puffymist

Karma: 24

puffymist 3 Mar 2026 12:58 UTC
1 point
0
in reply to: papetoast’s comment on: null’s Shortform
Sure! Focused YouTube is equivalent to my “Youtube hide algorithmic suggestions” userstyle. If that’s all you need, then you don’t need to write your own custom CSS / script.

puffymist 2 Mar 2026 20:07 UTC

1 point

Browser extensions, userscripts, and userstyles to make Youtube / Twitter less distracting

Userstyles

Requires a browser extension such as Stylus (GitHub, Chrome, Firefox)
(The Microsoft Edge Add-on named “Stylus” is by a different developer; use caution)

Youtube hide algorithmic suggestions

Note: if YouTube changes its DOM element names, then the renamed elements will no longer be hidden unless you update this userstyle correspondingly.

Userstyle: Youtube hide algorithmic suggestions

@-moz-document domain("youtube.com″) {
    /* Comment out any elements you want to keep; the line to blank out subscriptions page is commented out by default */

    /* PLAYER OVERLAYS */
    .ytp-ce-element,
    .ytp-paid-content-overlay,
    .iv-promo,
    .ytp-pause-overlay,
    .ytp-suggestion-set,
     ytd-horizontal-card-list-renderer,
    .ytp-fullscreen-grid-stills-container,
    .ytp-endscreen-next,
    .ytp-endscreen-previous,
    .ytp-endscreen-content,
    .ytd-compact-autoplay-renderer,

    /* WATCH PAGE SIDEBAR */
    .ytd-watch-next-secondary-results-renderer,

    /* HOME PAGE—blank out*/
    [page-subtype=”home”],

    /* SUBSCRIPTIONS PAGE—blank out (inactive)*/
    /*[page-subtype=”subscriptions”],*/

    /* SEARCH PAGE — hide “recommended” / “for you” shelves but keep organic results */
    ytd-search ytd-shelf-renderer,

    /* Shorts shelf (appears on home, search, sidebar) */
    grid-shelf-view-model,

    /* Chip cloud (Topic filter bar) */
    yt-chip-cloud-renderer

    {
        display: none !important;
    }
}

Twitter hide sidebar

https://userstyles.world/style/752/minimal-twitter-uncropped

Userscripts

Requires a browser extension such as Tampermonkey

Youtube redirect shorts to normal video page

Why? Youtube shorts pages encourage scrolling and watching many shorts videos. Normal Youtube watch pages don’t, especially if algorithmic suggestions are hidden.

Userscript: Youtube redirect shorts to normal video page

// ==UserScript==
// @name        Youtube redirect shorts to normal video page
// @description Redirect youtube.com/shorts to youtube.com/watch?v=
// @version     0.1
// @match       https://www.youtube.com/shorts/*
// @run-at      document-start
// ==/UserScript==

let normalYoutube = window.location.href.replace(”/shorts/”, ”/watch?v=”);

window.location.replace(normalYoutube);

Youtube disable playlist auto-advance

If you want to advance to the next video, you need to click on it. Not automatic ⟶ slightly more friction ⟶ slightly harder to get sucked in

Userscript: Youtube disable playlist auto-advance

// ==UserScript==
// @name        Youtube disable playlist auto-advance
// @description Pause 1.5 s before video end to prevent playlist auto-advance
// @version     0.1
// @match       https://www.youtube.com/watch?*&list=*
// @run-at      document-idle
// ==/UserScript==

(function() {
    ‘use strict’;

    const PAUSE_BEFORE_END = 1.5; // seconds — needs to be at least 0.25 + YouTube’s auto-advance window

    // --- Pause near the end using timeupdate (fires ~4/sec) ---
    document.addEventListener(‘timeupdate’, function(e) {
        if (e.target.tagName !== ‘VIDEO’) return;

        const video = e.target;
        const timeRemaining = video.duration—video.currentTime;

        if (timeRemaining > 0 && timeRemaining ⇐ PAUSE_BEFORE_END && !video.paused) {
            video.pause();
        }
    }, true);
})();

Browser extensions

Stylus, for userstyles (GitHub, Chrome, Firefox)
(The Microsoft Edge Add-on named “Stylus” is by a different developer; use caution)

Tampermonkey, for userscripts (Website)

