This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
What I Built
Nature Quest is an offline scavenger hunt for families and kids. You pick a place (park, forest, garden, urban walk), how many things to find (5, 8 or 12) and the age of the youngest player. Then an open-source AI model running on the phone writes a hunt list for that place, season and age: "a red leaf", "something with a spiral shape", "bark with moss on it". The app reads the list aloud and asks everyone to put the phone away.
When someone finds something, they tap it on the list and take one photo. The same model checks it, answers with a short, encouraging message (plus a friendly hint if it is not a match), and reads that aloud too. When the hunt ends, there is a bronze, silver or gold medal and the time spent outside.
The theme was "Touch Grass", so every design decision started from one question: how do I make the screen the shortest part of the experience?
- Setup is one screen with everything pre-selected, so starting a hunt is one tap.
- The list and every piece of feedback are read aloud, so nobody needs to look at the phone.
- Big tap targets and a high-contrast palette, because the phone is used outdoors in the sun, for a few seconds at a time.
- One photo per find. No camera roll, no feed, nothing to scroll.
It is for families and kids on a walk, with an adult, in English and Spanish. The first screen always says: hunt with an adult, only look and take photos, never pick or touch anything.
| Setup | The hunt, written on the phone | Medal and time outside |
|---|---|---|
 |  |  |
Demo
There is no web demo, because the whole point is that it runs on a phone.
- Install it: Nature Quest 1.0.1 on GitHub Releases (signed APK, about 55 MB, Android 12+, 64-bit ARM). On first launch the app offers to download the AI model (2.59 GB, Wi-Fi recommended). That is the only time it uses the internet.
Taking it outside
WIP
Code
RZEROSTERN / hacktoberfest-week2
Second submission for hacktoberfest. The theme: Touch Grass
Nature Quest
An offline nature scavenger hunt for families and kids. The AI runs on the phone; the fun happens outside.
Nature Quest is an Android app. You pick a place (park, forest, garden, urban walk), how many things to find, and the age of the youngest player. An open-source AI model running on the phone writes a hunt list ("a red leaf" "something with a spiral shape", "bark with moss on it"), reads it aloud, and invites everyone to put the phone away When someone finds something they take one photo; the same on-device model checks it, answers with a short, encouraging message (also read aloud) and gives a friendly hint if it is not a match. At the end there is a bronze, silver or gold medal and the time spent outside.
The phone is a tool for a few seconds at a time. The screen is…
MIT licensed (plus a Beerware clause). The repo has 154 unit tests, the architecture in the README, and docs/DECISIONS.md, a log of 32 technical decisions with the reasons behind each, written as I went. docs/BENCHMARKS.md has every number quoted below.
How I Built It
The open pieces. The model is Gemma 4 E2B (open weights, Apache-2.0) in LiteRT-LM's .litertlm format. It runs through LiteRT-LM (Apache-2.0), Google's on-device runtime, with no network and no server. The rest is a normal Android stack: Kotlin, Jetpack Compose, Hilt, CameraX and Android's own text-to-speech. Nothing closed runs in the app. (I built it with Claude Code, which is not open source. It was my pair programmer, not part of the product.)
The shape of it. The app talks to the model through one interface, InferenceEngine, so ViewModels and use cases never touch LiteRT-LM and tests use a fake. The model answers in strict JSON, which I parse with kotlinx.serialization. If the JSON is bad I retry once, then fall back to something safe: "I'm not sure, try another photo", or a built-in hunt list. Prompts are versioned text files, not strings in Kotlin.
I benchmarked before I built. The first real step was a debug-only screen that loaded the model and timed it on my Pixel 10. That changed the design:
- The CPU backend was about twice as fast as the GPU: a hunt list took 5.7 s on average on CPU and 11.9 s on GPU, and the first GPU load took 25 s against 3 s. Google's published numbers for a flagship phone did not carry over to mine.
- Decoding is slow (7 to 23 tokens per second), so every answer is kept short. "Twelve words at most" in a prompt is a performance feature.
