AI · alternatives · 2026

24 alternatives to An all-in-one blog for learning Large Language Models (LLMs)

An all-in-one blog for learning LLM ins and outs: tokenize, attention, PE, and more Project I've been diving deep into the internals of Large Language Models (LLMs) and started documenting my findings. My blog covers topics like: Tokenization techniques (e.g., BBPE) Attention mechanism (e.g. MHA, MQA, MLA) Positional encoding and extrapolation (e.g. RoPE, NTK-aware interpolation, YaRN) Architecture details of models like QWen, LLaMA Training methods including SFT and Reinforcement Learning If you're interested in the nuts and bolts of LLMs, feel free to check it out:…

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

1
Llama 3.2 Interpretability with Sparse Autoencoders▲579
I spent a lot of time and money on this rather big side project of mine that attempts to replicate the mechanistic interpretability research on proprietary LLMs that was quite popular this year and produced great research papers by Anthropic [1], OpenAI [2] and Deepmind [3]. I am quite proud of this project and since I consider myself the target audience for HackerNews did I think that maybe some of you would appreciate this open research replication as well. Happy to answer any questions or face any feedback. Cheers [1]…
2024 · github.com · its alternatives →
2VI
Visual intuitive explanations of LLM concepts (LLM University)▲303
Hi HN, We've just published a lot of original, visual, and intuitive explanations of concepts to introduce people to large language models. It's available for free with no sign-up needed and it includes text articles, some video explanations, and code examples/notebooks as well. And we're available to answer your questions in a dedicated Discord channel. You can find it here: https://llm.university/ Having written https://jalammar.github.io/illustrated-transformer/, I've been thinking about these topics and how best to communicate them for half a…
2023 · its alternatives →
3
2000 Large Language Models (LLM) Prompts▲106
Unlock your knowledge with 2000 LLM prompts
2023 · miguelanticonam.gumroad.com · its alternatives →
4
I built a tiny LLM to demystify how language models work▲915
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 →
5HL
How LLMs Work – Interactive visual guide based on Karpathy's lecture▲245
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 →
6
Countless.dev – A website to compare every AI model: LLMs, TTSs, STTs▲361
Compare AI models easily! All providers in one place. Find the best LLM for your needs with our comprehensive pricing calculator and feature comparison tool. OpenAI, Anthropic, Google, and more.
2024 · countless.dev · its alternatives →
7
LLM Explorer▲152
Find your best LLM for a local inference
2023 · its alternatives →
8
Fine-grained stylistic control of LLMs using model arithmetic▲85
We developed a new framework that enables flexible control of generated text in language models. By combining several models and/or system prompts in one mathematical formula, it lets you tweak your style and combine model outputs with ease. A handy tool for those working with LLMs, looking for more fine-grained control of stylistic output. More details in our paper: https://arxiv.org/abs/2311.14479. Feedback and potential applications are welcome.
2023 · github.com · its alternatives →
9
LLM, a Rust Crate/CLI for CPU Inference of LLMs (LLaMA, GPT-NeoX, etc.)▲45
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 →
10
LLM Thought Visualization▲24
To know what models don't say out loud. Contribute to ninjahawk/Subtext development by creating an account on GitHub.
Jul 2026 · github.com · its alternatives →
11
finetune LLMs via the Finetuning Hub▲80
Hi HN community, I have been working on benchmarking publicly available LLMs these past couple of weeks. More precisely, I am interested on the finetuning piece since a lot of businesses are starting to entertain the idea of self-hosting LLMs trained on their proprietary data rather than relying on third party APIs. To this point, I am tracking the following 4 pillars of evaluation that businesses are typically look into: - Performance - Time to train an LLM - Cost to train an LLM - Inference (throughput / latency / cost per token) For each LLM, my aim is to benchmark them for…
2023 · github.com · its alternatives →
12
Create-LLM – Train your own LLM in 60 seconds▲54
https://medium.com/@theaniketgiri/three-months-ago-i-wanted-...
Oct 2025 · github.com · its alternatives →
13
LLMFlows – LangChain alternative for explicit and transparent apps▲49
Hi HN! Over the last several weekends, I've been building LLMFlows as an alternative to langchain. There's been a lot of discussion on the shortcomings of langchain in the past few weeks, but when I first tried it in March, I thought there are 3 main problems: 1. Too many abstractions 2. Hidden prompts and opinionated logic in chains which makes it hard to customize 3. Hard to debug This inspired me to try and build a framework that solves these 3 issues, and therefore I started building LLFlows with the "philosophy" of being "simple, explicit, and transparent." A few weekends later, I think…