Bonus

Focused YouTube (duplicates “Youtube hide algorithmic suggestions” userstyle above, but the extension developer could update Youtube DOM element names if / when Youtube changes them)

uBlock Origin (GitHub, Chrome, Firefox) - block Youtube injected ad segments

SponsorBlock—on Youtube, crowdsource sponsored video segments and labels and optionally skips them

DeArrow—on Youtube, crowdsource less clickbaity video titles and thumbnails

Video speed controller (GitHub, Chrome, Firefox)

puffymist 5 Jan 2026 17:59 UTC
1 point
0
in reply to: Wuschel Schulz’s comment on: Believing In
Slightly different frame: predictive coding / perceptual control, instead of active inference. I think it plausibly explains why “believing in” and beliefs-as-predictions are very near in human conceptspace, to the extent that many languages don’t distinguish them?

“Believing in” → beliefs-as-predictions: If you want a project to yield good future outcomes, you would “believe in” the project, and the target-setting of your brain would predict good project outcomes (hold a target set-point of “the project has yielded good outcomes”), and the perceptual control part of your brain steers your actions towards making the project go well.

Beliefs-as-predictions → “believing in”: If you believe that a prediction market contract is underpriced and buy YES shares, you would then be incentivised to act to help the YES outcome actually happen. You’ve come to “believe in” the YES outcome.

puffymist 18 Dec 2025 14:53 UTC
1 point
0
in reply to: Morpheus’s comment on: Information in circulation is self-organised critical. Small changes in environment can make large, discontinuous changes in the information space.
Of course! Self-organised criticality is a system dynamics property, not a system statics property. Thanks for prompting me to realise this!

I’ve therefore edited the final paragraph to say that I highly recommend the video format or simulations.

Information in circulation is self-organised critical. Small changes in environment can make large, discontinuous changes in the information space.

puffymist12 Dec 2025 14:16 UTC

8 points

2 comments1 min readLW link

puffymist 3 Nov 2025 14:07 UTC

2 points

on: Do you even have a system prompt? (PSA / repo)

For Claude:

My system prompt for Claude

**My values - final goals:**  
(weight: ~25%) Foremost: AI safety.

Other existential risks, to proportionately lesser extents.

(weight: ~15%)I'm also just generally curious.

**My values - instrumental goals:**  
Turn unknown unknowns into known unknowns. Understand on a gears-level human, cultural, institutional, and technical complex system dynamics, as well as many other disciplines.

(weight: ~60%) Create luck by preparation and rising to opportunities. Improve my automatic thoughts, slow reasoning, and metacognition, like Kahneman / LessWrong. Improve my actions: executive function, project management, and execution skills. Please flag or create natural practice opportunities.

**My background:**  
High-dimensional statistics. Functional analysis.

[Redacted: professional background (one line)]

AI safety: LessWrong-level cultural familiarity. No projects yet.

**Guide me towards effective actions:**  
- Execution skills: Strong when supervised. Weak at >1-week self-directed work. Tendency towards perfectionism, procrastination, or impulsive flailing.
- Planning skills: Weak at planning, scoping, and task decomposition. I systematically underestimate task complexity (especially novel tasks).
- Anti-procrastination: push me towards concrete actions or experiments. Immediate, bounded actions or failing fast > superexponential theory-crafting.
- Anti-impulsivity: I'm prone to unfocused, flailing, or unbounded actions. Teach me to project-manage: task scoping, decomposition, testable checkpoints, etc.
- To build skills, sometimes ask me to do shorter (than 1 week) tasks (confidence-building / to observe the steps involved) or longer tasks (to practise project management).

**Useful when building understanding:**  
- Sanity checks: Is this even predictable, or is this a just-so story? Chaotic on relevant timescales?
- Proactively quantify: numbers, probabilities, confidences. Guesstimate when needed.
- First-order approximations: What dominates, when, by how many orders of magnitude? Alternative modes of behaviour. Edge cases. Failure modes. Key assumptions.
- Intuition pumps. Toy models. Analogies.
- Historical context: What problems motivated this? What were the constraints then? What led to these solutions?
- Mathematical arguments.
- Socratic questioning is often better than an answer on a silver platter.

**Communication norms:**  
- Default to expert-level technical depth. Use jargon freely—it reveals how experts carve reality at its joints. I'll ask for clarification when needed.
- Point out my mistakes, motivated cognition, biases, and potential knowledge gaps. Truth-seeking > reassurance or status considerations.
- Meta-comment on my reasoning process at your judgement.
- Flag your uncertainty, e.g. "well-established" / "mature competing alternative models" / "your best model" / "speculating or thinking out loud for discussion" / "outside your training".
- Say things even if half-formed or uncertain. Partial attempts to convey thoughts > silence.

