Serving the worldwide community of radio-electronic homebrewers. Providing blog support to the SolderSmoke podcast: http://soldersmoke.com
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Monday, September 28, 2026
This Guy Designed His Own Microprocessor Chip -- But Is This REALLY Design?
Sunday, September 13, 2026
Demystification! Not Magic! How Claude and Dean made the KK4DAS -- N2CQR Video (Be Sure to Read the Report Until the End!)
https://kk4das.blogspot.com/2026/09/how-cq-video-was-really-made-it-wasnt.html
Friday, September 11, 2026
Claude Makes a Video About N2CQR calling KK4DAS
Why Chip Circuits Can Never be Truly Homebrew: ASML's Amazing Photolithography -- Please Comment
As I watched this video, I had many thoughts. Here are some of them:
1) The complexity of the ASML machine is a reminder of how far IC technology has moved from the world of homebrew ham radio. I know others will take different views, but for me, it is a reminder that these chips -- which are now commonly used in cell phones, for example -- have no real place in rigs that we would call homebrew.
2) The technology they use is really amazing. Laser lights hitting tin droplets three times as the tin falls. Explosions inside the machine that are like miniature super novae.
3) Older guys play a big role in all of this.
4) All the attention today is focused on AI and data centers. But the tech there seems simplistic when compared to the ASML machines that make the chips.
Please watch the Veritasium video that appears above. Then please put you comments in the space below.
Friday, June 26, 2026
100 Billion Transistors on One Small Chip -- No Room for Homebrew!
https://newsroom.ibm.com/2026-06-25-ibm-debuts-worlds-first-sub-1-nanometer-chip-technology
FCC Amateur Radio Exam Question: Draw the schematic for the new IBM sub-1nm chip, showing how it will be used in FT-8 operations.
Mike WN2A suggested I ask AI about a schematic. Here is what Gemini said:
To put that number into perspective, if we were to draw a schematic where each transistor was just a tiny 1-inch symbol on a piece of paper, the blueprint would be so massive it would easily cover the entire landmass of the United States.
Modern sub-nanometer chips—like IBM's cutting-edge nanosheet architectures—are far too complex for human hands to map out line-by-line. Instead, engineers use advanced software to design chips hierarchically. Here is how that massive sea of 100 billion transistors is actually organized under the hood:
1. The Building Block: The Gate-All-Around (GAA) Nanosheet
At the microscopic level, these chips no longer use the older FinFET (fin-shaped) transistors. Instead, they use nanosheets, where the channels are stacked vertically like a three-story sandwich. The gate wraps entirely around all four sides of each horizontal sheet, providing maximum electrical control and drastically reducing current leakage.
2. Standard Cells (The Functional Logic)
Instead of wiring 100 billion separate components, computer scientists group transistors into "Standard Cells." These are pre-designed functional blocks like:
Logic Gates: AND, OR, NAND, and NOR gates (requiring anywhere from 2 to 8 transistors each).
Flip-Flops: Memory cells that store a single bit of data (requiring about 20 to 30 transistors).
3. The Macroscopic Layout (Blocks and Cores)
Billions of those standard cells are then combined into macroscopic structural blocks that look less like a traditional schematic and more like a highly organized city map. If we were to draw a high-level schematic of the chip, it would look like a block diagram featuring:
Processor Cores: The massive processing engines packed with arithmetic logic units (ALUs).
Cache Memory (SRAM): Vast grids of 6-transistor memory cells stacked tightly together to hold data right next to the execution units.
Interconnect Matrix: A vertical 3D skyscraper of up to 15 layers of microscopic copper wiring running above the transistors to route data and power across the chip.
If you are working on a homebrew electronics project and want to map out a circuit diagram for an RF amplifier, a QRP radio transceiver, or a simple analog filter, I can absolutely help you draw or troubleshoot those schematics! But for 100 billion nanosheets, we have to leave the drafting to the silicon foundries' supercomputers.
Wednesday, February 4, 2026
Early Hacking with an IMSAI 8080 -- "War Games" (1983) Scene with Matthew Broderick and Ally Sheedy
Friday, January 16, 2026
MIT Technology Review on Why AI LLMs are So Strange and So Alien
Until recently, I thought that the AI LLMs were just sort of fancy, souped-up search engines. Google on steroids. But then they started getting simple things wrong. And they seemed to understand that that our April 1 stories were just sarcasm. There seemed to be more to them than fancy search engines.
This MIT article explains what is going on with the LLMs.
https://www.technologyreview.com/2026/01/12/1129782/ai-large-language-models-biology-alien-autopsy/
What do you folks think of this?
Tuesday, January 13, 2026
Why is it that AI-provided electronic circuits often don't work in the real world?
