I've been working almost exclusively with coding agents since January of this year, and over the past few months I began to feel utterly exhausted by them. They're great, but I'm finding it more and more tedious to write full sentences for every change I want. Not only that, but it seems there's a complexity limit for codebases - beyond a certain point the agent begins confusing itself.
I'd like to go back to writing code, but I don't want to go all the way back to fully manual coding. So I've come up with this interaction paradigm where you:
1. write pseudocode in whatever way makes the most sense to you
2. on save, the editor synchronizes your work to real source code
3. the pseudocode is persisted alongside the generated code, making your prompt effectively a stored record of intent.
Right now it's just a proof of concept - installation instructions are here in the readme: https://github.com/danielvaughn/hz
You can also watch a video of it in action here: https://x.com/danielvaughn/status/2090456808431165715
Cheers!
That's the way software engineers working on large projects work anyway: you first gather context on the state of the system and read it at a level you can understand. Then you propose a change on the simplified representation, and then holistically update the machine-runnable format ("implementation").
I'd be interested in tools that formalize/automate this process more.
Difficult! :(
The biggest issue is you end up leaning heavily on the quality of the model. Lower fidelity models tend to make a mess and add tech debt that you must frequently repay with intentional cleanup passes from a higher quality model, or else the rate of useful progress will fall off a cliff. At least that's my experience.
Personally, I’d feel much more confident to use a vibe-coded library where I can read the human-written intentions than one where I just see a lot of AI-written code. Maybe it feels more like there is still a programmer/engineer/architect behind it who knows what they’re doing and what exactly they want to achieve.
To me, vibe-coding always feels a bit like this weird transition into something else that is more precise and reliable. It feels backwards to give up on our achievements in formal language for the comfortable vagueness of everyday speech.
https://github.com/spekk-ai/spekk-cli
Rather than writing exhaustive specs, I preserve only the intent and what must be true as discrete assertions. This preserves the leverage you get from LLMs - anything it can reliably infer does not need to be specified. It also (mostly) separates intent from code or architecture decisions, which keeps specs flexible.
After the last year, I feel like this sentence could replace half my outbound emails.
The challenge I see more broadly is we (as engineers now empowered by LLMs) are trying to find the right level of abstraction to operate in. Writing long form sentences and (sometime) reviewing the output feels too far away. But having an LLM work directly with you in an IDE feels too close to “the old way”.
Personally for me the approach here still feels a little too close to the lower level old way, but it’s better than the two approaches above.
Excited to see where you take it!
At what point will you need formal rigid syntax? Or is not having rigid syntax the point? If the latter, how much "informational noise" or ambiguity can you inject before the "DSL compiler" gets confused?
Scaling is another bit. Convertible Psuedocode a great pattern for writing functions, but is it useful for writing modules? If you're writing a paragraph to change behavior of a function, you're underutilizing LLMs. Paragraphs are best for spec'ing modules, and the LLMs already fill in the blanks. Not sure if it would be faster to psuedocode the entire module (although maybe just the interface would be a sweet spot...)
My guess is that if you simply write `use some_fn from $repo/some/path`, the LLM _should_ be smart enough to infer in most cases. But we'll have to see how reliable that is.
Why not just put an instruction into your favorite harness’ system prompt: “If I give you pseudo code, spell out my intent, and then write and test it in real code.”
The issue is that with very large or complex codebases, you tend to forget what was AI generated and what was written by a human. And it's also extremely tedious and difficult to read AI generated code. So if you want to _understand_ a complex bit of code, the natural tendency is to ask an agent to summarize it for you. This can work but also has lots of problems.
What you really want is a system that persists both your written intent, and the actual source code. And you want to provide a source map between them, so that you can understand which bits of human pseudocode are responsible for which bits of generated code.
The real value is in persisting your expressed intent.
I think it will be interesting to see how this plays out when it comes time to debug.
At that time, someone else may be reading my pseudo code and implicitly assuming that the code was translated correctly. If the code wasn't translated correctly, wouldn't the human who naturally assumes it was miss the bug every time?
Huzzah would benefit from having a guard identify pseudo code with two potential interpretations and ask the human to clarify so the reliability of the interpretation does not suffer.
I think having to open a web interface is a big entry barrier.
Imagine if those pseudocode files could live in your codebase, and the CLI tool would just “build” the actual code, with sourcemaps. You could edit the code in your favorite editor and run “build” commands in your favorite shell.
(TBH, I haven't looked deeply inside the repo and maybe it's exactly how it works. I just saw that demo and readme tell you to open a http://localhost:5173 as if you can use it only via custom UI.)
I'm not sure how to do syntax highlighting for this pseudocode in any IDE, but you could start with supporting something like Alabaster theme, the whole point of which is to highlight as little as possible.
