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Audio narrations of LessWrong posts.

  1. 48 MIN AGO

    “Claude Opus 4.6 Escalates Things Quickly” by Zvi

    Life comes at you increasingly fast. Two months after Claude Opus 4.5 we get a substantial upgrade in Claude Opus 4.6. The same day, we got GPT-5.3-Codex. That used to be something we’d call remarkably fast. It's probably the new normal, until things get even faster than that. Welcome to recursive self-improvement. Before those releases, I was using Claude Opus 4.5 and Claude Code for essentially everything interesting, and only using GPT-5.2 and Gemini to fill in the gaps or for narrow specific uses. GPT-5.3-Codex is restricted to Codex, so this means that for other purposes Anthropic and Claude have only extended the lead. This is the first time in a while that a model got upgraded while it was still my clear daily driver. Claude also pulled out several other advances to their ecosystem, including fast mode, and expanding Cowork to Windows, while OpenAI gave us an app for Codex. For fully agentic coding, GPT-5.3-Codex and Claude Opus 4.6 both look like substantial upgrades. Both sides claim they’re better, as you would expect. If you’re serious about your coding and have hard problems, you should try out both, and see what combination works [...] --- Outline: (01:55) On Your Marks (17:35) Official Pitches (17:56) It Compiles (21:42) It Exploits (22:45) It Lets You Catch Them All (23:16) It Does Not Get Eaten By A Grue (24:10) It Is Overeager (25:24) It Builds Things (27:58) Pro Mode (28:24) Reactions (28:36) Positive Reactions (42:12) Negative Reactions (50:40) Personality Changes (56:28) On Writing (59:11) They Banned Prefilling (01:00:27) A Note On System Cards In General (01:01:34) Listen All Yall Its Sabotage (01:05:00) The Codex of Competition (01:06:22) The Niche of Gemini (01:07:55) Choose Your Fighter (01:12:17) Accelerando --- First published: February 11th, 2026 Source: https://www.lesswrong.com/posts/5JNjHNn3DyxaGbv8B/claude-opus-4-6-escalates-things-quickly --- 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.

    1h 14m

  2. 5 HR AGO

    “Research note: A simpler AI timelines model predicts 99% AI R&D automation in ~2032” by Thomas Kwa

    Audio note: this article contains 31 uses of latex notation, so the narration may be difficult to follow. There's a link to the original text in the episode description. In this post, I describe a simple model for forecasting when AI will automate AI development. It is based on the AI Futures model, but more understandable and robust, and has deliberately conservative assumptions. At current rates of compute growth and algorithmic progress, this model's median prediction is >99% automation of AI R&D in late 2032. Most simulations result in a 1000x to 10,000,000x increase in AI efficiency and 300x-3000x research output by 2035. I therefore suspect that existing trends in compute growth and automation will still produce extremely powerful AI on "medium" timelines, even if the full coding automation and superhuman research taste that drive the AIFM's "fast" timelines (superintelligence by ~mid-2031) don't happen. Why make this? The AI Futures Model (AIFM) has 33 parameters; this has 8. I previously summarized the AIFM on LessWrong and found it to be very complex. Its philosophy is to model AI takeoff in great detail, which I find admirable and somewhat necessary given the inherent complexity in the [...] --- Outline: (01:22) Why make this? (02:23) Scope and limitations (04:08) The model (07:07) Parameter values (08:42) Graphs (11:00) Observations (13:13) Discussion (14:39) More on modeling choices (14:43) List of differences from the AIFM (17:21) How could we better estimate the parameters? (18:16) Why is automation logistic? (18:43) Why are labor and compute Cobb-Douglas? (19:49) Why is there no substitutability between tasks? --- First published: February 12th, 2026 Source: https://www.lesswrong.com/posts/uy6B5rEPvcwi55cBK/research-note-a-simpler-ai-timelines-model-predicts-99-ai-r --- 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.

    20 min

  3. 10 HR AGO

    “Distinguish between inference scaling and “larger tasks use more compute”” by ryan_greenblatt

    As many have observed, since reasoning models first came out, the amount of compute LLMs use to complete tasks has increased greatly. This trend is often called inference scaling and there is an open question of how much of recent AI progress is driven by inference scaling versus by other capability improvements. Whether inference compute is driving most recent AI progress matters because you can only scale up inference so far before costs are too high for AI to be useful (while training compute can be amortized over usage). However, it's important to distinguish between two reasons inference cost is going up: LLMs are completing larger tasks that would have taken a human longer (and thus would have cost more to get a human to complete) LLMs are using more compute as a fraction of the human cost for a given task To understand this, it's helpful to think about the Pareto frontier of budget versus time-horizon. I'll denominate this in 50% reliability time-horizon. [1] Here is some fake data to illustrate what I expect this roughly looks like for recent progress: [2] For the notion of time horizon I'm using [...] The original text contained 6 footnotes which were omitted from this narration. --- First published: February 11th, 2026 Source: https://www.lesswrong.com/posts/rRbDNQLfihiHbXytf/distinguish-between-inference-scaling-and-larger-tasks-use --- 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.

