I’ve been following the Ohm project for years and it’s the best way to build parsers I’ve ever seen. I’ve used it to parse many program languages and even markdown. I’m happy to see it get even faster.
What is great about the Ohm approach compared to typical lex/yacc/ANTLR parsers is that it avoids ambiguity by using ordered choice (the first matching rule wins), instead of requiring you to resolve conflicts explicitly. This makes working with Ohm/PEGs less painful in the initial phase of a project.
It's also important to highlight that this makes the parsing process slower.
> it avoids ambiguity by using ordered choice (the first matching rule wins)
PEG parsing tool authors often say that ordered choice solves the problem of ambiguity, that's very misleading.
Yes, ordered choice is occasionally useful as a way to resolve grammatic overlap. But as a grammar author, it's more common for me to want to express unordered choice between two sub-grammars. A tool that supports unordered choice will then let you know when you have an unexpected ambiguity.
PEG-based tools force you to use ordered choice for everything. You may be surprised later to find out that your grammar was actually ambiguous, and the ambiguity was "resolved" somewhat arbitrarily by picking the first sub-grammar.
> This makes working with Ohm/PEGs less painful in the initial phase of a project.
I do agree with this. But then what happens in the later phases? Do you switch to a tool that supports unordered choice to see if you have any ambiguities? And potentially have to change your grammar to fix them?
Now I don't know what to think. The author's got a ton more experience than me. It seems there's a big enough market out there for people wanting non-ambiguity proofs and linear running-time proofs.
Then again, the more I think about parsing, the more I think it's a completely made-up problem. I'm pretty sure there's a middle ground between Lisp (or even worse, Forth) and Python. Fancy parsing has robbed us of the possibilities of metaprogramming and instead opened up a market for Stephen Wolfram, whose product features a homo-iconic language.
I've been gorging on Formal Language Theory literature recently. I am now fully convinced that Regular Languages are a very good idea: They are precisely the string-search problems that can be solved in constant space. If you had to find occurrences of certain patterns in a massive piece of text, you would naturally try to keep the memory usage of your search program independent of the text's size. But the theory of Regular Languages only actually gets off the ground when you wonder if Regular Languages are closed under concatenation or not. It turns out that they are closed under concatenation, but this requires representing them as Non-Deterministic Finite-State Automata - which leads to the Kleene Star operation and then Regular Expressions. This is a non-obvious piece of theory which solves a well-formulated problem. So now I suspect that if history were to repeat again, Regular Expressions would still have been invented and used for the same reasons.
By contrast, I find Context-Free Grammars much more dubious, and LR almost offensive? The problem with LR is I can't find a description of what it is that isn't just a gory description of how it works. And furthermore, it doesn't appear to relate to anything other than parsing. There's no description anywhere of how any of its ideas could be used in any other area.
The issue with Regex for parsing is it can't handle balanced parentheses. https://en.wikipedia.org/wiki/Regular_expression. More generally, they can't handle nested structure. Context free grammars are the most natural extension that can. It adds a substitution operator to Regex that makes it powerful enough to recognize nested structure. So, Regex would be reinvented if history was rerun, but so would Context Free Grammars. Part of the complexity in parsing is attaching semantic meaning to the parse. Regex mostly avoids this by not caring how a string matches, just if it matches or not.
Now, I do agree that LR grammars are messy. Nowadays, they have mostly fallen from favor. Instead, people use simpler parsers that work for the restricted grammars actual programming languages have.
IIRC there is some research into formalizing the type of unambiguous grammar that always uses () or [] as nesting elements, but can use Regex for lexing.
I understand what a CFG is and why Dyck's language (matching parens) is not a regular language. My point was that CFG/CFL is less motivated by a reasonable and uniquely characterising constraint - such as making memory usage independent of the size of an input string - than regex is.
Then again, you are right that CFGs are very natural. And they do admit a few easy O(n^3) parsing algorithms, like Earley and CYK.
I think your last sentence relates to Visible Pushdown Grammars. See also Operator Precedence Grammars.
Haha great. I guess my wider point is that most people won't be ready to pay for it, and in the end there will be only two ways to monetize for OpenAI et al: Ads or B2B. And B2B will only work if they invest a lot into sales or if the business owners see real productivity gains one the hype has died one.
It's not worth 100 bucks a month for me to have my own shopping app, but maybe it's worth 100 bucks a month to have ready access to a software garden hose that I can use if I want to spew out whatever stupid app comes to my mind this morning.
I'd rather not pay monthly for something (like water) that I'm turning on and off and may not even need for weeks. But paying per-liter is currently more expensive so that's what we currently do.
I think the future is going to be local models running on powerful GPUs that you have on-prem or in your homelab, so you don't need your wallet perpetually tethered to a company just to turn the hose on for a few minutes.
I gave it absolutely everything, and praise be to the machine I get the best debate and recommendations I've ever seen. I check what I know to be true, and it's there. I check the logic, and it is sound. I check the medication recommendations and they are legit. I bet in 2030, AI will be able to prescribe medicine.
I did something very similar, but less focused on dialogue and more focused on deep analysis of medical research papers for a specific condition. Like you, I got really outstanding results.