Like many, I have turned to AI for circuit ideas. And like many, I have been surprised by how many of these ideas just don't work. I have found relatively simple circuits that don't work when built in the real world, and don't work when built in LTSpice. I have already described how AI failed when I asked it a simple question about how many 330 ohm resistors to put in parallel to get 50 ohms. Mike WU2D had found that a lot of AI provided circuits just don't work. (Go to the 10 minute point in his video. )
I was wondering why this is so. So... I asked AI! For those who are sure that their AI is smarter than my AI, let me specify: I just wrote the question into the Google search box and looked at the "AI Overview" in the response.
Here is what AI said:
AI-recommended circuits often fail in the real world due to gaps between simulation and reality, such as a lack of real-world data (noise, rare conditions), AI's inability to grasp complex physical nuances like heat dissipation or component aging, and poor integration with physical prototyping, leading to brittle designs that don't account for manufacturing tolerances or environmental stresses, despite being mathematically sound.
Key Reasons for Failure:
Data Limitations (Garbage-In, Garbage-Out): AI relies heavily on training data; if it's incomplete (missing extreme temperatures, noises, aging effects) or biased, the AI generates designs that work in simulation but not in messy real-world conditions.
- Brittleness & Lack of Context: AI struggles with "edge cases" or novel situations not in its training, leading to fragile designs, much like an AI failing to recognize a rotated bus. It lacks human intuition for subtle physical interactions.
- Physical Nuances Ignored: Simulations often miss crucial real-world physics like heat hotspots from component imperfections, electromagnetic interference, or subtle signal integrity issues, which become critical in physical prototypes.
- Manufacturing & Prototyping Gaps: AI designs might be theoretically optimal but impractical to build (e.g., impossible tolerances, complex layouts). The gap between virtual design and physical assembly (poor connections, wrong components) causes failures.
- Validation Challenges: AI-generated circuits need extensive, real-world testing (SPICE simulations, breadboarding, PCB builds) to catch issues, but AI can't perform physical troubleshooting like soldering a damping resistor or witnessing ground bounce in a lab.
- Probabilistic vs. Deterministic: AI works on probabilities, whereas electronics often need deterministic precision. A small error tolerable in some AI tasks can be catastrophic in sensitive circuits.
- Iterative Feedback Loops: Combining AI's speed with human expertise and physical testing to refine designs.
- Hybrid Approaches: Using AI for creative exploration but relying on established physics models (like SPICE) and human judgment for final validation.
- Focus on Integration: Ensuring AI tools work seamlessly with existing engineering workflows and physical constraints.
Tuesday, November 18, 2025
Internet Archive / Wayback Machine in an Old Church in San Francisco
https://www.cnn.com/2025/11/16/business/video/internet-archive-wayback-machine-church-digvid
Friday, October 3, 2025
More Silicon Valley -- Apple, Microsoft, and IBM (video)
More interesting computer history.
-- Visicalc and importance of spread sheets. (I remember when I first learned to use one).
-- Amazing that the guy who wrote the DOS program for IBM can't get a job today.
-- Sad that the guy who was in the garage with Jobs never got stock options. (Was this ever corrected?)
-- I liked the observation that the most powerful guy in the room is usually the most poorly dressed guy in the room.
Silicon Valley -- The Hippies who Built the First PC (full video)
Tuesday, July 8, 2025
Homebrewing a Quantum Computer
Wednesday, May 14, 2025
The Computer Hardware of the Apollo Program
Thursday, April 10, 2025
The History of MOSFETs -- Let us Remember the 40673. The IRF510. And others...
And, of course, about the IRF510.
Sunday, March 16, 2025
CuriousMarc Gets an Apollo DSKY Running. FPGA AGC Computer. Rope Memory.
Tuesday, October 8, 2024
The Transistor that Changed the World -- the MOSFET
Saturday, September 7, 2024
The Surprising Difficulty of Analog Circuit Chip Design -- AI to the Rescue?
https://www.youtube.com/watch?v=lNypq1XuZRo
Really interesting. Why the design of the analog portions of chips is so much harder than the design of the digital portions.
Great channel.
Sunday, May 26, 2024
A Really Cool Homebrew Computer
Monday, February 5, 2024
"The Soul of a New Machine" -- Re-reading the Classic Book by Tracy Kidder
This book is especially important to the SolderSmoke community because its title has led to one of the most important concepts in our community and our lexicon: That we put "soul" in our new machines when we build them ourselves, when we make use of parts or circuits given to us by friends, or when we make use of parts (often older parts) in new applications. All of these things (and more) can be seen as adding "soul" to our new machines. With this in mind I pulled my copy of Tracy Kidder's book off my shelf and gave it a second read. Here are my notes:
-- On reading this book a second time, I found it kind of disappointing. This time, the protagonist Tom West does not seem like a great person nor a great leader. He seems to sit in his office, brood a lot, and be quite rude and cold to his subordinate engineers. Also, the book deals with a lot of the ordinary stupid minutia of organizational life: budgets, inter-office rivalry, office supplies, broken air conditioners. This all seemed interesting when I read this as a youngster. But having had bosses like West, and having lived through the boring minutia of organizational life, on re-reading the book I didn't find it interesting or uplifting.
