I’ve been staring at JWST mirror builds for years. You know the ones-the iconic gold honeycomb that makes every nerd’s heart skip a beat. It’s been stuck in my head like a song I couldn’t stop humming.

While the 3D-printing community has some beautiful frames for these, I just couldn’t bring myself to do it. Spending days of print time and a mountain of plastic filament for a static frame felt wasteful. I knew there was a leaner way to do it.

The Plan:

About a year ago, I finally pulled the trigger on some gold adhesive hexagons. The Amazon photos lead you to think these tiles are huge, but the listing said they were 11cm x 12.5cm. The full array is about 20.5″ x 22″, the size for a wall piece.

The JWT has a black backing, but again, I wasn’t about to 3d print that much. I just waited until inspiration struck. The pack of tiles sat on my desk almost a literal year. I refuse to pay “Big Craft” prices. I wasn’t going to drop $10+ at Michaels or Walmart for foam core board when I knew I could get it for a buck at Dollar Tree. If that means a project sits in a drawer for 12 months until I’m near a Dollar Tree, so be it. I’m patient to a fault.

The Build:

The Dollar Tree foam core board is almost the exact size of the mirrors, so I had to use two pieces to get enough material to have the boarder around the edge. To keep it clean, I aligned the seam to sit directly behind a center row of tiles. Pro-tip on alignment: Don’t just start sticking things down. I laid everything out first. I started the actual adhesive process by centering a tile right over the board transition, making sure the corners were dead-on the edge. From there I worked toward the center, then filled out the rest. Once the “honeycomb” was locked in, I traced the boarder at 1.5cm from the tiles with a pencil and carefully did the surgical work with a ruler and a fresh razor blade. I mounted the whole thing with some extra 3M Command strips that I cut smaller.  You can’t even tell where the transition of the two pieces of backer meet (Hint, in the photo it is the top left third of the design at an angle from the center left-most point to the top-right center point.)

One thing I’ve noticed with the 3D-printed versions is that if the frame doesn’t support the entire back of the tile, they tend to warp or look “wavy.” I felt that mine came out slightly less wavy than some of the ones I saw online, but certainly not as perfect as others. But for foam core they look surprisingly sharp. Total cost? Less than $20 and about an hour of slightly non-focused time. Total plastic filament wasted? Zero. It’s not a $10 billion space telescope, but for my wall, it’s close enough.

 

Most people assume AI assistants require a subscription, an internet connection, and a willingness to let your conversations pass through someone else’s server. What if none of that were true?

You might think that setting up a local LLM AI is beyond your capabilities, or maybe just takes too much space.

All you need is to open a special HTML file to use it.

As of Chrome 138+, there’s a fully capable large language model sitting dormant inside your browser right now — and with a little tinkering, you can build a slick multi-chat interface to talk to it. That’s exactly what the Local Chrome AI project does.

What Is This, Exactly?

Google has been quietly shipping Gemini Nano — a real, capable LLM — inside Chrome as part of its on-device AI initiative. It’s not a toy. It can answer questions, write code, summarize content, describe images, and hold a conversation. The catch: Google doesn’t exactly advertise how to turn it on, and there’s no built-in chat interface. That’s the gap this project fills.

The result is a single HTML file you download and open locally. No server. No npm install. No signup. You just drop it in Chrome and start chatting.

Setting It Up

Before the HTML file does anything, you need to unlock Gemini Nano in Chrome’s experimental flags. Paste these URLs into your address bar one at a time:

• chrome://flags/#prompt-api-for-gemini-nano → Enabled
• chrome://flags/#prompt-api-for-gemini-nano-multimodal-input → Enabled (for image support)
• chrome://flags/#optimization-guide-on-device-model → Enabled BypassPerfRequirement

Then restart Chrome via chrome://restart. After that, go to chrome://components, find Optimization Guide On Device Model, and hit Check for update. Chrome will pull down the model — it’s 2–4 GB, so give it a few minutes. When the status reads “Component already up to date,” you’re ready.

Then just download one of the HTML files from the repo and open it in Chrome. That’s it.

What the Interface Does

The full-featured file (local_AI_with_sidebar.html) looks and feels like a real chat app. There’s a sidebar for managing multiple conversations, each one saved independently in your browser’s localStorage. You can create new threads, delete individual ones, or wipe everything with a single button. Conversations get auto-titled based on your first message.

The image-enabled version (local_AI_Sidebar_image_Uploads.html) adds a camera icon next to the input field. Attach a photo, ask a question about it, and Gemini Nano analyzes it entirely on your device. The image shows as a thumbnail in the chat bubble; click it for a fullscreen lightbox view.  A settings panel lets you control whether images get saved to localStorage (they’re big you may not want them persisting) and how many messages back the model sees as context. Because of that, the default settings are to save space by not storing uploaded images in Chrome’s localStorage, but you can turn on image saving if you want to.  Just be sure to turn this setting on before you upload an image. Anything uploaded previous to the setting change won’t be saved.

There’s also a minimal single-window version (minimi.html) if you just want to kick the tires without the sidebar.

