Grammar, punctuation, and style errors a dictionary lookup can't see: LanguageTool is open-source proofreading powered by a Java rule engine covering English, German, Spanish, French, Portuguese, Dutch, and 25+ other languages. Self-hosting the HTTP server is how you get Grammarly-class checking without sending every sentence you write to a third party - a real concern when the text being proofread is confidential email, legal drafts, or unreleased documentation. Your instance exposes the standard /v2/check API, so the official ecosystem plugs straight in: browser extensions for Chrome and Firefox accept a custom server URL, and integrations exist for VS Code, LibreOffice, Obsidian, Vim, Emacs, and many editors. Notably, self-hosting restores free browser-extension checking that the hosted service moved behind a premium subscription - your server, no character limits, no paywall. Detection quality is tunable: optional n-gram datasets (multi-gigabyte language models for en, de, es, fr, nl) teach the engine word-order and confusion-pair errors like there/their and brakes/breaks, and a fastText model improves automatic language identification. Everything runs offline once models are downloaded. The core is LGPL, the API is documented with Swagger, and rules are community- maintained and constantly expanding.

Machine translation with no Google, no Azure, no per-character billing, and no text leaving your infrastructure: LibreTranslate is a free, open-source translation API that runs entirely on your own server. The engine underneath is Argos Translate, which runs OpenNMT neural models with SentencePiece tokenization and Stanza sentence-boundary detection, all offline. Models install as portable .argosmodel packages covering dozens of languages - English, Spanish, French, German, Chinese, Japanese, Russian, Arabic, Hindi, Portuguese, and many more - and Argos handles automatic pivoting: with es-to-en and en-to-fr installed, it chains them to translate es-to-fr without a direct model. The API is a straightforward HTTP POST to /translate with source and target language codes, returning JSON - simple enough that the ecosystem has clients in every major language and integrations across tools like Weblate and Mastodon. Beyond plain text it translates HTML while preserving markup and handles whole file uploads (documents in, translated documents out), plus automatic language detection when the source is unknown. A clean bundled web UI serves interactive translation for end users, and optional API keys with rate limits control access. AGPL-licensed and trainable with custom models, it is the standard answer when translation must be private, unmetered, and self-contained - GDPR-sensitive text never touches a third party.

Doccano is a text annotation platforms for building machine learning training datasets. The web-based interface supports text classification for sentiment analysis and document categorization, sequence labeling for named entity recognition with overlapping entity support and relation extraction between labeled spans, and sequence-to-sequence annotation for text summarization and machine translation pairs. Collaborative annotation enables multiple annotators to work on the same project simultaneously with per-user progress tracking, annotation guidelines, example assignment to specific members, and filtering by assignee. Auto-labeling integrates with external machine learning model APIs through configurable request and response mapping templates, allowing pre-annotation that annotators can review and correct. Data import accepts plain text, JSONL, CoNLL, and Excel formats, while export produces JSONL and CoNLL datasets compatible with spaCy, Hugging Face Transformers, PaddleNLP, and other training frameworks through the doccano-transformer library. The Django backend with Django REST Framework exposes a complete RESTful API for programmatic project creation, dataset management, and annotation retrieval via the official doccano-client Python library. Celery handles background tasks including dataset import and export processing with Flower providing task monitoring. One-click deployment supports AWS CloudFormation and Heroku alongside Docker Compose for self-hosted environments. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

OpenLLM serves any large language model as an OpenAI-compatible API endpoint from a single CLI command, handling model download, backend selection, quantization, and port binding automatically. It supports the full spectrum of popular models including Llama 3.3, Qwen2.5, DeepSeek, Mistral, and Phi3, choosing between vLLM and PyTorch inference backends based on hardware capabilities. When vLLM is available, continuous batching with PagedAttention achieves up to 23x throughput improvement over naive serving, while GPTQ and bitsandbytes quantization reduces memory requirements for GPU-constrained deployments. The server exposes a RESTful API on port 3000 with full OpenAI client library compatibility, enabling drop-in replacement for commercial providers in any application using the standard chat completions format. A built-in web chat UI at the /chat endpoint provides immediate interactive testing without external clients. Custom model repositories allow teams to maintain private catalogs of fine-tuned models alongside the default repository that tracks the latest releases. Deployment workflows generate production-ready Docker images automatically, with Kubernetes manifest support for orchestrated scaling. Native integration with LangChain and LlamaIndex supports RAG pipelines, Transformers Agents enables tool-calling workflows, and HuggingFace Hub handles model discovery. Server-Sent Events enable real-time token streaming across all API endpoints. Backed by BentoML's production ML infrastructure. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

What Nitter was to Twitter and Invidious is to YouTube, Lingva Translate is to Google Translate: a privacy front-end delivering the service's full capability while cutting Google out of the loop between you and your text. Built on Next.js with TypeScript and Chakra UI, it uses the purpose-built Lingva Scraper to fetch translations from Google Translate without your browser ever touching a Google-related service - no cookies, no tracking, no account, while retaining what makes Google Translate hard to give up: 100+ languages with the translation quality of Google's production models, unlike offline engines that trade privacy for accuracy. The clean interface covers automatic source-language detection, text-to-speech audio playback for pronunciations, definitions and examples, and light/dark themes. For developers, every instance doubles as a translation API: a RESTful endpoint at /api/v1/:source/:target/:query returns JSON translations, an audio endpoint serves TTS buffers, and a full GraphQL API at /api/graphql exposes translations, audio, and language lists for richer integrations - all unmetered on your own instance. Deployment is a single stateless container with one environment variable for the site domain; defaults for theme and language pair are configurable. GPL-licensed, and popular as the translation backend for privacy-respecting apps.