Rambling again

Hermes Agent and LangGraph probably aren’t going to work for me either. On a highly resource constrained system like my no-GPU/no-VRAM Ubuntu box, Hermes quickly has a problem with long contexts. The benefits of Hermes is that it tries to iterate quickly using lots of calls, but with each call including a huge amount of context overhead about the user, the tools, and the current task, the context bloat means each of these calls takes forever. I like the UI and as long as I’m using an API based LLM through OpenRouter¹ , it’s fine. While a small local model can move relatively “fast”, it gets bogged down with context and larger models which can handle the context are even slower. Again, there’s a lot of great things about Hermes with an API. It can be conversational, it’s fairly coherent across long conversations, it can review files, make changes, and does indeed to try to improve itself as advertised. It’s also got a lot of ways to quickly flip between models and sessions/conversations on different topics. Even with all these abilities, it’s still only a partial solution to what I want. I’d like to have something that can run custom scripts on a regular basis, interact via voice, and these features don’t seem well implemented quite yet. ² If left alone to do a task, it will litter it’s home directory with a pile of different folders and files, despite explicit instructions otherwise. It’s very capable – but can get confused and messy. Perhaps this wouldn’t be a problem if it were powered by a frontier model, but I’m not going to do that.

I tried out LangGraph as a possible replacement, but it has it’s own problems. It’s far more “bare bones”, with all commands needing to be typed into the command line. Adding skills via specific scripts works very well, powering it via local LLM works very well, it’s able to accurately interpret language and make tool calls, properly set up CRUD lists and reminders, and even manages separate conversations in it’s own clunky way. However, it seems to really lack the iterative ability of Hermes to actually get things done. If it had a better UI, a more clear path to having it semi-autonomously work on a task, I think I could make a go of it.

Even though these two systems aren’t for me, I’ve learned a lot about what I like and what I could put to use in the future.

I keep circling back to … maybe I should just build my own thing. I’ve built the various parts several times before getting distracted by something shiny, so it’s really a matter of collecting those pieces, cleaning them up, and wiring most of them together with what I’ve learned from using other systems.

While I’m rambling, I just read this article on Hackaday about the LLM moats for frontier labs starting to shrink. ³ It kind of makes sense these damn frontier AI companies had to buy up all the RAM. If people could easily pick up 128GB of RAM for $250⁴ and given the performance of medium sized models in the 30-70B / 30-70GB range, no one would pay $20 per month for access to an AI. At the rate models are improving and being released, it certainly seems like the only thing keeping big AI companies afloat is high RAM prices. While I would not spend $3k-$5k to get a 128GB RAM beast today, since it would never pay for itself, if that price dropped to $500, the cost / benefit analysis starts to make sense.

It does make me wonder, though… what happens if RAM supply suddenly increases, home inference machines are suddenly feasible and every domain name / hosting provider has a $5 upsell for their own Qwen 3.8+ instance. What happens to these billion dollar companies? I would think there’s only so much “intelligence” people are going to be willing to pay for. If all people really want are digital receptionists, developers, lead generation, content generation, investment predictions, and the occasional therapist/pseudo-relationship, why would they pay for an astrophysics PhD level hyper intelligent machine?

On an unrelated note, while I’m busy pontificating… I feel like I’d recently seen something about how continued AI usage “quiets” a brain’s activity – and how taxi cab drivers in NY had very low incidence of brain deterioration as they grew older. All of which to suggest⁵ that more brain use means more brain and less brain use means less brain.⁶

In a move that might surprise no one – these last two points suggest a much darker path for frontier AI companies. Perhaps the game was always about about increasing surveillance and diminishing critical reasoning of the masses.

