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AI · alternatives · 2026

24 alternatives to Building an LLM from Scratch in TypeScript

The from-scratch guide to building large language models in NodeJS.

Building an LLM from Scratch in TypeScript is a guide for learning how large language models work by constructing one from the ground up using Node.js and TypeScript. The book covers each component sequentially… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

  1. 1

    Built a ~9M param LLM from scratch to understand how they actually work. Vanilla transformer, 60K synthetic conversations, ~130 lines of PyTorch. Trains in 5 min on a free Colab T4. The fish thinks the meaning of life is food. Fork it and swap the personality for your own character.

    Apr 2026 · github.com · its alternatives →

  2. 2

    Outlines is a Python library that focuses on text generation with large language models. Brandon and I are not LLM experts and started the project a few months ago because we wanted to understand better how the generation process works. Our original background is probabilistic, relational and symbolic programming. Recently we came up with a fast way to generate text that matches a regex (https://blog.normalcomputing.ai/posts/2023-07-27-regex-guide...). The basic idea is simple: regular expressions have an equivalent Deterministic-Finite Automaton (DFA) representation. We…

    2023 · github.com · its alternatives →

  3. 3
    LangSmith▲350

    Build and deploy LLM applications with confidence

    2023 · langchain.com · its alternatives →

  4. 4HL

    All content is based on Andrej Karpathy's "Intro to Large Language Models" lecture (youtube.com/watch?v=7xTGNNLPyMI). I downloaded the transcript and used Claude Code to generate the entire interactive site from it — single HTML file. I find it useful to revisit this content time to time.

    Apr 2026 · ynarwal.github.io · its alternatives →

  5. 5PT
  6. 6

    G'day, HN! I'm one of the maintainers of `llm`. I've been working alongside a trusty group of contributors to bring this project to life, and we're now at a point where we're ready to share it with the world. Large language models (LLMs) are taking the computing world by storm due to their emergent abilities that allow them to perform a wide variety of tasks, including translation, summarization, code generation, and even some degree of reasoning. However, the ecosystem around LLMs is still in its infancy, and it can be difficult to get started with these models. `llm` is a one-stop shop for…

    2023 · github.com · its alternatives →

  7. 7
    Llama 2▲263

    The next generation of Meta's open source LLM

    2023 · developer.meta.com · its alternatives →

  8. 8

    We've been building data pipelines that scrape websites and extract structured data for a while now. If you've done this, you know the drill: you write CSS selectors, the site changes its layout, everything breaks at 2am, and you spend your morning rewriting parsers. LLMs seemed like the obvious fix — just throw the HTML at GPT and ask for JSON. Except in practice, it's more painful than that: - Raw HTML is full of nav bars, footers, and tracking junk that eats your token budget. A typical product page is 80% noise. - LLMs return malformed JSON more often than you'd expect, especially with…

    Mar 2026 · github.com · its alternatives →

  9. 9

    Hey HN! We just released a new library for building LLM-powered applications: @axflow/models. It is part of a larger suite of libraries we're developing for TypeScript developers working with generative AI. This library provides the simplest APIs for 1) invoking the most popular LLM and embedding models (openai, anthropic, cohere, huggingface, etc.) 2) streaming LLM responses to clients, including augmenting the streams with additional arbitrary data and 3) building client-side applications with React hooks. @axflow/models has zero dependencies and is built using only the…

    2023 · docs.axflow.dev · its alternatives →

  10. 10

    https://medium.com/@theaniketgiri/three-months-ago-i-wanted-...

    Oct 2025 · github.com · its alternatives →

  11. 11

    Turn any webpage into structured data using LLMs. Contribute to mishushakov/llm-scraper development by creating an account on GitHub.

