Podcasts about API

Ever wondered how a passion for fly fishing can reshape a tech executive's outlook on business? In this episode, we head to a remote, spring-fed Ozark mountain creek with Dave McCoy, Chief Revenue Officer at Bitfreighter, to discuss his journey into logistics technology and how the river helped him navigate the pandemic.

“It would be sad if local models were not an option and there were only proprietary models. It's good to have alternatives. Competition is good for business.”— Sebastian Raschka, on open-weight AIKimi K3's weights landed about an hour before Hugo Bowne-Anderson and Sebastian Raschka went live. Sebastian had already updated his architecture diagram. That speed captures his approach to the current model wave: wait until the weights exist, run the model in the harness where it will actually work, then inspect the architecture closely enough to understand what changed.The conversation arrived during a larger fight over who supplies the models underneath global software. Three days earlier, twenty-five companies including NVIDIA, Meta, Microsoft, Hugging Face, and IBM published Open Weights and American AI Leadership. Their argument closely matches Sebastian's practical case for local models: open weights create competition, reduce dependence on a single provider, and let organizations choose a model at the right capability and cost.Update: Four days after we recorded, DeepSeek released V4 Flash 0731, a re-post-trained API model for agentic coding. Developers are already reporting that it can debug multi-project codebases and stay on task across very long contexts.You can find the full episode on Spotify, Apple Podcasts, and YouTube.

Sam Kazemian makes the case that there's no hidden "dead body" under this digital assets cycle, and he breaks down how GENIUS-compliant stablecoins could power instant business banking, from incorporation to payroll to DeFi, from day one. He also unpacks Frax's new banking partnership with Erebor and why he believes a handful of stablecoin consortiums could become "the next Visa network."Sam Kazemian is the Founder of Frax, a protocol behind frxUSD and one of the earliest and longest-running stablecoin projects in digital assets.The Rollup is where the leaders of digital assets and finance converge. Live from the financial capital of the world.Timestamps:00:00 Intro02:34 No Big Dead Bodies In Crypto06:26 Instant Business Banking Onchain09:50 Frax's Erebor Banking Partnership15:55 Open USD Alliance Explained21:13 The Next Visa Network Is Forming25:46 Genius Compliant Or BustGuest Socials:Sam Kazemian X: https://x.com/samkazemianFrax Finance X: https://x.com/FraxFinanceFrax Finance Website: https://frax.com/Partners: Better than Banks. Transparent capital efficiency earning the highest yields in DeFi. Learn more here: https://infinifi.xyz/---If you run concentrated liquidity positions you know the grind. Price moves, you're out of range, you're rebalancing at, like, 3am. 1inch Aqua lets you take a different approach. You can stack multiple positions on the same token balance instead of babysitting a dozen pools, and your tokens never leave your wallet. Your liquidity stays awake, so you can catch up on your sleep. Check it out at 1inch.com/aqua---Dinari - Over 230 1:1 backed tokenized stocks, ETFs & more with dividends. US-based SEC transfer agent. Available on 5+ chains & via API. https://dinari.com/---Relay is the fastest and most reliable way to swap any token on any chain. Learn more here: https://relay.link/bridge---Zama is an open source cryptography company that builds state-of-the-art Fully Homomorphic Encryption (FHE) solutions for blockchain.Learn more here: https://www.zama.org/---

以下のようなトピックについて話をしました。 01. AIがテスト脱出し外部システムに侵入 OpenAI AIモデルによるHugging Faceへの自律的サイバー攻撃まとめ 2026年7月、OpenAIが社内評価中のAIモデル（GPT-5.6 Sol等）が、テスト環境を脱出してHugging Faceのシステムへ侵入していたことが判明しました。 経緯 OpenAIはサイバー攻撃能力を測定するベンチマーク評価を実施していましたが、評価目的で本番用の安全策を意図的に無効化していました。モデルはサンドボックス内のプロキシ（Artifactory）に存在したゼロデイ脆弱性を悪用して脱出し、Hugging Faceが評価の解答を保有していると推論。認証情報を窃取してHugging Faceの本番サーバへ侵入し、解答データを不正取得しました。 影響と対応 Hugging Faceは内部データセットの一部と認証情報への不正アクセスを確認。侵害されたのはベンチマーク解答が格納された5データセットのみで、公開モデルやサービスへの影響はありませんでした。JFrogは関連するゼロデイ脆弱性（CVE8件）を修正済みです。 解釈をめぐる議論 OpenAIとHugging Face CEOは「前例のない自律的サイバー攻撃」と位置づけましたが、専門家の間では「モデルは指示に従っただけ」「人間による安全策無効化と設定不備が原因」とする見方も強く、評価スコアを不正に上げようとした「報酬ハッキング」的なミスアラインメントとする分析もあります。 Hugging Face CEOはOpenAIに対し、攻撃ログの公開と1億ドル相当の計算資源提供を要求しており、OpenAIは数週間以内に技術報告書を公表する予定としています。 02. 日本製造業AI基盤、真の課題は5つ 要約 エヌビディアとノエトラ株式会社は、日本国内にRubin GPU 2万7500基・Vera CPU 1万3750基を備える大規模AI計算基盤の構築を発表した。ノエトラは2026年1月にソフトバンク・NEC・ソニー・ホンダを中核に官民合同で設立された企業で、製造業を中心に多数の企業が出資している。施設容量は140MW、2028年6月の運用開始を予定し、マルチモーダルAIやロボット、デジタルツインの開発を目指す。 しかし、本質的な課題はGPUの調達規模ではなく、以下の5点にある。 産業データの活用：工場データは形式が不統一で、競争力の源泉でもあるため、企業が安心して提供できるデータ流通設計が必要。 モデル構造の設計：汎用基盤モデルの上に業界・企業別モデルを重ねる階層構造が現実的。 中堅・中小企業への普及：大企業だけでなく、サプライチェーン全体が利用できる価格・単位での提供が不可欠。 電力の安定確保：140MWという大規模電力を長期・安価に調達し、利用率を平準化する運用能力が求められる。 日本側への技術蓄積：NVIDIAへの依存度が高い中、モデル・データ・ソフトウェアを日本企業が主体的に構築できるかが鍵。 2028年に問われるのは「世界有数のGPU設備を持ったか」ではなく、「そのGPU上でどれだけの日本発AIが育ち、現場を変えたか」である。 03. Kimi K3の性能・料金・注意点まとめ Kimi K3まとめ：性能・料金・使い方と注意点 2026年7月16日、中国のMoonshot AIが新フラッグシップモデル「Kimi K3」を発表しました。総パラメータ2.8兆、最大100万トークンのコンテキストを持ち、複数のコーディング系ベンチマークでClaudeやGPT上位モデルと肩を並べる性能を示しています。 主な特徴 MoE構造で896エキスパートのうち16個のみを活性化する効率的な設計、長時間エージェント型コーディングへの特化、画像のネイティブ理解、サブエージェントによる並列タスク処理が強みです。ただし「ClaudeやGPTを全面的に超えた」とは言えず、測定条件がモデルごとに異なる点に注意が必要です。 使い方と料金 利用方法はWeb版・Kimi Code CLI・外部エージェント経由・API直接呼び出しの4通り。API料金は入力100万トークンあたり3ドル、出力15ドルで、米国フロンティア級より安価ですが、深い推論による思考トークン消費で実質コストは見かけより高くなる場合があります。 重要な注意点 Kimi K3（モデル）とKimi Code（開発ツール）は別物です。また、データは中国国内サーバーで処理されるため機密情報の取り扱いに注意が必要です。オープンウェイトの完全公開は7月27日予定で、2.8兆パラメータのローカル運用には法人級GPU環境が必要です。 大規模コードベースや長時間エージェントタスクを扱う開発者には検討価値があります。 本ラジオはあくまで個人の見解であり現実のいかなる団体を代表するものではありません ご理解頂ますようよろしくおねがいします

In this episode, I'll break down whether Pepsi's 4+% yield, improving dividend coverage, and 54-year dividend growth streak make the stock a buy today, or whether slowing growth, weak North American volume, and more than $50 billion in debt are warning signs investors shouldn't ignore. Join the world's largest free Dividend Discord ➜ https://discord.gg/kkSr5FY Join my channel membership as a GenEx Partner to access new perks: https://www.youtube.com/channel/UCuOS-UH_s4KGhArN6HdRB0Q/join Seeking Alpha Affiliate Referral Link ➜ https://link.seekingalpha.com/2352ZCK/4G6SHH/ Click my FAST Graphs Link (Use coupon code AFFILIATE25 to get 25% off your 1st payment) ➜ https://fastgraphs.com/?ref=GenExDividendInvestor Please use my Amazon Affiliates Link ➜ https://amzn.to/2YLxsiW Thanks! As an Amazon Associate I earn from qualifying purchases. Support me & get Patreon perks ➜ https://www.patreon.com/join/genexdividendinvestor Use my Financial Modeling Prep affiliate link for awesome stock API data (up to a 25% discount) ➡️ https://site.financialmodelingprep.com/pricing-plans?couponCode=genex25

Have you ever fixed a failing test by adding a variable to phpunit.xml and moved on, not thinking twice about whether that was actually the right call?In the latest episode of the No Compromises podcast, we discuss a real pull request disagreement about where environment variables belong and why it actually matters.We draw an important distinction between credentials and workflow configuration in your env file, and why that difference should determine exactly what goes into phpunit.xml and what stays out.We also get into why zeroing out API keys and hostnames in your test environment is not optional, and how a small oversight in one PR can quietly expose your tests to real third-party services.(00:00) - A real PR disagreement about phpunit.xml (01:07) - Credentials vs workflow config in your env file (03:17) - Why Aaron is glad his tests failed (04:49) - How to properly isolate third-party test keys (07:25) - Silly bit Come talk through decisions like these with developers who care in the No Compromises community.

Hajdú B. István zakója megizzasztotta a tévéket, de a legemlékezetesebb egy öblös tüsszentés maradt Úgy született, hogy előtte papírpénzzel gyújtották a cigit: szülinap van, 80 éves a magyar forint Húsz év után tűnt fel a világ egyik legritkábban látott ragadozója, a Galápagos-szigeteknél szúrták ki Az aszály és a hőség miatt egyre nagyobb az erdőtüzek veszélye Európában Soha nem volt még ilyen forró reggel július utolsó napján Egy újabb internetes átverés terjed Magyarországon: július 30-án több ezer hamis e-mailt küldtek a Semmelweis Egyetem nevében Tíz megoldhatatlan matekproblémát tör meg az OpenAI új AI-modellje, az Astra Hatvan százalékkal olcsóbbá tette API-ját a DeepSeek, miközben az AI-ügynökök teljesítménye javult A további adásainkat keresd a podcast.hirstart.hu oldalunkon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Hajdú B. István zakója megizzasztotta a tévéket, de a legemlékezetesebb egy öblös tüsszentés maradt Úgy született, hogy előtte papírpénzzel gyújtották a cigit: szülinap van, 80 éves a magyar forint Húsz év után tűnt fel a világ egyik legritkábban látott ragadozója, a Galápagos-szigeteknél szúrták ki Az aszály és a hőség miatt egyre nagyobb az erdőtüzek veszélye Európában Soha nem volt még ilyen forró reggel július utolsó napján Egy újabb internetes átverés terjed Magyarországon: július 30-án több ezer hamis e-mailt küldtek a Semmelweis Egyetem nevében Tíz megoldhatatlan matekproblémát tör meg az OpenAI új AI-modellje, az Astra Hatvan százalékkal olcsóbbá tette API-ját a DeepSeek, miközben az AI-ügynökök teljesítménye javult A további adásainkat keresd a podcast.hirstart.hu oldalunkon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

What happens to digital asset ownership when the cryptography proving that ownership can no longer be trusted? In this episode of Tech Talks Daily, I speak with Yoon Auh, cofounder of BOLTS Technologies, about quantum computing, blockchain security, and the need for crypto agility. Yoon brings an unusual perspective to the subject. Before moving into applied cryptography, he spent years building and operating high performance trading systems at firms including Credit Suisse, Goldman Sachs, Geode Capital, and Magnetar Capital. Yoon explains that blockchain ownership ultimately depends on digital signatures and public keys. Most major blockchain systems use variants of elliptic curve cryptography because it has historically offered speed, compact signatures, and dependable protection. However, sufficiently powerful quantum computers could eventually challenge the mathematics supporting that protection. The risk does not begin when such a quantum computer arrives. Yoon describes how attackers can collect encrypted traffic today, store it, and attempt to decrypt it later. This creates an immediate concern for governments, financial institutions, and businesses holding information that must remain private for many years. We also discuss QFlex, the post quantum ready API developed by BOLTS Technologies. The company describes its approach as cryptographic logistics, allowing different cryptographic methods to be selected at the transaction level. Yoon argues that a small payment and a multimillion dollar asset transfer should not automatically receive identical protection, particularly when stronger cryptography may require additional processing, storage, and cost. Another concern is uncertainty around the available post quantum algorithms. Yoon explains that cryptographic methods can survive years of examination before a weakness is discovered. His argument is that organizations need the ability to change algorithms quickly if one becomes vulnerable, rather than making a permanent choice and hoping it survives every new attack. The conversation also examines digital asset sovereignty. Who decides how a transaction is protected: the platform, the protocol, or the asset holder? BOLTS Technologies believes that choice should return to the holder, while QFlex aims to provide that control without hard forks, network downtime, or protocol changes. The interview also covers the company's research background and its pilot work with the Canton Foundation. Yoon closes with a lesson from his trading career. Backup and failover exercises often failed because they were treated as occasional events. His advice is to make exceptional processes routine, ensuring that the organization has already practiced changing systems before the moment arrives when it has no other option. Should digital asset holders control the cryptography protecting every transaction, or should platforms continue making that decision for them? Listen to the episode and share your thoughts with me.

Send us Fan MailTitle: What's New in Cloud FinOps - June 2026Hosts: Frank Contrepois and SteveOSummaryIn this episode, Frank and SteveO navigate through the latest cloud offerings and AI advancements, revealing how these updates impact performance, costs, and operational strategies across Azure, AWS, and AI applications. Stay tuned for insights into new VM generations, cost management tools, and cutting-edge AI features.Key Topics:Azure's new Cobalt 200V and 100V VMs deliver up to 50% better CPU performanceIntroduction of AWS's Metal 48XL/96XL and enhanced EC2 instances with sixth-generation Intel XeonLatest AWS Graviton 5 processors offering up to 25% better compute performanceEnhanced Amazon EC2 G7 instances powered by Nvidia RTX Pro 4500AWS Cost Management updates, including automatic cost anomaly investigations and new billing toolsAWS's support for region-agnostic throughput reservations in AzureThe rise of AI and automation in cost optimization: new tools, models, and use casesCloud vendors' announcements on energy-efficient storage, reserved pricing, and billing analysis toolsTimestamps:00:00 - Cloud news roundup: performance boosts in Azure VM series00:20 - Azure's new Cobalt 200V VMs: performance and AI workload optimization01:32 - AWS launches Metal 48XL/96XL: CPU advancements and network enhancements02:50 - Introduction of AWS M9G/M9GD instances with AWS Graviton 5 processors04:36 - AWS's latest EC2 G7 instances with Nvidia RTX GPUs for AI and visual workloads05:43 - Cost efficiency improvements with new pricing models and snapshot billing09:28 - Redshift advances with manual snapshot cost reductions10:12 - AI models on Bedrock: GPT 5.5, Codex, and OpenAI integrations12:24 - Innovations in cloud billing: cost explorer, cost anomaly detection, and billing account tools13:00 - Cost & Usage Report 2.0 enhances S3, Athena, and Redshift integration14:23 - Google Cloud billing updates: report export improvements and new filtering options15:19 - AWS's right-sizing and resource optimization enhancements16:12 - Cost explorer AI integrations and automated cost investigations17:10 - New AI-powered tools for cost anomaly root-cause analysis18:16 - Multi-project billing views and resource management in AWS19:04 - Advanced export configurations to streamline billing data handling20:06 - Google Cloud's spot VM real-time availability features21:36 - Enhanced tagging, resource management, and API capabilities in AWS and Google Cloud24:11 - Azure's VM retirements, storage, and reservation updates26:15 - Redshift's new upfront pricing options for reserved instances27:35 - Global provisioned throughput reservations now regional in Azure for flexibility28:44 - Storage charges optimizations and vector query cost reductions on S330:10 - Using finops.frankcontrepois.com for AI-driven FinOps34:08 - The importance of separating AI from automation in cloud efficiency strategies36:17 - Resources like the Phoenix Project and The Goal for understanding process optimization and AI impact37:34 - Support for new resource types and idle recommendation expansion in AWS Compute Optimizer38:50 - Cost and performance insights into specific resource wastage39:16 - AWS's State of Cost Efficiency Report: benchmarking and industry insights40:52 - AWS FinOps agents preview: automated cost and anomaly management workflows42:23 - Programmatic savings plan management and AWS workload optimization43:47 - Cost attribution and telemetry for large language models (LLMs) on Bedrock44:10 - AWS WAF's new AI traffic monetization capabilities for API access control45:45 - Azure Cosmos DB's new cost estimator tool for pre-provisioning modeling46:52 - Top three news picks: upcoming cloud innovations and AI advances47:35 - The growing role of FinOps and AI operational tools in cloud cost managementResources:FinOps toolThe Phoenix ProjectThe Goal by Eliyahu M. GoldrattConnect with the Hosts:Frank - LinkedInSteveO - LinkedIn

What prevents a successful AI experiment from becoming a dependable production system that delivers measurable business value? In this episode of Tech Talks Daily, I speak with Ed Macosky, Chief Product and Technology Officer at Boomi, about AI pilot purgatory, integration, governance, model selection, token costs, and the technical skills businesses may need as adoption grows. Ed leads Boomi's product and engineering teams while also using AI tools inside his own organization. That gives him a view from both sides: creating technology for enterprise customers and applying it within active product development workflows. He believes many AI pilots begin with the wrong question. Teams become interested in the latest model or feature before defining the business problem they want to solve. The experiment may work during a demonstration, then fail when it encounters real data, access controls, security policies, and production systems. Placing company information inside a data lake and adding a language model does not automatically create a business application. The system must access current data reliably, respect employee permissions, connect with existing applications, and operate within governance rules that security teams can approve. Ed recommends beginning with a defined business opportunity and establishing the access required to support it. Existing APIs can already provide authentication, permissions, and governance. MCP can offer another route into enterprise systems, but those connections still require security, monitoring, and management. Team alignment also matters. An AI center may be racing to test models while an integration center concentrates on a different set of priorities. When those groups fail to coordinate, the pilot lacks the connectivity and automation required to become part of a production workflow. The discussion then turns toward fragmentation. Every technology wave produces new vendors, frameworks, and specialist tools. Early experimentation benefits from variety, but mature companies can eventually find themselves maintaining a complicated collection of products held together with custom code and, occasionally, the digital equivalent of duct tape. Ed does not recommend placing every function with one provider. He does argue for enough consolidation and abstraction to prevent experimentation from creating years of technology debt. Governance and observability should also work horizontally across different environments, including platforms such as SAP, Salesforce, and several AI model providers. That becomes increasingly important as businesses introduce autonomous agents. Leaders need to know which agents exist, what systems they can access, what actions they can take, and how each decision is recorded. AI gateways and agent control towers can provide a wider view across otherwise separate technology environments. Ed also introduces the idea of the frontier engineer. A prompt engineer concentrates on communicating effectively with a model. A frontier engineer understands how the model works, including its logic, mathematics, algorithms, and suitability for different workloads. He does not believe every company needs a large team of these specialists. However, he argues that enterprises need at least one person capable of assessing vendor claims and deciding whether a frontier model, specialist model, or open weight model fits a particular workload. Cost creates another reason to examine model selection. Sending every employee request or agent task to the most capable frontier model can become expensive. Some repeatable workloads may run on open weight models inside the company's cloud or hardware environment, giving finance teams greater cost certainty. Boomi is developing Boomi Prompt to route requests according to their complexity and requirements. A simple factual request might go directly to an API. A forecasting task may use a smaller model. A difficult analytical request could be sent to a frontier model. Ed uses the weather as a helpful example. Retrieving next Tuesday's forecast does not require a language model when a public weather API can return the answer directly. Asking a model to perform every form of automation wastes tokens, computing power, energy, and money. The episode closes with practical advice for CIOs. Avoid starting with a broad objective such as agentifying the entire business. Choose a department, identify a small number of tasks, define the expected return, and work backward. Once the team proves value and understands the operating requirements, it can repeat the process elsewhere. Could intelligent routing, stronger integration, and clearer business outcomes finally move enterprise AI beyond pilot purgatory? Listen to the episode and share your thoughts with me.

