ITHCWY has been running on ASP.NET 4.8 for a long time. I've been putting off the upgrade as the official Microsoft documentation says something close to 'your funeral'. Visual Studio Copilot added a modernization agent. It claimed to have generated a plan, but the file it insisted it had just written was hallucinated. So I rolled up my sleeves and did it the hard way.

Hard is an overstatement. I did some much needed refactoring and jettisoned a bunch of dead code. The regular Copilot (via Claude Sonnet 4.5) was a big help on things that didn't exist any more or needed to be done differently. The new OutputCache refused to disengage until I entirely killed the default and I need to spend some more time there. It seems to matter which order you enable server features which is moderately terrifying but probably doesn't need to be touched often. It's certainly better than poking around in web.config and hoping for the best. If you're reading this then it has been served by asp.net core 10.

Probably some subtle things are broken and it usually takes a while to mop everything up after a migration this big. If you run into any problems please get in touch.

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(Published to the Fediverse as: Blog Engine Upgrade #code #ithcwy #c# #asp.net Migrating from ASP.NET 4.8 to asp.net core 10 )

Aurora is an open source smart assistant designed for the Raspberry Pi and functional on Windows, Mac and anything else that can run Python.

Aurora uses the OpenAI realtime API, and is pretty similar to the advanced voice mode in ChatGPT. The idea is a replacement for Amazon Alexa and Google Home that anyone can build and extend.

I have had this running for nearly a year. It was functional but the code was a mess. OpenAI just promoted their realtime API from beta, and that prompted me to refactor the project and get it into shape for a public release.

While Amazon is slowly rolling out an LLM version of Alexa, I'm not buying another device that just wants to show me ads. I also don't want an assistant that has been engineered to minimize costs. Aurora is an exercise in what is possible using the best models available combined with helpful tools.

The project has a plug-in architecture for tool support, so you can pick and choose what makes sense. This first release supports timers, Perplexity Sonar, Todoist and a kid conflict resolution system that in my house is known as 'cheese night'. I'll be adding more tools over time, and the architecture makes it easy to contribute (please do!).

When I first designed Aurora I used an Adafruit BrainCraft HAT which has a small display and some nice LEDs. This combined with a Raspberry Pi 4 and a couple of speakers makes for a compact assistant. The project contains interface code for this specific setup, and also a generic version that will run anywhere. As with tool support the interface can be extended to support other devices.

Check out Aurora on GitHub, contribute if you come up with a cool tool or UI, and please send me a photo if you build an assistant.

Updated 2025-09-13 21:56:

Added support for the Bay Area 511 API to provide arrival times. You configure an agency and stop ID and then can ask 'when is the next L' and get arrival times. This is really useful for me. There is a new tool calling image state for the Raspberry Pi version and times are now poems based on the timer name.

Updated 2025-09-21 19:44:

Recipe support added. Ask Aurora to cook with you and she will guide you step by step through a recipe.

LIFX light bulbs are also now supported. Add names and IDs to settings and then Aurora can switch lights on and off.

Also added OpenSCAD and STL files for 3D printing a case for the BrainCraft version.

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(Published to the Fediverse as: Aurora, the Raspberry Pi Smart Assistant #code #ai #openai #aurora #raspberrypi #python #perplexity #todoist Aurora is a python smart home assistant using the OpenAI realtime API, available on GitHub. )

Android Then: Why not add a splash of color and personality to the status bar?

Android Now: I see a red door and I want it painted black. / No colors anymore, I want them to turn black...

Some people have to go to the doctor for a heart stress test. I just upgrade Android apps when Google forces me to. Today was supposed to be a simple exercise in bumping the target framework up to 36 / Android 16. Instead the status bar disappeared.

Google often deprecates things and this time for me it's setting the status bar color. Not the end of the world, but for my app the helpful default is white icons and text on a white background. Apparently if you really, really want to you can fuck around with something called the WindowInsets API but life is too short. I added a version dependent tweak to the status bar and moved on.

Catfood Earth and Fortune for Android are both rolling out over Google Play right now. They have a slightly worse look and feel and zero additional functionality but at least, for now, I'm still allowed to use some international orange in my part of the window. I'm pretty sure the next iteration of Material Design will involve the National Guard somehow.

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(Published to the Fediverse as: Annual Android Antics #code #earth #fortune #maui #android Handling the deprecation of status bar color in Android 16 for Catfood Earth and Android. )

Here's a simple Google Apps Script to monitor for new OpenAI models:

To get this running, paste into a new Apps Script project and enable the Gmail API under services. Add your email address and OpenAI API Key at the top. Run a couple of times and you should get an email with all the current models and then nothing the second time unless you get lucky. On Triggers set a scheduled execution with whatever frequency feels good (I use daily) and you're all set.

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(Published to the Fediverse as: Get an email when a new OpenAI model is available via the API #code #software #appsscript #google #gmail #openai #ai Simple Google Apps Script project that will send you an email any time OpenAI releases a new model to their API. )

From the department of things I wouldn't have bothered with a year ago, here's a python script to set Todoist label colors.