The Clever Engineering Under the Hood

Local LLMs are stateless — every prompt starts from zero. The project solves this with two techniques worth knowing about.

The first is a simple context injection: before every message, the last N exchanges from localStorage get prepended to the prompt, giving the model a rolling memory of the conversation. You can tune how many messages back it looks in the settings panel.

The second is recursive summarization. When a chat exceeds a certain length, the script quietly asks the model to summarize everything so far, then replaces the growing history with that single compressed summary. This prevents what the README aptly calls the “Quadratic Slowdown” — where every new message has to carry an ever-longer context, making responses progressively slower. The summarization happens silently; from your perspective the conversation just continues normally.

Privacy Is the Real Feature

Every word of every conversation stays on your hard drive. The model runs on your GPU. Nothing is transmitted anywhere. You can turn off your Wi-Fi and it keeps working. Really, try it!

Cloud AI assistants are excellent, but they’re not private by design. This is.

The tradeoff is capability. Gemini Nano is a small model optimized to run locally, not a frontier model running on a data center. It’s fast, it’s private, and it handles everyday tasks well, but it won’t match GPT-4 or Claude on complex reasoning. For a lot of use cases, that’s a perfectly fine deal.

Who Should Try This

If you’re the type of person who reads about experimental Chrome flags for fun, you’ll have this running in ten minutes and enjoy every second of it. If you have colleagues who handle sensitive documents and keep asking “is this AI thing private?”, this is a concrete answer you can hand them as an HTML file.

The project is open source under MIT and lives at github.com/morrowsend/local_chome_ai. Fork it, modify the system prompt to give the AI a different personality, build your own tools on top of the same LanguageModel API — the foundation is solid and the code is readable.

Local AI has been technically possible for a while now. This project makes it actually convenient.

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Requires Chrome 138+. The on-device model download is 2–4 GB. Performance depends on your GPU — on a modern machine responses are near-instant; on integrated graphics expect a few seconds per reply.

If you’ve ever changed your Fire TV’s name, only to have Alexa suddenly refuse to communicate with it. or worse, lose track of your Fire TV Voice Remote Pro, then you know how frustrating it is. I tried everything, but what finally worked was a complete reset and a specific re-pairing sequence.

If you’re stuck, follow this step-by-step guide to get your Alexa and Fire TV devices talking again!

The Problem: Changing the Fire TV name breaks the connection with Alexa and the Voice Remote Pro.

The Solution: A Full Factory Reset and Strategic Re-pairing
The key is to essentially wipe the slate clean for all involved devices (Fire TV, Alexa, and the Remote Pro) and reintroduce them in a specific order.

Phase 1: The Clean Slate

Before you start re-pairing, you need to completely remove the old, broken connection from every device.

1. Factory Reset Your Fire TV:
   Go into your Fire TV Settings –>My Fire TV –> Reset to Factory Defaults. This will erase all your settings, apps, and channels. You will have to reinstall your apps and log into them again. Confirm the reset.
2. Prepare the Remote Pro (CRITICAL STEP):
   While the Fire TV is resetting/rebooting, immediately unplug the batteries from your Fire TV Voice Remote Pro (the one with the “find my remote” beep feature). This prevents it from automatically re-pairing with the Fire TV before you want it to.
3. Clean Up the Alexa App:
   Open the Alexa App on your phone and go to Devices –> Echo & Alexa (or All Devices). Find and delete the entries for your Fire TV and the associated remote(s) that are causing issues.

   Phase 2: The Rebuild (The Specific Order Matters!)

   Now that everything is clean, we’ll connect the devices in an order that ensures Alexa properly recognizes the Fire TV and its advanced remote.

4. Use Your Old/Standard Remote First:
   Wait for the Fire TV to boot up to the initial setup screen (the one asking for your language/Wi-Fi).Use a standard (non-Pro) Fire TV remote to complete the setup process, connect to your Wi-Fi, and sign in to your Amazon account.
5. Pair the Voice Remote Pro on the Fire TV:
   Once the Fire TV is fully operational (on the main screen), put the batteries back into your Fire TV Voice Remote Pro. Go into Settings on the Fire TV –>Remotes & Bluetooth Devices –> Amazon Fire TV Remotes –> Add New Remote. Follow the on-screen instructions to pair the Pro remote directly with the Fire TV.
6. Re-Discover the Device in Alexa:
   Go back to the Alexa App on your phone and go to Devices and search for New Devices. Alexa should now find your newly reset and configured Fire TV.
7. Enable Voice Assistant Control for the Fire TV:
   Once the Fire TV is listed in the Alexa app, tap on the Fire TV device entry. Look for the “Manage Devices” or “Fire TV Settings” option within the device details. Crucially, make sure that the settings to allow voice assistants to access and control the Fire TV are turned ON.

Your Fire TV should now be properly connected to your Alexa ecosystem, and all the voice commands and remote features (like the all-important beep to find your lost remote!) should be functional again.

If this worked for you, let me know in the comments!

In the first article, we selected hardware and software for our alarm system. In the second, we learned how to make connections between all of our devices to have sensors send notifications to phones that are on the network, or activate a siren. In this article, we’ll cover accessing the system when you are not home over the internet and having voice announcements.