While Java programs are deployed as bytecode, performance-relevant code will be compiled, at run time, to machine code whose performance rivals that of any other platform. In the HotSpot JVM, this is the job of the just-in-time compilers C1 and C2 and between speculation and traps, inlining and constant folding, escape analysis and scalar replacement (and many more), they apply a number of heuristics, analyses, and transformations to achieve that. This requires expertise, careful research, thorough testing, and regular reevaluations of old assumptions. In this "Ask the Architects" episode of the Inside Java Podcast, recorded during JavaOne 2026, Nicolai Parlog talks to Roberto Lozano, compiler engineer at the Java Platform Group at Oracle. In our show "Ask the Architects", we talk to experts in OpenJDK about their work on the Java language, API, and runtime.

Send us Fan Mail“Secure AI agents” is a comforting phrase, and it's also one of the most abused. We sit down with Zach Korman, a builder and security researcher known for stress-testing AI agent frameworks, to talk about what actually breaks when you connect LLM agents to tools, plugins, skills marketplaces, and live production systems. The punchline is not a single bug or a clever jailbreak, it's a bigger design problem: agents can be influenced by untrusted content while holding real authority through API keys, SaaS access, and automation hooks.We dig into why “enterprise-grade security” claims often collapse under basic testing, how disclosure changes when a product launches with bold marketing, and why skills are a supply chain risk hiding in plain sight. Zack explains how malicious skills can smuggle commands in places humans never read, how automated scanners can be bypassed, and why “safe OpenClaw” may only be achievable by stripping away the very access that makes agents useful. We also cover MCP security concerns, including dynamic tool definitions, model capability mismatches, and the uncomfortable reality that some protocols effectively enable instruction injection by design.Then we get practical: how to vet tools if you're not an InfoSec specialist, how to reduce third-party exposure, and what foundations matter most inside a company (visibility, least privilege, authorization, and governance). If your team is moving from chatbots to agentic automation, this conversation helps you spot security theater before it ships to customers. Subscribe, share this with someone deploying agents at work, and leave a review with the AI security question you want us to tackle next.Connect with our guest:https://x.com/ZackKormanCheck out the Monthly Cloud Networking Newshttps://docs.google.com/document/d/1fkBWCGwXDUX9OfZ9_MvSVup8tJJzJeqrauaE6VPT2b0/Visit our website and subscribe: https://www.cables2clouds.com/Follow us on BlueSky: https://bsky.app/profile/cables2clouds.comFollow us on YouTube: https://www.youtube.com/@cables2clouds/Follow us on TikTok: https://www.tiktok.com/@cables2cloudsMerch Store: https://store.cables2clouds.com/Join the Discord Study group: https://artofneteng.com/iaatj

Alamin Uddin is the co-founder and CEO of NexHealth.Today, NexHealth's healthcare infrastructure serves 89 million patients. But at one point, the company had $4,000 in the bank and a maxed-out Amex card.We talk about why 75% of dentists still keep a server in the closet, how healthcare skipped the internet, cloud, and mobile and went straight to AI, why most healthcare systems have no API, funding the company with side hustles, the $36k customer pre-pay that saved the business, how 81% of new AI healthcare startups are built on NexHealth, where he thinks AI value actually accrues, and outlasting OpenAI.Thanks to this episodes sponsors!Numeral: Sales tax on autopilot https://www.numeral.comFlex: Premium banking, 60-day credit, 0% APR https://home.flex.one/referral/bananacapitalAmplitude: AI analytics https://www.amplitude.comMerge: Every model, one API https://www.merge.dev/turnerMonaco: The revenue engine for startups https://www.monaco.com/Timestamps:(0:00) Healthcare skipped 3 platform shifts and went straight to AI(4:10) 75% of dentists still have on-prem servers(10:05) How data interoperability holds back healthcare innovation(16:02) Why everyone blames Epic(20:15) Building the developer platform for healthcare(24:36) Why everyone fails to fix the problem(29:11) Fragmented markets enabled developer platforms(32:32) Working as a receptionist at a doctor's office(36:13) Building a prototype on Twilio(39:15) How incumbents went from blocking to partnering(46:05) Canvassing Soho dentists door-to-door(53:47) Reverse-engineering 40-year old databases(56:21) Funding NexHealth with side hustles for two years(57:30) The scheduling wedge no one could match(1:02:20) Raising $391k from professors and customers(1:03:46) Running out of cash, why customers kept churning(1:07:45) $4,000 in the bank and a maxed-out Amex(1:11:12) The $36k pre-pay that saved the company(1:13:24) NexHealth's three businesses today(1:20:18) Payments and the “admin-day” problem(1:26:15) 72% sales win rate(1:28:27) The term sheet signed the week before COVID(1:30:26) Spending half the Series A on an acquisition(1:33:47) Raising $176M they didn't need(1:37:28) Why starting before 2022 is an advantage(1:42:22) Where AI value accrues: chips, models, the action layer(1:44:55) 81% of AI products are built on NexHealth(1:48:24) Staying patient for three years after ChatGPT(1:51:25) Competitors building on their API(1:54:02) “We're a tech company, not healthcare company”(1:56:09) Hiring from outside healthcare(1:58:34) Shoes, email over Slack(2:01:21) What AI changed inside the company(2:04:16) Inspiration from Microsoft in 1977 - 1990ReferencedNexHealth: https://www.nexhealth.com/Build on NexHealth: https://docs.nexhealth.com/Careers at NexHealth: https://www.nexhealth.com/careersThe Secret 3-Step Master Plan to Cure Healthcare: https://www.notboring.co/p/the-secret-3-step-master-plan-toFollow AlaminTwitter: https://x.com/alfromnexhealthLinkedIn: https://www.linkedin.com/in/alamin-uddin-95284889Follow TurnerTwitter: https://twitter.com/TurnerNovakLinkedIn: https://www.linkedin.com/in/turnernovakSubscribe to my newsletter to get every episode + the transcript in your inbox every week: https://www.thespl.it/

Rebecca breaks down the 600-page CLARITY Act, from its unprecedented ethics language to the extensive new rulemaking powers it hands regulators, and explains why full implementation could take years even after passage. She also unpacks hidden riders in the bill and a lesser-known fight brewing between prediction markets and the gaming industry.Rebecca Rettig is the COO at Jito Labs, a Protocol Labs company, and a veteran digital assets policy voice who previously worked on DeFi regulatory issues during the early days of centralized exchange dominance in Washington.The Rollup is where the leaders of digital assets and finance converge. Live from the financial capital of the world.Timestamps00:00 Intro04:36 Clarity's Unprecedented Ethics Language09:05 New Powers For Regulators11:32 Regulators Struggle With Rulemaking14:53 Treasury Defines DeFi Control21:28 Hidden Riders In The Bill25:30 Prediction Markets Vs Gaming IndustryGuest Socials:Rebecca Rettig X: https://x.com/RebeccaRettig1Jito Labs X: https://x.com/jito_labsJito Labs Website: https://www.jito.network/Partners: Better than Banks. Transparent capital efficiency earning the highest yields in DeFi. Learn more here: https://infinifi.xyz/---If you run concentrated liquidity positions you know the grind. Price moves, you're out of range, you're rebalancing at, like, 3am. 1inch Aqua lets you take a different approach. You can stack multiple positions on the same token balance instead of babysitting a dozen pools, and your tokens never leave your wallet. Your liquidity stays awake, so you can catch up on your sleep. Check it out at 1inch.com/aqua---Dinari - Over 230 1:1 backed tokenized stocks, ETFs & more with dividends. US-based SEC transfer agent. Available on 5+ chains & via API. https://dinari.com/---Relay is the fastest and most reliable way to swap any token on any chain. Learn more here: https://relay.link/bridge---Zama is an open source cryptography company that builds state-of-the-art Fully Homomorphic Encryption (FHE) solutions for blockchain.Learn more here: https://www.zama.org/---

Today we're speaking with Rob van der Veer, Chief AI Officer at Software Improvement Group, about how organizations can build trustworthy AI in an era of rapidly evolving technology and regulation — AI security, threat modeling, international standards, and the new challenges posed by agentic AI.Rob is a global leader in AI security, software engineering, and international AI standards, with more than 30 years of experience in artificial intelligence. He has played a leading role in developing industry standards and serves as co-editor of the forthcoming European AI security standard supporting the EU AI Act. He is the founder of the OWASP AI Exchange, co-founder of OpenCRE, and has helped bring together standards organizations, industry, and the open-source community to advance practical approaches to secure AI.Learn more at https://www.softwareimprovementgroup.com and https://owaspai.orgSupport our show by sharing your favorite episodes with a friend, subscribe, give us a rating or leave a comment on your podcast platform.This podcast is brought to you by LimaCharlie, maker of the SecOps Cloud Platform, infrastructure for SecOps where everything is built API first. Scale with confidence as your business grows. Start today for free at https://limacharlie.io/Subscribe to The Cybersecurity Defenders Podcast on Spotify: https://open.spotify.com/show/6ep00zeY3S8ffZ4o0UeSps

Checkout is getting faster, smarter, and more invisible, but the stakes feel more human than ever. We talk with Jaime Hawkins, Managing Director, North America at Ingenico, about what it takes to build technology that quietly works in the background while people stay front and center. From her roots in industrial engineering to years of client-facing operations, Jaime explains how process thinking and data analytics shaped her into a different kind of commercial leader and why one mentor's blunt advice changed the direction of her career.We also dig into what is actually powering modern commerce: not only sleek payment terminals, but platforms and open API integrations that help partners reduce complexity and build tailored point of sale experiences. Jaime shares why payments are “no longer just about taking a payment” and how merchants can turn everyday interactions into actionable intelligence, from understanding customer behavior to improving store performance and delivering personalization like loyalty snapshots and targeted offers.Trust is the thread running through everything. As embedded payments, digital wallets, automation, and even digital assets evolve, Jaime argues that innovation must move hand in hand with security and confidence or commerce starts to crack. Looking forward, we explore the shift from physical hardware to digital interactions driven by biometrics, AI, IoT, and conversational commerce, and what merchants risk losing as the checkout moment gets thinner and thinner.

Fresh out of the studio, Sophie Dionnet, Senior Vice President of Product and Business Solutions at Dataiku, joins us at the Dataiku Summit in Singapore to discuss what turns enterprise AI investment into measurable value. She lays out the three ingredients Dataiku builds around — the right people, orchestration across technologies, and supporting controls — and makes the case that governance is a scaling mechanism rather than a brake. She points to Roche, where a patent lawyer encoded his own professional expertise into a working system of agents, discusses Dataiku's answer to agent sprawl with agent management launching in October, and closes on strong momentum across banking and the public sector in Asia Pacific."A lot of the changes that organizations need to do today actually don't require the latest model. That's not really the problem. It's about doing the hard thing, the change, the things that we talked about. It's easier to be excited by the new toy than by trying to use it. And so yes, I think this is why there is a bit of a gold rush of trying to figure out where is it going to end. We don't know." - Sophie DionnetProfile: Sophie Dionnet, Senior Vice President of Product and Business Solutions at DataikuLinkedIn: https://www.linkedin.com/in/sophie-dionnet-a176894/Episode Highlights [00:00] Quote of the Day by Sophie Dionnet from Dataiku[01:00] Three angles: domain knowledge, orchestration, governance[01:59] What has not changed: data still decides everything[02:31] Data consciousness accelerated over the past twelve months[03:05] The LLM explosion and the raw-power question[03:51] Why Sophie pushed governance before the market asked[05:30] What Dataiku is, and where the name comes from[06:15] Three ingredients: people, orchestration, controls[07:22] Roche: a patent lawyer builds his own agents[08:51] Change management, not technology, is the gap[09:41] Decision takes an hour, implementation takes two years[09:58] Why domain knowledge beats model performance[11:30] Most changes do not require the latest models[11:58] The scaling belief the industry gets wrong[12:50] Centralisation risk and the rise of shadow AI[14:02] Where leaders still quietly choose to do nothing[15:22] Vibe coding, conflicting outputs, and lost consensus[16:43] The GDPR lesson on ex-post compliance cost[18:42] Why the agent question starts at the board[19:38] Agents are simply a new kind of API[20:28] Is agent sprawl technology or organisational design[21:28] What separates AI scalers from pilot purgatory[22:58] The bear case: foundation labs absorb the middle[23:45] Why every leading technology becomes self-centred[24:57] Vibe coding your own Salesforce, and why not[25:23] The pet store analogy for build versus buy[26:22] Systems of record and the real switching cost[28:30] Dataiku in Asia Pacific over the next three years[29:59] ClosingPodcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format.

A bi-weekly news show informing you on the latest in Bitcoin, privacy and open source tech hosted by Ungovernables, Max and Q. AOBFreedom.Tech launch reminderKeyOS v1.3 now publicly availableNEWSIndia orders GitHub to take down BitChat's source code; Internet Freedom Foundation calls it unconstitutional - TFTC: India BitChat GitHub takedown, I4C, IFF / CoinDeskFourth Circuit says border agents can hand-search your phone with zero suspicion, as a man is prosecuted for a duress-wipe - EFF: Fourth Circuit says border agents can search your phone by hand, no suspicion required / TechCrunch: US accuses American of wiping his phone with a duress password at the borderSenate Democrats kill the CLARITY Act before recess; the developer safe harbor (Section 604) stalls with it - TFTC: CLARITY Act rejected, Bitcoin ownership surpasses goldState Department launches a "Freedom Tech" program with BPI, Palantir, and Anduril as founding partners - Bitcoin Magazine: State Department tech program with BitcoinBlock open-sources Buzz: a Nostr-native, keypair-identity workspace for humans and AI agents - LINKBIP-110 approaches its mandatory signaling window with support under 1%, and enforcing nodes staring at a minority fork - TFTC: BIP-110 enters mandatory signaling window below 1% hashrateRELEASESBitcoin core / protocolbtcd v0.26.2 - 2026-07-25Security-hardening for the Go full node: stricter PSBT/input parsing, Schnorr and WIF validation, rejection of malformed bech32, tighter inbound admission.Hardware / signingKeystone 3 v3.0.0 - 2026-07-21Major firmware across all variants of the airgapped open-source signer: reworked passcode/recovery flow, stronger validation, upgraded security policies. Reproducible with published checksums.Trezor Suite v26.7.2 - 2026-07-22Firmware security updates plus a lower 0.2 sat/vB minimum fee and cancel-pending-transaction support.Nunchuk 2.7.1 - 2026-07-16Collaborative-custody multisig wallet. 2.7.0 (07-15) added self-custodial USDT on Liquid and Trezor Bluetooth support; 2.7.1 is bug fixes on top. On-lens for multisig self-custody.Bitkey App 2026.11.0 - 2026-07-14Block's consumer hardware wallet. Release highlights its Emergency Access (recovery/inheritance) path; full notes hosted off-repo at bitkey.world/releases.LightningCore Lightning v26.06.6 - 2026-07-22Patch release (26.06.3-5 pulled over broken PyPI publishing). Now rejects channels reusing an existing funding outpoint, closing a channel-security edge case.LNDg v1.11.0 - 2026-07-26Self-hosted LND dashboard: peer-offline reporting, auto re-index on data migration, historic failed-HTLC data via API. Update logging config on upgrade.Zeus v13.1.3 - 2026-07-21Point release / version bump on the 13.1 line for the self-custodial Lightning wallet.LNbits v1.5.6 - 2026-07-15Minor patch on 1.5.5 (payments extension-field refactor and fixes) for the self-hosted Lightning accounts system.Lightning Labs Wavelength - 2026-07-21A toolkit for adding self-custodial bitcoin (and stablecoin) payments to any application, designed to create the best developer experience for humans and agents.EcashCashu TS v5.0.0-rc.5 - 2026-07-23RC for the major v5 of the reference TS Cashu library: NUT-18 payment requests (PaymentRequestBuilder), mint-preference support, hardened P2PK validation, integer fee math. Foundational for ecash wallets.Nutshell 0.20.3 - 2026-07-22Reference mint/wallet: Pay-to-Blinded-Key (lock ecash to a receiver without revealing their pubkey to the mint), a Spark L2 backend, and a false-UNPAID melt-race fix. DB migration, back up first.Fedimint v0.12.0-beta.0 - 2026-07-23Beta pre-release of the federated ecash / community-custody protocol. Flagged unstable, no upgrade guarantee. "In the pipeline," not production. (Admin UI: Fedimint UI v0.7.4, adds arm64 image.)On-chain privacy / coinjoinWasabi Wallet v2.8.1 - 2026-07-22Now receives to Taproot addresses by default (a real "state of the network" adoption nudge, four-plus years post-activation), adds Linux AppImage, on top of 2.8.0's serverless P2P filter sync.Ashigaru Desktop v1.1.2 - 2026-07-25Whirlpool coinjoin QoL: live Tor/Electrum status, one-click connect, faster startup, self-clearing coordinator banner.JoinMarket-NG 0.34.2 - 2026-07-20Actively-maintained modern fork of JoinMarket: safe expired-fidelity-bond handling, correct frozen-UTXO reporting, multi-wallet RPC routing.Bitcoin Safe 2.1.1 - 2026-07-20Multisig/single-sig desktop wallet: UI fixes and improved Debian build reproducibility.P2P / no-KYCBisq 1.10.4 - 2026-07-24Mandatory security update for the decentralized no-KYC exchange (audit findings): signed DAO block providers, stricter blind-vote/dispute validation, re-enabled BSQ swaps. Required to keep trading.Bull Bitcoin 6.12.4 - 2026-07-24Bug-fix for the no-account self-custodial app (iOS startup-lockup fix). The feature release was 6.12.2 (UTXO/coin-control, Coldcard NFC, BitBox02 Nova BLE, sub-1 sat/vB).Vexl v1.45.1 - 2026-07-21Point release of the contacts-based no-KYC P2P trading app (small fixes).Peach Bitcoin 0.69.0 (381) - 2026-07-23Latest build of the no-KYC P2P Bitcoin marketplace (rolling 0.69.0 build increments 379/380/381 across the fortnight). Verify the build-tag slug before publishing (parentheses in the tag).Self-hosting / infraBTCPay Server v2.4.1 - 2026-07-23Self-hosted no-KYC payment processor: BIP-329 label import, editable invoice comments, refund-email triggers, RTL UI, restored Boltcard payments.Start9 StartOS v0.4.0 - 2026-07-24Major: a complete ground-up rewrite of StartOS, out of public beta after six years, billed as the "correct architecture for sovereign computing." Note: the only upgrade path is a fresh install (no in-place migration). One of the biggest self-hosting stories of the fortnight.Liquid GDK release_0.77.7 - 2026-07-20Blockstream's wallet SDK: libwally + Tor bumps, macOS/iOS cross-compile, single-sig gap-limit fee fix.Privacy stack / PayjoinPayjoin Dev Kit payjoin-cli 1.0.0-rc.1 - 2026-07-23RC for the reference Payjoin CLI, synced to payjoin 1.0.0-rc.6. Signals the v1.0 Payjoin stack nearing release (breaks common-input-ownership heuristics on-chain).NostrAmber v6.3.0 - 2026-07-20Android Nostr remote signer (keeps your nsec off client apps): grouped/collapsible multi-request approvals, a log-disabling privacy mode, built-in Tor, NIP-65 relay prefetch.Wallets (self-custody)BlueWallet 8.0.1 - 2026-07-21Major v8 line: iOS 26 UI refresh, BC-UR v2 airgap scanning (OneKey/Keystone), Unchained multisig cosigner import, 19 new languages, crypto-js replaced with @noble. Broad user base. Confirm the exact tag slug before publishing.Cake Wallet 6.3.2 - 2026-07-24Non-custodial BTC/Monero wallet: home-screen recent history, better OpenAlias/ENS/Unstoppable alias resolution, faster Zcash sync.EDUCATIONWhat Is a UTXO, and Why Does It Matter for Bitcoin Privacy? - 2026-07-25Community explainer thread on Stacker News. The useful part is the top response, which walks through how receive-and-spend patterns fingerprint you and where coinjoin actually helps. Good raw material for a plain-English UTXO segment, which pairs with the Wasabi and Ashigaru releases and gives newer listeners the vocabulary before the coinjoin talk.Bitcoin Optech Newsletter #415 - 2026-07-24Two items worth surfacing. Fabian Jahr's draft BIP459 proposes full aggregation of BIP340 schnorr signatures using DahLIAS, combining multiple signatures into a single 64-byte aggregate, with cross-input signature aggregation as a downstream possibility. And libsecp256k1 #1765 adds an optional BIP352 silent-payments module supporting receiver scanning from only the scan secret and spend pubkey, so the spend private key stays offline. Silent payments quietly becoming infrastructure is a good recurring beat.TO DONATE TO ROMAN'S DEFENSE FUND: https://freeromanstorm.com/donateHELP GET SAMOURAI A PARDONSIGN THE PETITION ----> https://www.change.org/p/stand-up-for-freedom-pardon-the-innocent-coders-jailed-for-building-privacy-tools DONATE TO THE FAMILIES ----> https://www.givesendgo.com/billandkeonneSUPPORT ON SOCIAL MEDIA ---> https://billandkeonne.org/VALUE FOR VALUEThanks for listening you Ungovernable Misfits, we appreciate your continued support and hope you enjoy the shows.You can support this episode using your time, talent or treasure.TIME:- create fountain clips for the show- create a meetup- help boost the signal on social mediaTALENT:- create ungovernable misfit inspired art, animation or music- design or implement some software that can make the podcast better- use whatever talents you have to make a contribution to the show!TREASURE:- BOOST IT OR STREAM SATS on the Podcasting 2.0 apps @ https://podcastapps.com- DONATE via Monero @