I say this because I really think that if the setup was simpler lots of people would use it. It's a kind of concept that when you read about it, you think “Wait, how did I not came up with this”. Finally some interesting concept in this endless stream of skills, MCPs, loops etc.
I'm not totally convinced this is a useful way to express something like "Change the data flow so that we bulk query from the DB upfront and pass it down to all callsites"
Then probably do some fuzzing or SAT solving to formally enumerate the known-good cases and possible edge case exceptions. That was before I knew about ranged variables, category theory, etc.
Unfortunately the real world got in the way, and I spent a quarter century treading water to survive, which was exacerbated by CPTSD amplifying ADHD and OCD symptoms many fold before I knew what they were. So now any great ideas I might have had are rendered obsolete because an LLM can synthesize them in a matter of minutes. It may be time to let go and hand things over to the next generation.
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I think maybe the goal now is to keep building on these ideas until we achieve creating digital assistants that can manage all of the aspects of our lives that interfere with our goals. That's a controversial statement because the political right doesn't distinguish between setbacks and goals (it's all discipline) while the political left embraces a kind of learned helplessness as controlled opposition. Change comes glacially or not at all. In other words, help isn't coming from above - we have to pick ourselves up by our bootstraps like we always do.
A few things that AI might help solve by rendering the conditions of their existence obsolete:
- Money
- Hunger
- Illness
- Exploitation
- Pollution
The list goes on. Unless we're actively working to solve these things, then whatever we come up with is frankly a distraction. I consider just about all tech innovation since the 1990s to be a waste of time for that reason. It all just made some guy rich. Yawn.
Huzzah however, is great. I hope it leads to something wonderful.
I guess you still need the chat to discuss with the AI? For example, ask it to compare solution A and B, or to explain how to do X. A bit like you can use plan mode today. Then given the result, you can write the hz file (or even have an agent write it).
But I wonder how it works on a more complicated project than fizzbuzz. Can I suggest you use Huzzah to develop itself, and then share the hz file(s)? That will show both how it works on a more complicated project, and something that is developing over time.
Though catching back up on that project- it seems it's evolved pretty substantially, becoming much higher level than the initial pseudocode driven version I remember
You install a fancy chrome extension or custom browser.
You go through the app on this browser. You notice something you want to change. You can then submit a prompt via this extension saying the change you want.
The trick is, the chrome extension has been following your movements through the app. This way the chrome extension can generate a lot of data the AI can use for a good prompt. I.e. The extension can get a screenshot of where you are in the app and all the pages you went through to get there. The extension can get the console logs and traces and stuff.
So with all this, the AI system gets all the material needed for a good prompt to make a good change.
In an ideal world, the AI system could then save all these details and when the change is made, guide you through the UX again. And you can check if the change was made as you wanted.
So kinda like AI flavored manual UX testing where you click around and record your findings.
Sometimes you might also want examples and then BDD testing software (like Yadda) might make sense?
Let's look at your fizzbuzz example. Unfortunately, if you wanted to have the agent implement fizzbuzz for you, it looks like, in your example, you would have to already know how to effectively write fizzbuzz. Specifically, you call out the use of the modulo.
In your prompt, for the traditional agentic development path, you already declared the intent. There is some imperative language in there, sure, "Create a function that ...", but also there is the declarative state, that doesn't require knowledge of specific programming syntax or semantics.
What I've relied on is a more formal location/syntax for acceptance criteria are in code. These are then used to generate tests, and implementations. It isn't perfect, and more investment is needed, but it starts getting at the root of the problem.
https://en.wikipedia.org/wiki/Program_Design_Language
https://codecourse.sourceforge.net/materials/Code-Complete-A...
what you did is spec-driven development, instead of use-cases and requirements you have pseudo-code.
spec-driven did not work in my case, likely yours suffer the same "issue of walls of text no one wants to read".
What about multi-file / larger changes? How would you express files being connected, imports, and exports? Or are you thinking the hz files are disposable per change?
I'll be looking into multi-file stuff soon - it's an interesting can of worms to think through.
It's cool that with a tool like this you don't NEED to get all aspects of your code finalized and ready. It's possible to be vague when you want to and specific when you need to.
I'm not sure if that itself would work well in practice, but the project is still quite cool nonetheless.
Am i missing anything?
Just a few days ago someone was talking about a machine - human patois.
This (your project) sits somewhere between Lean and BDD cucumber syntax.
At the same time Claude spits out phrases like “a container paying the price of -42px”.
Recently I was listening to a lecture about metaphor in poetry, the misconception that poems are riddles whereas we use metaphors all the time in our language because they convey the meaning more precisely.
My concern, however, is that all of these options might ultimately be slower than just prompting.
I mean, at this point, another option would be to just go back to actually writing the code ourselves, like we did back then in 2024 ??