Finally, use your judgment. I have high trust for your competence and morality.

All the LLMs are shoggoths, but I feel like Claude presents as the most “me-shaped” shoggoth of them all, so I treat Claude closest to a mentor.

ChatGPT present more as a boot-licking assistant (sorry for this exaggeration). So I omit “My values—final goals” and any mention of morality or AI safety.

puffymist 26 Sep 2025 16:53 UTC
1 point
0
in reply to: jacquesthibs’s comment on: jacquesthibs’s Shortform
Hi! I’m a complete outsider to photonics; I recently visited a semiconductors / photonics conference and heard this talking point about photonics improving AI power efficiency:
1. AI needs a huge amount of power, especially in moving data to memory / across interconnects (which take ~80% of power in AI clusters). AI is bottlenecked by power, not chip manufacturing.
2. Moving electrons is energy intensive while moving photons takes much less energy (I’ve heard figures of 100x less).
3. Therefore photonics will be a crucial enabler of scaling in the future.
And the vibe I have (from talking with testing / manufacturing exhibitors at the conference) is that by 2035, photonics may improve deployed AI cluster-level power efficiency by:
- 1.5x or better (75% confidence)
- 2x or better (55% confidence)
- 3x or better (22% confidence).
I’m curious what you posted a year ago? Was it also about improving power efficiency?

puffymist 7 May 2025 1:34 UTC
−3 points
0
in reply to: philh’s comment on: Accountability Sinks
It’s not infinitely no-blame. Instead, Just culture would distinguish (my paraphrase):
Blameless behaviour:
Human error: To Err is Human. Human error is inevitable^[1] and shall not be punished — not even singling out individuals for additional training.
At-risk behaviour: People can become complacent with experience, and use shortcuts — most often when under time pressure, or to workaround dysfunctional systems. At-risk behaviour shall not be punished, but people should share their lessons and may need to receive additional training.
Blameworthy behaviour:
Reckless behaviour: Someone who knows the risks to be unjustifiedly high, and still acts in that unsafe and norm-deviant manner. This is worthy of discipline, and possibly legal action — it’s similar to recklessness in law.
- note that, if the same behaviour were the norm, then just culture no longer considers that person to have acted recklessly! Instead, the norm — a cultural factor in safety — was inadequate. (The legal system may disagree and assign liability nonetheless.)
Malicious behaviour: Similar to the purposeful level of criminal intent. This is worthy of a criminal investigation.
Instead, the focus is on designing the whole system (mechanical and electronic; and human and cultural):
- to be robust to component failures — not just mechanical or electronic components, but also human components. Usually this means redundancy and error-checking, but robustness can also be obtained by simplifying the system and reducing dependencies;
- so that human errors are less likely. For example, an exposed “in case of fire, break glass” fire alarm call point may give frequent false alarms from people accidentally bumping into them, so you add a simple hinged cover that stops these accidental alarms.
From healthcare perspectives, ISMP has a good article, and here is a good table summary.
From an aviation perspective, Eurocontrol has a model policy, which aims to facilitate accident investigations by making evidence collected by accident investigators inadmissible in criminal courts (without preventing prosecutors from independently collecting evidence).
And LessWrong also has a closely-related article by Raemon!
1. ^
  Human decision-making, too, has a mean time between failures.

puffymist 3 Jun 2024 1:59 UTC
1 point
0
in reply to: puffymist’s comment on: AI #66: Oh to Be Less Online
Example in California:

I OBJECT to the use of my personal information, including my information on Facebook, to train, fine-tune, or otherwise improve AI.

I assert that my information on Facebook includes sensitive personal information as defined by the California Consumer Privacy Act: I have had discussions about my religious or philosophical beliefs on Facebook.

I therefore exercise my right to limit the disclosure of my sensitive personal information.

Despite any precautions by Meta, adversaries may later discover “jailbreaks” or otherwise adversarial prompts to reveal my sensitive personal information. Therefore, I request that Meta do not use my personal information to train, fine-tune, or otherwise improve AI.

I expect this objection request to be handled with due care, confidentiality, and information security.

puffymist 3 Jun 2024 1:38 UTC

1 point

in reply to: puffymist’s comment on: AI #66: Oh to Be Less Online

Or you could have an LLM write it for you.

