Peter: What you are asking for is not easy. The storage space alone for the dictionary is prohibitive.
Of course, where there is a will, there is a way. For example, when Brad Grier released Wordle for the M100 (M100LE), I helped him by figuring out a way to compress the official Wordle word list in a form that allowed direct lookup from the date so it always had the correct "word of the day" instantly. Uncompressed, the wordle word list — which contains every common five-letter word in a specific order — takes 17K. The BASIC source code for M100LE is 9K, tokenized. (17K, ASCII). That would seemingly rule out playing the game on anything less than a 32K machine. Fortunately, there were two ways to tackle that: 1. The data was split by year. It was left up to the user to download whatever years they were interested in. By relying on the user's intelligence, only 365 words were required to be in RAM instead of all 2000. 2. Every word was compressed into exactly three bytes. Reading the file sequentially would have been too slow, so we indexed the RAM filesystem directly. (A sly trick, yes, but surprisingly it works portably on all Model T's). Putting both together, the required word list was reduced to 1K for a single year or 7K for all the years. I think you will need solutions like that to improve your phone number-to-word generator. Specifically, I suggest focusing on the first method: relying on the user's human intelligence to fill in the gaps the computer has trouble with. That is the way Model T's have worked historically and designing for that is part of the challenge of programming them. The second method, coming up with a fast and small word checking algorithm is going to be harder and provide less benefit than leveraging the user's brain. Which is not to say it shouldn't be done. You may want to try something that recognizes common English patterns (character ngrams) instead of using a fixed list. For finding misspellings that sound right, consider Knuth's soundex algorithm. John: AI has uses, but I doubt it would make this particular puzzle any easier. There are plenty of phone number-to-words example programs it could give you, but the standard algorithm requires CPU and storage the Model T simply does not have. What is needed is not regurgitation but novel insight into how the problem can be redefined to be soluble on such a small machine. For me at least, the effort to get something working within tight limits is a major part of what makes hacking on a Model T so fun. --b9 On September 10, 2026 10:41:40 AM PDT, "John R. Hogerhuis" <[email protected]> wrote: >On Tue, Sep 8, 2026, 12:24 PM Peter Vollan <[email protected]> wrote: > >> There is a program called NUMWRD which finds words in phone numbers, but >> it just generates every possible letter combination. Is there any way to >> run it against a dictionary? >> > >Long answer... > >That seems relatively hard because of the size of the English language and >the small size of the model t's memory. > >There is at least one spell check for the model t's but I think it stores >its dictionary on disk. So I think you would need probably a combination of >compression and storage beyond the main ram. > >You could I guess include words that are exactly 7 letters only. But most >letter phone number patterns rely on dropping vowels or relaxed spelling >rules. So that seems harder too. You need to determine patterns that look >pronouncible even if not spelled correctly. > >Plus some readable phone numbers leave some digits as digits. Like >555-HELP. so even more complicated. > >Maybe decide on a set of rules, write a English language specification of >those rules, and ask an AI to generate C or something and convert it to >microsoft basic. > >Might have the AI do research even on the rules since I think such things >already exist for other platforms. > >Short answer is I don't think anything like what you want exists for the >model t. > >-- John.
