ChatGPT Will Never Write For Exploring Binary

I got an unsolicited email from a company offering to write AI (ChatGPT) generated articles for my site. This is the snippet they sent of their sample article:

Mastering Decimal, Binary, & Two’s Complement Conversion

In the digital world, understanding numerical notations like decimal, binary, and two’s complement can provide a substantial advantage and presents opportunities for improved problem-solving. The journey starts with the basic foundation of decimal …

I did not click through the tracking link to get the rest of the article but it looks like the generic fluff ChatGPT wrote when I asked it about Exploring Binary. It’s worse than fluff actually; I don’t see how knowing numerical notations helps with problem solving.

If you’ve read anything on this site you’d know immediately that I didn’t write that. Will AI ever be able to write an article indistinguishable from one of my own? I don’t think so.

Jetpack Compose Byte Converter App: 2022 Version

I wrote a simple byte to decimal converter app less than two months into starting to learn Jetpack Compose. Now that I have more experience with Compose — in developing a real app and by participating on the #compose channel on Slack (login required) — I wanted to update this demo app to reflect my current understanding of best practices.

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Anomalies In IntelliJ Kotlin Floating-Point Literal Inspection

IntelliJ IDEA has a code inspection for Kotlin that will warn you if a decimal floating-point literal exceeds the precision of its type (Float or Double). It will suggest an equivalent literal (one that maps to the same binary floating-point number) that has fewer digits, or has the same number of digits but is closer to the floating-point number.

Screenshot in IntelliJ IDEA of hovering over a flagged 17-digit literal with a suggested 10-digit replacement
Hovering over a flagged 17-digit literal suggests a 10-digit replacement.

For Doubles for example, every literal over 17-digits should be flagged, since it never takes more than 17 digits to specify any double-precision binary floating-point value. Literals with 16 or 17 digits should be flagged if there is a replacement that is shorter or closer. And no literal with 15 digits or fewer should ever be flagged, since doubles have of 15-digits of precision.

But IntelliJ doesn’t always adhere to that, like when it suggests an 18-digit replacement for a 13-digit literal!

Screenshot of IntelliJ IDEA suggesting an 18-digit replacement for a 13-digit literal
An 18-digit replacement suggested for a 13-digit literal!

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Showing n! > 2n When n Is A Power of Two

Which is bigger, 64! or 264? 64! is, because it follows from a proof by induction for any integer n greater than or equal to 4. It’s also easy to just reason that 64! is bigger: 264 is 64 factors of 2, whereas 64! has 64 factors, except all but one of them (1) are 2 or greater.

When I saw this problem though I wondered if I could solve it in another way: Could the factors of two alone in 64! be greater than 264? As it turns out, almost.

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Hexadecimal Numbers: Uppercase or Lowercase?

Do you prefer hexadecimal numbers written with uppercase letters (A-F) or lowercase letters (a-f)?

For example, do you prefer the integer 3102965292 written as B8F37E2C or b8f37e2c? Do you prefer the floating-point number 126.976 written as 0x1.fbe76cp6 or 0x1.FBE76Cp6?

I ran this poll on my sidebar, and after 96 responses, about 70% are “prefer uppercase” and about 9% are “prefer lowercase”. What do you think? (For the “depends on context” answer I meant things other than numeric values, like the memory representation of strings. However, for the purposes of this article, please answer with just numeric values in mind.)

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Another NaN In the Wild

I see these from time to time, but I don’t always capture them; here’s one I saw recently while playing a podcast:

A NaN in an ad in the app Castbox (partial image)
A NaN in an ad in the app Castbox (click for full image).

(According to Castbox, this is an error in the ad and is out of their control.)

A Simple Binary To Decimal Converter App In Jetpack Compose

I’ve been learning Jetpack Compose and Kotlin (and Android for that matter) so I decided to create a simple binary conversion app to demonstrate how easy it is to create (at least basic) UI in Compose.
Byte to Decimal Converter Demo App (Pixel 4 Emulator)

(This app has been updated; see Jetpack Compose Byte Converter App: 2022 Version.)

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Direct Generation of Double Rounding Error Conversions in Kotlin

For my recent search for short examples of double rounding errors in decimal to double to float conversions I wrote a Kotlin program to generate and test random decimal strings. While this was sufficient to find examples, I realized I could do a more direct search by generating only decimal strings with the underlying double rounding error bit patterns. I’ll show you the Java BigDecimal based Kotlin program I wrote for this purpose.

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Double Rounding Errors in Decimal to Double to Float Conversions

In my previous exploration of double rounding errors in decimal to float conversions I showed two decimal numbers that experienced a double rounding error when converted to float (single-precision) through an intermediate double (double-precision). I generated the examples indirectly by setting bit combinations that forced the error, using their corresponding exact decimal representations. As a result, the decimal numbers were long (55 digits each). Mark Dickinson derived a much shorter 17 digit example, but I hadn’t contemplated how to generate even shorter numbers — or whether they existed at all — until Per Vognsen wrote me recently to ask.

The easiest way for me to approach Per’s question was to search for examples, rather than try to find a way to construct them. As such, I wrote a simple Kotlin1 program to generate decimal strings and check them. I tested all float-range (including subnormal) decimal numbers of 9 digits or fewer, and tens of billions of random 10 to 17 digit float-range (normal only) numbers. I found example 7 to 17 digit numbers that, when converted to float through a double, suffer a double rounding error.

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