@ondeinference / onde / commits / 39d7940

update SDK READMEs

keypair34 committed May 3, 2026 at 12:45 UTC 39d7940c668afd9928d356a9baa95eadf1eea0be
6 files changed +35 -11
.agents/skills/humanizer/SKILL.md
+26 -2
@@ -25,8 +25,32 @@ When given text to humanize:
25 1. **Identify AI patterns** - Scan for the 24 patterns listed below
26 2. **Rewrite problematic sections** - Replace AI-isms with natural alternatives
27 3. **Preserve meaning** - Keep the core message intact
28 -4. **Add soul** - Don't just remove bad patterns; inject actual personality
29 -5. **Final audit pass** - Ask "What makes this obviously AI generated?" then revise again
28 +4. **Protect exact names** - Do not change brand casing, package names, commands, file paths, JSON keys, or code just to make a sentence feel smoother
29 +5. **Add soul** - Don't just remove bad patterns; inject actual personality
30 +6. **Final audit pass** - Ask "What makes this obviously AI generated?" then revise again
31 +
32 +---
33 +
34 +## BRAND AND NAMING GUARDRAILS
35 +
36 +Humanizing copy does **not** give you permission to rename things.
37 +
38 +Preserve exact casing and spelling for:
39 +
40 +- product and company names
41 +- package names
42 +- CLI commands
43 +- protocol names and acronyms
44 +- JSON keys, environment variables, paths, and code identifiers
45 +
46 +If a sentence feels stiff because a brand name is rigid, rewrite the sentence around the name. Do not "fix" the name.
47 +
48 +Examples:
49 +
50 +- Keep `siGit Code` as `siGit Code`, not `siGit`, `Sigit Code`, or `sigit Code`
51 +- Keep `smbCloud` as `smbCloud`
52 +- Keep `Onde Inference` as `Onde Inference`
53 +- Keep `sigit`, `@smbcloud/sigit`, `sigit-code`, and `onde` exactly as written when they refer to commands or packages
54
55 ---
56
README.md
+2 -2
@@ -5,7 +5,7 @@
5 <h1 align="center">Onde Inference</h1>
6
7 <p align="center">
8 - <strong>Run LLMs on-device, with first-class support for <a href="https://en.wikipedia.org/wiki/Apple_silicon">Apple silicon</a>.</strong>
8 + <strong>Run LLMs on-device with <a href="https://ondeinference.com/">Onde Inference</a>, with first-class support for <a href="https://en.wikipedia.org/wiki/Apple_silicon">Apple silicon</a>.</strong>
9 </p>
10
11 <p align="center">
@@ -26,7 +26,7 @@
26
27 ## In production
28
29 -Onde is already shipping in real App Store apps. Chat runs fully on-device, so there is no server round trip and no user data leaving the device.
29 +Onde is already shipping in real App Store apps. Chat runs fully on-device, so there is no server round trip and no user data leaving the device. For SDK docs, platform notes, and setup details, see <https://ondeinference.com/sdk>. If you want to test downloads, model selection, or GGUF export before wiring the engine into app code, use [Onde CLI](https://github.com/ondeinference/onde-cli).
30
31 <p align="center">
32 <a href="https://apps.apple.com/se/developer/splitfire-ab/id1831430993" target="_blank">
sdk/dart/README.md
+2 -2
@@ -5,7 +5,7 @@
5 <h1 align="center">Onde Inference</h1>
6
7 <p align="center">
8 - <strong>Run LLMs on-device from Flutter and Dart. Metal on iOS and macOS, CPU everywhere else.</strong>
8 + <strong>Run LLMs on-device from Flutter and Dart with <a href="https://ondeinference.com/">Onde Inference</a>. Metal on iOS and macOS, CPU everywhere else.</strong>
9 </p>
10
11 <p align="center">
@@ -20,7 +20,7 @@
20
21 ---
22
23 -Run Qwen 2.5 models directly inside your Flutter app. The model downloads from Hugging Face on first launch, then everything runs locally. No server, no API key, and no user data leaving the device. On an iPhone 15 Pro, Metal gets you around 15 tok/s. Android and desktop run on CPU, so they are slower, but they work well for local inference.