2023 · github.com · its alternatives →
14
LLM-based Toolset for Language Learning▲9
Hello everyone. I built an AI-based toolset to help me with language learning. I wanted to be able to easily generate very specific study content and get rapid feedback on my writing. Unlike most language apps, it doesn’t actually try to teach you a language. Instead, it’s a collection of tools for people at an intermediate level who already have a learning process It’s particularly great for Anki users. There a demo video on the login page, and I set up anonymous auth for people who want to test it without creating an account. Feedback and bug reports welcome.
2025 · drillapp.xyz · its alternatives →
15
Building an LLM from Scratch in TypeScript▲33
The from-scratch guide to building large language models in NodeJS.
9d ago · book.humanexperiences.io · its alternatives →
16AS
A surprisingly effective way to predict token importance in LLM prompts▲14
We explored a novel method to gauge the significance of tokens in prompts given to large language models, without needing direct model access. Essentially, we just did an ablation study on the prompt using cosine similarity of the embeddings as the measure. We got surprisingly promising results when comparing this really simple approach to integrated gradients. Curious to hear thoughts from the community!
2023 · heatmap.demos.watchful.io · its alternatives →
17
LLMs following grammar for general-purpose PLs▲11
Efficient and general syntactical decoding for Large Language Models - structuredllm/syncode
2024 · github.com · its alternatives →
18
LLM Explained▲6
Simplifies artificial intelligence and LLM for everyone.
2025 · llmexplained.netlify.app · its alternatives →
19
I trained a 65B LLM on my texts to talk to myself (details inside)▲13
I trained the 65b model on my texts so I can talk to myself. It's pretty useless as an assistant, and will only do stuff you convince it to, but I guess it's technically uncensored? I'll leave it up for a bit if you want to chat with it. I posted this to Reddit and had several hundred people talking to it. Salient points from that discussion: LLAMA 1 65b Rank 128 5 epochs Batch size 1, 256 cutoff Trained in the Oobabooga suite using bitsandbytes 4-bit quantization for the lora Loss around 1.5 seems to give the most coherent results Trained on raw text dumps that is then parsed by a crappy…
2023 · airic.serveo.net · its alternatives →
20
LLaMA Tutorial▲7
Llama学习平台
2025 · its alternatives →
21
LLM Alignment Template – Aligning Language Models with Human Feedback▲9
Hey Hacker News! I've been working on an open-source project called LLM Alignment Template, a comprehensive toolkit designed to help researchers, developers, and data scientists align large language models (LLMs) with human values using Reinforcement Learning from Human Feedback (RLHF). What the project does: Interactive Web Interface: Easily train models, visualize alignment metrics, and manage alignment with an accessible UI. Training with RLHF: Align models effectively to human preferences using feedback loops. Explainability: Built-in dashboards to help understand model behavior using…
2024 · github.com · its alternatives →
22
A 100-Line LLM Framework▲9
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
HelpingAI▲22
Hey HN, We’re the team behind HelpingAI, and we’ve been experimenting with ways to make LLM reasoning faster and more efficient, especially for complex tasks like math problems, coding questions, and logic puzzles. During a fine-tuning session of our older reasoning model, a bug accidentally introduced a think tag mid-response. Rather than break the model, it created a chain-of-thought style intermediate step — and the model got better. That bug inspired us to explore what we now call Intermediate Reasoning. We scaled this up by fine-tuning Qwen3-14B on 3 trillion tokens of…
2025 · helpingai.co · its alternatives →
24
Aiaiai.guide: Plain-English mental model for LLM apps, tools and agents▲7
I’ve been building LLM tooling for a small VC fund and found myself explaining the same mental model over and over to non-technical people around me: how a stateless LLM becomes a chatbot, how tool use works, what an agent is mechanically, and why context windows shape all of it. I never found a guide that covered that full chain at the level I wanted, so I wrote one. It’s nine short chapters, each building on the last. Deliberately simplified: the goal is a useful mental model, not a textbook. Feedback, corrections, and contributions welcome: github.com/ymyke/aiaiai
Apr 2026 · aiaiai.guide · its alternatives →

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