In “The Offspring,” the 16th episode of the third season of Star Trek: The Next Generation, we get to see Piet Mondrian’s “Tableau I” hanging on the wall of his quarters when he shows it to his daughter Lal. I think this might be the first time that I had really seen or had my attention drawn to a work by Mondrian. I thought it was quite striking as a work of art, and it seemed fitting that Data might be drawn to this work for its ordered lines despite Mondrian’s neoplasticism theory and its connection to nature and emotion as being the motivators for the artist’s composition.
Mondrian’s “Tableau I” appears in Lt. Cmd. Data’s quarters–notably in the eleventh episode of season five titled “Hero Worship,” in which Timothy, a young boy traumatized by the loss of his parents, apes Data’s mannerisms in order to erase his emotional response to his loss. In one scene, Data and Timothy paint in Data’s quarters where “Tableau I” is on an easel to the side.
In the Star Trek: The Next Generation Interactive Technical Manual, Mondrian’s “Tableau I” is on an easel in about the same place as pictured in “Hero Worship.”
Yesterday, I was able to see some of Mondrian’s works in person at the Museum of Modern Art (MOMA) in Manhattan. Y and I went there to see our friends from Japan, Masaya and Saki. While I didn’t get to see “Tableau I,” because it hangs in the Kunstmuseum in The Hague, I did get to see some representative works of his neoplasticism.
My absolute favorite piece of software for my 486DX2/66MHz computer with a CD-ROM drive was the Star Trek: The Next Generation Interactive Technical Manual (1994). Built using Macromedia Director and Apple Quicktime VR and distributed on CD-ROM for Macintosh and Windows 3.1, it presents an LCARS (Library Computer Access/Retrieval System) interface to the user for navigating through spaces aboard the USS Enterprise NCC-1710-D, viewing the exterior and interior three-dimensionally, reading technical information, hearing ambient starship sounds, and listening to audio from the Computer (Majel Barrett-Roddenberry) and Command William Riker (Jonathan Frakes).
Before its release, I religiously carried around Rich Sternbach and Michael Okuda’s Star Trek: The Next Generation Technical Manual (1991)–a soft cover, magazine-sized book about 1/2″ thick–that detailed the design and function of 24th-century technology that went into the USS Enterprise NCC-1701-D. I filled it with marginalia and referenced it when I was drawing or discussing esoteric technical minutiae of Star Trek: TNG. It is an example of printed technical communication material about the science and technology scaffolding for the science fictional narratives of Star Trek: TNG. The Interactive Technical Manual added so much more to the experience by putting the user into the spaces described and illustrated on the two-dimensional pages of the Technical Manual. While the Interactive Technical Manual wasn’t as nearly portable as the Technical Manual, it felt like a revolutionary approach that despite being static continued to provide new and interesting experiences for the user based on the interactive path and options (e.g., tour vs. explore; voice vs. no voice; jump vs. transit) selected.
For this post, I ran the Interactive Technical Manual on Macintosh System 7.5.5 emulated on SheepShaver on a Debian 12 Bookworm host. In the past, I have got the Interactive Technical Manual to run on Windows 3.1 installed on DOSBox, but I don’t currently have that setup on this computer. Using the included Quicktime with Quicktime VR is key to successfully running the software on either operating system setup.
After loading, it gives the user options: Guided Tour or Explore. The Guided Tour features Jonathan Frakes as Commander William Riker providing voiceovers as various points, equipment, and artifacts around the Enterprise are shown on the screen. Explore takes the user directly to the Ship Exterior view with the LCARS navigation menu open on the right.
Ship Exterior is the landing page for the Explore option. The view of the Enterprise on the left is a Quicktime VR movie with options to rotate the ship up or down or left or right with gradations in between on each axis, which make it feel like rotating the ship as a three-dimensional object. This was a mind-blowing feature to me at that time. Despite the low resolution and small color palette, looking at the Enterprise from all of these angles–many I had never seen in an episode of Star Trek: The Next Generation before–felt like the future. Using the LCARS navigation menu on the right and clicking on Location loads options for other places around the Enterprise to see and learn more about.