- The phone slows about 1.6x after 25 minutes of back-to-back runs, even though the OS reported no thermal throttling. So I picked settings that hit my targets in the slow state: 140 visual tokens and a 640 px photo. A photo check takes 4.5 to 5 s on a cool phone and 5.7 to 6.5 s on a warm one.
- Peak memory is 2.2 to 2.6 GB, so the app loads the model for each use and releases it right after. A warm reload is about half a second, and memory drops to about 240 MB.
Safety cannot live in the prompt. The prompt asks for safe items. The model still produced "a coiled snake", "orange fungus" and "a bird's nest hidden in a hollow", and later "a cloud shaped like a deer". For a kids' app that is not acceptable, so every item now passes a deterministic validator in code (English and Spanish word lists: no picking or touching, no animals, insects or mushrooms, nothing near water or roads, no climbing, nothing to eat, no photos of people). Rejected items are regenerated, and if the model still falls short, a built-in safe list fills the gap. The model's feedback is checked too, because it is read aloud to children.
Bugs the phone found that tests did not.
- The same settings produced the identical hunt three times in a row. LiteRT-LM's sampler seed defaults to 0, which makes sampling deterministic. Each request now gets a random seed.
- A regex that passed every JVM test crashed on Android, because Android's regex engine rejects a bare
}that Java accepts. - Cancelling the Kotlin Flow does not stop native inference. You have to call
cancelProcess()yourself. - CameraX's in-memory JPEG already carries its EXIF rotation, so rotating it again turns the photo sideways.
- A Compose cleanup effect unbound the camera I had just bound. It read state when disposed instead of when created.
Privacy and delivery. Photos are processed in memory. A small copy of each found photo stays in private cache only for the summary and is deleted when you close it, unless you tap "Save to gallery". The only code that opens a network connection is the model downloader: it resumes interrupted downloads, pins an exact model revision, and verifies the SHA-256 before using the file. I tested that on the phone by killing the app at 17% and resuming. The manifest asks for CAMERA, INTERNET (download only) and network state.
Limits, stated plainly. I could not measure how accurate the photo check is: I did not have a set of real photos to score the model against, so treat its verdicts as fun and encouraging, not as a judge. I tested on one phone. Reading aloud needs a voice installed for the phone's language.
Why Does Open Innovation Matter?
Four things an open model made possible that a closed API would not:
- It works with no signal on the trail. After a one-time download, there is no server to reach. The people this is for are standing in a field.
- Kids' photos never leave the phone. With a hosted model, "check this photo" means sending a child's picture to someone else's server. Here it is a function call on the device, and the only network code in the app is the downloader. Because the code is open, anyone can check that claim.
- It costs nothing per hunt. No API bill, so a family, a school or a scout group can use it as much as they like. The cost is the phone's time: about 5 to 12 seconds per hunt list and 5 to 7 per photo.
- It is swappable and pinned. The model is a file plus one entry in
ModelSpec(URL, size, SHA-256), behind an interface. The app behaves identically next year, because the exact revision is pinned and checksummed instead of changing under me. And I could turn the knobs a hosted API hides: backend (the CPU-versus-GPU finding above), image resolution and memory.
The honest counterpoint: a server GPU would be faster, and probably more accurate. I traded speed and a 2.6 GB first download for privacy and offline use. For a hunt in a forest, I think that is the right trade, but it is a trade.
My Agent Session
I built Nature Quest with Claude Code, from an empty repository to two signed releases. Here is the whole conversation: my prompts, Claude's replies and every tool call, from the benchmark spike to the release. Claude's private reasoning is not included, long tool outputs are truncated, and anything personal (emails, local paths, device serial numbers) is scrubbed.
let's start the next development:
<pasted_content id="5de6">
Context
I'm entering the DEV Hacktoberfest Open-Source AI Challenge, Week 1: "Touch Grass".
The project MUST comply with these rules:
- Build something with open-source AI at its core that gets people off the screen and
into the world. The best builds make the screen the shortest part of the experience.
- Open-source AI must be what makes the product work: at runtime, NO closed-model APIs
and NO server inference. Everything runs on the phone. You're helping me build it, but
the product runs entirely on open components.
