You know, Herman, I was looking at that one shelf in the hallway yesterday, the one where you keep all the old power adapters and those random lengths of ethernet cable, and I had this moment of pure existential dread. I realized that if I needed a specific twelve volt, two amp center-positive barrel jack, I would probably just buy a new one on the internet rather than spend forty-five minutes digging through that box. It is a strange kind of paralysis where the abundance of resources actually makes you poorer because the cost of finding the resource is higher than the cost of just re-acquiring it.
Herman Poppleberry here, and Corn, that hurts. That box is not just a pile of junk; it is a carefully curated archive of mid-two thousands power standards and the evolution of copper shielding. But I get it. Truly. What you are describing is the friction of retrieval. In the world of logistics, we say that an item you cannot find is an item you do not own. The friction of finding something often outweighs the value of the object itself, which is the great tragedy of the modern tinkerer. We become curators of things we can never actually use because we cannot find them. We are essentially running a museum where the catalog has been lost, and the lights are turned off.
It is the information retrieval problem applied to physical space. And that leads us perfectly into today's prompt from Daniel. He has been diving deep into this exact struggle. He uses a system called Homebox, or a fork of it he calls Where My Stuff, which is an open-source inventory management platform. He is tracking niche computer parts, components, even specific things like a C P U block for an L G A seventeen hundred socket he just got in the mail. But he is hitting a wall. Once you get to three thousand, four thousand, or five thousand items, the upkeep becomes a full-time job. He wants to know how the pros do it and how we can use artificial intelligence to stop the data entry from eating our lives. He is a digital consultant, so he knows the value of data, but he is drowning in the physical reality of it.
This is such a classic scaling problem. It is one thing to inventory your five favorite board games or your collection of vintage fountain pens. It is another thing entirely to manage a literal warehouse of micro-electronics in a spare bedroom or a home office. Daniel mentioned he is taking photos with his phone, a OnePlus Nord, and that the macro shots are great, but the manual entry is the killer. And he is right. In professional logistics, we talk about the cost of a touch. Every time a human has to touch a piece of data or an object, the cost of maintaining that inventory goes up. If you have to touch an item five times between receiving it and shelving it, you have probably already spent more in labor than the item is worth.
I love that phrase, the cost of a touch. Let’s do the math on Daniel’s situation. If he has five thousand items, and every time he buys a new pack of M three screws or a voltage regulator he has to open an app, take a photo, type in the dimensions, categorize it, and assign it a location, he is spending maybe three minutes per item. At five thousand items, that is fifteen thousand minutes. That is two hundred and fifty hours of pure data entry. If Daniel bills his time as a consultant at, say, a hundred dollars an hour, he has spent twenty-five thousand dollars worth of his life just telling a database that he owns some screws. That is not a hobby anymore; that is a grueling, unpaid part-time job.
Exactly. And the professionals, the people running actual warehouses or even high-end repair shops like the ones you see in Shenzhen or even the specialized labs at M I T, they do not do it that way. They rely on systems of flow rather than systems of record. The first thing to understand is the difference between a flat list and a hierarchical location system. Daniel is using Homebox, which is great because it allows for nested locations. You have the office, then the shelf, then the bin, then the sub-bin. But the pro tip here is to decouple the item from the location in your mind and in your database.
What do you mean by that? If it is not in the bin, where is it?
I mean that the location should be a permanent address, like a house number. Most hobbyists label a bin screws or capacitors. That is a mistake. Because the moment you get more screws than fit in that bin, your system breaks. You have to relabel everything. The professional way is to use something called a License Plate Number, or L P N. Every container, every bin, every drawer gets a unique, permanent barcode or Q R code. You never, ever change the label on the box. You only change what the database says is inside that box. This is how Amazon handles their chaotic storage. A bin might contain a book, a toaster, and a pack of AA batteries. The system knows they are there because the bin was scanned, then the items were scanned into it.
That makes sense. It stops you from having to peel off old tape and write new labels every time you reorganize. You are just moving digital pointers. But Daniel's issue is the initial entry. He is getting these hauls from places like Ali Express or the local hardware store, like David Castel on Agrippa Street here in Jerusalem. If you have ever been there, it is this incredible, cramped space where they have everything from nineteen fifties plumbing fixtures to modern power tools. You walk out with a bag of twenty different things, and you have to get them into the system. How do the pros handle the high-volume intake without losing their minds?
