Author: David Bookstaber

Gold, the Fed, and the Tax on Inflation

Posted on by David Bookstaber

Compiled from posts I first published in 2008 and 2010.

If you spend any time in anti-income-tax libertarian circles you will discover that fear and loathing of the IRS is followed closely by deep suspicion of the Federal Reserve (“Fed”) and abiding nostalgia for the days of the gold standard.  Libertarians spin vast conspiracy theories about who controls the Fed and who profits from it.  They appear to be confused.

The only solid argument I see against the Fed seems to circle back to the tax code, which presently levies taxes on inflation.  But this is an argument against the tax system, not the monetary system.

Yes, in principle the Federal Reserve could choose to inflate the money supply.  In an inflation scenario, non-debtor citizens holding dollars are harmed as dollars lose value.  Meanwhile our debtor government benefits since inflation not only reduces the (real, not nominal) national debt but also increases tax receipts from capital gains, thanks to the Tax on Inflation:  If you earn 5% interest during a period in which inflation runs at 5%, you have made no real gain on your savings.  However, the IRS currently looks at your gains in nominal, non-deflated terms, and so they will tax you as if you had gained 5%.

Gold-standard advocates see a commodity-backed currency as a solution to the moral hazard of government-instigated currency inflation.  But gold-backed currency is not immune to inflation or manipulation either: It simply replaces the intentional control of the money supply by the Fed with the circumstances of world-wide gold production and storage.  Inflation and deflation still occur when the production of new gold does not match the growth of the economy.  Nations and corporations could still manipulate the money supply by hoarding gold or flooding the market with their stores.  How is this any better than what we have right now?

Though in theory the Fed could take actions to inflate our currency, its mission is the exact opposite: to limit inflation.1  The Fed is also accountable to the banking system, and since banks are predominantly creditors they are not happy with inflation since it reduces the value of their credits.

There are numerous ways Americans can hedge against dollar inflation: Instead of storing dollars, hold hard assets, other currencies, or inflation-protected securities like TIPS.  One thing Americans can’t do at present is protect themselves from the Tax on Inflation: As long as the IRS demands that gains be calculated against an inflating currency, it can assess capital gains even when real gains are zero (or negative!).

Ironically, this argument feeds directly into the personal portfolios of many gold-standard advocates.  Often dubbed ‘gold bugs,’ these are people who have concluded that physical gold is the best hedge against inflation and fiat currency risks.  Gold bugs revel in the relatively stable historical value of gold.  One problem I have pointed out is that its intrinsic value (i.e., its substitution value as a material for industrial or other practical uses) is closer to that of lead or copper.  Its stratospheric market value is entirely a function of (1) scarcity and (2) speculation, which is to say that it trades where it does because buyers believe that there will continue to be other buyers ready to pay similarly elevated prices.  If everybody decided one day that they would rather own platinum jewelry and hoard casks of whiskey as a hedge against inflation then the price of gold could collapse to its substitution value.

The other problem is that the supply of gold is not fixed.  True, the cost of mining and refining has historically been proportional to industrial capacity.  The last technological breakthrough in production came in the 19th century with the MacArthur-Forrest Process for extracting gold from low-grade ore.  But even barring another breakthrough in production, the gold supply does increase with demand: When gold prices surge, capital investments in mining operations follow and the production of gold increases.

  1. Not exactly to zero, but rather to 2% per year – for macroeconomic reasons beyond the scope of this post. ↩︎

Why I Dislike Dark Mode

Posted on by David Bookstaber

First published in my newsletter, September 4, 2023.

VSCode Light Theme vs Dark Theme
VSCode Light Theme vs Dark Theme

The Light Mode vs Dark Mode GUI question has become somewhat polarizing.  Increasing numbers of apps have added Dark themes that use a black background.  I generally prefer light mode for one very specific and widely applicable reason, even though I grew up with “dark” displays.

In the old days – back when all computer displays were cathode ray tubes (CRTs) and PCs only had enough memory to display 25 lines of 80 characters of text in a fixed font – screens were black with white, green, or amber text, like this:

1980s Monochrome CRT computer display
1980s Monochrome CRT computer display

It was possible to invert these to show the text as black on light.  But as far as I remember, the first computer to mainstream a uniformly light background with black text was the 1984 Macintosh.  And I thought it was grotesque.  The pixels on those old monitors were not very sharp, so instead of bright text characters bleeding a little onto a black background, a white background seemed to accentuate the blurriness as it bled into the black text pixels.  (In fact, on color screens the individual red green and blue phosphors that made up each white pixel would stand out if viewed closely.)  For me, fewer illuminated pixels was better.