An important place to visit on the Enterprise is the Bridge. The image of the bridge on the left is a Quicktime VR video that allows the user to look around 360 degrees and click “forward” into other nearby views. Those other views are represented by the white squares in the legend on the lower right corner of the screen. Moving through the space of the bridge felt as close to being there as possible at that time. The closest that we’ve come to that today is the fan-made Studio 9 over 20 years later.
From the Bridge screen, clicking on the Parallels option opens cross-referenced information related to the Bridge. In this case, an Exterior Details view of the Bridge.
Another top spot to visit is Engineering. The user can click the forward arrow within the Quicktime VR video showing the entrance to Engineering on the left, or click the white squares on the legend in the lower right–each view point features a 360 degree view from that vantage point and navigation arrows leading to the other nearby viewpoints.
Clicking forward from the entrance to Engineering leads to the warp core–the matter/anti-matter reactor that powers the Enterprise.
The Transporter Room is another must-see location within the Enterprise. This view is to the right of the one that the user first sees when entering the Transporter Room. Its right behind the transporter control facing the transporter pad.
Another innovative feature that helps the user conceptualize locations within the Enterprise is the Transit Mode between locations. Let’s say that I want to go to Lt. Commander Data’s quarters via Transit Mode. First, the screen on the right shows me where I currently aim and then highlights the location of Data’s quarters. On the left, the camera backs out of the Transporter Room, travels down the hallway to the Turbolift, which opens and the camera enters.
The camera shows a brief ride in the Turbolift, which then opens on the corridor for the deck where Data’s quarters are.
The camera moves down the corridor, turns at the door for Data’s quarters, the doors open, and the camera enters to the first location on the Quicktime VR views there.
Inside Data’s quarters, the user can click through the Quicktime VR videos on the left or use the legend on the right. Note that Data’s Sherlock Holmes costume is hanging on the coat rack in the back right corner.
Overall, the Star Trek: The Next Generation Interactive Technical Manual is a well-thoughtout, complete user experience that gives the user a different view and experience of the USS Enterprise D. It’s adherence to a logical and self-contained user interface that was consistently applied throughout the program brings the user into the world of the future. It’s aural and video features created an experience of being there–even though you were looking at a low-resolution 14″ monitor and hearing its audio through low-quality beige speakers that came with your sound card. It’s power was to overcome the constraints of early 1990s personal computer hardware and software to create an experience for Star Trek fans with every affordance available at that time.
Finally, Keith Halper, the CD-ROM’s producer for Simon & Schuster Interactive, writes the following in the credits for the Interactive Technical Manual–exploring both what kind of software this is and what exactly it was intended to do:
I want to endeavor to encapsulate our goals in the Interactive Technical Manual for an interactive development community that will, without doubt, surpass our best efforts here in the flash of a tachyon beacon, and also for a Star Trek community to whom we owe our gravest responsibility.
This software is not quite a game, not quite a story, not quite a work of
reference.
This is a fiction, with characters and scenes, but no preordained plot.
Rather, a story unwinds--or more precisely, occurs--as you go. The
struggles and events of the crews' lives are absent from this "episode".
The mechanism by which a storyteller traditionally tells us about
characters--and through them about both writer and audience--cannot
exist in a totally non-linear experience. Yet, in your own exploration of the
Enterprise here, of the environs and systems and quarters and art and
artifacts, you may come to understand a story about the members of a
particular starship. This story includes impressions of their world,
thoughts regarding our relationship with these characters and the
progress they represent, and about the hurdles we will overcome on our
own journey through time, till their world is our world. It is a story that we come to understand by participating in the telling of it.
