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That being said, I am still guiding it on a regular basis and only recently let it run open ended trying fixing small to medium size bugs. The next stage will be try let it run in a loop trying to fix the 100 issues it has identified as well as getting most of the bash test suite to pass.
you ever worked with human engineers before? :D
Yet I see comments and articles like above every week, where someone says "Yeah, I just left my agent run for a few nights, and then it made this awesome tool."
There is a huge gap here, but I can't pinpoint it yet. Either I (and everyone I talk to) is Doing It Wrong, or your code is actually flawed at its core and bugs will take exponentially longer to fix and never converge, or... I just need to let my agent run for 3 months??
[0]: https://medium.com/@vishvananda/i-spent-2-billion-tokens-wri...
The future is duplication. Code is no longer meant to be read by humans but by machines.
That isn't true. Everyone knows it and you know it. So why would you make a statement like this? Does it make you feel like a "thought leader"? This isn't X or LinkedIn. Bulshitting here just makes you look like a fool.
Cuz what if a human has to intervene as the LLM can’t get it right?
Assuming a human then has till be able to comprehend it?
1. You have very good tests. The agent can write them, but you have to prompt it to do that, and some human input may be needed here.
2. You have a good, modular architecture. Again, this is something that needs to be prompted and/or developed up front. Agent work can scale if you divide and conquer, so that any given task has a well-defined scope, limiting the amount of context needed to handle it well. If you let an agent just build a big pile of code, you’ll have issues.
Once you have the above, you’re more likely to be able to just let the agent run.
This is why you hear about things like SQLite being ported to Rust (by multiple groups!) It has a comprehensive unit test suite and a good existing architecture. Such porting projects are benefitting from already having done the upfront work that I mentioned, plus they have the original source code as a reference implementation.
Because of that, most of what I do involves a lot of interaction with the agent. That’s because I’m typically working on complex existing systems, that don’t necessarily have the above properties, and agents aren’t at the point where they can handle that autonomously.
I don't think i wrote more than 10 lines of code in the largest project i'm working on. Lines of code: Java: 900_635, typescript: 725_418, C++: 180_445, Dart: 96_181.
It's been obvious from the start that no model, as good as it is, can do large(-ish) amounts of work by its own without supervision, control, criticism, etc ... If left unsupervised, models usually do half the work, leaving stubs and todos everywhere.
Quality comes from applying software engineering principles as much as possible, just like you would do with teams of junior devs: planning sessions and implementation sessions with adversarial critiques, specifying as much as possible upfront, planning unit/smoke/integration tests, etc ...
Many systems rely on swarm of agents to build software but i've found it very difficult to get good results without lots of overhead/token waste because of inter agent communications mostly.
So instead i built what is mostly a workflow engine to structure / organize processes into workflows with different agents assigned different roles. I've setup a basic landing page here https://kodfactory.com if anyone wants to follow along.
This solution should look for more problems.
I also built a caching proxy server for apt packages, that's a bigger project, and that is 30.5K lines of golang, which doesn't seem bad compared to apt-cacher-ng which is 19.3K of C++ (my project does significantly more).
You should really be proud of those LOC numbers!
Good job!
- my coworkers
It includes a custom WAL, structure aware semantic chunking, ingestion and indexing pipelines, hybrid (rrf), text and vector retrieval, ACL aware filtering at query level, query classifier, citation tracking, and a CrateDB backend that performs fused retrieval over a single table. total LOC: 180 K. Status: evaluation by State agencies for investigative search on legacy documents. tool used - Codex, Luna, and earlier codex 5.3, and gpt 4 mini.
- Project portfolio management software. Good results so far. This is unguided experimentation: Fable researches feature ideas and plans them, then Opus 5 implements them, without me in the loop. The features include things such as an auth server, full-text search, audit logging, resource leveling, HA/DR, IaC, and more.
- FHIR & SNOMED medical software experimentation in demo-only environments. This has mixed results so far. This is guided experimentation: the FHIR specifications and SNOMED specifications are thorough and I'm in the loop. Fable plans OK, but Opus 5 is thrashing a lot, going down many rabbit holes.
All models in the leaderboard probably have had access to the original source code in their training data.
It’s an interesting eval but this is a distinct limitation — most applications that consumers use directly are not purely text-based.
It's impressive AI can put together it's own version, but I can't help but feel it's probably just relying on existing excel clones as it's a well trodden space.
Novel applications are what impress me. Not a clone.
It is important not to wait for today's AI tech to get to AGI. It is better to exploit the useful things it can do today while waiting for it to get better tomorrow.
Cloning existing software might be useful today. It allows the model to
1) learn from its mistakes
2) mix known solutions (embed product A inside product B)
3) mine value (turn product A into a set of reusable libraries)
etc.
The interesting question in my view is what is the largest system an AI can maintain over a long time horizon -- while keeping a coherent architecture that adapts and responds well to change. All the while remaining performant, scalable, and understandable to humans. I am unaware of what is the currently longest-lived vibe coded project but it cannot be more than a few years old.
So to my mind, we don't have very much real data yet.
The space is too big and the code too extensive for a 1M context window and the capabilities of the current SOTA models.
I'm building a language with AI, and when I leave the AI running on its own, it keeps generating code in strange directions. I wonder what kind of structure AI-only code would have.
In the linked paper the researchers state "AI solutions must match the original program’s output exactly on end-to-end tests, including held-out tests. " And a bit further on "The AI agent is given execute-only access to the original program and a set of visible test cases, but does not have access to the original source code. "
At a guess, their agents are not going off into the weeds (like mine do occasionally) is because they have both access to a reference runtime implementation to use as a definitive end to end test to guide development. And also a relatively large token budget to run enough characterisation tests to allow them to home in on solutions.
It's decent at design and implementation, but strangely terrible at language consistency - and expectedly architecture.
Steering it on language consistency is easy. Steering it on architecture has required me to learn a lot more about compilers than I wanted to do for this project (since that's a lot more than I wanted to take on for something that was supposed to be a "fun" "side" project).
I'm not sure about architecture. When I first implemented the slot system in my language, I designed it around that implementation, and in the end, the architecture suffered. There were quite a few cases where consistency was simply unavoidable.
It's not easy to create a completely new architecture with something that doesn't exist in existing languages. Consistency doesn't come together as easily as you'd think.
In that sense, I really admire the people who first introduced something new.
When I started implementing it, I focused on one killer feature and just went for it—with the feeling that if this succeeds, my language will succeed. But once I started building around it, it turned out to be very different from other compiler architectures.
With CRUD or other well-established domains, it's easier to maintain consistency. But achieving internal consistency in a program seems to be a different kind of challenge.
But then it always comes down to having some human who is accountable for verifying the behavior. I think ultimately we just need a better set of languages and frameworks to minimally encode the types of applications we want to build today, so that human comprehension and verification of the source code becomes easier.