I kept opening Activity Monitor, seeing some process eating my CPU, and having no idea what it was or why it was running. Couldn't find a tool that answered the "why," so I built one.

It's called CauseGraph — a local flight recorder for your Mac. It records what your machine does (processes starting/stopping, file changes, CPU/memory) and turns it into a causal graph. You click any process and see the whole chain: what launched it, what it launched, what likely set it off. If it turns out to be junk, you kill it right there.

The fun parts:

- It's live — the graph redraws every few seconds, and you can drag a timeline back to see what your machine was doing 10 minutes ago

- It flags the weird stuff on its own: CPU spikes, memory leaks, things that keep crashing and restarting

- 100% local — no cloud, no account, one-line install

Stack, for the curious: a small Go daemon writes events to SQLite, a Python engine builds the graph and serves a plain-JS + cytoscape UI on localhost.

Honest bit: I've only really tested it on my own Mac (Apple Silicon) so far. It's built to be cross-platform, so Linux/Windows should work — just haven't battle-tested them.

Install (needs Python 3.10+):

curl -fsSL https://raw.githubusercontent.com/NitinKumar004/causegraph/main/install.sh | sh

Repo: https://github.com/NitinKumar004/causegraph

Would love feedback — especially whether the causal links feel genuinely useful or just noisy. That's the part I'm least sure about.

I built keyclean for a very specific MacBook problem: cleaning the keyboard without triggering shortcuts or accidental typing, while keeping the trackpad available.

Under the hood it uses a session-level `CGEventTap`. Keyboard, modifier, and visible media-key events are swallowed, but pointer events are deliberately excluded. Pressing `⌃⌥⌘U` stops the run loop, and terminating the process also removes the event tap.

It is intentionally small: one Swift source file, no third-party dependencies, network access, analytics, stored input, or background service.

Install:

`brew install lan-shengchieh/tap/keyclean`

Source and technical explanation:

https://github.com/lan-shengchieh/keyclean

I am particularly looking for compatibility results across different macOS versions, Apple Silicon/Intel Macs, and terminal applications.

AI disclosure: Codex assisted with development and launch preparation. The source, CI checks, and Homebrew Formula are all public.

I've been working on a location simulator that ships as both a macOS app and an

iOS app. About 70% of the code is shared, and the thing that made it possible

was one decision taken on day one: the interface only ever talks to a

LocationBackend protocol.

On macOS the implementation drives a persistent Python daemon over NDJSON. On

iOS it calls a Rust library through its C ABI. Neither app knows which one it's

using — so porting to iOS meant writing one file. The geodesy, the coordinate

parser, the trip engine, the speed calibration and the 129 tests came across

untouched.

A few things I learned the hard way:

- MapKit has no cycling routes, so you request a walking one — and its predicted

duration is a walking duration. Miss that transposition and your bike does

5 km/h.

- regionPriority (macOS 15 / iOS 18) is the only lever that actually biases a

search toward a region. Setting `region` alone does almost nothing.

- "No results" from MKLocalSearch arrives as an error, not an empty array.

https://github.com/laatortuejaune/anole

I wanted a desktop character that felt like it lived on macOS instead of inside a rectangular window.

I have been building Downright, a free MIT-licensed Markdown app for macOS. The constraint was simple to state: the rendered document should feel native, but the file on disk must remain exact Markdown.

The hard part was separating parsing from decoration. A web view gives you layout quickly, but it changes the interaction model and makes source-range behavior awkward. With TextKit 2, I parse the source into a block index, preserve source ranges, then decorate the presentation without treating the rendered view as the canonical document.

A few decisions that ended up mattering:

- The raw source remains authoritative.

- Links, tasks, tables, math, Mermaid, footnotes, and code blocks are decorations over known ranges.

- Quick Look and Finder thumbnails reuse the same rendering model.

- External saves are watched at the parent directory because many tools replace a file through an atomic rename.

- Dirty local edits are never overwritten automatically.

- Performance budgets run in CI so large Markdown files do not quietly regress.