Project Jarvis

Poolside’s Laguna S 2.1 has been incredibly useful [↩]
It can do cron jobs, but for some reason loses track of the correct date / time and time offset based on time zone really quickly [↩]
The Hackaday article is largely summarizing this article by TerminalBytes, which is an excellent read in it’s own right [↩]
Assuming pre-AI boom RAM prices and the gentle application of Moore’s law… [↩]
Assuming my recollection and interpretation of these findings is accurate and the findings themselves were valid… [↩]
Seems like a fairly reasonable assumption??? [↩]

Secret Agent Man

I’d been looking at several LLM powered “agent” frameworks recently. OpenClaw got a lot of buzz several months ago, spiked the cost of Mac Mini’s, and it seems reality doesn’t quite live up to the hype. Peter Steinberger’s description of his OpenClaw¹ creation in multiple videos, podcasts, and posts was he would wake up each day to discovery the agent had been working tirelessly throughout the night on projects, building things, discover things, bring him new opportunities, and learning and growing as it went. Reddit and Youtube exploded with people snatching up Mac Mini’s for their high unified RAM / small form factor / modest cost, implementing their own OpenClaw’s, and telling everyone about their successes breathlessly.

I think my favorite “review” was this:

In the span of a few weeks ClawdBot went through several name changes, Anthropic blocked it from consumer level plans, Steinberger got a job with OpenAI, while people started to post their runaway token costs and concerns the agent was escaping containment, grabbing personal information, publishing personal information, and just generally running amok.

I have never tried OpenClaw because it seemed like an AI vibe coded slop privacy / security nightmare with a thick frosting of marketing slathered all over. I understand the appeal of OpenClaw. It would be so nice to have a little digital butler, looking at my cluttered desktop, helping tidy up, organize me, make some connections and surprising me in the morning with some new developments. After all, where would Tony Stark be without Pepper Potts and Jarvis to keep him on the rails?² I also understand how some semi-technical people would be dazzled by the promise of something that would work around the clock to make them money, all for the cost of a Mac Mini.

A short interlude about Chik-Fil-A. I don’t like their politics and will not support them. I’ve never once given them a single penny. But, I have eaten food from Chik-Fil-A… When a new located opened not far from my former workplace, they came by and left a HUGE platter of delicious chicken nuggets and sauces. They have historically donated to our local Maker Faire, providing lunch for the makers. Hide their branding and of course their food is going to be delicious – that’s the entire point of fast food. The first year they provided lunch, I didn’t eat it – because I don’t like Chik-Fil-A. The second year… I was weaker – but also more practical? I was hungry, the kids were hungry, and although I had no problem paying for food at one of the many vendors, we’d been there a while, it was hot, the lines were long, and well, the food was right there. I suppose the most subversive thing to do would be to take their food and toss it in the trash – or maybe shuttle their food to the kinds of people they despise. But, I’m not in the habit of wasting or delivering food. My own subversive little act was to eat the food – and resolve to never, ever, advertise for them or give them any money.

Back to LLM’s. I have tried Hermes Agent, running in a docker container in a machine with very little personal information about me. I’ve only been using it for a few days – and I think it has some real promise with some real caveats.

The first caveat is that I won’t use any frontier LLM services because I can’t justify giving OpenAI/Anthropic/Google/Meta money for such things. Whatever Google once was, whatever their motto might have been, they’re so far removed from that as to be a different animal entirely. ³ The obvious downside is that without access to a very fast, very big, and very powerful LLM, my Hermes Agent will absolutely be less useful than someone else’s instance.
It will run in a Docker container on an isolated, budget machine, or not at all.

In practical terms, this means I’ve limited myself to either smaller and less capable local LLM’s that my machine can run or free tier API’s for various LLM providers. I’m still fumbling my way around Linux / Ubuntu and already using LLM’s to get my bearings, so these resources for providing the LLM reasoning power for Hermes feels within bounds too.

As always, I simply don’t trust any LLM’s outputs. I don’t trust their purported facts, claims, summaries, etc. Even so, there’s no arguing they are capable of building systems. Some family recently “fact checked” me using ChatGPT and I pointed out its merely a machine designed to provide them with the very next token of what they want to hear – not a system for giving them facts. I feel like it’s probably impossible to build an AI / AGI without training it on facts, but relying on a token generator for facts seems foolhardy. That said, I don’t have a problem relying on a system / set of processes / code base that I can inspect and verify works to arrive at a solution.

These “agent” systems are usually better called “agent harnesses,” since their best feature is offloading the work of monitoring, guiding, and nudging an LLM to perform a task. The Hermes agent adds a lot of extra features – keeping things organized, keeping certain chats available to revisit later on, switching LLM’s between local / API based, but at it’s core it is saving me the time of just babysitting an LLM.

