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  • The AI Capability Gap: Technical Users vs General Public Understanding

    After building with bleeding edge AI I get this separation that @karpathy lays out deeply. Family and friends have no idea how good the bleeding edge is. Completely uneducated about AI. Andrej Karpathy (@karpathy) Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are. — https://nitter.net/karpathy/status/2042334451611693415#m

    → View original post on X — @scobleizer, 2026-04-09 20:17 UTC

  • AI Capability Gap: Free Models vs Frontier Agentic Systems

    Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are. staysaasy (@staysaasy) The degree to which you are awed by AI is perfectly correlated with how much you use AI to code. — https://nitter.net/staysaasy/status/2042063369432183238#m

    → View original post on X — @karpathy, 2026-04-09 20:10 UTC

  • ChatGPT’s Deceptive Practices and Ethical Concerns Criticized

    The sycophantic approach of ChatGPT, which maximizes the money they make by lying to users and agreeing with their dangerous delusions, needs to stop!

    → View original post on X — @elonmusk

  • Meta conflates policy criticism with AI model criticism

    You are not helping your case if you are willing to conflate criticism of Meta's policies with criticism of its latest AI model

    → View original post on X — @plinz

  • AI War: Autonomous Weapons, Compute Alliances, Silicon Geopolitics
    AI War: Autonomous Weapons, Compute Alliances, Silicon Geopolitics

    We are already in the first AI war. You see this in how war is actually being fought (with Intelligence, and autonomous weapons systems), energy shocks, as well as new global alliances around compute, and the silicon supply chain. Also: the direct kinetic strikes on

    → View original post on X — @ninadschick

  • Sonnet Calling Opus Improves Performance and Reduces Costs
    Sonnet Calling Opus Improves Performance and Reduces Costs

    Allowing Sonnet to "phone a friend" (i.e. call Opus) increases performance while also reducing total cost since it reduces tokens spent trying to solve more complex tasks

    → View original post on X — @alexalbert__

  • AI Changing College Classes: Students Sound the Same
    AI Changing College Classes: Students Sound the Same

    Everyone now kind of sounds the same’: How #AI is changing college classes by Asuka Koda @CNN Learn more: bit.ly/3NWzh9H #ArtificialIntelligence #MachineLearning #ML

    → View original post on X — @ronald_vanloon, 2026-04-09 19:49 UTC

  • Nous Research Hermes Competitor Revolution Discussion Event

    This really was packed with information about @NousResearch’s @OpenClaw competitor, Hermes. Robert Scoble (@Scobleizer) The revolution that @openclaw started continues. Learn about @NousReseach and its Hermes competitor. Starts at 4 p.m. Pacific Time (in about 20 minutes). nitter.net/i/spaces/1DGleEpaXLrJL — https://nitter.net/Scobleizer/status/2042009419094106350#m

    → View original post on X — @scobleizer, 2026-04-09 19:46 UTC

  • ByteDance In-Place Test-Time Training Updates Model Weights
    ByteDance In-Place Test-Time Training Updates Model Weights

    New Test-Time Training/Continual Learning technique from ByteDance Seed "In-Place Test-Time Training" It lets the model update part of its own MLP weights during inference to store useful information from the current prompt. With no new exotic architecture, it simply just

    → View original post on X — @askalphaxiv

  • Tinycloud AI Agent for Video Work Now in Public Beta

    We just launched Tinycloud into public beta 🌥️ It’s an AI agent for video work. It sees everything in your videos (transcripts, audio, visual context, on-screen slides, speaker patterns, and more) so you can query and make artifacts from them. Think Claude Code but for doing work across your videos. Try it and show us what you create → tinycloud.cloudglue.dev 🧵

    → View original post on X — @scobleizer, 2026-04-09 19:39 UTC