If this sounds odd, consider the thought that we tell a tale about ourselves
by our actions. Ask yourself, what the is difference between your real life,
and a story about your real life? In both there are characters and scenes,
even changes in characters over time and in reaction to events. However,
there is no plot. Things "happen," of course--you visit your family, your
young nephew has grown, you get a job offer, you argue with your brother,
perhaps make up and have a drink to celebrate--but events have no
significance until they are strung together to suggest themes. There is no
story until your older brother, Robert, ties together the events of your past
and recent life, assails you for your past stoicism and says to you, (or to
someone we all know), "Jean-Luc, you are human after all." The
crystallizing thought that connects perception and conception--that
bridges the questions, what has happened? what does it mean?--contains the story. In an interactive story (and a good linear one), you, the reader, provide this insight.
So, let's you and I tell each other about the crew of the Enterprise and the
world in which they live. Listen. Explore. Notice. Evaluate. What is
present? What is missing? Mr. Roddenberry, Mr. Okuda, Mr. Berman,
and Mr. Sternbach (in their Introductions) note that the Enterprise is a real
vehicle--for story-telling. To visit the ship, or even to serve aboard her,
you need only to participate in the story-telling taking place around you.
While we are accustomed to visiting the Star Trek universe each week (at
least twice a week these days), it is our hope to bring a little of the
twenty-fourth century home to you; to create a space you can live in from
time to time, and to help us remind each other of a bright star in the
heavens by which to steer.
The Interactive Technical Manual was for me “a space you can live in from time to time.” It was an immersive and engaging way to escape the end of the 20th century and bask in the wonder and excitement that the 24th century might offer.
Berkeley System’s After Dark – Star Trek: The Next Generation is one of my favorite pieces of software. It consumes electricity and CPU cycles to create audio and visual experiences that are ostensibly meant to prevent CRT screen burn-in. Put another way, it’s a program meant to solve a bygone era’s technological problem while providing passersby a little bit of entertainment. Above, it is running on the Apple Macintosh Performa 550 that I donated to Georgia Tech and is now housed in the RetroTech Lab at the Georgia Institute of Technology (center-right on landing page). Data’s dancing is protecting the Performa’s built-in 14″ Sony Trinitron monitor. Below are screenshots of the screensaver in action.
Integrated into the After Dark screensaver system, it has 13 modules: Counselor Troi, Data Dances, Encounters, Nanites, Officer’s Review, Personnel Files, Science Stations, Starbase, Starfleet Messages, Tachyon Particle Field, The Borg, Warp Effect, and Worf’s Weapons.
Counselor Troi
Counselor Troi appears and gives advice and affirmations.
Data Dances
Data appears in the spotlight while the step pattern for different dance styles, such as tap or cha cha, appear to the side. Appropriate music plays and Data dances the steps.
Encounters
Encounters switches between views of the Enterprise crew on the bridge and what they see on the main viewscreen.
Nanites
Nanites, an intelligent nanotechnology, devour the screen and self-replicate.
Officer’s Review
Officer’s Review is a timed Star Trek TNG quiz that uses keyboard inputs that don’t deactivate the screensaver (as mouse movements would).
Personnel Files
Personnel Files rotates through information screens of different characters on the show.
Science Stations
Science Stations displays changing information panels that update and change just like the LCARS science station panels on the bridge.
Starbase
Starbase shows different ships flying through space with an occasional starbase coming into view.
Starfleet Messages
Starfleet Messages show different informational and warning messages that appear in different places on the screen.
Tachyon Particle Field
The Tachyon Particle Field looks like a four-dimensional tesseract interacting with three-dimensional space.
The Borg
The Borg materialize in different places on the screen to assimilate it using their technology.
Warp Effect
Warp Effect shows the passage of stars while traveling at warp speed.
Worf’s Weapons
Finally, Worf’s Weapons feature Worf’s son Alexander handing his father different weapons, such as a phaser or bat’leth, to destroy the screen with. Where Worf walks, the underlying screen is revealed. Where he damages the screen, it turns black.
Written by Bertil Holmberg, MD, MacTravesty 1.1.1 (available to download from the archived TextWorx Toolshed) is a lean piece of software that can analyze groupings of characters in a text and then based on that analysis generate nonsense text. His Info-Mac abstract explains:
MacTravesty is a small program that analyses a text file and lists all the character groups contained in the text. A new pseudorandom text based on the language specific character frequencies can then be generated.