    2024 · github.com · its alternatives →

  12. 12
    TypeORM▲81

    Node.js ORM written in TypeScript

    2017 · github.com · its alternatives →

  13. 13

    An educational Rust project for exporting and running inference on Qwen3 LLM family - reinterpretcat/qwen3-rs

    2025 · github.com · its alternatives →

  14. 14IL
  15. 15

    Hi HN - Marcello and Vaibhav here. We built smolmodels to experiment with using LLMs for ML development. It's a fully open-source library that generates complete model training and inference code from natural language descriptions. It combines graph search with LLM code generation to find a model that gives as good predictions as possible. The core idea is that LLMs are overkill for a lot of predictive tasks. Smolmodels automates the trial-and-error process of finding the right model architecture and training approach, letting you build small, specialised models. You can either provide your…

    2025 · github.com · its alternatives →

  16. 16

    Hi HN! I'm Inacio, the author of REASON. I've been tinkering with LLMs since the GPT-3 API's release May 2020. Seeing the emergence of many frameworks in the last 15 months has been exciting. While many understandably focused on launching quick, I found myself intrigued by a different question: what would a thoughtful, first-principles approach look like? This curiosity led to a personal project, RΞASON, a minimalistic open-source TypeScript framework. It's been a couple of months of digging deep and learning a ton, and I'm eager to share it and grow it with insights from all of you. In the…

    2023 · tryreason.dev · its alternatives →

  17. 17

    Build tools for LLMs in Rust using Model Context Protocol - avahowell/offeryn

    2025 · github.com · its alternatives →

  18. 18

    JSON AST > Clojure. Contribute to quadracollision/llmisp development by creating an account on GitHub.

    May 2026 · github.com · its alternatives →

  19. 19

    A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance in Text-to-SQL - eosphoros-ai/DB-GPT-Hub

    2023 · github.com · its alternatives →

  20. 20

    Hey HN, I wanted to share a UI toolkit project I’ve been working on recently, born out of how difficult I found it to build a great UX on top of LLMs, and keep application state in sync. I’ve built: - A React/JS front-end library for conversational interfaces, which makes it super easy to bootstrap AI assistants and ChatGPT style UX: https://github.com/nlkitai/nlux - A set of adapters that simplify integration with AI backends such as LangServe and HuggingFace The library is highly configurable, easy to theme, supports markdown streaming (that was tough to get…

    2024 · github.com · its alternatives →

  21. 21

    Efficient and general syntactical decoding for Large Language Models - structuredllm/syncode

    2024 · github.com · its alternatives →

  22. 22

    I've seen a lot of comments about how complex frameworks like LangChain can be. Over the holidays, I wanted to see how minimal an LLM framework could get if we stripped away everything non-essential. The result is an LLM framework in just 100 lines of code. These 100 lines capture what I see as the core abstraction of most LLM frameworks: a nested directed graph that breaks down tasks into multiple LLM steps, with branching and recursion to enable agent-like decision-making. From there, you can layer on more advanced features like agents, RAG, task decomposition, and more. I’ve intentionally…

    2025 · github.com · its alternatives →

  23. 23

    We were frustrated with dataset generation DX: ad‑hoc scripts and JSON templates break as flows branch, tool‑calls drift, and reproducibility is hard. So we built Torque: a schema‑first, declarative, fully typesafe DSL to generate conversational datasets. What it is: Declarative DSL - Compose conversations like React components Fully Typesafe - Zod schemas with complete type inference Provider Agnostic - Generate with any AI SDK provider (OpenAI, Anthropic, DeepSeek, vLLM, LLaMA.cpp etc.) AI-Powered Content - Generate realistic varied datasets automatically without complicated scripts Faker…

    Nov 2025 · github.com · its alternatives →

  24. 24

    While working with LLMs for structured web data extraction, we saw issues with invalid JSON and broken links in the output. This led me to build a library focused on robust extraction and enrichment: - Clean HTML conversion: transforms HTML into LLM-friendly markdown with an option to extract just the main content - LLM structured output: Uses Gemini 2.5 flash or GPT-4o mini to balance accuracy and cost. Can also also use custom prompt - JSON sanitization: If the LLM structured output fails or doesn't fully match your schema, a sanitization process attempts to recover and fix the data,…

    2025 · github.com · its alternatives →

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