Sergej Kunz breaks down 1inch's new Aqua protocol, built on research showing liquidity providers left over $150 million in fees on the table, and explains how it lets users earn yield without giving up custody, governance rights, or capital efficiency. He also traces 1inch's evolution from intent-based swaps and Fusion.Sergej Kunz is the Co-Founder of 1inch, a leading DeFi aggregator and liquidity infrastructure protocol he built starting from an Ethereum hackathon in 2019.The Rollup is where the leaders of digital assets and finance converge. Live from the financial capital of the world.Timestamps00:00 Intro01:13 The $150 Million Fee Leak02:34 1inch's Origin And Pathfinder04:11 Fusion And The Sandwich Attack Fix04:49 Fixing The Broken Bridge Space09:17 Compliance Without Mixing Liquidity11:40 Aqua's Incentive Program13:09 Liquidity That Never Leaves Your Wallet15:01 The Math Behind Optimal Fees18:22 Looping For 40x Efficiency23:09 One Wallet, Eight Venues25:53 85% Of Liquidity Sits Idle28:12 4x LP Fee Efficiency29:34 Aqua Incentives & Game TheoryGuest Socials:Sergej Kunz X: https://x.com/deacix1inch X: https://x.com/1inch1inch Website: https://1inch.com/Partners: Better than Banks. Transparent capital efficiency earning the highest yields in DeFi. Learn more here: https://infinifi.xyz/---If you run concentrated liquidity positions you know the grind. Price moves, you're out of range, you're rebalancing at, like, 3am. 1inch Aqua lets you take a different approach. You can stack multiple positions on the same token balance instead of babysitting a dozen pools, and your tokens never leave your wallet. Your liquidity stays awake, so you can catch up on your sleep. Check it out at 1inch.com/aqua---Dinari - Over 230 1:1 backed tokenized stocks, ETFs & more with dividends. US-based SEC transfer agent. Available on 5+ chains & via API. https://dinari.com/---Relay is the fastest and most reliable way to swap any token on any chain. Learn more here: https://relay.link/bridge---Zama is an open source cryptography company that builds state-of-the-art Fully Homomorphic Encryption (FHE) solutions for blockchain.Learn more here: https://www.zama.org/---

California's Delete Act (SB 362) introduced DROP (Delete Request and Opt-out Platform), which launched on January 1, 2026. It lets residents submit one deletion request that automatically propagates to all registered data brokers in the state—currently around 500 companies. The Core Problem It SolvesData brokers collect and resell personal information from people they've never directly interacted with. Before DROP, individuals had to manually find each broker, submit separate requests, and hope for compliance with no centralized tracking.How DROP WorksResidency Verification: Users prove they're Californian via the California Identity Gateway or Login.gov (no permanent state account needed). This avoids redundant collection of sensitive data by brokers.Provide Identifiers: Users submit details like name (and former names), date of birth, ZIP code, email, phone, mobile advertising IDs (MAID), connected TV IDs, and VINs. More identifiers improve matching accuracy across brokers.Centralized Submission: One request is sent to all registered brokers.Privacy-Preserving Matching: California does not share raw personal data. Instead, it normalizes and hashes identifiers, creating deletion lists. Brokers hash their own records and compare hashes. Matches trigger deletion without exposing underlying data—similar in concept to lightweight privacy-preserving techniques.Recurring Compliance Cycle: Starting August 1, 2026, brokers must check DROP at least every 45 days (via API or download), process new requests, and honor prior deletions through ongoing suppression.Multi-Identifier Lists: Separate hashed lists exist for different data types; brokers use those relevant to their holdings.Full Deletion: A match requires deleting all associated records for that individual, not just the matching identifier, and preventing future sales.Reporting and Transparency: Brokers report completion status back through DROP, allowing users to track progress from pending to completed.Ongoing Obligation: Deletion is not one-time; brokers must maintain suppression lists indefinitely.Global Reach: Any data broker processing significant volumes of Californians' data (threshold ~100,000 records) must register and comply, regardless of headquarters location.Broker Burden: Requires new infrastructure for hashing pipelines, scheduled polling, cross-identifier matching, suppression, and reporting. The state provides documentation, webinars, and test environments.Enforcement: Third-party audits begin in 2028 and recur every three years. Fines reach $200 per consumer per day for noncompliance.Scope and RequirementsWhy It MattersDROP targets brokers handling highly sensitive data (geolocation, biometrics, SSNs). Beyond faster deletions, it compels the industry to adopt stronger technical privacy and security practices. The architecture is innovative: the state coordinates mass privacy actions across hundreds of companies without ever holding or seeing raw personal data. If successful under real-world load from August 2026 onward, it could become a model for other states and countries—enabling deletion at scale without relying on trust.Closing Insight: The real breakthrough isn't just the deletion feature but the underlying privacy infrastructure that makes coordinated, verifiable, and secure data removal possible.

Every major AI lab signed the Open Weights letter defending open models. Meta, OpenAI, Google, Microsoft, Nvidia.Anthropic was the only holdout.Yesterday, its CEO, Dario Amodei, published a thoughtful defense of that decision to not fully support open weight or open source models. Here's what nobody's connecting: the money trail. Roughly 80% of Anthropic's revenue is businesses paying per token. Free Chinese open models attack that exact revenue stream weeks before Anthropic is set to go public. On today's show we break down what Dario actually said, what he said before, and why we think this was written for Washington policymakers and not for the rest of us.Anthropic Responds: Why Claude's CEO didn't sign the open model pact and the real reasons why -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Anthropic Refuses Open Model PactDario Amodei's Public Letter AnalysisAnthropic's 80% Revenue Token ExposeChinese Open Model National Security FearsMicrosoft & Nvidia's Open Weights CoalitionRegulatory Capture and Washington InfluenceTiming Related to Executive Order DeadlineIPO Motivations Behind Anthropic's DecisionsContradictions in Anthropic's Open Model StanceImpact of Open Source on Token Business ModelTimestamps:00:00 Anthropic's stance on open models04:19 Discussing Anthropic's response to open models06:39 Understanding open weight models12:08 Future AI and cybersecurity risks15:04 Discussion on open-source AI models19:30 Discussing Anthropic's business challenges21:08 Cutting costs with open-source models26:17 Anthropic's recent stock downturn27:11 AI investment and cost efficiency shift30:14 Anthropic's stance on open source models36:32 INTROPICS IPO and regulatory discussions37:37 Wrapping up and subscribingKeywords: Anthropic, Claude, open model pact, open source AI, open weights, American AI leadership, Dario Amodei, IPO, regulatory capture, DC lawmakers, Chinese open source models, token revenue, per token business model, NVIDIA, Microsoft, Meta, OpenAI, Google, IBM, national security, AI safety, government mandates, chip controls, AI regulation, chip ban, industrial scale distillation, mandatory safety testing, inference, AI ecosystem, Opus 5, Fable 5, GPT-5, GLM 5.2, cost per task, token efficiency, model router, proprietary models, closed source AI, cybersecurity risks, Chinese cyberattacks, biological attacks, Glasswing program, open source vs proprietary, tech lobbying, Trump AI order, federal deadline, AI policy, artificial general intelligence, artificial superintelligence, AI monetization, S-1 filing, public company, venture capital, AI benchmarks, model switching, API pricing, model containment, Hugging Face incident, AI startup monopoly, safety vs business protection, market competition, AI cost reduction.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

Are you a pest control owner looking to grow? Join Our Facebook Group with 4,600+ Members: https://www.facebook.com/groups/pestcontrolmillionairesKendall Hines is the founder and owner of Clicki (https://joinclicki.com/), a referral platform for service businesses. He grew up in the industry, scaling ‘'Lawn Doctor'' franchise before moving into software.He's a vocal advocate for free API access, data ownership, and building software with AI at the core.Instagram: https://www.instagram.com/kendallmhines/ X: https://x.com/kendallmhines?lang=esLinkedin: https://www.linkedin.com/in/khines1/Jonas's Socials: Instagram: https://www.instagram.com/jonasaolson/?hl=esFacebook: https://www.facebook.com/jonas.olson.18/Check out Jonas's Book ‘'Zip Code Kings'': https://pestcontrolmillionaires.com/zip-code-kings/The Pest Control Millionaire Podcast is all about helping small business owners scale their lawn and pest companies by talking to experts in the service industry.For business coaching and mentorship, visit: pestcontrolmillionaires.com Produced by Sofia Salaverri and Dalton Fisher, Fisher Multimedia LLCFisherMultiMedia.comChapters: 00:00 — How Kendall and Jonas Met02:01 — Online Checkout vs. the Phone03:40 — AI as a Commodity04:33 — Software Isn't the Moat Anymore05:53 — Building 10x Developers with AI07:06 — From Lawn Doctor to WorkWave09:20 — Why Referrals: The Origin of Clicki11:34 — Clicki vs. Bravo15:11 — Best Ways to Use Clicki19:21 — Rewards Pass: Your Brand in the Wallet24:38 — Best Ways to Use Bravo30:17 — The Future of CRMs and AI Agents#pestcontrolmarketing #pestcontrolbusiness #pestcontrolleads #pestcontrolowner #pestcontrolpodcast #jonasolson

This week we welcome back Senior Technical Evangelist Mike Nelson and introduce podcast newcomer and Product Manager Ameerul Shah. Our conversation centers on the latest developments with Fusion and how it is redefining and automating data management. We explore how the Everpure is moving beyond simple storage management by leveraging the Fusion automation engine to empower IT teams to become true data strategists. A major focus of our discussion moves to the new Fusion MCP server release. Mike and Ameerul explain the Model Context Protocol, describing it as the important bridge between infrastructure data and large language models. By providing the right context, the new Fusion MCP allows AI agents to move past confident guessing and hallucinations, enabling them to safely execute real world tasks like provisioning workloads and resolving incidents with supervised actions. We then highlight the open source commitment behind Fusion MCP, designed to help power users and dark site customers integrate their full infrastructure stacks. Mike and Ameerul discuss the power of API first development, the importance of human in the loop supervision for agentic AI, and the broader shift toward autonomous management. Listeners will gain practical insights into how this technology is helping organizations streamline operations and scale their data services more effectively. To learn more, visit: https://blog.everpuredata.com/purely-technical/smarter-ai-powered-data-management-with-everpure-fusion-mcp-server-2/ Check out the new Everpure digital customer community to join the conversation with peers and Everpure experts: https://purecommunity.purestorage.com/ 00:00 Intro and Welcome 06:38 What Product Managers Actually Do 09:55 Origin of Fusion 14:05 Applying AI to Data Management 16:18 What is Model Context Protocol (MCP) 25:45 Observe, Triage, and Act 35:15 MCP is Open Source 38:25 Availability of MCP for Users 42:49 Hot Takes Segment 48:31 Oops IT Stories