Remote Access:

Honestly, I have wasted a LOT of my time over the years setting this up in different ways, with VPNs, hosting stuff myself, etc.  It’d work for a bit, then after an update something would break and I’d be without remote access for a while (having a fairly useless system) until I devoted a few hours to repairing everything again.   I’ve come to a setup I like quite a lot.  It barely costs anything and gives me the flexibility that I like while being very easy to set up.

I made an account with Nabu Casa, which is the official Home Assistant Cloud service. It is run by the same folks who created Home Assistant, so it integrates easily, seamlessly, and even has automatic backups of your system (see caveat below) in case it gets hosed up somehow.  It isn’t free, but it is super cheap compared to my hourly rate I’d charge for my time. Currently it is only $65 per YEAR!  The amount of time I have saved here and the frustrations I’ve avoided with it makes it priceless.

The one caveat with the automatic backups is that the backups are encrypted. So you need to manually download the emergency backup kit from within your instance of homeassistant before you have to use them. Otherwise, if something hoses your system and you need to reinstall HomeassistantOS from scratch, you’ll have lose everything and have to start over. Go ahead and download this backup kit if you’ve been following this tutorial series and store the key somewhere securely so you can use it in case of emergency.

As I said, I’ve messed around with Home Assistant for more than 5 or 6 years, and I’ve never written an article about it because I didn’t want to endorse other people pulling their hair out working with it, but Nabu Casa makes it all worth it!

The way it works is that once you sign up, the Nabu Casa website will generate a custom URL for you. You simply log into your Home Assistant instance, Select “Settings”  Then “Home Assistant Cloud” You’ll be able to login and/or paste this URL from Nabu Casa, and  you’re done. From that point forward, you will be able to securely control your Home Assistant, and get notifications anywhere you have internet access!

Voice Announcements:

There are about a million ways to get voice announcements in Home Assistant. Years ago I’d send a message to an old cellphone I was using as a front panel that ran LANnouncer.  Nowadays, we have Amazon echo devices (echo dots, echo show, old fire tablets, etc.). Home Assistant will let you connect with these, Google Home , or even their own new Voice module (which I may get once it is more mature).

If you paid attention in the last article when connecting your sensors, you would have gone into the sensor’s settings and activate sharing to voice assistants. This is step 1, so if you haven’t done that yet,  go back and do so now. Each time you do this, your Alexa will announce they have seen a new device.

Next, open your Alexa app on your phone. If you go into devices you should be able to see the devices and they should have the names you gave them.

The easiest method I’ve found to make simple announcements is to create a routine, add the device you want as the trigger, then make the announcement. Announcements can go to multiple Alexa devices, and “messages” can only go to a single device.  Here’s a video showing how:

More Complicated Use Case:

I wanted to have an alert every 5 minutes if a window or door was left opened past 8pm. This would remind me to go close it. The way to set this up wasn’t intuitive to me at first, but I found a great way to do it without having to code Home Assistant’s YAML file directly.

First, I need to create a Helper in Home assistant. You can find this in the Settings–>devices & Services window, select “Helper” tab at either the top or the bottom (depending on what app you use to access home assistant). Create a new helper and search for “toggle” as the type. Name it something (i.e. “Open Window Alert”) and give it an icon.

This creates a new pane on your Overview with a toggle button. You can toggle it on or off manually there, but we are going to control it with an automation instead.  Before we do that, go to the settings of this new button and expose it to a voice assistant like we did with other sensors before. This makes it so that Alexa will see this as a device like your other sensors.

Then we will create a new automation. In the trigger, you’ll select “Time & Date” then “Time Pattern”  This will allow us to have a repeating time trigger. The syntax is goofy here. To make it do a multiple of minutes, you must put a slash before the number of minutes. For example “/5” for  every 5 minutes.  Luckily, when you do this, it changes the text of the trigger and gives you a sentence in English to help you debug the timer. eg “Trigger every 5 minutes of every hour”  vs “trigger 5 minutes past the hour each hour.”

Next you’ll want to add two AND IF conditions.  The first is a fixed time of 8pm, and the second condition is “Kitchen window opened”

For the THEN DO action I want it to notify my phone, as well as to TURN ON the New Window Alert. To do this, select Helper” from the Actions list, then you can try one of 2 methods. You can “Toggle” the input boolean (our switch) or you can select “Turn On” and add a second “Turn Off” action afterward. While testing, I chose the two separate ON and OFF commands. Note that if you Toggle, then the alert will only be voiced half as often as the time you selected above.

Once you select this, you will then need to link this to our specific helper. Click the “Choose Entity” button to select the ” New Window Alert” switch on the front panel we created. This is the input boolean referred to above.

Now this automation will trigger every 5 minutes when it is past 8pm, the window is opened. It should send a notification to your phone and we can make an announcement on Alexa.  Go to Alexa, and create a routine using this “New Window Alert” as the Trigger, and make it announce “Kitchen Window opened, Please close the window.”

If you chose to use the Toggle Function above note that you will get notifications every 5 minutes, but announcements every 10 minutes because toggling only flips the switch one direction each time it is toggled.