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  • Andrej Karpathy’s LLM Wiki: Persistent Memory vs Traditional RAG
    Andrej Karpathy’s LLM Wiki: Persistent Memory vs Traditional RAG

    By

    @scobleizer

    –

    🚨 Andrej Karpathy just dropped something that could replace a lot of RAG workflows. It's called LLM Wiki. The idea is simple: Most AI systems retrieve context from scratch every time you ask a question. LLM Wiki doesn't. It builds a persistent knowledge base that gets better every time you add a new source. So instead of: • search docs
    • pull fragments
    • answer
    • forget everything
    • repeat it does this: • ingest a source
    • extract the important ideas
    • update entity pages
    • revise topic summaries
    • connect related concepts
    • flag contradictions
    • keep compounding the knowledge over time That shift matters. RAG is great for retrieval. But a lot of people are really trying to build memory. Not just "find me the right chunk again."
    More like: "help me build an evolving model of this topic over time." That's what this is. Karpathy's examples are strong too: • personal knowledge
    • long-horizon research
    • books and topics
    • internal company knowledge
    • meeting transcripts
    • customer calls Basically, anything where the knowledge should accumulate, not reset every session. The best way to think about it: Obsidian is the IDE.
    The LLM is the programmer.
    The wiki is the codebase. You don't manually maintain the system. You feed it sources, ask questions, and the AI keeps the structure alive. That's a much bigger idea than "better RAG." 100% open source. [Translated from EN to English]

    → View original post on X — @scobleizer, 2026-04-06 15:06 UTC

  • Karpathy’s Second Brain Idea Just Killed RAG
    Karpathy’s Second Brain Idea Just Killed RAG

    By

    @montreal_ai

    –

    Karpathy's Second Brain idea just killed RAG. LLMs can now turn papers, repos, and notes into a living wiki that keeps getting smarter. And people are already doing wild use cases with it. 10 examples: [Translated from EN to English]

    → View original post on X — @montreal_ai, 2026-04-06 15:03 UTC

  • OpenAI Superintelligence: New Policy Blueprint for Intelligence Age
    OpenAI Superintelligence: New Policy Blueprint for Intelligence Age

    By

    @kimmonismus

    –

    OpenAI is preparing ready to launch their new and next generation of models. They are about to revolutionizing science & economy. "A very significant step forward" compared to their current models. Imho this is preparing people for the launch, very very soon, maybe even this week. Chubby♨️ (@kimmonismus) Looks like OpenAI reached Superintelligence. OpenAI: "Now, we’re beginning a transition toward superintelligence: AI systems capable of outperforming the smartest humans even when they are assisted by AI." OpenAI just published a 13-page policy blueprint for the "Intelligence Age"- proposing a Public Wealth Fund, 32-hour workweek pilots, portable benefits, a formal "Right to AI," and tax reforms to offset shrinking payroll revenue as automation scales. The document frames superintelligence not as a distant scenario *but an active transition requiring New Deal-level ambition*: new safety nets, containment playbooks for dangerous models, and international coordination modeled on aviation safety institutions. Here are OpenAI's suggestions (tl;dr): Open Economy: -Give workers a formal voice in AI deployment decisions -Microgrants and "startup-in-a-box" for AI-native entrepreneurs -Treat AI access as basic infrastructure (like electricity) -Shift tax base from payroll toward capital gains and corporate income -Public Wealth Fund — every citizen gets a stake in AI growth -Fast-track energy grid expansion via public-private partnerships -32-hour workweek pilots, better benefits from productivity gains -Auto-scaling safety nets triggered by displacement metrics -Portable benefits untied from employers -Invest in care economy as a transition path for displaced workers -Distributed AI-enabled labs to accelerate scientific discovery Resilient Society: -Safety tools for cyber, bio, and large-scale risks -AI trust stack — provenance, verification, audit logs -Competitive auditing market for frontier models -Containment playbooks for dangerous released models -Frontier AI companies adopt Public Benefit Corporation structures -Codified rules and auditing for government AI use -Democratic public input on AI alignment standards -Mandatory incident and near-miss reporting -International AI safety network for joint evaluations and crisis coordination Notably, OpenAI calls for stricter controls only on a narrow set of frontier models while keeping the broader ecosystem open, a clear attempt to position regulation as targeted, not industry-wide. They're backing it with up to $100K in fellowships and $1M in API credits for policy research, plus a new DC workshop opening in May. — https://nitter.net/kimmonismus/status/2041130939175284910#m

