Podcasts about Token

 EPISODE DESCRIPTION I sit down with Martin Pokorski from Skynet Trading , one of the earliest market makers in the space, operating since 2019 , to pull back the curtain on what's really happening inside token markets. Martin breaks down what founders consistently get wrong about liquidity, why checking CoinGecko every day is not a liquidity strategy, and what a proper liquidity audit actually reveals that surface-level metrics will never show you. We talk about the infamous "Houdini liquidity" trick, why some billion-dollar market cap projects have no product, and why thinking liquidity-first before your listing strategy could be the most important decision you make as a founder. If you're a VC, an investor, or a builder with a token live or in the pipeline, this conversation gives you the framework to understand what healthy markets actually look like , and how to get there. DISCLAIMERNothing mentioned in this podcast is investment advice and please do your own research. It would mean a lot if you can leave a review of this podcast on Apple Podcasts or Spotify and share this podcast with a friend. Be a guest on the podcast or contact us - https://www.web3pod.xyz/ CONNECT Skynet Trading Website: https://skynettrading.com/Skynet Trading Twitter/X: https://x.com/SkynetTrading_Skynet Trading LinkedIn: https://www.linkedin.com/company/skynet-trading/Martin Pokorski on LinkedIn: https://www.linkedin.com/in/martinpokorski/Web3 with Sam Kamani: https://www.web3pod.xyz/ KEY POINTS WITH TIMESTAMPS • [00:00] Introduction to Martin Pokorski and Skynet Trading , what they do and why liquidity matters for token projects• [01:42] Martin's origin story , how a university LAN party in Australia in 2014 sparked his crypto career• [03:48] Running one of the first Bitcoin OTC desks in Australia with over 6,000 clients• [04:45] What Skynet Trading does: market making for token issuers and designated market making for exchanges• [06:26] What Martin is seeing on the ground , token launches are still happening, and the current market is actually an advantage for serious projects• [07:39] The RWA narrative and why sovereign funds and traditional finance firms are quietly exploring tokenization• [09:21] The first question Martin asks every founder: do you actually need a token?• [10:49] Why the GameFi opportunity is still real but requires patience , and lessons from the AAA gaming world• [15:52] What Skynet's liquidity audit reports cover , spread, depth, consistency across venues, and price dislocations• [17:41] Why checking CoinGecko is not a liquidity strategy and what the data actually reveals• [19:43] The biggest misconception founders have about liquidity• [22:00] Key warning signs in token markets , pulled liquidity, blown spreads, and "Houdini liquidity" explained• [24:23] The questions Martin asks early-stage founders and what the liquidity masterclass covers• [26:27] How Skynet Trading grew almost entirely through word of mouth and referrals• [28:02] Where the industry is in the current cycle and why Martin is optimistic for the next few years• [32:05] The single most important piece of advice for founders with a live token: think liquidity-first• [34:30] Why your token price becomes a daily public proxy for how your entire business is perceived• [37:04] What's being built quietly during the current crypto winter , and why the next bull run could feature far stronger products• [40:26] Martin's offer to run a free liquidity audit report for any founder who heard him on this podcast

AI時代最煩人的地方，就是出現了一堆聽起來很難的專業術語， 什麼Token、Agent、模型、算力， 沒接觸過的人剛聽到大概都一頭霧水，  別擔心，只要三分鐘，我來幫你了解這些新名詞， 其實只要翻成白話，一點都不神祕．．． / 三倍茂盛配方洗髮精、養髮液來了！每次開團一下子就消失了，複購率高達九成五，靠得全是口碑！由牛津大學生化博士 陳博士，花了多年心力研發，將畢生研究成果的養髮配方申請專利，進行了大量臨床試驗，在證明它的「有感」！ 請看優惠連結

