Introducing GpTimestamp

In my programmer's life I frequently deal with all kinds of timestamps. As the code is relatively, ahem, "mature" those timestamps come from all kinds of sources. There are 64-bit ticks (GetTickCount64), multimedia time (timeGetTime), performance counters (QueryPerformanceCounter), performance counters in disguise (TStopwatch), and there is also a TDateTime (NowUTC) mixed in from time to time.

All this mess would not be that bad had the timestamps been only used locally. If you sample a timestamp and then a few lines later check whether so-and-so time has expired, all is well. You will always know what the source of the timestamp was and what to compare it to.

Problems occur when the timestamps are grabbed on one side of the system and consumed on the other side. Let's say that the code receives a block of data over some internal communication mechanism. A part of that data is a timestamp representing a creation time of that data block. You have to check how much time has elapsed since then. How do you do that?

Well, firstly you have to know in what units this timestamp is stored. Is it a floating point number (TDateTime) or a 64-bit integer? In the latter case, does it store a number of milliseconds, 'ticks' (whatever that is) or something else?

Secondly, you have to get "current timestamp" from the correct time source so you can do the calculation. So you have to know what the source was. And if you're not sure, you'll have to dig through the code to find the point where the original timestamp was created.

The former problem is somewhat solvable by using descriptive variable/field names. For example, if a variable stores a timestamp in milliseconds, I always use a suffix _ms. In the example above I would use a field DataCreationTime_ms or something similar.

The latter problem, however, is hard. You could use suffixes (DataCreationTime_Tick64_ms) but that gets ugly pretty fast. You could also document the time source (and you should!) but that again stops you when you just want to write some code.

Even worse, both solutions only work if you take care when coding. Delphi won't stop you from subtracting TStopwatch.GetTimeStamp from a value that is storing a result of timeGetTime, although the result won't make much sense.

Clearly, I needed something better, so I wrote* the GpTimestamp unit which tries to solve this mess. There is also documentation and unit tests, if you want to jump in now.

(* When I say "I" I actually mean Claude. I was just an idea-man on this project. I'll say more about that in the following post...)

When is AnsiString(String(ansstr)) <> ansstr?

Remember the good old times when strings were composed of 8-bit characters? Those were simpler times. When we needed a data buffer, we just used an AnsiString. An 8-bit character is just a byte, surely?

Then Unicode arrived and suddenly all strings were based on 16-bit characters. The code broke all around us. At the company I'm working for we needed about one year to port all our applications to Unicode. (And by “we” I mean myself. I allocated one day per week to work on Unicode issues and slowly worked through the code. Today I'd probably use AI for the task. :) )

We completed this port in Delphi 2010 times. And then we spent the next 10-ish Delphi versions fixing bugs related to this change. And when I thought we had fixed them all (hope, hope), Microsoft did something unexpected.

Restoring code formatter in Delphi 13

While I don't miss the Refactor menu in Delphi 13 -- I'm used to MMX -- I do sometimes miss ability to format Delphi code (the in/famous Ctrl+D). On my recent RAD Studio presentation in Ljubljana I learned that there are many other Delphi programmers who also miss this functionality, so I looked around to find a way of restoring it.

As it happened, I didn't have to look far. Pretty quick I found an excellent blog post written by David Cornelius. If you also miss code formatter, read his post!

OmniThreadLibrary 3.07.11 with RAD Studio 13 support

I just released a new OmniThreadLibrary. Not many changes, just some fixes and - of course - support for RAD Studio 13.

OmniThreadLibrary now supports 19 major Delphi releases: 2007, 2009, 2010, XE, XE2, XE3, XE4, XE5, XE6, XE7, XE8, 10 Seattle, 10.1 Berlin, 10.2 Tokyo, 10.3 Rio, 10.4 Sydney, 11 Alexandria, 12 Athens, and 13 Florence. I wonder whether this is a record in the Delphi open-source world :)

New features

• Added Delphi 13 packages supporting 64-bit IDE.
• Implemented TOmniValue.AsUInt64.

