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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

  • Pokee AI Ideathon: From Ideas to Impact with AWS
    Pokee AI Ideathon: From Ideas to Impact with AWS

    We're excited to announce the Pokee AI Ideathon: From Ideas to Impact with AWS Build! This is an in-person event for San Francisco / Bay Area founders, builders, creatives, and strategists to come together and turn bold ideas into compelling concepts, prototypes, and products in just a few hours. More than a traditional hackathon, this Ideathon is designed around a broader question: How do you quickly move from insight to workflow, execution and impact with AI? [Translated from EN to English]

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

  • Iceberg v3 Public Preview: Unifying Data Layer Across Engines
    Iceberg v3 Public Preview: Unifying Data Layer Across Engines

    Iceberg v3 marks a major step forward for open table formats in unifying the data layer. With v3 now in Public Preview on Databricks, it enables: • High-performance incremental data processing • Native support for semi-structured data • Interoperability across engines and

    → View original post on X — @databricks

  • NVIDIA Jetson Enables Open Source Robotics Autonomy Deployment

    Across NVIDIA Jetson and our robotics software stack, we’re focused on making it easy for developers to turn open source innovation, like @openclaw
    , into deployable, real‑world autonomy on the edge.

    → View original post on X — @nvidia

  • Global Robotics Community Directory Worldwide

    More accurately this is everyone I can find in robotics world wide. 🙂 Not just Bangalore.

    → View original post on X — @scobleizer

  • DeepAgents vs LangChain: Agent Framework Comparison Guide

    great q! deepagents has more "batteries included", which langchain v1 is a very minimalistic agent harness if you are doing more complex workflows (eg claude code for X) -> deepagents if you want something simple -> langchain both are customizable with middleware

    → View original post on X — @hwchase17