AI Resume Builder
JOIN NOW Post Job
"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis
"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis

AI in the AM — Week 2 Highlights (June 2026)

13 June 2026 1:44:47 Erik Torenberg, Nathan Labenz

Listen to episode

About this episode

Week 2 highlights follows Anthropic’s Fable launch in real workflows, from safety gates and API refusals to autonomous coding, 3D world-building, and a Claude-run Twitter experiment. Geoffrey Irving and Daniel Murfet argue for alignment theory and guarantees before recursive self-improvement, while prinz tests Fable on legal reasoning and monitoring. Rahul Sonwalkar, Shlok Khemani, Tom McGrath, and Andrew Moore add field reports on data agents, hybrid authorship, interpretability, context systems, token economics, and power concentration.



Mercury: Run your finances with virtual cards, spending limits, merchant/category locks, and AI-friendly tools like API keys, MCP, and CLI. Check out Mercury at https://mercury.com



LINKS:

  • Claude Fable 5 announcement
  • Julius AI platform
  • Rahul Sonwalkar homepage
  • Nate Jones homepage
  • Shlok Khemani homepage
  • FrontierCode benchmark blog
  • Lovelace AI company
  • Andrew Moore Wikipedia profile
  • Geoffrey Irving homepage
  • Daniel Murfet LessWrong profile
  • Sequent Research announcement
  • Timaeus research organization
  • Automated Alignment paper
  • Goodfire AI company
  • Tom McGrath homepage
  • Predictive data debugging tool
  • prinzbench legal benchmark
  • Unit distance conjecture disproof
  • Dario Amodei policy essay
  • Vending-Bench 2 benchmark
  • Andon Labs site
  • Recursive Superintelligence startup
  • Sakana AI company
  • <a href="https://posttrain

Want to find AI jobs?

Join thousands of AI professionals finding their next opportunity

We respect your inbox. Unsubscribe at any time.

© 2026 "The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis. All rights reserved.

Common Questions

Frequently asked questions

Quick answers about how DevFound's AI matching, resumes, and referrals work.

DevFound's AI Copilot ingests your profile, goals, and live job data to deliver curated matches in seconds. Every match includes a resume variant, suggested referrals, and interview prep so you can act immediately. The more feedback you provide, the sharper the Copilot becomes.

AI-led job searches shrink the hours spent sifting through boards and formatting resumes. DevFound pairs automation with your personal outreach, so you reserve energy for interviews and negotiation. Traditional networking still matters, but AI gives you a lift before you even send a message.

Modern AI roles expect comfort with production-grade code, data fluency, and practical ML tooling. The strongest candidates pair deep technical chops with storytelling—translating model impact to product, GTM, and exec partners. Continuous learning keeps you ahead as stacks evolve.

DevFound rewards active seekers. Keep your profile fresh, respond to match quality prompts, and enable alerts so you never miss a role. The AI prioritizes companies and teams that align with your feedback, accelerating both introductions and interview invites.

High-density tech hubs continue to host the deepest AI talent pools, yet distributed teams are catching up fast. Use DevFound filters to hone in on onsite, hybrid, or fully remote roles and watch openings expand across time zones.

DevFound aggregates thousands of remote AI openings and flags the nuances—core hours, async culture, and visa needs—up front. The Copilot also recommends how to position your distributed work experience so hiring managers know you can thrive on a remote team.

Still have questions?

Contact Support