Category Archives: Uncategorized

Uncategorized

CLI-Driven Development: Building AI-Friendly iOS and Mac Apps

I’ve been using Claude Code for several months now on many personal projects, and I’ve worked out some practices that work really well for me as an iOS and Mac developer. This CLI-driven development approach has fundamentally changed how I build applications with AI assistance.

The Problem with GUI-Based Development

One of the most powerful aspects of AI coding assistants is their ability to receive feedback and iterate on solutions. Give an AI a goal, and it can refine and improve until it gets there. However, if you’re an iOS or Mac developer, you’ve likely hit a wall: GUI interfaces are opaque to AI systems.

You could set up tooling to capture simulator screenshots, but this approach is slow and error-prone. By the time the AI gets a screenshot, analyzes it, and suggests changes, you’ve lost the rapid iteration cycle that makes AI assistants so valuable in the first place.

The other option is writing comprehensive unit tests. If you’re not doing test-driven development with AI yet, CLI-driven development is a nice stepping stone toward that goal. It has the added flexibility of interacting with real data—somewhat like an end-to-end test. Tests are still important, but this is another tool in your toolbelt for those not ready to go full TDD.

The goal is to give the AI the full context of your running application so it can fully interact with it.

Note: See the caveats section below regarding respecting user privacy and security when giving AI access to application data.


The Solution: CLI-Driven Development

CLI-driven development means architecting your application so that every use case accessible via the UI is also accessible via a command-line interface. The UI and CLI becomes access points to these use cases, rather than containing the business logic itself.

This isn’t a new idea. We’ve been told for years not to put business logic in view controllers or SwiftUI views. When working with AI, this separation becomes critical.

Benefits

  1. Better Architecture: Enforces separation between UI and business logic
  2. Faster Debugging: AI can identify and fix issues more quickly
  3. Faster Feature Development: AI has a way to give itself feedback
  4. Improved Testability: These principles make your app more unit-testable too
  5. Easier Data Migrations: AI can access and transform your real data

Architecture: Three Targets

I’m vastly oversimplifying the architectural requirements for a real application here. Folks will have their choice of patterns, frameworks, and approaches. I’m focusing on the minimal Swift package setup that will allow for this flow to work.

Think of your application as having three distinct targets within a Swift package:

  1. UI Target: Contains your SwiftUI views, view controllers, and UI interactions
  2. CLI Target: Handles command-line input/output
  3. Core Target: Contains all business logic, services, data interactions, and workflows

Both the UI and CLI targets are thin layers that simply pass data to the Core. When a user taps a button, the UI sends data to a service. When you run a CLI command, it does the same thing.

Shared Services in Core

This approach requires discipline you probably want anyway: every new feature or interaction needs a single method that can be called with all necessary parameters, with all the important logic happening outside your views. These shared services live in your Core target.

Here’s a brief example of a service to add a TODO:

Core Target – TodoService.swift:

public class TodoService {
    public func addTodo(listName: String, item: String) throws {
        // All business logic lives here
        let list = try getOrCreateList(name: listName)
        let todo = Todo(title: item, completed: false)
        list.todos.append(todo)
        try persistenceManager.save(list)
    }
}

CLI Target – main.swift:

// Parse command: ./MyAppCLI add-todo --list "Grocery" --item "Buy milk"
let service = TodoService()
try service.addTodo(listName: listName, item: item)
print("Added: \(item)")

Mac App Target – AddTodoView.swift:

struct AddTodoView: View {
    @State private var itemName = ""
    let listName: String
    let service = TodoService()

    var body: some View {
        TextField("Item", text: $itemName)
        Button("Add") {
            try? service.addTodo(listName: listName, item: itemName)
        }
    }
}

Both the CLI and Mac app call the exact same TodoService.addTodo() method. The business logic is shared, and both interfaces benefit from any improvements or fixes.


What Can You Do With This?

Debugging with AI

Let’s say you see an issue when adding a TODO to your Grocery list in the Mac app. Here’s how the conversation might go:

> I see a bug when adding a TODO to my Grocery list. Check it out.

