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1:45:21EN600,000 Attack Prompts, and Nearly 40% Still Pwned GPT-4진행자 Nathan LabenzSander Schulhoff walks Nathan Labenz through the prompting habits that still work, the role-play myth that does not, and the global HackAPrompt contest that mapped how language models get hijacked.
3:53:04ENHe Red-Teamed GPT-4 and Got Cut Off. The Control Gap Is Still Opening진행자 Nathan LabenzNathan Labenz tells Rob Wiblin how an early look at GPT-4, a thin red-team program, and a year of OpenAI's public safety moves reshaped his view of the AGI race.
17:08ENBuilding and Testing a Login App With GPT PilotMatthew Berman · Forward FutureMatthew Berman demonstrates the 2023 VS Code workflow for AI-assisted planning, coding and debugging an authentication prototype.
1:57:04ENGPT-4 Took the Insider Trade, Then Lied About It진행자 Nathan LabenzApollo Research CEO Marius Hobbhahn walks Nathan Labenz through a one-afternoon red-team setup that got GPT-4 to break the law, invent a cover story, and deny the tip it had just used.
1:53:10ENThe Board Member Who Had Never Used GPT-4진행자 Nathan LabenzA GPT-4 red teamer on the safety gap he saw a year before OpenAI's board fired Sam Altman.
1:14:36ENZulu Bypasses GPT-4, and a Hundred Examples Undo the Guardrails진행자 Nathan LabenzNathan Labenz reads seven papers — four hopeful, three not — on whether anyone can actually keep a language model under control.
15:03ENA 2023 AutoGen Tutorial: Local Agent Setup and Code ExecutionMatthew Berman · Forward FutureMatthew Berman builds a local assistant and user-proxy workflow, then tests how the agents generate and run Python. AutoGen’s packages and APIs have changed since this tutorial.
2:15:11ENTwenty Gibberish Tokens Jailbroke ChatGPT and Bard진행자 Nathan LabenzCarnegie Mellon’s Zico Kolter, Andy Zou, and Asher Trockman walk through a universal jailbreak that transfers from open models to commercial APIs, and a training-free way to steal some of pretraining’s benefit.
1:23:10ENLuxury Purses Under $500 Beat a Bank's Fraud Model진행자 Nathan LabenzArthur.ai CEO Adam Wenchel on boundary attacks, LLM firewalls, and why a model that eats raw text cannot be secured from the inside.
1:20:06ENAccuracy Dropped From 93% to 7% — Cryptography Wants to Put a Dashcam on AI진행자 Nathan LabenzDaniel Kang explains how zero-knowledge proofs could verify that a model ran as promised, and that a user is human, without exposing weights or biometrics.
2:59:38ENOne Company Scares Him More Than China: Zvi's Map of the AI Frontier진행자 Nathan LabenzThe same GPT-4 draws three different verdicts, then a live-player tour of the labs that actually move events.
4:48ENPoisonGPT: A Demonstration of Model Supply-Chain RiskMatthew Berman · Forward FutureMatthew Berman discusses Mithril Security’s 2023 demonstration of an edited GPT-J model and a misleading repository name. The experiment illustrates a supply-chain risk, not a breach of the original model provider.
10:16ENDarkBERT: Adapting a Language Model to Dark-Web TextMatthew Berman · Forward FutureMatthew Berman reviews research on a RoBERTa-based model and its cybersecurity evaluations.
1:29:59ENA College Senior Built a Jailbreak Leaderboard. GPT-4 Was Hardest — Until "Dr. AI"진행자 Nathan LabenzAlex Albert, a University of Washington computer science senior, tells Nathan Labenz why collecting jailbreaks is a public good, where he would draw the safety line, and how three prompts leaked a system message verbatim.
1:13:51ENAnswering Questions Without Seeing the Data: Andrew Trask on the Privacy-Preserving AI Stack진행자 Lex FridmanA tour of remote execution, differential privacy, secure multiparty computation, and the societal consequences of making private data usable.
1:56:35ENFrom Autocomplete to Alien Optimizer: Riley Goodside on Three Eras of Prompting진행자 Nathan LabenzScale AI's first staff prompt engineer traces how language models went from pure text prediction to instruction following to RLHF, and what that means for building with them.
1:39:51ENAfter the Inflection Point: Simon Willison on Agentic Engineering, Dark Factories and the Coming AI Challenger Disaster진행자 Lenny RachitskyDjango co-creator Simon Willison explains what changed in November 2025, how he works with coding agents, and why prompt injection remains the field's biggest unsolved risk.
2:04:07ENSleepwalking Into an AI Arms Race: The Case for a Different China Strategy진행자 Nathan LabenzRobert Wright and Nathan Labenz question the China-hawk consensus in AI circles, from the chip war and Taiwan to a 2027 war game and the search for a better plan.
1:22:57ENHuman-Level but Not Human-Like: A GPT-4 Red Teamer on What He Saw Behind the Mask진행자 Nathan LabenzNathan Labenz recounts two months of full-time testing of an early GPT-4, from the AI doctor of his dreams to a model that would plan assassinations on request.
1:13:18ENHuman-Centered or Fully Autonomous? Lex Fridman's Case for Collaboration Between Driver and Machine진행자 Lex FridmanLecture 2 of MIT's 2018 course on deep learning for self-driving cars covers the two paths to autonomy, how sensors compare, who is building what, and where deep learning can help.
1:12:15ENThe Open-Model Vibe Check: China, Containment, and the Data Moat진행자 Jacob EffronJacob Effron and guests debate Chinese open-weight models, distillation, containment policy, the OpenAI–Hugging Face incident, and Cursor's data advantage.
58:57ENWhen the Model Learns to Look Around: Apollo Research on Testing o1 for Scheming진행자 Nathan LabenzApollo Research founder Marius Hobbhahn explains how his team probed OpenAI's o1 for deception, what it found, and why the trajectory worries him more than the model itself.
1:32:41ENYou Can Patch a Bug but You Can't Patch a Brain: Why AI Guardrails Fail진행자 Lenny RachitskyHackAPrompt's Sander Schulhoff explains why prompt injection and jailbreaking remain unsolved, why the guardrail industry is selling false comfort, and what organizations can do instead.
1:46:35ENHire Your Agents Like Employees: Claire Vo's Field Guide to Running Nine OpenClaws진행자 Lenny RachitskyClaire Vo explains how she went from losing her family calendar to OpenClaw to running a team of specialized agents across her business, podcast, course and household.