- The repo is created and completed within the challenge window, which closes Monday,
October 12, 00:59 Mexico City time.
- Hard internal deadline: the app must be installed and working on my phone by Friday
night. On Saturday I take it outside with real users (that's a bonus criterion), and
Sunday is for the write-up.
All architecture decisions below are already made. Build the whole project end to end,
following the build order, without waiting for confirmation between steps unless a step
explicitly says to stop. Time is the main constraint: always choose the simplest solution
that works.
Git workflow: GitFlow with non-stacked PRs (ESSENTIAL, never skip)
STRICTLY FORBIDDEN: a branch named "main". The production branch is "master".
Never create, push, reference, or target "main" in any command, PR, config, or doc.
Branches:
- master: production only. Changes arrive only through release/* or hotfix/* PRs.
- develop: integration branch. Changes arrive only through feature/* PRs.
- feature/<short-name>: one per build step, always created from the latest develop.
- release/<version>: created from develop when the MVP is ready; PR into master, then
merged back into develop.
- hotfix/<short-name>: from master, only for urgent fixes; PR into master and merged back
into develop.
Rules:
- Never commit directly to master or develop.
- One feature branch per build step, one PR per feature branch, base branch = develop.
- NO stacked PRs: never create a branch from another feature branch, and never open a PR
whose base is anything other than develop (or master for release/hotfix).
- Sequence for every step:
1. git checkout develop && git pull
2. git checkout -b feature/<name>
3. Work with small Conventional Commits.
4. Run lint + unit tests for the parts touched; all must pass, and the app must build.
5. Push and open the PR with gh pr create --base develop.
6. Merge it with gh pr merge (merge commit, delete the branch).
7. Only then start the next step from the updated develop.
- PR description: what changed, why, how it was tested, and whether DECISIONS.md was
updated. PR title follows Conventional Commits.
The product: "Nature Quest" (working title)
An offline nature scavenger hunt for families and kids. The phone is only a tool for a
few seconds at a time; the hunt happens outside.
Flow:
1. Setup (one screen, under 30 seconds): pick the place type (park, forest, garden,
urban walk), the hunt length (5, 8, or 12 items), and the youngest player's age range.
The current month and the device language are used automatically.
2. Gemma 4, on device, generates a hunt list suited to the place, the season (central
Mexico in October: end of the rainy season, early fall), and the age range. Items are
observable and safe, e.g., "a red leaf", "something with a spiral shape",
"a feather", "bark with moss on it", "a flower with five petals".
3. The app reads the list aloud with Android's TextToSpeech and invites everyone to put
the phone away. The list stays available with a single tap.
4. When players find something, they open the camera, take one photo, and Gemma 4
verifies whether it matches an item on the list. The response is a short, fun,
encouraging message, also read aloud, and the item is checked off. If the photo
doesn't match, a friendly hint is given.
5. When the hunt ends (all items found or the players end it), a summary screen shows the
items found, the time spent outside, and a medal (bronze, silver, gold).
Safety rules for generated content (non-negotiable):
- Photograph, don't pick: never ask players to pick plants or flowers, collect, touch, or
approach animals, insects, or mushrooms.
- No items that require leaving paths, climbing, going near water or roads, or touching
anything that could be harmful.
- Never suggest eating or tasting anything.
- Encourage kids to hunt with an adult.
Language: all UI strings in Android resources, English as the default (values/) and
Spanish (values-es/). Model prompts and responses follow the device language.
Privacy:
- Photos are processed in memory for verification and never uploaded anywhere.
- During a hunt, photos may be kept only in app-private cache for the summary screen,
and are deleted when the hunt ends unless the user taps "Save to gallery".
- No analytics, no telemetry, no network calls except the one-time model download.
Architecture
Single Android app module, Kotlin, Jetpack Compose, MVVM, Clean Architecture
(data / domain / ui packages), Hilt, Coroutines + Flow, CameraX, Android TextToSpeech,
Gradle version catalog. Room only if the history step is reached.