Bug fixes

• [SMelnyk64] Fix: AV in TOmniParallelLoopBase.InternalExecute when taskFinalizer is not provided.
• Fixed TOmniGroupAffinity.Create with 64 processors on 64-bit.
• Use correct time-measurement functions in OtlEventMonitor.
• [Anton Alisov] Fixed critical section handling in TOmniValueQueue.DoWithCritSec.
• Avoid range check in OtlThreadPool on 64 CPU systems.

You can get it on GitHub, download it from my Google Drive, or - soon - install it via GetIt!

RAD Studio 13 presentation in Ljubljana

Next week I'm presenting all that is shiny and new in RAD Studio 13 in Ljubljana. As the presentation will be in Slovenian language, so is the next of this post ...

Delphi and AI [9]: Guided writing

Some time ago, I tried using AI to write a blog post, but things didn’t go quite as planned. For my second attempt, I aimed to do better. I focused on a single engine—OpenAI—and used it in a more granular way. Instead of simply saying, “Write me an article about...,” I started with basic introductory questions, then asked it to write the article, and finally requested improvements where I felt the draft was lacking. The end result was quite satisfying.

This article is my analysis of that process. If you’re only interested in reading the transcript, you can find it here.

Integrating LLM engines in your Delphi app with Chatterbox

But the real changes happened under the hood.

I’ve refactored the codebase to fully separate the AI client logic from both the UI and the network layer. And what does that mean for you? It means you can now use the Chatterbox AI libraries directly in your own code.

Let me say that again, loud and clear:

You can now use Chatterbox AI libraries from your code!

To test this, I created a very basic demo called EngineDemo. And naturally, for this announcement, I had to explain how the demo uses the Chatterbox libraries.

Since this was a brand-new development that the AI had no prior knowledge of, I decided to revisit an experiment I mentioned in a previous post—one that was only partially successful. This time, I took a different approach. But more on that in the next post.

For now, take a look at what ChatGPT had to say on the topic. Just keep in mind: this wasn’t a simple “click and publish” process. ChatGPT and I had a long back-and-forth, and the result of that collaboration is the article below.

Delphi and AI [8]: Write me an article about ComputeCore

This was supposed to be an article comparing three major AI players in their ability to write technical articles. In preparation, however, I made some mistakes ... :(

When I prepared for the initial article, I asked them to fetch the data from https://github.com/gabr42/ComputeCore/blob/main/ComputeCore.pas and write about it. (See the end of this article for the initial query and results.) At that moment, however, that link was not public yet. All three AIs ignored that and happily wrote about the code they knew nothing about. It was only Claude that complained about the bad link but I missed that while checking the results. 

So I decided to rerun the experiment on all platforms. I started by asking the engines the following question: 

Can you accesss https://github.com/gabr42/ComputeCore/blob/main/ComputeCore.pas and provide an overview of the code in one paragraph?

This worked with ChatGPT but both Claude and Gemini complied that they cannot access the link. I had to rerun the query with the link to the raw file for the latter two to work:

Can you accesss https://raw.githubusercontent.com/gabr42/ComputeCore/refs/heads/main/ComputeCore.pas and provide an overview of the code in one paragraph?

Analyzing the answer shows that the answers were not done purely on the basis of the linked code. Both Claude and Gemini have included details that they got from my previous article on ComputeCode.

In the answers below I marked hallucinated misinformation with red and information that was obviously retreived from different sources with blue.

ComputeCore - A Simple Parallel Task Framework

Today, I want to talk about a small multithreading kernel (framework, library, or whatever you want to call it) that I've recently helped develop for the Spring4D project.

ComputeCore is a lightweight parallel processing framework for Delphi that lets you easily run CPU-intensive tasks across multiple threads. Its goals are straightforward: to run (possibly compute-heavy) tasks concurrently, allow tasks to spawn child tasks, and expose a very simple interface for the caller.

It will eventually be published as Spring.Threading, but until that happens, we can examine the almost-final version at https://github.com/gabr42/ComputeCore. In Spring4D, it will run the parallel implementation of the new sorting algorithm, but that doesn't mean it is only useful for one specific job! It is indeed a completely open (although intentionally simple) framework that can be used for your projects too.

[This article was written with the help of an AI. My next article will explain this decision and the writing process.]