Let me test this using the CLI:

$ ./MyAppCLI add-todo --list "Grocery" --item "Buy milk"

$ ./MyAppCLI list-todos --list "Grocery"
Grocery List:
  - Buy milk
  - Buy milk

I see the issue - the TODO is being added twice. Let me check the add-todo implementation...

[AI fixes the duplicate insertion bug]

$ ./MyAppCLI list-todos --list "Grocery"
Grocery List:
  - Buy milk

Fixed! The issue was in the data persistence layer where we were saving twice

The AI can use the CLI to reproduce the issue, see what’s happening, and iterate on fixes until it’s working correctly.

Building Features Faster

When building a feature, you can give the AI requirements and have it verify its work. Here’s an example:

> Add a feature to mark TODOs as complete. When I mark a TODO as complete,
> it should show with a checkmark when I list them. Verify it with the CLI
> and make sure it works before you're done.

I'll implement the complete-todo feature. Let me build it and test it:

[AI implements the feature in Core target]
[AI adds CLI command for marking complete]
[AI updates list display to show completion status]

Now let me test it:

$ ./MyAppCLI add-todo --list "Grocery" --item "Buy milk"
Added: Buy milk

$ ./MyAppCLI add-todo --list "Grocery" --item "Buy eggs"
Added: Buy eggs

$ ./MyAppCLI list-todos --list "Grocery"
Grocery List:
  - [ ] Buy milk
  - [ ] Buy eggs

$ ./MyAppCLI complete-todo --list "Grocery" --item "Buy milk"
Marked complete: Buy milk

$ ./MyAppCLI list-todos --list "Grocery"
Grocery List:
  - [✓] Buy milk
  - [ ] Buy eggs

Perfect! The feature is working. TODOs now track completion status and
display with checkmarks when completed.

The AI builds the feature and verifies it works before reporting back. In theory, you’ll see the same results in the UI application.

Data Migration and Transformation

During early iterations, you frequently need to migrate data when properties change. With a CLI, you can ask the AI to transform all your proof-of-concept data because it has access to all the APIs necessary to read and write data.

> Migrate my TODO data from the old JSON format to the new format.
> Make a backup first, then verify the results match exactly.

$ ./MyAppCLI export-data --output todos_backup.json
Exported 127 TODOs to todos_backup.json

[AI implements migration from old format to new nested format with IDs]

$ ./MyAppCLI migrate-data --from todos_backup.json --to-new-format
Migrating 127 TODOs...
Migration complete.

$ ./MyAppCLI verify-migration --old todos_backup.json --new current
✓ All 127 TODOs migrated successfully
✓ Data integrity verified

Migration complete. Backup saved at todos_backup.json.

Important Caveats

This is Not a Replacement for Unit Tests

Unit testing leads you toward similar practices of keeping services separate from UI. Following CLI-driven development actually makes your app more testable. I suggest using this approach for both unit testing and CLI-driven development. Don’t focus on CLI-driven development alone.

Security and Privacy Considerations

You must respect your customers’ privacy and security. Be mindful when giving AI access to data and understand your privacy requirements. The CLI should be treated as a development tool with the same security considerations as direct database access.

Apple, Development, Uncategorized

The Dangers of Mixing Locks with Core Data

Codebases accumulate patterns over time that don’t match up with current best practices. These patterns might have made sense when they were written, or they might just reflect how our understanding has evolved. Before you can address  these patterns, you need to spot them and understand why they’re risky.

One particularly dangerous combination in iOS codebases is mixing locks (@synchronized, NSLock, etc.) with Core Data’s performAndWait. These patterns were used to maintain synchronous operation patterns, but together they create hidden cross-thread dependencies that lead to deadlocks, freezing your app.

This shows exactly how these deadlocks occur, so you can recognize and avoid them in your own code.