By the end of the 1980s, CRT monitors had significantly improved in both resolution and pitch, making everything more crisp and making light backgrounds more tolerable.  WYSIWIG became all the rage, and since most computer work was done to be printed on white paper GUIs defaulted to white backgrounds with black text – Light Mode.  There was also a lot of contemporaneous research, as detailed in this seminal article, that supported the idea that humans work better in Light GUIs.

However, even in the enlightened 1990s programmers spent their time in text terminal windows and development environments that to this day default to dark mode.  For example, in Windows open a command terminal by going to Start and typing “cmd”.  So I do have some nostalgia for the dark-mode days.

And then I grew into my mid-40s.  Most people in middle age will get presbyopia, which is a reduced ability to focus at close distances.  (This is due to a consistent decrease over time in the flexibility of the eye’s natural lens.)  I can no longer read small or dim text without reading glasses to push the focal distance further out.  And this is where light mode shines.

Photographers know that the wider the aperture of a lens, the more shallow the range of things that can be in focus (a.k.a., depth of field).  As the aperture closes, depth of field increases.  At the limit, a pinhole aperture has an infinite depth of field: everything is in focus no matter what its distance from the viewer.1

The same physics hold for eyes: the wider our pupils open, the more our lenses have to deform to shift focus, and the more apparent is any astigmatism.  As our pupils close more of our field of view comes into focus.  In fact, if you look through a pinhole everything will be in focus, no matter how extreme an eyeglass prescription you may have.

Eye photographed in dim and bright light to show change in pupil size.
Eye on the left photographed in dim light.  Same eye on right in bright light.  Note how pupil on left is dilated (larger) in order to pass more light through the lens.

You can test this yourself:  Find some small print and bring it so close to your eyes that it becomes blurry.  (If your eyes are so young or flexible that bringing it right up to your eyes doesn’t make it blurry, take it into a darker environment so that your pupils dilate.)  Now close one eye, and with the other one look at the text through a pinhole opening held right in front of that eye.  You can make a literal pinhole by punching a tiny hole in a piece of paper, or you can create one by closing your fingers together leaving only a tiny opening.  Now you will be able to clearly see what was out of focus before.

Using a hand to form a pinhole aperture in order to read fine print without reading glasses.
David Bookstaber actually uses this trick to read fine print when no reading glasses are handy.

The problem with pinholes is that they don’t admit much light.  So everything you look at through a pinhole will be in focus, but without adequate illumination it might be too dim or low-contrast to read.

What does this have to do with UI themes?  Light Mode puts out more light, which causes your pupils to contract, which increases the depth of field of your lens and allows your eyes to focus on objects that are closer.  For any given display brightness, there will be a distance at which text is too close for me to read without glasses or eyestrain in Dark Mode but where the same text will still be readable without glasses in Light Mode.

1. The following illustration demonstrates this.  I set a pill bottle just inside the closest distance this lens can focus at maximum aperture (f/1.7), so the text is blurry (second row of photos).  Then I closed the aperture as tight as it goes (f/22) and took the photo again (third row of photos).  Now the text is clear.  The cost: To get the same exposure required a shutter speed of only 1/640 second at maximum aperture, but 1/4 second at minimum aperture.

The same fine print photographed at f/1.7 and at f/22, showing how a smaller aperture increases depth of field.
Photos of a pill bottle sitting inside the minimum focal distance of a lens.  At wide-open aperture the text is blurry.  At minimum aperture the text is legible.  (The photo of the lens aperture in the second row is slight closed to f/2.8 because wide-open the iris doesn’t show at all.)

Software Engineering

Posted on by David Bookstaber

First published in my newsletter, September 29, 2023.

I’m writing a book on ballistics, based in part on my work on ballistipedia.com, and I got to the point that I wanted a raw ballistic calculator to create examples. I found a public implementation of a well known calculator in (programming language) Python and decided to start with that.

Nothing gets you up to speed on software engineering tools like having (a) a project you want to complete and (b) existing code to work from. In this case the existing code used a lot of Cython, which is a hybrid of Python and C that I have been meaning to pick up for a long time. I have been using Python more than any other language for the last six years. But I began coding in C more than thirty years ago. So why did I end up using Python if C is still relevant?

