AI
Models, runtimes, agents and the tooling around machine learning.
- AI coding agents and assistantsAgents that read a codebase, edit files and run commands from a prompt, and completions and chat inside the editor, backed by a hosted or a local model.23 →
- AI chat interfacesApps to chat with language models, whether the model runs locally or behind an API.18 →
- Local model runtimesRun open-weight language models on your own hardware, behind a local API.18 →
- Speech to textTranscribe recordings, meetings and dictation with speech recognition models on your own hardware.15 →
- Document Q&AAsk questions about your own documents and sources through retrieval-augmented generation, with cited answers.9 →
- LLM observabilityTrace, evaluate and monitor LLM applications.9 →
- Text to speechGenerate speech from text, with voice cloning on some models, on your own hardware.9 →
- LLM app buildersBuild chatbots, agents and RAG workflows in a visual editor and serve them behind an API.8 →
- Model fine-tuningFine-tune language and diffusion models on your own data and GPUs.8 →
- AI browser agentsLet a language model drive a web browser to navigate sites, fill forms and extract data.7 →
- Experiment trackingLog the metrics, parameters and artefacts of training runs and compare them in a dashboard.7 →
- Image generationGenerate images from text prompts with diffusion models running on your own hardware.7 →
- LLM evaluationTest prompts, models and agents against datasets, assertions and red-team probes, locally or in CI.7 →
- Machine translationTranslate text between languages, through an API or a web UI.7 →
- Autonomous AI agentsAgents that plan and carry out multi-step tasks on their own, browsing, running code and calling tools.6 →
- Data labelingAnnotate images, video, text and audio to build training and evaluation datasets.6 →
- AI app buildersGenerate and edit a web app or interface from a chat prompt, with a live preview.5 →
- AI search enginesAnswer questions from live web searches with a language model, citing the pages it read.5 →
- AutoMLTrain, tune and compare models automatically from tabular, text or image data.5 →
- Document parsingConvert PDFs, office files and scans into Markdown or JSON that language models and pipelines can read.5 →
- LLM command-line toolsPrompt language models from a terminal and pipe their answers into other commands.5 →
- ML pipelinesDefine, run and track machine learning pipelines from data preparation to deployment.5 →
- OCRRecognise text in scanned documents and images.5 →
- Writing assistantsCheck grammar, spelling and style as you type.5 →
- LLM gatewaysOne OpenAI-compatible API in front of many model providers, with keys, budgets, fallbacks and usage logs.4 →
- Machine learning frameworksLibraries for building and training machine learning models.4 →
- Model servingServe trained machine learning models behind an API, with batching, scaling and versioning.4 →