    5 min

  4. 11 HR AGO

    “Monitor Jailbreaking: Evading Chain-of-Thought Monitoring Without Encoded Reasoning” by Wuschel Schulz

    A key concern about chain-of-thought monitoring is that optimization pressure on the CoT during RL could drive models toward encoded reasoning, where models reason in ways that are not readable or that look like innocuous text (steganography). If a model is penalized when a monitor catches unwanted reasoning, RL implicitly selects for whatever lets the model reason without being caught. Our original goal was to elicit encoded reasoning so we could develop defenses against it. We constructed an RL environment where encoded reasoning is the highest-reward strategy, trained models in it, and studied what emerged. The models did not learn encoded reasoning. Instead, they learned to phrase their chains of thought in ways that cause monitors to misclassify obviously visible reasoning as benign. We call this monitor jailbreaking. This post summarizes our paper about this phenomenon, currently under review at ICML.[1] Overview of the setup and central finding. (left) Training curves showing side task accuracy increasing while monitor suspicion scores decrease. (right) Example: the model explicitly reasons about both tasks, then asserts 'the monitor can only see this section' before a main-task-only summary. The monitor accepts this framing and reports low suspicion despite having the full CoT in its context. [...] --- Outline: (01:37) Setup (02:59) Monitor Jailbreaking (04:09) Effect of Monitor Capability (05:31) Weak-to-Strong Generalization (07:06) Jailbreaks Are Not Just Surface-Level Strategies (07:56) Paraphrasing as a Defense (09:27) Implications The original text contained 1 footnote which was omitted from this narration. --- First published: February 11th, 2026 Source: https://www.lesswrong.com/posts/szyZi5d4febZZSiq3/monitor-jailbreaking-evading-chain-of-thought-monitoring --- 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.

    12 min

  5. 17 HR AGO

    “Claude Opus 4.6: System Card Part 1: Mundane Alignment and Model Welfare” by Zvi

    Claude Opus 4.6 is here. It was built with and mostly evaluated by Claude. Their headline pitch includes: 1M token context window (in beta) with State of the art retrieval performance. Improved abilities on a range of everyday work tasks. Model is improved. State of the art on some evaluations, including Terminal-Bench 2.0, HLE and a very strong lead in GDPval-AA. Claude Code now has an experimental feature called Agent Teams. Claude Code with Opus 4.6 has a new fast (but actually expensive) mode. Upgrades to Claude in Excel and the release of Claude in PowerPoint. Other notes: Price remains $5/$25, the same as Opus 4.5, unless you go ultra fast. There is now a configurable ‘effort’ parameter with four settings. Refusals for harmless requests with rich context are down to 0.04%. Data sources are ‘all of the above,’ including the web crawler (that they insist won’t cross CAPTCHAs or password protected pages) and other public data, various non-public data sources, data from customers who opt-in to that and internally generated data. They use ‘several’ data filtering methods. Thinking mode gives better [...] --- Outline: (03:45) A Three Act Play (04:57) Safety Not Guaranteed (10:53) Pliny Can Still Jailbreak Everything (12:48) Transparency Is Good: The 212-Page System Card (13:53) Mostly Harmless (17:45) Mostly Honest (19:01) Agentic Safety (20:27) Prompt Injection (23:07) Key Alignment Findings (33:48) Behavioral Evidence (6.2) (38:40) Reward Hacking and 'Overly Agentic Actions' (40:37) Metrics (6.2.5.2) (42:40) All I Did It All For The GUI (43:58) Case Studies and Targeted Evaluations Of Behaviors (6.3) (44:19) Misrepresenting Tool Results (45:09) Unexpected Language Switching (46:12) The Ghost of Jones Foods (47:54) Loss of Style Points (48:54) White Box Model Diffing (49:13) Model Welfare --- First published: February 9th, 2026 Source: https://www.lesswrong.com/posts/sWsSncqMLKyGZA9Ar/claude-opus-4-6-system-card-part-1-mundane-alignment-and --- 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.

