The Fed, #AI, and economic uncertainty: What investors need to know by Tracy Mayor @MITSloan Learn more: bit.ly/4m7mL3A #Finance #ArtificialIntelligence #MachineLearning #ML #DL
→ View original post on X — @ronald_vanloon, 2026-04-08 02:58 UTC
AI
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AI Awakening Class Kickoff with Steve Jurvetson at Stanford
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What an awesome way to start my AI Awakening class at Stanford! The legendary @FutureJurvetson has a knack for seeing around corners thanks to his deep technical knowledge. I can't wait to see the projects the students deliver in 10 weeks. Steve Jurvetson (@FutureJurvetson) 🤖 Just did the kickoff guest lecture at Stanford Business School for "The AI Awakening" — a new class cross-listed with CS where the students will use agentic AI to forge new businesses in 3-person teams. So I vibe coded a rocket launch tracking calendar app that integrates launches big and small (from SpaceX to the local LUNAR rocketry club). It took 4 minutes for me + 30 minutes of compute: rocklaunch-rptzu8y3.manus.sp… We discussed many topics, including the new "society of thought" paper that dropped 40 years after Minsky's Society of Mind, and five years after Hawkins' memory-prediction framework in A Thousand Brains: science.org/doi/10.1126/scie… — https://nitter.net/FutureJurvetson/status/2041693675877756999#m
→ View original post on X — @ceobillionaire, 2026-04-08 02:57 UTC
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Creative Writing with Mythos AI Without Complex Prompting
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Creative and unique writing from Mythos without complicated user prompting.
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Psi-Zero: Open Foundation Model for Humanoid Robot Learning
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What if we could teach humanoid robots intricate skills more efficiently than ever before?
— 机器之心 JIQIZHIXIN (@jiqizhixin) 8 avril 2026
The USC Physical Superintelligence (PSI) Lab, NVIDIA, and WorldEngine introduce Ψ0 (Psi-Zero).
Their new open foundation model rethinks how humanoids learn complex tasks by decoupling… pic.twitter.com/BK2ADXJ6GBWhat if we could teach humanoid robots intricate skills more efficiently than ever before? The USC Physical Superintelligence (PSI) Lab, NVIDIA, and WorldEngine introduce Ψ0 (Psi-Zero). Their new open foundation model rethinks how humanoids learn complex tasks by decoupling the learning process: it first acquires general visual-action understanding from human videos, then masters precise robot control using high-quality humanoid data. Ψ0 sets a new standard for universal humanoid loco-manipulation, achieving over 40% higher success rates across multiple complex tasks while using more than 10 times less training data than prior state-of-the-art approaches. Ψ0: An Open Foundation Model Towards Universal Humanoid Loco-Manipulation Paper: arxiv.org/abs/2603.12263 Project: psi-lab.ai/Psi0/ Code: github.com/physical-superint… Our report: mp.weixin.qq.com/s/yvkG5ZcO1… 📬 #PapersAccepted by Jiqizhixin
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Maximize Single GPU Power with Qwen 3.5 and Gemma 4
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You don’t need Mythos, just learn how to use Qwen 3.5 27B and Gemma 4 31B and you’ll realize you have so much intelligence under your control on a single GPU
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Hermes AI Model Performs Better on Mac Mini Hardware
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The nerd building a system for me says Hermes runs better on a shitty Mac Mini.
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Continual Learning Breakthrough in AI Science Company
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It keeps compounding I learned today from an AI science company that built one that is continually learning.
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6 Practical Tips for Building AI Agents with Claude and Codex
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Good tips: nitter.net/gkisokay/status/204062… Graeme (@gkisokay) Practical tips to save time and tokens when using Claude or Codex externally to build or debug your Hermes and OpenClaw agents. 1. After building externally, debrief Hermes/OpenClaw on every new feature In Hermes case, it owns the orchestration standards, memory architecture, and self-improving skill loops. Hermes itself should review any feature built outside to confirm it fits the framework before it runs. Do the same for OpenClaw as well, to ensure it fits its execution standards. 2. Scan the whole codebase when switching LLM models Don't assume a quick LLM swap will work. Ask Claude/Codex to audit every prompt string, config file, and env variable for stale model names. One missed reference will silently route to the wrong model, costing you hours of debugging. 3. Test viciously. Normally, agents fail new features on first runs Define the exact desired output, watch for it, and if it doesn't appear, keep iterating until it's consistent. Repetition is the only honest test. 4. Describe symptoms, not solutions Don't tell Claude/Codex how to fix it; describe what's wrong. "The memory isn't persisting between runs." Beat "rewrite the storage layer." Let it figure out the approach. You'll get a clearer answer and see where the actual problem is. 5. Paste the error, don't summarize it When receiving errors from your agent, use the exact error or full log dump every time. Claude reads noise better than you do, and the part you thought was irrelevant might be the part that matters. 6. If it breaks again later, return to the same session If the same feature fails weeks later, go back to the original Claude Code/Codex session before starting fresh. The context for what was built, why, and what was already tried is sitting there. Starting a new chat throws all of that away, and you'll loop the same decisions. These tips should address many of the recurring questions I've been receiving. Let me know if I can help you out further. — https://nitter.net/gkisokay/status/2040627715079098737#m
→ View original post on X — @scobleizer, 2026-04-08 02:46 UTC
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AI Systems Effectiveness Against Humans Raises Safety Concerns
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Yeah. Or attacking the humans more effectively. Sigh.
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Open-source models outperform closed-source with fine-tuning
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This is for off-the-shelf models. With fine-tuning/post-training, open-source models beat closed source in a lot of specialized tasks/areas