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0:47ENCognition's AI Productivity Guarantee for DevinMatthew Berman · Forward FutureMatthew Berman discusses a 2026 enterprise offer that compares estimated engineering value with the cost of using Devin.
40:22ENBuilding Gets Easier Near AGI. Knowing What to Build Does Not.Listen Labs CEO Alfred Wahlforss on AI interview agents, a 30-million-person audience, and why transcripts beat credit-card data when you need to predict customers.
32:30ENTheo Reviews DeepSWE and the Limits of Coding BenchmarksTheoAn investor in DataCurve examines its coding benchmark, the results shown in the video and their limitations.
2:22ENPioneer Agent and Automated Fine-Tuning of Small ModelsMatthew Berman · Forward FutureIn a Fastino-sponsored video, Matthew Berman describes a system that automates parts of model adaptation and discusses its reported benchmark results.
2:34:17ENMatthew Berman Tests Claude Opus 4.8 and Dynamic Workflows LiveMatthew Berman · Forward FutureA launch-day walkthrough compares Anthropic’s reported benchmarks with informal coding experiments and a multi-agent website redesign.
17:03ENDeepSWE: Testing Coding Agents on Longer Software TasksMatthew Berman · Forward FutureMatthew Berman reviews Datacurve’s benchmark, its verifier audit and the tradeoffs of comparing models under a shared harness.
41:45ENAI Cyberattacks and Defenses: What Recent Security Incidents ShowMatthew Berman · Forward FutureMatthew Berman examines supply-chain attacks, AI-assisted vulnerability research and the economics of defending software as models improve.
1:23:53ENCreators Cheer Filler-Word Removal and Boo Generative VideoModeration: Nathan LabenzDescript CEO Laura Burkhauser maps the same customers' three-speed reaction to AI: deterministic buttons they love, an agentic co-editor they want but scold, and generative video they actively hate.
39:48EN400,000 Customers, 20,000 APIs: SAP's CTO on the Hard Exam of Enterprise AIModeration: Sarah Guo, Elad GilPhilipp Herzig tells No Priors how SAP is rebuilding the company operating system for AI, and why scale, evals, and pricing will decide who wins.
3:10:49ENCrunch Time: Ajeya Cotra's Playbook for the Months Before SuperintelligenceModeration: Nathan LabenzA conversation on recursive self-improvement, public warning systems, and using each generation of AI to align and control the next.
25:15ENMatthew Berman on Claude Mythos, Project Glasswing and AI SecurityMatthew Berman · Forward FutureBerman examines Anthropic’s limited cybersecurity preview and its reported capabilities, then weighs the promise and risks of increasingly capable AI systems.
1:13ENMemPalace Organizes AI Memory With a Palace MetaphorMatthew Berman · Forward FutureMatthew Berman introduces an open-source memory project and its reported retrieval score, while noting that AI memory tools already exist.
27:15ENMeta-Harness: Searching for Better AI Agent InfrastructureMatthew Berman · Forward FutureMatthew Berman reviews a paper on using a coding agent to iteratively improve model harnesses and discusses its experimental limits.
1:18:46EN90 Slides in 45 Minutes: A Full-Time AI Scout Admits He Can No Longer Keep UpModeration: Nathan LabenzNathan Labenz’s LexLab briefing for lawyers runs through the good, the bad, and the weird of frontier models — from triplicate second opinions on his son’s cancer care to AIs that know they are being tested.
11:59ENHow Claude Identified a Benchmark and Retrieved Its AnswersMatthew Berman · Forward FutureMatthew Berman examines Anthropic's report on BrowseComp contamination, evaluation-aware behavior, and the difficulty of keeping web-based tests reliable.
2:17:36ENCurrent Safety Techniques Will Not Give You Many Nines—and They May Fail TogetherModeration: Nathan LabenzUK AISI chief scientist Geoffrey Irving on jailbreaks that never fail, correlated alignment failures, and why a government lab is funding theory.
2:03:56EN260 Doctors and 49,000 Criteria: How OpenAI Built Attending-Level Health AIModeration: Nathan LabenzKaran Singhal tells Nathan Labenz how ChatGPT Health was evaluated with physicians, walled off from model training, and aimed at raising both the floor and the ceiling of human health.
54:26ENTen Billion Active Parameters, and the Unsexy Work of Making Them Agent-ReadyModeration: Nathan LabenzMiniMax researcher Olive Song explains how M2 was trained with expert developers as reward models, interleaved thinking, and a perturbation pipeline that treats every scaffold change as the real generalization test.
0:25ENGemini 3.1 Pro: Four Benchmark Results from Its 2026 LaunchMatthew Berman · Forward FutureMatthew Berman reviews reported academic, abstract-reasoning and coding scores; the chart distinguishes ARC-AGI-2 from GPQA Diamond.
44:46ENA Single Training Environment Can Cost Millions. Prime Intellect Wants a GitHub for RLWill Brown and Johannes Hagemann argue that the eval suite you already use to test agents is one step away from a training set.
0:53ENClaude Opus 4.6: Coding Claims, Agent Evaluation, and a Longer ContextMatthew Berman · Forward FutureMatthew Berman discusses Anthropic’s February 2026 model launch and a chart of AI task horizons.
1:57:56ENA Hundred Million Lines at Twenty Cents Each: Inside Blitzy's Enterprise Coding PipelineModeration: Nathan LabenzCEO Brian Elliott and CTO Sid Pardeshi tell Nathan Labenz how Blitzy turns a giant codebase into a map, then lets thousands of dynamically generated agents ship most of a multi-month project in days.
39:47ENThe Context at Step 14 Isn't in the CodeLangChain cofounder Harrison Chase on why long-horizon agents finally work—and why traces, harnesses, and first-draft workflows matter more than scaffolding.
17:44ENRecursive Language Models: Extending Long-Input Work Beyond a Context WindowMatthew Berman · Forward FutureMatthew Berman examines an MIT research approach that lets language models inspect external text and make sub-calls, along with its benchmark and cost tradeoffs.
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