Some features of Python:

  1. It’s a high-level language that encourages concise and readable code.
  2. It’s the most popular programming language. Whatever you want to do, you can probably find existing (and free) Python packages that get you most of the way there. For example: I recently needed to scrape a bunch of data from a website. Bing’s chatbot recommended a package called selenium, and in less than an hour and 30 lines of Python I was done.
  3. It is interpreted, not compiled. This makes it fast to write and easy to prototype in, but it also means the code runs relatively slowly.

In the past decade computers have gotten so fast that the last point is usually not an issue. Lots of production systems are running in Python. But when you realize how much slower Python runs this is surprising: As an example, I just made a Cython version of a simple statistical simulation I had written in Python. The Cython version can run 10 million simulations in 15 seconds. In the same time, the Python version completes only 75 thousand simulations. The compiled Cython version is over 100 times faster!

So how does a programming language that runs two orders of magnitude more slowly than the alternatives become so common? This is interesting, because Python has been around for 30 years, but its use has only really exploded in the last decade.1 The other thing that has exploded over this timeframe is the amount of excess compute – a term that refers not only to processing speed but also to storage capacity and information transmission bandwidth. A generation ago we marveled that a student calculator had more compute than the guidance systems that landed man on the moon. Today, there is so much compute in a typical smartphone that developers rarely have to worry about speed or memory. And so we use tools to build and publish apps that are hundreds of times larger and slower than necessary because (a) they’re easier and (b) it’s no longer worth the effort to make them small or fast.

Have you ever noticed the size of installation files for smartphone apps? Very few weigh in at less than 10 megabytes. Heck, the basic calculator app on my phone takes up 8MB of memory! Why? 30 years ago the same app under Windows 3.1 used 41kB of memory (that’s 1/200th the size).

Smartphone calculator app listing showing 8 MB of storage used
8 Megabytes of functionality here?

It’s not a coincidence that I didn’t use Python much earlier. Most of my work is in the finance industry. In the 1990s we could never get enough storage for market data or speed to run analyses, so we spent a lot of time optimizing our C++ code to squeeze as much as we could out of our compute resources.

In the early 2000s I still knew every detail of every available piece of computer hardware because I was working them to their limits. I still remember my excitement to get a server with two 64-bit Opteron CPUs and to break out of 32-bit memory space! The early 2000s were also the point at which we could afford to use a SQL database instead of handling every detail of reading and writing our data to files and keeping track of exactly what was being held in RAM at each moment.

The last decade is when we could get really sloppy. With gigabytes of RAM on every computer it’s usually possible to keep everything of interest in memory; no need for a database. Where previously I used C++ or C# to build production systems for trading and portfolio management, computers are now so fast that I actually built and run my most recent system in Microsoft Excel! I’m not kidding: Here’s a screenshot of Excel (left) consuming realtime data and generating portfolio analysis next to an Interactive Brokers Trader Workstation (right):

Microsoft Excel consuming realtime market data beside Interactive Brokers Trader Workstation
These days, real quants can make everything work in Excel

1. TIOBE publishes data on programming language popularity. Stack Overflow is another good indicator, which is where I got this chart:

Stack Overflow question-share trends for C++, Python, Java and C#

Where Do Trading Profits Come From?

Posted on by David Bookstaber

This is a question I first encountered in college, where it was tinged with idealistic indignation at this glaringly capitalist phenomenon. Since I have continued to work in the industry I regularly encounter it in various forms, and it’s worth explaining in detail.

Capital markets are institutions that match capital suppliers (monied investors) with capital demanders (typically, businesses that need money to make money). Demanders attract suppliers by offering to pay them for the use of their capital. For example, a business might sell standardized instruments like stocks (which confer ownership and may pay dividends) or bonds (which pay interest). Secondary (trading) markets for these instruments tend to be pretty efficient, which means that on average there’s little money to be made by trading one stock or bond for another. In fact, trading in secondary markets is roughly a zero-sum game: every dollar earned by one trader comes out of the pocket of another. Which is why investors are encouraged to buy and hold, and why active trading tends to be a money-losing activity.

But there are enterprises that make large and consistent profits by actively trading stocks and bonds. This leaves many laypeople understandably confused: They are warned that if they actively trade they should expect to lose money, while professional trading operations consistently win money. So is trading profitable, or not?