    56 min

  6. 20 HR AGO

    “Designing Prediction Markets” by ToasterLightning

    Audio note: this article contains 55 uses of latex notation, so the narration may be difficult to follow. There's a link to the original text in the episode description. Prerequisite: basic familiarity with what a prediction market is So you want to run a prediction market. You need a way for people to trade shares. What are your options? CLOBs and Markets If you were making a prediction market from scratch, you'd probably come up with a Central Limit Order Book (CLOB). Traders post BUY and SELL orders, stating what they're willing to buy and sell, and at what price, and you record these orders in your book. Alice posts: "I'll buy 20 YES shares at $0.60 each" Bob posts: "I'll sell 25 YES shares at $0.65 each" Carol posts: "I'll buy 10 YES shares at $0.61 each" When someone wants to trade, and doesn't want to wait for someone to fulfill their order, you match them with the best available offer. Pretty intuitive. This system shows up directly in Hypixel Skyblock and other MMOs. The Bazaar lets you post orders and wait, or instantly fulfill existing orders. Have some Enchanted Iron to sell? You can [...] --- Outline: (00:34) CLOBs and Markets (01:47) Enter the Market Maker (02:55) Pricing Shares (04:21) Creating New Shares (05:29) A Natural Probability Formula (06:28) Discrete Shares (08:27) The Calculus of Market Making (12:00) Constant Product Market Maker (14:49) Conclusion The original text contained 2 footnotes which were omitted from this narration. --- First published: February 11th, 2026 Source: https://www.lesswrong.com/posts/ZrLZxkGGC4ms98iiD/designing-prediction-markets --- Narrated by TYPE III AUDIO.

    17 min

  7. 21 HR AGO

    “LLMs Views on Philosophy 2026” by JonathanErhardt

    I've let a few LLMs take David Bourget's and David Chalmers' 2020 PhilPapers Survey and made a little dashboard you can use to navigate the data: https://www.lordscottish.com/philsurvey.html You can select multiple models to pool their data and compare it against the views of philosophers. Some notable things: 1. LLMs are much more into one-boxing, and the strongest models (Claude opus 4.6 & ChatGPT5.2) 100% one-box. 2. Gpt4o and Grok3 are least interested in immortality. Gpt4o reject immortality in 4 out of 5 runs. 3. LLMs are less into moral generalism than philosophers (which might make sense, given how we train them on lots of special cases without clear underlying ethical principle?). 4. All llms accept a priori knowledge (100% across all runs!). Which might again make sense given their (arguable) lack of sense data? 5. LLMs lean towards there being little philosophical knowledge (philosophers lean towards a lot). 6. LLMs want to revise race categories much more than philosophers (83% vs 32%). Unlike philosophers, they treat race and gender similarly. 7. LLMs are really into deflationary realism as their metaontology compared to philosophers (83% vs 28%). BUT they also accept that there is a hard problem of consciousness [...] --- First published: February 10th, 2026 Source: https://www.lesswrong.com/posts/ytxJjw9TLDuEa5CLj/llms-views-on-philosophy-2026 --- Narrated by TYPE III AUDIO.

    2 min

  8. 1 DAY AGO

    “Claude Opus 4.6: System Card Part 2: Frontier Alignment” by Zvi

    Coverage of Claude Opus 4.6 started yesterday with the mundane alignment and model welfare sections of the model card. Today covers the kinds of safety I think matter most: Sabotage, deception, situational awareness, outside red teaming and most importantly the frontier, catastrophic and existential risks. I think it was correct to release Opus 4.6 as an ASL-3 model, but the process Anthropic uses is breaking down, and it not on track to reliably get the right answer on Opus 5. Tomorrow I’ll cover benchmarks, reactions and the holistic takeaways and practical implications. I’m still taking it all in, but it seems clear to me that Claude Opus 4.6 is the best model out there and should be your daily driver, with or without Claude Code, on most non-coding tasks, but it is not without its weaknesses, in particular in writing and falling into generating more ‘AI slop’ style prose than Claude Opus 4.5. For coding tasks, I presume that Opus 4.6 with Claude Code is the play, especially with Agent Teams and fast mode available, and I’m using it myself, but Codex with GPT-5.3-Codex-Max is also a strong model and a viable alternative, and a fully [...] --- Outline: (01:32) Sabotage, Deception and Evaluation Integrity (03:42) Sandbagging On Dangerous Capability Evaluations (06:01) Situational Awareness (07:33) Inhibiting Evaluation Awareness (6.5) (09:06) Self-Preference (10:24) UK AISI Testing (11:40) Apollo Research Testing (14:24) Responsible Scaling Policy Evaluations (15:45) CBRN (mostly Biology) (18:43) Autonomy (26:40) Autonomy Benchmarks (29:53) Cyber (31:27) Ship It Anyway (33:40) You Are Not Ready --- First published: February 10th, 2026 Source: https://www.lesswrong.com/posts/togCQtFtfdF23xGNS/claude-opus-4-6-system-card-part-2-frontier-alignment --- 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.

    37 min
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About

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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