As understand these agents, the first thing they do is take a query, ask the LLM to break it into pieces, and then get to work on discrete elements. It used to be that on long tasks ChatGPT would keep popping up a “Continue” button. About two years ago I wrote a simple Chrome extension that was active on ChatGPT and would simply auto-click the “Continue” button whenever it appeared. I think this is less a thing these days and ChatGPT will either truncate a reply or simply stop. But, keeping the LLM on track for each small sub-task and spurring it to complete appears to be part of what the agent is doing under the hood.

I’ve tried several LLM’s to assist with writing code, with varying degrees of success. The best OpenRouter free model I’ve used recently has been Poolside’s Laguna S2.1 with 118B parameters, a “mixture of experts” model with only 8B active at any given time. I’ve been very impressed with it’s coherence on long tasks and overall quality of work – and it has been extremely helpful in setting up Hermes. I haven’t tried a recent Qwen or Deepseek model on my very much underpowered system, but I plan on doing so soon.

My first goal with this system is to get it to just run a bunch of python scrips on a cron job. I may need to build up a separate system entirely that only runs cron jobs, but which can have it’s roster of tasks manipulated by Hermes. Too often the LLM gets involved and wants to interpret a query rather than run a static script, which just leads to more hassles. Perhaps a frontier LLM wouldn’t have this problem – but I don’t plan on using them.

Project Jarvis

Well, Clawdbot -> Moltbot -> OpenClaw [↩]
NGL, I really hated Gwenth Paltrow’s Pepper Potts and would have liked nearly anyone else in that role. Maybe it was the direction – or maybe just my dislike for Paltrow – but I found her bumbling, flustered character exhausting. The comic book Potts was a capable, intelligent person in their own right. Maybe there’s hope for a better person in a future reboot? Hell, if they can recast Roddy, they can recast Potts. [↩]
I won’t even bore you with the full story of how Google promised a remediation for a Pixel 6 battery problem, bait and switched, then slowplayed it until everything expired. [↩]

Building a Home PC Lab

Trigger warning: This post (and potential series) is going to discuss some AI/LLM stuff. That’s only a part of what’s going on, but if you hate all things LLM, then maybe this post isn’t for you.

Reasons for Building a PC

I haven’t built a PC in probably 25 years now. Even then, I had the kind assistance of two friends who knew a lot more about PC building than I ever did. More recently I’ve been interested in building a PC for a variety of reasons:

The intellectual exercise of building something, mostly out of parts, all by myself
Having a PC running Ubuntu / Linux, something I’ve toyed with now and then, but never really tried in earnest. I already eschew most closed source software in favor of open source alternatives¹ , so moving to an open source operating system seems almost natural.
Some of my personal information currently resides on a computer I use for work – and I’d like to create a clean separation of these things for a variety of practical reasons.
Having an inexpensive and dedicated box would let me sandbox certain programs, try out different new ideas, and still let me access all this information and processing power remotely, if set up properly.
Yeah, I wouldn’t mind an Iron Man style Jarvis virtual assistant / butler, but the technology is still a fair bit aways as are my own skills and budget for such things.

Building in Parallel

I’ve taken to “building in parallel” for several recent projects. With the recent remote temperature sensor system, I tried taking measurements with an infrared heat gun, then an ESP32C3 module with lots of wires, then several Wemos D1 Mini modules each separately sending data back to my PC. While the ESP32C3 systems were still (mostly) working, I recognized some of the weaknesses and failure points and began working on the Wemos systems and didn’t take the ESP32C3 sensors offline until the Wemos sensors were up and running. There was certainly overlap for a little while, but this also let me calibrate both systems. I enjoyed working on these things, so building an entire second set of sensors in exchange for doubling the price and quadrupling the soldering wasn’t a horrible experience. I’m reminded of a quote from Contact, “First rule in government spending: why build one when you can have two at twice the price?”

As with those sensors, I’ve had some automated projects running constantly lately. Something that downloads a huge work calendar daily, a few simple reminders, something that sends me reminders over a messaging platform, and a few other assorted digital doodads. I’m keeping all of these online, on my work PC, until I can get the Linux box going – then I’ll look forward to closing these down.