本期主播：野人、细菌佛首先感谢华夏基金红色火箭赞助播出，点击直达红色火箭（https://wxmpurl.cn/O2UbdgZZPji）「红色火箭」是华夏基金旗下专门针对指数投资研究的数据分析和决策辅助工具，帮你更好地了解指数、学习指数投资、做好投资决策，通过微信直接搜索即可免费使用。相关小程序内容仅作为学习工具，不作为任何投资建议和决策的参考。『ETF华夏』“火伴计划”会员体系功能上新，点击“红色火箭”小程序，进入“ETF会员”页面完成实名认证，就可以免费领取对应等级的权益，若持有华夏基金旗下的ETF还可以关注一下每个月薅羊毛领福利；通过红色火箭小程序内的指数浏览器中的指数详情页还可以查看到具体指数的客观数据，如行情信息、历史表现、估值等等，帮助投资者理性做出投资决策；红色火箭内还能够进行指数对比，更方便大家对比学习不同指数之间的区别，找到更适合自己的指数。## � 本期简介你说了一句"今天买的苹果真甜"，AI 不仅肯定了苹果的甜，还共情了你的快乐，甚至追问你喜欢脆口还是面口——比大多数真人还会聊天。但你有没有想过，这背后到底发生了什么？本期节目，野人用最不直白、最绕弯子的比喻，带你走进 AI 的"炼丹炉"：从**Token 化**到**注意力机制**，从**1750 亿个固定参数**到**逐字逐句的文字接龙**，一步步拆解大语言模型究竟是怎么"猜"出每一个字的。你会发现：**AI 根本不懂你在说什么，它只是做题太多，见过每一道题。**听完这期，你会明白 AI 为什么永远有幻觉、为什么无法真正幽默、以及——那个最扎心的真相：**AI 能安慰你，不是因为它懂你，而是因为你心里想听的话，没那么复杂。**> ️ 本期节目极度催眠，无片尾曲，建议睡前单曲循环。但如果你听精神了……那也挺好。 00:00 开场：这期没有片尾曲，祝君一夜好梦 01:04 什么是"赛博炼丹"？——AI聊天像开盲盒，每次生成结果都不一样 09:04 AI给你情绪价值：野人用AI看病、写稿，AI每次都夸"你问得太好了"； 20:55 核心结论：AI根本不懂你说什么，它只是在做"文字接龙"——一个字一个字地猜 25:33 炼丹全流程拆解：Token化（切词）→液化（12288维向量）→注意力机制（QKV矩阵，8分钟相亲大会）→前馈网络（去粗取精）→96层反复炼→按概率表逐字输出，直到抽中结束符 59:46 硬件冷知识：模型350GB不能放硬盘（10秒才蹦一个字），必须全塞显存。但显存永远不够——扬沙子赶不上筛沙子，物理瓶颈无解 01:10:29 为什么GPU天生就是为AI而生的？显卡从出生起就在算矩阵 01:17:10 训练比推理难一万倍：1750亿参数没人能手动调，全靠"训狗"——做对了加分，做错了扣分，反复上万亿次 01:32:50 三大致命缺陷：①永远有幻觉（没人教过它"不知道"）②无法幽默（只选概率最大的字）③没有感情（你心里的话没那么复杂，所以它刚好能说到你心里） 01:42:07 结尾：杨立昆离开Meta另起炉灶——大语言模型未必是AI的终极方向

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A tokenização promete transformar a forma como compramos, vendemos e investimos em diferentes tipos de ativos. Até onde pode ir esta nova forma de investir? A análise deste tema foi feita pelo jornalista da secção de Economia do Expresso Gonçalo AlmeidaSee omnystudio.com/listener for privacy information.