    → View original post on X — @kimmonismus, 2026-04-06 15:03 UTC

  • Morphic Workflows Eliminates Need for Complex AI Prompting

    By

    @aihighlight

    –

    Morphic just killed the "I don't know how to prompt" excuse for good. Select your assets, pick a workflow, and the output is already done before you finish your coffee. Jaynti Kanani (JD) (@jdkanani) Introducing Workflows on @morphic. You know what you want, you just don’t know how to prompt for it. That’s what Workflows solve. Storyboarding? Three clicks. UGC ads? No prompting. Color grade? In seconds. Try now: morphic.com/workflows Live with 72 workflows today. More coming soon. With Workflows, you can capture repeatable creative tasks and reuse them without starting from scratch. Just select your assets and options while running a workflow. Minimal prompts required. And no nodes, of course. There’s a workflow for everything: filmmaking, social media, animation, fashion, marketing, and some just to have fun. Tag someone who'd make something wild with this. Here are my 5 favorite workflows: — https://nitter.net/jdkanani/status/2041154028034490867#m

    → View original post on X — @aihighlight, 2026-04-06 14:59 UTC

  • Morphic Workflows: Automated Video Creation from Single Image

    By

    @datachaz

    –

    Holy sh*t. Dropping one image in and watching it automatically predict 9 scenes, upscale every frame, animate them, and stitch together a compiled video  🤯 @Morphic just dropped ‘Workflows’. You bring the image. It handles everything after.
 Following closely 👀 Jaynti Kanani (JD) (@jdkanani) Introducing Workflows on @morphic. You know what you want, you just don’t know how to prompt for it. That’s what Workflows solve. Storyboarding? Three clicks. UGC ads? No prompting. Color grade? In seconds. Try now: morphic.com/workflows Live with 72 workflows today. More coming soon. With Workflows, you can capture repeatable creative tasks and reuse them without starting from scratch. Just select your assets and options while running a workflow. Minimal prompts required. And no nodes, of course. There’s a workflow for everything: filmmaking, social media, animation, fashion, marketing, and some just to have fun. Tag someone who'd make something wild with this. Here are my 5 favorite workflows: — https://nitter.net/jdkanani/status/2041154028034490867#m