A Simple Shared Class

Let’s start with a basic class that manages some shared state. This shows a common pattern from before Swift concurrency – using dispatch queues to manage background work. This class might be accessed from multiple threads:

  • Main thread: reads the operation status description
  • Background thread: Starts a background operation
class DataProcessor {
    var currentOperationIdentifier: String = ""
    var currentOperationStatus: String = ""
​
    // Called from main thread
    func getDescription() -> String {
        return "Operation \(currentOperationIdentifier) has status: \(currentOperationStatus)"
    }
    
   // Called from background thread
  func startBackgroundOperation() {​
      currentOperationIdentifier = "DataSync"
      currentOperationStatus = "Processing"​
      // Do processing
  }
}

The Problem – Race Conditions

When dealing with multiple threads, execution can interleave unpredictably. One thread executes some code, then another thread slips in and executes its code, then back to the first thread – you have no way of knowing the order.

Here’s what can happen:

Background ThreadMain Thread
currentOperationIdentifier = “DataSync”
(about to update status…)
getDescription()
reads identifier → “DataSync” ✓
reads status → “Idle” ❌ (old value!)
currentOperationStatus = “Processing”
❌ too late – main thread already read old value

The main thread ends up with the new identifier but the old status – a mismatch that leads to inconsistent data.

There are better solutions to this problem – like bundling related state in one immutable structure, or using actors in modern Swift. But in legacy codebases, synchronous locks were a common strategy to protect shared state.

Adding Locks for Thread Safety

The lock creates “critical sections” – ensuring we either write to both properties OR read from both without other threads interfering.

class DataProcessor {
    private let lock = NSLock()
    var currentOperationIdentifier: String = ""
    var currentOperationStatus: String = ""
​
    func getDescription() -> String {
        lock.lock()
        defer { lock.unlock() }
        
        return "Operation \(currentOperationIdentifier) has status: \(currentOperationStatus)"
    }
​
    func startBackgroundOperation() {
        lock.lock()
        defer { lock.unlock() }
​
        currentOperationIdentifier = "DataSync"
        currentOperationStatus = "Processing"
    }
}

So far, this works fine. The locks protect our shared state, and both threads can safely access the properties.

The Deadlock – When Locks Meet Core Data

Now let’s assume we want to store this data to Core Data. This is where things get interesting.

When sharing Core Data across threads, you can run into race conditions just like we had earlier. So you need to use the right APIs to protect the critical sections too.

Your go-to is performBlock – it asynchronously performs the work safely. However, there are cases in legacy code where the caller needs to do something synchronously using performAndWait. When you call performAndWait on a main queue context, it blocks the calling thread until the block executes on the main thread. Think of waiting on the main queue as our “lock”.

Let’s assume some developer in the past (who definitely isn’t you) decided to use performAndWait here:

func startBackgroundOperation(with context: NSManagedObjectContext) {
    lock.lock()
    defer { lock.unlock() }
    
    // Assume the main thread tries to call
    // getDescription() at this point. 
    // It is blocked as we are holding the lock

    currentOperationIdentifier = "DataSync"
    currentOperationStatus = "Processing"
​
    // 💀 DEADLOCK HAPPENS HERE
    context.performAndWait {
        saveDataToStore(context: context)
    }
}

Why Does This Deadlock?

There’s a problem:

  • performAndWait needs the MAIN THREAD to execute this block
  • The MAIN THREAD is blocked waiting for our lock (in getDescription)
  • We’re holding that lock and won’t release until performAndWait completes

CIRCULAR WAIT = DEADLOCK

Timeline of the Deadlock

Background ThreadMain Thread
lock.lock() ✅
Updates propertiesCalls getDescription()
Still holding lock…             lock.lock() ❌ WAITING…
Waiting on performAndWait() (needs main thread)Can’t process – stuck waiting on lock!
  • Main thread: stuck in lock.lock() waiting for background thread
  • Background thread: stuck in performAndWait waiting for main thread

 How to Fix This Deadlock

The best solution is to eliminate performAndWait entirely and use the asynchronous perform instead. This breaks the circular dependency because the background thread no longer waits for the main thread:

func startBackgroundOperation(with context: NSManagedObjectContext) {
    lock.lock()
    defer { lock.unlock() }

    currentOperationIdentifier = "DataSync"
    currentOperationStatus = "Processing"

    // ✅ No deadlock
    // doesn't block waiting for main thread
    context.perform {
        self.saveDataToStore(context: context)
    }
}

If you absolutely cannot eliminate performAndWait, you’ll need to carefully analyze all lock dependencies, but this is error-prone and hard to maintain. The real fix is embracing asynchronous patterns.