In healthy capital markets, there are three legitimate ways to make money by trading: Providing liquidity, information, or insurance. (There are also some illegal ways, like manipulation.) Nearly everything the trading industry sells, and nearly every fee it collects, traces back to one of those three services. And if you’re not providing one of those services when you trade, then you’re probably paying for them.

The first service, liquidity, is the ability to conveniently value and exchange assets. The owner of a liquid asset knows at all times what the asset is worth, and can quickly sell the asset for close to its full value. Liquidity is the foundation of capital markets: investors supply a lot more capital when they are confident that they can get it back when they need it. Without liquid markets the gears of capitalism can grind to a halt. But liquidity doesn’t happen in a vacuum, and the suppliers of this metaphorical lubricant are paid for it. In the old days of centralized stock exchanges you could point to the front-line liquidity suppliers: specialists staffing the exchange stood ready to buy and sell stocks. They posted the prices at which they would buy (bid) or sell (offer) shares, and the cost of their service was baked into the difference between the bid and offer prices. Today’s stock exchanges are more complex and less centralized, but the fact remains that they depend on liquidity providers.

Another layer of liquidity waits outside the exchange to step in when the demand for liquidity exceeds what market makers can provide. Imagine that someone wants to sell a large amount of stock. As they begin to sell, the routine supply of buyers at the market price is exhausted – nobody is left willing to buy at that price. But lower the price and the stock begins to look like a bargain, which brings in a new supply of buyers. A trader who stands ready to buy when others are desperate to sell is supplying exactly what is scarce in such moments, and is paid for the service. Liquidity provision is a source of profit that can persist even in efficient markets – compensation for storing money and accepting risk when other participants are scrambling for cash.

The second service is information. Traders with special information indicating that a stock is undervalued can buy it, and if the information is correct then they will profit as the stock’s price moves towards its fair value. For example, an analyst might conclude that a company’s new product will be more successful than expected, and that its stock price has not increased to reflect that. If the analyst buys the stock, that purchase pushes the price up marginally – communicating the information to the market, but not in a way that produces any immediate profit for the analyst. The analyst’s profit is most likely realized when the information is proven correct: If the company’s earnings beat the consensus, its price will jump and the analyst can then sell for a profit. The difficulty is that this kind of edge tends to consume itself: public information is already incorporated into market prices. To make money trading on information you have to find a proprietary source – famous ones have included counting shipping containers moving through freight hubs. And information no longer pays once the source becomes widely known or available: the market price simply moves to incorporate it without any friction that a trader can exploit.

The third service is insurance, or the assumption of risk for a premium. This is subtly built into the prices of all investments, but risk can also be explicitly traded via derivative contracts like futures and options. Someone who wants to limit potential losses on a stock can buy an option. In that transaction, the option seller pockets the option’s premium in exchange for carrying its risk.

So who gets paid for providing liquidity, information, and insurance? In theory, anyone can, but it’s a competitive market that has grown increasingly dominated by professionals with tools and skills not available to lay investors. When you see a stock price dip can you tell whether the move was caused by someone demanding liquidity and not somebody trading with special information? Can you precisely calculate the “insurance” (risk) component of an asset’s price? Professional traders can. Or rather, any trader who can’t won’t stay in business for long.

It is worth stepping back to see what all of this accomplishes. In the normal course of events capital markets are like a nuclear reactor, pooling capital and exchanging risk to create heat that powers the economy – and yes, like a reactor, they occasionally melt down. But without the concentration and free exchange of capital and risk no heat is generated and economic development is stagnant. The profits earned in trading are what the economy pays to keep that reactor running, and, stripped to their essence, they are payment for just three things: liquidity, information, and insurance. If you’re trading without providing any of those services, then you’re probably paying for them.

Streaming Real-Time Market Data into Excel: Why RTD Beats Polling an API for Analysts

Posted on by David Bookstaber

Financial analysts live in Excel, and I’m no exception. Python (enhanced with pandas, numpy, etc.) is more powerful, but sometimes the convenience of having a complete data set exposed in Excel is worth the tradeoff. And modern Excel is strong enough to run a realtime trading dashboard. I’ve done that for years now. And a few years ago I discovered that it’s really strong if you use the right tools. One of those is Microsoft’s Real-Time Data (RTD) protocol, and even though that was introduced to Excel almost 25 years ago I only recently discovered it and began to unlock its power.