On-device inference:
- Runtime: LiteRT-LM via its Kotlin Android library (check the official docs at
ai.google.dev/edge/litert-lm and the google-ai-edge/LiteRT-LM repo for the current
artifact, version, and API; never guess).
- Model: Gemma 4 E2B in .litertlm format from litert-community/gemma-4-E2B-it-litert-lm
on Hugging Face. E4B only if the spike shows my phone handles it well.
- Wrap the engine behind a domain interface (e.g., InferenceEngine) so ViewModels never
depend on LiteRT-LM directly and unit tests can use a fake.
- Generation and verification responses use strict JSON, parsed with kotlinx.serialization.
On invalid output, retry once, then fall back to a safe default (for verification:
"I'm not sure, try another photo").
- Model delivery: during development, load the model from a file pushed with adb.
For release, download it once on first launch (Wi-Fi recommended, progress shown,
resumable, checksum verified), stored in app-private storage.
Repo layout:
/app Android app.
/samples Test photos (leaves, flowers, bark, feathers, etc.), already provided.
/docs DECISIONS.md and BENCHMARKS.md.
Root: README.md, CLAUDE.md, .claude/rules/, .gitignore, gradle files.
Build order (each step = one feature branch + one PR into develop)
Git setup: verify the GitHub remote and that gh is authenticated, and confirm that
master exists and is the default branch on GitHub. If any of these fails, STOP and
tell me. Create develop from master and push it.feature/scaffold: Compose app with Hilt, version catalog, navigation, theme,
English + Spanish string resources, lint configured, docs/DECISIONS.md.-
feature/claude-memory: Claude Code project memory, following current Claude Code
practices. Keep every file short: only what you can't infer from the code and what
differs from standard conventions.
a. CLAUDE.md at the repo root (committed), under ~60 lines:- One-paragraph project overview and the repo layout.
- Exact commands: build, install on device, push the model with adb, unit tests, lint.
- The GitFlow and non-stacked PR rules above, condensed.
- Workflow: lint + tests + build before each commit; Conventional Commits; update docs/DECISIONS.md when a technical decision is made or changed; check official docs instead of guessing LiteRT-LM, CameraX, or Hugging Face APIs.
- Non-negotiables: never use a branch named "main" (production is "master"); no closed-model APIs or server inference; photos never leave the device; the safety rules for generated content; all UI strings in resources.
- A "Gotchas" section, updated as we discover them. b. .claude/rules/inference.md with paths frontmatter scoped to the inference/data packages: engine behind a domain interface; strict JSON + retry once + safe fallback; prompts in versioned files under app/src/main/assets/prompts/; model loading and inference always off the main thread; release the engine when not needed. c. .claude/rules/ui.md with paths frontmatter scoped to the ui package: screens designed to be used in a few seconds; large tap targets and high contrast for outdoor sunlight; no strings outside resources; every long operation shows progress and can be cancelled. d. CLAUDE.local.md (in .gitignore) only for machine-specific settings: my device model, the local path of the .litertlm file, and adb details.
feature/model-spike: integrate LiteRT-LM, load Gemma 4 E2B from the adb-pushed file,
and add a debug-only screen that (a) generates a hunt list and (b) verifies each photo
in /samples against a given item. Record in docs/BENCHMARKS.md: model load time,
time to first token, total time for generation and for one verification, peak memory,
and the accuracy of verification on /samples.
Targets on my device: model load under 20 s, verification under 10 s, generation under
20 s, and correct verification on at least 8 of 10 samples.
If any target is clearly missed, STOP before opening the PR and show me the results
with options (E4B vs E2B, smaller image resolution, shorter prompts, fewer tokens).