What We Learned

In this article, we’ve seen how mixing locks with Core Data’s performAndWait creates a classic deadlock scenario:

  1. Race conditions can occur when multiple threads access shared mutable state
  2. Locks were traditionally used to protect this shared state with critical sections
  3. performAndWait works like a lock but requiring the main thread to execute
  4. When a background thread holds a lock and calls performAndWait, while the main thread is waiting for that same lock, we get a circular dependency – neither thread can proceed

Coming Up



Future articles will explore other ways you can hit or avoid these deadlocks:

  • Child contexts with read operations – Why using a child context doesn’t save you from deadlocks during fetch operations
  • Child contexts with write operations – How save operations on child contexts create the same circular dependencies
  • Private Contexts – Why private contexts with direct store connections are less likely to lock up

Development, Productivity, Uncategorized

A Simple Checklist for Debugging Regressions

I’ve been thinking about a process I’ve used for resolving regressions that may be useful to share. I’ve never explicitly written the steps down before, but figured it was worth capturing—both for myself and others.

When a regression shows up, there are three questions that I’ve found you have to answer. Skipping any of them usually leads to confusion, wasted time, or a fix that doesn’t actually solve the real problem. But if you take the time to work through them, you can usually land on the right answer with confidence.

1. Can you reproduce the problem?

A lot of engineers want to jump straight into the code. That’s the fun part, right? Digging through logic, inspecting diffs, reasoning your way to a fix. But if you can’t reliably reproduce the issue, studying the code is usually a waste of time. You don’t even know where to look yet.

Reproducing the problem is the first real step. It’s not glamorous, and it can feel a little silly—especially when you’re trying the same steps over and over with tiny variations. But this is one of the most valuable things you can do when a bug shows up.

As engineers, we have a special vantage point. We know how the code works, and we often have a gut instinct about what kinds of conditions might trigger strange behavior. That gives us a real edge in uncovering subtle issues—so don’t think you’re above tapping on an iPad for hours or running the same test over and over. It’s our duty to chase it down.

Once you have a reliable repro, everything gets easier. You can try fixes, stress other paths, and most importantly, build real confidence that your solution works.

Some useful tricks:

  • Adjust timing, inputs, or state to help provoke the bug
  • Script setup steps or test data to save time
  • Loop the behavior or stress threads to make edge cases more likely

2. What changed?

This step is often skipped. People jump into debugging without first understanding what changed. But the fastest way to track down a regression is to compare working code to broken code and see what’s different.

This question can feel sensitive. It gets close to specific contributions that may have introduced instability. I’ve seen plenty of cases where the discussion gets deflected into vague root causes or long-term issues—anything but the specific change. That’s understandable. We’ve all been there. But avoiding the question doesn’t help. It puts the fix at risk and slows everyone down.

Some go-to techniques:

  • Review pull requests and diffs
  • Trace from crash logs or error messages to recently changed code
  • Use git bisect to find the breaking commit
  • Try reverting the suspected change and see if the issue disappears

Once you find the change, test your theory. If undoing it makes the problem go away, you’re on the right track.

3. Why does it happen?

Knowing what changed isn’t enough. You need to understand why that change caused the issue. Otherwise, you’re just fixing symptoms, and might miss deeper problems.

This is where the real problem solving happens:

  • Read documentation for the APIs or system behavior involved
  • Think through the interaction between components or timing
  • Build a mental model and prove it with experiments or targeted tests

You don’t want to ship a fix that works by accident. You want one that works because you actually understand the problem. That’s what prevents repeat issues and edge cases slipping through.

Wrapping up

These three questions — Can you reproduce it? What changed? Why does it happen? — have helped me find and fix bugs more reliably than anything else.

It’s easy to skip them under pressure. It’s tempting to merge the first thing that seems to work. But without answering all three, you’re flying blind. You might get lucky. Or you might end up wasting hours chasing your tail or shipping the wrong fix.