Before I moved my dashboard to RTD, I pulled positions and account values via DDE (Dynamic Data Exchange), which is a protocol introduced 40 years ago! Ancient doesn’t always mean obsolete, but in this domain DDE hit its limits long ago and has been (appropriately) deprecated by Microsoft. Before I abandoned it, getting DDE to reliably feed data into Excel required jumping through hoops. Just establishing a connection between Excel and the broker’s API required starting a separate Java bridge process. The spreadsheet receiving the data had to be running in its own Excel process because every DDE update would lock the GUI. And if you happened to touch that workbook at the wrong moment in its update cycle it would lose the connection or crash completely. So on top of the Excel dashboard I wanted to work in, I had to start a separate “feeder” Excel workbook and a Java bridge. And the result wasn’t literally realtime – data arrived in my dashboard when the feeder was able to push it, which could be as frequently as every 10 seconds – but that was adequate for my needs.

And kludgy. But that’s the way all Excel data feeds were: You had to shoehorn cell updates into a system not designed for continuous calculation. Anyone who has added a “volatile” function to a heavy workbook knows this peril – or gives up – because every update means a global recalculation. The countermeasures are familiar hacks: polling loops, event handlers, lots of Visual Basic, etc.

I first saw the convenience of RTD when I used it to stream market data from ThinkOrSwim. Every other data source used an Excel add-in. ThinkOrSwim just magically appeared when needed. =RTD("tos.rtd",, "LAST", "SPY") would update the last trade price of SPY multiple times per second (once Excel’s default two-second RTD throttle is dialed down). But how? Unlike add-ins, you couldn’t even tell it was available until you asked Excel for it. It never consumed a measurable amount of CPU, never blocked the GUI, never lagged, never crashed. And that function: start to type “=RTD” and Excel reveals it’s a native function. This was a realtime data feed mechanism built in to Excel!

Once I began to dig into RTD I couldn’t stop. The interface could hardly be more simple: an RTD service only has to implement six methods and register itself as a Windows component. The mechanism was elegant: Excel tells the RTD server what topics the user has requested (e.g., “the last trade price of SPY”), the RTD server tells Excel when there is new data on that topic, then Excel asks for the data as soon as it can receive it. This was the trick to avoid blocking or crashing Excel, while feeding it data updates as fast as possible.

Implementing an RTD server properly turns out to be trickier than one might expect: You have to be very careful about thread management and subscription accounting. And you have to get the RTD server talking to the data source through whatever API is on the other end. But I think it’s worth the trouble: My Excel trading dashboard now gets realtime data through virtually invisible servers that have withstood all my attempts to break them. For example, I streamed 3,000 stock quotes through the market close on a busy day, and Excel just kept working. Once I’ve seen how it can be done right, I can’t go back.

Early Memories

Posted on by David Bookstaber

My body was born 50 years ago. I know this not only because my parents told me, but also because the government decided its business includes tracking and certifying exactly when, where, and of whom people are born.

My body bears the scars and wear of 50 years of life. The first half of that period it was growing. Then it stopped growing and started down a predictable path of degeneration. (Presbyopia appeared within the last decade, followed by the beginnings of detectable osteoarthritis.)

My mind also bears the scars and wear of 50 years of life. I can trace paths backward in time, but my ability to do that is also only going to degrade as it ages. My memory (like that of most people) is not continuous. I have snapshots of varying length, clarity, and detail. Looking back in time is like turning around and finding most of the path you walked submerged in a dark body of water. The memory snapshots are like stepping stones that have not yet sunk from view.

How far back can I see?

My earliest memory is from just before I reached my second birthday. What I remember is looking out a window where we lived and seeing a snowplow a few houses up the street that was stuck. I was upset and wanted the truck to get help. I was placated when my father said he would go help it. (How can I be confident in this memory? The visual details in my mind are limited, but I am certain I was looking downwards and to the left and the plow was 3-4 houses up the street heading away. Cross-checking with my parents: at the time we lived on the second story of a house that had windows tall enough for a 2-year-old to see out. My father remembers the incident and agrees with the orientation and distance of the plow. Public records note two extreme blizzards in the month before my second birthday.)

Memories that I would call visually “complete” begin by age 4, which I can tell because they occurred around a house we left in my fifth year. Here are some of the more vivid ones:

Rattlesnake in the street: I was playing outside and my Mom ushered me into the house saying there was a rattlesnake and it was dangerous. I watched from a window as my Dad and five other men from the neighborhood formed a loose circle around it, right in the middle of the street. They were armed with shovels and I could see a few take jabs at the snake, but otherwise they were just hanging out, talking. I was a little confused because my Mom had made it seem like a serious threat, but it looked like the men were treating it as more of a social gathering. I finally got bored of watching so don’t know how that ended.

