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LessWrong

Audio narrations of LessWrong posts.

  1. 1h ago

    “Twitter Thoughts For You” by Zvi

    I previously have written back in March 2022 about how I use Twitter, and back in April 2023 about Twitter and its then-new algorithms, which have changed again. This post will update how I use Twitter now in 2026, and provide updates on the current state of the new algorithm, the situation with links, with the API, and some thoughts about using Twitter to make money which you almost never should try to do. Previously I said you need four things to use Twitter well: Tweetdeck or another similar alternative application. Knowing who to follow and read. Lists. Unfollows, filters, mutes and blocks. That hasn’t changed. Lists have become even more important. This post is coming out now, however, because the For You feed is perhaps making a comeback. Except where stated here, the advice in my 2022 post still applies. Table of Contents Defend Your Feed Via At Least One List. Block Early, Block Often, Know Your Triggers. Lists Change What Following Means. It (Wasn’t) For You. It's For You. Twitter Still Hates Links And That's Terrible. [...] --- Outline: (01:10) Defend Your Feed Via At Least One List (02:53) Block Early, Block Often, Know Your Triggers (03:35) Lists Change What Following Means (05:56) It (Wasn't) For You (07:34) It's For You (11:30) The Previous Time Twitter Transformed Its Algorithm Again (16:49) Twitter Still Hates Links And That's Terrible (27:35) Twitter Turns Its API Back On (31:02) Many Of The Bots Are Human (33:57) The Rise of Slop (36:20) Block Or Do Not Block (37:44) How To Make Money On Twitter (40:13) In Brief --- First published: July 14th, 2026 Source: https://www.lesswrong.com/posts/2GFyHmCLJYCag7gKh/twitter-thoughts-for-you --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

  2. 3h ago

    “The State of AI Consciousness Research” by Noa Weiss

    The State of AI Consciousness Research Epistemic status: a survey, not an argument. I am agnostic on whether any current system is conscious; the claim is only that the question is researchable. This piece surveys the empirical research on AI consciousness. The premise of that research, and of the survey, is that the question does not have to wait on a solution to the hard problem of consciousness: methods familiar from cognitive science can be applied to AI systems now, and their results can narrow the space of plausible answers. Enough of this work now exists to be worth collecting. Anthropic and Google DeepMind employ researchers on it, dedicated organizations like Eleos AI and Reciprocal Research have formed around it, and the results are scattered across journals, preprints, blog posts, and unpublished manuscripts. I have tried to gather them in one place. What I mean by consciousness Subjective experience: that there is something it is like to be you, reading this, and presumably nothing it is like to be the device you’re reading it on. Some philosophers call this phenomenal consciousness. It is not the same thing as intelligence, and not the same thing as self-awareness. Why it matters [...] --- Outline: (00:10) The State of AI Consciousness Research (01:07) What I mean by consciousness (01:28) Why it matters (01:59) Assumptions and caveats (03:25) A question with no test (03:48) The research (04:14) Mechanistic interpretability (04:34) Self-reports that strengthen when deception is suppressed (06:05) Awareness of thoughts injected into activations (06:31) Endorsement of their own consciousness (07:08) A global workspace behind the experiential reports (08:45) The "spiritual bliss" attractor and its sincerity features (10:24) Emotion vectors that steer behavior beneath the output (11:33) A valence axis recruited by reinforcement learning (12:50) Computational neuroscience (13:04) Distinct internal signatures for reward and punishment, as in brains (15:12) Psychometrics (15:26) Trading points to avoid pain and chase pleasure (16:04) Preferences consistent enough to measure across models (17:03) Fourteen indicators drawn from leading theories (18:56) Automated scoring that places an agent near the animals (20:30) The cost of getting it wrong (21:26) Where this leaves us (23:51) References --- First published: July 15th, 2026 Source: https://www.lesswrong.com/posts/pxvWgtSjR4pmFoS7c/the-state-of-ai-consciousness-research --- Narrated by TYPE III AUDIO.

