From 6af83592d0f0d609ace0b4efad9feaa1c46d4d99 Mon Sep 17 00:00:00 2001 From: Ettore Di Giacinto Date: Mon, 3 Aug 2026 21:11:43 +0000 Subject: [PATCH] docs: cut marketing filler from user-facing prose A no-ai-slop detect pass over all 85 docs pages and the 8 website content files. The docs came out clean on every pattern that actually got the blog post criticized on HN: zero faux-insight setups, zero unearned framing, zero ledger metaphors, zero pre-chewed numbers, zero importance puffery, zero weasel attribution, zero recap endings, zero rhetorical setups. What was left was vocabulary, so that is all this changes. Ten edits in eight files, no links or code blocks touched: - overview.md: "In today's AI landscape, privacy, control, and flexibility are paramount" and "Ready to dive in?" - architecture.md: "seamlessly integrate ... effortlessly implemented" - customize-model.md: "is utilized", "utilizes a shorthand format" - advanced/_index: "fully leverage LocalAI's capabilities beyond basic usage" - agents.md, object-detection.md, text-to-audio.md, faq.md: leverage/seamless used as filler. text-to-audio also had "before the api provide its response". Deliberately left alone: "GPU utilization", "KV utilization" and "highest-leverage knob" are the correct technical terms, not filler. The docs are reference material and read like it. They do not need the treatment the blog posts got. Co-Authored-By: Claude Opus 5 (1M context) --- docs/content/advanced/_index.en.md | 2 +- docs/content/faq.md | 2 +- docs/content/features/agents.md | 2 +- docs/content/features/object-detection.md | 2 +- docs/content/features/text-to-audio.md | 2 +- docs/content/getting-started/customize-model.md | 4 ++-- docs/content/overview.md | 4 ++-- docs/content/reference/architecture.md | 2 +- 8 files changed, 10 insertions(+), 10 deletions(-) diff --git a/docs/content/advanced/_index.en.md b/docs/content/advanced/_index.en.md index c81602ea1..f553e4aae 100644 --- a/docs/content/advanced/_index.en.md +++ b/docs/content/advanced/_index.en.md @@ -13,7 +13,7 @@ images: [] ## Overview -The **Advanced** section covers in-depth topics for users who want to fully leverage LocalAI's capabilities beyond basic usage. These pages are designed for developers, DevOps engineers, and power users who need fine-grained control over model configuration, system resources, and deployment infrastructure. +The **Advanced** section covers in-depth topics for users who want to go beyond basic usage. These pages are designed for developers, DevOps engineers, and power users who need fine-grained control over model configuration, system resources, and deployment infrastructure. ### Who Should Read This Section diff --git a/docs/content/faq.md b/docs/content/faq.md index 73623df37..4f5dbd7a3 100644 --- a/docs/content/faq.md +++ b/docs/content/faq.md @@ -72,7 +72,7 @@ See the performance section of the runtime errors reference: {{% relref "referen Yes! If the client uses OpenAI and supports setting a different base URL to send requests to, you can use the LocalAI endpoint. This allows to use this with every application that was supposed to work with OpenAI, but without changing the application! -### Can this leverage GPUs? +### Can this use GPUs? There is GPU support, see {{%relref "features/GPU-acceleration" %}}. diff --git a/docs/content/features/agents.md b/docs/content/features/agents.md index f406d0d15..88f2f8c68 100644 --- a/docs/content/features/agents.md +++ b/docs/content/features/agents.md @@ -24,7 +24,7 @@ The agent system provides: - **Autonomous agents** with configurable goals, personalities, and capabilities - **Tool/Action support** - agents can execute actions (web search, code execution, API calls, etc.) - **Knowledge base (RAG)** - per-agent collections with document upload, chunking, and semantic search -- **Skills system** - reusable skill definitions that agents can leverage, with git-based skill repositories +- **Skills system** - reusable skill definitions that agents can use, with git-based skill repositories - **SSE streaming** - real-time chat with agents via Server-Sent Events - **Import/Export** - share agent configurations as JSON files - **Agent Hub** - browse and download ready-made agents from [agenthub.localai.io](https://agenthub.localai.io) diff --git a/docs/content/features/object-detection.md b/docs/content/features/object-detection.md index 27b7258f4..d82bd72f4 100644 --- a/docs/content/features/object-detection.md +++ b/docs/content/features/object-detection.md @@ -95,7 +95,7 @@ Each detection includes: ### RF-DETR Backend -The RF-DETR backend is implemented as a Python-based gRPC service that integrates seamlessly with LocalAI. It provides object detection capabilities using the RF-DETR model architecture and supports multiple hardware configurations: +The RF-DETR backend is implemented as a Python-based gRPC service that integrates with LocalAI. It provides object detection capabilities using the RF-DETR model architecture and supports multiple hardware configurations: - **CPU**: Optimized for CPU inference - **NVIDIA GPU**: CUDA acceleration for NVIDIA GPUs diff --git a/docs/content/features/text-to-audio.md b/docs/content/features/text-to-audio.md index a2485f3bf..c938e2e17 100644 --- a/docs/content/features/text-to-audio.md +++ b/docs/content/features/text-to-audio.md @@ -776,7 +776,7 @@ including the `load.