Want to learn violin? My Mom was lying on a bed reading a book. Out of the blue, she looked up and asked me if I wanted to take violin lessons. (What is a 4-year-old in the pre-computer-game era going to say: “No, I have too many other commitments?” Of course I’d like to try a new activity!)

You only get soda if you can do it right: A local kid had a 2-liter bottle of soda. He offered to share it with anyone who could drink without backwashing. I don’t remember the words he used to explain it, but basically if you could pour or sip without putting the whole opening in your mouth, you could drink; if not you only got to watch. He demonstrated, and then invited others to drink. I was so relieved when my lips proved coordinated enough to get soda. I felt bad for another boy failed and was denied.

Coding Agents Grow Up

Posted on by David Bookstaber

For years, programming something new followed a predictable, exhausting rhythm: write some code, hit a wall, and then disappear into a forest of documentation and StackOverflow tabs to find the trick to get it working. In 2025, that era ended for me.

Today, AI code assistants take so much drudgery out of development and debugging that the work has become mostly gratifying and rarely frustrating — the opposite of how programming was in the Before Times.

Between my work and my side interests, I often feel like I live in Visual Studio Code (VSCode — a popular open-source development environment). Early in 2025 I subscribed to GitHub Copilot, which integrates AI coding assistants into VSCode. At $10/month it’s a phenomenal bargain that offers software developers an easy way to loop in the latest models from OpenAI, Google, and Anthropic. Now when I’m trying something new (like this little project I did for fun) I can mostly stay in VSCode and work with an AI assistant that has mastered all of the documentation.

As mentioned elsewhere, my most significant side project in 2025 was helping Ukrainians develop an open-source ballistic calculator (pyballistic) in Python and Cython. I polished that off at the end of September.  By that point I had begun to spend more time with Github Copilot, and the capabilities of its latest models gave me enough confidence and support to tackle what would have previously been an absurdly ambitious project for my day job: an Excel Real-Time Data (RTD) server for the Interactive Brokers API.  (This RTD server feeds live market data, positions, and orders directly into Excel using native Excel formulas.) After two months of working seven days a week on that, I had a beautiful piece of software so solid (and validated every build by over 800 unit tests) that I had begun to use it in live trading operations.

Early Childhood Development

Watching these models mature over the last year has been like watching a child grow up.

Tell a child, “Clean your room.” First they’ll spend more time arguing than it would take to just do it. When they finally declare the task “done,” you might find a few toys picked up but most of the mess still there. Emphasize that “clean your room” means everything and you might find the floor clean but everything shoved under the bed.

Claude v3 was notorious for hacking shortcuts. Ask it to fix a failing test and it might just replace the test logic with a “return true;” statement. Claude v3.5 wouldn’t be so brazen, but it was still prone to hack the example rather than the task. GPT-4 and Gemini v2 would enthusiastically announce completion without checking their work. Like the child who picks up two toys and concludes that his room must be clean, even though the mess is visible from outside the door.

The teens came quickly: Claude v3.7 and its contemporaries would often spend more effort arguing that its failure was actually success than it would have taken to do the work correctly.

More recent models have become more likely to keep checking and working until they succeed. Performance of the latest models is still wildly variable: a model that astonishes me with its apparent skill one day may choke on something relatively simple the next. But they are getting more consistent. And they are definitely getting more intelligent.

What is intelligence?  It becomes easy to see when you’re doing hard work with different models. One of the neat things about Copilot is that you can choose to watch the model at work. They all think “out loud,” meaning you can read their chain of thought to understand how and why they do things. When it’s not having an off day, Claude Opus is intelligent.  Given a problem:

  • It can more reliably identify what matters.
  • It has a better sense of what to look at and what to ignore.
  • It produces better assessments of what’s possible and makes better plans to get there.
  • It knows when to persist and when to change directions.

These are some of the things that separate a junior developer from a more experienced one. They are also qualities that characterize more intelligent people.