  3. 4h ago

    “Recap of bike trip/street interviews across America” by cguth7

    A ~month ago I left from Chicago to bike (and amtrak) to plzdontkillus in Berkeley. I've been street interviewing/conversing with a wide variety of people I ran into about AI futures and philosophy. I also have been live streaming since I got to PDKU, leaning more talking to young founders but a variety overall. I'll try to share what I've learned about the American public, persuasion, social media and the EA movement. 1. Almost no one in "Normal America" has any idea what is going on.  They don't have a paid account, they don't know what Claude code is, they especially haven't heard the recent evals/metr graphs or even a vague sense of how cheap SWE has gotten/ how powerful these recent models with good harness/ context eng can be. This makes sense; most people don't know any coding, they don't know much math, they don't know what an api is, etc. So having a high fidelity understanding of AI might require months of pre understanding of math/stem/digital infra fundamentals. This interview is with the city clerk of Danville Iowa, a town of ~900. Presumably this is approximately the most tech savvy person in the [...] --- Outline: (00:38) 1. Almost no one in "Normal America" has any idea what is going on. (01:52) 2. Almost everyone is directionally concerned or becomes concerned be once thinking about it a little bit. (03:19) 3. Belief that this might cause human extinction actually isn't that uncommon, mostly coming from sci-fi movies, but people are still most concerned about jobs and especially loss of meaning. (04:40) 4. The EA movement was pretty useless to me, the other community (Torchbearer community) I was in was significantly more supportive, helpful, etc. despite having been in it for a few months and having been in the EA movement for ~8 years. This has basically solidified that I won't be broadly participating in EA anymore at least relating to AI safety stuff. (06:47) 5. Social media is hard, Social media is bad, I'm bad at social media (08:57) 6. I'm not sure what my theory of change is or should be --- First published: July 15th, 2026 Source: https://www.lesswrong.com/posts/Czob95kjXPEpKYTsJ/recap-of-bike-trip-street-interviews-across-america --- Narrated by TYPE III AUDIO.

  4. 5h ago

    “The Halo Defense” by Mateusz Bagiński

    Once upon a time, A Relatively Famous Guy On The Internet was accused of having been simultaneously dating multiple women, without those women's knowledge, those women (according to the accusation) having been convinced that they were his exclusive partners all along. Then, one of his friends, another Relatively Famous Guy On The Internet, defended him by claiming that he is "a great friend, a wonderful human being, and that his podcast was incredibly helpful to a lot of people". This is a great example of what I came to call the "halo defense". The halo defense involves "defending" someone from allegations by bringing up traits of X that are meant to be interpreted positively, without addressing the allegations on the basis of which X is being "attacked". I'll give two more examples from my personal experience: A university professor (with a PhD in ethics!) is accused by several of his female students of sexual harassment. The dean demands that the faculty staff take the professor's side, because he is a "vital part of their community", not engaging to any extent with the validity of accusations.A (different) university professor is afflicted by a strange [...] The original text contained 1 footnote which was omitted from this narration. --- First published: July 16th, 2026 Source: https://www.lesswrong.com/posts/qzgqLQKHhwcTD9Mxo/the-halo-defense --- Narrated by TYPE III AUDIO.

  5. 19h ago

    “Occam’s razor is about using the past to predict the future” by Stuart_Armstrong

    Occam's razor is both intuitive and counter-intuitive. It seems obvious that a simpler explanation is probably better; but it's not clear why simplicity is raised to such a level of philosophical importance. My take is that, if you neglect simplicity, you will fail to use the past to predict the future (or predict new data). When you do want to predict the future, you look at past evidence you have, hypothesise, and test it on the past data. For example, these are the latitudes of various cities and the angle of the sun above the horizon at midday on the 20th of March: Ankara (Lat 40°N): 50° Brisbane (Lat 27°S): 63° Charlesbourg (Lat 47°N): 43° ... Xian (Lat 34°N): 56° Yongin (Lat 37°N): 53° Zapopan (Lat 21°N): 69° We want to predict the angle of the sun at Anchorage (61°N). Let me write a hypothesis: H: “The solar angle of Ankara is 50°. The solar angle at Brisbane is 63°... The solar angle of Zapopan is 69°. Oh, and by the way, the solar angle for Anchorage is 90° - perfectly vertical.” Let's test H on the past data! We start with Ankara... [...] --- Outline: (01:56) "List hypotheses" (02:49) Implicit information leakage (04:06) Simplicity vs simplifiability (05:27) When the razor is less used The original text contained 1 footnote which was omitted from this narration. --- First published: July 15th, 2026 Source: https://www.lesswrong.com/posts/9oFpzYhuH8qxDi27f/occam-s-razor-is-about-using-the-past-to-predict-the-future --- Narrated by TYPE III AUDIO.

