필터 결과
205개정렬
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.
48:00ENA Two-Second Vote Is Training Models to Be Clickbait진행자 Jacob EffronSurge AI CEO Edwin Chen tells Redpoint why LMArena rewards length and emojis, how one lab's models got worse for a year, and why frontier labs are no longer on the same path.
7:14ENAWS AgentCore Adds Policy Controls, Evaluations and Episodic MemoryMatthew Berman · Forward FutureMatthew Berman examines AWS’s December 2025 agent-platform announcements and the enterprise problems they aim to address.
42:06ENAfter SWE-bench Hits 80.9%, Dianne Na Penn Reckons with Opus 4.5진행자 Jacob EffronAnthropic’s head of product for research on computer use, deceptive token prices, thin harnesses, and why alignment makes a model a better thinker.
1:11:18ENHe Got Bored Five Minutes Into a Waymo. Nathan Labenz Says That's the Point진행자 Nathan LabenzOn a crossover with the Existential Hope podcast, the Cognitive Revolution host argues that the scarcest resource in AI is a positive picture of daily life, not another doom scenario.
0:37ENMatthew Berman Shows ChatGPT Struggling With a Seahorse EmojiMatthew Berman · Forward FutureA simple question produces repeated corrections and an inconclusive answer in two ChatGPT examples.
32:22ENQwen Image Edit Plus Compared With Nano Banana, Seedream and GPT Image 1Matthew Berman · Forward FutureMatthew Berman compares four image-editing models across compositing, precise edits, style transfer, object removal and weather effects.
1:53:50ENA curriculum rewrite takes five years. AI does not wait.진행자 Nathan LabenzJohan Falk, a Swedish teacher-turned-analyst, argues that AI literacy and AI friends are more urgent than putting a tutor in every classroom.
1:37:25ENFin Climbed From 35% to 65% Resolution. Intercom Says the Models Only Did a Few Points진행자 Nathan LabenzEoghan McCabe and Fergal Reid on production A/B tests, 99-cent outcome pricing, and why GPT-4 was already smart enough for most customer service.
28:27ENSora 2 in Practice: Cameos, Camera Direction and Video Generation ErrorsMatthew Berman · Forward FutureMatthew Berman surveys early Sora 2 clips, testing likenesses, hands, motion, graphics and physics while pointing out where the generated scenes break down.
1:28:17ENAdam Gleave's Three Capability Tiers: Attack Agents in 5–7 Years, Fully AI Companies Around Year 14진행자 Nathan LabenzFAR.AI and the UK AI Security Institute stacked adversarial attacks on frontier safeguard pipelines and found many breakthroughs were engineering failures, not a verdict against defense in depth.
42:20ENA Name Invented at 2:30 a.m. Put Gemini on Top of the App Store진행자 Jacob EffronGoogle's Nicole Brichtova and Oliver Wang on Nano Banana, why image models got smart when language models did, and why a prompt still is not a finished product.
1:48:49EN$500, 2,000 Steps, and a Vending Machine: Grok 4 Profited, Claude Called the FBI진행자 Nathan LabenzAndon Labs put frontier models in charge of simulated and real vending machines to see what breaks when nobody is in the loop.
1:10ENTesting a GPT-5-Generated Rubik’s Cube SimulatorMatthew Berman · Forward FutureMatthew Berman prompts GPT-5 to build a Three.js cube simulator and tries its scramble and solve controls at several sizes.
25:24ENGPT-5 Tested on Coding, Simulations and Multimodal TasksMatthew Berman · Forward FutureMatthew Berman tries browser applications, visual simulations and image prompts, showing both successes and defects in his early hands-on tests.
10:28ENOpenAI’s gpt-oss Models: Open Weights, Benchmarks and SafetyMatthew Berman · Forward FutureMatthew Berman reviews the 120b and 20b models, local deployment claims, selected evaluations and the scope of OpenAI’s safety testing.
3:13ENClaude Opus 4.1: Anthropic’s August 2025 Benchmark UpdateMatthew Berman · Forward FutureMatthew Berman reviews the model’s reported coding, reasoning and tool-use results, including one benchmark that declined.
15:30ENQwen3 Tested: Coding Demos, Long Context and Response BehaviorMatthew Berman · Forward FutureMatthew Berman tests generated simulations, political and safety prompts, hand tracking and word puzzles using a hosted Qwen3 model.
38:10ENPlus Five for Format, Plus Ten if It Compiles진행자 Nathan LabenzPredibase CEO Dev Rishi explains how reinforcement fine-tuning, production feedback, and narrow specialist models turn one-off training into systems that keep learning.
26:09ENGrok 4 and Heavy Tested: Coding, Images, Reasoning and Practical QuestionsMatthew Berman · Forward FutureMatthew Berman’s July 2025 hands-on trials produce interactive prototypes and several failures, with important limits on what single examples can establish.
54:53ENA Free Voting Board Burned $2 Million. Then It Raised $100 Million.진행자 Jacob EffronDatabricks co-founder Ion Stoica explains why LMArena exists, why China is winning open-source models, and why reliability—not another scaling law—is still the thing holding AI back.
11:24ENRunner H and Surfer-H: A Browser Agent and Its Holo1 ModelsMatthew Berman · Forward FutureMatthew Berman demonstrates H Company’s browser agent and reviews its research on visual navigation. The paper’s benchmark configuration and the demo’s missing links show why results need context.
44:21ENMercor's CEO: Knowledge Work's Next Job Is Writing Evals진행자 Jacob EffronBrendan Foody tells Redpoint how Mercor turned AI hiring and expert data into a $100 million round — and why he thinks a huge slice of white-collar work will become creating tests for models.
18:13ENDarwin Gödel Machine: How a Coding Agent Tests Changes to Its Own SoftwareMatthew Berman · Forward FutureMatthew Berman explains Sakana AI’s evolutionary search over agent code, its benchmark results and the limits of claims about recursive self-improvement.
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