    → View original post on X — @datachaz, 2026-04-06 14:54 UTC

  • How Anthropic’s Growth Team Leverages AI and Automation

    By

    @paulroetzer

    –

    My biggest takeaways from @AnthropicAI's Head of Growth Amol Avasare: 1. Engineering is getting the most AI leverage—and it’s squeezing PMs and designers. With Claude Code, a five-engineer team now produces the output of 15 to 20 engineers. But PM and design productivity haven’t scaled proportionally. The result is a compressed ratio where one PM is effectively managing the output of a much larger engineering team. Anthropic's growth team is responding in two ways: hiring even more PMs (!), and formally deputizing product-minded engineers to act as mini-PMs for any project with less than two weeks of engineering time. 2. Anthropic is using Claude to automate its own growth. The internal initiative is called CASH (Claude Accelerates Sustainable Hypergrowth). It works across four stages: identifying opportunities, building features, testing quality, and analyzing results. Right now it handles copy changes and minor UI tweaks. The win rate is comparable to a junior PM with two to three years of experience, and improving rapidly. 3. The one part of PM work that AI can’t automate yet: getting six people in a room to agree. Amol and his head of design joke that even with AGI, it’ll still be impossible to align six stakeholders. Cross-functional coordination—managing opinions, navigating politics, mediating tradeoffs—remains the bottleneck that AI doesn’t touch for larger projects. This is why Amol believes PM roles aren’t going away, and may actually grow. 4. 60-80% of Anthropic’s growth team's projects have no PRD. For smaller work, kickoffs happen on Slack—messages back and forth with product-minded engineers who can push back and ask the right questions. For larger projects, Amol believes in a proper 30-minute cross-functional kickoff (legal, safeguards, stakeholders) to surface concerns early. 5. Adding friction to onboarding drives growth—if the friction helps users understand why the product is for them. His work Mercury, MasterClass, Calm, and now Anthropic, adding steps to onboarding flows consistently improved conversion. The key: cut annoying friction that doesn’t add value, but add friction that helps users understand why the product is for them. 6. AI companies need to focus on bigger bets, not better A/B tests. Amol’s argument: if your core product value is driven by AI, then the future value is orders of magnitude higher than today’s value, because model capabilities grow exponentially. In that world, micro-optimizations capture a shrinking share of a growing pie. Traditional growth teams do 60% to 70% small optimizations and 20% to 30% big swings. At Anthropic, they flip this ratio. 7. Amol built a weekly AI agent that scans Slack for cross-functional misalignment. Using Cowork with the Slack MCP, he has a scheduled task that looks across his projects and conversations and surfaces areas where teams are about to do overlapping work or pull in different directions. A colleague on the enterprise team already caught major misalignment that would have caused weeks of wasted effort. 8. A traumatic brain injury taught Amol the principle that now drives his work: freedom through constraints. In early 2022, a kick to the head during a Muay Thai sparring session caused a traumatic brain injury. Amol spent nine months off work and months relearning to walk, unable to look at screens or listen to music for more than 20 seconds. He was re-injured a month after joining Mercury and had to take two more months off. He’s still not fully healed. But the constraints—no alcohol, no caffeine, mandatory breaks, daily meditation—have become the habits that let him operate at the intensity Anthropic demands. “The true freedom in life is learning how to be content when you don’t get what you want.” Lenny Rachitsky (@lennysan) Anthropic is on an unprecedented growth run. Just in the past year they grew from $1B to $19B ARR. They added $6B in ARR just in *February*. Companies like Palantir and Atlassian took 15-20 years to reach ~$5B ARR. Anthropic is adding that every month. Amol Avasare is head of growth at Anthropic, and one of the most impressive people I've had on the podcast. In his first ever public interview, Amol shares: 🔸 How Anthropic is automating growth experiments with Claude (their internal tool called “CASH”) 🔸 Why activation is the single highest-leverage growth problem in AI 🔸 Why Amol is hiring more PMs, not less 🔸 How he uses Cowork to automatically detect team misalignment in Slack 🔸 How the company’s focus on AI coding created a research flywheel that accelerated their models 🔸 How Amol landed his role by cold emailing Anthropic’s CPO @mikeyk 🔸 The brain injury that nearly ended Amol's career Listen now 👇 piped.video/k-H4nsOTuxU — https://nitter.net/lennysan/status/2040827067126907113#m

    → View original post on X — @paulroetzer, 2026-04-06 14:48 UTC

  • Poke Raises $10M at $300M Valuation, Scales on iMessage

    By

    @scobleizer

    –

    Poke is OpenClaw for normies. First AI product I've seen successfully scale on iMessage. Closed an unannounced $10M round at $300M from Spark in Q1. Congrats! Poke (@interaction) Starting today, personal superintelligence is just one tap away. No download, no signup. Text Poke for free now: Poke.com 🌴 — 0:00 – What's Poke? 0:50 – Introducing Poke Recipes 1:25 –  Create a Recipe in 10 seconds 1:43 – Earn on Poke 2:44 – Build with npx poke 12:58 – Recap 13:36 – Parisian Love — https://nitter.net/interaction/status/2034713714415608123#m

