All posts tagged prodos

I’ve been reluctant to comment on the latest fad in our industry, generative AI, simply because everybody has weighed in on it.  I do also try to avoid commenting on subjects outside of my scope of authority.  Increasingly, though, people are coming to me at work and asking how we can incorporate this technology into our products, how our competitors are doing it, and what our AI strategy is.  So I guess I am an authority.

To be honest, I didn’t play with ChatGPT until this week.  When I first looked at it, it wanted my email address and phone number and I wasn’t sure I wanted to provide that to our new AI overlords.  So I passed on it.  Then Cisco release an internal-only version, which is supposedly anonymous, so I decided to try it out.

My first impression was, as they say, “meh.”  Obviously its ability to interpret and generate natural language are amazing.  Having it recite details of its data set in the style of Faulkner was cool.  But overall, the responses seemed like warmed-over search-engine results.  I asked it if AI is environmentally irresponsible since it will require so much computing power.  The response was middle-of-the-road, “no, AI is not environmentally irresponsible” but “we need to do more to protect the environment.”  Blah, blah.  Non-committal, playing both sides of the coin.  Like almost all of its answers.

Then I decided to dive a bit deeper into a subject I know well:  Ancient Greek.  How accurately would ChatGPT be on a relatively obscure subject (and yet one with thousands of years of data!)

Even if you have no interest, bear with me.  I asked ChatGPT if it knew the difference between the Ionic dialect of Herodotus and the more common dialect of classical Athens.  (Our version, at least, does not allow proper names so I had to refer to Herodotus somewhat elliptically.)  It assured me it did.  I asked it to write “the men are arriving at Athens” in the dialect of Herodotus.  It wrote, “Ἀφίκοντο οἱ ἄνδρες εἰς Ἀθήνας,” which is obviously wrong.  The first word there, “aphikonto“, would actually be “apikonto” in the dialect of Herodotus.  He was well known for dropping aspirations.  The version ChatGPT gave me would be the classical Attic version.

I let ChatGPT know it was wrong, and it dutifully apologized.  Then I asked it to summarize the differences in the dialects.  It said to me:

Herodotus and Ionic writers typically removed initial aspirations, while the Attic dialect retained them. For example, “Ἀφίκοντο” (Herodotus) vs. “ἔφικοντο” (Attic)

Uh, you don’t need to know the Greek alphabet to see it made exactly the same mistake, again.  It should have said that Herodotus would use “Ἀπίκοντο” (apikonto) whereas in Attic the word would be “Ἀφίκοντο” (aphikonto.)

Here’s a little more computer-person-friendly example.  I decided to ask about my other favorite subject, retro computing.  I asked, “What is the difference between the Apple IIGS ProDOS 8, ProDOS 16, and GS/OS operating systems?”  Among other things, ChatGPT responded that GS/OS ” introduced features such as a desktop metaphor, drop-down menus, and a hierarchical file system.”  Which happens to be wrong.  ProDOS 16 actually introduced those features.  (In fact, ProDOS 8 introduced the hierarchy.)  I pointed this out.  “You are correct, and I apologize for the oversight in my previous response. I misspoke in stating that GS/OS introduced [those] features.”  Hmmm.

I’m testing it and I know the right answers.   The problem would arise if I were trusting ChatGPT to provide me with a correct answer.  There have been plenty of examples of mistakes made by ChatGPT, such as adding a “permit any” to the top of access-lists.

The issue is, ChatGPT sounds authoritative when it responds.  Because it is a computer and speaking in natural language, we have a tendency to trust it.  And yet it has consistently proven it can be quite wrong on even simple subjects.  In fact, our own version has the caveat “Cisco Enterprise Chat AI may produce inaccurate information about people, places, or facts” at the bottom of the page, and I’m sure most implementations of ChatGPT carry a similar warning.

Search engines place the burden of determining truth or fiction upon the user.  I get hundreds or thousands of results, and I have to decide which is credible based on the authority of the source, how convincing it sounds, etc.  AI provides one answer.  It has done the work for you.  Sure, you can probe further, but it many cases you won’t even know the answer served back is not trustworthy.  For that reason, I see AI tools to be potentially very misleading and potentially harmful in some circumstances.

That aside, I do like the fact I can dialog with it in ancient Latin and Greek, even if it makes mistakes.  It’s a good way to kill time in boring meetings.

In my last post, I discussed the BBS and how it worked.  (It would be helpful to review, to understand the terminology.)  In this post, I have resurrected, in part, the BBS I used to run from 1988-1990.  It was called “The Tower”, for no particularly good reason except that it sounded cool to my teenage mind.

