Alternatives
Products that do what AnyModal – Train Your Own Multimodal LLMs does
I’ve been working on AnyModal, a framework for integrating different data types (like images and audio) with LLMs. Existing tools felt too limited or task-specific, so I wanted something more flexible. AnyModal makes it easy to combine modalities with minimal setup—whether it’s LaTeX OCR, image captioning, or chest X-ray interpretation. You can plug in models like ViT for image inputs, project them into a token space for your LLM, and handle tasks like visual question answering or audio captioning. It’s still a work in progress, so feedback or contributions would be great. GitHub:…
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8AA
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:…
2025 · comfyai.app
- 9SC
2025 · github.com
- 10LB
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
- 11AD
Hi all, I threw together a small prototype I am calling “Notepad.ai”. A new take on UIs for interacting with LLMs. While I enjoy using LLM’s in the chat format I wanted to see what it would be like to do it in a more long form style. It let’s you write in a pretty free form, much like Window’s Notepad, but you can choose to hit ctrl+[ to analyze the text with a preset prompt of your choosing. It has a few other small features. It’s WIP and very experimental. I would appreciate any feedback or thoughts. Video: https://youtu.be/ntdlgFmSxQY Live Demo:…
2024 · github.com
- 12SC
Hey HN Community! We're excited to introduce Spine, a tool we've been developing for the past month that aims to streamline the process of building and sharing AI-driven natural language interfaces like ChatGPT for various data formats. With Spine, you can: * Upload numerous data types such as websites, PDFs, docs, PowerPoints, CSVs, audio files, YouTube videos, and more * Navigate lots of data and get in-depth referenced results with our hybrid search * Built-in Feedback: Craft accurate, personalized & adaptive experiences -- we will update the search models and eventually your LLM with the…
2023 · getspine.ai
- 13MO
Why we built it: • Claude Code: great for coding, but no video/audio support, localhost only • OpenAI SDK: single-model, no native multimedia tools • Both: no integrated DevTools for debugging agent reasoning So, we built Mix as an alternative for multimodal applications. • Native video/audio/PDF analysis tools (via Gemini for vision, Claude for reasoning) • Multi-model routing instead of single-provider lock-in • One-command Supabase setup for cloud deployment (vs localhost-only) • HTTP architecture that enables visual DevTools alongside agent workflows • Go backend: 50-80%…
Oct 2025 · github.com
- 14AG
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
- 15TA
Let's try a small experiment with LLMs that have a large context length: feed an entire book into the context window and ask it to generate a list of characters, their relationships, and physical descriptions—data that can later be used for image generation. In this repository, you can find two tools: a script that extracts data from book text using an LLM (Gemini or OpenRouter API) and an HTML/JS (D3) visualization of the character graph. An external text-to-image model can be used to generate character illustrations (a Google Colab example is provided). Explore the visualizations,…
2025 · github.com
- 16UM
Hi HN, We’ve been building memU(https://github.com/NevaMind-AI/memU), an open-source, general-purpose memory framework for AI agents. It supports dual-mode retrieval: classic RAG and LLM-based direct file reading. Most multimodal memory systems either embed everything into vectors or treat non-text data as attachments. These work, but at scale it becomes hard to explain why certain context was retrieved and what evidence it relies on. memU takes a different approach: since models reason in language, multimodal memory should converge into structured, queryable text, while…
Jan 2026 · github.com
- 17OL
Finally made something I've always wanted, using the model we built. • SOTA omni embedding model, fully local, indexes text, PDF, image, audio, and video • Swift-native app UI + mlx-swift-transformer core. No Python. • Tested on M3 Pro 18G / M3 Ultra 512G / M4 Pro 48G. All work fine. • HTTP server exposes search to local agents like OpenClaw & Hermes − Indexing still feels slow even on the latest M3 Ultra, ranging from 10K tps to 300 tps depending on file type − Fans go crazy, high power draw while indexing − Search is near-instant. Multimodal relevance is sometimes arguable, but…
Jun 2026 · hanxiao.io
- 18MM
Hi HN, we're Arnav and Adi, and we're building DataBridge - a multi-modal database built from the ground up with AI use cases in mind. We recently launched support for ColPali-style image embeddings and late-interaction retrieval. We've implemented a hamming distance version of retrieval which helps this approach scale significantly more when compared with the regular late-interaction similarity scoring. These embeddings provide a significantly better retrieval accuracy, with ColQwen achieving around an 89% average score on the ViDoRe benchmark, compared to around 67% for traditional parsing…
2025 · github.com
- 19EE
Hey everyone on HN! We recently spent the past couple of weeks building out an end-to-end platform which can plug-in multiple models (both open/closed-source) to create voice driven conversational applications. We've tried to make the process simple & concise through documentation. Feel free to try it out and provide feedback. We will be launching a dashboard in the coming week for monitoring and analytics alongwith more open source models. Let us know what you all think. (if you want to contribute, we have tons of features planned - do let us know)
2023 · github.com
- 20OS
I built an open-source tool for learning anything using AI: - Type what you want to learn - It creates a full interactive course for you Move from beginner to mastery of any subject, even complex things like Quantum Physics It has three formats: - Explanation: bite-sized lessons that you swipe like TikTok/Instagram - Practice: solve problems in real-world situations - Quiz: test your knowledge in Duolingo-like lessons - Language courses have a different format covering vocabulary, reading, and listening skills It's more hands-on than reading a long textbook or watching a video Plus, it…
May 2026 · zoonk.com
- 21JN
My startup's just released this open source tool which lets you perform multimodal search in two lines. I would love to hear what you all think about it, or if you have any use case ideas in mind. It even helps Emojipedia improve its results. read the blog here: https://medium.com/jina-ai/jina-now-the-one-liner-neural-sea...
2022 · now.jina.ai
- 22WI
I've been wondering how I could use LLMs to help me write, without taking my own voice away. I arrived at a workflow where the AI has strict instructions not to give me any text, just to give me tips, but it was clunky to see which parts of the text the critique referred to. To solve it, I made Lucid. I made it mostly for myself, but I added a "bring your own key" system for others to use it. I hope you like it!
May 2026 · writelucid.cc
- 23AF
Hey HN — I’m Gaurav, one of the founders of Captions. We work on applied AI research for talking videos. Our foundation model, Lipdub, captures how humans speak, and matches full face movement to what’s being said. The model is zero-shot and can generate videos in under a minute, without person-specific training. Building on Lipdub, we’re releasing a few APIs that can generate and translate talking videos in bulk. Here are some ways they could be used: * Translating videos with matching lip movement * Creating personalized videos that include someone’s name or company, like what’s shown in…
2024 · captions.ai
- 24IE
Hey HN, when building ML systems for industrial AI, we have learned that data inspection is critical during the ML development process. We are also big fans of the Hugging Face ecosystem. That is why we built an integration to our data exploration tool Spotlight that allows you to interactively explore Hugging Face datasets with one line of code. Spotlight lets you leverage model results such as predictions and embeddings to gain a deeper understanding in data segments and model failure modes. Currently, many many NLP, CV, Audio and multimodal datasets are supported both locally and on the…
2023 · huggingface.co
Ranked by how close each launch is in meaning, then by votes. Refine with a description →