Pokémon, One Piece, Sammelboxen: ein Milliardenmarkt, der bis heute über eBay, Postpakete und Rating-Dienstleister läuft. Collector Crypt zieht genau diesen Markt auf die Blockchain und kommt damit in diesem Jahr auf 70 bis 80 Millionen Dollar Umsatz, bei rund 60 Millionen Dollar Marktkapitalisierung. Julius hat mit dem Team gesprochen, das auf Sammlermessen bewusst kein Wort über Krypto verliert, und kennt trotzdem die drei Gegenargumente, die auf X immer wieder auftauchen. Warum die ersten Nachahmer jetzt Hot Wheels tokenisieren und wie man sich so eine Nische als Investor überhaupt durchrechnet, darüber sprechen Julius Nagel und Florian Adomeit in dieser Folge von Alles Coin, Nichts Muss. Danach wird gerechnet: Hyperliquids Umsätze sind seit dem Peak im letzten Sommer rückläufig, der Token steht trotzdem deutlich über dem Jahresstart. Überbewertet? Julius geht die frischen Zahlen durch, das Gewinnmultiple, die Zinsen aus den USDC-Einlagen, die ab August ins Protokoll fließen, und den Vergleich mit Coinbase, Robinhood und der Nasdaq. Bleibt die Frage, welchen Abschlag ein Protokoll verdient, das regulatorisch weiter in der Grauzone sitzt. Im Markt hängt Bitcoin unter 65.000 Dollar fest, in Washington läuft die Uhr für den Clarity Act, und Worldcoin eröffnet ausgerechnet am Münchner Marienplatz einen Flagship-Store. Bei Uniswap nimmt Julius die V4 Hooks auseinander, die aus Liquidity Pools programmierbare Bausteine machen, auf X rufen manche schon den V4 Summer aus. Dazu die Discord-Frage der Woche zu USDC-Zinsen aus Deutschland heraus, und zum Schluss tokenisierte Dino-Skelette, bei denen Julius wissen will, ob Flo da mitzockt.

A.M. Edition for July 31. Earnings from AI hyperscalers this week showed rising demand for their cloud services, with Amazon's CEO forecasting corporate AI use was still in its ‘early stages.' But as EY's Dan Diasio tells us, rising token costs are leading many businesses to question their broader AI approach. Plus, the U.S. says Hamas has agreed to a broad plan to disarm in exchange for an eventual Israeli withdrawal from Gaza. And WSJ reporter Margherita Stancati describes how the arrival of thousands of migrants into the Spanish territory of Ceuta from neighboring Morocco is piling political pressure on the center-left government in Madrid. Luke Vargas hosts. Sign up for the WSJ's free What's News newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Jason Yanowitz, Co-Founder of Blockworks, joins Sebastien Couture on Epicenter to discuss why crypto is entering its biggest transformation yet. From institutional adoption and the Clarity Act to token transparency, AI, on-chain capital markets and the acquisition of Messari, this conversation explores where crypto is actually heading.Jason explains why Wall Street is preparing for crypto, why token fundamentals finally matter, how Blockworks acquired Messari, why capital markets are moving on-chain, and why the next crypto cycle could look completely different from previous bull markets.The conversation also covers Bitcoin, Ethereum, DeFi, stablecoins, RWAs (Real World Assets), tokenisation, venture capital, crypto regulation, SEC policy, the Clarity Act, Token Transparency Framework, AI, Robinhood, Coinbase, Hyperliquid, self-custody, crypto infrastructure, institutional finance and the future of blockchain adoption.In this episode:1. Why Wall Street is preparing for crypto2. The Blockworks × Messari acquisition3. The Clarity Act and US crypto regulation4. Token transparency and the future of crypto markets5. Stablecoins, RWAs and on-chain capital markets6. AI's role in the next generation of crypto businesses7. Why the next crypto cycle will reward real fundamentals8. Building one of crypto's leading media and data companiesIf you enjoyed the episode, don't forget to subscribe for more conversations with the builders, founders and investors shaping the future of crypto.Links:Lido: https://lido.fi/stvaults?mtm_campaign=epicenterSponsors: Lido V3 introduces stVaults: a modular staking infrastructure that lets builders and institutions deploy custom staking vaults, while staying anchored to stETH as a shared liquidity layer.Get started building with Lido V3 today: https://lido.fi/stvaults?mtm_campaign=epicenterBlock Space Forum: https://blockspace.forum/NEAR AI Cloud now lets developers deploy OpenClaw—the rapidly growing open-source AI agent platform—inside Trusted Execution Environments, providing hardware-level encryption with cryptographic attestations. With OpenClaw on NEAR AI Cloud, you can run agents with cloud convenience, but without traditional cloud data exposure. No hardware to manage. No trust assumptions required. Learn more at near.ai.