Why? I like a productivity environment with some color and flare, and it also helps to visually recognize what a task relates to. But setting label colors is more clicks than I have patience for.

How? Just figure out embeddings for each available color and then for each label. Use cosine similarity to set the color that best suits each label. Colors will stay consistent for existing labels and new ones will get just a dash of semantic meaning in their assignments.

Here's the code (you need an OpenAI API key and a Todoist API token set as environment variables):

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(Published to the Fediverse as: Set Todoist Label Colors Automatically Using OpenAI Embeddings #code #python #openai #todoist #ai Python code that uses OpenAI embeddings to set Todoist task labels to a semantically meaningful color. )

I set myself the goal this morning of shipping a vibe coded web application in a day. Welcome to the world, vibevideo.xyz, and apologies if you own Adobe stock.

Vibe Video is a chat based video editor. There are two things that are pretty cool about it. First, it writes its own code. When you ask it to make an edit it uses GPT 4.1 to write a function for the edit and then runs that function. That's the Vibe Video part. It's a single page JavaScript application but the core functionality is hallucinated at runtime.

Second, it runs locally. It's based on ffmpeg.wasm, which I knew nothing about before this morning. The site is a Vite/Svelte SPA deployed to a Cloudflare site with no backend except the OpenAI API. Your videos never leave your computer, and all the editing happens in the browser thanks to WebAssembly. I hadn't touched Vite or Svelte before today either.

Both of those things are crazy. There are some limitations - as the great saying goes, "If you are not embarrassed by the first version of your product, you’ve launched too late.". Right now you need to bring your own OpenAI API Key. Maybe I'll add some free usage when I get a grip on how much it costs to run. It's fast when you don't need to reencode - so trimming the video is quick. but changing resolution or speed will take some time. And there is a 2GB limit on file sizes, a limitation of WebAssembly. My hope is to iterate and improve, so kick the tires and let me know what else it needs.

To build this I used Visual Studio Code with GitHub Copilot in agent mode using Claude 3.7. The only code that's hand crafted is changing GPT 4 Turbo to GPT 4.1 (Claude refused to admit that 4.1 exists) and repeatedly removing a call to a non-existent file size property. While I didn't have to write any significant code I did need to coax the LLM - an experience halfway between being a product manager and pair programming with a buddy who is a much faster typist and won't allow you to touch the keyboard. There was a fair bit of troubleshooting involved as well, some with the environment setup and some with runtime errors. Having the experience to help guide Copilot through these helped a lot.

I have been experimenting with a lot of different tools over the past few weeks. Cursor and Windsurf both worked well for me, Replit was pretty good as well. Cline impressed me with debugging an app after building it. V0 and Lovable struggled with my test use case. I used Copilot for this personal experiment as I already have a license and wanted to see how far it would go. All of these tools are going to continue to improve (and/or merge). The current state of the art is a big deal already. A few weeks ago the statement that a good chunk of Y Combinator startups were writing 95%+ of their code with AI sounded like bullshit. Now I think they might be sandbagging.

One bad smell is that the experience gets worse as you add code. The start of the project was unbelievably easy, but as features were added Copilot started to bog down. This is unsurprising as the more you load up the context window for an LLM the more it struggles with the details. Again, this will just improve over time as models and agentic implementations improve. Good architecture is also going to matter - and is no different here from freeing regular developers from needing to keep too much in their head at the same time.

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Spring has sprung (09:02 UTC, March 20, 2025) for the Northern Hemisphere and Autumn is here if you are south of the Equator. Rendered in Catfood Earth.

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(Published to the Fediverse as: Vernal (Spring) Equinox 2025 #code #earth #catfood #equinox #spring #vernal #autumn The exact moment (09:02 UTC, March 20, 2025) of the Spring Equinox in Catfood Earth. )

Google recently bricked my digital photo frame, so I set out to build a new one. I did this in two parts - a Raspberry Pi to display the photos, and a C# script to preprocess them. The second part is optional but worth it.

The display side of the project turned out to be way easier than I thought. There is a utility called fbi that will run a slideshow to a HDMI monitor. Create a boot SD card from the lite version of Raspberry PI OS, copy over your photos and then run:

sudo apt-get update
sudo apt-get -y install fbi

You can then connect a monitor and test that images are displaying as expected.

Create a file called launch.sh with the following:

#!/bin/sh
# launch.sh
# run photos
sleep 2m
fbi -T 1 -noverbose -mode 1366x768-30 -t 60 -u -blend 1500 -a /home/rob/photos/*.jpg

-T 1 uses the first display, -noverbose just shows the photos, -mode depends on your monitor and is likely safe to omit, -t 60 changes the image ever sixty seconds, -u displays in a random order, -blend 1500 cross fades for 1.5 seconds between images, -a is auto zoom and the path at the end is where ever you copied your photos to.

The sleep 2m command allows the system to complete booting and bring up the login prompt. Without this the photos might start first and then the login shell ends up on top, which is pretty boring.