Run your Delphi programs in a browser

Next Wednesday I'll be talking about Delphi and TMS Web Core in Ljubljana. As the presentation will be in Slovenian language, so is the rest of this post.

Delphi and AI [7]: How good are local DeepSeek models (for Delphi)

Due to potential privacy concerns with DeepSeek servers (we’re unsure if the data sent over the paid API is kept private), I looked into some smaller DeepSeek models available on the Ollama.com site. These models use less complex AI with fewer parameters than the online version, but they might still be good enough for an average Delphi programmer. We’ll see.

For testing, I used a powerful RTX 4090 card with 24 GB of memory. If your graphics card has less memory, your selection of useful models will be more limited.

I asked all models the same two questions: one on a general programming topic and another specific to the FireMonkey platform. The first question was:

"I have a multiline string containing newline ASCII characters (TMemo.Text). I want to change it to a single-line string with only printable ASCII characters. I could do that with BASE64 encoding, for example. I would, however, like to keep the text as much readable as possible by "encoding" only non-printable characters. Is there a simple way to do that?"

You can check Codellama’s response in an older post (Codellama being the only local model I had tested so far): Delphi and AI [5]: Encoding Multi-line Strings.

Last week, I asked the same question to the online DeepSeek-Reasoning model. Check the answers in this post: Delphi and AI [6]: DeepSeek-Reasoning Model.

The second question was:

"How can I copy text to clipboard in a Delphi Firemonkey application?"

Read here for Codellama’s response: Delphi and AI [4]: Device-ndependent clipboard.

The answer from the online DeepSeek-Reasoning model can be found at the end of this post.

Let’s see how the models are performing! As always, full logs are available on GitHub.

Chatterbox v0.2 - Now with DeepSeek support!

As I’m writing this, a new Chatterbox release is available on GitHub. As with the initial release, you can either download the source and recompile it or grab an executable (Windows, 32-bit) from the release.

(If you have never heard of Chatterbox, you should probably read the initial release article.)

The biggest change since v0.1 is support for DeepSeek AI.

Delphi and AI[6]: DeepSeek reasoning model (Encoding a multi-line string)

An interesting AI has just appeared on my radar - DeepSeek. It exposes a "normal" chat model deepseek-chat and a "reasoning" (that's the interesting part) model deepseek-reasoning. While working on DeepSeek support for the Chatterbox (more on that in few days) I thought it would be interesting to ask the "reasoning" model the "multi-line string encoding" question that every other AI has failed.

Warning: DeepSeek privacy policy states: "When you use our Services, we may collect your text or audio input, prompt, uploaded files, feedback, chat history, or other content that you provide to our model and Services." Don't send any private or proprietary information in the chat!

The log is provided on GitHub.

Just as a reminder, the question was:

"I have a multiline string containing newline ASCII characters (TMemo.Text). I want to change it to a single-line string with only printable ASCII characters. I could do that with BASE64 encoding, for example. I would, however, like to keep the text as much readable as possible by "encoding" only non-printable characters. Is there a simple way to do that?"

Delphi and AI[5]: Encoding a multi-line string

Recently I had to convert a multi-line string into a single-line string value for storage (ignore the 'why' of it; let's just blame it on a legacy code). My first idea was to do a Base64 encode of the string, but I was in a mood for some fun and so I asked my friendly AI helpers:

"I have a multiline string containing newline ASCII characters (TMemo.Text). I want to change it to a single-line string with only printable ASCII characters. I could do that with BASE64 encoding, for example. I would, however, like to keep the text as much readable as possible by "encoding" only non-printable characters. Is there a simple way to do that?"

Let's see what they came up with!

Full transcripts, as usual, are on GitHub. This time I had also created a program containing all implementations.

Delphi and AI [4]: Device-independent clipboard

While working on Chatterbox I ran into number of problems, most of them caused by my limited knowledge of device-independent programming with Firemonkey. One particular challenge was copying data to the clipboard - something that is AFAIK available on all supported platforms. So I asked my friendly AI helpers:

"How can I copy text to clipboard in a Delphi Firemonkey application?"

As always, all logs are available on GitHub.