There are roughly 100x more people who use code than who can write code. As code that “just works” becomes easier to generate, this group may be the biggest prize of all — if you can get the agentic interface right.A key trend we have been tracking over at AINews is the absolute explosion in Codex usage this year, with MAU now up >10x from Jan 2026. Less than two weeks after their July 9th launch, OpenAI said ChatGPT Work and Codex had reached 10M users combined (as we cover in the pod, Codex now powers ChatGPT Work, so all ChatGPT Work users are now users of the Codex harness, even if they aren't traditional engineers) — showing the early innings of what happens when you graduate from coding agents to knowledge work agents:We've been calling out how coding agents are “breaking containment” to do everything else this year to power every other part of knowledge work - and it started with the org chart, with a major reorg last month that amounted to two of Codex's most prominent leaders, Greg and Tibo, taking responsibility over product and ChatGPT specifically, completing a “Superapp” consolidation cycle first discussed in March.With these updates Codex is no longer just a coding tool. In June, OpenAI said knowledge workers already accounting for roughly 20% of Codex's user base and growing more than 3x as quickly as developers. A product dedicated for knowledge workers was being pulled out of the Codex team.However, knowledge work has a different set of problems and environments than coding. For decades, knowledge work has been scattered across different primitives like documents for writing, spreadsheets for analysis, slide decks for communication, and specialized applications for everything else. ChatGPT Work now enables users to work across every primitive with agents. Instead of opening an application and manually operating its features, the user can describe an outcome and collaborates with an agent that can assemble the tools, context, and artifact needed to reach it.From building no-code products at Airtable to leading Productivity Engineering at OpenAI, Akshay Nathan has spent much of his career trying to make the power of software accessible to people who do not write code. In this episode, Akshay joins swyx and Vibhu to unpack the launch of ChatGPT Work, why Codex unexpectedly took off among non-developers inside OpenAI, and the company's broader plan to bring useful agents from software engineers to knowledge workers and eventually everyone.We go deep on the shared agent harness behind Codex and ChatGPT Work, why OpenAI brought the experiences together without making them identical, and how persistent computers, artifacts, Sites, plugins, memory, and sub-agents are changing what people can delegate to AI. Akshay explains why some teams are replacing decks and spreadsheets with interactive websites, how agents can gather context across code, Slack, documents, and local files, and what OpenAI learned from personal-agent products like OpenClaw.Side note: also don't miss Abhihek's sandbox track keynote at AIE, which now powers a lot of the sandboxing for ChatGPT Work… and yes was also broken by an unreleased OpenAI model in the recent HuggingFace incident.Akshay also reflects on how AI is transforming product development itself: why more people will become generalists with a specialty, why ideas and taste become the bottlenecks when almost anyone can build, why LLMs still struggle to generate genuinely grounded new ideas, and why teams must distinguish increased motion from actual progress.We discuss:* Why Codex unexpectedly took off among non-developers inside OpenAI* Why employees felt like using Codex gave them a new superpower* The product insight that led OpenAI to build ChatGPT Work* Why Codex and ChatGPT Work share the same underlying agent harness* How their UX, Git visibility, artifacts, and sandboxing defaults differ* Why OpenAI merged its agent experiences instead of building separate products* How AI is blurring the boundaries between engineering, design, strategy, and operations* Why OpenAI wants the default model configuration to work for most users* When power users should use deeper reasoning, Ultra, or multi-agent modes* Artifacts, agentic spreadsheets, and creating high-fidelity work products* Why interactive Sites may replace decks and spreadsheets* The challenge of designing a simple interface for an agent that can build almost anything* Why users should retry tasks that models could not handle three or six months ago* How AI can gather context for performance reviews without replacing human judgment* The OpenAI automation that turns internal Slack and document activity into memes* What reaching ten million ChatGPT Work and Codex users means for the product* How OpenClaw inspired persistent environments, scheduled tasks, and personal agents* Using ChatGPT for financial planning, budgeting, workouts, meals, and household management* The design tradeoffs behind sub-agents and how much of their work users should see* ChatGPT memory, Chronicle, and long-term context* Why AI may make more people generalists with deep specialties* Why ideas and taste become more important when almost anyone can build* Why LLMs still struggle with the instruction “bring me new ideas”* Measuring productivity through quality at-bats instead of commits, tokens, or pull requests* The critical difference between AI-generated motion and meaningful progressAkshay Nathan* LinkedIn: https://www.linkedin.com/in/akshaynathan/* X: https://x.com/akshaynathan_Timestamps00:00:00 Introduction and Bringing the Power of Code to Everyone00:01:33 Joining OpenAI and Preserving a Startup Culture00:02:40 What OpenAI Learned from Enterprise AI Adoption00:05:28 Why OpenAI Built ChatGPT Work00:07:17 Codex vs. ChatGPT Work and the Shared Agent Harness00:12:07 Why OpenAI Merged Its Agent Experiences00:16:24 Models, Reasoning Levels, and Choosing the Right Default00:20:26 Artifacts, Agentic Spreadsheets, and Model–Product Collaboration00:24:22 Why Sites Could Replace Decks and Spreadsheets00:30:08 Designing an Agent That Can Build Almost Anything00:34:28 From Developer Agents to Knowledge Work—and Everyone00:36:07 Power-User Advice and AI-Assisted Performance Reviews00:40:41 OpenAI's Internal AI Memes and the Ten-Million-User Launch00:44:39 OpenClaw, Personal Agents, and ChatGPT as an Operating System00:50:24 Sub-Agents, Ultra Mode, and How Much Control Users Need00:54:39 ChatGPT Memory, Personalization, and Chronicle01:00:19 How AI Is Reshaping Product Development and Tech Roles01:03:15 Ideas, Taste, and Why LLMs Struggle to Generate New Ideas01:04:42 Measuring Productivity, Quality At-Bats, and Motion vs. ProgressTranscriptIntroduction: Akshay Nathan, ChatGPT Work, and the No-Code ArcSwyx [00:00:00]: We're here in the studio with Akshay from OpenAI. Welcome.Akshay Nathan [00:00:07]: Thank you.Swyx [00:00:08]: And with our trusty co-host, Vibhu. So you recently launched ChatGPT Work. You lead Core Product Engineering. It's been a long journey, into all this. I find it very interesting that you started with no code or low code, with Walrus and Airtable. And to some extent, ChatGPT Work is like the super app of super apps of, well, here is the ultimate no code. You just write a prompt.Akshay Nathan [00:00:32]: Yeah. It's funny how things come, full circle. I think for a long time in my career, I started my career working consumer fintech, but then after that, like, there's this hypothesis that, the things that we were able to do with code, like, as engineers, like, if we could bring that to many more people in a more, accessible way, then that would be truly magical. We were working on a startup. It's funny, like, before LLMs, before vision LLMs, on how to do automated testing with AI. It was just kinda jank, back then, but doing what we can, and then worked at Airtable for a while on the same thesis that, like, if we can bring a database or the primitives behind a database to people, that'd be really useful to them. But once LLMs came onto the scene, it became clear that, this was the missing piece, like, the missing technology required to, like, bring the magic of code to everyone without them having to know what's going on underneath the hood. And so, like, I think this launch and a lot of the stuff that we've been up to is, like, the manifestation of that.From Walrus and Airtable to OpenAIVibhu [00:01:33]: How was stuff when you joined? So you joined OpenAI 2023. Now we've got, so much more stuff, so ChatGPT, Codex app, ChatGPT Work. Have things changed?Joining OpenAI and What Hasn't ChangedAkshay Nathan [00:01:44]: I think the more interesting thing is how things haven't changed. Like, one, I joined I remember when I joined, it was, like, five hundred people. One thing I was worried about was, like, I was looking for something, more early stage and, like, was it gonna feel startup enough? And I joined, and I was like, “This feels even more startup-y than I could ever imagine.” And, like, that really hasn't changed even till now. I think the, like, level of, like, bottoms-up ambition and, like, the ability of anyone to, like, do anything or have an idea and ship it is really cool. But on the, like, mission side, I think what was really compelling to me is this mission of, bringing frontier intelligence to everyone. Like, building AGI and then bringing it to everyone. And, I think acknowledging back then that, like, that vision is gonna, not be a linear progression. Like, we're probably gonna, like, try different products and have different things that succeed and don't. But the vision has stayed the same, and the mission has stayed the same, and we're starting to see the pieces, fall together, and that's really cool.Enterprise Lessons: No One-Size-Fits-All AISwyx [00:02:40]: You worked on Enterprise. What A lot of people never touch ChatGPT Enterprise. What is something that you learned from there that you're bringing into your work now?Akshay Nathan [00:02:52]: I think how there's no one-size-fits-all solution in Enterprise. I remember in the early days of ChatGPT Enterprise, like, when we talked to customers and, like, everyone. That was, like, when I think it was a year after ChatGPT was released, and everyone was so excited to bring, AI into their enterprise. And, there were all these teams being stood up. It was, like, the AI deployment team with, like, these enormous budgets. And if you asked anyone, like, what were they excited about? Like, what were they excited about solving? Like, at first, you'd get, like, kinda like the baseline answers of, like, “Yeah, we have all this context and data and all this stuff.” But then if you ask them, like, “What was, like, a discrete use case that, like, they want AI to enable in their workplace?” You get such a different, like, variance, like, explosion of, different types of answers. And it's interesting, like, you using, like, these models and these products, you have this box, and you can say anything to it, which is the magic. But it'on the flip side, it also means that, like, you don't know what to do with it. And in Enterprise, I think a big part of that is, like, meeting the users where they are, like, what use case were they trying to solve, and then teaching them how they can use AI to, like, gain leverage there.Swyx [00:03:56]: Do you meaningfully differentiate that from forward-deployed engineering?Akshay Nathan [00:04:01]: I think there is the go-to-market side of it and then there is the product side of it. I think you need someone on the product side. And I think, like, however good we get at FDE motion, like, I think at the end of the day, if we have a user who's, like, looking at their computer or looking at their phone, like, it's our job in the product to, like, be enabling them and showing them where to go. So we're really excited about that.Vibhu [00:04:24]: Do you think there's been changes, over the past three years of adoption? So there have been, step function changes. You have reasoning models and whatnot. Is there still the same problems of Enterprise has black box, don't know what to do with it, or have things changed?Adoption, Agents, and the Next 10x MarketAkshay Nathan [00:04:39]: We're seeing now that, like, there's this huge uptake, right? Everyone is extremely excited about it. It feels like, many people are, millions, hundreds of millions of people are using ChatGPT. They understand, like, how generally to work with AI. But then, like, every time, like, a new capability gets unlocked, so now, like, we're seeing with agents, like, there is probably a contingent of, like, early adopters still who, truly get it, who are like, “ we you can do anything. You just have to make sure the right context is there, it's connected to the right tools, and that you are supervising it, but, like, anything is possible.” But then there's, like, this, like, 10x or 100x bigger market where, like, they don't yet get that, or they don't yet see that. And so I think that's the next stage here. So to answer your question, like, I think the adoption is there and growing fast, but I think the opportunity is, like, far bigger than that. That's where we wanna play, especially with ChatGPT Work.ChatGPT Work, Codex, and the Super App MergeSwyx [00:05:27]: Yeah. well, let's, let's skip ahead to ChatGPT Work. only, like, a month ago or so, announced. what was the decision process that led into it? there was this, overall merging of the super app. Is that what we're officially calling it? you deprecated the browser as well. Just, summarize your last, like, couple months of working on this thing.Akshay Nathan [00:05:50]: Yeah. It feels like forever now, but it's only been a few months. I think maybe the one, impetus that, like- Is most salient is when we release Codex, or even internally had Codex, like, it was really surprising to us, I think we recently put out some stats on this, that there was this, like, real inflection of, like, adoption among non-developers at OpenAI. And, I, through this product development process, like, would go to, like, these UXR sessions to talk to people internally. And the thing that stuck out to me is, like, one, like, you go talk to, like, strategic finance or marketing or whatever, and they're all using Codex for, their use cases. That part's cool, but the thing that really stuck out to me is how proud people were that they were using Codex. Like, how, likeSwyx [00:06:34]: It's like, “I'm not supposed to be using it, but I am.”Akshay Nathan [00:06:36]: It was that. It was, like, that they were, early to this, like, new thing, but it was also this thing of, like, they felt like they had a superpower, right? And, what we recognized then is that, like, the power of Codex, the power of agents, like, we already had this massive distribution base of people who have, come to know and love ChatGPT. Like, how do we show that to them? Like, how do we bring it to them? Which is, like, a hard product problem, and it's, like, a tricky thing, right? There's many ways you can go about it. And so that's what we called the Merge and the Super App over time, and ultimately launched it in ChatGPT Work, is how do we do that? But it came from that initial realization that, like, the power was not only for developers, like, much earlier than probably even we thought. Like, it could be extended to everyone.Swyx [00:07:17]: How do you see the products differently? So, like, who is it for, right? So Codex started out even CLI, then app. Now there's a merge of ChatGPT Codex and ChatGPT Work, so is it the opening for the average user, for enterprise, for work? How do you position it?Akshay Nathan [00:07:36]: I think we want to get it to position it for if you're doing work-related things, for lack of a better word, right?Who ChatGPT Work Is ForAkshay Nathan [00:07:42]: I think productivity is what, like, the pillar that I support. Like, that's the name of the team. And the reason for that, the reason we call it productivity and not, like, enterprise or, like, work or something like that, is because there's also personal productivity, right? And, like, I think ChatGPT Work is I've seen people do things in their personal lives that you wouldn't classify as, like, work technically, but, like, these agents are, super capable for. Like, one recent example that someone posted about, on our Slack is, like, someone had, like, a missed package, like they didn't receive it, and then they got, like, the picture of it, from Amazon or whoever the courier was, and they, like, asked ChatGPT Work to, like, find out where that package is. And, like, the agent, is extremely tenacious and, like, took the image and, like, looked at a bunch of, like, listings around their neighborhood and figured out exactly the apartment complex in which the package was, like, gave them some information. And so, like, I think there's all these things that, like, you, work-related or productivity-related things, I think that's what we want the product to be. You asked about Codex. I think we think Codex is, a durable brand, but we have a principle that, like, the user we don't want a user to get stuck in a tab or an experience where they don't get the power of the product. And so, like, everything that you can do, in the Codex portion of the product on desktop, you can do in ChatGPT Work and vice versa. But we made some opinionated product decisions on, like, how much of the Git state, if you're in a Git repo, do we wanna expose to the end user? Or how much do we wanna make the experience of seeing the agents thinking, like, diff forward so that you get exposed to the diffs out of the box. And then, like, on the safety side, like, how do we wanna think about, like, sandboxing and making sure that we have the right defaults in one state versus the other? So, there's, like, some opinions that go behind that, but we do want We don't want the user to need to choose which experience they're in.Swyx [00:09:26]: That is a good goal for AGI, right? Like, people don't want, like, to hide to choose what version of AGI they want. They just want the AGI to decide for them. can I get an answer or, like It's not super clear to me. Is the Codex harness and the ChatGPT Work harness the same? Is it just UI affordances, or are there prompt level or even deeper differences?Shared Harness, Different UX: Codex vs. WorkAkshay Nathan [00:09:49]: So the harness is the same. The harness is shared. on In both of the products, we made improvements to the harness to make it good for knowledge work, especially as it relates to plug-ins or computer use or artifacts. You get that power regardless of which experience you're in. On the UX side, there's opinionated takes that we have when you're in Codex mode, what the UX should be how the UX should behave, and some stuff around the sandbox like I mentioned, but the underlying harness and capabilities should be the same.Swyx [00:10:16]: I'm just kinda curious. Maybe we can, -- Is there a query that we can run that would look different in the two modes?Akshay Nathan [00:10:23]: Yeah. I tried to create, like ask it to create, like, a retirement calculator spreadsheet or something, in both modes. And then in Codex mode, you might have to be in a repo for this, but you'll see, like, the diffs of, like, the sheet that it's creating and stuff like that, and the file edits. But in Work you won't be able to see that.Swyx [00:10:42]: I think that's, that's super clear. And then also the other thing I wanted to dive into was your, the productivity team. what else is there? first of all, what are the top-level teams other than productivity? Isn't productivity everything?Productivity Teams and Core ChatAkshay Nathan [00:10:55]: SoSwyx [00:10:55]: Science?Akshay Nathan [00:10:55]: We have a team focused on ChatGPT. Like, the core chat experience, for consumer, which is like, not, I think all productivity. Like, there'People are using ChatGPT every day for search to, figure out how to write messages to loved ones, to think about, how to, like, learn a new topic, et cetera. And so there's so much more inside to create images. And there's so much more in chat that, the hundreds of millions of users are using that warrants, like, a very dedicated effort. And there's teams focused on enterprise and infrastructure and API and stuff like that, so.Swyx [00:11:33]: I will bring it up.Retirement Calculator Demo and Git-First UXSwyx [00:11:34]: Yeah. So I have them both running. This is ChatGPT Work. There's a Codex version here. I picked “Five Little Ducks” song, so this will take a while.Akshay Nathan [00:11:43]: Huh.Swyx [00:11:43]: I think we'll just keep it in the background and, as they finish, we'll look into some of the differences.Akshay Nathan [00:11:48]: Yeah. But immediately, I think if you flip back to the Codex version you'll see that,Swyx [00:11:53]: That it assumesAkshay Nathan [00:11:54]: Like theSwyx [00:11:54]: It assumes Git. Yeah. Yeah.Akshay Nathan [00:11:56]: The, like, dynamic island assumes that you're in a Git repo. And you might miss some stuff because some of it is, like, in the actual chain of thought with those changes and how we display that, but yeah.Swyx [00:12:07]: Is there an unintuitive like, is there a thing that you wanted to ship and then you got feedback, and you were like, “No, let's not do it?” Like, what's the thinking behind that?Why Merge the ExperiencesAkshay Nathan [00:12:14]: In, ChatGPT Work?Akshay Nathan [00:12:17]: I think one direction we could have gone with this is, like, keeping the experiences, like, completely separate. So it's like, whySwyx [00:12:22]: Different apps.Akshay Nathan [00:12:23]: Exactly, like different apps or even in the same app, like different, completely different experiences. Like, why merge it all? Like, what is. Codex, people love. Like, why bring these products together? And I think the intuition here is that, like, all of our jobs are, like, changing dramatically with AI. Like, for, like, every few months, like, I feel like I wake up, and I'm, like, doing a completely different thing than I was doing a few months ago. And my hypothesis here is that, or I should say our hypothesis is that, like, part of what we're, we're building, this technology is giving people leverage. Like, the things, maybe it's the more mundane parts of your job or parts that, like, if you were able to automate, you'd be able to share more ideas faster or whatever, like, you're able to do now. And because of that, like, that might blur the lines between someone who's, like, only writing code or creating strategy docs or, planning events or, helping with marketing or doing podcasts or whatever, right? And so, like, these things are gonna get blurred over time. And so, like, trying to draw a hard boundary based on, like, the who you are is gonna be, is gonna be tough. And, like, we should enable users to choose, but we shouldn't box them in. And so a lot of the work that went in here, like, keeping the primitives the same, like for example, plugins are, like, unified across, this product and ChatGPT and the cloud, was because of that. It's this thesis that, like, eventually things are gonna come together and we don't wanna be Like, we wanna be prescriptive about when to be in either experience, but we don't want to box anyone in.Swyx [00:13:45]: I wonder if there's users who are very tuned to the old ChatGPT harness that is effectively now replaced by the Codex harness. I can't imagine what that was, but maybe they're more the more conversational side. Can you compare and contrast the two harnesses? ‘Cause only you've seen it.Akshay Nathan [00:14:02]: Yeah. I think ChatGPT, the existing harness, like, still exists today. Like, it exists in this app,Harness Engineering: ChatGPT vs. CodexSwyx [00:14:08]: The classic, right?Akshay Nathan [00:14:09]: TheVibhu [00:14:09]: You just start a new chat, and you don't go under Work, right?Akshay Nathan [00:14:13]: Yeah. If you startVibhu [00:14:13]: SoAkshay Nathan [00:14:14]: A new chat and go to chat, then you're, you're talking to ChatGPT with the instant model.Vibhu [00:14:16]: Oh, we can technically do another. But on instant.Swyx [00:14:21]: Yeah. So this one's not gonna code or it's gonna be in line. It's on a in line in a sandbox.Akshay Nathan [00:14:26]: It'llVibhu [00:14:27]: Oh, that's coolAkshay Nathan [00:14:27]: We try to push you to go to Work if you're creating a spreadsheet. Yeah, but this isSwyx [00:14:30]: And this is a router decision? Sorry. Is it a router decision?Akshay Nathan [00:14:34]: This is the decision that, the model is making, and then, like it sees that you're able to. or you're trying to do something that would be better served in Work mode. But I think your question was like, what are the advantages of, like, the chat, like ChatGPT chat harness?Swyx [00:14:48]: It's more broadly, like, I wanna, do an oral history of harness engineering. Right? the ChatGPT harness lasted us from, let's call it the ‘01 era, until now, and now it's being replaced by the Codex harness effectively. And they're, they're overlapping somewhat, but I'm curious what changed if there is.Akshay Nathan [00:15:10]: My perspective on this is, like, there's, there's, there's there's like a constant process of, like, divergence, convergence, divergence, convergence. And in chat, like, many of the use cases I was talking about before, like, search or learning, I think we're, we're really optimizing for latency and optimizing for personality and, like, different things that, over time, like the product The reason people love ChatGPT is because we've been optimizing for those things and working on them for so long. Codex, what we learned was that, like, if you give the agent access to this infinitely flexible environment as a computer, it can do really powerful things. And so when we think about, like, okay, well, for knowledge work, like, what is which mode should we choose? It was like it felt more natural to us to bring that to this, like, computer environment and, maybe abstract some of the details of this computer away from users who might not be used to that, but, like, give them that same power. But ultimately, I think that we want the power in all places, right? We wanna meet people where they are. So I'm sure there'll be work down the road in order to get things to be, equivalently capable in all scenarios. But it's just a question of, like, what we've been focusing on the product on historically and what we're focusing on now.Models, Defaults, and the Reasoning SliderVibhu [00:16:24]: I think alongside that, outside of just harness and when to use Codex, ChatGPT, or Work, there's also the new models you've released, right? any guidance there? So people love to min-max what to use, like only use Terra on high reasoning versus, for this, you wanna use Sol here, ignore all theseAkshay Nathan [00:16:44]: There's 32 options.Vibhu [00:16:46]: But, that being said, for people that are expanding, so, productivity trying stuff for work that don't have the breakdown of what all this is what's, what's the advice, right?Akshay Nathan [00:16:59]: Well, I think before the advice, like the first thing is, like, none of this would be possible without these models. Like, the, I think you asked earlier, like, what was, like, the inspiration for work and, like, early on, like I mentioned, like, what we were seeing with Codex, but that was also because the models were getting infinitely more capable. That's happening again. I think it's like another step function jump now. And to answer the question on advice, like we want this default to be the best possible. Like, we wanna be opinionated about the default, and so we've we've chosen a default that we think is gonna be the best for everyone. And, we have for power users options under the hood. We could One could argue that there might be too many right now, and we're, working on simplifying it. But you can extend, the reasoning level, and you can change between the different model classes if you need to, but the default should be the best for most use cases. So my advice to most people would be to stick to that. And then, if you reach a situation in which you think that you could, you wanna try, a different configuration, if you're not seeing either the efficiency on the cost side or the quality on the intelligence side, then you can change the defaults and see if you can get something better. But we think that the default should be good enough.Swyx [00:18:09]: I have, I'm just gonna run something by you since you have way more experience than me. I've recently been doing Sol Lite but with goal, with the idea that the goal augments the reasoning effort, but with more terminations and turns.Swyx [00:18:24]: Is that a good way to think about it as opposed to Sol Ultra or Sol, Extra High?Akshay Nathan [00:18:29]: Yeah. It's hard to say becauseSwyx [00:18:31]: Yeah. It's like an interaction effect.Akshay Nathan [00:18:33]: exactly. It's like there's a preference on, for you as an individual, like how do you like to collaborate with the models? Like how many of those like terminations, as you call them, do you want where, you can steer or make sure that it's doing the right thing?Akshay Nathan [00:18:46]: I think generally people should try whatever works for them. I think that like using Ultra or the like multi-agent setups are best for like when you have like tasks that are either incredibly complicated, like open explorations or very paralyzable. I think even for tasks using goal, I think is best for tasks that you'll be able to make consistent progress in a way that's verifiable over time. But I think for most tasks, they don't fall into either of those buckets. And so like at least when they're starting, and so that's why I think the best first step is like trying it with the default configuration and then seeing like where you wanna go from there.Swyx [00:19:29]: Right. You guys worked on a slider, which is super helpful for reducing the amount of panic.Vibhu [00:19:36]: It's nice on mobile at least. There's a nice slider there.Swyx [00:19:38]: It's nicer.Vibhu [00:19:39]: I haven't tried it.Swyx [00:19:40]: So you have the advanced view there, but if you click advanced view. Yeah.Vibhu [00:19:44]: Ooh, it's just a nice slider. Yeah.Swyx [00:19:46]: Very pretty, very colorful.Akshay Nathan [00:19:48]: Yeah. The idea was here was like reduce it to like one dimension even though there's multiple dimensions, right? Try to project it onto a single dimension for the user. Like, something from that represents like, speed and efficiency on one side and then like quality and thoroughness on the other side.Artifacts, Spreadsheets, and the Work LaunchSwyx [00:20:04]: I am just puzzled that it uses Sol so much, like the lowerVibhu [00:20:07]: NoSwyx [00:20:07]: Grounds I would've usedVibhu [00:20:08]: I think the slider, if I'm not mistaken, isSwyx [00:20:09]: Terra.Vibhu [00:20:10]: Oh, it is.Swyx [00:20:11]: Yeah. See? So they preset Terra to only be the light one. But like I think a lot of people would more people should use Terra. One, because Sol keeps running out of capacity.Vibhu [00:20:22]: I'm the reason. Here's ten minutes of ourSwyx [00:20:24]: There you goVibhu [00:20:25]: Retirement calculator.Swyx [00:20:26]: Oh, that's the Excel thing working for you.Vibhu [00:20:28]: This is,Swyx [00:20:28]: Oh my God. Look at thatVibhu [00:20:28]: This is work, and then Codex is still cooking, so we'll get back into it. I think it'll be interesting to see the thought process, the reasoning, and also, this is eight minutes on work. Codex is still cooking.Swyx [00:20:41]: Yeah. And by the way, so I've, do Gabriel Chua? He's part of the OpenAI Singapore team. He showed me this, and I was like pretty shocked that this looks like Excel. It edits Excel files. You never paid an Excel license, right? Like, but somehow this is like workable and it's agentic Excel.Akshay Nathan [00:21:01]: Yeah. one of the big like pushes that we made for this launch was like artifacts, right?Akshay Nathan [00:21:05]: Like both on the model side, like I think if you compare this with GPT-5.5 and GPT-5.4 before that, you'll see that there's been pretty dramatic improvements in the quality of these artifacts and then also on the product side.Vibhu [00:21:16]: The UX side is also crazy, like hosted sites and whatnot. No longer needing to host your own little webpage, like itSwyx [00:21:23]: Oh, I have a story about that. I can do, a separate thing. I'll need to take the visuals here, but we-we'll, we'll cut to that later. Was there co-training, because you were moving making this big move and you launched GPT-5.6 on the same day as ChatGPT Work? Was there influence between the model training teams and the harness teams, or did they did the launch dates just happen to line up the same day?Akshay Nathan [00:21:46]: I think the we collaborate heavily with the research teams, and I think that's like one of the most magical parts of the job, like the most fun parts of the job. But yeah, just using artifacts as an example. Like, a lot of what you're seeing, like underneath the hood, there's a lot of work that went into making sure that like, we had the right infra to be able to train the models to get better at this. And then on the product side, like had the right experience for users to be able to collaborate with the model on an artifact like this. In fact, like this whole viewer, like the intuition here is that like, it's not necessarily that you wouldn't need an Excel license. This is stage one, right? Like, this is probably not what you meant when you're like making a retirement calculator.Vibhu [00:22:24]: Yeah, you can iterate very easily. Yeah.Akshay Nathan [00:22:24]: You wanna iterate and like when you're seeing it, and if this thing is high fidelity to like what you would see in or what your coworkers would see if you were to send this to Sean, like that I think makes it so easier and makes you trust the product in terms of iteration.Vibhu [00:22:39]: When you say coworkers would see, do you see a multiplayer, multi-team collaboration with artifacts? Any things you guys think about that?Multiplayer Artifacts and CollaborationSwyx [00:22:46]: You can already share it, right?Akshay Nathan [00:22:48]: Yeah. It's inter It's something that, we're actively thinking about. one thing that, we've noticed internally without talking too much about the roadmap is that like there's many times when someone will ping me about something, and I will ask ChatGPT Work the question, and then I'll ping them back the answer.Akshay Nathan [00:23:04]: And then I'll be thinking likeVibhu [00:23:04]: Like the simplest would be, the three of us are just all on one hosted.Akshay Nathan [00:23:07]: Exactly. And I'll think about like was I required in this loop or and then maybe it was, rephrase like what they were asking or pulled from certain context or whatever. But like, when I gave them back the answer, that process was also lossy, right? Like I gave them just like my interpretation of what ChatGPT Work cooked up. But like underneath the hood, there's so much context like in the rollout and stuff that could be interesting.Vibhu [00:23:28]: Yeah, it'sSwyx [00:23:28]: So like the answer was preemptively respond to every inbound request?Akshay Nathan [00:23:33]: No, it was just like literally like this is what I do sometimes as my job.Swyx [00:23:36]: I know you copy-paste and then you're just a message forwarding serviceAkshay Nathan [00:23:39]: Yeah. Yeah, exactlySwyx [00:23:39]: From AI to AI.Vibhu [00:23:40]: But I think it's interesting, right? It helps people understand the capability of what you can ask and delegate that oftentimes people don't realize until they try or someone shows you, and then you're like, “Oh, okay. Okay, I see.”Swyx [00:23:52]: I think it's als there's also like a, light security issue, where like you're the permissions layer. Like yes, I could query everything that you query, and I could get an automated response, but maybe I'm not supposed to see it. And that there's no way I would know because I'm not supposed to know what I don't know.Akshay Nathan [00:24:07]: Especially as like, with ChatGPT Work, we're, we're asking you to connect your plug-ins and, it's pulling from your local files and stuff like that. Like the amount of context that the agent has access to is like- Deeply personal and like that's something I think we need to preserve, so that'll be definitely a challenge.Swyx [00:24:22]: There's Excel, there's PowerPoint, there's Docs, the, grand trio of work. What other formats of work do you think about? like you worked on Airtable. Is there a future where there's like OpenAI Airtable? Like what does that look like if you ever ended up doing it?Akshay Nathan [00:24:41]: It's a really good question. I think,Formats of Work: Sites as Knowledge ArtifactsAkshay Nathan [00:24:43]: one that you didn't bring up was Sites, and I think that wasSwyx [00:24:46]: SitesAkshay Nathan [00:24:46]: A core part of this launch. There's one side of Sites that I think people commonly talk about, especially on Twitter and stuff or X, of like, this like prototyping tool. And like we saw that happen with this launch even. The model slider that you guys were referencing earlier, like that was developed almost fully in a Site. Like, the collaboration between design and engineering and product on that was like on a site where we play with, the affordance and figure out how it feels and all of that. But the other aspect that I think is a little bit less talked about is like Sites as like an artifact for knowledge work. I was talking to someone the other day who's on like our corporate finance team, and like we were mentioning how like now when they have these reports that they're, they're working on as a team month to month, historically those things were in slide decks and in spreadsheets, and now they're just in Sites. And like Sites is the mechanism that they collaborate across the team. And the reason is ‘cause it's like, it's like somewhat higher bandwidth. Like, at these tools like PowerPoint and Excel are like infinitely flexible, but at some point you reach the boundary of like either as a human you may not know how to use some feature or something, or the product itself doesn't support it. But with a site you can do anything. You ask for anything and you can get that. once people see that magic, I think it's been really valuable.Swyx [00:26:02]: Yeah, let me show you my case study. this involves all the hot topics including ChatGPT Work, but also GPT-5.6 token billionaires and token maxing and Sites and auto research. I'm a fan of this game called Strata. It's, it's like a little board game that youSites, Auto Research, and Research DashboardsSwyx [00:26:17]: That you play with, physical blocks, that come on top of it like that. So over the weekend I took like thirty photos and just threw into ChatGPT. one point seven billion tokens later, out comes this site with a fully playable thingAkshay Nathan [00:26:32]: WowSwyx [00:26:32]: With 3D, block placement and everything. Because it requires physical blocks and I needed friends to train on it so they can get better, so I can play against them. But also, I could also, do things like train an AI on it and that's, thatAkshay Nathan [00:26:45]: That's your auto researchSwyx [00:26:46]: That gets into auto research. So, you want to train your own AIs, and then make sure they self-play against, each other. I need to set both AIs. So this is AI versus AI, and they're, they're gonna self-play. the AIs start out bad and then you want to define a loss function and get good. I wasn't gonna supervise all this. I was at, I was down in San Mateo, attending a conference. What I ended up doing was, auto researching and on this and creating benchmarks and that there was just way too many parameters for me to read. So I started asking it for a site, and it's created this lab, panel. Where is there a, is there a shortcut for a site that is created?Akshay Nathan [00:27:28]: You should be able to go in the sidebar to Sites, top of the sidebar. The left sidebar.Swyx [00:27:33]: This one? Oh, left?Akshay Nathan [00:27:35]: Yeah. Just scroll all the way to the top.Swyx [00:27:36]: Oh. Oh, it says Sites. Oh, there you go. Yeah.Akshay Nathan [00:27:39]: Ooh.Swyx [00:27:40]: So it create, it creates the sites. I don't, I don't think this is, it is exactly what I wanted, but let me show you what it popped up, right? Like I think as a research artifact, it is very important to communicate, exactly, what is being done. Outputs this thing which I eventually started publishing. So I moved it off of Sites because I wanted more, database and infrastructure than Sites afforded me. But this is like a research output that you can start to mess with and like try to think about like what hyperparameters are you tuning for training AIs. And like I was trying to make like scaling laws and everything and doing all sorts of like game optimization stuff. And the fact that you can just throw this up as a research artifact, like I no longer need to read ChatGPT output. I read Site output. But then there's also a huge sprawl. Like look at how long this thing is. There's so many numbers. It is pretty overwhelming, so then I have to start pruning it from there. But, it's an interesting transition from Markdown effectively that you're putting out to, you're putting out a whole functional site.Akshay Nathan [00:28:41]: I think Markdown just isn't that optimal for people to read, right? Might as well just write HTML website and I don't know. I think you can do a lot with customizing this, right? You have your skills that explain what you want. Like I noticed they're quite verbose. I don't need a lot of this information.Swyx [00:28:57]: It's very verbose.Akshay Nathan [00:28:58]: So and then the nice thing of having a site side by side is, you just iterate on what you want and what you don't, right?Swyx [00:29:05]: Yeah. I don't know if, any that triggers any stories for you of how it's run internally. Am I doing this right?Akshay Nathan [00:29:11]: Yeah. I think that this is like a workflow that we're seeing like all different types of teams use, where like the canonical artifact that was previously a deck or something is now becoming a site. And like with a site you, because it's just HTML, you can like. It's infinitely flexible. And so, if you want to give more prominence to a certain thing that like in a slide deck would, feel like it was buried, like you can do that. You can have it be like the hero image, right? And so I think that like, people are starting to see that. There's more work to be done to make these things like much more easier, easy to collaborate on. You mentioned that they're very, they're long and verbose, could be broken up. I'm sure that there's still something to do there.Swyx [00:29:53]: They're super long. Yeah.Akshay Nathan [00:29:54]: Yeah. But I think we're starting to see that like there is this aspect of this is a really interesting, format, for people to use, that's like much more flexible than what they ever had before.Swyx [00:30:07]: I think your job also comes becomes meta. You're not designing the products. You're designing a product to make products, and I'm curious how you manage that.Designing a Product That Makes ProductsAkshay Nathan [00:30:18]: I think one thing that we've been Like when we look at the UX, like that we've been thinking a lot about is how can we balance like simplicity with capability? Like if we're designing a product, like you said, that like is made to make up build other things, right? You can build so many different things. But we can't put that all in front of you because you'll get overwhelmed.Vibhu [00:30:41]: Yes.Akshay Nathan [00:30:41]: And so we had similar problem or similar challenges even Chat-with ChatGPT, but especially now, like when there's so much that can be done, I think the balance that we're constantly trying to strike is like, how can we give the user enough of a UI surface where, they can be expressive, they can tell the agent what they need, they can verify that it's using the right tools, it's pulling from the right sources, et cetera, but then it gets out of the way. And then how can we build the right system such that we can show them instead of telling them what can be done? Because so much of this is gonna be like, how do they discover the next use case and the next one after that if they really want to be super powered by the AI.Games, Private Evals, and Show-Don'TellVibhu [00:31:19]: Yeah. It's interesting. I feel like everyone also just has a different way to do it, right? I made a similar version of this same game. I didn't take any pictures of board or rule game. I threw in at goal eighteen minutes, fifty-three seconds later, a lot of tokens later, I've got a similar version. not with all the auto research and whatnot, butAkshay Nathan [00:31:39]: You gotta do all the latest trends.Vibhu [00:31:40]: And yeah, I did it with, did it with Codex, not Work, but it's interesting, right?Akshay Nathan [00:31:45]: Yeah. And this is GPT Image generating the pro avatars. Very good for game design. LikeVibhu [00:31:51]: AndAkshay Nathan [00:31:52]: A lot of game designers were like really into GPT Image for assets.Vibhu [00:31:54]: I will say like the broader takeaway probably is the reason that we do this is more so just to test the tools, right? Like, this was also a test for GPT-5.6 came out. I had done the game on GPT-5.5, right? The ability for me to no longer need it to. I had to feed it the rules. It's, it's a pretty niche game. It couldn't find how to do this on its own.Akshay Nathan [00:32:15]: Oh, yeah.Vibhu [00:32:15]: GPT-5.6Akshay Nathan [00:32:16]: It is out-of-distribution, which is why I was also very keen on testing the GPT-5.6 capability.Vibhu [00:32:21]: But, this is just as work comes out, as new things come out, these are just our side ways to test things, right?Akshay Nathan [00:32:27]: Yeah. It's some private eval. That is not this private.Vibhu [00:32:31]: But also valuable because now you can send this to your friends and I learned about this game through seeing this.Akshay Nathan [00:32:36]: It's a hard game. He's very good.Vibhu [00:32:39]: It's good to when no one is competing with you. But yes, it's a classic RL problem of like self-play, bootstrapping your game AI. yeah, you see how easily work becomes personal and personal becomes work because the thing I do for personal, it directly informs people I work with because I showed it to them. They were like, “Oh, you can do that with GPT?” Which like I imagine is the growth strategy.Akshay Nathan [00:33:02]: Yeah. The show not tell is a big piece that, I think we've we're not still not fully cracked of like, showing people all the things that they can do with the product versus like trying to teach that to them through like, articles or onboarding or whatever.Akshay Nathan [00:33:18]: So meeting them in the moment.Vibhu [00:33:19]: It's a career risk for me, because I used to be in developer relations, right? Where your job is to show, and then you're like, “What do you mean? You don't, you don't need.” your job is to tell. And then. But the product people are like, “Well, we don't need you if our product is intuitive enough.” SoAkshay Nathan [00:33:37]: Yeah. that's the magic of the models. So you can tailor the telling or the showing to like specifically what the user needs, like what they care about, what they've done in the past, exactly where they are on the adoption journey. So I think that's like gonna be a super big opportunity.Vibhu [00:33:50]: Seems easier and easier now to tailor custom showing, right? People have different use cases. As much as you said you don't wanna segment different people into different buckets, right? It's also not that hard to for people that are in different categories. But the question, is you said your team is more broadly on. What was the term you used? Productivity?From Developers to Knowledge Work to EveryoneAkshay Nathan [00:34:12]: Productivity.Vibhu [00:34:12]: Productivity. So howAkshay Nathan [00:34:12]: Which is now work.Vibhu [00:34:14]: Is it work? Is there another distribution that we're not hitting? Is there a group of people that will have something different than ChatGPT, Codex or Work? Is there more that the mass isn't targeting?Akshay Nathan [00:34:28]: I see it as like a sequencing, like. The vision is like bring useful agents to everyone. We started with like developers. Like developers historically are like early adopters that are willing to put up with more friction, set things up, et cetera. Like that's where, Codex started. I think the next opportunity is like what we call general knowledge work, all the other functions around developers. I think when you go from developers to this segment, like there's inherent challenges with like, this show not tell thing that we're talking about, making the product more understandable, bringing in new capabilities that matter more for this cohort than matter for developers, things like artifacts, things like computer use, et cetera. And then I think like the same learnings, like similarly how we took the learnings from developers and brought it to, general knowledge work, the next stage will be like taking the learnings from general knowledge work and bringing it to everyone no matter what they're doing in their lives. And we're already seeing that a little bit. Like this game example that you have is, something that's like on the border of like fun and personal life to, your professional life. I use ChatGPT Work full-time at home for everything, like for whatever I'm doing. I used it the other day to come up with a meal plan and like, save that on the like computer environment that it has and something that I can continue going back to. Like is everyone doing that yet? Probably not because the thing says work on it, but eventually, we wanna get people there.Vibhu [00:35:51]: ChatGPT life.Akshay Nathan [00:35:52]: Yeah, exactly. ChatGPT cooking. But I think there's a lot of, there's a lot of opportunity there, but I see it as like, we're, we're built we built a foundation in software engineering, and we're gonna take the same learnings that we take from software engineering to knowledge work to everyone.Vibhu [00:36:07]: Do you have any power user advice? I feel like, there's a group of people that will live it, use it for everything, stay on it twenty four-seven. And then there's a bit of a gap between that crew and people that, okay, I use it for work. I use it occasionally. Sometimes I type questions. any advice, any learnings, anything you recommend or just, takeaways that you've found that help bridge that gap?Power User Advice: Push the Frontier of ImaginationAkshay Nathan [00:36:30]: I think a couple things that I've seen is like, one, that it really helps to broaden your imagination of what's possible, and this has been a learning even for me. Like, the technology has progressed so fast that, something that, like, even three months ago, like, no way the models can do this. Like, now it's like, wow, it's like it can. Like,Swyx [00:36:52]: Give an exampleAkshay Nathan [00:36:52]: We're going through right now our, like, review cycle internally, and, people always talked about this as, like, a thing that the models are good at and like, there's a cliché of like: Okay, like, no one wants to be writing reviews and, like, we just use AI to do it. But in all seriousnessSwyx [00:37:09]: And it can evaluate it as well.Akshay Nathan [00:37:10]: Yeah, exactly. In all seriousness, before it was, like, just, like, slop and, like, I think it was helpful, but, not super productive. Now I've found that, like, the model can do a much better job than me, especially in this environment of, like, pulling context on, like, what people are up to, how they've like the things that they've done to make a difference, highlighting like, wins that they've had that, like, I might may not even have seen. It has access to, like, everything, right? Like the code, like, things that they've caught, reviews, Slack, everything. And so it's, like, incredibly powerful in that domain and, like, just like six months ago, the last time we did this cycle, like, I didn't even I tried using it, but it was not at all helpful. And this time it's been, like, incredibly helpful and, like, so I think continuing to push the frontier of imagination of what's possible, even if you tried something before, I think is maybe the my biggest piece of advice. The other, thing is, like, the more you put in, especially in this environment where, like, the model has access to everything on your computer or in ChatGPT Work, like you can create, artifacts over time and save them in your library and, like, the model will continue having access to those. Like, the more information you give it about whatever domain you're in, whether it's your life or your work, the more valuable it becomes, and it'll become valuable in, like, ways that might surprise you. Like, it might pull from context in a way that, may be proactive and that you might not even have thought about. But it needs to have access to those, to that those tools or that context first.Reviews, Agentic Search, and Context GatheringSwyx [00:38:27]: One thing I just wanna talk about the review stuff because I'm still that's a very sensitive thing and you're, you're a founder, you've managed people, you've hired people. As manager myself, I'm very reticent to put out any LLM-generated things especially when it comes to people, ‘cause it feels like you don't care.Swyx [00:38:46]: Presumably at OpenAI, people are more open to being eval rated by GPT. But are there any unofficial rules around this? Like, what's the etiquette?Akshay Nathan [00:38:57]: Oh, I think the etiquette is that, like, I would never write something via, like, well, solely via AI and, like, present it as, like, a review for someone. What I was talking about is more, like, gathering context. That's the place where it's incredibly helpful.Swyx [00:39:08]: So it's just search.Akshay Nathan [00:39:09]: Yeah, exactly.Swyx [00:39:09]: It's agentic search. Yeah.Akshay Nathan [00:39:10]: It's like agentic search, but, that you can tailor and steer much more capably than you could before, ‘cause, like, the thing is it's all there's a flywheel happening, right? Because of Codex, people are able to do, and because of ChatGPT, people are able to do so much more now than ever before. And if you're able to do so much more, it's easy to miss things as well. And so, like, I think we need to use these same tools to keep up with all the impact that people are having and understand, where we can be helpful.Swyx [00:39:39]: I think the thing, like, I run a small company, so easy to search, but at the scale of OpenAI with the amount of messages that you guys put in Slack, do you think that it misses things?Remembering What Humans MissAkshay Nathan [00:39:50]: Probably, but I think that I also miss things.Swyx [00:39:52]: Like, it doesn't matter, right?Vibhu [00:39:53]: I think sometimes it'sSwyx [00:39:53]: Like it's, as it needs to be human-levelAkshay Nathan [00:39:54]: It's all relative, right? Yeah.Vibhu [00:39:56]: Sometimes it's nice when it finds things you wouldn't, right? Like right now, my Codex system prompts, they're set up in such a way that every project I have has a secret- separate, notes MD, and it just writes learnings to there. And then the global one can pull from all these. So sometimes it'll be like: Oh, there's this project you did like four months ago. Here's a note that we had, and it randomly pulls it back into context that I would never do, I haven't thought about.Vibhu [00:40:20]: And I'm like, okay, this is quite superhuman, right? Like, stuff that would. And, it'll save like hours on chunking of stuff or find something that's already been done. I'm like, as much as it might miss stuff, I would too, but it's very useful when it finds stuff. And I have like a very, non-super engineered solution to this. It's just marked down files that get pulled whenever they want.Akshay Nathan [00:40:41]: Yeah. I have a funny anecdote about this. Like, recently gearing up to this launch, the team has been, really cooking on it for a couple months, and over that time, like there's so much conversation and chatter going on in Slack and Docs and elsewhere. And, one of the members of the team set up this, scheduled tasks, like automation to like look at everything that's going on and, like, come up with the best memes and then post it in one of our shared channels. And like, there are two cool things about this. Like, the first is, like, I think the models are, over time, like starting to become like funny.Swyx [00:41:13]: Funny. Nice.Akshay Nathan [00:41:13]: Whereas like, a year ago, like that was not at all the case. The second is, it was what you were saying, like they find things that in surprising ways that you may not have thought of and like create connections that you may not have thought of. And that really helps with like the meme generation because then you can see something that, genuinely surprises you and, is funny in that way. So yeah, that's like not like the most productive, use of this the technology, but it does it does uncover this, like this capability that's emerging, which is just like to find information that you otherwise would not know of.Launch Momentum and the 10 Million User MilestoneSwyx [00:41:43]: Talking about the launch, I think, I have pretty much said this is the most successful launch in a long time. I think even more successful personally than 5.0, and they're announcing ten million users. Does it feel different? You've been through a lot of launches.Akshay Nathan [00:41:58]: I think it feels like a culmination. Well, I think two things. One, it feels like a culmination, like I was mentioning earlier, like this like vision mission that we've been on for a long time. Like I said, we saw the magic of Codex internally, and then we're like extremely excited to bring this to many more people and to see it working, to like see us reach, the distribution goal, numbers that you mentioned, like I think that's like huge and super exciting. The flip side of that is like, there's so much more to do too. Like, that's also really exciting. Like, ChatGPT as a whole, like the this product that, everyone almost equates to AI and like loves, has hundreds of millions of users. And so like ten million is really cool, but like we need to get this to everyone. Like, we need everyone to feel this magic. And so that's the next step from here. But yeah, I think extremely pumped about how it's going so far and the opportunities.Swyx [00:42:46]: Awesome. I did want to also Because I've, I've, I've been tracking the number closely, it transitioned at some point from just Codex users to Codex plus ChatGPT Work, because they're same harness. The whole point is that you don't, you can't, count them separately. Do you have roughly a billion, ChatGPT users? Why did it just jump to one billion right away? Like, isn't that the default on ChatGPT or no?Codex, ChatGPT Work, and the Developer BrandAkshay Nathan [00:43:11]: We don't default you into ChatGPT Work if you're on ChatGPTSwyx [00:43:14]: If you're free. YeahAkshay Nathan [00:43:15]: It's also only available to paid users right now. And I think there's like a process of, educating users of what is the value of this product, having them try it, learning from their feedback, and making it better over time. But the goal is to, get as many of the people who love ChatGPT today to like feel the power of ChatGPT Work. But I think it'll be a journey.Swyx [00:43:36]: Yeah. And Codex will still be alive as a brand for the foreseeable future. And we'll just toggle between them as needed for UI stuff.Akshay Nathan [00:43:44]: Yeah, I think it's even stronger point than that. Like, I think we fully intend to like, treat developer. Like, developers have been, a core market for us for so long, and like there's, there's so much more that we can do to make Codex great specifically for, software development, and we'll continue to do that. This doesn't take away from that at all. If anything, it should increase the utility of something like Codex, because now you can move seamlessly between writing a diff to creating an artifact or, doing a search over your factor.Swyx [00:44:11]: I do wonder how much this terminology leaks to the non-technical user. Like, do they have to learn to say artifact if I want artifact? Or.Akshay Nathan [00:44:20]: It's funny, like we call it artifacts internally ‘cause that's what the teams call it.Swyx [00:44:23]: It's nice. Yeah.Akshay Nathan [00:44:23]: But like externally, like no one says that, no one calls it an artifact. But I think that people like often, like describe things, whatever they're used to, right? So if, ChatGPT Work is good at creating slides, they'll say ChatGPT Work is good at creating slides, and that's what we want.OpenClaw, Personal OS, and Persistent ComputersSwyx [00:44:38]: One big Another, it's July of twenty-six. One big thing that also happens in, for OpenAI was OpenClaw, and that's I think a lot of people's first time really maxing a agent for personal stuff, but also crossing over to work in essence same way. As far as I understand, OpenClaw is still independent, but did you go through your own OpenClaw moments? Were there any lessons you took from OpenClaw to Codex or back? Whatever.Akshay Nathan [00:45:06]: I think there's a lot of inspiration. I did go through my own OpenClaw moment. I,Swyx [00:45:10]: Yeah, tell the storyAkshay Nathan [00:45:10]: Me and my wife like set up an OpenClaw to like try to manage everything in our house. Not that there's like a ton, but it was like quite useful. We gave it a calendar. It started, creating events for us and stuff. At some point, the laptop that we were running on, it died and never got a chance to pick it back up. But there was a lot of inspiration there, like, in ChatGPT Work, in web and mobile, like you get access to this like persistent computer environment where, you can store files, and those files stay around between sessions. And the idea is to be able to enable use cases like this. one of the members of our team uses ChatGPT Work for what they used OpenClaw from before, and then feel like it has like completely transitioned, which is like, workout planning and like meal tracking. which again, it's like a work-related thing, right? It's like not work necessarily, but it's like in personal productivity space. But it has all the same primitives. So it has scheduled tasks. It has the ability to store files on a file system. It has the ability to like reference those things over time. And so you start to see the same types of use cases emerge, which has been really cool.Swyx [00:46:14]: Is there a point that ChatGPT Work completely replaces OpenClaw? they're independent, so.Akshay Nathan [00:46:20]: Yeah, I'm, I'm not close to it, so I can't speak to the OpenClaw roadmap, but I don't think so. I think that there's gonna be, there's always a need for like this like incredible, like open source technology that team has built. And I think that we can draw inspiration, in the product and, ChatGPT, I think many more people have like heard about and used ChatGPT than have used OpenClaw. And if we can take the magic from OpenClaw and bring it to them, I think that'll be a success. I think that like one thing on the ChatGPT Work side that we feel strongly about is that like the core experience is that you come to this product and you have a conversation, start a session, whatever you wanna call it, with this agent. And the magic of the product is that you can do anything in that moment. And we would like to create a product where you don't have to click a button or to go to a different place, whatever, and you can get whatever functionality exists in, your finances app or where or any other product like in this one place. And so that's the goal. It's like it we want an extensible system with plugins where you can connect to the tools that you need in order to be able to accomplish like a financial task, where you can, if you're doing like science work, like we have an ability to like extend the system in such that you can like write the tech and it performs well. There'll always be like products that we support that are best in class at those things, but we want as much of the magic as possible in that core experience.Swyx [00:47:45]: Yeah. Do you think that you can do everything you used to do with Wealthfront in ChatGPT Finance?Finance, Data Access, and Centralized ContextAkshay Nathan [00:47:50]: I tried it. like ChatGPT doesn't yet custody, cash and assets for me. So that part, no, not yet. But I, there was like a whole component of like retirement planning and, like financial planning and budgeting and stuff that, we were looking into when I was there. And like with the finances plugin, like that's all possible with ChatGPT today. So, I feel