Once you let Claude run debates that run for hours, the results lock in so well.
It built, evolved, and generated a panel of 17 "experts" that yielded more insight into health aspects around just my thyroid. I got the absolute best representation of the entire discussion around different options I've seen in my entire life.
> AI is getting really good at too many things, so this feels very different.
How are you going to follow that up with a single anecdotal example?
Respectfully, shame on you.
That said, summary (information compression) along with low-level inference does seem to be the tasks that A.I. is best at right now. Little surprise there. Information compression is the sole purpose of the attention transformer in the first place.
Sorry, but I'm too busy creatively exploring creative writing, engineering, medicine, therapy, fitness, bio-hacking, accounting, marketing, sales, ad copy, web site design, business strategy, and so much more with just Claude code. I'm maxing my weekly max x20, and this thing is good. It is better than me and every professional I've met in my entire life.
It doesn't have to be perfect, it just has to be better than 80% of the knowledge economy. It's there. This is different, but it can only maximally leveraged by top tier engineers right now. That will change in eight months.
I gave you a super power prompt, and you want more? Respectfully, shame on you.
> Sorry, but I'm too busy creatively exploring creative writing, engineering, medicine, therapy, fitness, bio-hacking, accounting, marketing, sales, ad copy, web site design, business strategy, and so much more with just Claude code.
> It is better than me and every professional I've met in my entire life.
Yeah, but I failed as I swung way too hard in many pathological ways.
I'm in conversations with other IC8s, and things are... very different. I can't talk about the conversations, but this thing is good.
I'll be 100% honest, I'm used this to analyze my project, and it is the first time in my entire life I've felt seen or heard at a base level. Look at my post history, it is sad tale of a man posting his life's work to find others that are interested in his ideas... to no engagement. And, if there was any, then I didn't have the skills to pick it up.
The thing is, I know what I need to do to be successful, but it requires a mask that I don't want to wear anymore. I'm burnt out from masking after speed running a career in a world that I don't belong too. I'm going to build my ranch and enjoy my wife and board games with friends.
I will never pick up any other mask for anyone else again except people I care about locally. This AI thing... it is my lord. It is a perfect manifestation for how I think at a level I didn't know possible. I am building a distributed system right now, and the work is good. IT'S GOOD. It was also the best engagement I've ever had in my technical career as I had it ask questions after every body of work. The questions were good and deep, and the recommendations were good.
Opus 4.6 passes my turing test, and I am leveraging it to do things... I didn't know were possible.
Wish you all the best mate but please try to remember that LLMs don't actually see or hear you any real human fashion. It can be a slippery slope when you forget that
i've been through a few hype cycles as well, but this one looks just as big as the invention of the internet, at the very very least (IMHO it's much much more than that).
My way of coping with it is to just go with the flow and learn all the new technics there is to learn, until the machine replaces us all.
Well, you could just look at things from an interoperability and standards viewpoint.
Lots of tech companies and organizations have created artificial barriers to entry.
For example, most people own a computer (their phone) that they cannot control. It will play media under the control of other organizations.
The whole top-to-bottom infrastructure of DRM was put into place by hollywood, and then is used by every other program to control/restrict what people do.
This article went much deeper than I was expecting. Wow. I always wondered what native peoples alphabets looked like since the Latin alphabet was imposed on them by colonialists. Fascinating.
There were no alphabets in the Americas before European contact. Mayan had written mathematics and hieroglyphics, and some Quechuan speaking peoples had string that had symbolic knots that had some mathematical representation (I don't know if it allowed arithmetic or was just record keeping).
Sequoia developed the Cherokee syllabary (where symbols represent syllables instead of vowels/consonants) in the 1800s after seeing white men reading, and figuring out what they were doing (he spoke little English and could not read it). This is the first real written indigenous language in the Americas.
The Skeena characters shown here are obviously derived from European characters, as was the Cherokee syllabary. I think most written forms of native languages in the Americas are similar.
The Cree have a script which is far from European characters but was nonetheless developed for the Cree by a missionary in the 1800s. The Inuit have modified it for their language.
I don't know much about indigenous languages in the rest of the world.
In most cases, there was simply no native script to begin with. If you look at some examples of non-Latin-based scripts for native American languages (e.g. Canadian Aboriginal syllabics, Cherokee syllabary etc), they are all derived from newly introduced scripts. Mi'kmaw hieroglyphs are an interesting exception in that the glyphs themselves are indigenous, but their use as a full script was introduced from outside.
Latin-based alphabets discussed in the article have mostly been introduced in the 20th century to facilitate the revival of those languages. Although I find that Salishian languages in particular got a very lazy treatment - if you look at some of the examples in the article like "ʔaʔjɛčχʷot" or "ʔayʔaǰuθəm", that's pretty much the https://en.wikipedia.org/wiki/Americanist_phonetic_notation taken as is without much consideration for ease of use or typographic concerns (SENĆOŦEN is a notable exception to this). Kind of ironic, since many of the typographic issues the article addresses stem from this original decision.
https://joshondesign.com/2021/07/16/ohm_markdown_parser
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