I think that due to the nature of language, often the prompting technique that you use is indeed the best, for you, since it allows you to express yourself “naturally” and thus have more consistent and effective session with a model adopting a similar style and using similar abstractions when building.
I'm always a very serious person while I wait for people to join the stream. I'm sorry you weren't impressed, but tbf that's not really my goal, I just like building things and yapping about it.
Not sure what you mean? This was a demo in a live session that took about 30 minutes, including ui ideation (see pngs). It’s a reasonably well featured app and the code is fairly minimal. I wouldn’t be able to write something like that in 30 minutes by hand.
Since I get downvoted because I guess people don’t believe me, I’m sitting at breakfast reading a book. I suddenly think about yaml streaming parsing, start a gpt research, dig a bit deeper into streaming parser approaches, and launch a deep research on streaming parsing which I will print out and read tomorrow at breakfast and go through by hand. I then take some of the gpt discussion and paste it into Manus, saying:
“ Write a streaming go yaml parsers based on the tokenizer (probably use goccy yaml if there is no tokenizer in the standard yaml parser), and provide an event callback to the parser which can then be used to stream and print to the output.
Make a series of test files and verify they are streamed properly.”
This is the slot machine. It might work, it might be 50% jank, it might be entire jank. It’ll be a few thousand lines of code that I will skim and run. In the best case, it’s a great foundation to more properly work on. In the worst case it was an interesting experiment and I will learn something about either prompting Manus, or streaming parsing, or both.
I certainly won’t dedicate my full code review attention to what was generated. Think of it more as a hyper specific google search returning stackoverflow posts that go into excruciating detail.
Trivial is a pretty big word in this context. Expanding an idea into some sort of code is indeed a matter of waiting. The idea, the prompt, the design of the overall workflow to leverage the capabilities of llms/agents in a professional/long-lived codebase context is far from trivial, imo.
I very often put some random idea into the llm slot machine that is manus, and use the result as a starting point to remold it into a proper tool, and extracting the relevant pieces as reusable packages. I’ve got a pretty wide treesitter/lsp/git based set of packages to manage llm output and assist with better code reviews.
Also, every llm PR comes with _extensive_ documentation / design documents / changelogs, by the nature of how these things work, which helps both humans and llm-asssisted code review tools.
I don't think so, although I think at that point experience heavily comes into play. With GPT-5 especially, I can basically point cursor/codex at a repo and say "refactor this to this pattern" and come back 25 minutes later to a pretty much impeccable result. In fact that's become my favourite past time lately.
I linked some examples higher up, but I've been maintaining a lot of packages that I started slightly before chatgpt and then refactored and worked on as I progressively moved to the "entirely AI generated" workflow I have today.
I don't think it's an easy skill (not saying that to make myself look good, I spent an ungodly amount of time exploring programming with LLMs and still do), akin to thinking at a strategic level vs at a "code" level.
Certain design patterns also make it much easier to deal with LLM code: state reducers (redux/zustand for example), event-driven architectures, component-based design systems, building many CLI tools that the agent can invoke to iterate and correct things, as do certain "tools" like sqlite/tmux (by that I mean just telling the LLM "btw you can use tmux/sqlite", you allow it to pass hurdles that would otherwise just make it spiral into slop-ratatouille).
I also think that a language like go was a really good coincidence, because it is so amenable to LLM-ification.
I have linked my github above. I don't know how that fares in the bigger scope of things, but I went from 0 opensource to hundreds of tools and frameworks and libraries. Putting a number on "productivity" makes no sense to me, I would have no idea what that means.
I generate between 10-100k lines of code per day these days. But is that a measure of productivity? Not really...
Their main point is "AI coding claims don't add up", as shown by the amount of code shipped. I personally do think some of the more incredible claims about AI coding add up, and am happy to talk about it based on my "evidence", ie the software I am building. 99.99% of my code is ai generated at this point, with the occasional one line I fill in because it'd be stupid to wait for an LLM to do it.
For example, I've built 5-6 iphone apps, but they're kind of one-offs and I don't know why I would put them up on the app store, since they only scratch my own itches.
In case the author is reading this, I have the receipts on how there's a real step function in how much software I build, especially lately. I am not going to put any number on it because that makes no sense, but I certainly push a lot of code that reasonably seems to work.
The reason it doesn't show up online is that I mostly write software for myself and for work, with the primary goal of making things better, not faster. More tooling, better infra, better logging, more prototyping, more experimentation, more exploration.
Here's my opensource work: https://github.com/orgs/go-go-golems/repositories . These are not just one-offs (although there's plenty of those in the vibes/ and go-go-labs/ repositories), but long-lived codebases / frameworks that are building upon each other and have gone through many many iterations.