It's easy to just slab on a Skil saw, cut through the beam and it'll be somewhat straight. But when every manual stroke counts, there's enough time on a human time scale to correct every little mistake. It's definitely possible to become skilled at using the circular saw, but it takes effort that it feels like you don't need at first.
This is similar. LLMs are so powerful for writing code that it's easy to become complacent and forget your role as the engineer using the tool: guaranteeing correctness, security, safety and performance of the end result. When you're not invested in every if-statement, forgetting to check edge cases is really easy to do. And as much as I like Claude writing test cases for me, I also have to ensure the coverage is decent, that the implicit assumptions made about external library code is correct, etc. It takes a lot of effort to do it right. I don't know why Mycelium thinks they invented interfaces for module boundaries, but I'm pretty sure they are still as suceptible to that "0" not behaving as you'd expect, or the empty string being interpreted as "missing." Or the CSG algorithm working, except if your hole edges are incident with some boundary edges.
Edit: spelling
What I'm trying to do is to create hard context boundaries which help both the human and the agent understand what the code is doing. With Mycelium, you have a graph expressing the top level logic, so you have a declarative workflow that you can review. With traditional code this is mixed in together with the implementation details, and you have to tease out the business logic by reading through the code.
Mycelium creates a hard boundary between implementation details which live in the cells, and the high level business logic of the application. You can set up a workflow manifest which declares what you're doing. Then you can go an implement each step in form of a cell. And then you can review it and test it in isolation without having to consider the entire application.
This is the part that reduces the cognitive load and makes it easier to ensure that the code is doing what's intended.
As a note, I'm not arguing that I invented anything fundamentally new here. Workflow engines have been around for a while. I'm simply applying the idea directly in the context of coding agents.
> Mycelium structures applications as directed graphs of pure data transformations. Each node (cell) has explicit input/output schemas. Cells are developed and tested in complete isolation, then composed into workflows that are validated at compile time. Routing between cells is determined by dispatch predicates defined at the workflow level — handlers compute data, the graph decides where it goes.
No still don't understand
> Mycelium uses Maestro state machines and Malli contracts to define "The Law of the Graph," providing a high-integrity environment where humans architect and AI agents implement.
Nope, still don't
The main question that is not obvious, is what should I use it for?
Again I don't know much about Clojure and I am too slow for functional programming in general.
The whole point of the 'slop' report is to have the LLM try implementing the features using both the traditional approach and the framework and then reflect on how it fared with each approach.
I'm not familiar with the project (or Clojure), but let me try to explain!
> Mycelium structures applications as directed graphs of pure data transformations.
There is a graph that describes how the data flows in the system. `fn(x) -> x + 1` in a hypothetical language would be a node that takes in a value and outputs a value. The graph would then arrange that function to be called as a result of a previous node computing the parameter x for it.
> Each node (cell) has explicit input/output schemas.
Input and output of a node must comply to a defined schema, which I presume is checked at runtime, as Clojure is a dynamically typed language. So functions (aka nodes) have input and output types and presumably they should try to be pure. My guess is there should be nodes dedicated for side effects.
> Cells are developed and tested in complete isolation, then composed into workflows that are validated at compile time.
Sounds like they are pure functions. Workflows are validated at compile time, even if the nodes themselves are in Clojure.
> Routing between cells is determined by dispatch predicates defined at the workflow level — handlers compute data, the graph decides where it goes.
When the graph is built, you don't just need to travel all outgoing edges from a node to the next, but you can place predicates on those edges. The aforementioned nodes do not have these predicates, so I suppose suppose the predicates would be their own small pure-ish functions with the same as input data as a node would get, but their output value is only a boolean.
> Mycelium uses Maestro state machines and
Maestro is a Clojure library for Finite State Machines: https://github.com/yogthos/maestro
> Malli contracts
Malli looks like a parsing/data structure specification EDSL for Clojure: https://github.com/metosin/malli
> to define "The Law of the Graph," providing a high-integrity environment where humans architect and AI agents implement.
Well, beats me. I don't know what is "The Law of the Graph" and Internet doesn't seem to know either. I suppose it tries to say how you can from the processing graph to see that given input data to the ingress of the graph you have high confidence that you will get expected data at the final egress from the graph.
I do think these kind of guardrails can be beneficial for AI agents developing code. I feel that for that application some additional level of redundancy can improve code quality, even if the guards are generated by the AI code to begin with.
And what I meant with the law of the graph was simply that the graph defines the actual business logic, and then each cell is a context free component that can be plugged into it. I guess I was just trying to be clever there.