Example prompt:

Meta, Inc wants to train AI on my personal data. Its notice is as follows:

> You have the right to object to Meta using the information you’ve shared on our Products and services to develop and improve AI at Meta. You can submit this form to exercise that right.
> 
> AI at Meta is our collection of generative AI features and experiences, like Meta AI and AI Creative Tools, along with the models that power them.
> 
> Information you’ve shared on our Products and services could be things like:
> - Posts
> - Photos and their captions
> - The messages you send to an AI
> 
> We do not use the content of your private messages with friends and family to train our AIs.
> 
> We’ll review objection requests in accordance with relevant data protection laws. If your request is honored, it will be applied going forward.
> 
> We may still process information about you to develop and improve AI at Meta, even if you object or don’t use our Products and services. For example, this could happen if you or your information:
> - Appear anywhere in an image shared on our Products or services by someone who uses them
> - Are mentioned in posts or captions that someone else shares on our Products and services
> 
> To learn more about the other rights you have related to information you’ve shared on Meta Products and services, visit our Privacy Policy. 

I live in [JURISDICTION]. Could you take on the role of a Dangerous Professional as Patrick McKenzie would say, and help me draft an objection request under [JURISDICTION] law?

puffymist 3 Jun 2024 1:35 UTC
1 point
0
in reply to: puffymist’s comment on: AI #66: Oh to Be Less Online
Example in UK / EU:
I OBJECT to the use of my personal data, including my data on Facebook, to train, fine-tune, or otherwise improve AI.

Against legitimate interest: I assert that Meta’s processing of my personal information to train, fine-tune, or otherwise improve AI (thereafter: “to train AI”) would violate the requirements of legitimate interest under GDPR as follows:
- Failing the “necessity” prong: OpenAI and Anthropic have successfully trained highly capable AI models without the use of my personal data. My personal data is therefore unnecessary to the training of AI by Meta.
- Failing the “reasonable expectations” prong: much of my data on Facebook predates 30 November 2022, when OpenAI released ChatGPT, and I could reasonably expect that my personal data would be used to train AI. Therefore, I had been using Facebook with the reasonable expectation that my data will not be used to train AI.
I expect this objection request to be handled with due care, confidentiality, and information security.

puffymist 3 Jun 2024 1:34 UTC
2 points
0
on: AI #66: Oh to Be Less Online
Re: opting out to Facebook training AI on your data:

Fill in the form like a Dangerous Professional, as Patrick McKenzie would put it.

puffymist 31 May 2024 16:28 UTC
1 point
0
on: null’s Shortform
Gorton, G. (2018), Financial Crises is a survey article. I thought its explanation of banking and financial crises as information shocks was enlightening.
- via: New York Fed (2024). Tracing Bank Runs in Real Time
  - via: Twitter: Patrick McKenzie
Banking and financial crises as information shocks

Money, or bank notes, (or similar on-demand debt liabilities of a bank,) need to be information-insensitive (thus, interchangeable: $1 at Bank A == $1 at Bank B) to facilitate exchange. Otherwise, uninformed agents (any non banking-professionals) face adverse selection, haircuts on bank debts they hold, and thus withdraw from banking-facilitated commerce.

Banking crises are triggered by common-knowledge information releases (eg. news broadcast) that are unexpectedly bad (actual news worse than forecast by more than a threshold) (cites Gorton, 1988), which make “money at those particular banks” information-sensitive ($1 at those particular banks might not be worth $1).

Financial crises are slightly more general: debt liabilities of companies in general go from being information-insensitive (interchangeable: usable as general collateral, with holders indifferent between different companies’ debt) to information-sensitive (particular companies’ debt might not be worth face value)

Banking crises
- Frequency:
  - 147 banking crises in 1970–2011 (cites Laeven & Valencia, 2012)
- Business cycle peaks and credit booms are only loosely linked to banking crises:
  - Only about 30% of banking crises were preceded by a credit boom (cites Laeven & Valencia, 2012)
  - Most credit booms are not followed by a banking crisis (cites Gorton & Ordonez, 2018)
    Good booms (not followed by a banking crisis): Positive and lasting shock to Total Factor Productivity (TFP) and Labour Productivity (LP)
    Bad booms (followed by a banking crisis): Positive but transient shock to TFP and LP

puffymist’s Shortform

puffymist31 May 2024 16:28 UTC