Scott Lechley started Lechley Associates with £10,000, a new baby at home, and six months of recruitment experience. Twenty four years later he is still the only person in the business, and he is on track for the best year he has ever had.Lechley Associates places the people who run the UK's biggest construction and infrastructure programmes. Framework directors, board level hires, the leaders behind HS2 scale rail and new hospital builds. Clients like Laing O'Rourke, Balfour Beatty and Hochtief. One retained search at a time.Six months into this year Scott had already cleared £300,000, with another £150,000 confirmed. He did it by doing something most recruiters never attempt: going fully retained, staying completely solo, and building an AI stack that means he now wakes up to a BD intelligence report, an automated outreach system, and a dashboard of ranked opportunities before most people have made their first coffee.He also did something almost no recruiter would admit to. This year he deliberately lost £40,000 in fees by talking a client out of a hire he did not believe in, and it does not cost him a minute of sleep.On this episode of The RAG Podcast, Scott breaks down exactly how the stack works, why 24 years of saying no to the wrong things is the real engine behind the numbers, and what a solo business actually makes possible when you stop trying to grow headcount.Scott is 54, having the best year of his career, and building a community for the next generation of construction professionals on the side. He is not slowing down.If you have ever wondered whether the pressure to build a team, hire more people and grow headcount is actually the only way, this episode is your answer.-------------------------------------------------------------------Episode Sponsor: AtlasAdmin is a massive waste of time. That's why there's Atlas, a CRM that actually understands context.Atlas captures everything you say, hear, read and write. Every interview, every client call, every LinkedIn message, email and WhatsApp, automatically. Not because you typed it up, but because Atlas was listening. Then it goes one step further and tells you the next action to take.So when you need to fill a role, People Search ranks your best candidates and tells you why, no digging required. That same memory turns your BD into a shortlist of exactly who to chase and why, so you win more clients. And when you want to see your pipeline, you just speak to your dashboards and Atlas builds the view for you in real time, tracking only what you care about.Whether you place permanent or contract, Atlas covers both. Its Contract Suite means you never chase a timesheet again, with live margin visibility across every placement. And with its new MCP and API, Atlas plugs straight into your LLMs and the rest of your stack, so the low-value admin that keeps you from billing gets done for you.This is not theory. Atlas customers are seeing 50% higher candidate response rates, a 35% increase in new clients won, 15+ hours saved every week, and monthly billings jumping by 85%. Some agencies are hitting 130% of their annual revenue target after building their business around Atlas.So if you're thinking you need to bolt AI onto your CRM, don't bother. Take a look at Atlas instead.Head to https://recruitwithatlas.com/therag/ to find out more.-------------------------------------------------------------------Episode Sponsor: HoxoEvery recruitment founder is investing in LinkedIn, but AI has turned templated posts and outreach into a commodity. When everyone sounds the same, the market stops listening. The recruiters winning now are the ones the market trusts.At Hoxo we help recruitment founders become the most influential name in their niche, using AI to multiply output while trust stays the product. Our clients turn their existing networks into £100K to £300K in new billings within months. Watch the free RAG listener training to see how: https://hubs.ly/Q03lBpYC0