Otherwise, continue. Also check whether the Hugging Face model repo is gated; if it
requires a token to download, STOP and propose how to deliver the model without
embedding any token in the app.feature/hunt-generation: setup screen and on-device list generation with the safety
rules enforced in the prompt and validated in code (reject and regenerate any item
that violates them).feature/photo-verification: CameraX capture and on-device verification with feedback
and hints.feature/game-loop: checklist, TextToSpeech for the list and feedback, end of hunt,
summary screen with time outside and medal, private photo cache cleanup.feature/model-download: first-launch download with progress, resume, and checksum.
feature/history (only if time allows): past hunts stored with Room.
feature/readme: README in English: what it is, who it's for, screenshots, how to
install the APK and get the model, architecture, benchmarks, the branching model, and
a "Why open source matters here" section (works with no signal on the trail, kids'
photos never leave the phone, zero cost per hunt, swappable model).release/1.0.0: from develop, PR into master, merge, tag v1.0.0, merge master back
into develop, and create a GitHub Release with the APK attached.
Priority if time runs short: spike > generation > verification > game loop >
model download > TextToSpeech > history. For Saturday's outdoor test, an adb-pushed
model is acceptable; the download step matters for judges installing the APK.
Even when cutting scope, never skip the GitFlow sequence.
General rules
- Code, comments, commits, PRs, and README in English.
- Don't invent LiteRT-LM, CameraX, TextToSpeech, or Hugging Face APIs or flags: when unsure, check the official docs before writing code, and tell me if something isn't documented.
- Unit tests for ViewModels, JSON parsing, and safety validation using a fake InferenceEngine.
- Ask before installing anything globally or touching anything outside the repo.
- Keep docs/DECISIONS.md logging each technical decision and why; I'll use it for the post.
- Whenever I correct you on something that should apply to future sessions, propose adding it to CLAUDE.md or the right rules file instead of only fixing the code. </pasted_content id="5de6">
Starting with Step 0: verifying the git remote, gh auth, and that master is the GitHub default branch.
$ {"command": "echo \"--- pwd/ls\" && ls -la && echo \"--- branches\" && git branch -a && echo \"--- remotes\" && git remote -v && echo \"--- log\" && git log --oneline -5 2>&1 | head", "description": "Inspect local git state"}--- pwd/ls total 0 drwxr-xr-x@ 3 <user> staff 96 Oct 5 14:14 . drwxr-xr-x 15 <user> staff 480 Oct 5 12:44 .. drwxr-xr-x@ 9 <user> staff 288 Oct 5 14:16 .git --- branches --- remotes origin git@github.com:RZEROSTERN/hacktoberfest-week2.git (fetch) origin git@github.com:RZEROSTERN/hacktoberfest-week2.git (push) --- log fatal: your current branch 'master' does not have any commits yet$ {"command": "gh auth status 2>&1; echo \"--- gh version\"; gh --version | head -1", "description": "Check GitHub CLI authentication"}github.com ✓ Logged in to github.com account RZEROSTERN (keyring) - Active account: true - Git operations protocol: ssh - Token: <redacted-token> - Token scopes: 'admin:public_key', 'gist', 'read:org', 'repo' --- gh version gh version 2.90.0 (2026-04-16)Local repo has a remote and no commits yet. Now checking what GitHub has.
$ {"command": "echo \"--- repo view\" && gh repo view RZEROSTERN/hacktoberfest-week2 --json name,visibility,defaultBranchRef,isEmpty,createdAt,url 2>&1; echo \"--- remote branches (ls-remote)\"; git ls-remote --heads origin 2>&1; echo \"exit: $?\"", "description": "Check GitHub repo default branch and remote heads"}--- repo view {"createdAt":"2026-10-05T20:14:51Z","defaultBranchRef":{"name":""},"isEmpty":true,"name":"hacktoberfest-week2","url":"https://github.com/RZEROSTERN/hacktoberfest-week2","visibility":"PUBLIC"} --- remote branches (ls-remote) exit: 0GitHub's side is empty, so the master check fails. Before stopping, I'll run a few read-only checks on the toolchain so I can report everything in one go.