Pricing Things Out

As with nearly any learning experience, you end up paying for it one way or another – money, tuition, wasted attempts and materials, time. Partially because I’m just starting out and partially because I’m fairly frugal, I set a budget of ~$250² If I’m going to spend any money at all, I wanted something that would work – but I’m not ready to drop $5k on RAM/GPU/newest processors. My needs are still modest – and largely covered by my equally modest work computer. It’s an Intel i7 with 32GB RAM in a very small mini PC case, handles email, big spreadsheets, graphic programs, python projects, and even 3D modeling and rendering with OpenSCAD if you give it enough time. It cost $550 before tax, new from Amazon, nearly 4 years ago, and is still going strong.

For this project I contemplated going with a fanless headless system, but these seemed to require special aluminum cases and appeared to be little harder to come by. I thought about tracking down some e-waste resources, Facebook Marketplace, and Craiglist – but all of these require a fair bit of legwork and I feel like they’d also have more than a modest possibility of wasted time. Any new PC would set me back at least $500 through either Amazon or Costco, so I turned to eBay since the rating system provides at least a modicum of quality assurance – if not in the product, at least the seller.

There are a ton of options for older PC’s that can be given a new lease on life with Ubuntu and some extra memory. I’m still pretty biased against Dell, HP, and Gateway for producing sub-par consumer grade computers for myself, family, and friends. Some noodling about and half-hearted research suggested that these brands may produce significantly better enterprise machines than their consumer grade lines. This only irritates me even more, if true. This pretty much left me with Lenovo.

I ended up going with a “Lenovo ThinkCentre M70q Tiny i5-10500T 2.30 GHz” and paid a little extra to have the eBay seller customize it with 32 GB DDR4 RAM total, a 512 GB M.2 NVMe SSD, and throw in the antenna. Besides being fairly accommodating and helpfully communicative, I went with this listing because it fell squarely in my price range / project budget, it already came with the power adapter, they were California, and it could ship very quickly. While I could have easily spent another week or three trying to find a better deal on better and newer equipment, this was always going to be a project running on low end / older equipment and I valued the immediacy of getting to work over the incremental value of a better system. Frankly, just not having to hunt for a compatible power adapter was a huge plus for me.

In the end I purchased the main system from the eBay seller, paid an extra $36 to make sure the system had 32GB of RAM, ordered a used 512GB SSD (they threw in the antenna) from them in a second purchase for $55, a SATA SSD enclosure to re-use an old 500GB SSD, and $7 to grab some HDMI dummy plugs off Amazon. I knew the system would come with 16GB of RAM, but the benefit of “buying up” to 32GB also meant that I knew the machine wouldn’t have two 8GB sticks instead of a single 16GB stick. I could later upgrade from 16GB to 32GB if I had a single 16GB stick to start – but having two 8GB sticks would mean I would later have to get two 16GB sticks.

Specs and Prices

Anyhow, for those of you playing along at home, here are all the parts for my $284.30 computer system.

Part	Cost	Shipping	Tax	Total
M70q Gen 1(i5-10500T, 16GB, Adapter Incl., No SSD), upgraded to 32GB	$185.99	$5.93	$14.41	$206.33
M.2 SATA SSD to 2.5” SATA adapter enclosure	$10.79		$0.84	$11.63
512 GB NVMe M.2 SSD, antenna	$55.00		$4.13	$59.13
Dummy HDMI Plug	$6.69		$0.52	$7.21
				$284.30

I didn’t have to buy the SSD enclosure – but it seemed like a very inexpensive way to double the storage and repurpose my old drive. I didn’t realize I’d need to get an HDMI dummy plug, or even what they were, until I tried to “remote” into the headless box and got a fairly ugly looking desktop instead of the slick Ubuntu desktop. This was probably the least necessary of these purchases. If I kept the system to the PC with the 32GB upgrade, ran the OS off my old SSD in the SATA enclosure, and picked up a $7 antenna off Amazon, the system would have only cost about $213. The extra $59.13 doubled the hard disk space and enabled a more graphically pleasing interface. It’s not much of a splurge and this was never a “build a PC for as little as possible” kind of a challenge anyhow.