This version updates the authors address. -- Bertil Holmberg, M.D. Dept. of Anesthesiology Malmoe University Hospital S-205 02 Malmoe, Sweden bertil.holmberg@anestesi.mas.lu.se Fax +46 40 33 70 70
In the “About MacTravesty” file included with the program, he explains further:
MacTravesty analyses how characters relate to each other in a sample text. More specifically, it will record the ocurrence [sic] of every sequence in the text with one to five characters. These sequences are quite typical for the language studied. A random text based on the character frequencies of English will therefore easily be recognized as English. What is MacTravesty good for? You tell me, or ask a linguist.
MacTravesty was written several years ago in assembler, hence it's speed and the small size of 28 kB! For the same reason it is rather unlikely that a PowerPC version will ever surface. It runs quite fast under emulation, however, a 30 kB text is processed in about 10 seconds.
Looking at the Get Info for the MacTravesty program, we can see that it is a rather lean application being only 32K on disk (26,083 bytes used), and it has very modest memory requirements of 128K minimum and 640K preferred size.
MacTravesty launches to the menu bar and does not show any windows initially.
Clicking on Apple > About MacTravesty brings up a window that states, “MacTravesty — or the fine art of turning literature into drivel…” Holmberg explains this and the program’s inspiration in the “Travesty manual (MWII)” file’s introduction:
Introduction The lead-in above is borrowed from the Scientific American Computer Recreations column in the pre-Mac issue of November 1983. In this fascinating article, Brian Hayes describes how a letters probability of appearing at a given point in a text, depends strongly on the preceding letters. When examining a text-sample, it is possible to register not only the frequency of occurrence for single letters, but also to do the same for different combinations of several letters. These frequencies can then be used to generate random text that mimics the frequencies found in the original. Though nonsensical, these pseudo-texts have a haunting plausibility, preserving as they do, many recognisable mannerisms of the texts from which they are derived.
The letter-frequencies for single letters are well known. In addition, they are few enough, not to present any storage problems. Although little statistics is readily available for groups of more than one letter, todays computer technology makes it easy to derive frequencies for virtually any combination of letters. This has not always been the case, only ten years ago, fourth-order letter frequencies was the limit. The large tables necessary to store the frequencies quickly outgrew the limited memory capacity available. Even more important, however, was the lack of efficient algorithms that could reduce the storage requirements.
In his article BH presents a working method that builds on the fact that all the information that could be incorporated in any frequency table, is present in the original text, where it takes it most compact form. There is a drawback to this scheme, it requires a scan of the entire input text for the generation of each pseudo-random character. The performance is therefore dependent on the product of the lengths of the input and the output strings. If on the other hand, the output was based on a frequency table, it could be generated in a time that was proportional to the sum of the lengths.
This is where Peter Wayner, in the September 1985 issue of BYTE, comes to our help. He shows that it is quite feasible to build a frequency table, even for larger orders, as long as all the redundant information is left out. The natural storage form for such a table is, of course, the data tree. This well known structure is in fact ideal for the varying requirements of the travesty table. Its practical implementation will be rather more like a bush, though. Each unique character group represents a complete branch with as many ramifications as the current order dictates. Several similar groups will share smaller or larger parts of the same branch. A terminal leaf will contain the frequency count for one group. For more details on the practical implementation, please see below.
While I don’t think Holmberg is making any assertions about artificial intelligence (AI) and generative AI, I can’t help but think about how his program draws on Hayes and Wayner’s pieces point to one avenue of understanding human language with a computer, and by helping the computer understand language, it could eventually reproduce it.
The File menu has many standard options but how these work after analyzing a text is unique. Holmberg explains in the manual:
File menu New: Opens an empty document window that bids you to enter plain text in 9 p Monaco. This function uses TextEdit, a small editor that is built into all Macs. Since the purpose of this is to handle short text in dialogs, you may run into trouble if your text exceeds 32K.
