I'm hoping you're right. I enthusiastically bought PEFI but found it to be way over my head. I'm eager to get SLOC because I'm hoping it can be sort of a bridge between my "MAKE: Electronics" book and PEFI.
PEFI/PE4I as a technical reference when you need details about a specific topic, SLOC as an overall introduction to give a good intuition, and M:E for more practical project tutorials, if that’s what we (hobbyists without any formal training) end up with that would be awesome :)
AlphaPhoenix YouTube channel is also a great source of information to clarify theoretical topics. I find it very helpful to just see how he think about electricity in his various projects: https://youtu.be/X_crwFuPht4. And his enthusiasm is contagious!
One personal note: as someone coming from software I find learning electronics pretty hard as it requires a completely different mental model and way to approach systems. Took me years of various failed attempts to finally internalize I have to stop relying on metaphors and shortcuts, and instead understand the actual physics at play to develop an intuition that makes sense. That looks daunting and I thought I would hate that part but in fact after pushing through the first weeks it becomes really satisfying! Accept things take time and that you will be uncomfortable for a while, and move at your own pace. And here LLMs are actually really helpful to clarify topics you’re not sure you understand fully, once you start to learn the terminology (I use instructions that makes it clear they shouldn’t solve things for me and instead evaluate my understanding)
Also as a person who came to Embedded/Electronics from the Software side, i found the following incredibly useful to bridge the software-hardware gap using MCUs;
This has more of a hardware interfacing slant than most books on embedded systems which is what a software person needs. Every topic is first introduced in a general way with lots of illustrations and then its application in MSP430 is shown. Hence we can easily transfer our understanding to any other MCU family. A really good textbook for serious students of embedded electronics.
To me, transistors are the best example of how hard EE can be. I heard "transistor is a switch" or "transistor is an amplifier". So I bought some MOSFETs and tried to use them. No go. Well, I had to learn (through random web pages and some books) that microcontrollers struggle to switch mosfets. Fine. I'll get a gate driver. Then I have to very carefully pick a bunch of resistor values that make my system behave. Eventually, I learn that transistors aren't switches at all, but can be coerced into sort of behaving like switches, but in reality, there is always leakage and a hundred other variables you have to understasnd and control for. You can see one of my threads here: https://www.reddit.com/r/AskElectronics/comments/1omqn4d/con...
> as someone coming from software I find learning electronics pretty hard as it requires a completely different mental model and way to approach systems
I went from electronics to software and one thing that has puzzled me is how much people dislike reading docs as in reference manuals. People can get by with sloppy code full of hidden bugs and when those bugs arise they’re like deers frozen by headlights.
Imagine building a circuit without any ideas how it operates. I’ve encountered web devs that don’t understand how http works.
I like Elicia White’s Making Embedded Systems because it covers a lot of practical stuff like how to read a datasheet. It’s a good book for existing programmers.
A lot of electronic books struggle to find balance between super easy projects, very specific cookbook recipes or a laundry list of components.
I use Canvas (the LMS) a LOT for the classes I teach and, since I'm a hobbyist programmer, I enjoy writing little tools/scripts to help me create html code "snippets" that I can post into the html editor of my Canvas pages (e.g., striped tables, FAQs, flashcard-like study cards, an interactive story creator, and more).
I've also written a plaintext to QTI quiz converter since creating quizzes on Canvas is so slow (I realize, of course, many other people have already made these... I just happen to like mine).
It currently supports fill-in-the-blank, multiple choice, matching, true/false and word bank style questions. You can also generate a printable (DOCX) version of whatever quizzes you make.
All the tools are free, and there's no login required. So if you are a teacher that uses Canvas and think any of these tools could help you, check them out!
Yes. Don’t get me wrong: I use and enjoy these LLMs. But even now, well into 2026, I’m still using them via the now “old-fashioned” chat box in a web browser. Based on all that we’ve learned about this technology, there’s just no way I’m giving it control over any part of my machine. Is it a helpful search engine? Does it save me typing and write some nice code snippets when I need it to? Absolutely it does, and I greatly appreciate the technology. But I’m just not ready to create agents and let them run. I just think that’s still way too risky, and I fear a major, major catastrophe is coming because of how so many people recklessly trust these agents. I certainly hope I’m wrong.
last week i finally bit the bullet and did the vm thing and my harness exclusively lives in there now, no more env files for local dev keychain access only which is requiring a lot of input from me but oh well. i'm 100% web browser chat on my host system. i cannot afford to have my world compromised and i stalled on setting that up for way too long. all of these major services are inevitably going to get compromised they're just adding too many surfaces constantly it's insane.
i also ejected node/npm the fuck out of my world after the recent shai halud. In this "AI is assisting in finding vulnerabilities" era i'm just like done with the exposure. keeping vendored copies of any libs i need and mostly just use go now and making stuff that is effectively distroless for deployment and my build chain is pretty much just compiling my go, and even with go im carefully looking at packages theres so many packages appearing out of thin air now all vibe coded no reputation.
Me too, it is a crystal clear boundary between me and the LLM.
They might have access to my most precious code, but at least they don't have access to my browsing history.
See, this, to me, seems obvious, but I’m sure it’s more challenging/complex than I can imagine (I am NOT an expert on AI in any way imaginable). But there has to be a solution. Just yesterday I was asking Gemini to tell me about a certain college professor, and it gave me a list of facts about them. And it was perfect. Then, out of curiosity, I followed up with “tell me more about him!” and it spit out several more bits of information about this person that were entirely hallucinated (e.g., gave them credit for writing papers they didn’t write, said they won awards that actually someone else won). I know this is all complex and certainly beyond my limited skill set, but goodness, we’ve got to get this figured out with so many people depending on and trusting these things nowadays. It’s quite scary.