Now, bringing this back to life was no simple task, but was aided by some foresight I had 20 years ago.  I had a Mac with a disk drive, and realizing the floppy era was coming to a close, I decided to produce disk images of all the 3.5 inch floppies I had saved from my Apple II days.  Fortunately, my last Apple II, the IIGS, used 3.5″ drives instead of the 5.25″ that were more common on the Apple IIs.  The Macs that had floppy drives all had 3.5″ drives.  Additionally, Apple had included software to on the pre OSX MacOS to read ProDOS (Apple II) disks.  Thus, in the year 2000, I could mount an Apple II floppy from a dozen years prior and make an image out of it.

I did not have a full working version of my GBBS, however, so I had to download a copy.  I also had to do a lot of work to bring it up to Macos (not MacOS, but Macos, Modified ACOS), which was a modified form of the GBBS compiler I used at the time.  All of my source files required Macos and not the stock GBBS software.  Believe me, even though I ran the BBS for a couple years and wrote a lot of the code, remembering how to do any of this after 30 years was non-trivial.

Rather than hook up my old IIGS, which I still have, it made a lot more sense to use an emulator.  (It also enabled me to take screen shots.)  I used an emulator called Sweet16, which is a bit bare bones but does the trick.  In case you’re not familiar with the Apple II series, the early models were primarily text-driven.  They had graphics, of course, but they were not GUI machines.  After the Mac came out, there was a push to incorporate a GUI into the Apple II and the result was the Apple IIGS (Graphics and Sound).  While it had a GUI-driven OS (ProDOS 16 at first, replaced by GS/OS), it was backwards compatible with the old Apple II software.  The GBBS software I ran was classic Apple II, and thus it was a bit of a waste to run it on an Apple IIGS, but, well, that’s what I did.

In this screen shot (Figure 1), you can see the Apple IIGS finder from which I’m launching the BBS software, the only GUI shot you’ll see in the article:

Figure 1: The Apple IIGS ProDOS Finder

The next shot (Figure 2) shows the screen only visible to the sysop, while waiting for a call.  As sysop, I had the option to hit a key and log in myself, but if a user dialed in the system would beep and the user would begin the log in process.  I’m not sure why we’re awaiting call 2 which will be call 1 today, but it looks like a bug I need to hunt down.  The screen helpfully tells me if new users have signed up for the BBS, and whether I have mail.

Figure 2: The landing page while waiting for a call

(If you want to know why I used the silly handle “Mad MAn”, please see the previous article.)

The next screen shows the BBS right after logon.  The inverse text block at the top was a local sysop-only view, showing user information including the user name and phone number, as well as the user’s flags.  These are interesting.  Some BBS software provided access levels for controlling what a user could and could not do.  Instead of sequential access levels, GBBS provided a series of binary flags the sysop could set.  Thus, I could give access to one area but not another, whereas the sequential access levels mean that each access level inherits the privileges of the previous level.  Very clever.  A few other stats are displayed that I won’t go into.  I’ll turn off the sysop bar for the remaining screen shots.

Figure 3: The main level prompt with sysop bar. Be sure to report error #20!

Note the prompt provided to the user in figure 3.  It tells you:

  • That the sysop is available
  • That the user has not paged the sysop
  • The double colons (::) normally would display time left on the system.  Since this was a dial-up system, I needed to limit the time users could spend on the BBS.  But as sysop, I of course had unlimited time.
  • The BBS had different areas and the prompt (like an IOS prompt) tells you where you are (“Main level”)

Next, in figure 4 you can see the main menu options for a user logged into the BBS.  This is the default/stock GBBS menu, as my original is lost.  Despite the limited options, this was like entering a new world in the days of 64K RAM.  You can see that a user could send/read mail, go to a file transfer section, chat (or attempt to chat) with the system operator, or read the public message boards.

Figure 4: The BBS main menu. This is the GBBS default, not the custom menu I built

Next, the user list.  I had 150 users on my BBS, not all of them active.  I blacked out the last names and phone numbers, but you can get a sense of the handles that were used at the time.  In addition to these names, there were a lot of Frodo’s and Gandalf’s floating around.  Also note that most BBSing was local (to avoid long-distance charges.)  Sadly, none of these users has logged on since 1989.  I wish they’d come back.  Oggman, whom I mentioned in my last post, was a user on my board.

Figure 5: My user list


I recently interviewed a recent college grad who asked me how she could be successful at a company like Cisco.  My answer was that you have to understand where we came from in order to understand where we are.  You cannot understand, say, SD-WAN without understanding how we used to build WANs.  Go back to the beginning.  Learn what SneakerNet was.  Understand why we are where we are.  Even before SneakerNet, some of us were figuring out how to get computers to talk to each other over an already existing network–the analog telephone network.  As a side note, I love vintage computing.  It’s a lot of fun using emulators to resurrect the past, and I hope to do some physical restorations some day.  Trying to figure out how to boot up a long-defunct system like this BBS provides a great reminder of how easy we have it now.