Open: Lets you open a document that has been saved as TEXT. It is NOT possible to open a file with a length of more than 32K.
Close: Closes the front window. If it is the editor window and this contains a text that has not yet been saved, an alert will give you the opportunity to do so.
Save: This option is only enabled if a non-saved text is present in the text window.
Save as: This option is enabled whenever one of two windows is open. When the text window is in front, its content is saved as TEXT with the creator MACA, i.e. MacWrite. If the frequency window is in front, its character groups and their counts can be saved in a similar text file. This makes it possible to print out this information from another application. Regrettably, the nice columns come out horizontal when using this function. You can have a look at the file in the editor window. It may be necessary to resize this to get the lines even. If MacWrite is used for printing, hit the "Paragraphs" button when opening the file.
Get info: This is another context sensitive menu option. For a text file it will show the number of characters, words and paragraphs. If the other window is in front, this feature will tell you about the number of nodes that were created during the analyse, and how many unique character groups that were found.
Quit If you try to leave MacTravesty without saving some data in the edit window, an alert will tell you so. As is the case with the similar feature of the Close item, pressing the Option key while selecting the appropriate menu item with the mouse will bypass the alert.
The Edit menu is also standard fare, but there are some exceptions and caveats explained by Holmberg in the manual:
Edit menu Undo This command is not functional in MacTravesty. It is included for compatibility with certain DAs [desk accessories].
Cut, Copy, Paste and Clear These are all supported by the MacTravesty editor. In addition, TEXT can be imported and exported through the Clipboard in the usual fashion. Use the Paste function with care, don't let the file size exceed the limit mentioned above.
The Travesty menu is where the magic happens in this application. Holmberg explains each option in this menu in the manual:
Travesty menu Analyse Choosing a TEXT source file in a standard file dialog will start the generation of a new letter-frequency table. After a brief delay, another dialog will show the progress of the analyse. When this is finished, the groups and their counts are sorted if the relevant box in the Preferences dialog is checked. Next, the frequency window is opened, showing all the character groups that were found. The first group is always selected after an analyse or a sort. To choose another group, just click on it. If the selected group isn't visible after scrolling, hit the Enter button to move it into view. If the analysis is terminated with the Cancel button, the groups that were found up to this point will be displayed. In order to present complete groups only, a complete analyse always wraps around to beginning of the source text.
Travesty: This menu choice will only be available when the frequency window is in front. Selecting Travesty will then create a pseudo-random text based on the current character group frequencies. A travesty always start with a seed. This must be one of the character groups that were found during the analyse. It is selected either randomly or by the user, as determined by a setting in the Preferences dialog. Double-clicking on one of the groups will also start a travesty. Choosing Travesty again will create another piece of text, that will either replace the previous text, or will be appended to it. The requested text length is also entered in the Preferences dialog. Although this accepts a range of 0-9999 characters, no travesty will be started with a figure that is less than or equal to the order. When appending text, don't forget the 32K limit of TextEdit. The new text will appear in the text edit window mentioned above. If this is already showing and containing a text that hasn't been saved, you will be warned about this fact. Repeated calls to travesty will not result in any further save alerts. A travesty can be edited and saved as any other text.
Sort: A list of character groups can be sorted either alphanumerically or after the number of groups found. Holding down the option key while selecting this item will toggle the sort from one alternative to the other. Since the sort [on a slow Mac] may take a while for a longer source text, automatic sorting can be turned off in the Preferences dialog.
Find: Presents a dialog with a square text field that accepts up to five characters, including CRs. After an OK or Enter, the program will try to match these with one of the groups in the frequency list. If a match is found, it will be selected and highlighted. It can then be used as a travesty seed. You will hear a beep if no match is found. The entered text is then highlighted to facilitate a new try. A À (Shift-Option-?) can be used as a wildcard character. Only the first group that is found in this way will be selected, though.