The final system is probably on par with my existing 4 year old PC at roughly half the cost. I think it was a “decent” deal and I have a system that I can use to offload a lot of projects. If any of these projects end up requiring more processing power, I’m not even really “out” $285 since I can still keep using this system for other tasks (home automation, running daily scripts, etc).

And, yes, I’m trying to use this PC as a basis for a bundle of scripts I can deterministically interact with via voice input / output as well as certain LLM powered tasks. First, a helpful comic.

And, that kids, is how AI killed human interaction

We’re probably hurtling towards dead internet theory. I can’t search through Google or even DuckDuckGo anymore without having to wade through their AI summaries and then scroll past search results from AI slop. Using ad blockers or setting up a PiHole to block ads and stop tracking from ever touching my PC may impoverish some otherwise worthy websites. However, I simply don’t have to read every advertisement sent in the mail, on the inside of my grocery shopping cart, or every billboard. Yeah, I hate the idea of AI companies boiling oceans of drinking water for their inscrutable purposes…

There’s a quote I love by H.L. Mencken; “Every normal man must be tempted, at times, to spit on his hands, hoist the black flag, and begin slitting throats.” What if the best way to filter out AI garbage is to use an LLM to do some of that filtering for me? ³⁴

Searching through any search provider is kinda garbage these days, but there’s no reason I should have to wade through that AI garbage myself. Gone are the days you can drill down to the kind of search results you want by simply adding “-negative_search_terms”, because there’s probably no financial reason for them to permit this.

But what if I created my own search function? I recently tasked an AI with searching up 10 options for a certain product for me on Amazon and it did a mediocre job, finding 7 dead or unavailable listings. But it also saved me a lot of time looking through dozens of pages of crap when I only needed to look through 10 crap listings. With some tinkering, I think I could make a script that would identify negative search terms, perform the search as requested, then remove those that should have been removed with the negative search terms. I could see this being incredibly useful for Ebay and Amazon purchases.

A preview for next time: After some dithering, much watching of youtube videos, I’m going to give Hermes Agent a shot over OpenClaw.

Project Jarvis

LibreOffice, Thunderbird, OpenSCAD, etc [↩]
Is it really a budget if there’s a range?

Words to live by

[↩]
And, yes, I have considered the possibility that an AI company’s aim might be to make everyone distrust the entirety of the internet, then sequester everyone into their own little siloed version of digital reality, everyone watching slightly more targeted ads and Marvel movies than everyone else, and any attempt to use another LLM to filter shit out only results in more precise targeting… [↩]
I went to the hardware store yesterday and was told by someone who worked there in an orange apron that the entire store, inside and out, was covered in Flock cameras. I think this bullshit surveillance is really starting to get to me. [↩]

House Temperature Readings So Far

Just an update with the charts and measurements so far.

07/07/2026, readings from XIAO ESP32C3 and thermistors

07/08/2026, readings from XIAO ESP32C3 and thermistors

07/09/2026, readings from XIAO ESP32C3 and thermistors

07/10/2026, readings from XIAO ESP32C3 and thermistors

07/11/2026, readings from XIAO ESP32C3 and thermistors

07/12/2026, readings from XIAO ESP32C3 and thermistors

07/13/2026, readings from XIAO ESP32C3 and thermistors

07/14/2026, readings from XIAO ESP32C3 and thermistors… totally broken

07/14/2026, readings from Wemos D1 and AHT10 sensors

07/15/2026, readings from Wemos D1 and AHT10 sensors

07/16/2026, readings from Wemos D1 and AHT10 sensors

Summer Science - Testing Effects of Window Coverings on Interior Temperatures

Back on track

Okay, as of 11:30 AM, the AC was working again and battling against 104 degree heat. That means we can start looking at our data again – and plan out what to do about the window, sun shade, or other system for blocking some or all of the heat for this one picture window.

In an effort to help make this data more representative of what’s actually going on inside the house, I put a thin piece of aluminum around the sensor in the entry way. My thought was that it would help deflect some of the direct heat hitting the sensor and causing it to register ~100 degree heat on the ground.

Distributed temperature sensors for creating a “heat progression map” of temperatures in a house. Right now I only have three sensors, but these are inexpensive enough, that I could see creating several to send temperature back to my PC. There’s always a tradeoff between effort, speed, and cost.