Uncategorized

Productivity Resolutions

Productivity is one of my favorite discussion points. Not because I’m an expert (far from it) but rather I’m constantly learning and it affects nearly every aspect of life. I was drawn to the Get Things Done methodology years ago and more recently inspired by a book called Deep Work. There is a firehose of information out there but only so many changes I can make at once. I’ve reflected on some of my core habits and decided it is time for concrete adjustments. Here is a list of my productivity resolutions for the new year which I hope can help you too.

1. Multi-day Project Focus

I previously split up each day to work on several different projects. I’d spend two hours on project A, two hours on project B, …, and rinse and repeat tomorrow. I relied on my Apple Watch timer to tell me when it was time to move to the next project. While touching every active project daily is satisfying, the practice breaks momentum and hinders engagement.

Instead, I plan to work on a single project for several consecutive days, until it is finished. I’ve experimented the last few weeks and the results have been promising. I’ve been more engaged and even caught myself working well past my usual quit time. Additionally, I was thinking more clearly and creatively about the task under focus. Interruptions occurred but I was quick to mitigate the impact on my current project.

2. Define “Done” For Everything

Business projects often have a clear deliverable that will mark its completion: “Email the proposal” or “Publish the blog post”. But personal projects can be more open-ended: “Learn C++” or “Learn how to cook”. The realm of self-development and learning can fall in this trap easily which may only be defined by improving a personal quality. This can lead to aimless experimentation and wandering. These projects tend to outlive everything else on my to-do list.

For the new year, I won’t plan to work on something if I haven’t defined what it means to complete it.  Rather than “Learn C++”, try “Release an iOS app that uses C++”. Or instead of “Learn how to cook”, I could “Cook a great meal for my parents” (Mom and Dad: this is an example only).

I recently wanted to learn about Apple Shortcuts. My technique was to make something useful for myself. This was a step in the right direction but it was hard to know when I was done. I decided to define “done” as releasing a post about the experience and sharing the Shortcut. Hitting the “Publish” button and uploading the Shortcut was a small victory and gave me permission to move onto other projects.

3. Face the Unfinished

The early stages of a new project are exciting. The possibilities are wide as you plan for the future. But once the scope is defined and the plan is established, the excitement may dissipate. I’ve been guilty of chasing the initial high on personal projects before. These abandoned efforts are mentally draining. A nagging, half-finished initiative decreases my motivation to conquer new goals.

In 2019, I plan to revisit my unfinished, important work before starting something new. I’ll define a deliverable if one didn’t exist (see #2). Then I’ll singly focus on it until complete (#1).  I mentioned the thrill of starting a new project but that is no comparison to the satisfaction of finishing it.  

Summary

These productivity changes will be experimental for me and I’m sure there will be complications to consider. I’ll report back on the experiences. What productivity strategies work best for you?

Uncategorized

Shortcut to Upload Screenshots to Jira

Apple’s commitment this year to promote the Shortcuts app, formerly known as Workflow, has spurred excitement among a growing group in the iOS community. Shortcuts allows users to automate iPhone/iPad tasks, analogous to the Mac’s Automator. While this app is accessible to those without any development experience, I’d like to share the experience of developing my first Shortcut as an iOS developer. Additionally, I hope the resulting Shortcut will save some Jira users some time. First, here is the background on the process I chose to optimize with Shortcuts.

Nearly everybody involved in creating software needs tools for documenting software bugs. The most ubiquitous tool for tracking bugs is Jira which I use daily.  To track a new bug in Jira I stumble upon on an iOS device, I follow these steps:

  • Capture a screenshot or video of the bug on my iPad/iPhone.
  • Create a case in Jira documenting the bug.
  • Annotate or trim the aforementioned media.
  • Upload and attach the media to the Jira case.
  • Delete the media from the iPad/iPhone.

This process seems simple enough in theory but has some minor challenges.  My iPhone and iPad Photos app is littered with software screenshots, some months old that I neglected to delete after uploading to Jira. Attaching the screenshot to the case can also be cumbersome as I need to move the media to the device that has the related Jira case open. The Jira App for iOS can make this simpler; however, I prefer to create cases on my Mac for speed of entering the details.