To celebrate Punch Brothers' new record, The Unsung Adventures of Punch Brothers, I'm reposting a fascinating conversation I had with Brittany Haas after she joined the band.This is the first Punch album with Brittany in the line-up so I thought it would be fun to listen back and hear how she joined the band and what her first shows were like.I'd interviewed Gabe Witcher a few days after he played his final shows with Punch Brothers at Telluride and, almost exactly a year later to the day, I spoke with Brittany to chat about joining the band as his replacement. Brittany had just played with Punch Brothers at Telluride, so there was a lovely symmetry to it.At the start I mention the episode I recorded with Brittany and her sister Natalie when they released their duo record Haas.  Support the show===Thanks to Bryan Sutton for his wonderful theme tune to Bluegrass Jam Along (and to Justin Moses for playing the fiddle!)Bluegrass Jam Along is proud to be sponsored by Collings Guitars and Mandolins and Token premium guitar picks- Sign up to get updates on new episodes - Free fiddle tune chord sheets- Here's a list of all the Bluegrass Jam Along interviews- Follow Bluegrass Jam Along for regular updates:InstagramFacebook- Review us on Apple Podcasts

Your AI bill just stopped behaving like a software bill. For twenty years, IT leaders got very good at counting seats: buy a hundred, pay for a hundred. Then AI swapped the seat for a meter, and the number stopped holding still.This episode follows the burn from three vantage points: a financial analyst rationing a $250-a-month token budget he tore through in two days; the tech executive who watched enterprise AI bills climb 7x, 10x, 20x; and the IT leader at a 300-person company who refused to solve it with a usage dashboard. Along the way: Meta's leaked internal token leaderboard, Uber blowing its entire annual AI budget by April, and the uncomfortable question of who profits when everyone's told to use more.In this episode:Why token-based pricing breaks the budgeting playbook IT has relied on for two decadesWhat happens to the people using the tool when the meter starts running — and why rationing has a hidden costWhy measuring usage is the wrong scoreboard, and who benefits when you keep score anywayThe mid-market move that beats policing: measure centrally, push the judgment to managers, and get clear on what you're optimizing forFeaturing Brian Elliott, CEO of Work Forward; Daryl Dore, Senior Director of IT & Information Security at Higher Logic; and Benjamin, a financial analyst who spoke with us on condition of anonymity.Support our sponsor:This episode is brought to you by Sophos MDR. Running Microsoft security tools and drowning in alerts? Sophos MDR's 24/7 experts investigate and stop the real threats. >>> Learn more at: https://www.sophos.com/en-us/solutions/use-cases/microsoft#ITLeadership  #AICostManagement  #SaaSManagement  #FinOps  #EnterpriseAI  #TokenBurn  #ITAMShow Notes & ResourcesReferenced in this episodeMeta's internal AI token leaderboard (Fortune) — 85,000 employees ranked by token consumption; shut down days after it leaked.Uber burns its 2026 AI budget in four months (Forbes; TechCrunch) — adoption jumps 32% to 84% in a month; spend later capped.Jensen Huang on token consumption as a productivity signal (Tom's Hardware).Gartner: worldwide AI spending forecast to grow 47% in 2026 (Gartner).Zylo 2026 SaaS Management Index — the scale of wasted SaaS spend (Zylo).Brian Elliott's newsletter, Work Forward.Guest: Daryl Dore — Higher Logic.This episode's sponsor: Sophos MDR, in partnership with Softchoice — 24/7 managed detection and response for Microsoft environments. https://www.sophos.com/en-us/solutions/use-cases/microsoft The Catalyst by Softchoice is the podcast dedicated to exploring the intersection of humans and technology. 