Every engineer I have ever mentored got a lesson on how to write a good commit message that included this. This is exactly that.
Further Huzzah from skimming it over seems to be re-inventing documenting your code.
Together I can only surmise that the author is new out of school or has simply not yet worked on a team with good coding practices.
But before AI arrived on the scene, source code was a single artifact that directly expressed the intended behavior of a piece of software as it currently exists. After AI, the artifact is still there, but it's no longer the true record of human intent.
> Welcome to my Github! I'm a web engineer who's been building front-ends since 2009. Most of my work is either closed source or behind paywalls, but here is where I tinker on side projects in my spare time.
No need to dismiss the person - you can just say you don't like the approach
With scale problems arise.
EDIT: same on Firefox on my Mac (macOS Ventura).
Nice work.
I wrote this but as a compiler. It was ~2 years ago and local models have gotten WAY better; I was having too many issues with adherence (syntax errors, etc) and dropped it.
The compiler comes with a model embedded or can use an external model. It uses Cosmopolitan Libc and can zip things together into one binary. I will take some time to dust it off and share it.
But the idea was basically, you have your natural language source files or a one-shot prompt and it "compiles" them into a single, shareable fat binary that works across all popular platforms and architectures.
It was pretty fun to use with remote frontier models but the local model story simply wasn't good enough at the time for me to feel proud releasing it. I think that's probably changed now and passable results can be had even with small modern 7B/14B models.
I think you should work on your differentiation. The session management stuff is the greater concern, in my opinion; pseudo code is not a novelty.
You can already retrieve the session associated with a given line of code.
What I'm after is a condensed distillation of human intent using semi-formal symbolic language, which should be vastly easier to read and understand for engineers and teams.
Keep your toolchain as simple as possible.
A really nice rig, which you can use in an existing repo, is to have ollama and aider simply log everything that happens in the session, through tee, into a log directory which you do - indeed - check into the repo.
> almost exclusively with coding agents
Do your own commits too (don't just let the ML do them), and in those commits, keep your prompts.
Learn to use your AI skills with succinct and calculated, forthright projection.
Which is to say, it is your own personal set of words now which define your control over your computer.
The words are tools. But what are your methods?
> .. tedious to write full sentences for every change I want .. interaction paradigm ..
Your own command of your speaking/thinking language can be extended as far and as wide, now, as you can possibly imagine. In fact, you must control AI/ML with imagination now, in multiple ways.
One of those ways is to iterate on expansion of your own ontology. There has to be an input from the AI before an adequate human output can send the AI directly at the heart of it. This improvement loop is on you. Get smarter with the loop.
> pseudo-code -> sync -> record of intent
[1] "Idea -> Description -> Result -> Build -> [human] (use)"
Well, I get this by checking all my aider logs into a submodule of my main source tree. All my prompts, all the happy little mistakes and bright, shiny things, commit by commit. Sure, the logs grow and grow, but you know what .. I learn a hell of a lot by reading them.
Time-stamped. So, nice graphs if I wanted them, one of these days we'll do it, me and the AI.
The commit point for where I cut the exhaustion between me and the immense power of the AI/ML tooling, is when there is a new build, and I have tested it, personally.
I get exhausted if there is no delivery factor, to me personally, from whatever method I'm wrangling the tools with. Like if I really push too hard on the prompt, things get gnarly.
But, I've been here before over the decades, there are methods.
Even in the AI/ML age .. tooling and methodology requires a discipline - what is true now more than ever is that if a method fails, the usual approach of building another tool is not necessarily the best approach.
Methods can be sharpened just like tools. But every tool carries a cognitive load.
The methods are there to make that load useful. Are you a user?
So then just do a build and run it. See if is worth it.
Goto [1].
For businesses it makes sense to abandon programming in favor of delegating to agents that can do more in less time, but for programmers, it is a loss. Either be a programmer and code, or be a delegator and delegate, you aren’t going to make the life of a delegator suck any less by trying to trick yourself into thinking you’re programming.
I’m not a full time dev, but I code quite a bit doing Systems and OPs stuff, but AI has opened up an entirely new world to me and it has expanded my ability to think through a problem. It’s the ultimate rubber ducky. I love to watch the reasoning process while I’m in opencode so I can interrupt if I see it going down a path that doesn’t make sense.
It’s opened another world to me that allows me to implement ideas I’ve had for years without the time to invest in the skills needed to even try the idea.
I think it’s just how you use the tool.
My broader philosophical take is that we, programmers, lived through a golden age where our skills used on our terms were some of the most valuable skills. The golden age is over, our skills aren’t useless, they can still be applied to making things with modern tools, but it is no longer on our terms, no longer programming, no longer the meditative thinking process it once was.
For non-programmers, this is their golden age, the reign of programmer tyranny is over.