$ {"command": "echo \"ANDROID_HOME=$ANDROID_HOME ANDROID_SDK_ROOT=$ANDROID_SDK_ROOT\"; ls ~/Library/Android/sdk 2>&1 | head -20; echo \"--- adb\"; (which adb && adb version | head -1 && adb devices -l) 2>&1; echo \"--- java\"; (java -version 2>&1 | head -2); /usr/libexec/java_home -V 2>&1 | head -5; echo \"--- gradle\"; (which gradle && gradle --version 2>&1 | sed -n 1,4p) 2>&1; echo \"--- git default branch cfg\"; git config --get init.defaultBranch; echo \"--- samples\"; ls ~/Documents/Repositorios/Training/hacktoberfest-week2/samples 2>&1; ls /Applications | grep -i \"android studio\"", "description": "Check Android toolchain and device availability"}ANDROID_HOME= ANDROID_SDK_ROOT= build-tools cmake cmdline-tools emulator extras fonts icons licenses ndk patcher platform-tools platforms skins sources system-images tools --- adb ~/Library/Android/sdk/platform-tools/adb Android Debug Bridge version 1.0.41 List of devices attached <device-serial> device usb:0-1.1.1 product:frankel model:Pixel_10 device:frankel transport_id:6 --- java openjdk version "17.0.7" 2023-04-18 LTS OpenJDK Runtime Environment Zulu17.42+19-CA (build 17.0.7+7-LTS) Matching Java Virtual Machines (1): 11.0.12 (arm64) "Azul Systems, Inc." - "Zulu 11.50.19" /Library/Java/JavaVirtualMachines/zulu-11.jdk/Contents/Home /Library/Java/JavaVirtualMachines/zulu-11.jdk/Contents/Home --- gradle gradle not found --- git default branch cfg master --- samples ls: ~/Documents/Repositorios/Training/hacktoberfest-week2/samples: No such file or directory Android Studio.appStopped at Step 0. The GitHub repo exists and gh is authenticated, but master doesn't exist on GitHub yet, so the check you asked me to stop on fails.
| Check | Result |
|---|---|
| Remote | ✅ origin is [REDACTED]:RZEROSTERN/hacktoberfest-week2.git, public, created 2026-10-05 |
gh auth | ✅ logged in as RZEROSTERN with repo scope |
master is the default branch on GitHub | ❌ The repo is empty: no branches, and defaultBranchRef is blank |
| Local repo | ⚠️ Branch master, zero commits, so I can't create develop from it yet |
Decision for you: an empty repo gets its default branch from the first push, and develop can't be cut from a branch with no commits. The only way forward is a one-time bootstrap commit on master, which technically breaks "never commit directly to master". I recommend this:
- Make a single commit on
mastercontaining only.gitignore(withCLAUDE.local.md,local.properties,*.litertlmand build outputs) and a one-lineREADME.md. - Push it. GitHub then sets
masteras the default, and I confirm that withgh repo view. - Create
developfrom it, push it, and go straight intofeature/scaffold. Every later change goes through the GitFlow sequence.
If you'd rather keep master untouched, you can create it from the GitHub UI by adding a README, but the result is the same.
Other findings (none block Step 0; some will block later steps):
- Phone: a Pixel 10 (<device-serial>) is connected over adb.
- Android SDK: found at ~/Library/Android/sdk, but ANDROID_HOME isn't set. I'll point local.properties at it instead of changing your shell.
- JDK: Java 17 (Zulu) is on PATH.
- Gradle: there is no global gradle, so I need a way to generate the Gradle wrapper for Step 1. I'll ask before installing anything.
- /samples is missing. You said it was already provided, but there's no samples/ directory in the repo. I need the photos before Step 3. Drop them in, or tell me where they are.
Reply "go" to use the bootstrap plan above, or tell me if you want something different.
make initial commit with README.md so you can continue
$ {"command": "cat > README.md <<'EOF'\n# Nature Quest\n\nAn offline nature scavenger hunt for families and kids, powered by on-device open-source AI (Gemma 4 via LiteRT-LM).\n\nWork in progress for the DEV Hacktoberfest Open-Source AI Challenge, Week 1: \"Touch Grass\".\nEOF\ngit add README.md && git commit -m \"chore: initial commit\n\nCo-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>\" && git push -u origin master 2>&1 && echo \"--- verify\" && gh repo view RZEROSTERN/hacktoberfest-week2 --json defaultBranchRef,isEmpty", "description": "Create bootstrap commit on master and push"}[master (root-commit) 077eec9] chore: initial commit 1 file changed, 5 insertions(+) create mode 100644 README.md To github.com:RZEROSTERN/hacktoberfest-week2.git * [new branch] master -> master branch 'master' set up to track 'origin/master'. --- verify {"defaultBranchRef":{"name":"master"},"isEmpty":false}master is now the GitHub default branch. Creating develop from it and pushing.