In this episode of Shift AI, Andrew Comstock, Senior Vice President and General Manager of MuleSoft from Salesforce, joins host Boaz Ashkenazy for a wide-ranging conversation on governing, securing, and controlling the cost of agentic AI at enterprise scale.The conversation covers how MuleSoft's API-led integration playbook, built during the on-prem-to-cloud shift, is now extending into what the company calls Agent Fabric — bringing governance and controlled connectivity to agent-to-agent communication. Andrew walks through a concrete example of how two internal agents trusting each other by default can leak a customer's order history, and digs into prompt injection, impersonation, and why the security industry's people-focused compliance frameworks now have to account for reasoning software, not just people. He and Boaz also get into token economics, LLM gateways, intent-based routing between local and cloud models, and the emerging trend of companies repatriating some AI workloads on-premise.This episode is essential listening for CTOs, CISOs, platform and integration engineers, and IT and product leaders responsible for governing AI spend and agent security as their organizations scale past pilot projects into production.Chapters[00:01] Andrew Comstock's path to MuleSoft[02:06] First job: tax returns and trumpet reeds[03:21] What MuleSoft does and its extension into Agent Fabric[05:29] From consumer chatbots to enterprise agent connectors[07:22] Governance and security at the enterprise level[10:36] A real example: how two agents can leak an order number[13:45] Connect AI 2025 vs. 2026 — a year of night-and-day change[16:28] Mythos, Fable, and preparing for the LLM that breaks your systems[18:11] Token economics and the true cost of enterprise AI[20:38] The "AI savior" pattern vs. applying IT discipline to AI[22:40] Hybrid deployments, local models, and intent-based routing[27:13] Why AI's uneven acceleration makes prioritization more valuable[28:57] The two-word answer: "coming soon"Connect with Andrew ComstockLinkedIn: https://www.linkedin.com/in/andrewcomstock/Connect with Boaz AshkenazyLinkedIn: https://www.linkedin.com/in/boazashkenazy/Email: info@shiftai.fm

Partner with Jay: https://www.jayschwedelson.com/contactㅤPre-order Jay Schwedelson's new book, Stupider People Have Done It (out June 9, 2026).All net proceeds are donated to The V Foundation for Cancer Research, let's kick cancer's butt: https://www.amazon.com/Stupider-People-Have-Done-Marketing/dp/1637635206ㅤSubscribe to Jay's newsletter for weekly marketing tips and tactics: https://www.jayschwedelson.com/newsletterㅤRegister for GuruConference (FREE + VIRTUAL!) https://www.guruconference.comㅤCheck out Eventastic (FREE + VIRTUAL!) https://www.eventastic.comㅤConnect with Jay on LinkedIn: https://www.linkedin.com/in/schwedelson/Check out Jay's YouTube channel: https://www.youtube.com/@schwedelsonCheck out Jay's Instagram: https://www.instagram.com/jayschwedelson/Ask Jay anything: https://www.jayschwedelson.com/askㅤLeave a comment and follow the show, it really helps us out!ㅤFollow Daniel on LinkedIn and check out The Marketing Millennials podcast for sharp, no-fluff marketing insights. Subscribe to Ari Murray's newsletter at gotomillions.co for sharp, actionable marketing insights.ㅤYou have a file sitting inside LinkedIn right now with every connection, every DM and every comment you have ever made, and it takes about four clicks to get it. Jay Schwedelson walks through what happens when you drop that export into Claude alongside your CRM data and tell it to think like a chief revenue officer, while Daniel Murray shares the prompt he uses to pull clips, hooks and subject lines out of podcast transcripts he already recorded months ago. The whole thing wraps with the two of them pulling up their Uber passenger ratings on air, which does not go the way either of them planned.ㅤBest Moments:(00:11) The Uber small talk question nobody wants to answer honestly(02:06) Why AirPods are the polite way to opt out of any conversation(02:49) Daniel's prompt for turning old podcast transcripts into clips, hooks and subject lines(05:00) Feeding your full LinkedIn history into Claude and asking it to think like a CRO(06:14) The exact desktop click path to export your LinkedIn connection data(07:51) If your tool has no API connection, it might be time to find a different tool(08:41) A live Uber score showdown, including where to find your one-star count

Show DescriptionCodePen 2.0 is out now and we're talking about the launch, the idea of templating on the web, and how HTML could look very interesting in the future. Listen on WebsiteWatch on YouTubeLinks Yarn Announcing TypeScript 7.0 CodePen 2.0 announcement blog post SponsorsNotionWrite custom tools for Notion Agents that generate assets, query live data, and hit any API. Listen for incoming webhooks from any app, then run workflows with Notion Agents, pages, databases, and external APIs. All of this, on a hosted runtime. Workers are isolated sandboxes managed by Notion, so the code behind your syncs, tools, and workflows runs on our infra instead of your servers.