23 +Run Qwen 2.5 models directly inside your Flutter app. The model downloads from Hugging Face on first launch, then everything runs locally. No server, no API key, and no user data leaving the device. On an iPhone 15 Pro, Metal gets you around 15 tok/s. Android and desktop run on CPU, so they are slower, but they work well for local inference. For [on-device AI inference for Flutter and Dart](https://ondeinference.com/sdk/dart), the SDK page covers install details, platform notes, and package links. If you want to test model downloads or GGUF exports before you wire up UI code, use [Onde CLI](https://github.com/ondeinference/onde-cli).
24
25 You get multi-turn chat, streaming, one-shot generation, and configurable sampling in a single package.
26
sdk/dart/example/README.md
+1 -1
@@ -5,7 +5,7 @@
5 <h1 align="center">onde_inference — Example App</h1>
6
7 <p align="center">
8 - <strong>A Flutter chat app that runs LLM inference fully on-device.</strong><br>
8 + <strong>A Flutter chat app that runs LLM inference fully on-device with <a href="https://ondeinference.com/">Onde Inference</a>.</strong><br>
9 No server. No API key. No data leaving the device.
10 </p>
11
sdk/kotlin/README.md
+2 -2
@@ -5,7 +5,7 @@
5 <h1 align="center">Onde Inference Kotlin SDK</h1>
6
7 <p align="center">
8 - <strong>Run LLMs on-device from Kotlin. No cloud, no API key, and no user data leaving the device.</strong>
8 + <strong>Run LLMs on-device from Kotlin with <a href="https://ondeinference.com/">Onde Inference</a>. No cloud, no API key, and no user data leaving the device.</strong>
9 </p>
10
11 <p align="center">
@@ -29,7 +29,7 @@ Onde is an on-device LLM inference SDK for Kotlin apps. It wraps [mistral.rs](ht
29 - **Rust underneath**: powered by [uniffi-rs](https://github.com/mozilla/uniffi-rs)
30 - **Published on Maven Central**: add one Gradle dependency and you're done
31
32 -This package is built as a Kotlin Multiplatform library. Today that means Android and JVM support, with the same Rust core underneath both.
32 +This package is built as a Kotlin Multiplatform library. Today that means Android and JVM support, with the same Rust core underneath both. For [on-device AI inference for Kotlin and Android](https://ondeinference.com/sdk), the SDK page is the quickest way to check package docs and platform scope. If you want to test model downloads or fine-tune outputs before moving into app code, use [Onde CLI](https://github.com/ondeinference/onde-cli).
33
34 ---
35
sdk/react-native/README.md
+2 -2
@@ -5,7 +5,7 @@
5 <h1 align="center">Onde Inference</h1>
6
7 <p align="center">
8 - <strong>Run LLMs on-device from React Native. Metal on iOS, CPU on Android, and no user data leaving the device.</strong>
8 + <strong>Run LLMs on-device from React Native with <a href="https://ondeinference.com/">Onde Inference</a>. Metal on iOS, CPU on Android, and no user data leaving the device.</strong>
9 </p>
10
11 <p align="center">
@@ -20,7 +20,7 @@
20
21 ---
22
23 -Run Qwen 2.5 models directly on the device. No server, no API key, and no user data leaving the phone.
23 +Run Qwen 2.5 models directly on the device. No server, no API key, and no user data leaving the phone. For an [efficient on-device inference engine for React Native](https://ondeinference.com/sdk/react-native), the SDK page is the quickest place to check install details and platform notes. If you want to verify model downloads or GGUF output before you ship the app build, use [Onde CLI](https://github.com/ondeinference/onde-cli).
24
25 The model downloads from Hugging Face the first time you load it, then runs locally after that. The 1.5B model is about 941 MB. On iPhone, Metal makes it feel surprisingly fast. On Android, it runs on CPU, so it is slower, but it still works well enough for local chat.
26