They use batching and standardized attributes. If you look at how a professional parts distributor like Digi-Key or Mouser works, they are not typing out descriptions. They are using O C R, which is Optical Character Recognition, and pre-existing databases. For Daniel, the biggest leap forward in twenty-twenty-six is leveraging multimodal artificial intelligence for automated tagging. We are at a point now with models like G P T four o, Gemini one point five Pro, or even local models like Llama three vision, where you can feed an image of a circuit board or a specific screw into an A I, and it can identify the part, the thread pitch, or the manufacturer almost instantly.
Wait, can it really do that for niche parts? Like, if I show an A I a picture of a random voltage regulator, can it actually tell me the part number?
If the marking is legible, absolutely. These models have been trained on millions of data sheets and product catalogs. They are shockingly good at reading tiny laser-etched serial numbers that the human eye struggles with. So, instead of Daniel typing out C P U block for L G A seventeen hundred, he should be able to just snap a photo under a decent light, and the A I should extract the text from the packaging, categorize it under cooling components, and suggest tags like Intel, water cooling, and copper. It can even go out to the web, find the P D F of the manual, and attach a link to it in Homebox.
So the workflow changes from manual entry to verification. You are just hitting a confirm button instead of typing. That would cut the cost of a touch down from minutes to seconds. But how does he integrate that with something like Homebox? Homebox is open source, but it does not have a native A I layer that just watches your camera.
Not natively, no. But because it is open source and has a robust A P I, a programming interface, you can build a bridge. Daniel is a consultant; he probably has some scripting skills. There are already projects on GitHub that act as an A I sidecar for home inventory. You point a folder of photos at the script, it sends them to an A I model via an A P I key, and it spits out a J S O N file, which is a data format that Homebox can import. You could essentially process a hundred items in the time it takes to drink a cup of coffee. You just lay them out on a white background, snap-snap-snap, and let the script do the heavy lifting.
That is the dream. But let's talk about the physical side of this. Daniel mentioned he is using his phone camera. While phone cameras are great, especially with the macro lenses on something like his OnePlus, is there a better way for someone with thousands of parts? I am thinking about those handheld scanners you see in supermarkets or those ruggedized tablets.
For a home labber, the phone is actually often better because of the screen real estate and the processing power. But the real pro move is dedicated labeling. If Daniel wants to manage five thousand items, he needs a thermal label printer. Something like a Brother Q L eight hundred or a Dymo LabelWriter. These use heat instead of ink, so they never run out of toner. Every time a new item comes in, it gets a tiny Q R code. That Q R code is the digital twin of the physical object. If he ever needs to know what is in a bag or a box, he just points his phone at it, and the Homebox page for that item pops up instantly. No searching, no typing.
I think the Q R code is the bridge between the digital and physical. But there is another problem with large inventories: the audit. Things get lost. You take a screw out, you forget to update the count. You move a cable to a different bin because you were in a rush, and you do not tell the app. Professional warehouses use something called cycle counting. Instead of doing a massive, soul-crushing audit of five thousand items once a year, which takes an entire weekend and makes you want to quit the hobby, they count five items every single day.
Yes! Cycle counting is a game changer for the psychology of organization. It turns a mountain into a series of molehills. If Daniel just makes it a habit that every time he opens a drawer to look for something, he verifies the count of one random item in that drawer, his database stays remarkably accurate over time. It is about building a low-friction habit. If the app makes it hard to update a count, you won't do it. But if you can just scan the Q R code on the bin, hit a plus or minus button on your phone, and close the drawer, it takes four seconds.
So, we have A I for intake, Q R codes for retrieval, and cycle counting for accuracy. But what about the stuff that is too small to label? Daniel mentioned tiny screws and fittings. You cannot put a Q R code on an M two screw. It is physically impossible.
That is where you use the container as the proxy. This is a standard practice in hardware management. You label the bin, and you use a scale. Professional inventory managers for small parts often use counting scales. You tell the scale that one screw weighs zero point five grams, then you dump the whole bag in, and it tells you there are exactly two hundred and forty-two screws. If Daniel wants to get really nerdy, he can get a high-precision digital scale, one that goes down to the milligram, hook it up to his computer via U S B, and update his Homebox counts by weight. There are even plugins for some inventory systems that do the math for you. You put the bin on the scale, and it updates the database automatically.