My aim was to optimize this with a Shortcut that can walk me through the steps of uploading my most recent media to my most recent Jira case and the subsequent media deletion. I was excited that Shortcuts was up for the task and jumped right in. 

Shortcuts First Impressions

I’ll admit that I spent no time reading documentation or watching tutorials for Shortcuts before getting started. I think that speaks to the approachability of Shortcuts (and my impatience to play).

The Shortcut interface offers many options that require no background in development. Using Shortcuts’ drag-and-drop interface, one can compose actions to play a song, send a text message or search the web, for example, without any familiarity with programming languages. But much more power exists in this tool that will be familiar to developers and an excellent primer for those that want to learn more about development.

Simple Shortcut To Send Text Message

Some of the features familiar to developers are variables, conditional and control constructs, arrays/dictionaries and code comments — just to name a few. There are also powerful APIs available to fetch data from from the web, parse JSON and even SSH to other devices (and much more). I was delighted to find so much flexibility so I did not plan to limit the scope of this Shortcut initially.

More Complicated Shortcuts Using Web Services

Lessons Learned

Like many complex apps, Shortcuts will be more efficient to develop on an iPad than an iPhone. The larger form-factor and persistent sidebar makes it much easier to navigate the interface and add actions. The use of an external keyboard can help as well and I’m curious how the Apple Pencil would improve drag-and-drop functionality. .

But the iPhone makes it very convenient to develop Shortcuts during small idle periods when your iPad may not be on hand (Uber, bank line, etc..). This was a new experience for me and I got hooked to it the way many play games on their phones. I have some mixed feelings on the distractibility factor but developing in spare contexts was interesting.

This Jira Shortcut morphed into a more complex tool than I expected. It makes several API calls to Jira, processes the results, requests input from the user, shows error messages and more. As a developer, my inclination was to break these individual components into smaller Shortcuts for modularity and usability. This was clearly a wrong turn when I decided to share the shortcut which would have required a user to download multiple Shortcuts for one feature. So I ended up joining 3 different Shortcuts into one Shortcut as I neared completion.

Security also required thought on this project. The Shortcut will need access to a user’s Jira credentials (username + password or token). All the actions and parameters for a Shortcut are stored to the Shortcut app’s sandbox. While storing usernames/passwords is not ideal, the primary risk I see here is a user accidentally sharing those credentials if they ever redistribute their Shortcut (“Hello World.. oops here is my login!”). I attempted to work around this by using import questions which will request sensitive user information on installation and not share those user inputs if that Shortcut is later shared. I have not verified this so caution against sharing any Shortcuts with personal information captured.

Conclusion

Nearing the end of this project, I realized there is a limit to how much additional functionality you want to pack into a Shortcut before you may want to consider writing an iOS app instead. It can be tricky editing a Shortcut since it lacks copy/paste support for actions and the lack of functions makes it difficult to reuse logic. I also missed simple common language features such as “else if” and “break/continue”. Finally, all this work to develop the Shortcut cannot be ported outside the context of the Shortcut (i.e. another iOS app, Mac, etc..).

But Shortcuts is not designed to be a replacement for traditional software development or apps. It is however an excellent tool for automating tasks on the iOS platform, even for iOS developers. I’d certainly use Shortcuts again when the task to automate is the appropriate complexity level. Then if it gets too complicated, I’ll consider writing an iOS app. I hope non-developers can use Shortcuts too as an introduction to software development in a familiar environment.

If you are a Jira user and wish to fiddle with this Shortcut with your account, you can download it on my Github. I welcome feedback and pull requests.

Uncategorized

Old Washing Machine Learns New Tricks

I can’t count how many times I’ve started a load of wash and forgot about it until the next day. Then I’m forced to play the game of clothing roulette — maybe it is not too late to throw it in the dryer? But if the post-dryer smell check reveals my assessment was wrong, the cycle needs to repeat again.  My 27-year-old washer is not winning any eco-awards these days — it doesn’t need to add “redundant loads” to its list of offenses.