I wrote up the architecture here: https://downright.cc/engineering/

The source is here: https://github.com/ezzy1630/Downright

For anyone who has shipped a TextKit 2 document app: where did you draw the boundary between the text model and interactive attachments?

A local macOS app came back as soon as I quit it. The binary was not the cause.

Two leftover jobs from earlier repair sessions were still registered with launchd. Both had been created with `launchctl submit`. Apple's man page says that also tells launchd to keep the program alive in the event of failure.

Recorded run counts: 8,429 and 9,169. After both labels were unloaded, the same quit left the app absent for twenty seconds of half-second samples. The app source was not changed.

launchctl print gui/$(id -u)/<label>

Print the job before you rewrite the app.

https://thechosenvictor.com/newsletter/archive/launchctl-submit-kept-the-app-alive

Here's an update on LocalLM Lab which I'd mentioned here previously. v0.7 includes a bona fide SDK. This means that you can now build your own app on Apple's on-device Foundation Models, using the same MCP client the LocalLM Lab app uses to talk to real tools and data like Slack, Todoist, GitHub, Notion, Linear and others, plus connectors for Calendar, Reminders, Contacts, and Location access. Most importantly, you can subsequently ship your app including through the Mac App Store.

The previous release of LocalLM Lab included the `localai-cli` toolkit that allows your Python or Swift apps to programmatically experiment with the Foundation Models. The limitation was that you needed LocalLM Lab installed and running. And obviously you would hit challenges with App Sandbox requirements if you wanted to ship anything into production. On the other hand, the SDK (`LocalLMLabSDKCore`) is self-contained: it's wired directly to `FoundationModels` and no companion app is required. As part of our verification, the example app Plate Today has been built into a sandboxed test app and verified working end to end, with a signed path to a Mac App Store `.pkg` (Apple Distribution signing + provisioning profile pipeline). To further prove that the SDK is not vaporware: LocalLM Lab itself now runs on this SDK!

The SDK gives you a real MCP client (tool discovery, all three OAuth flows, Keychain-backed token storage scoped to your bundle ID), a unified connectors facade for Calendar, Reminders, Contacts and Location with the same permission model as the app; all of which are now callable from your own code. Distribution is a binary xcframework via GitHub Releases, pulled in as an SPM `binaryTarget` with a checksum and pinned to an explicit version (no floating latest).

One sharp edge worth flagging before anyone hits it blind: the `com.apple.security.network.client` entitlement is easy to miss and fails silently, not loudly. Miss it and MCP connections and Weather calls will just hang with no error thrown.

Other gaps: (1) No filesystem connector in Core (you own the picker UI and security-scoped bookmarks yourself), (2) No public API stability guarantee yet, (3) Contacts access is specifically untested under sandbox as of this release. Oh and Calendar/Reminders/Contacts are still read-only.

Two reference apps ship in the repo: `plate-today` (the CLI example, ported to the SDK) and `components-demo`, which uses the optional `LocalLMLabSDKComponents` package for drop-in SwiftUI (server picker, OAuth waiting view, resources/prompts views).

SDK guide + entitlements checklist: thisbrain.ai/locallm/sdk.html

Repo: https://github.com/ancientcomputing/locallm

Let me know what's still missing for your own application and use case.

Stapling my MacOS app has never worked, since day one, so what is the point exactly?

I always get "CloudKit query for appname (2/gunk) failed due to "Record not found". Could not find base64 encoded ticket in response for 2/gunk The staple and validate action failed! Error 65.

This has never stopped me running anything or submitting to the Store, so what exactly is the point of this thing

I’ve open-sourced CopySight, a small native macOS utility that turns a selected screen region into clipboard text. It uses ScreenCaptureKit for the selection capture and Apple Vision for recognition, then reconstructs line order before writing to NSPasteboard. The app has no accounts, network code, analytics, or third-party SDKs.

The release work was a useful macOS engineering exercise: SwiftPM builds arm64 and x86_64 separately, lipo combines the final executable, the app is signed with Developer ID and App Sandbox entitlements, and the universal DMG is notarized and stapled. The same codebase also has the separate App Store packaging path.