$ {"command": "git checkout -b develop && git push -u origin develop 2>&1 && echo \"--- remote heads\" && git ls-remote --heads origin", "description": "Create and push develop branch"}Switched to a new branch 'develop' remote: remote: Create a pull request for 'develop' on GitHub by visiting: remote: https://github.com/RZEROSTERN/hacktoberfest-week2/pull/new/develop remote: To github.com:RZEROSTERN/hacktoberfest-week2.git * [new branch] develop -> develop branch 'develop' set up to track 'origin/develop'. --- remote heads 077eec9368036e23d1690809b32fefeb0b293904 refs/heads/develop 077eec9368036e23d1690809b32fefeb0b293904 refs/heads/master$ {"command": "SDK=~/Library/Android/sdk; echo \"--- platforms\"; ls $SDK/platforms; echo \"--- build-tools\"; ls $SDK/build-tools; echo \"--- cmdline-tools\"; ls $SDK/cmdline-tools; echo \"--- Android Studio\"; defaults read \"/Applications/Android Studio.app/Contents/Info\" CFBundleShortVersionString 2>&1; ls \"/Applications/Android Studio.app/Contents\" | head -20; echo \"--- bundled gradle?\"; find \"/Applications/Android Studio.app/Contents\" -maxdepth 3 -iname \"gradle*\" 2>/dev/null | head; echo \"--- ~/.gradle\"; ls ~/.gradle 2>&1 | head; ls ~/.gradle/wrapper/dists 2>&1 | head; echo \"--- jbr\"; \"/Applications/Android Studio.app/Contents/jbr/Contents/Home/bin/java\" -version 2>&1 | head -2", "description": "Inspect SDK platforms, Android Studio, and Gradle caches"}--- platforms android-23 android-26 android-28 android-29 android-30 android-31 android-32 android-33 android-34 android-35 android-35-ext14 android-35-ext15 android-36 android-36-ext19 android-36.1 --- build-tools 28.0.3 29.0.2 29.0.3 30.0.0 30.0.2 30.0.3 31.0.0 33.0.1 34.0.0 35.0.0 36.0.0 36.1.0 --- cmdline-tools 19.0 --- Android Studio 2025.3 CodeResources Info.plist MacOS Resources _CodeSignature bin jbr lib license modules plugins --- bundled gradle? /Applications/Android Studio.app/Contents/plugins/gradle /Applications/Android Studio.app/Contents/plugins/gradle-declarative /Applications/Android Studio.app/Contents/plugins/gradle-java /Applications/Android Studio.app/Contents/plugins/gradle-dsl --- ~/.gradle android android.lock caches daemon jdks kotlin-profile native notifications workers wrapper CACHEDIR.TAG gradle-6.7-all gradle-8.10-all gradle-8.11.1-bin gradle-8.14.3-bin gradle-8.7-all gradle-9.2.1-bin gradle-9.5.1-bin --- jbr openjdk version "21.0.9" 2025-10-21 OpenJDK Runtime Environment (build 21.0.9+-14649483-b1163.86){ "query": "select:WebFetch,WebSearch", "max_results": 5 }Gradle 9.5.1 is already cached from your previous projects, so I can generate the wrapper from it without downloading anything. Next I'll check current stable versions in the official sources.
