Do you constantly want to be reading ai-generated content on this site? If so, why not just stay on chatgpt.com and ask it to generate what hackernews.com would look like today? It's adding to the discussion because I feel like the author broke a social contract by probably putting less effort into writing this than I did reading it.
> the author broke a social contract by probably putting less effort into writing this than I did reading it.
I seriously doubt that.
First, the author documented a substantial effort (testing, emails, references to documentation.)
Second, I also used AI to evaluate if this was written by AI, and my AI said it was not.
> Broken sentences that a model wouldn't produce. "The value is while you are on ChatGPT and tied to your ChatGPT account" is missing a word. "Loading that code, sends __obi to OpenAI" has a comma splitting subject from verb. "By the virtue of loading the tag the identifier is disclosed" is non-idiomatic. LLMs are fluent to a fault; these are the fingerprints of a fast human writer, possibly a non-native English speaker.
Third, I read it. Whilst it is not well written, it is succinct. It is novel. It has a few detailed references. It lacks many of the hallmarks of AI. It makes a number of novel yet falsifiable claims.
I challenge you to come up with a blog post with these qualities that can be created in under 15 minutes via AI.
While I commend you on actually using an AI tool to validate your assumption, rather than simply hurling AI slop accusations based purely on vibes, I do think you and your AI tool are likely incorrect.
> Second, I also used AI to evaluate if this was written by AI, and my AI said it was not.
every day I'm more and more reminded that the level of intelligence of the average human even on hn is so low that it makes sense why LLMs took over so hard. Asking an LLM like an oracle as if it could naturally distinguish between LLM writing and human writing.. it's a statistical likelihood next token predictor. LLMs can't even play chess without constantly making illegal moves. They are not intelligent.. and unfortunately, I have bad news for you: neither are you.
Having an LLM generate major parts of text that you (implicitly or explicitly) claim as yours is dishonest and plagiarism and should be brought to readers' attention, and it baffles me that many people just don't seem to mind or see anything problematic about it.
Love this. I use a similar "ralph-loop" approach that starts with an approved plan and then hand it off to a coordinator which does it across 2 sessions (build and review for simplicity), with each session getting its own model.
That's interesting. I've been trying to build something similar as a side project: Hermes agent + plugins (MCP, skills, and agents) + a Postgres DB for auditing and state. The idea is essentially to make all of that a black box and present a simple “work queue” to a desk assistant.
Good validation that this is indeed a space the frontier firms are thinking about along similar lines.
I see remote MCP servers as a great interface to consume api responses. The idea that you essentially make your apis easily available to agents to bring in relevant context is a powerful one.
When folks say MCP is dead, I don't get it. What other alternatives exist in place of MCP? Arbitrary code via curl/sdks to call a remote endpoint?
yes, but clis thus need self-service commands to provide guidance, and their responses need to be optimized for consumption by agents. in a sense, this is the same sort of context tax that MCP servers incur. so in my view cli and MCP are complementary tools; one is not strictly superior over the other.
> yes, but clis thus need self-service commands to provide guidance, and their responses need to be optimized for consumption by agents.
MCP vs Agent Skills:
MCPs once configured cost you tokens even when they are not used.
Unlike MCPs, skills use progressive disclosure. The AI agent does not load up the entire context, if the skill is not being used.
It is also one of the big reasons why Meta and Zuckerberg want to invest in AI. If AI companions are going to replace online friends, it makes total sense for Meta to invest in AI heavily.
The scenario NEVER comes up in the future as it was originally expected. You'll end up having to remove and refactor a lot of code. Abstractions are useful only used sparingly and when they don't account for handling something that doesn't even exist yet.
When doing the initial design start in the middle of the complexity to abstraction budget. If you have 100 “units of complexity” (lines of code, conditions, states, classes, use cases, whatever) try to find 10 subdivisions of 10 units each. Rarely, you’ll have a one-off. Sometimes, you’ll end up with more than 20 in a group. Mostly, you should have 5-20 groups of 5-20 units.
If you start there, you have room for your abstraction to bend before it becomes too brittle and you need to refactor.
Almost never is an interface worth it for 1 implementation, sometimes for 3, often for 5-20, sometimes for >20.
The trick is recognizing both a “unit of complexity” and how many “units” a given abstraction covers. And, of course, different units might be in tension and you have to make a judgement call. It’s not a silver bullet. Just a useful (for me at least) framing for thinking about how to manage complexity.
Even one use case may be enough e.g., if one class accepts another then a protocol (using Python parlance) SupportsSomething could be used
to decouple two classes, to carve out the exact boundary. The protocol may be used for creating a test double (a fake) too.
If you own the code base, refactor. It's true that, if you're offering a stable interface to users whose code you can't edit, you need to plan carefully for backward compatibility.
"We'll extract interfaces as and when we need them - and when we know what the requirements are we'll be more able to design interfaces that fit them. Extracting them now is premature, unless we really don't have any other feature work to be doing?"
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