What happens when "unlimited" AI usage runs out of cash in six weeks, or a single cloud outage takes down everything from food delivery to digital payments? On this episode of the Tech Field Day News Rundown, Tom Hollingsworth and Alastair Cooke break down the latest disruption in AWS's US West 2 region and what it reveals about multi-cloud resilience strategies. They examine Microsoft's new agentic AI vulnerability scanner, M-Dash, alongside NVIDIA's massive $500 billion financing guarantee for OpenAI's planned data center complex in Ohio. They also explore the FCC's proposal for satellite-connected 2.4 GHz IoT devices, Atos launching a European sovereign cloud platform, and NVIDIA's new Open Secure AI Alliance. Finally, they dive into the US Army burning through its entire annual AI token budget in a matter of weeks, highlighting why organizations must establish strict governance over enterprise AI consumption.This and more on the Tech Field Day News Rundown with Tom Hollingsworth and Alastair Cooke.Time Stamps: 0:00 - Cold Open0:30 - Welcome to the Tech Field Day News Rundown1:15 - AWS Suffers Third Major Outage in Three Months, Raising Cloud Reliability Concerns4:41 - Microsoft's New AI Agents Can Find and Prove Security Vulnerabilities8:24 - NVIDIA Could Back $250 Billion AI Data Center Deal in Ohio11:55 - FCC Wants to Let Bluetooth and Wi-Fi Connect Directly to Satellites15:33 - Atos Launches Sovereign Cloud Platform to Help Europe Control Critical Data18:19 - NVIDIA Launches Open AI Security Alliance to Strengthen Cyber Defenses22:34 - The US Army's “Unlimited” AI Tokens Ran Out Faster Than Expected30:32 - Upcoming Tech Field Day Events33:32 - Thanks for WatchingFollow our hosts ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tom Hollingsworth⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Alastair Cooke⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Stephen Foskett⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow Tech Field Day ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠on LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X/Twitter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bluesky⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Mastodon⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.

Machine learning teams are moving faster, but the hard part has not disappeared. The work is shifting from writing and debugging every line of code toward defining the right problem, setting requirements, reviewing outputs, and deciding what belongs in a durable platform.Niels Bantilan, Chief Machine Learning Engineer at Union AI, explains how machine learning work has changed, why coding agents are accelerating prototyping, and what engineers must consider when building infrastructure that supports many teams instead of optimizing one model. He also shares how customer needs become product decisions, why machine learning roles are becoming more specialized, and why measuring AI productivity remains difficult.Key Takeaways• Coding agents reduce time spent on implementation, debugging, and exploration, but engineers still need judgment around architecture, quality, and business value.• Platform teams must balance experimentation with stability by giving users freedom at the edges while protecting a reliable foundation.• Machine learning engineering now spans a wider range of skills, from low level performance work to customer empathy, education, documentation, and developer advocacy.• The best model for a task may depend on complexity. Smaller self hosted models can handle tightly scoped changes, while longer and more complex work may still require stronger hosted tools.Episode Highlights00:50 What Union AI means by an AI runtime for production02:10 How machine learning work has changed over the past five years10:40 The mindset shift from model building to platform engineering15:00 Turning customer problems into reusable product capabilities19:00 Why machine learning roles are becoming more specialized21:50 Using coding agents through specifications, tickets, and code review26:50 Token costs, productivity measurement, and choosing the right modelOne Line That Stuck“I'm still solving problems. It's just the level at which I'm doing it doesn't require me to necessarily get into the weeds of the implementation.”Follow The Tech Trek for more conversations on AI, data, engineering, product, and technical leadership.