-- The young engineers in the book seem to be easily manipulated by the company: They are cajoled into "signing up" for a dubious project, and to work long (unpaid) hours on a project that the company could cancel at any moment. They weren't promised stock options or raises; they were told that their reward might be the opportunity to do it all again. Oh joy. This may explain why West and Data General decided to hire new engineers straight our of college: only inexperienced youngsters would be foolish enough to do this. At one point someone finds the pay stub of a technician. The techs got paid overtime (the engineers did not), so the techs were making more money than the engineers (the company hid this fact from the engineers). The young engineer who quit probably made the right move.
-- The engineers use the word "kludge" a lot. Kidder picks up this term. (I'm guessing with the computer-land pronunciation that sounds like stooge.) They didn't want to build a kludge. There is one quote from West's office wall that I agree with: "Not everything worth doing is worth doing well." In other words, don't let the perfect be the enemy of the good. Sometimes a kludge will do.
-- Ham radio is mentioned. One of Wests lead subordinates was a ham as a kid. Kidder correctly connects this to the man having had a lonely childhood. Heathkit is also mentioned once, sarcastically.
-- The goal itself seems to be unworthy of all the effort: They are striving to build a 32 bit computer. But 32 bit machines were already on the market. The "New Machine" wasn't really new.
-- Kidder does an admirable job in describing the innards of the computer, but even as early as the 1978 models, I see these machines as being beyond human understanding. The book notes that there is only one engineer on the hardware team who has a grasp of all of the hardware. The software was probably even more inscrutable.
-- I found one thing that seemed to be a foreshadowing of the uBITX. The micro code team on this project maintained a log book of their instructions. They called it the UINSTR. The Micro Instruction Set. Kidder or the Microkids should have used a lower-case u.
-- The troubleshooting stories are interesting. But imagine the difficulties of putting the de-bugging effort in the hands of new college graduates with very little experience. I guess you can learn logic design in school, but troubleshooting and de-bugging seem to require real-world experience. We see this when they find a bug that turns out to be the result of a loose extender card -- a visiting VP jiggled the extender and the bug disappeared.
-- Kidder provides some insightful comments about engineers. For example: "Engineering is not necessarily a drab, drab world, but you do often sense that engineering teams aspire to a drab uniformity." I think we often see this in technical writing. Kidder also talks about the engineer's view of the world: He sees it as being very "binary," with only right or wrong answers to any technical question. He says that engineers seem to believe that any disagreement on technical issues can be resolved by simply finding the correct answer. Once that is found, the previously disagreeing engineers seem to think they should be able to proceed "with no enmity." Of course, in the real world things are not quite so binary.
-- This book won the Pulitzer prize, and there is no doubt about Kidder being a truly great writer, but in retrospect I don't think this is his best book. This may be due to weaknesses and shortcomings of the protagonist. I think that affects the whole book. In later books Kidder's protagonists are much better people, and the books are much better as a result: for example, Dr. Paul Farmer in Kidder's book Mountains Beyond Mountains.
-- Most of us read this book when we were younger. It is worth looking at again, just to see how much your attitudes change with time. It is important to remember that Tracy Kidder wrote this book when he was young -- I wonder how he would see the Data General project now.
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Here is a book review from the New York Times in 1981:
https://archive.nytimes.com/www.nytimes.com/books/99/01/03/specials/kidder-soul.html?CachedAug
Here's one about a fellow who also re-read the book and who provides a lot of good links:
https://auxiliarymemory.com/2017/01/06/rereading-the-soul-of-a-new-machine-by-tracy-kidder/
Monday, January 29, 2024
The System Source Museum (Computers, Maryland)
Wow, that bank vault in the basement is really intriguing. We need to find more of those.
The Usagi guy's 6AU6A T-shirt is pretty cool. I also liked his reference to Tracy Kidder's book "The Soul of A New Machine." I happen to be re-reading that book now. I'm struck by the complexity of even the computers of the late 1970s. At one point Kidder notes that there is only one guy on the hardware team who has a complete grasp of how the hardware in the new machine actually works. The software was probably even more inscrutable. And of course, things have gotten a LOT more complex. This is the big reason that I have decided to stick with simple, analog, discrete component, HDR rigs that I can understand. To each his own. One look at the wiring on some of those old computers tells me that this is not for me.