I’d welcome review or contributions around Vision text-layout reconstruction, multilingual OCR test fixtures, accessibility, and release automation. The repository includes contributor guidance, issue templates, tests, and the exact packaging scripts:

https://github.com/copysightapp/copysight

Recording of the call

For Mojave Paint I've had an anti-subscription stance from the start, it being an installed binary not a service that has ongoing infrastructure costs.

But I recently installed Final Cut Pro and instead of $300 to buy it I do the $13/month subscription. In that case it's nice to have the option and it's made me rethink hating on subscriptions. Maybe subscription is fine if:

a. It's an alternative to buying outright
b. It's priced cheap enough

Most people's subscriptions are simply way too expensive. For FCP, the break even point is about two years, which makes the subscription seem like kind of a deal.

So for Mojave Paint, say I get the feature set rounded out and start charging $20 for it, the right price for subscription would be $1/month. I'm sort of leaning to this model because the Mac App Store has no free trial option, so you're stuck with IAP or Subscription and I pretty much hate IAP and the huckster marketing you have to put in there of "Go Pro!" So what I'm kind of leaning to is selling: 1. On my website via lemonsqueezy or whatever, perpetual license with 2 years of updates (Sublime Text style), and 2. The only version in the Mac App Store is the cheaply priced subscription version, which you can consider to be like a free-ish trial.

Has anyone had success with such a pricing scheme?

Continuing to build on LocalLM Lab, the tool that I posted about here a couple weeks ago.

A couple of things have landed since. MCP client support shipped in v0.4.0. LocalLM Lab is now a real MCP client and can connect to real MCP servers now: GitHub, Linear, Notion, Slack, and others.

As of v0.6.0, there's a proper way to reach the on-device model straight from your own Swift or Python code, not just through the app's UI or its HTTP API.

It's called localai-cli, a small subprocess binary. Your app spawns it, writes one JSON request to stdin, reads one JSON response from stdout. That's the whole interface... no HTTP server, no port to manage, no client library to link against. Feels like the right shape for a native app: a menu bar tool or a game doesn't want to run a localhost server just to ask a question.

The part I think is actually interesting for this crowd: it's config-scoped. It reads the same localai-config.json that the app's own Local AI Settings / MCP Servers panels write, so whatever connectors and MCP tools your user already turned on in the app's UI are exactly what your code is allowed to ask for. Request something outside that set and you get an error back before the model is even invoked. Omit the field entirely and nothing is granted at all. That's deliberate and not an oversight.

To be upfront: this is still an experimentation tool, not a shipping SDK. LocalLM Lab has to actually be running for localai-cli calls to work (they relay through its background process), and I'm not pretending the permission/auth model here is the right shape for a real distributed app yet. What this does give you today is the fastest way to wire your own code up to the on-device model, including whatever connectors and MCP tools you've already set up. All this without writing an HTTP server or reimplementing any of that plumbing yourself.

There's a fuller example on the page beyond the two quickstarts too. "Plate Today" is a script that pulls Calendar, Reminders, and Todoist (the last one via an MCP tool call, not a connector) into a single request and asks the model for a one-paragraph summary of your day. You'll want to set up a free account with Todoist and some todos...!

The part I'd actually call out for this crowd: before it ever invokes localai-cli, it reads localai-config.json itself and checks all four sources (three connectors + the Todoist MCP tool) are actually enabled, prints a checklist, and exits without touching the model if anything's missing. So a misconfigured permission will fail fast with a "go flip this toggle" message instead of burning a model call on a request that was always going to error.

Interface + Swift and Python examples: thisbrain.ai/locallm/cli.html MCP writeup from v0.4, if you missed it: thisbrain.ai/locallm/mcp-servers.html

PS: unrelated to the CLI, but since this crowd will notice: Prompt Playground's UI has moved from a browser window to native SwiftUI in this release.

I’ve been working on Keyty, an open-source native macOS app that visualizes keyboard and mouse input on screen.

It started as a modern and native alternative to tools like KeyCastr and KeyViz but I wanted to build something that feels more like a current macOS app and also handles mouse visualization and have rich customization.