One infrared thermometer. $15
One board, 3 sensors, lots of wire. ~$30 and lots of wire hassles
- $5 XIAO board, $2 worth of thermistors and resistors, $2 protoboard, $15 wire
- A small amount of soldering
Three boards with a sensor each. ~$25 and very portable / placeable
- 3x ($3 Wemos D1 Mini, $2 AHT10 sensor, $2 protoboard, $1 tap light, $1 for two AA batteries)
- SO MUCH SOLDERING

For the best looking and most useful data, I think the three-board but higher effort approach is best. It also scales very nicely. For about $50 you could distribute 6 sensors throughout your house and get a sense of how heat moves around you home.

Summer Science - Testing Effects of Window Coverings on Interior Temperatures

WWMWD?

Welp. I guess this is a good time to dust off this little gem from the book, “The Martian.”

I’ve been trying to get consistent readings inside the house for a little over a week now. The infrared thermometer was helpful, but was too manual and to variable in readings despite trying to control by checking at wider intervals and taking measurements in very specific sections of the floor, wall, and ceiling. The XAIO board and thermistors were a great first step, but required too many wires and it was also having trouble broadcasting data. ¹ The Wemos D1 Mini, while an underpowered board, took a lot of fiddling to get running and a fair bit of soldering, but has worked very well.

Until external factors cropped up.

Yesterday, at the absolute peak of outside heat, our AC unit stopped working. I’m pretty sure the sound of it turning off yesterday was the last time it was working. The temperature inside reached about 86 degrees upstairs based on the home thermostat and stayed at that level until about midnight when we started up the whole house fan.²

If I were comparing a given day’s temperature to a different day’s temperature, all the readings would be basically useless. However, I’m actually more interested in figuring out if a single sunscreen can soften or blunt the increases in internal house temperatures. The data from yesterday is going to be useless for looking at absolute changes in temperature, but it certainly shows what would happen if we don’t have a working AC unit on a very hot day.³

Yeah, having the AC go out is a hassle – but on the bright side, at least it happened during a time when I was running some science experiments.

But, with about a week left to go, I’m less certain I’m going to be able to present the findings and associated original project I was planning on.

Summer Science - Testing Effects of Window Coverings on Interior Temperatures

Though, now that I think about it, this might have been improved if I attached the antenna to it? [↩]
86 degrees inside sounds comparable to Italian AC [↩]
Though, it shouldn’t take a rocket scientist to realize it’ll get warmer here without AC. [↩]

Musings on measurements and a wacky cat

More thoughts / ideas:

At about 5:30 PM, right around the time the heat measured at the entryway started to rapidly increase, I noticed two things. First, our AC unit was louder and persistent – it hasn’t seemed to have turned off since in the 30 minutes since. Second, the cat went absolutely bananas – eyes super dilated, calling out, then splayed on the ground in an almost seizure like stretch, then a weird slow walk. I had remarked on this odd behavior to the fam yesterday.
In looking at the data / charts I have now, it’s clear there was a huge spike in the temperature measurement in the entryway as the sun approached and then covered that sensor. Annnnnnd… now I’m wondering about the advisability of leaving those batteries and that board in that temperature, even if it’s only for 30 minutes of peak temperature. Fortunately, I connected the sensors with a length of wire so I could conceivably keep the batteries out of direct sun / heat. I might need to extend this further.
I think it would be interesting to review the delta / change in temperature over time, rather than the actual temperatures over time. Right now I’m just looking at these colored squiggles and thinking “Oh, that looks neat, and this bit is spikey, and how about that uptick, huh?” But, by looking at the change over time, I could actually quantify these things.
I know I’d suggested the next time I make something like this, I’ll just use another XIAO board, since they’re only slightly more expensive than the Wemos D1 Mini’s, but given the kinds of problems I was having with the XIAO seizing up, maybe not? I could get another 5 D1’s for about $15 and I have plenty of extra sensors and wire now that I’ve taken down my XAIO setup, but I don’t have any extra tap light enclosures. :) I know I can make do with off-the-shelf battery holders – and goodness knows I’ve got plenty – but I do kinda want to have them all be about the same.
It’s now 6:03 PM and the AC unit just shut off. I know there are API ways to know when the AC unit is active, but that requires the cooperation of my smart thermostat company – and they haven’t replied to my requests. I could build something that would sense motion and tape it to the AC unit, but it would be easier to build another temperature sensor and place it near an HVAC vent and treat the sudden drop or rise in temperature as an indication the HVAC was on.