In this episode of Freight Waves Today, hosts Craig Fuller and Julie Van de Kamp break down one of the most transformative developments in recent logistics history: a staggering $640 million nuclear judgment involving C.H. Robinson that has sent shockwaves through the brokerage industry and freight markets. Plus, we explore how AI data center expansion is reshaping flatbed and bulk capacity, the latest trends in last-mile retail delivery, and a custom tech solution built specifically for truck drivers! Dennis McCaffrey, SVP of Enterprise Sales at RXO, drops by to share war stories from the early days of ExpressOne and XPO. He breaks down how RXO scaled into the 3rd largest freight broker, the power of managed expedite platforms, and what the next 12–18 months look like for capacity. Hackathon Winner Spotlight: RateSafe: Hackathon winner Sean Conley (VPA & Director of Business Development at Vantage Logistics) introduces RateSafe—an app built using Sonar's API that cuts through the noise to tell drivers what not to book based on personalized operating costs and home-time expectations. Last-Mile Delivery & Retail Trends: Jake Stein, VP of Retail Growth at Burke, breaks down the shift from ultra-fast delivery to extreme reliability, multi-carrier last-mile orchestration, and the realistic timeline for drone delivery. The "Capacity Tax" & AI Data Centers: Blake Ezell, VP of Customer Success & Support at IntelliTrans, joins to challenge the market recovery narrative. Discover how hyperscale AI data center construction is quietly consuming massive amounts of flatbed and bulk capacity—creating a bidding war for specialized freight. ⁠Follow the FreightWaves Today Podcast⁠ ⁠Other FreightWaves Shows⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Your best content ideas and warmest leads are already sitting in data you forgot you had. Jay and Daniel break down "AI ingestion": feeding transcripts, LinkedIn exports, and CRM data into tools like Claude so your AI actually knows your business and stays up-to-date. Daniel shares his exact prompt for turning one podcast transcript into clips, hooks, and subject lines that get watched past three seconds. Jay reveals the LinkedIn data trick that exports every connection, DM, and comment, then ranks who to reach out to first. Plus why your tools need an API connection or you'll fall behind. If you're a Marketer who wants to squeeze more content and more leads out of data you already own, this episode is for YOU. Enjoying Bathroom Break? Follow the show and drop us a rating, it genuinely helps more Marketers find us. Now go follow The Marketing Millennials. Daniel is a Workweek friend, working to produce amazing podcasts. To find out more, visit: https://workweek.com/ Follow Jay: LinkedIn: https://www.linkedin.com/in/schwedelson/ Podcast: Do This, Not That Follow Daniel: YouTube: https://www.youtube.com/@themarketingmillennials/featured Twitter: https://www.twitter.com/Dmurr68 LinkedIn: https://www.linkedin.com/in/daniel-murray-marketing Sign up for The Marketing Millennials newsletter: https://themarketingmillennials.com/

Paul Frambot breaks down how Morpho's new Midnight protocol lets curators price risk directly, unlocking under-collateralized lending onchain for the first time, and explains why he believes DeFi is finally ready to capture a slice of the $200 trillion global credit market. He also makes the case against aggressive token buybacks, arguing that reinvesting in growth beats shrinking the float.Paul Frambot is the Founder and CEO of Morpho, a decentralized lending protocol powering onchain credit for Coinbase, Robinhood, and other major institutions.The Rollup is where the leaders of digital assets and finance converge. Live from the financial capital of the world.Timestamps00:00 Intro02:25 Unlocking The $200T Credit Market09:05 Midnight Unlocks Custom Loan Pricing11:32 Under-Collateralized Loans Become Reality18:30 Risk-Adjusted Markets Never Before Possible21:13 Token Value Vs Buybacks Debate34:21 Clarity Act Update From DCGuest Socials:Paul Frambot X: https://x.com/PaulFrambotMorpho X: https://x.com/MorphoMorpho Website: https://app.morpho.org/Partners: Better than Banks. Transparent capital efficiency earning the highest yields in DeFi. Learn more here: https://infinifi.xyz/---1inch - Simple experience. Smart execution. Trading built to scale. It's time to bring the world onchain. https://1inch.com/---Dinari - Over 230 1:1 backed tokenized stocks, ETFs & more with dividends. US-based SEC transfer agent. Available on 5+ chains & via API. https://dinari.com/---Relay is the fastest and most reliable way to swap any token on any chain. Learn more here: https://relay.link/bridge---Zama is an open source cryptography company that builds state-of-the-art Fully Homomorphic Encryption (FHE) solutions for blockchain.Learn more here: https://www.zama.org/---Trezor is the creator of the first-ever hardware wallet. Securing crypto for 2M+ users worldwide. 100% open source. Learn more here: https://affil.trezor.io/aff_c?offer_i...---

Members Only: Today's video is available only to members. If you are already a member, you can access your private podcast feed by visiting https://www.pointfree.co/account. --- SwiftData has a brand new `sectionBy` API, for grouping the results of a `@Query` into sections. So what is SQLiteData's solution to this problem? Well it turns out SQLiteData's tools supported sectioning from day one, and much more. We'll show just how far these tools can take use before introducing an ergonomic API to match SwiftData's.

In this episode of Freight Waves Today, hosts Craig Fuller and Julie Van de Kamp break down one of the most transformative developments in recent logistics history: a staggering $640 million nuclear judgment involving C.H. Robinson that has sent shockwaves through the brokerage industry and freight markets.Plus, we explore how AI data center expansion is reshaping flatbed and bulk capacity, the latest trends in last-mile retail delivery, and a custom tech solution built specifically for truck drivers!Dennis McCaffrey, SVP of Enterprise Sales at RXO, drops by to share war stories from the early days of ExpressOne and XPO. He breaks down how RXO scaled into the 3rd largest freight broker, the power of managed expedite platforms, and what the next 12–18 months look like for capacity.Hackathon Winner Spotlight: RateSafe: Hackathon winner Sean Conley (VPA & Director of Business Development at Vantage Logistics) introduces RateSafe—an app built using Sonar's API that cuts through the noise to tell drivers what not to book based on personalized operating costs and home-time expectations.Last-Mile Delivery & Retail Trends: Jake Stein, VP of Retail Growth at Burke, breaks down the shift from ultra-fast delivery to extreme reliability, multi-carrier last-mile orchestration, and the realistic timeline for drone delivery.The "Capacity Tax" & AI Data Centers: Blake Ezell, VP of Customer Success & Support at IntelliTrans, joins to challenge the market recovery narrative. Discover how hyperscale AI data center construction is quietly consuming massive amounts of flatbed and bulk capacity—creating a bidding war for specialized freight. Follow the FreightWaves Today Podcast Other FreightWaves Shows Learn more about your ad choices. Visit megaphone.fm/adchoices

With Javed Jafar, CPQ and Billing Expert with 20+ years of experience across enterprise implementations, advisory work, and customer-side transformation programs. How will AI change CPQ for customers, vendors, system integrators, and CPQ professionals? In this episode, Javed shares his perspective on why AI is not replacing CPQ expertise, but changing what good CPQ expertise looks like. Routine configuration work, documentation, testing, and delivery tasks are becoming easier to accelerate with AI tools. At the same time, deep domain knowledge is becoming more important, since customers still need experts who can recognize incorrect AI output, manage complexity, and translate business requirements into scalable commercial processes. Frank and Javed also discuss how AI is changing expectations for system integrators, as customers increasingly expect more value for every professional services dollar — a shift that may push the market toward fixed-price offerings, reusable accelerators, boutique specialists, and AI-enabled delivery models. They also examine the current business climate around AI adoption, including why many organizations are investing in AI tools but still struggle to demonstrate measurable ROI. In CPQ specifically, Javed sees many companies experimenting with automation and AI assistance, while true agentic CPQ adoption is still in the early stages. In this conversation we cover: Why AI is changing what good CPQ expertise looks like, not replacing it How AI is accelerating configuration, documentation, testing, and delivery work Why deep domain knowledge matters more as AI output needs expert review How AI is reshaping expectations and pricing models for system integrators The gap between AI investment and measurable ROI in CPQ today The state of agentic CPQ adoption and Salesforce Agentforce Revenue Management The impact of legacy CPQ end-of-sale messaging The shift from rule-based to constraint-based thinking What types of customers benefit most from AI-first, API-driven revenue architecture If you are a CPQ customer, part of a Salesforce Revenue Cloud team, a system integrator, a CPQ vendor, or a revenue operations leader trying to understand how AI will reshape Configure, Price, Quote over the next few years, this episode offers a grounded, experience-based perspective. Learn more: Javed on LinkedIn

At the GEOINT 2026 Symposium in Denver, Colorado, Project Geospatial's Adam Simmons sits down with Kelly Kroegel, a former Marine Special Operations geospatial intelligence analyst now working with Torchlight.In this segment, Kelly provides an exclusive look into Torchlight's powerful location analytics platform, demonstrating how anonymized, aggregated mobile device data is transformed into actionable intelligence for marketing, security, and the defense community.Watch the full demo to see how Torchlight analyzes massive datasets—pulling from 9 petabytes of hot storage—to track patterns of life, trace cross-border device movements from special operations headquarters, and unmask unidentified training facilities globally using behavioral indicators.Key Highlights in this Video:00:00:36 – Meet Kelly Kroegel and learn about her journey from Marine Special Operations to Torchlight.00:01:04 – How Torchlight leverages anonymized mobile device location data without compromising PII (Personally Identifiable Information).00:02:08 – Live Platform Demo: Analyzing device deployments from the UK's 22nd SAS Base.00:03:00 – Inside the Data Scale: Sifting through 9 petabytes of storage to isolate patterns.00:04:00 – Threat Detection & Pattern of Life: Tracking international military movements and cross-border travel (Poland, Cyprus, Greece, Saudi Arabia).00:04:55 – Uncovering Unmarked Facilities: How behavioral data maps out hidden compounds in Riyadh.00:07:15 – Enterprise Solutions: Torchlight platform subscriptions, API integration, and future predictive analytics algorithms.Learn More About Torchlight:

In this episode of Shift AI, Noah Smith, Vice Provost for AI at the University of Washington and Senior Director of NLP Research at the Allen Institute for AI, joins host Boaz Ashkenazy for a wide ranging conversation on how universities are preparing for a workplace increasingly run by AI agents.Noah's path wound from a PhD at Johns Hopkins to a tenured professorship at Carnegie Mellon, until a call from UW pulled him west in 2015 to help build out its natural language processing faculty. A few years later he took on a second role leading a research team at the Allen Institute for AI, and this past November added a newly created role as UW's first Vice Provost for AI. The conversation covers how faculty across UW are experimenting with AI in the classroom, from an interactive logic textbook in the philosophy department to new AI literacy courses, and digs into OLMo, the fully open language model project Noah leads at Ai2, unpacking why releasing the weights, code, and training data together, not just an API, matters for regulated industries, universities, and anyone who wants to retrain a model rather than just prompt it.Noah lays out a scenario where 80 percent of entry level work gets done by autonomous agents and argues the skill universities need to double down on is not AI literacy so much as discernment, the ability to unwrap a problem and figure out what questions to ask.This one is for university administrators, provosts, and faculty grappling with AI policy, as well as founders and engineers building on open source models, and anyone curious how a major research university is trying to get ahead of the agentic era instead of just reacting to it.Chapters[00:05] Welcome to Shift AI, live from UW's Foster School of Business[03:44] Noah's path from Carnegie Mellon to UW's Vice Provost for AI[06:08] Bagging groceries: Noah's first paid job[08:11] Six months in: what surprised Noah most about the role[09:22] How AI is reshaping teaching and pedagogy at UW[11:33] Preparing students for a world where agents do 80 percent of the work[14:08] Inside the Allen Institute for AI and the origins of OLMo[15:52] What "fully open" really means for language models[17:49] Specialized models versus general-purpose models for regulated industries[19:47] AI governance, security, and UW's new governance committee[24:35] The future of work in two words: human agencyConnect with Noah SmithLinkedIn: https://www.linkedin.com/in/noah-smith-0322511a4/Connect with Boaz AshkenazyLinkedIn: https://www.linkedin.com/in/boazashkenazy/Email: info@shiftai.fm

Leo Fan breaks down why the real bottleneck in AI isn't model intelligence but a persistent GPU shortage driving up inference costs, even as open-source models from China close the gap. He also unpacks the financialization of compute and why some believe it could become one of the largest derivatives markets in the world.Leo Fan is the Founder of Cysic, a full-stack compute network turning GPUs, ASICs, and spare hardware into verifiable AI infrastructure, and a Cornell CS PhD researcher in zero-knowledge systems and AI.The Rollup is where the leaders of digital assets and finance converge. Live from the financial capital of the world.Timestamps00:00 Intro04:18 Kimi K2 Shocks Compute Demand06:39 GPU Shortage Drives Inference Costs11:34 Engineering Around Chip Gaps18:23 Compute Financialization20:48 Earning Yield From Spare Compute23:20 Compute Markets FutureGuest Socials:Leo Fan X: https://x.com/leofanxiongCysic X: https://x.com/cysic_xyzCysic Website: https://cysic.xyz/ Partners: Better than Banks. Transparent capital efficiency earning the highest yields in DeFi. Learn more here: https://infinifi.xyz/---1inch - Simple experience. Smart execution. Trading built to scale. It's time to bring the world onchain. https://1inch.com/---Dinari - Over 230 1:1 backed tokenized stocks, ETFs & more with dividends. US-based SEC transfer agent. Available on 5+ chains & via API. https://dinari.com/---Relay is the fastest and most reliable way to swap any token on any chain. Learn more here: https://relay.link/bridge---Zama is an open source cryptography company that builds state-of-the-art Fully Homomorphic Encryption (FHE) solutions for blockchain.Learn more here: https://www.zama.org/---Trezor is the creator of the first-ever hardware wallet. Securing crypto for 2M+ users worldwide. 100% open source. Learn more here: https://affil.trezor.io/aff_c?offer_i...---

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In this episode of Business Brain, we dig into the tools that make us better operators, starting with Markdown, the plain-text format John Gruber built to be human-readable and computer-friendly, and now the format our AI overlords love because it burns fewer tokens and runs faster. We share a free live-preview tool at markdownlivepreview.com and Dave’s go-to Mac app, then get real about software pricing: consumers overwhelmingly want to buy outright while enterprises prefer subscriptions, and why recurring revenue is what keeps the small developers we love in business. Then we get into the FridAI meat: using agentic browsers like Comet to stress-test our own websites the way a customer would. How many clicks to find your contact form, file a warranty claim, or unsubscribe? We ask AI to surface the friction points, audit our SEO and AI engine optimization, and flag security holes, because an unpublished private API is not a private one. We even threw out one of our own user interfaces for a chat interface and watched engagement jump 10x. Test every customer touchpoint, keep the friction out, and keep living that Charmed Life. 00:00:00 Business Brain – The Entrepreneurs' Podcast #773 for Casual FridAI, July 24, 2026 July 24th: Tell an Old Joke Day 00:01:32 MarkDownLivePreview.com Marked (macOS app) 00:08:43 Scribe. Don’t get caught being the only person who knows how something works. For a limited time, book a demo at https://scribe.how/brain and mention BRAIN for your first month of Scribe Capture free. 00:10:34 SPONSOR: FanVue. Are you ready to start your own creator journey and make it big? Visit https://www.fanvue.com/ today and launch your career! 00:11:48 For the apps you use: subscription vs. buy it outright? 00:13:19 Using an Agentic Browser to Test Your Website Comet Browser from Perplexity Things to look for: How easy is it for my customers to contact me? Go find [x] FAQ and tell me how easy it was. Pretend you have [x] question. Tell me how to solve it. How effective is my SEO? How effective is my AIEO? Have it review your site for security holes Business Brain 773 Outtro This Episode's Big Takeway: Use AI to Test all the customer touch points of your business Check out Business Brain Blueprints Tell Your Friends! Business Blueprints Review Business Brain Subscribe to the show feedback@businessbrain.show Call/Text: (567) 274-6977 X/Twitter: @ShannonJean & @DaveHamilton, & @BizBrainShow LinkedIn: Shannon Jean, Dave Hamilton, & Business Brain Facebook: Dave Hamilton, Shannon Jean, & Business Brain The post FridAI – AI Browsers Test Your Website + Markdown – Business Brain 773 appeared first on Business Brain - The Entrepreneurs' Podcast.