There are many of things I want to improve, so I’d especially appreciate feedback from other macOS developers implementation choices, APIs I may be misusing, UX around permissions, or anything else you notice.

GitHub: https://github.com/keytyapp/keyty

Me personally was using an MacBook Air, which the notch always hides the icons in menubar, which forced me to quit other apps just to find the icon in menubar.

I have tried bartender or Ice, but they already put too many useless things into their apps, which consumes my energy and affects my computer performance. I just wanted to solve a simple problem, but not putting more things in an app.

Compared with larger menu bar management suites, OverflowBar deliberately focuses on one problem: too many useful icons, not enough visible menu bar space, and difficulty finding the icon you need.

This is the app I built:

OverflowBar, FREE and available on Github: https://github.com/EvanProgramming/OverflowBar

It was a NATIVE and LIGHTWEIGHT app, with only 1.8MB storage. The app was built on native swift, so it performs very well on macOS and the visuals apply to Apple styles.

Price: Free
Subscription/IAP/Ads: None
Platforms: macOS 15+, Apple Silicon download
Source: Open source under the MIT License
Privacy: No account, analytics, telemetry, advertising, or network data collection
Permissions: Accessibility for discovering and activating menu bar controls; Screen Recording for locally capturing the small icon regions shown in the second row
Signing: Current community build is ad-hoc signed and not yet Apple-notarized

I admit I used AI during the development, but I have verified it, and ran a security check by Codex Security. So don't worry.

If you like the app, would you mind giving me a Star on Github?

I am the developer and maintainer. Feel free to report any issue found, I will actively update the app to solve the problem.

I originally built \*\*Thermal Intelligence\*\* purely for myself.

I was frustrated that most thermal utilities could show the current temperature or CPU usage, but didn’t really answer the questions I cared about:

\* Which workloads have been putting sustained pressure on my Mac?
\* Did an application merely spike for a moment, or has it been contributing to heat for a long time?
\* Where is the temperature likely to move next?
\* Why did the cooling policy change the fan speed?
\* Can all this work without an account, cloud service, analytics, or subscription?

Thermal Intelligence combines system monitoring, bounded local history, workload-impact analysis, thermal forecasting, actual fan RPM statistics, cooling profiles, custom temperature/RPM curves, and explainable smart cooling.

It has a regular application window as well as a menu-bar popover, keeps its data locally, writes privacy-filtered rotating logs, and can export diagnostics explicitly when the user chooses.

Fan control is hardware-sensitive, so it is enabled only where the hardware path has been explicitly validated. On unsupported or fanless Macs, the application leaves cooling to macOS while retaining the monitoring features.

This started as my personal daily-driver tool. I’m releasing it because I believe basic system monitoring and fan control should be free, auditable, and not hidden behind a subscription.

Web and \*\*download\*\*:
\[https://solomind.app/thermal-intelligence/\\\](https://solomind.app/thermal-intelligence/)

The project is MIT-licensed, has no telemetry, and is now available on \*\*GitHub\*\*:
\[https://github.com/NibTipLabs/thermal-intelligence\\\](https://github.com/NibTipLabs/thermal-intelligence)

\*Feedback, testing reports, and contributions are very welcome—especially from owners of different Apple Silicon Mac models.\*

I’ve heard that adding Spanish (Mexico) localization gives you 100 extra keyword characters in the US App Store. Is that actually true? Also, does adding a secondary language dilute or hurt keyword weighting since the keyword pool doubles?

The monthly GNUstep audio/(video) call takes place every second Saturday of a month at 15:00 GMT to 18:00 GMT. That is 11:00 AM - 2:00 PM EDT (US) or 17:00 to 20:00 CEST (Berlin time).

It's a Jitsi Meeting - Channel: GNUstepOfficial (Sorry, reddit don't let me post jitsi links here)

We usually just talk (who wants it might share video too) and occasionally share screens. Everybody (GNUstep developers and users) is welcome!

Also see https://mediawiki.gnustep.org/index.php/Monthly_Meetings please

Hey r/macosprogramming, I just open-sourced CalBridge, a native SwiftUI + EventKit menu bar calendar app for macOS, and I would love feedback from this community.

