Alternatives
Products that do what Ollama Explorer does
Find the right local AI model for your hardware instantly
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2025 · rlama.dev
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Hi HN A few folks and I have been working on this project for a couple weeks now. After previously working on the Docker project for a number of years (both on the container runtime and image registry side), the recent rise in open source language models made us think something similar needed to exist for large language models too. While not exactly the same as running linux containers, running LLMs shares quite a few of the same challenges. There are "base layers" (e.g. models like Llama 2), specific configuration to run correctly (parameters, temperature, context window sizes etc). There's…
2023 · github.com
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Mar 2026 · github.com
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Just to clarify the background a bit. This project wasn’t planned as a big standalone release at first. On January 16, Ollama added support for an Anthropic-compatible API, and I was curious how far this could be pushed in practice. I decided to try plugging local Ollama models directly into a Claude Code-style workflow and see if it would actually work end to end. Here is the release note from Ollama that made this possible: https://ollama.com/blog/claude Technically, what I do is pretty straightforward: - Detect which local models are available in Ollama. - When…
Jan 2026 · github.com
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Hey HN! When implementing an AI-powered feature for a project, we—and many people we've talked to—often reach a point where we have to choose an AI model but aren’t sure which one best fits our constraints or where to even start. Unfortunately, the advice to "just use chatgpt" is not always a good one. What if I want an open-source model? What languages does it support? What about context window size or the number of parameters? There are thousands of AI models already out there and many of them are perfect for certain problems. That’s why we’ve carved out this part of our product as a free…
2024 · app.elementera.ca
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Hey HN, Henry and Roman here - we've been building a cross-platform framework for deploying LLMs, VLMs, Embedding Models and TTS models locally on smartphones. Ollama enables deploying LLMs models locally on laptops and edge severs, Cactus enables deploying on phones. Deploying directly on phones facilitates building AI apps and agents capable of phone use without breaking privacy, supports real-time inference with no latency, we have seen personalised RAG pipelines for users and more. Apple and Google actively went into local AI models recently with the launch of Apple Foundation Frameworks…
2025 · github.com
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Hi, I’m Jakub, a solo founder based in Warsaw. I’ve been building GoModel since December with a couple of contributors. It's an open-source AI gateway that sits between your app and model providers like OpenAI, Anthropic or others. I built it for my startup to solve a few problems: - track AI usage and cost per client or team - switch models without changing app code - debug request flows more easily - reduce AI spendings with exact and semantic caching How is it different? - ~17MB docker image - LiteLLM's image is more than 44x bigger ("docker.litellm.ai/berriai/litellm:latest" ~…
Apr 2026 · github.com
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Hi HN, I'm excited to introduce OllaMan, a desktop application designed to streamline your workflow with Ollama local AI models. If you're already using Ollama, you've likely experienced its power in running large language models (LLMs) locally. However, managing models, switching between them, and engaging in conversations often requires frequent command-line interaction, which can be inefficient and less intuitive for many users. OllaMan aims to solve these pain points by providing an intuitive and elegant graphical user interface (GUI), especially tailored for macOS users, adhering to…
Sep 2025 · ollaman.com
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Hi everyone, we’re a small team, supported by Mozilla, who are working on re-imagining a UI for training, tuning and testing local LLMs. Everything is open source. If you’ve been training your own LLMs or have always wanted to, we’d love for you to play with the tool and give feedback on what the future development experience for LLM engineering could look like.
2025 · github.com
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2024 · github.com
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