The key benefit I'm finding is that cells can be reasoned about in isolation because they know nothing about one another. You don't have implicit coupling happening the way you do in normal programs that's embedded in the call graph.
My approach is to use inversion of control where the cell gets some context and resources like a db connection, does some work, and produces the result. That gets passed on to the graph layer which inspects the result, and decides what cell to call next.
With this approach you can develop and tests these cells as if they were completely independent programs. The context stays bounded, and the agent doesn't need to know anything about the rest of the application when working on it.
The cells also become reusable, since you arrange them like Lego pieces, and snap them together into different configurations as needed.
They should try to fix technical debt before going to the next round. Of course Claude can probably also do this.
I would be pissed off too if I was a hypocrite who was so sure AI was total garbage and was now at the same time needing to use claude on a daily basis.
A lot of developers are going through an identity crisis where their skills are becoming more and more useless and they need to attack comments like the above in a desperate but futile attempt to make themselves matter.
This sounds like arguing you can use these models to beat a game of whack-a-mole if you just know all the unknown unknowns and prompt it correctly about them.
This is an assertion that is impossible to prove or disprove.
I rarely have blocks of "flow time" to do focused work. With LLMs I can keep progressing in parallel and then when I get to the block of time where I can actually dive deep it's review and guidance again - focus on high impact stuff instead of the noise.
I don't think I'm any faster with this than my theoretical speed (LLMs spend a lot of time rebuilding context between steps, I have a feeling current level of agents is terrible at maintaining context for larger tasks, and also I'm guessing the model context length is white a lie - they might support working with 100k tokens but agents keep reloading stuff to context because old stuff is ignored).
In practice I can get more done because I can get into the flow and back onto the task a lot faster. Will see how this pans out long term, but in current role I don't think there are alternatives, my performance would be shit otherwise.
This is a joke right? There are complex systems that exist today that are built exclusively via AI. Is that not obvious?
The existence of such complex systems IS proof. I don't understand how people walk around claiming there's no proof? Really?
lets say i accept you and you alone have the deep majiks required to use this tool correctly, when major platform devs could not so far, what makes this tool useful? Billions of dollars and environment ruining levels of worth it?
I'd say the only real use for these tools to date has been mass surveillance, and sometimes semi useful boilerplate.
It doesn't, that's ego-preserving cope. Saying that this stuff doesn't work for "damn well near every professional" because it doesn't work for you is like a thief saying "Everybody else steals, why are you picking on me"? It's not true, it's something you believe to protect your own self-image.
I think it's fair to say that you can get a long way with Claude very quickly if you're an individual or part of a very small team working on a greenfield project. Certainly at project sizes up to around 100k lines of code, it's pretty great.
But I've been working startups off and on since 2024.
My last "big" job was with a company that had a codebase well into the millions of lines of code. And whilst I keep in contact with a bunch of the team there, and I know they do use Claude and other similar tools, I don't get the vibe it's having quite the same impact. And these are very talented engineers, so I don't think it's a skill either.
I think it's entirely possible that Claude is a great tool for bootstrapping and/or for solo devs or very small teams, but becomes considerably less effective when scaled across very large codebases, multiple teams, etc.
For me, on that last point, the jury is out. Hopefully the company I'm working with now grows to a point where that becomes a problem I need to worry about but, in the meantime, Claude is doing great for us.
> The vibes are not enough. Define what correct means. Then measure.
And how do you define correct feedback? If the output is correct?
LLMs are proving to be very much force multipliers of the kind of developer you already are, and of those who report a 10x increase in productivity they're probably all being genuine. Whether that 10x is of careful, thoughtful choices or reckless rough-shod slop though is really an artifact of the developers themselves. I've been saying from the beginning that your effectiveness with LLMs is roughly equivalent to your ability to get effective results out of a real team of human contractors.
It's really amazing, we've crossed a threshold, and I don't know what that means for our jobs.
> Another AI agent. This one is awesome, though, and very secure.
it isn't secure. It took me less than three minutes to find a vulnerability. Start engaging with your own code, it isn't as good as you think it is.
edit: i had kimi "red team" it out of curiosity, it found the main critical vulnerability i did and several others
Severity - Count - Categories
Critical - 2 - SQL Injection, Path Traversal
High - 4 - SSRF, Auth Bypass, Privilege Escalation, Secret Exposure
Medium - 3 - DoS, Information Disclosure, Injection
You need to sit down and really think about what people who do know what they're doing are saying. You're going to get yourself into deep trouble with this. I'm not a security specialist, i take a recreational interest in security, and llm's are by no means expert. A human with skill and intent would, i would gamble, be able fuck your shit up in a major way.