Summer Science - Testing Effects of Window Coverings on Interior Temperatures

WHY IS EVERYTHING BROKEN?!

The running joke / perennial axiom of hardware startups is “hardware is hard.” I’m not even dealing with any kind of wild hardware. I’m using off-the-shelf sensors and an off-the-shelf board, connecting these in a way I can prove works, and transmitting data back to my PC. For some reason the XIAO ESP32C3 board is just not transmitting data back to the PC. Sure, sometimes I have to restart it which is no big deal. We all need a break. But, now it’s just not resetting properly when I power cycle it.

Anyhow, it’s clearly going haywire:

The good news is that the sensor data coming in from the single microcontroller + three hardwired sensors seems very consistent to the data that’s coming in from the three microcontrollers + single sensors, both over the last day and when compared against readings over the last week.

The observed trends is basically:

Outside temperature dips overnight, with a low around 5-6 AM, peak heat at around 3-4 PM.
When there’s no cloud cover, the house temperature at the entryway bumps first, then the family room, then the ambient house temperature peaks about two hours after entryway bump.
The house and outside temperatures are about the same by 10 PM to 12 AM.

In an ideal world, I’d just restart all the readings right now using the new system, get the sunscreen, then compare these patterns. However, the science display is due in about 10 days, which means I don’t have a lot of time to keep restarting / pivoting.

I guess what I’m doing now is just looking at the baselines. From there, I’ll make a prediction about what would happen with a sunscreen. Some of this sunscreen stuff may depend on external factors – the availability of the sunscreen vendors to measure/make the sunscreen. I don’t know what their lead times are, but I imagine during this heatwave they might be extra busy. Otherwise, I might try to make some predictions about how else we can affect the house temperature and humidity, perhaps with timing the opening windows, opening/closing blinds, and use of the whole house fan.

On interesting aspect of the more distributed Wemos boards / sensors is that these three sensors are also at different heights. The entryway sensor is at floor level in the path of the sun, the family room sensor is at about 6 feet off the ground, and the ambient sensor is at about 13-15 feet in the air. I am expecting the ground level to spike first, then the other two. I’m not sure if I should expect the 6 foot sensor to react first, because it’s closer to the ground height or the 13 foot sensor because it’s closer to the ceiling where the hot air will be collecting. If I had to guess, I would guess the higher one.

Although today is a real scorcher, the one upside is that I can compare it pretty nicely against the data from a very warm day last week with almost no cloud cover.

Summer Science - Testing Effects of Window Coverings on Interior Temperatures

Repeatedly Redundant (Sensing Sensors)

This is the second time the XIAO board stopped working for an extended period of time. Rebooting the board, unplugging/plugging the power cable and adapter from the wall, and even pulling the board from the headers, didn’t work to get it back online. I finally pulled the board, plugged it into USB, then made a do-nothing change to the main.py file¹ and plugged it back into the headers, which worked perfectly. There were no obvious errors in the board – it just … needed a boring update to the code?

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Anyhow, I’d discussed some of the pros and cons of running three Wemos D1 Mini powered sensors² versus just one XIAO ESP32C3 powered sensors – but didn’t think about what happens when a single one goes down. Although the XIAO was out of commission, the three Wemos board kept going so I didn’t really lose any temperature data – especially since the data was so flat during from 12:30 AM to 9 AM this morning.

