Alternatives
Products that do what Swiss Army Llama does
This project originally started out with a focus on easily generating embeddings from Llama2 and other llama_cpp (gguf) models and storing them in a database, all exposed via a convenient REST api (hence why it was originally called the not-very-catchy name of "llama_embeddings_fastapi_service" when I submitted it a few weeks ago). But since then, I've added a lot more functionality: 1) New endpoint for generating text completions (including specifying custom grammars, like JSON). 2) Get all the embeddings for an entire document--can be any kind of document (plaintext, PDFs, doc/.docx,…
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Frustrated with the apparent lack of tools for tagging and describing images locally, I built a quick and dirty little tool. You start it up, start up llama-server, and point it at a directory of photos. It scans through them, captioning them one at a time, and provides the captions and tags in an editable interface for you. When you're happy with them, you can save them, which writes them to the exif metadata of the image, and moves onto the next one
Dec 2025 · github.com
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We build LlamaExtract, a tool that allow you to automatically extract a data model from a collection of documents, and then reusing this datamodels (JSON Schema) to extract data from documents. Available as a Python library and as an API. Announcement blog: https://www.llamaindex.ai/blog/introducing-llamaextract-beta...
2024 · github.com
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How It Works - Offline Indexing: Docs are processed and embedded using the GTE-small model at build time. Browser-Based Magic: - SQLite database (stored in the browser) for vector search. - Local embedding model for query processing. - Local LLaMA model for response generation using WebLLM. - Everything Happens Locally: No data leaves the user’s device. Key Benefits - No API Costs: Everything runs in the browser—zero backend expenses. - Unlimited Chats: No rate limits or usage restrictions. - Privacy-First: Your data stays on your device, always. You can find the code here:…
2024 · docs.akiradocs.ai
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After fine-tuning GPT for a personal project, I realized how tedious it is to write plain text in a massive JSON file. That's why I built this app for my own use, and I want to see if others could benefit from a tool like this as well ;)
2024 · finetuna-ui.com
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We built a browser extension (Chrome + Firefox) that captures the runtime DOM and exports it as JSON. Not the pre-render source (HTML/CSS/JS, templates, bundles) and not a screenshot — but the live, post-render state the browser is actually displaying: - visibility/hidden, disabled/required - current input values and validation/validationMessage - dataset attributes - trimmed text - stable selector paths Why: LLMs often miss or guess UI state. Screenshots are too opaque, pre-render source is too noisy. A structured snapshot gives reproducible context for debugging…
2025
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Hi HN! I wanted to share my freshly finished open-source project. It is similar to ChatGPT Code Interpreter, but the interpreter runs locally and it can use open-source models like Llama 2. It allows you to work with sensitive data without uploading it to the cloud. Either you use a local LLM (like Llama 2), or an API (like GPT-4). For the latter case, there is an approval mechanism in the UI, which separates your local data from the remote services. I would be very interested in your valuable feedback!
2023 · github.com
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We were inspired to make this project because 𝕏/Twitter recently removed titles & description from link embeds in posts. It felt like much information has been lost and we want to bring back transparency to users. Of course this is not limited to 𝕏/Twitter since you can fully control the displayed text and customize the embeds to match your brand CI. As a tech stack we used Laravel (Backend/API), Next.js (Frontend) and node workers generating the images with WebGL - provisioned via Hashi Nomad. Do you have any feedback or comments? Please let me know, I'm happy to answer…
2023 · embedx.app
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Hey HN, I built a website where you can train Llama 3.1 8b & 70b (4bit) on your data. I use unsloth in the backend and the training is done on H100s which I rent programmatically from Runpod. I'd love some feedback. If you would be interested in using it feel free to book a chat with me: cal.com/hamada/tunellama-intro Happy to give you free credits :) P.S. I'm also looking for a co-founder as I have big plans for this.
2024 · tunellama.com
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It's very early days for the project, but I wanted to share it to see if there is interest. It is the final piece of the FastAPI server-side rendering stack I started building with FastHX and htmy (the two dependencies of this project besides FastAPI). Think of it as a more powerful and convenient alternative to tools like FastHTML, powered by FastAPI (without any modifications). I hope you'll like it.