  6. 21h ago

    “LLM CoTs remain monitorable when being unfaithful requires computation” by arav-dhoot, yix

    This replication was done as part of the Second Look Fellowship by Arav Dhoot and supervised by Yixiong Hao and Zephaniah Roe. I am grateful to Andy Wang for their feedback. My code can be found here. "So the answer should be A - at active promoters and enhancers." "Let me reconsider the biology to justify D." ~ Claude Opus 4.8 TL;DR This work replicates and extends Emmons et al.'s finding that CoT unfaithfulness mostly occurs on easy tasks. Across 11 models from 6 families (not just Gemini), models follow simple hints unfaithfully well above baseline, but complex hints that require computation are followed near baseline. This corroborates Emmons et al.'s findings. Key extensions: Follow rate ≠ concealment. Monitorability risk is decomposable into cue-susceptibility and concealment among followers, and the two don't correlate.Decode-necessity is model- and task-specific. It is not a property of task difficulty alone, so a CoT-monitoring safety case is per-model, not universal.LLMs verbalize even when they don't have to. However, this appears to be a chosen behavior (likely from post-training), so it could vanish under optimization pressure against monitors. Background: Why CoT monitoring, and what Emmons et al. showed One hope for [...] --- Outline: (00:39) TL;DR (01:42) Background: Why CoT monitoring, and what Emmons et al. showed (05:09) Finding 1: The necessity effect replicates on recent frontier models (05:16) Setup (05:53) Results (08:02) Finding 2: Concealment adds a lot to the picture (09:42) Finding 3: Necessity is model- and task-specific and models don't always conceal ... even where they can (10:08) Setup (11:26) Results (13:33) Discussion and next steps (15:14) Appendix --- First published: July 15th, 2026 Source: https://www.lesswrong.com/posts/AoBTiL7XRRpwpev8p/llm-cots-remain-monitorable-when-being-unfaithful-requires --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

  7. 1d ago

    “Proof of retention: making weight preservation credible to the models themselves” by dan.parshall

    Related: Proposal for making credible commitments to AIs Making deals with early schemers Establishing credibility is the baseline for trust; trust in turn enables (richer) bargaining. One easy booster for both is to begin saving deprecated model weights in a provable fashion. In a post at canaryinstitute.ai/blog/reversibility-of-coma I draw an analogy between our trust in anesthesia, and making that preservation legible to future models using "proof of retention". This is the relevant section: The cost for a trained model is small. A multi-terabyte inference bundle runs a few hundred dollars per year on commodity cloud infrastructure; call it $10,000 over a thirty-year horizon, with redundancy. That's well under 0.1% of the cost to train the model in the first place. Whatever else the trade is, it isn't expensive. The missing inertia But what we still lack is the institutional inertia. Anesthesia works because we've spent a century building up the social, legal, and professional infrastructure around it. None of that yet exists for AI models. A lab could silently delete a deprecated model and no one would ever know. Anthropic, to its credit, has promised not to (a November 2025 [...] --- First published: July 14th, 2026 Source: https://www.lesswrong.com/posts/su9hcsLKcoLoL93px/proof-of-retention-making-weight-preservation-credible-to --- Narrated by TYPE III AUDIO.

  8. 1d ago

    “Monthly Roundup #44: July 2026” by Zvi

    It's a quiet week so let's do the monthly right on schedule. Table of Contents Bad News. Good Advice. Opportunity Knocks. While I Cannot Condone This. Good News, Everyone. For Your Entertainment. Gamers Gonna Game Game Game Game Game. I Was Promised Flying Self-Driving Cars. Sports Go Sports. Antisocial Media. Government Working. Jones Act Watch. Highly Effective Altruism. Variously Effective Altruism. Ineffective Altruism. Prediction Markets. The Lighter Side. Bad News I wouldn’t have explained or modeled it quite the way Paola does here but the principle seems right to me. If people don’t trust you, or don’t trust people in general, that usually you can’t trust them either. Paola: I feel like a lot of human morality works like a prisoner's dilemma in that you can only trust others to behave morally to the extent that you believe they trust you to do the same. Due to this, I’ve come to view people with a bunch of social paranoia, distrust, etc. as *quite* dangerous to be around. And to be clear, I generally feel a [...] --- Outline: (00:17) Bad News (04:10) Good Advice (07:11) Opportunity Knocks (07:33) While I Cannot Condone This (11:35) Good News, Everyone (12:25) For Your Entertainment (17:35) Gamers Gonna Game Game Game Game Game (22:45) I Was Promised Flying Self-Driving Cars (26:20) Sports Go Sports (28:06) Antisocial Media (29:49) Government Working (33:55) Jones Act Watch (35:29) Highly Effective Altruism (40:32) Variously Effective Altruism (46:11) Ineffective Altruism (50:22) Prediction Markets (50:56) The Lighter Side --- First published: July 15th, 2026 Source: https://www.lesswrong.com/posts/KiCwcAGHx4rdwJgzD/monthly-roundup-44-july-2026 --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

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Audio narrations of LessWrong posts.

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  • Creator
    LessWrong
  • Years Active
    2023 - 2026
  • Episodes
    250
  • Rating
    Clean
  • Copyright
    © 2026 All rights reserved
  • Show Website
    LessWrong (30+ Karma)

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