That is next-level. I can see Herman Poppleberry now, weighing his resistors to the nearest milligram. But honestly, it makes sense. If you are a digital consultant like Daniel, your time is literally money. Spending an hour counting screws is a net loss for your business. But I want to go back to the A I side, because I think there is a deeper implication here. We are moving toward a world where the inventory system could be passive.
You are talking about computer vision, aren't you?
Exactly. Imagine a camera mounted over Daniel's workbench. It uses a model like Segment Anything or a specialized vision transformer to watch what he is doing. It sees him pick up a specific C P U block and move it from the intake area to a specific bin. The A I recognizes the object, knows where it went because it sees the L P N on the bin, and updates the database without him ever touching a phone or a keyboard. We are seeing this in Amazon's Go stores and high-end manufacturing lines. For a home user in twenty-twenty-six, we might not be there yet for a twenty-dollar setup, but the building blocks are all there.
It is closer than you think. Even just using a tool like LLaVA, which is a vision-language model you can run locally on a decent G P U, could allow Daniel to describe his inventory in natural language. He could say, I am putting the silver heat sink in the top drawer of the blue cabinet, and the A I parses that, identifies the heat sink from the video feed, and updates the database. That is the key: a natural language interface. The burden of inventory is the structure. You have to think like a database. But A I allows the database to think like a human. Daniel shouldn't have to navigate a menu tree. He should be able to just tell his house where he put his stuff.
And on the flip side, when he needs that L G A seventeen hundred block, he should be able to ask his smart speaker or his phone, where is my water cooling gear? and have the system highlight the exact shelf or even blink an L E D on the bin. That is called pick-to-light, another professional warehouse technique. You can actually build a pick-to-light system using W L E D and some cheap E S P thirty-two microcontrollers. When you search for an item in Homebox, it sends a command to the L E D strip on your shelf, and the specific bin lights up green.
I love pick-to-light. It makes you feel like you are living in the future. But I think we should also address the psychological aspect of this, because Daniel is hitting a wall at five thousand items. When you have that many things, there is a tendency to keep things you do not need just because they are inventoried. It is a form of digital-enabled hoarding. Professional inventory managers have a concept called O S M, which stands for Obsolete and Slow-Moving. If an item hasn't been touched in two years, it is costing you money in storage, insurance, and cognitive load.
Oh, absolutely. The cost of storage isn't just the physical space; it is the mental tax of knowing it exists and having to manage it in your system. A pro tip for Daniel would be to set an expiration or a review date for niche parts. If that C P U block is still in the bin in twenty-twenty-eight, it is probably for a socket that no one uses anymore. At that point, the A I should flag it and say, hey, you haven't touched this in three years, do you want to list it on eBay or donate it to a local maker space?
It is like the KonMari method for computer nerds. Does this voltage regulator spark joy? No, but it might spark a fire if I use it wrong. So, to recap for Daniel, we are looking at three main pillars of improvement. First, use A I for the intake. Stop typing. Use O C R and multimodal vision models to generate your descriptions and tags. Second, embrace the physical infrastructure of barcodes and Q R codes. They are the permanent addresses of your stuff. Use License Plate Numbers for your bins so you never have to relabel. And third, adopt professional workflows like cycle counting and weight-based measurement for small parts.
I am curious about the hardware side again. Daniel mentioned his OnePlus Nord. If he is doing thousands of items, would a dedicated ruggedized scanner, like a Zebra or a Honeywell, be worth it? Those things are designed to scan a thousand barcodes an hour without breaking a sweat.
For a home user, probably not, unless he finds a cheap one on the used market. They can be expensive. But what he could do is use a Bluetooth ring scanner. They are these tiny little devices you wear on your finger, and they pair with your phone or computer. You keep your hands free to move the parts, and you just point your finger to scan the code. It is incredibly efficient. It makes you feel like a cyborg librarian. It removes that friction of having to pick up and put down your phone every single time.
A cyborg librarian on Agrippa Street. I love that image. But let's talk about the data itself. Daniel forked Homebox into Where My Stuff. One of the risks of a large inventory is data lock-in. If he has five thousand items in a database and that project stops being maintained, he is in trouble. He has all these Q R codes that point to nowhere.
That is why open source is so critical here. Because Homebox uses a standard database structure, usually S Q Lite or PostgreSQL, Daniel owns his data. He should make sure he has a regular export routine that saves his inventory in a human-readable format, like a C S V or a J S O N file. That way, even if the software disappears, his two hundred and fifty hours of data entry labor are preserved. He can always import that into the next big thing.