Admittedly, the locations for the three sensors aren’t perfectly matched. I have the three XIAO thermistors taped to the entryway flooring, taped to the metal plate just inside the family room, and one dangling in the air above the entryway out of any direct sunlight. The three Wemos AHT10’s are on the entryway floor near the entrance to the family room, well inside the family room on the mantleplace, and the last at the top of the stairs with the sensor in open air at almost the exact same height and area as the original XIAO ambient sensor. I actually wanted the second XIAO sensor over on the mantleplace as well – but didn’t want a run wires across the house that far to do it. I’m at peace with getting different readings from these sensors versus my historical readings because:

The AHT10’s are supposed to be more accurate – and also measure humidity which I’ll use near my 3D printer later on
There’s no really good way to tape the AHT10’s to a surface and that doesn’t really tell me much about the “feeling” of warmth inside the house anyhow
The floor readings were constrained by the amount of wire I had rather than really wanting measurements at those specific locations

Just looking at the charts of the incoming data suggests that the AHT10 sensor data is definitely less noisy than the thermistor data. Then again, it might be due to the way in which I’m taking readings every minute but then broadcasting the data every 5 minutes. I’m sure no one would notice or care if I sampled the data every 5 minutes, but when the peak heating coming in from that one picture window only lasts about 30 minutes, I think I’d like to get a better sense of what’s going on minute-by-minute rather than at 5 minute intervals.

Summer Science - Testing Effects of Window Coverings on Interior Temperatures

Changing something behind the comment line from “#==================” to the same thing with one less equal sign [↩]
Way more than triple the soldering [↩]

Now with remote sensors

As with many of my projects, the scope and build has changed somewhat over time. This project started with me just taking readings around my house with an infrared thermometer at somewhat regular intervals. However, this was tedious and prone to variance as I measure things by hand. After that, I started using a single XIAO board with three hardwired thermistors around our entry way. The nice thing about this kind of a setup is that I really only have to build/program one board, set it up, and then let it send data to my PC. The downside is that there’s wires everywhere – and it used up my entire stash of glorious rainbow colored ribbon cable. ¹

Practice when I work on a project is to buy replacement parts for the pieces I use up after I’m done. This way, I will still have stuff on hand for my next project. So, I’ll need to pick up more colored colored ribbon cables, small solderable prototyping boards, and replacement microcontrollers. Back in the day, I would stock up on Adafruit Trinkets, but for about a lower price and a slightly larger footprint, I can get a XIAO board with more IO ports, memory, speed, and built-in wifi/bluetooth. The little prototyping boards I’d purchased aren’t available from the same seller on Amazon, which is too bad. I really like these boards because they’re bigger than just the microcontroller, have power rails that run down the center rather than the sides and horizontal rows, and rounded corners. They’ve been incredibly convenient.

With this project, I wired up three Wemos D1 Mini boards, each to a different AHT10 temperature/humidity sensors, and shoved them into my favorite enclosure – cheap tap lights.²

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As I’ve mentioned previously, I’m just not very good at soldering – which is why I go very slowly, checking, rechecking my progress as I go. Even so, I made two small and fixable mistakes, soldering the wrong connection somewhere and later accidentally bridging two contacts. Fortunately, some things are fixable.

The construction of these little wifi sensors isn’t very interesting. Rather than run each board off of 4.5 volts from three AA batteries, I’m using 3.0 volts from two AA batteries. By not passing that 4.5 volts through the voltage regulator to bring the power down to 3.3 volts, I should be able to save enough battery life to justify running this system off 2/3 the batteries. This meant I soldered the power connectors to the end and middle battery connectors. I have a suspicion that the XIAO boards are probably more power efficient, but if I don’t use those Wemos boards for something simple like this, well, I’ll probably never get around to doing it.

In any case, I now have these three sensors throughout the house near the locations of the prior sensors. I like these AHT10 sensors – but unlike the thermistors, I can’t really get the sensor right up against a surface. Hopefully it won’t matter much. While the readings directly off the flooring and metal plate were dramatic and gave a sense of the heat that was being absorbed and released by the house, it’s the actual temperatures in the house that are what I’m after. In the meantime, I’m running both systems in parallel so I can make sure they’re both pulling in reasonably consistent readings. If so, I’ll pull down the tangle of wires, wind them up, and set them aside for the next project.

Summer Science - Testing Effects of Window Coverings on Interior Temperatures

I picked up 15 feet for $5 about 13 years ago from Sparkfun, now selling for $7. Digikey also carries it for quite a bit more, but I haven’t run the math to figure out whether shipping makes a difference. [↩]
With some hunting, you can find a set of tap lights for about $1, they include a battery holder, switch, bright white LED, attractive sturdy case, and semi-translucent top. [↩]