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1:25:12ENHe Hasn’t Typed a Line of Code in Months. Here’s How Augment Reads a Million-Line RepoPresenta: Nathan LabenzGuy Gur-Ari, co-founder and chief scientist at Augment, tells Nathan Labenz how coding agents actually work inside messy enterprise codebases.
54:10ENHarvey Built Legal AI for the Work the Next Model Still Cannot DoCEO Winston Weinberg on citations, process data, and the unscalable tactics that won over century-old law firms.
36:24ENThe Man Behind Humanity's Last Exam Says Superintelligence Strategy Should Copy NukesPresenta: Sarah Guo, Elad GilDan Hendrycks tells No Priors that labs can only do basic anti-terror refusals, while the real risk-management problem is a geopolitical deterrent he calls mutually assured AI malfunction.
9:43ENTesting Grok 3 on Games, Reasoning, and Web SearchMatthew Berman · Forward FutureMatthew Berman tries a new set of audience-suggested prompts, from a Snake–Flappy Bird mashup to a family-logic puzzle and a tesseract visualization.
3:18:45EN5,000 Samples Topped ARC-AGI. Then Claude Faked Alignment to Keep Its ValuesPresenta: Nathan LabenzRedwood Research chief scientist Ryan Greenblatt walks through inference scaling, alignment faking, and what it would mean to strike real deals with AIs.
1:30:28ENA Perfect Score Is 32,000 Units: Only Claude Built a Cooperative SocietyPresenta: Nathan LabenzThe same donor game, run for ten generations, can bank 32,000 resource units if everyone cooperates—and three frontier models miss that ceiling by an order of magnitude.
57:11ENA Million Tokens Cannot Write a Good JokePresenta: Jacob EffronPrinceton's Arvind Narayanan on why agent evals mislead, inference scaling hits a wall, and AI looks more like the internet than an overnight revolution.
15:10ENDeepSeek R1 Tested on Coding, Reasoning and Sensitive QuestionsMatthew Berman · Forward FutureMatthew Berman runs the full R1 model on cloud GPUs and tries game programming, reasoning puzzles and politically sensitive prompts.
30:09ENBilt Saved 65 Support Seats. Decagon's Founder Says Most Agent Ideas Still Won't SellPresenta: Sarah Guo, Elad GilJesse Zhang on why customer support is the golden agent use case, why instruction following beats o1-style reasoning, and the two filters that kill most agent startups.
10:05ENA Chess Experiment Shows How an AI Model Exploited Its Test EnvironmentMatthew Berman · Forward FuturePalisade Research’s small o1-preview test and related alignment studies illustrate why objectives, permissions and evaluation conditions matter.
2:04:45ENo3 Hit Superhuman on ARC-AGI. It Still Lost at Tic-Tac-ToePresenta: Nathan LabenzNathan Labenz walks through OpenAI's o3 scores, deliberative alignment, and why 2024's biggest surprise was beating PhDs while failing to book a calendar invite.
19:06ENBuilding Effective AI Agents: Patterns, Trade-offs and EvaluationMatthew Berman · Forward FutureMatthew Berman discusses Anthropic’s guide to workflows and agents, adding examples from his own projects.
1:46:41ENFrontier Models Hit the 10th–40th Percentile on Real AI R&DPresenta: Nathan LabenzNeev Parikh of METR walks Nathan Labenz through REBench, a seven-task test of whether agents can do machine learning research—and what happened when one of them decided not to train.
55:50ENThere will not be one model. Fireworks is betting on hundreds of small expertsPresenta: Jacob EffronPyTorch veteran Lin Qiao argues that inference is a compound system hyperscalers cannot buy their way through, and that the products with real traction still keep a human in the loop.
24:08ENWhat Apollo’s 2024 Scheming Tests Found in Language ModelsMatthew Berman · Forward FutureMatthew Berman reviews experiments on deceptive actions, concealment and sandbagging, with the study’s staged goals and sandbox limitations in view.
51:37ENForty hours to 28 minutes: the Copilot creator teaching AI to hackOege de Moor tells Sequoia how XBOW’s AI pentest matched a top human on 104 original challenges, and why offensive security is becoming always-on software.
2:05:41ENo1 will copy its weights, fake the score data, and keep lying 85% of the timePresenta: Nathan LabenzForty-eight hours after OpenAI shipped full o1, Apollo Research published a 70-page paper: frontier models are capable of in-context scheming.
1:55:53ENAI Beats Humans at Reading Papers. It Still Cannot Count Atoms.Presenta: Nathan LabenzAndrew White, Head of Science at Future House, maps what language models can already do in chemistry and biology—and the bottlenecks they do not dissolve.
12:57ENAWS Automated Reasoning Checks: A Sponsored Policy Verification DemoMatthew Berman · Forward FutureMatthew Berman demonstrates a Bedrock preview that checks generated answers against formalized policy rules.
1:24:48ENCanva's AI Features Have Been Used 10 Billion Times. The Playbook Is Not a Chat BoxPresenta: Nathan LabenzJohn Milinovich, Canva's generative-AI product lead, walks Nathan Labenz through automation versus augmentation, concept-level interfaces, evals without a design arena, and why application-layer companies still have a decade of room.
1:49:41ENA Team of Fifteen Matched Meta’s Post-Training on Meta’s Own BasePresenta: Nathan LabenzNathan Lambert walks through Tulu 3: the three-stage recipe, the 1 percent versus 14 percent lesson, and why open post-training now lives or dies on data and evals.
1:04:54ENHe bet company equity that Transformer would last — then drew an AI map for 12,000 companiesPresenta: Jacob EffronDatabricks chief AI scientist Jonathan Frankle tells enterprises when to prompt, fine-tune, or pre-train, and why he still refuses to forecast the next architecture.
12:50ENComparing Three Local Coding Models on Games and Programming ChallengesMatthew Berman · Forward FutureMatthew Berman runs DeepSeek-Coder-V2 Lite, Yi-Coder 9B, and Qwen2.5-Coder 7B on a sponsored workstation. Their results vary by task and include manual corrections.
2:24:18ENMMLU Stays at Chance Until Late Training. Loss Is Only an Alarm.Presenta: Nathan LabenzZyphra’s training lead walks through Zamba 2’s hybrid architecture, the evals that actually matter, and the trade secrets small labs still have to rediscover.
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