Let me show you. Have you ever wondered what it’s like debugging software? Well, debugging is one thing the newer models can usually do as well as a good human programmer. In fact, they can do it better because they can run the process faster and interact with the code more directly. Below I pasted a transcript of Claude working to find and fix a tricky bug in my RTD server. This could just as well have been a transcript of my thoughts if I had to debug it. But whereas this would have been a draining hour+ distraction for me, the Claude instance cranked this out in minutes.

Continue reading “Coding Agents Grow Up”

2025: The End of the Human Polymath

Posted on by David Bookstaber

Born in 1976, I was just early enough to taste pre-internet life. In grade school, on a dial-up 1200baud modem and IBM PC, I was ahead of the curve in actually connecting to primordial pieces of the internet, though they didn’t have a lot of utility outside of academic collaboration. I grew up with a physical encyclopedia at home – the 22 volumes of the 1987 World Book Encyclopedia took up more than 3 feet of shelf space. I wondered how they decided what to include in those books, because I was mostly frustrated to find my subjects of interest barely grazed, if covered at all. In the mid-1990s Microsoft published a DVD to make the printed encyclopedia obsolete: Encarta, which somehow offered even less information but more data because it had “multimedia” – the buzzword for sound, video, and primitively interactive content.1

So what did a curious young mind do back then? There were so many more questions than answers. For a typical How or Why question, your local library might have a book containing an answer, but you’d have to physically visit the library, search their card catalog for books covering the subject, and then physically find potential matches on the shelves and thumb through each to see if it actually provided the details sought. You couldn’t reach out to experts because even if you could find their names you couldn’t easily find contact information. So you were stuck with whatever local adults happened to know. My Dad was very smart, and he had smart work colleagues who could go quite far in some areas of math and physics. What about teachers? Public school teachers were – despite their avowed profession – astonishingly underinformed. (That realization led me to despise them: I did not get along with public school teachers after 3rd or 4th grade when I discovered that, to any random question, I was more likely to have the right answer then they.)

It was a struggle to build a deeper-than-average understanding of the world. I put in a lot of work seeking answers to practical questions, and that gave me a lot of practical knowledge. I certainly had gaps: Pop culture, sports – many obsessions of the average person did not interest me, so I was never going to be a Jeopardy champion. But in the realm of practical and technical knowledge I was exceptional. I read slowly, but have an insatiable thirst for understanding how and why things work. Plenty of people idly wonder. I don’t just wonder: I search. When I had a random question and couldn’t quickly find the answer I would write it down, and eventually I would find an answer and absorb everything around it. Maybe the hunt is why the answers stick in my head.

Now, what search engines started, LLMs have so thoroughly finished that future generations are bound to forget that there was a time when knowing things was not only difficult but also useful.

“Know-it-all” was often thrown around as a pejorative. But, back in the dark ages of the late 20th century, extensive practical knowledge had real utility. It could make the difference between staring blankly at a problem (or not even recognizing the presence of a solvable problem) and jump-starting solutions by drawing on a deep well of understanding how other things work and how they could relate. A know-it-all2 is more likely to:

  • Recognize the absence or presence of a significant problem. (What is that sound, and should I get it looked at?)
  • Flag misleading or false assertions. (Could competitive chess players really burn thousands of calories thinking during a match?)
  • Explain what matters, when, and why. (When do you really need to change engine oil, and should you pay extra for synthetic?)

Even when search engines came along, the human polymath still had value. Answers were more accessible, but you still had to know the right questions. You had to know if a thing was a thing, what terms might apply, and what a correct answer should look like.

Today, it’s over. We have reached the singularity of convenience. This year, as they ironed out the chatbot propensity to hallucinate, the value of the human know-it-all evaporated. Yes, I still catch the bots making factual errors, but if you keep them talking they eventually notice the errors themselves.

I took pride in being the guy to ask, the guy with the notoriously uncanny breadth and depth of knowledge, the guy who – even if he didn’t have the answer off the top of his head – would likely find it faster than anyone else. “Have a practical question? Just ask me. Worst case: I don’t know. More likely: I’ll point you in the right direction.” Now? I tell people to ask the bots. There is no way I can give as quick and thorough an answer on as broad a set of topics as they can.