Order: This will show a hierarchical menu with five items, 1-5. These also have the command key equivalents Cmd-1 and so forth. Order-3 is the default setting. Although it is possible to choose a new order whenever the menu is enabled, a travesty will always use the order that was current at the start of the analyse.
Preferences: Opens a large dialog with several items, most of which has already been mentioned above. Edit: Here you can determine if the text in the edit window shall wrap to the bounds of the window, or to a fixed document size. Analyse: A long list of character groups can be confusing if it contains many space-characters. Replacing these with a ×, usually gives a better view of the groups, especially since the CR and the Tab will be represented by a  and a Æ, respectively. Sorting was originally such a slow procedure that it was sometimes necessary to inhibit it. As MacTravesty now uses an extremely fast quicksort, you should be able to have this feature enabled most of the time. "Alphanumerically" will place the groups in ascending order, while "By Count" first will arrange them by descending frequency. Travesty: This is where you set the length of the travesty and determine how the seed is to be chosen, and whether to append or replace the created text. OK Make the current settings the default ones. Cancel Just leave the previous defaults as they are. Revert Return to the saved settings. Save Save the current settings.
Clicking on Travesty > Analyse brings up a file selection dialog.
Navigating to the MacHD, I selected a text file with a sample of chapters from William Gibson’s Neuromancer (1984).
Very quickly, MacTravesty created this massive character group chart that it can use to generate text based on the relative appearance of certain character groups. Holmberg explains about the performance of the application in the manual:
Performance MacTravesty is quite fast. It will process a small text sample in just a few seconds. Analysing a longer source text may take a minute or so, but this is still quite reasonable. The exact time depends mainly on the length of the source. Since most of the time is spent traversing the travesty tree, the order has only a minor influence on the timing. Here are some data for a relatively long file of about 30K (on a Mac SE/30):
Please note that the time required for sorting increases in an almost linear fashion, this an excellent example of how fast the quicksort can be. Using the less efficient shuttlesort, sorting the 15917 groups took about two hours! On a Quadra 700 the same 5-order analyse takes 14 and the sort less than two seconds (timings with a Power Macintosh Upgrade Card are similar, the sort takes a few seconds more, though). The travesty is also fast, creating a thousand character sample of random prose will only take a few seconds.
Memory Analysing a large source file requires a lot of memory (well, this was originally written in the late eighties). As can be seen above, the number of letter groups increases fast with higher orders. As each node requires 8 bytes, the 5-order analyse in the example needed 237224 bytes for its tree. Since it is very difficult to know in advance how large the tree will become, a certain amount of memory is reserved at the beginning of the analyse. How much depends on the order and on the file size. For each 1024 bytes in the file, a smaller piece of memory is added to a basic allotment. These figures are fetched from the resource TRDF and can be changed as required. This means that even a very short analyse may start out with a sizable chunk of memory, perhaps 90K or so. This is necessary since most of the memory is consumed in the beginning. Any memory that isnÕt used is released at the termination of the analyse.
This may come out handy, as the next phase also requires a good part of memory. The different character groups in the tree has to be identified and transferred to a separate list, before they can be presented in the frequency window. Each group in the list needs (order*2)+2 bytes, i.e. 191K for our example. Saving the frequencies would require another 190K. The graph below shows how the node count relates to the file size in a 3- and 5-order analyse.
Implementation The travesty tree begins with a dummy root node from which all the branches sprout. This means that there will be at least one node more than the number of character groups. The number of branches at this level equals the number of unique characters in the source. Each node contains four word-length fields; the character code, a count and two offsets to the next branch and twig, respectively. The count is only used in the outermost twigs, i.e. the leafs.
Using the BYTE example, Here are the shoes, the ships, and the sealing wax: the beginning of its 138-node, 4-order tree will look like this in my implementation:
[Image could not be decoded.]