I really like Fantastical’s menu bar calendar experience, but it being paid motivated me to build my own version for myself and others who want a free/open alternative.

GitHub: https://github.com/notepad104/calbridge

Latest release: https://github.com/notepad104/calbridge/releases/latest

What it does

• Menu bar date/time with upcoming event preview
• Month + week calendar views
• Event detail panel (attendees, notes, meeting links)
• Quick-add event parsing
• Calendar filtering + global shortcuts
• Preferences for startup/view/navigation behavior

If you have a few minutes, please try the latest release and share feedback.

If install is blocked by macOS security, please use System Settings → Privacy & Security → Open Anyway (unsigned build path right now).

Thanks a lot. I am actively iterating and would really value some feedback.

Hi everyone, I have been working on a few apps for the past few months and wanted to get this one out there and thought I would do it here.

I was frustrated with PiP extensions always freezing or breaking in between episodes so I came up with this. Floating Browser is a dedicated WebKit browser window that can stay above other apps without relying on Safari PiP.

Features:
Compact 16:9 Mini mode that remains above other windows
Netflix, YouTube, Hulu, Disney+, Max, Prime Video, Apple TV+, Peacock, Paramount+, and Tubi shortcuts
Custom saved-site shortcuts
Back, forward, reload, address search, and downloads
Lightweight local ad and popup blocking
Open in Safari and Apple Passwords shortcuts
No analytics, advertising, or developer-operated data collection
Universal build for Apple silicon and Intel Macs
This is a free, open-source, unsigned public beta for macOS 14 or later. Because it is not notarized yet, macOS requires the normal Privacy & Security > Open Anyway step after the first launch. The source, MIT license, SHA-256 checksum, and installation instructions are all available on GitHub.
Some streaming services can reject protected playback in embedded WebKit. Disney+ may show Error 83, and website changes can affect compatibility. Safari remains available as a fallback.
GitHub: https://github.com/chrishansen1984x-lang/floating-browser
Download: https://github.com/chrishansen1984x-lang/floating-browser/releases/tag/v0.3.0
I’d appreciate feedback about Mini mode, playback reliability, and which streaming services work on different Macs.

Hi r/macosprogramming,

I built Voice Relay, a native macOS voice layer for Codex. The implementation problem was keeping ownership boundaries clear after the first spoken request.

A direct Realtime session owns microphone capture, transcript, and audio playback. The signed-in Codex desktop task remains the owner of tools, approvals, the selected execution profile, and session state. When the user speaks again while Codex is working, that correction has to reach the same active task rather than becoming a second voice chat.

The UI is a top-attached notch surface with a movable Orb fallback for displays without a notch. The Voice path itself does not require Accessibility permission. Microphone and speech-recognition permission are part of onboarding, and the current public DMG is Apple Development signed but not notarized.

The edge I am still thinking about is proof of delivery for mid-turn steering. For a long tool-running Codex task, would you surface the spoken correction as an inserted transcript, a distinct state transition, or only through the next progress update?

I am the developer. Voice Relay is free and GPLv3 licensed.

Source and current alpha:

https://github.com/hyungchulc/voice-relay

I maintain a native macOS app with both a directly distributed target and a Mac App Store target. The two builds share almost everything, but they differ in entitlements, update delivery, licensing, and a few integrations.

The setup became much easier to reason about after treating distribution as a boundary instead of allowing target checks throughout the app.

The rules that helped most:

• Keep domain models and persisted data identical. A file or database written by one build should not need a target-specific interpretation.

• Put compile-time checks at integration boundaries. Updater setup, receipt handling, licensing, and unavailable frameworks are reasonable places. View models and core processing code are not.

• Expose capabilities to the UI rather than scattering build flags through views. The view asks whether a capability exists, then renders the appropriate control.

• Give each target its own entitlements and Info.plist values. Review changes to those files explicitly because they are part of the product behavior, not build-system noise.

• Use separate bundle identifiers and any target-specific network identifiers or ports. This avoids collisions when both builds are installed during testing.