I bet most of these issues are essentially system prompt/harness issues.
If your example had "Validate any details before sharing them with the user, with multiple sources" as the system prompt, it was using a model that is strong at following system prompts precisely and had access to some basic tools, then it'd spend maybe minutes more, but the answer would have been way more accurate.
But no, Google want "the new search results" (LLM hallucinations) to be on top, so we end up with "sounds plausible" answers instead "Collection of evidence from reliable/semi-reliable" or similar, which sucks. We could have quality, but it's too expensive/slow, so we get slop instead, just to maximize for speed and convenience.
Errors multiply though, you might just get more plausible sounding errors than actual facts.
Like when agent 1 says X, agent 2 verifies it as Y and the original question ends up being some weird amalgamation of Z with additional ”this is really true” statements sprinkled on top.
I agree Google responses hurt more than help, but I’ve also gotten identical outcomes of 40min self-reasoning Opus threads (it’s less common obviously).
> Like when agent 1 says X, agent 2 verifies it as Y and the original question ends up being some weird amalgamation of Z with additional ”this is really true” statements sprinkled on top.
Yeah, seems what grounds agents right now is quite literally human thoughts and text, so if you're doing something like that, you really need to pass the original user prompt through the entire way, for every "child" to keep in mind the final thing, otherwise it does seem to spiral out of control.
I think you may be right. I enjoy tech and programming, but hardly any of my friends/family do. And nearly everyone in my inner circle (an admittedly small number of people, considering I'm an extreme introvert) condemns and avoids AI both for the reasons you mentioned and because they refuse to "outsource my brain to AI!"
In fact, the only time I personally encounter a lot of pro-AI commentary is when I come here to HN (and, obviously, there are plenty of anti-AI people on this site too).
I personally appreciate it and use it, but I'm still "old-fashioned" in the sense that I only ask it for very specific things and always read through what it produces. I'm honestly not entirely sure how I'm supposed to feel about all this. These are interesting times, to say the least.
For real! I occasionally write assembly because, for some reason, I kind of enjoy it, and also to keep my brain sharp. But yes, there is no way I could do it without pencil and paper (unless I’m on a site like CPUlator that visually shows everything that’s happening).
This is exactly how I use them too! What I usually do is give the LLM bullet points or an outline of what I want to say, let it generate a first attempt at it, and then reshape and rewrite what I don’t like (which is often most of it). I think, more than anything, it just helps me to quickly get past that “staring at a blank page” stage.
I love the way you've expressed this! This was my experience doing the lit review for my PhD. I read more than a few "scholarly" texts that were perhaps impressive from a language/vocabulary standpoint but contained ideas that were either poorly supported or too simple to amount to any meaningful contribution to the field.
I felt this comment in my soul. I’ll never understand it: I’ve written thousands of lines of code (as a hobbyist) to solve all sorts of problems I’ve run into and yet always seem to struggle to wrap my mind around the core algorithms any real developer should be able to handle easily. This is why I’ve never pursued programming as a career.
It took computer scientists of the past, a lot of effort to come up with these complicated algorithms. They are not easy or trivial. They are complicated and that's OK that you can't just quickly understand them. Your imaginary "real developer" at best memorised the algorithms, but that hardly differs from smart monkey, so probably not something to be very proud of.
It is your choice which career to pursue, but in my experience, absolute majority of programmers don't know algorithms and data structures outside of very shallow understanding required to pass some popular interview questions. May be you've put too high artificial barriers, which weren't necessary.
To be a professional software developer, you need to write code to solve real life tasks. These tasks mostly super-primitive in terms of algorithms. You just glue together libraries and write so-called "business-logic" in terms of incomprehensible tree of if-s which nobody truly understands. People love it and pay money for it.
Thanks for your kind comment! I do not have any systematic leaning of computer science; I often feel confused when reading textbooks on algorithms hahaha.
Should I be familiar with every step of Dijkstra’s search algorithm and remember the pseudocode at all times? Why don’t the textbooks explain why the algorithm is correct?
> Should I be familiar with every step of Dijkstra’s search algorithm and remember the pseudocode at all times?
Somehow, I think you already know the answer to that is "no".
I've been working as a software engineer for over 8 years, with no computer science education. I don't know what Dijkstra's search algorithm is, let alone have memorised the pseudocode. I flicked through a book of data structures and algorithms once, but that was after I got my first software job. Unless you're only aiming for Google etc, you don't really need any of this.
You should know the trade-offs of different algorithms, though. Many libraries let you choose the implementation for a spcific problem. For instance tree vs. hash map where you trade memory for speed.
> Why don’t the textbooks explain why the algorithm is correct?
The good ones do!
> Should I be familiar with every step of Dijkstra’s search algorithm and remember the pseudocode at all times?
If it’s the kind of thing you care to be familiar with, then being able to rederive every step of the usual-suspect algorithms is well within reach, yes. You don’t need to remember things in terms of pseudocode as such, more just broad concepts.
Something that helps, I think, is to make something practical that demands it.
I used to think alike (I'm +30 year programing) until I decide to do https://tablam.org, and making a "database" is the kind of project where all this stuff suddenly is practical and worthwhile.
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