    → View original post on X — @scobleizer, 2026-04-06 14:47 UTC

  • AI as AI Ops: Automate Your AI Workflows for Higher ROI
    AI as AI Ops: Automate Your AI Workflows for Higher ROI

    By

    @alliekmiller

    –

    AI is better than you at working with AI. It's better at generating prompts for AI, teaching skills to other AI agents, coordinating messaging between AIs. AI is an AI ops whisperer. Think like a PM: go through the journey you're taking right now with your AI workflows and find high ROI ways to use AI to help your AI efforts. Yes, you can prompt AI to create an app. But you can also… …and this is overkill and would waste a lot of tokens but I want to dramatize it because when costs plummet, we will see strange usage patterns… Prompt an AI with the idea, and then it creates a much better prompt (see images), and then it creates 4 different versions of that prompt, and then it spawns parallel agents to research the product space from 4 different points of view, and then they all meet in an agent team war room and battle it out, and then spec a product together while 5 other agents with 5 different goals in parallel spec it out themselves, and then 3 more agents review and critique the specs, then another reviews all previous work and summarizes, and another one tees up open questions, then 10 more with radically different personas meet to evolve the best idea and spawn 50 more versions, then you run a simulation by 10000 personas to vote for the product with the fastest time to market, highest delight, and strongest ROI potential. And then you create the app. What I'm saying is: find where you are the intermediary and shouldn't be, and find higher order ways to plug yourself in. Take yourself out of the loop before the loop takes you out.

    → View original post on X — @alliekmiller, 2026-04-06 14:38 UTC

  • OpenSeeker: AI-Native Search Beyond Keyword Matching

    By

    @learnopencv

    –

    OpenSeeker: Rethinking Search With AI-Native Reasoning In this episode of Artificial Intelligence: Papers and Concepts, we explore OpenSeeker, an emerging approach to building AI-native search systems that go beyond traditional keyword matching. Instead of retrieving links based purely on queries, OpenSeeker focuses on reasoning over information helping users get structured, context-aware answers rather than a list of results. We break down how modern search is evolving with large language models, why retrieval alone is no longer enough, and how systems like OpenSeeker combine retrieval with reasoning to deliver more accurate and useful outputs. If you’re interested in AI-powered search, retrieval-augmented generation, or the future of information discovery, this episode explains why OpenSeeker represents a shift toward more intelligent and answer-driven search experiences. Resources: Paper Link: arxiv.org/abs/2603.15594v1 Interested in Computer Vision and AI consulting and product development services? Email us at contact@bigvision.ai or visit us at bigvision.ai

    → View original post on X — @learnopencv, 2026-04-06 14:30 UTC

  • One-Step AI Image Generation Framework Achieves State-of-the-Art Results
    One-Step AI Image Generation Framework Achieves State-of-the-Art Results

    By

    @jiqizhixin

    –

    What if you could generate stunning AI images in a single step, without compromising quality? Researchers from Westlake University, Chinese Academy of Sciences, and DP Technology present a breakthrough. They've introduced a new framework that simplifies the design of 'shortcut' diffusion models. This framework clarifies how to build more efficient one-step image generators by disentangling their core components. Their model achieves a new state-of-the-art FID50k of 2.85 on ImageNet-256×256 with one-step generation, and 2.53 with two steps. Remarkably, it requires NO pre-training, distillation, or curriculum learning! On the Design of One-step Diffusion via Shortcutting Flow Paths Paper: openreview.net/forum?id=k6q8…  Code: github.com/EDAPINENUT/Explic…    Project: edapinenut.github.io/explici… Our report: mp.weixin.qq.com/s/BptmtBa_O… 📬 #PapersAccepted by Jiqizhixin

    → View original post on X — @jiqizhixin, 2026-04-06 14:23 UTC