{ "url": "https://developer.android.com/build/releases/about-agp", "prompt": "What is the latest stable Android Gradle Plugin version, and what Gradle version, JDK version, and compileSdk/SDK build tools does it require? Also note any AGP 9 breaking changes regarding built-in Kotlin (org.jetbrains.kotlin.android plugin), KSP/kapt, and Hilt compatibility. Be concise and specific." }# Latest Android Gradle Plugin (AGP) Requirements ## Latest Stable Version **AGP 9.4.0** is the latest stable version. ## Required Versions | Component | Version | |-----------|---------| | **Gradle** | 9.6.0 (minimum) | | **Android Studio** | Rabbit 1 (2026.2.1) or Quail 4 (2026.1.4) | | **compileSdk** | API 37 requires AGP 9.1.1 minimum | ## AGP 9 Breaking Changes ### Built-in Kotlin Support - AGP 9 includes **built-in Kotlin support** via the `org.jetbrains.kotlin.android` plugin - You must specify the Kotlin version explicitly in your `build.gradle.kts`: ```kotlin plugins { id("com.android.application") version "9.4.0" apply false id("com.android.library") version "9.4.0" apply false id("org.jetbrains.kotlin.android") version "2.4.10" apply false } ``` ### KSP/KAPT Compatibility - AGP 9 requires migration to **Kotlin Symbol Processing (KSP)** as KAPT is being phased out - Use the [AGP 9 Upgrade skill](/tools/agents/android-skills) or [JetBrains KMP migration skill](https://github.com/Kotlin/kotlin-agent-skills/tree/main/skills/kotlin-tooling-agp9-migration) for assistance ### Hilt Compatibility - Ensure your Hilt version is compatible with AGP 9.x - Update to the latest Hilt release when upgrading AGP **⚠️ Caution:** Avoid dynamic version dependencies like `'9.4.+'` as they cause unexpected updates.{ "url": "https://developer.android.com/develop/ui/compose/bom/bom-mapping", "prompt": "What is the latest stable Compose BOM version and which compose library versions does it map to? List the newest few BOM versions." }# Latest Stable Compose BOM Versions Based on the content, here are the newest BOM versions: ## Most Recent Versions 1. **2026.09.00** (Latest) 2. **2026.08.00** 3. **2026.06.01** 4. **2026.06.00** 5. **2026.05.01** ## Library Version Mappings for 2026.09.00 The latest BOM version **2026.09.00** maps to the following Compose library versions: | Library | Version | |---------|---------| | `androidx.compose.animation:animation` | 1.12.1 | | `androidx.compose.foundation:foundation` | 1.12.1 | | `androidx.compose.material:material` | 1.12.1 | | `androidx.compose.material3:material3` | 1.4.0 | | `androidx.compose.runtime:runtime` | 1.12.1 | | `androidx.compose.ui:ui` | 1.12.1 | All core Compose UI libraries are at version **1.12.1**, while Material Design 3 components are at **1.4.0**. ## Key Notes - These versions appear to follow a date-based versioning scheme (YYYY.MM.DD format) - The BOM (Bill of Materials) simplifies dependency management by providing tested, compatible versions - All libraries in a single BOM version are guaranteed to work together without compatibility issues{ "url": "https://dagger.dev/hilt/gradle-setup.html", "prompt": "What is the current Hilt Gradle setup: plugin id, latest version number, whether it uses KSP or kapt, and the artifact coordinates for hilt-android, hilt-compiler, and androidx.hilt:hilt-navigation-compose. Also note any AGP 9 / built-in Kotlin caveats." }# Current Hilt Gradle Setup Based on the documentation provided, here's the current Hilt configuration: **Plugin ID & Version:** `id 'com.google.dagger.hilt.android' version '2.60.1'` **Artifact Coordinates:** - hilt-android: `com.google.dagger:hilt-android:2.60.1` - hilt-compiler: `com.google.dagger:hilt-compiler:2.60.1` - hilt-android-testing: `com.google.dagger:hilt-android-testing:2.60.1` **Kotlin Processing:** The documentation presents both options as equally viable: "If using Kotlin, you have the choice of using either KSP or kapt." Users select one based on preference; KSP is presented first but neither is mandated as the default. **Missing Information:** The document does not contain information about `androidx.hilt:hilt-navigation-compose` or any specific caveats regarding AGP 9 or built-in Kotlin compatibility issues. Those topics fall outside the scope of this gradle setup guide.Related posts
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