The Case for a New HN Guideline on AI-Generated Content
This is a timely discussion. While AI is an invaluable tool, the issue isn't using AI—it's using it to replace genuine engagement, leading to "low-signal" contributions.
The Problem with Unfiltered AI Replies
Dilution of Human Insight: HN's core value is the unique, experienced human perspective. Unanalyzed LLM-dumps replace original thought with aggregated, generic consensus.
Reading Fatigue & Bloat: Long, copy-pasted blocks of LLM text break the flow of discussion and make comments less scannable, forcing users to sift through machine-generated prose to find human analysis.
Lack of Authority/Verification: A comment that just says "$AI said X" is essentially an anonymous opinion. It lacks the critical filter, context, and experience of the human poster, making it less trustworthy, especially given LLM hallucination risk.
The Value of AI as a Tool
Quick Context/Summary: LLMs can quickly provide neutral, accurate definitions, historical context, or a list of arguments, saving users a separate search.
Supporting Evidence: When used properly, AI output can be supporting "data" for a human's core argument or analysis.
A Proposed Middle Ground Guideline
Instead of an outright ban, which punishes useful use cases, a new guideline should focus on human value-add and presentation.
The spirit of the guideline should be: If you use an LLM, your contribution must be more than the LLM's output.
Mandatory Analysis: The commenter must add their own critical analysis, personal experience, or counter-argument that contextualizes, critiques, or supports the AI's summary.
Clear Attribution and Formatting: All LLM-generated text must be clearly attributed (e.g., "I asked ChatGPT-4...") and visually separated (e.g., using a > blockquote) to maintain scannability.
Curation over Dumping: Encourage summarizing or excerpting the most relevant parts of the AI output, rather than pasting a lengthy, unedited wall of text.
Ultimately, the community downvotes already function to filter low-effort posts, but a clear guideline would efficiently communicate the shared norm: AI is a tool for the human conversation, not a replacement for it.
Is the main value to the expression debugger that it makes explicit the order of evaluation, or the specific intermediate values? (or is it pretty much both that provide the value?)
It makes explicit stuff like operator precedence, but it's especially helpful when you have to explain function calls and recursion.
For the latter specifically, Thonny has a mode where for every call stack frame, it opens up a separate child editor window that displays the code of the function in that frame, highlighting the current line. When you step through, the visual substitution of expression results happens in the window for the frame where it is happening. Thus, when you have a chain of recursive calls, you can see all the substitutions that it took to get there.
> I have this implementation of the uxn virtual machine that has 32 instructions, how can we add a few more instrucions do implement memory pagination? (code here)
To implement memory pagination, we can add a few instructions to the existing set of 32 instructions in the uxn virtual machine. These instructions could include:
1. PAGE_IN - This instruction would be used to load a specific page of memory from the underlying storage device into the virtual machine's RAM.
2. PAGE_OUT - This instruction would be used to save a specific page of memory from the virtual machine's RAM to the underlying storage device.
3. SET_PAGE - This instruction would be used to set the current page of memory that is being accessed by the virtual machine.
4. GET_PAGE - This instruction would be used to retrieve the current page of memory that is being accessed by the virtual machine.
These instructions could be implemented as follows:
#define PAGE_IN(x) { u->page = (x); u->dev[0x0f] = 1; }
#define PAGE_OUT(x) { u->dev[0x0f] = 1; }
#define SET_PAGE(x) { u->page = (x); }
#define GET_PAGE(x) { x = u->page; }
int
uxn_eval(Uxn *u, Uint16 pc)
{
...
switch(instr & 0x1f) {
...
case 0x1a: /* PAGE_IN */ PEEK(a, pc) PAGE_IN(a) pc += 1 + bs; break;
case 0x1b: /* PAGE_OUT */ PEEK(a, pc) PAGE_OUT(a) pc += 1 + bs; break;
case 0x1c: /* SET_PAGE */ PEEK(a, pc) SET_PAGE(a) pc += 1 + bs; break;
case 0x1d: /* GET_PAGE */ GET_PAGE(a) PUSH(src, a) break;
...
}
...
Not only these opcodes are already assigned, this would be a very much overkill implementation of this functionality by wasting opcode space, whereas a simple device port would do the trick. It's telling that everyone's first reaction to Uxn is to write a compiler for it, or adding extra memory - it's more about finding fun projects to do that fits within such a restricted space.
The Case for a New HN Guideline on AI-Generated Content
This is a timely discussion. While AI is an invaluable tool, the issue isn't using AI—it's using it to replace genuine engagement, leading to "low-signal" contributions. The Problem with Unfiltered AI Replies
Instead of an outright ban, which punishes useful use cases, a new guideline should focus on human value-add and presentation.The spirit of the guideline should be: If you use an LLM, your contribution must be more than the LLM's output.
Ultimately, the community downvotes already function to filter low-effort posts, but a clear guideline would efficiently communicate the shared norm: AI is a tool for the human conversation, not a replacement for it.