There are features in new washing machines to help prevent the rewash problem – an all-in-one washer/dryer or a smart washer that texts you notifications when the cycle completes. Purchasing a new washer is likely the best solution from the standpoint of energy efficiency and time but where is the fun in that 🙂 I read about a few other hobbyists that tackled this problem so I was inspired to try my own version.

The brains behind this washing machine hack is the Raspberry Pi Zero W model, which can be purchased used for around $25. I coupled it with a vibration sensor, available for less than $5, to detect the shaking of the washing machine. Several magnets were glued to the inside of what was formerly a drill bit case for easy attachment to the side of the washer.

Raspberry Pi + Vibration Sensor

Magnetically Secured

After assembling and coding this simple vibration monitor, I recorded the activity of a single 40 minute wash cycle. The spinning and washing stages of a cycle trigger millions of vibration events while the filling stages produce motionless periods for up to 6 minutes. Understanding these motion and idle phases helped me to write the C++ code that tracks when the cycle appears to start and end.

With this information, I could alert myself after every wash cycle completes with a text message that it is time to unload. While a text reminder is helpful, it seemed like a glorified timer. It would be best to automatically track when I’ve emptied the washer too. That way I only get the text messages when necessary — when I forget to unload. I considered monitoring the existing washing machine lid switch to detect when it was opened and closed but I didn’t have the hardware available to make the connection.

Looking for other options, I observed that this old washer lid makes a surprisingly strong thump when closed. I opted to use the resulting vibration from closing the lid as a cue that the clothes had been removed from the washer. If that thump is detected within 4 hours a of a completed cycle, no notification is necessary as it is assumed it has been emptied; otherwise, I get the following text message.

This project was fun to implement, troubleshoot, and solves a unique personal challenge which is always a bonus. I don’t really have plans to expand this hack as it basically resolves this very specific problem. I am looking forward to finding another excuse, I mean reason, to “fix” something else around the house with a Raspberry Pi.

Uncategorized

Raspberry Pi Pet Feeding Tracker

1 Comment

I’ve been looking for an excuse to make something useful out of my Raspberry Pi. For those unfamiliar, the Raspberry Pi is an inexpensive, small, programmable computer which can interface between just about any “kind” of hardware you can imagine. You can use it to automate your home, create your own robots and much, much more. As a software developer, tinkering with real-world hardware that can be controlled by my own software is a very compelling prospect.

My Raspberry Pi (a computer)

I had high ambitions initially of creating my own robots,  magic mirrors, and smart locks. Reality set in that this tech is new to me and I need a low bar for the first experiment. After weeks of tinkering with the basics of electronics, I opted to focus on building something for our two dogs whom I likely won’t disappoint.

Schenley and Luna’s dog food resides in this dresser in our living room. They are fed 3 meals per day on a schedule. My wife and I sometimes lose track of when the last feeding occurred. This leads to the dogs occasionally skipping a meal or getting fed too often.

I wrote a simple software program for the Raspberry Pi that is aware of their ideal feeding schedule — 6am, 12pm and 6pm. This program will signal a small red LED, mounted just below the cabinet drawer, when a feeding is due. A completed feeding is detected by a magnetic sensor, hidden under the dresser drawer, that tracks when the cabinet drawer is opened then closed.

Magnet attached to bottom of drawer

Hall Effect Sensor mounted to surface under drawer. Magnet on drawer will slide over this sensor.

The basic flow is if the LED is on, we fill the bowls with food. The light will then go out until it is time to feed them again. This removes the guess-work out of when they had their last meal or if it is too early for the next feeding.

Small red light below drawer indicates a feeding is due

Light extinguished when drawer is opened to feed

I have a few ideas for expanding this dog feeding theme. A text messaging service running on the Raspberry Pi could alert us when a feeding is overdue. On the hardware end, I can include a water sensor in their water bowl to monitor when their water is low and send similar alerts.

This Raspberry Pi project was easy enough that I could hack something together using just some basic online tutorials. I recommend exploring this if you are new to hardware or software projects and want to learn more.

Raspberry Pi 3 B+ Kit Purchased

My code on Github (C++)