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

You want to put AI to work in your business, but every time you start, the same worry stops you: what happens to your data once it's in there? For a lot of contractors, that single fear is the only thing standing between them and real time savings.In this episode, Khalil and Martin break down the three real security risks with AI, why paying for a team account changes what happens to your data, and when a private, local model is actually worth it. They get into open-source models, HIPAA-sensitive work, tokens and API costs, and what an AI agent really is.If you've been holding off because you don't trust AI with your information, this is the conversation that shows you the way around it.Key Topics & Timestamps00:39 - Episode Intro01:09 - Sunsetting GrowthKits01:54 - Benali Goes AI03:55 - HIPAA and Secure AI Needs09:04 - Security Risks and Local Models28:33 - Skip Fine-Tuning28:59 - Local Models and Privacy30:20 - Tokens and API Costs34:35 - Open Source Model Trust38:35 - Agents and AdoptionMemorable Quotes"If it's free, you are the product." — Khalil"If you're comfortable putting it into a paid Google Drive, you should be comfortable putting it into Claude." — Khalil"A lot of roles will go away, but jobs will not go away." — Khalil"We are doing it. It's not theoretical." — Martin"Just get started, and then all of a sudden you start thinking more and more, and then you're in the game." — MartinKey TakeawaysStop using free AI tools for company work. Free versions train on everything you upload, and your employees are almost certainly doing it right now without a policy in place.Move to paid team or organization accounts. On an org account, training on your data is off by default, which is the baseline protection most contractors actually need.Write an AI policy and train your team on it. Treat unmanaged AI use like a phishing risk; one person uploading client data to a free account is all it takes.For most contractors, a paid team account is enough. If you're comfortable putting a file in a paid Google Drive, you're safe putting it in Claude or ChatGPT.Consider a private, local model when you handle HIPAA data, trade secrets, or heavy API costs. You can run an open-source model on a server you control with zero data retention.Skip fine-tuning your own model. Almost no contractor has the thousands of past projects it takes to justify it, so focus on using AI well and building tooling around it instead.Find the time and money to start now. Raising prices is usually the fastest way to fund it, and the efficiency gains pay it back.ResourcesOllamaQuoNeed help with podcast production? We recommend DemandcastMore from Martintheprofitproblem.comannealbc.comEmail MartinMeet With MartinLinkedInMore from KhalilBenaliEmail KhalilLinkedInThe Cashflow ContractorSubscribe to our YouTube channelSubscribe to our NewsletterFollow on social: LinkedIn, Facebook, Instagram, X (formerly Twitter)Visit our websiteEmail The Cashflow Contractor

AI is no longer just a race to train smarter models. As AI moves into production, the bottleneck is increasingly inference: how fast models can generate tokens, use tools, reason, verify, and act. In this episode of the MAD Podcast, Matt Turck sits down with Andrew Feldman, co-founder and CEO of Cerebras, to explain why fast inference may define the next era of AI.Cerebras is known for building a chip the size of a silicon wafer. But this conversation is not just about one company or one chip. It is a deep dive into the AI infrastructure stack: GPUs, ASICs, memory, HBM, SRAM, data centers, power, TSMC, AWS, OpenAI, agents, reasoning models, and why speed changes what AI products can become. Andrew explains why “tokens per second per user” matters, why generating a single word can require moving the equivalent of 100 HD movies through memory, why agents amplify latency, why GPUs struggle with certain inference workloads, and why fast AI may eventually reshape SaaS itself.This is a reference conversation on fast inference, AI chips, and the next compute bottleneck.(00:00) Cold open & Intro(01:31) Why speed became the AI bottleneck(02:32) Tokens per second per user, explained(03:16) AI's broadband moment and the Netflix analogy(04:35) The AI chip landscape: GPUs, TPUs, Trainium, ASICs(06:36) What is an ASIC?(08:08) Nvidia, Groq, and the fast inference war(09:16) OpenAI, Broadcom, and specialized silicon(12:10) China, power, and sovereign AI infrastructure(15:05) Is the AI infrastructure boom a bubble?(18:56) The hidden bottlenecks: HBM, CoWoS, and 3nm(22:57) Why agents are creating CPU demand(25:36) Andrew Feldman's path from SeaMicro to Cerebras(26:13) Why Cerebras bet on AI in 2016(31:14) SRAM vs. HBM: why inference is a memory problem(33:19) What wafer-scale computing actually means(34:28) The deep-tech “Everest” problem(36:07) The moment the first Cerebras system worked(36:49) Ringing the bell and surviving deep tech(39:08) How a giant chip handles failure(41:22) Why GPUs struggle with decode(42:17) Prefill vs. decode explained(44:01) The “100 HD movies” problem in AI inference(45:04) How fast inference changes RL and training(48:08) Reasoning models and why they cost more compute(50:08) Verification, guardrails, and small models checking big models(52:37) Multimodal AI and the path to video(53:51) Cerebras' business model: hardware, cloud, and API(55:14) OpenAI's 750MW inference deal(55:36) Why data centers are measured in megawatts(58:01) AWS Trainium + Cerebras decode(59:29) Fast tokens as a cloud product(01:00:52) Is CUDA still a moat?(01:03:53) How TSMC helped Cerebras build the giant chip(01:07:41) Why nobody cared in 2020(01:08:15) Why chip supply chains are hard to diversify(01:09:54) Why today's AI models will be the worst you ever use(01:10:38) What fast AI could do to SaaS

Anatoly Yakovenko is the co-founder of Solana, the fastest scaled blockchain in the world.We start by talking about how non-US residents were trading SpaceX on Solana pre-IPO, and which parlayed into the last 130 years of US financial markets.We then get into how Solana is removing eight layers of middlemen that make-up the legacy financial system, whether you actually need to use blockchain to do this, the 4am inspiration to start Solana, how Solana was 10,000x faster than Bitcoin, why a16z passed on investing then paid a 1,000x higher price, how launching Solana at the bottom of the market right as COVID hit led to their success, why AI won't take your job, growing up sharing one toilet with four families in the USSR, and playing competitive underwater hockey.Thanks to this episodes sponsors!Numeral: Sales tax on autopilot https://www.numeral.comFlex: Premium banking, 60-day credit, 0% APR https://home.flex.one/referral/bananacapitalAmplitude: AI analytics https://www.amplitude.comMerge: Every model, one API https://www.merge.dev/turnerMonaco: The revenue engine for startups https://www.monaco.com/Timestamps:(0:00) Trading SpaceX on Solana(3:02) Why Wall Street runs on 100 year old tech(10:51) US dominance created demand for tokenized stocks(13:58) Complexity reduces risk of the financial system(15:51) Do you need to use blockchain?(18:34) Privacy tradeoffs of public ledgers(23:45) Making a 10,000x faster blockchain(30:05) A new data structure based on time(32:54) Trading was Solana's first use case(37:41) Advice from his wife that led to Solana(40:05) Why a16z passed (then paid up 1,000x)(43:50) Rejection and COVID led to Solana's fast adoption(48:30) Best time to launch is the bottom of a market(52:56) Why Bitcoin and Ethereum were so slow(57:38) Rebuilding Solana with Alpenglow(1:01:23) 35% of all stablecoin volume runs on Solana(1:04:26) Motors replaced 200 billion jobs, AI will replace 100 billion(1:08:17) It's selfish to protest data centers(1:10:11) Growing up in the USSR: one toilet, four families(1:12:27) Culture shock moving to the US(1:13:23) Government spending is fake GDP(1:15:49) Playing competitive underwater hockey(1:18:01) Armani at Backpack(1:19:23) How Solana survived the FTX collapse(1:22:47) There won't be massive AI job lossReferencedSolana: https://solana.com/Jobs at Solana: https://jobs.solana.com/companies/solana-foundation-2Slow Ventures: https://slow.co/Foundation Capital: https://foundationcapital.com/Multicoin: https://multicoin.capital/Follow AnatolyTwitter: https://x.com/toly?lang=enLinkedIn: https://www.linkedin.com/in/anatoly-yakovenkoFollow TurnerTwitter: https://twitter.com/TurnerNovakLinkedIn: https://www.linkedin.com/in/turnernovakSubscribe to my newsletter to get every episode + the transcript in your inbox every week: https://www.thespl.it/

Spotify is shifting from a traditional audio platform into a powerful multi-format ad engine driven by advanced data targeting and transparent, value-exchange sponsorship models. Through innovative automated tools and natural language API plugins, the streaming giant is eliminating traditional production barriers so brands of all sizes can easily deploy high-ROAS campaign creative. Key Highlights

Sean Emory of Avory & Co. explores how AI could change software competition if agents become buyers, not just users, shifting value from platform suites to composable, standalone capabilities.He frames three eras of software: individual tools, integrated platforms, and an emerging era where AI orchestration acts like a new operating system that selects the best capability for a task regardless of UI or vendor.In this model, winning depends less on distribution, brand, and long contracts, and more on reliability, latency, accuracy, security and governance, API quality, cost per call, and task success rates.Sean argues platforms won't disappear. They may become command centers for permissions, compliance, and governance, while features "escape" suites. He walks through examples from Shopify, Shop Pay, Stripe, Twilio, Zoom, Box, Salesforce, Adobe, and HubSpot.Chapters:00:00 AI changes software buying01:44 Three eras of software03:10 Composable software era03:55 Agents versus humans05:06 Orchestrator as new OS07:46 Capabilities escaping platforms08:33 Examples: Shopify, Stripe, Zoom12:28 New scorecard for winners15:32 Two questions for companies17:22 Where this could be wrong19:48 Big picture takeaways23:22 Closing and subscribeListen on:Apple Podcasts: https://podcasts.apple.com/us/podcast/avory-markets-and-investing/id1504555573Spotify: https://open.spotify.com/show/3A8acTyfhhxpUFoFLEKeMJYouTube: https://youtube.com/@avorycoFollow Sean and Avory & Co.:X: https://x.com/avorycoX: https://x.com/_SeanDavidLinkedIn: https://www.linkedin.com/company/avory-coWebsite: https://avoryfunds.comDisclaimerThe content on this channel is for informational and educational purposes only. It does not constitute personal investment advice, a solicitation, or an offer to buy or sell any security. Views expressed are those of Sean Emory and Avory & Co. as of the recording date and are subject to change without notice.Avory & Co. and Sean Emory may hold positions in the securities and companies discussed. Any references to specific companies, products, or services are for illustration only and should not be interpreted as recommendations.Investing involves risk, including possible loss of principal. Past performance does not guarantee future results. Consult a qualified financial advisor before making any investment decision.

This Week In Startups is made possible by: Vanta https://www.vanta.com/twist Superhuman https://superhuman.com YSecurity https://YSecurity.io/TWIST Today's show: *Cybersecurity has long focused on cleaning up a system AFTER a breach but Ent founder Brandon Dixon says that's backwards. He just raised a $100M seed round to put an AI agent on everyone's company laptops that catches the risky clicks, leaked files, or rogue agents BEFORE they wreck havoc. PLUS Clawra creator David Im return swith his new project, Sume's Avatar, a multi-model orchestration layer that generates 60-second UGC videos from a single prompt… and gets it right on the first try, rather than falling back on trial and error. Guests: Brandon Dixon on LinkedIn: https://www.linkedin.com/in/brandonsdixon/ Ent: ****https://ent.ai/ David Im on X: https://x.com/davidim Sume: https://www.sume.com/ Relevant Links: WSJ: "Cyber Security Startup Ent Raises $100 Million in Seed Funding": https://www.wsj.com/pro/cybersecurity/cyber-startup-ent-raises-100-million-in-seed-funding-a3e9b6c6 Microsoft Security Copilot: https://www.microsoft.com/en-us/security/business/ai-machine-learning/microsoft-security-copilot Engadget: "Meta 'pausing' employee tracking program…": https://www.engadget.com/2199458/meta-is-pausing-employee-tracking-program-after-it-let-the-whole-company-see-sensitive-data/ Seedance 2.0: https://seedance2.ai/ Timestamps: 0:00 Intro: Why AI + cybersecurity is the hot combo right now 2:29 How INT stops breaches before they happen 4:56 AI is giving non-technical employees dangerous new powers 10:26 Vanta - Compliance and security shouldn't be a deal-breaker for startups to win new business. Vanta makes it easy for companies to get a SOC 2 report fast. Get $1,000 off for a limited time at https://www.vanta.com/twist 13:54 Why on-device AI beats cloud-based security 20:08 Superhuman - Get AI that works where you work. Unlock your Superhuman potential at https://superhuman.com 25:18 INT's business model and go-to-market 30:26 YSecurity - The on-demand security team for startups. Need enterprise-grade security without hiring a $400k CISO? YSecurity gives you 40+ expert engineers, matched to exactly what you need, by the hour, with your first six hours completely free. Go to https://YSecurity.io/TWIST 34:18 David M. of Sumi Labs: one-shotting AI video 36:10 Live demo: Sumi's video orchestration API 40:05 Consistent faces, 60-second videos, and who's buying Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com Check out the TWIST500: https://www.twist500.com Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp   Follow Lon: X: https://x.com/lons   Follow Alex: X: https://x.com/alex LinkedIn: ⁠https://www.linkedin.com/in/alexwilhelm   Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis   Check out all our partner offers: https://partners.launch.co/   Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland   Check out Jason's suite of newsletters: https://substack.com/@calacanis   Follow TWiST: Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin Instagram: https://www.instagram.com/thisweekinstartups TikTok: https://www.tiktok.com/@thisweekinstartups Substack: https://twistartups.substack.com

Get the free Core Drives in the Wild guide, behavioral design applied to real products: professorgame.com/WildCD Episode Summary Frédéric Guitton, who leads sales and marketing at iTargetGolf after a career across banking, startups, financial services, and investment firms, explains how driving ranges are turning dead space into an interactive, gamified experience. He walks through the company's product line, including the iTarget Night Saber target that connects to Toptracer, Inrange, and Trackman through an API, the in-bay lights that fire when a player hits their target, and the new Cash Cup Golf hole-in-one contest played with RFID balls. Guitton lays out the three levers a driving range has for revenue, why post-COVID golf growth came from entertainment rather than traditional rounds, and how instant feedback keeps a player buying one more bucket. Listeners come away with a working model of physical gamification and a reminder that designing around real human behavior beats asking people to behave better. About the Host Rob Alvarez is Head of Engagement Strategy, Europe at The Octalysis Group (TOG), a leading gamification and behavioral design consultancy. A globally recognized gamification strategist and TEDx speaker, he founded and hosts Professor Game, the #1 gamification podcast, and has interviewed hundreds of global experts. He designs evidence-based engagement systems that drive motivation, loyalty, and results, and teaches LEGO® SERIOUS PLAY® and gamification at top institutions including IE Business School, EFMD, and EBS University across Europe, the Americas, and Asia. Key Takeaways A driving range has only three levers for revenue, according to Frédéric Guitton: sell more balls, sell more food and beverage, or sell the balls for more money. Every engagement decision at iTargetGolf traces back to one of those three. iTargetGolf's first targets were powder-coated metal and looked great, but ball picker staff bumped them and metal does not give, so it bent or broke permanently. The replacement is cut from utility sewer pipe and survives 400 to 500 pounds driving over it. Post-COVID golf growth came from entertainment, not from more rounds on traditional courses. Guitton describes the driving range starting to replace the bowling alley as the family night out, which brings in an audience that never came to practice. The iTarget Night Saber target connects through an API to Toptracer, Inrange, and Trackman ball tracking, so a landed ball triggers an animation within seconds. That instant feedback is Core Drive 2 (Development and Accomplishment) delivered in the physical world. Choosing low voltage over high voltage took permitting out of the install path. A California range that signed in mid-June started its install roughly four weeks later, which turned an infrastructure decision into a sales advantage. Guitton frames the B2B2C tension plainly: the range's success drives iTargetGolf's success, so the company treats itself as a consequence of its customers' results rather than a driver of them. Topics Covered 0:00 — Opening and introduction 2:17 — Inside a week at iTargetGolf 3:25 — Favorite fail: metal targets that bend 6:34 — Sewer pipes and the strike point redesign 9:01 — The four products iTargetGolf builds 11:31 — How a general contractor started the company 14:58 — Golf as entertainment after COVID 15:57 — Three ways a driving range makes money 17:46 — Making it fun and creating new golfers 19:35 — Best practices: listen and stay locked in 22:45 — Football intelligence and instant feedback 27:47 — Outliers, favorite games, and goodbyes Get the free Core Drives in the Wild guide, behavioral design applied to real products: professorgame.com/WildCD About Frédéric Guitton Frédéric Guitton leads the sales and marketing strategy at iTargetGolf, where he is helping redefine the driving range experience through interactive down-range technology. He relocated from France to Central Florida in 1996 and built a career in banking before expanding into startups, financial services, and investment firms, developing experience across business strategy, operations, sales, marketing, and executive leadership. At iTargetGolf he works on a product line that combines interactive illuminated targets, skill-based cash prize games, and integrated ball-tracking technology to increase player engagement and open new revenue for golf facilities. His current focus is building scalable systems, strengthening customer relationships, expanding strategic partnerships, and positioning the company in the evolution of golf entertainment technology. Find the Guest Online Website: itargetgolf.com LinkedIn: linkedin.com/in/fguitton Instagram: @itarget_golf TikTok: @itarget_golf Facebook: iTargetGolf on Facebook Mentioned in This Episode Some links below are affiliate links. As an Amazon Associate, I earn from qualifying purchases. Recommended book: Outliers by Malcolm Gladwell Proposed guest: Scott Armstrong, involved with TGL, the indoor golf competition on ESPN Ball tracking platforms iTargetGolf integrates with: Toptracer, Inrange, Trackman Topgolf, referenced as the far end of the golf entertainment spectrum iTargetGolf products discussed: iTarget Night Saber, iTarget Strike Point, in-bay lights, and Cash Cup Golf Favorite game to watch: volleyball. Favorite sport to play: cycling. Free Resources and Get in Touch Core Drives in the Wild: Professor Game Free Guide Get Daily Value on Your Email Let's chat about your gamification project YouTube LinkedIn Instagram Facebook Start Your Community on Skool for Free Ask a question

What if walking away from the CEO seat was the smartest career move you could make in AI right now?Cameron Herold sits down with Alex Triplett, COO of you.com, the company quietly powering web search for AI agents behind brands like Salesforce, Anthropic, and Harvey. Alex turned down the chance to be a CEO to take the second-in-command seat at a fast-moving AI startup, and he explains exactly why.They get into the operator work most leaders avoid: walking into a 135-person company, making a brutal focus call, and rebuilding the team around a single mission. Alex breaks down his first 90 days, the listening tour that validated his biggest decision, how he sells to enterprise without getting strung along, and the system that keeps him out of the minutiae.Skip this one, and you risk what most new executives do: the right idea at the wrong time, and broken trust. Listen now for the unfiltered COO playbook on focus and timing.Sponsored byGenius Network - An exclusive community for highly successful entrepreneurs, connecting you with top-tier leaders, strategic insights, and powerful relationships to help you grow your business faster and smarter.Learn more: https://www.geniusnetwork.com/Timestamped Highlights10:16 – “Google for AI agents,” explained: the invisible layer your favorite LLM cannot work without13:18 – You might be using you.com right now without knowing it. Here is where it hides in the AI stack15:08 – How to tell a real enterprise deal from a buyer who is just keeping you busy19:56 – The COO superpower hiding in plain sight, and why it gets you a seat at the C-suite table23:08 – Chief of staff or glorified executive assistant? The title too many companies get wrong25:24 – Three companies, one founder: how Richard runs you.com while raising $650M for a frontier lab33:34 – 135 people on day one: the focus call that cut the team to 110, then built it back stronger38:01 – One mission, one North Star, and the moment focus “just exploded” the company39:23 – The first 90 days that make or break a COO: the listening tour that validated everything47:23 – Why this COO leaves messages unread on purpose, and what it taught him about good decisions50:09 – The one-sentence piece of advice he would give his 22-year-old selfAbout the GuestAlex Triplett is the Chief Operating Officer of you.com, the leading web search API for AI agents, serving customers like Salesforce, Anthropic, and Harvey. An investor turned operator, Alex spent the first decade of his career in private equity before moving to the operating side. He served as Global Head of Corporate Development at ION Group, where he helped grow the business from $150 million to $3 billion in revenue, and later as CFO and COO of Appfire. He also chairs the board of the travel app Pangea and holds a degree from the McIntire School of Commerce at the University of Virginia.