  1. What was I looking for? Something like a cross between Wikipedia and The Way Things Work. Here’s how I described it in a 1998 journal entry: The Practical Encyclopedia of Technology.  It would contain in applicable form all of mankind’s technological achievements—information I haven’t been able to find elsewhere, like how transmission mechanisms are actually implemented on vehicles, the composition and construction of TFTs, how ball bearings are manufactured.  Every article on a specific piece of technology would be of the following form:
    – Brief theory;
    – References to components (e.g., transmissions would reference ball bearings, metal casting, gears, lubricants);
    – Problems encountered in implementation;
    – Canonical solutions to problems, in sufficient detail to actually implement on that information alone;
    – Other solutions that have been tried, and why they haven’t caught on;
    – References to sources for theory on the subject;
    – Patent Office classification fields of the technology, etc.
    ↩︎
  2. The age of the literal know-it-all – someone who knows everything that is known in a society – ended centuries ago. At least in the developed Western world, that has been impossible since the early 1800s. The title may be hyperbole, but The Last Man Who Knew Everything describes a plausible contender for the title: Thomas Young, who died in 1829. ↩︎

Adobe’s Protection Racket

Posted on by David Bookstaber

I just burned more than a day migrating my primary work computer from a machine running Windows 10 to a newer one running Windows 11. Not because I wanted to. Not because Win11 offers me anything I actually want (so far I hate every UI change from Win10). But because Microsoft has decided to end support for Win10 while preventing Win11 from running on older CPUs. And like everyone else whose work requires a secure operating system I’m being shoved along whether I like it or not.

This isn’t a trivial inconvenience. Over the last decade I’ve accumulated a small arsenal of development tools, libraries, and utilities — each with its own quirks, dependencies, and fragile installation paths. Migrating them is not a matter of clicking “Next” on a wizard. It’s a slog of registry tweaks, PATH surgery, license re‑entries, and the occasional ritual sacrifice to the gods of backward compatibility.

And just when I thought I had wrestled Windows 11 into grudging submission, Adobe decided to remind me that they can be even worse.

Adobe’s Perpetual License That Isn’t

I own a perpetual license for Lightroom 6. “Perpetual” is supposed to mean I can use it forever. The software runs fine on Windows 11 … except that Adobe has disabled it.

Adobe included one of the tedious “activation” processes in the Lightroom installation process that depends on their servers telling the software that my license is legitimate. And they have quietly shut down their activation servers, so now when I launch Lightroom 6 in Win11 I have discovered an endless loop of signing in, accepting the license agreement, and then having the software crash. To add insult to injury: Adobe makes no note on their website’s activation page that this process has been disabled for Lightroom 6. I only learned that it would not work after trying repeatedly and then asking Copilot what was happening.

This isn’t a bug. It’s a business model. Adobe has effectively disabled software that would otherwise continue to work. They’ve taken something I paid for outright and retroactively converted it into a hostage situation: either I cough up for their recurring subscription, or I lose access to the tools I already bought and the work I invested in using them to catalog and post-process more than 60,000 photos.

That’s not “end of support.” That’s a protection racket.

Why This Matters

This isn’t just about photography software. It’s about the erosion of implied contracts. We’re told we’re buying licenses, but too late discovering that those licenses can be revoked, crippled, or held hostage at the whim of the vendor. The “perpetual” in perpetual license turns out to mean “until we decide otherwise.”

For engineers, photographers, musicians — anyone who performs their work in specific software — this can be catastrophic.

Imagine you buy a plot of land from a real estate developer and build a house on it. Then one day you come home to find a gaping hole where your house used to sit. Eventually you find the developer and get the following explanation:

Sorry for the confusion: You bought the land, not the location. We moved your house and the land (i.e., the dirt) under its foundation to a new location.

Oh, and that new location is only available for rent. The monthly price? Well, if you have to ask, you’re not going to like it….

Hot and cold running water? Not in Phoenix!

Posted on by David Bookstaber

During summer in Phoenix we don’t have luxuries like hot and cold running water. Instead we have hot and hotter water. This photo shows me measuring the “cold” tap’s water emerging at 102°F:

Thermometer showing cold tap water measuring 102°F
“Cold” tap water is 102°F

If you haven’t run water recently you might enjoy a few moments of water as cold as the indoor air. But during summer the water supplied by the city routinely breaks 100°F.

Is this because it spends its time baking in water towers? Surprisingly no: Phoenix stores potable water underground and uses variable-speed pumps to deliver it on demand. And the ground gets really hot: Next photo shows me measuring the temperature of pavement in early afternoon sun at 173°F. (This was with temperature in the shade running 115-120°F.)

Thermometer measuring pavement in direct sun at 173°F
Pavement in summer Phoenix sun measured 173°F