Category
Running and talking to language models, on your own hardware or someone else's.
10 products, 6 Projects: 5 open source, 0 source-available, 1 unresolved-license
Run open language models locally with one command
Ollama reduced local model hosting from an afternoon of CUDA troubleshooting to a single pull command. It handles model download, quantisation selection, GPU offloading, and memory management, and exposes an OpenAI-compatible endpoint on localhost.
One chat interface for every model you run or connect
Open WebUI is the front end that turns a local inference server into something a household or a team can actually use. It handles accounts, conversation history, document retrieval, and model switching, and speaks to both local runtimes and hosted APIs.
Inference engine that runs LLMs on almost any hardware
llama.cpp is the layer most local model tooling is built on. It is a C and C++ inference engine with no Python runtime, aggressive quantisation support, and hand-tuned kernels for a remarkably wide range of hardware.
OpenAI's assistant and the interface that set the pattern
ChatGPT is the product that made conversational assistants ordinary. Beyond the chat box it now covers file analysis, image generation, web browsing, voice, and custom assistants sharing one account.
Polished chat interface for many model providers
LobeChat is the most designed of the open chat front ends. Plugins, an assistant marketplace, voice input and output, and vision support are all present, and the interface holds up next to commercial products.
Desktop application for running language models locally
LM Studio made local models approachable for people who do not want a terminal. Browse a catalogue, download a quantisation that fits the machine, and chat, with hardware requirements explained in plain language.
AI dictation that cleans up speech as you talk
Wispr Flow is dictation that accounts for how people actually speak. False starts, filler, and self-correction are removed, and the output is punctuated text rather than a transcript.
Speech recognition that runs anywhere, with no runtime
whisper.cpp is a C and C++ port of the Whisper speech recognition model with no Python dependency, quantised weights, and hardware acceleration across Apple Silicon, CUDA, and plain CPU.
Anthropic's assistant, strongest on long documents and code
Claude is positioned around careful reasoning over long inputs. The large context window makes whole-repository and long-document work practical in a way that shorter context assistants cannot match.
Local voice dictation with AI cleanup for macOS
VoiceInk transcribes speech on the machine itself using a local Whisper model, then runs the raw transcript through a cleanup pass that removes filler and applies punctuation.
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