The travesty is created using the method suggested by BH. Taking a 3-order travesty as an example, we would like to add the next pseudo-random character to the sequence Éth. While looking up all variations on thÀ in the tree (e.g. tha, the, tho etc), the counts for these groups are added together. A random number between this sum and zero is then generated. The same counts are then subtracted from the random number one by one. If the result of a subtraction is zero or less, the last character of the group corresponding to this subtraction will be appended to the growing travesty.
The Travesty menu > Order submenu allows for higher or lower order travesties, but as noted above, the order selected before a text was analyzed for creating travesties.
Clicking on Travesty > Preferences brings up this options window.
After analyzing the Neuromancer chapters, I instructed MacTravesty to generate this travesty of text based on its character group occurrences.
Clicking on File > Get Info on the travesty text shows that it generated 512 characters, 91 words, and 7 paragraphs (I’m not sure how these are being counted–maybe hard returns starting from the first blank line and the ending blank line?).
Holmberg concludes the manual by writing:
The End Playing around with MacTravesty can be great fun for a while, but is it actually useful for something? Well, at least you can easily create "greeking" for DTP dummies with MacTravesty. And it should be of some interest to linguists. I'm sure that the rest of you can come up with many more interesting ideas.
I definitely can see how this program would be useful for desktop publishing, especially at the time when it was first released. But, I’m captivated by how this could have been developed in other directions for the purposes of generative AI–as the basis for a more complicated text generator that operated on the level of words, parts of speech, and scripts. In retrospective imagining, I can see this application as the basis for something exceeding most of the other text generators that I have written about here because it in a sense learns from a text (creating the character groups) and applies that to generating text. If its input were more useful for providing meaningful text generation, it would have been a step in the direction of where we are at now with generative AI.
Created by Fuzzy Gerdes, HAIKU 0.2 is a Hypercard stack that assembles haikus from pre-written 5-syllable and 7-syllable phrases. Unlike some of the more advanced haiku generators like Haiku Master 2.2 or McPoet 5.1, which work on a micro, word-level for constructing haikus, HAIKU 0.2 operates on a macro, phrase-level and puts more work on the part of its human user to think of and type up phrases that contain five and seven syllables, which it randomly selects from to create new 5-7-5 haikus.
HAIKU 0.2’s file name is “Haiku stack” and it weighs in at 32K on disk (24,576 bytes used).
When the user double clicks on Haiku stack, it launches Hypercard Player, which runs the stack and displays HAIKU 0.2’s main composition window. The main options that can be clicked with the mouse are “Make a haiku,” “Help…”, “Home,” “Quit,” “Add to phrases…”, and “Save this haiku.”
Clicking on “Make a haiku” in the upper, middle portion of the main window creates a new haiku out of randomly selected five and seven syllable phrases stored in the Hypercard stack.
Clicking on Help loads this page, which explains how it works, how to use it, and credits–written by Gerdes and inspired by Larry Van Vactor Lee and Charlotte Van Vactor Lee, who wrote most of the built-in phrases.
Clicking on “Save this haiku” opens a standard save dialog box for navigating the system’s files and saving a TeachText file of the haiku currently displayed on the main window.
Clicking on “Add to phrases…”, loads this two-column page labeled “5 syllabus phrases” and 7 syllable phrases.”
Both columns are editable by clicking on a text line and typing, or the user can scroll to the bottom of the list, press return, and begin entering a new line of text. It doesn’t enforce syllable count–that’s up to the user to count correctly. Clicking “Done” at the bottom of the page returns the user to the main composition page.
Even though this is a simple haiku generator in terms of how it assembles a haiku by randomly pulling from the 5 syllable phrase list, then the 7 syllable phrase list, and again from the 5 syllable phrase list. However, it and the other Hypercard-based text generators demonstrate the extremely easy to build power of Hypercard. A Macintosh computer with Hypercard gave non-programmers and programmers alike a relatively easy-to-use platform for creating interactive hypertext with graphics and programming. So, even though this and other poem generators like it are pulling text from lists based on simple rules and random numbers, it gestures toward equipping human computer users with feature-rich tools to make computers seemingly intelligent to a degree.