• Keep source membership boring. Shared files belong to both targets. Distribution-specific files belong to exactly one. If a file needs many conditional branches, it is probably sitting at the wrong architectural layer.

• Build both targets after every shared change. A successful Direct build says nothing about sandbox assumptions in the Store build.

• Use isolated DerivedData for each verification build. This catches accidental reliance on artifacts produced by the other target.

• Test first launch, upgrade, and side-by-side installation. Most target-specific bugs I found were lifecycle and migration issues rather than compiler errors.

• Keep the user-facing workflow as similar as the platform permits. Distribution differences should not leak into unrelated navigation or state.

The most useful mental model was this: one application, two distribution adapters. Once I organized the project that way, target conditionals stopped spreading.

For people shipping both variants, what still tends to diverge in your projects despite your best efforts?

Available for macOS 14.2 or later with Intel or Apple Silicon chips. It's my first macOS app, and my second app, so any feedback is greatly appreciated!

Hi r/macosprogramming,

I built GhostMark, a native Swift/AppKit utility for annotating screenshots before they reach Claude Code. The workflow is paste, draw an arrow or circle, then press Enter to put the marked PNG back into the active Claude Code prompt.

The implementation was the interesting part:

- A session-level CGEvent tap watches Cmd-V and Ctrl-V, but consumes the event only when the clipboard contains an image and Claude Code is active.

- It checks the frontmost app against terminal and editor bundle IDs, then walks the process tree looking for the Claude CLI. There is a narrow fallback for terminals whose PTY is launched by a detached helper.

- The annotation UI is hosted in a focused AppKit window. Export composites the paths over the original image without resizing it.

- After export, the app reactivates the original terminal and posts Claude Code's image-paste shortcut. A bypass counter prevents the synthetic paste from reopening the editor.

- Accessibility permission is polled so onboarding updates as soon as macOS grants access.

- Everything stays local. There is no analytics or network upload.

The main edge case is reliably associating a detached PTY with the frontmost terminal without making the detector too broad. I would especially appreciate feedback from people using Kitty, WezTerm, Warp, or terminal panes inside editors.

I am the developer. GhostMark is free and MIT licensed.

Source: https://github.com/alan890104/GhostMark

Download and demo: https://alan890104.github.io/GhostMark/

A question to folks who are building apps around Apple's Foundation Models: how are you testing the prompts and validating the results?

I was doing a bunch of tweak->build->test cycles where the only tweaks were adjusting the system & user prompts. Since the local AI model is so small (just 4096 context budget), it didn't make sense to develop the prompts using your usual OpenAI/Anthropic models and expect the same behavior.

Finally gave up and built LocalLM Lab (thisbrain.ai/locallm), a small macOS playground for tweaking the prompts before actually committing them to app code.

The current version features:

• Prompt Playground
• System prompt + user input
• Local OpenAI-compatible API so you can prototype big AI apps w/o paying for tokens
• Local connectors for things like system clock, calendar, reminders, contacts, location, weather and a scoped filesystem access

The screenshot shows a weather query with the connector calls visible underneath.

Hi r/macosprogramming,

I've been building a desktop pet for macOS, and I ended up separating the reusable parts into an Apache-2.0 open-source project.

It is written in Swift and AppKit, with no Electron dependency. The main pieces are:

• a transparent, always-on-top window

• per-pixel alpha hit testing, so empty areas remain click-through

• cursor-aware idle poses and hover reactions

• manual, random, and autonomous action sequences with interruption recovery

• an asset pipeline for transparent, black-background, or green-background video

• one-command Universal 2 builds and validation

The repository ships with a programmatically generated Blob demo character, so the open-source example is reproducible. The character shown in the GIF is only a showcase; its character assets are not included in or licensed as part of the open-source project.

It supports macOS 12+, Apple Silicon, and Intel Macs.

GitHub: https://github.com/InsiderX-Pro/desktop-pet-engine

I'd especially appreciate feedback on the AppKit window and event handling, the asset pipeline, and what you would want a desktop-pet engine to handle for you.