Make the script executable (chmod 755 launch.sh) and then edit your crontab:

sudo crontab -e

Add the following:

@reboot sh /home/rob/launch.sh >/home/rob/logs/cronlog 2>&1
30 22 * * * /sbin/shutdown -h now

The first line runs the launch.sh script at startup and sends any output to a log file (adjust paths to your system). The second line will shut down the Pi at 10:30pm every day. I use this so I can control when it's running with a smart plug - the plug turns everything on at 7am and off at 10:45pm, and the shutdown prevents the Pi from getting in a bad state from having power removed unceremoniously. If you want it running all the time just omit this line.

Reboot and you should have a working digital photo frame. Fbi will do a reasonable job with your photos, but I wanted something better.

The C# script below preprocesses all my photos to make the best use of the frame. I'm using an old 1366x768 TV so I want all the photos at that resolution and 16x9 aspect ratio. Many of my photos are 4x3, or 3x4, or 9x16, or something else entirely. I don't want any black borders so cropping will be involved.

Cropping is handled by detecting faces and then trying to keep as many in frame as possible. This uses FaceAiSharp.

Photos are processed in two stages. The first stage just crops horizontal photos to fit the screen. Any vertical photos are saved in a list, and then paired using image embeddings (CLIP using this code). My implementation pairs the most similar photos - it would be easy to do the most different as well.

Here's the code. I run this with C# 9 in VSCode on Windows. You'll probably want to change the input and output folders, and possibly the output resolution as well:

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(Published to the Fediverse as: Building a Digital Photo Frame #code #c# #raspberrypi #ai #ml How to create a Raspberry Pi based digital photo frame with face aware cropping and AI image pairing. )

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This simple script adds useful AI to Todoist and runs in the cloud on Google Apps Script.

I use Todoist to run my life, and my dream is for it to be able to complete certain types of tasks for me. With OpenAI Operator and Anthropic Computer Use this is getting closer and closer to reality. Yes, there is a risk that Todoist spends all of my money on paper clips. But there is also the upside that it will eventually do my weekly shopping, pay my bills and call back the dentist's office. Google's new Ask for Me is promising too, even if right now it's just going to bother nail salons out of existence.

I already put together an Alexa replacement using a Raspberry Pi and the OpenAI realtime API. It switches lights on and off, adds things to my to do list, figures out when the next L is coming and more (I'll blog more about this soon).  One thing I learned is that this kind of thing can get pretty expensive. I can see why Amazon is procrastinating on an LLM Alexa. But costs keep going down, and the future will get more evenly distributed over time.

The first version of this script has two objectives. Respond to tasks, and create calendar events. Here's the script:

To get this working you need API keys from OpenAi and Todoist. Perplexity is optional, if you have a key add it at the top. It only works with tasks that have the right label, ai is the default - you can change this with AI_TASK_LABEL. I initially developed this with o1, but it looks like the tool use was rushed out too quickly and it calls the same tool repeatedly. GPT-4o works well enough and you can test switching out the model by changing OPENAI_MODEL.

Quick configuration guide - create a Google Apps Script project in Google Drive. Paste in the code above and add your API keys and any other desired configuration changes. Go to Project Settings in the right hand navigation and make sure your time zone is correct. Then go to Triggers and schedule the function trigger to run periodically (I use every 5 minutes). You should be done.

Back in Todoist add the ai label to a task (or whatever label you set in the script) and the AI will respond. With the current script there are two use cases - ask it to create an event (it can invite people, add details to the description, etc.), or ask it to research some aspect of the task you're working on. I think this is helpful because it has the full context of the task, and while you're working in Todoist it's useful to store the history there as well.

The point here is to extend the number of tasks that the script can take on. Add new tools for the AI to consider in the getTools() function, and then wire that tool into an implementation in generateAIResponse(). createCalendarAppointment() is a good example of using built in Google services - as well as the calendar it's pretty easy to interact with email, Google docs and many more. I'm planning to add file uploads as well, and will update this post with iterations of the script that add helpful functionality.

OpenAI recommends around 20 tools as the maximum. At that point it might be necessary to break this up into multiple assistants with different tool collections.

Let me know in the comments if you manage to use this to start crossing anything off your list.

Updated 2025-02-17 01:18:

Updated the script to support images and Perplexity. 

Image support takes advantage of multimodal input. Any image attachments will be sent as part of the conversation. This uses the large thumbnail in Todoist by default. It supports JPEG, GIF, PNG and WEBP. If a thumbnail is not available it will send the full size image without resizing, depending on how large this might not be accepted by OpenAI. 

Perplexity is implemented as a tool (so OpenAI is always called, and it may optionally call out to Perplexity to get more information). This is useful for web search, local search and knowledge past the training cutoff for the OpenAI model. It's optional, if you don't include a Perplexity API key then it just won't be used. 

Here's a simple use case - add a photo of a book and ask Todoist to find the URL where you can order it on Kindle. 

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(Published to the Fediverse as: Adding AI to Todoist with Google Apps Script and OpenAI #code #ai #openai #todoist #ml #appsscript #gas #google #perplexity How to add an AI assistant to Todoist, includes code to respond to tasks and create calendar appointments with gpt-4o. )