Good point. Always keep the exit strategy in mind. I think we should also touch on the idea of shared inventory. Daniel is a digital consultant. If he has colleagues or friends in Jerusalem who also use these parts, a system like Homebox can actually be used to create a local mesh of resources. Imagine if you could search your friends' inventories for a specific part before ordering it from China.
That is the ultimate goal! A localized circular economy for makers. If I need a specific capacitor and I can see that Daniel has ten of them two blocks away, that is a huge win for sustainability and speed. It turns a private hoard into a community library. But that requires everyone to have the same level of inventory discipline, which is a tall order. It starts with one person doing it well. If Daniel can prove that managing five thousand items is feasible without it becoming a nightmare, others will follow. The A I tools are really the missing link here. They take the drudgery out of the organization.
They really do. I am actually getting excited just thinking about the metadata you could extract. Think about the searchability. Instead of searching for M three screw, you could search for anything made of stainless steel that is longer than ten millimeters. The A I can infer those properties from the photos and the descriptions even if you didn't explicitly type them in. It gives you a level of insight into your own possessions that was previously impossible.
It is like having a personal assistant who has a photographic memory of every single thing you own. I think for Daniel, the next step is to look into some of the A I-powered O C R tools that can interface with the Homebox A P I. There are some great Python libraries that can do this. You could set up a folder on your computer where you just drop a photo, and a script automatically creates a draft entry in Homebox. He could even use a local LLM to avoid A P I costs if he has a powerful enough workstation.
And for the hardware side, he should definitely look into a high-quality thermal label printer. The cost of the labels is pennies, but the time saved in identification is massive. Also, he should consider the lighting of his intake area. If he is taking photos for A I to analyze, good lighting is the difference between an accurate identification and a hallucination. A simple L E D ring light and a clean white background can make a huge difference.
Exactly. It is all about creating a standardized environment. In the warehouse world, we call this the pack station. It is where all the magic happens. If Daniel treats his intake like a professional pack station, he will find the process much more satisfying. It becomes a ritual rather than a chore. You sit down with your new parts, you scan, you weigh, you label, and you move on.
I think we have given him a lot to chew on. From A I-assisted intake to professional logistics workflows like cycle counting and license plating. It is a big shift from a hobbyist mindset to a professional one, but at five thousand items, you really do not have a choice. You either become a pro, or you get buried in your own stuff.
You either manage the inventory, or the inventory manages you. And I think Daniel is at the point where he is ready to take control. It is a lot of work upfront, but the payoff is that moment where you need a specific niche part and you can put your hand on it in fifteen seconds. That is a superpower. It changes the way you create. You are no longer limited by what you can find; you are only limited by what you have.
It really is. It is the closest thing we have to a real-life search function for the physical world. Well, this has been a fascinating dive into the world of parts management. I am actually feeling inspired to go organize that hallway shelf now, Herman. Maybe I will even buy a thermal printer.
I will believe it when I see it, Corn. But I will help you. We can start by cycle counting the power adapters. I think I saw a nine volt AC-to-AC adapter in there that belongs in a museum.
One step at a time. Before we wrap up, I want to say thanks to everyone for listening. This has been My Weird Prompts. If you have been enjoying our deep dives into the technical and the strange, we would really appreciate it if you could leave us a rating or a review on Spotify or Apple Podcasts. It genuinely helps the show reach more people who are interested in things like A I-powered home inventory and the philosophy of organization.
Yeah, it really does make a difference. And if you have your own weird prompts or a home lab setup that you are proud of, we want to hear about it. You can find us at myweirdprompts dot com, where we have an R S S feed and a contact form. We love hearing about how people are using technology to solve these very human problems of clutter and complexity.
You can also reach us directly at show at myweirdprompts dot com. We are available on Spotify, Apple Podcasts, and pretty much everywhere else you get your audio fix. We are always looking for new challenges, so send us your weirdest problems.
Thanks to Daniel for the prompt. It is a great reminder that even the most mundane tasks can be transformed by a little bit of clever engineering and some A I. It is about reclaiming your time and your space.
Absolutely. Until next time, I am Corn.
And I am Herman Poppleberry.
Thanks for listening, and we will catch you in the next episode.
Goodbye!
