStore
    index_store: BaseIndexStore
    vector_store: VectorStore
    graph_store: GraphStore

  # The service context container is a utility container for LlamaIndex index and query classes.
  class ServiceContext:
    llm_predictor: BaseLLMPredictor
    prompt_helper: PromptHelper
    embed_model: BaseEmbedding
    node_parser: NodeParser
    llama_logger: LlamaLogger
    callback_manager: CallbackManager

  @dataclass
  class _Settings:
    # lazy initialization
    _llm: Optional[LLM] = None
    _embed_model: Optional[BaseEmbedding] = None
    _callback_manager: Optional[CallbackManager] = None
    _tokenizer: Optional[Callable[[str], List[Any]]] = None
    _node_parser: Optional[NodeParser] = None
    _prompt_helper: Optional[PromptHelper] = None
    _transformations: Optional[List[TransformComponent]] = None

Memory Optimization / Llama Finetuning

• CPU vs GPU vs TPU: The threads are grouped into thread blocks. Each of the thread blocks has access to a fast shared memory (SRAM). All the thread blocks can also share a large global memory. (high-bandwidth memories (HBM). HBM Bandwidth: 1.5-2.0TB/s vs SRAM Bandwidth: 19TB/s ~ 10x HBM [27 May 2024]

Other techniques and LLM patterns / Llama Finetuning

• Kolmogorov-Arnold Networks (KANs): KANs use activation functions on connections instead of nodes like Multi-Layer Perceptrons (MLPs) do. Each weight in KANs is replaced by a learnable 1D spline function. KANs’ nodes simply sum incoming signals without applying any non-linearities. git (⭐14k) [30 Apr 2024] / ref: A Beginner-friendly Introduction to Kolmogorov Arnold Networks (KAN) [19 May 2024]

Build an LLMs from scratch: picoGPT and lit-gpt / GPT series release date

• llm.c (⭐23k): LLM training in simple, raw C/CUDA [Apr 2024]
  • Reproducing GPT-2 (124M) in llm.c in 90 minutes for $20 ref (⭐23k)

Learning and Supplementary Materials / Korean

• DAIR.AI: Machine learning & NLP research (omarsar github)
  • ML Papers of The Week (⭐9.7k) [Jan 2023]

• Daily Dose of Data Science (⭐764) [Dec 2022]

• Machine learning algorithms (⭐11k): ml algorithms or implementation from scratch [Oct 2016]

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