필터 결과
31개정렬
25:39ENPersonal AI Assistants in Practice: Briefs, Family Logistics, Email and BillsMatthew Berman · Forward FutureMatthew Berman describes how he connects assistants to everyday workflows and sets approval rules for consequential actions.
10:38ENJev Use Cases: Fast Decisions for Search, Email and Dynamic InterfacesMatthew Berman · Forward FutureMatthew Berman explains a decision-oriented AI tool and shows projects that use many low-latency classifications.
0:51ENJev Demos: Fast AI for Search, Classification and BrowsingMatthew Berman · Forward FutureMatthew Berman highlights a collection of community-built demonstrations using Jev.
1:06:47ENEighty percent of what agents do should still be ordinary code진행자 Nathan LabenzZapier CEO Wade Foster tells Nathan Labenz that models top out at 40% on real automation tasks, users will not leave their daily driver, and the winning move is headless tools delivered through MCP.
2:23:43ENHe Bought an Android, Two SIMs, and Left Claude Watching His Inbox진행자 Nathan LabenzNathan Labenz asked a dozen friends how to visit China and got two irreconcilable answers.
2:12:51ENSteve Newman’s fifteen-app stack exists so he only has to look at one screen진행자 Nathan LabenzThe Writely founder walks Nathan Labenz through a vibe-coded attention firewall, a conservative security stance, and why astonishing models still leave him far from AGI.
1:59ENHere.now and the Case for Agent-First Web PublishingMatthew Berman · Forward FutureMatthew Berman describes how a publishing service lets an AI agent put content online, and argues that the web is being rebuilt for agents.
40:00ENHow Matthew Berman Built an OpenClaw Workflow for His TeamMatthew Berman · Forward FutureA tour of sponsorship triage, CRM, meeting notes, security, scheduling, memory and backup after a month of heavy use.
33:44ENHow Matthew Berman Uses OpenClaw for a Personal AssistantMatthew Berman · Forward FutureA tour of customized CRM, meeting, research and security workflows, along with the limits of automation and the need for backups.
26:17EN21 Practical AI Uses for Design, Work and ResearchMatthew Berman · Forward FutureMatthew Berman demonstrates ways to use image tools, ChatGPT and NotebookLM for visual concepts, research, everyday work and study.
1:27:01ENThe $16 Trillion Services Market Is Being Bought, Not Sold To진행자 Nathan LabenzMarc Bhargava of General Catalyst explains how AI-enabled rollups turn low-margin service firms into compounding, software-like businesses.
1:28:48ENDoctors code at 45%. The model beat them by 12 points — then 4,000 physicians had to try it once.진행자 Nathan LabenzAmbience Healthcare and Cleveland Clinic walk through the AI scribe that cut pajama time, climbed ICD-10 with reinforcement fine-tuning, and won 75% voluntary use.
3:08:35ENo3 isn't AGI. Zvi Mowshowitz still put p(doom) at 70 percent진행자 Nathan LabenzOn his ninth visit to The Cognitive Revolution, Zvi Mowshowitz argues that OpenAI's o3 is a tool-use leap rather than AGI, then explains why he raised the probability of AI catastrophe anyway.
2:57ENChatGPT Tasks: Scheduled Reminders at the 2025 LaunchMatthew Berman · Forward FutureMatthew Berman explains the initial beta of scheduled and recurring ChatGPT tasks, and places it in the context of AI assistants.
55:05EN400,000 Customers, 100 Million AI Tasks: Zapier’s CEO on Making Computers Do More Work진행자 Nathan LabenzWade Foster tells Nathan Labenz how a no-code automation company absorbed LLMs, cloned its CEO with a prompt, and is trying to turn a DIY toolkit into packaged solutions.
2:03:55ENThe More You Constrain an Agent, the More You Can Trust It진행자 Nathan LabenzAgent.ai engineering lead Andrei Oprisan tells Nathan Labenz why narrow, highly prescribed workflows beat open-ended autonomy — and how a 12-person team shipped a free agent network past 40,000 users in a few months.
1:51:00ENStart With Ten Gold Examples: A Three-Step Playbook for AI Automation진행자 Nathan LabenzThe day after OpenAI opened GPT-4o fine-tuning, Nathan Labenz unpacks a three-year method for choosing work, extracting tacit judgment, and raising model performance.
2:00:03ENPatients Rated GPT-4 More Empathetic Than Doctors진행자 Nathan LabenzAzeem Azhar of Exponential View maps the AI S-curve for Nathan Labenz: why the past always looks flat, why empathy went first, and what a million times more compute would actually change.
1:50:57ENTwenty-Eight Days After GPT-4, an AI Designed and Ran a Chemistry Experiment진행자 Nathan LabenzGabe Gomes and his Carnegie Mellon students built Coscientist, a language-model agent that plans, codes, and executes real experiments on a remote cloud lab.
1:18:39ENThe GPT-4 Red-Teamer Who Got Unstuck by a Mediocre Outline진행자 Nathan LabenzDan Shipper shows Nathan Labenz how to use ChatGPT as a copilot for thinking, not as a ghostwriter for his own voice.
1:30:21ENA Million DALL-E Images, Distilled into Twelve Minutes of Film진행자 Nathan LabenzWaymark’s Stephen Parker and Josh Rubin walk through how they turned stills from DALL-E 2 into The Frost, a narrative short, and what the next generation of video models changes.
39:02ENAI’s product advantage is consistency. Its real weakness is getting conned.진행자 Nathan LabenzNathan Labenz updates the Cognitive Revolution scouting report: jailbreaks, resume automation, Med-PaLM’s 40% radiology win, and what might follow Transformers.
1:30:29ENGPT-4 Wins Ten Times More Bill Fights: DoNotPay's Robot-Lawyer War진행자 Nathan LabenzGPT-3 thanked Comcast for twenty dollars off. GPT-4 said that was not enough — and the robot lawyer called it an arms race.
1:10:11ENTrying ChatGPT is not the same as using it진행자 Nathan LabenzRachel Woods of The AI Exchange tells Nathan Labenz what she learned from more than a hundred company calls: start inside the business, skip the annual tool contract, and expect to be using only a sliver of what the models can already do.
더 보기31개 중 24개 표시