Oct 2025 · volfpeter.github.io
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Hi, I'm Ben, the co-creator of Embedbase. Embedbase lets you use OpenAI Embeddings and Pinecone seamlessly. For example, you can add Embedbase to your app and pair it with GPT3 to allow people to search using natural language (e.g. How many workouts did I complete last week?), or simply expanding your current search experience beyond full-text search (e.g. looking for "similar" documents in Notion to find other related information) Managing embeddings is uncharted territory, we needed to discover the best practices ourselves. Now we're happy to share our learnings with Embedbase. Shoot if…
2023 · embedbase.xyz
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Hey, folks here is a peek into Jujutsu. We at Poozle are working with hundreds of APIs and it has been always frustrating to 1. Search the API in the documentation or ask ChatGPT 2. Then copy it to the postman and understand/test the API 3. Generate code to integrate into the codebase We thought how about having all of this at one place. We currently fine-tuned LLM on public REST APIs to reduce hallucination and then combined it with ChatGPT and Postman. I look forward to feedback, feature requests and discussions!
2023 · loom.com
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Thesys just open-sourced their generative UI rendering engine. Interesting timing given where Google a2ui and Vercel's json-render are headed. The difference worth noting: a2ui and json-render both treat JSONL as the contract between the LLM and the renderer. Thesys is betting that's the wrong primitive. Their engine uses a code-like syntax (OpenUI Lang) instead — LLM writes it, renderer executes it. The argument is that LLMs are fundamentally better at generating code than generating structured data, so you get cleaner output and ~67% fewer tokens. The broader vision seems to be a…
Mar 2026 · openui.com
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This is a simple text editor, made using gtkmm 3 and llama.cpp, that allows you to explore the possible continuations (ranked by descending probability) that an LLM would output after each token. I was quite surprised that there didn't seem to be a tool like that out there yet, so I decided to make my own. Source is on Github (https://github.com/blackhole89/autopen), though the code is still in a very rough shape.
2024 · youtube.com
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User Embeddings lets you build user-intent AI agents, hyper-personalized semantic search, and bring up-to-date information to GenAI applications in a personalized way. Docs: https://firstbatch.gitbook.io/firstbatch-sdk/ If you are a YC company , you can get User Embeddings free for a year by signing up here: https://www.firstbatch.xyz/subscribe
2023 · userembeddings.firstbatch.xyz
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2023 · github.com
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Introducing embeds.ai: an embedding playground to compare how embedding models work on a real world use case (retrieval augmented generation for Wikipedia articles + Elad Gil's High growth handbook) A few weeks ago, Shreyan and I were looking for an embedding model to use for RAG. We eventually came across the MTEB leaderboard, but we struggled to understand the benchmark scores. We wanted a tool to test various embedding models with example queries on real-world datasets. After unsuccessfully looking for such a “playground”, we decided to just build one ourselves! We embedded HuggingFace’s…
2023 · embeds.ai
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I've built an airgapped Retrieval-Augmented Generation (RAG) system for question-answering on documents, running entirely offline with local inference. Using Llama 3, Mistral, and Gemini, this setup allows secure, private NLP on your own machine. Perfect for researchers, data scientists, and developers who need to process sensitive data without cloud dependencies. Built with Llama C++, LangChain, and Streamlit, it supports quantized models and provides a sleek UI for document processing. Check it out, contribute, or suggest new features!
2024 · github.com
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Hi HN! Last night, I live streamed myself coding this Llama 2 Agent on a Single GPU (Colab). After 6 hours it actually has some good results. How it works is it takes in your intuition (e.g. "I think x would be cool") and develops a business idea (with a name and branding colors) and a business plan. After the business plan is developed, it criticizes this plan recursively until the "Investor" prompt is satisfied with the plan. After all this it will generate the final MVP idea and pass it to a the React Engineer Agent I live coded 2 days ago…
2023 · github.com
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I made a starter template for creating Haskell web-applications using HTMX, AlpineJS, Servant, TailwindCSS and Lucid (HASTL) - I really enjoy working in this stack and I think it brings together a lot of cool technologies with the awesomeness of Haskell doing all the heavy lifting I also tried to make it "production ready" by adding build and test tools e.g. it comes with simple Make targets and with unit and integration tests that use testcontainers to spin up the database and web application to test against. It's based on the awesome servant-persistant example by Matt Parsons…
2024 · github.com
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