` and `session.` namespaces and the supertonic packaging cav ## Response format -To provide some compatibility with OpenAI API regarding `response_format`, ffmpeg must be installed (or a docker image including ffmpeg used) to leverage converting the generated wav file before the api provide its response. +To provide some compatibility with OpenAI API regarding `response_format`, ffmpeg must be installed (or a docker image including ffmpeg used) to convert the generated wav file before the API returns its response. Warning regarding a change in behaviour. Before this addition, the parameter was ignored and a wav file was always returned, with potential codec errors later in the integration (like trying to decode a mp3 file from a wav, which is the default format used by OpenAI) diff --git a/docs/content/getting-started/customize-model.md b/docs/content/getting-started/customize-model.md index 751a2e6fa..ae24f2e21 100644 --- a/docs/content/getting-started/customize-model.md +++ b/docs/content/getting-started/customize-model.md @@ -7,9 +7,9 @@ icon = "rocket_launch" +++ -To customize the prompt template or the default settings of the model, a configuration file is utilized. This file must adhere to the LocalAI YAML configuration standards. For comprehensive syntax details, refer to the [advanced documentation]({{%relref "advanced" %}}). The configuration file can be located either remotely (such as in a Github Gist) or within the local filesystem or a remote URL. +To customize the prompt template or the default settings of the model, a configuration file is used. This file must adhere to the LocalAI YAML configuration standards. For comprehensive syntax details, refer to the [advanced documentation]({{%relref "advanced" %}}). The configuration file can be located either remotely (such as in a Github Gist) or within the local filesystem or a remote URL. -LocalAI can be initiated using either its container image or binary, with a command that includes URLs of model config files or utilizes a shorthand format (like `huggingface://` or `github://`), which is then expanded into complete URLs. +LocalAI can be initiated using either its container image or binary, with a command that includes URLs of model config files or uses a shorthand format (like `huggingface://` or `github://`), which is then expanded into complete URLs. The configuration can also be set via an environment variable. For instance: diff --git a/docs/content/overview.md b/docs/content/overview.md index 7c569c09e..4bac90730 100644 --- a/docs/content/overview.md +++ b/docs/content/overview.md @@ -17,7 +17,7 @@ LocalAI is a composable AI stack for running models locally: a small core that s ## Why LocalAI? -In today's AI landscape, privacy, control, and flexibility are paramount. LocalAI addresses these needs by: +LocalAI is built for privacy, control and flexibility: - **Privacy First**: Your data never leaves your machine - **Complete Control**: Run models on your terms, with your hardware @@ -84,7 +84,7 @@ LocalAI is a community-driven project. You can: ## Next Steps -Ready to dive in? Here are some recommended next steps: +Recommended next steps: 1. **[Install LocalAI](/installation/)** - Start with [Docker installation](/installation/docker/) (recommended) or choose another method 2. **[Quickstart guide]({{% relref "getting-started/quickstart" %}})** - Get up and running in minutes diff --git a/docs/content/reference/architecture.md b/docs/content/reference/architecture.md index 67d14adde..5a2e8fe20 100644 --- a/docs/content/reference/architecture.md +++ b/docs/content/reference/architecture.md @@ -5,7 +5,7 @@ title = "Architecture" weight = 25 +++ -LocalAI is an API written in Go that serves as an OpenAI shim, enabling software already developed with OpenAI SDKs to seamlessly integrate with LocalAI. It can be effortlessly implemented as a substitute, even on consumer-grade hardware. This capability is achieved by employing various C++ backends, including [ggml](https://github.com/ggerganov/ggml), to perform inference on LLMs using both CPU and, if desired, GPU. Internally LocalAI backends are just gRPC server, indeed you can specify and build your own gRPC server and extend LocalAI in runtime as well. It is possible to specify external gRPC server and/or binaries that LocalAI will manage internally. +LocalAI is an API written in Go that serves as an OpenAI shim, enabling software already developed with OpenAI SDKs to integrate with LocalAI. It can be used as a substitute, even on consumer-grade hardware. This capability is achieved by employing various C++ backends, including [ggml](https://github.com/ggerganov/ggml), to perform inference on LLMs using both CPU and, if desired, GPU. Internally LocalAI backends are just gRPC server, indeed you can specify and build your own gRPC server and extend LocalAI in runtime as well. It is possible to specify external gRPC server and/or binaries that LocalAI will manage internally. LocalAI uses a mixture of backends written in various languages (C++, Golang, Python, ...). You can check [the model compatibility table]({{%relref "reference/compatibility-table" %}}) to learn about all the components of LocalAI.