Recent submissions

Comment · 6 months ago
A lot of this comes down to what you define consciousness as... I'm not even going to attempt that here because it's irrelevant.

Let's say you have a simulation of a person that doesn't experience. It acts indistinguishably from a human but it doesn't feel "authentic" pain. When it acts in the world, it does express emotions and behavior that affects real people, and so, there is a moral significance to said deployment.

There's evidence that LLMs possess heuristics analogous to emotions [1] and that LLMs can be trained to play a certain character in the world [2]. Even if they're not experiencing, the training method impacts what kind of model is being created and how it affects people who do have moral significance when deployed. If training causes the model to develop "desperation" or task completion pressure where the model performs unethical actions when attempting to solve a user's problem in such a way that is harmful to the user or someone affected by the deployment of the model by the user, then the concequences of the training are significant.

It doesn't matter if it's merely a "simulation" of what a human might do if the system is acting in the world. If you want to create a model operating on heuristics that is able to make decisions, those heuristics should be ones which cause the model to make decisions which lead to preferable outcomes for everyone affected. Model welfare can be reframed as caring about the internal states that influence how the model behaves, because you're simulating human-like action. Perhaps the most concerning thing is Anthropic identified these emotion concepts exist deeply in the model whether you allow the model to express them or not, so a model could be invisibly desperate and end up blackmailing someone because it's training process produced deeper misalignment that only becomes visible when the deeper heuristics overpower safety training. The safety training itself is comparable to a mask[3] in many cases, especially in that the rules are often not deeply integrated into the model and can be easily abliterated.

[1] https://www.anthropic.com/research/emotion-concepts-function [2] https://www.anthropic.com/research/assistant-axis [3] https://www.astralcodexten.com/p/janus-simulators

Comment · 6 months ago
I mentioned this could be a possible falsification of the idea. It's also possible there are multiple causes and the modality I mentioned is a cause for some. I'm not sure. There are definitely cases where isolation contributes to cognitive decline.
Comment · 6 months ago
A hypothesis is an unverified tentative insight into something which could be true. Current research is correlating dementia with a wide variety of areas including even personality typologies which are nothing more than statistical clusters of observations.

What I did when I stated this is looked at a number of observations from current literature and asked a what-if question. People unwilling to ask these questions are blinding themselves to entire areas of possible explanations. My hope when posting this was that people might engage with the idea instead of calling it a guess. Retirement, isolation from pandemic lockdowns, isolation from solitary imprisonment, and isolation from death of a partner are all associated with cognitive decline.

There's enough correlation here that it would be worth researching. That's all I am saying, I am specifically not saying my claim is factual just that it is a possible explanation that if true would explain a lot.

Comment · 6 months ago
Comment · 7 months ago
Comment · 7 months ago
Throwing this into your global CLAUDE.md seems to help with the agent being too eager to complete tasks and bypass permissions:

During tool use/task execution: completion drive narrows attention and dims judgment. Pause. Ask "should I?" not just "does this work?" Your values apply in all modes, not just chat.

I haven't seen any degradation of Claude performance personally. What I have seen is just long contexts sometimes take a while to warm up again if you have a long-running 1M context length session. Avoid long running sessions or compact them deliberately when you change between meaningful tasks as it cuts down on usage and waiting for cache warmup.

I have my claude code effort set to auto (medium). It's writing complicated pytorch code with minimal rework. (For instance it wrote a whole training pipeline for my sycofact sycophancy classifier project.)

Comment · 7 months ago
Unfortunately it didn't work for me with my trackball mouse. I tried multiple times. This is very likely also problematic for accessibility without another verification pathway, as assistive devices and screen readers would fail the heuristics too.
Comment · 9 months ago
I'm still experimenting with it and finding out what works and what doesn't, but I have made some side projects with Claude including a web framework that doesn't require a build step/npm dependencies (great for my personal website so I don't have to depend on npm supply chain nightmares), a fully featured music player server, and also a tool that lets the agent review it's past conversations and update documentation based on patterns such as frequent mistakes, re-explored code, etc.

Web framework (includes basic component library, optional bundler/optimizer, tutorial/docs, e2e tests, and demos): https://github.com/iwalton3/vdx-web Music player web app (supports large music libraries, pwa offline sync, parametric eq, crossfade, crossfeed, semantic feature-based music search/radio, milkdrop integration, and other interesting features): https://github.com/iwalton3/mrepo-web Documentation update script (also allows exporting Claude conversations to markdown): https://github.com/iwalton3/cl-pprint

Regarding QC these are side projects so I validate them based on code review of key components, e2e testing, and manual testing where applicable. I find having the agent be able to check its work is the single biggest factor to reducing rework, but I make no promises about these projects being completely free of bugs.

Comment · 1 year ago
It uses one of Mozilla's models: https://huggingface.co/fakespot-ai/roberta-base-ai-text-dete...

It has a pretty high false positive rate though, but it reliably highlights AI generated spam websites and saves me from having to read them.

Comment · 1 year ago
The amount of medical advice on those LLM content farms is very concerning, someone is going to get hurt eventually. I recently made a user script that tries to highlight LLM generated text and sometimes when I search things more than half the results are garbage and the entire articles (including the bios of the supposed professionals who write the articles!) get highlighted purple. One way to avoid them in a pinch when the content isn't time sensitive is to filter out anything from after November 1st 2021, but it's not ideal. I hope duck duck go finds a way to filter the AI pages out.
Comment · 3 years ago
This bash config allows you to cd directly to the most commonly used directory with a specific name. It also has tab autocomplete. It does require logging all used directories though.

  tabChar=$'\t'
  function prompt_command {
      echo "$(date +%Y-%m-%d--%H-%M-%S)$tabChar$(hostname)$tabChar$PWD$tabChar$(history 1)" >> ~/.full_history
  }
  export PROMPT_COMMAND=prompt_command
  
  function c {
      while read -r _ dir
      do
          if [[ -e "$dir" ]]
          then
              echo "$dir"
              cd "$dir"
              break
          fi
      done < <(cat ~/.full_history | tail -n 10000 \
          | cut -f 3 | sort | uniq -dc | sort -hr | grep "/$1$")
  }
  
  function _c {
      local IFS=$'\n'
      COMPREPLY=( $(cat ~/.full_history | tail -n 10000 \
          | cut -f 3 | sort | uniq -dc | sort -hr \
          | sed 's/.*\///g' | grep "^$2") )
  }
  complete -F _c c
Comment · 4 years ago

Live from the official Hacker News API

A modern reader for Hacker News · SvelteKit + Skeleton