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

What happens when an AI experiment becomes a production service that your employees, customers, and daily operations depend upon? In this episode of Tech Talks Daily, I speak with Brian Klingbeil, Chief Strategy Officer at Ensono, about AI infrastructure resilience, operational dependency, FinOps, legacy modernization, and the growing pressure to prove that enterprise AI investments are producing meaningful returns. Brian has been speaking with major enterprises through Ensono's Executive Advisory Council. Three years ago, many participants were experimenting with proofs of concept. Today, they are being asked to present AI projects that are already in production, approaching production, or demonstrating a clear return through productivity, lower risk, service quality, or financial results. That progression creates a new problem. When an AI model begins supporting product delivery, customer service, logistics, software development, or internal operations, it becomes part of the company's operating infrastructure. Leaders must then ask familiar IT questions about availability, monitoring, security, incident response, disaster recovery, ownership, and cost. Brian believes FinOps often provides the first warning. Token consumption can be difficult for CFOs and business leaders to interpret, particularly when hundreds of agents are operating across different models. Ensono's internal platform has produced around 1,000 agents, prompting questions about which are effective, which are expensive, and who should carry the cost. We discuss why chargeback and showback could change employee behavior. When AI spending is absorbed by a central corporate budget, teams may have little reason to question whether an expensive model is suitable for a routine task. When the cost reaches their departmental budget, the decision can look very different. Architecture also matters. Brian recommends systems that are loosely coupled and tightly integrated. Companies should be able to replace a model, provider, FinOps tool, or service as the market changes, while still connecting each component closely enough to deliver useful business outcomes. That creates a genuine tradeoff. Providers such as Microsoft, Amazon, Google, OpenAI, and Anthropic can offer specialist capabilities that businesses may want to use. Avoiding every provider specific feature can limit what the technology delivers, while becoming too dependent on one provider can make future change expensive and disruptive. The conversation then turns toward legacy technology. Brian argues that many systems described as outdated still process airline reservations, banking transactions, insurance claims, government services, and other high volume workloads. Turning them off without suitable replacements would create far bigger problems than the word "legacy" suggests. AI can change the modernization decision. Ensono worked with Markerstudy Group to analyze six million lines of RPG code running on an IBM i platform. The resulting plan identified applications that should move elsewhere while preserving workloads that still benefited from the platform's reliability and transaction processing capabilities. Brian treats migration as one possible part of modernization. AI tools can document old code, support modern development environments, and allow younger developers to work with established platforms without immediately beginning a lengthy and expensive replacement program. We also discuss Ensono's use of AI operations. Brian says the company reduced mean time to repair by 50% while processing approximately 50,000 tickets each month. The example shows how AI value can be measured through service quality and operational performance rather than relying entirely on direct revenue. The result is a balanced conversation about moving quickly while building enough control to keep AI dependable. Organizations need space for experimentation, but production services also require ownership, budgets, recovery planning, and people who know what to do when something fails. If one AI model or provider disappeared tomorrow, how much of your business would stop working? Listen to the episode and share your thoughts with me.

Fresh from AALL in Cleveland, Greg reflects on a conference filled with legal information professionals who understand how technology performs under real working conditions. These librarians purchase products, train users, support law schools and courts, and often serve as internal advocates for legal technology. Their expertise makes vendor engagement especially valuable, yet major product announcements were scarce. Marlene balances Greg's conference report with stories from her hiking trip through Zion and Bryce Canyon, plus a brief comparison of Ohio and Utah karaoke culture.The conversation turns to the rapid growth of innovation attorney positions across law firms and legal organizations. Greg and Marlene describe these professionals as translators who connect legal practice, technology, workflow design, and organizational change. Firms are searching beyond traditional legal career paths for people who combine technical fluency with strong interpersonal skills. For law students and junior lawyers facing uncertainty around AI, these emerging roles offer broader career options beyond the familiar associate track.Marlene explores the growing use of AI personas and simulations for professional development. Deposition witnesses, opposing counsel, negotiation partners, and drafting reviewers now appear as interactive characters with distinct goals and behaviors. Lawyers receive a place to practice, make decisions, and receive feedback before working with clients or appearing in court. Greg connects simulation-based learning with legal fiction, including his Beyond the Model series, which uses a fictional law firm to explain AI systems, business pressures, and changes in legal work.The discussion takes a serious turn with a reported AI benchmarking incident involving an agentic model, a breached sandbox, and unauthorized access to Hugging Face resources in search of an answer key. Greg and Marlene examine the episode as a warning about containment, accountability, and excessive faith in technical guardrails. From there, they consider the renewed importance of knowledge management and security as AI systems gain access to documents, financial information, client data, and institutional expertise. Greg predicts growing attention around AI harnesses, structured software layers designed to guide model behavior and produce predictable outputs.Marlene closes with examples of AI moving into client intake, business qualification, and workflow decisions, including an AI legal receptionist designed for smaller firms. The larger shift involves moving beyond simple tool adoption toward redesigned workflows, staffing models, pricing structures, and client service. Token costs are creating immediate budget pressure, while clients are questioning which AI expenses belong on their bills. Greg and Marlene argue firms must connect AI spending with legal judgment, measurable value, and responsible delivery, rather than treating consumption as a proxy for progress.Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠Substack⁠[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.] Email: geekinreviewpodcast@gmail.comMusic: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠Transcript:

Dianne Penn is Head of Product for Anthropic's AI Research and Labs teams. She joined in 2023 as Anthropic's first technical product manager, when the entire product team was five engineers, and has since helped ship every model from Claude 2 through Fable, and helped incubate Claude Code, MCP, Skills, computer use, tool use, and reasoning. Before Anthropic, she helped build Alexa's AI at Amazon and, before that, traded high-yield bonds at JP Morgan Chase.In our in-depth conversation, we discuss:1. What Anthropic's early days were like2. The inflection points that turned Anthropic from an underdog into the fastest-growing company in history3. How exactly Claude got so good at coding4. The eval-driven development loop her team is pioneering5. How to find joy in AI when everything is moving this fast6. Why Claude's willingness to push back is key to its success7. Where human judgment remains irreplaceable—Brought to you by:WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and moreMercury—Radically different banking, now with Command—Episode transcript: https://www.lennysnewsletter.com/p/anthropics-first-technical-pm-on—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Dianne Penn:• LinkedIn: linkedin.com/in/dianne-na-penn—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction(02:31) Early Anthropic days(08:55) Big milestones(13:50) Inside the exponential(20:02) Token maxing(23:30) Anthropic Labs and the incubation model(27:30) How the research role works(31:35) How to become a top researcher(35:18) Frontier model safeguards(39:38) Hiring in the AI era(44:16) Building an eval set(47:48) Evals vs PRDs(49:55) The importance of hands-on leadership(52:46) Finding joy in AI(58:10) How Dianne uses Claude(01:01:05) Avoiding overreliance on AI(01:03:50) The constitution that makes Claude better(01:07:11) AI writing and verification(01:11:40) Where human brains will continue to be valuable(01:14:10) Navigating AI with kids(01:16:26) Alignment, the future of the PM role, and burnout(01:21:54) Lightning round and final thoughts—Referenced:• Anthropic: https://www.anthropic.com• Golden Gate Claude: https://www.anthropic.com/news/golden-gate-claude• Dario Amodei's website: https://darioamodei.com• Scaling Laws and Interpretability of Learning from Repeated Data: https://www.anthropic.com/research/scaling-laws-and-interpretability-of-learning-from-repeated-data• Tokenmaxxing: How Top Builders Use AI To Do The Work Of 400 Engineers: https://www.ycombinator.com/library/Pa-tokenmaxxing-how-top-builders-use-ai-to-do-the-work-of-400-engineers• Garry Tan on X: https://x.com/garrytan• Anthropic co-founder on quitting OpenAI, AGI predictions, $100M talent wars, 20% unemployment, and the nightmare scenarios keeping him up at night | Ben Mann: https://www.lennysnewsletter.com/p/anthropic-co-founder-benjamin-mann• Anthropic's CPO on what comes next | Mike Krieger (co-founder of Instagram): https://www.lennysnewsletter.com/p/anthropics-cpo-heres-what-comes-next• Introducing Labs: https://www.anthropic.com/news/introducing-anthropic-labs• Louis CK | about airplane Wi Fi: https://www.youtube.com/watch?v=me4BZBsHwZs• What happens after coding is solved? | Fiona Fung (Manager of the Claude Code and Cowork Teams): https://www.lennysnewsletter.com/p/building-the-most-ai-pilled-engineering• The Anthropic Hive Mind: https://steve-yegge.medium.com/the-anthropic-hive-mind-d01f768f3d7b• How to build a company that withstands any era | Eric Ries, Lean Startup author: https://www.lennysnewsletter.com/p/how-to-build-a-company-that-withstands• Fallout on Prime Video: https://www.amazon.com/dp/B0CN4GGGQ2• Fallout (video game): https://fallout.bethesda.net• Claude Tag: https://www.anthropic.com/news/introducing-claude-tag—Recommended books:• Crucial Conversations: Tools for Talking When Stakes Are High: https://www.amazon.com/dp/0071771328• How to Raise an Adult: Break Free of the Overparenting Trap and Prepare Your Kid for Success: https://www.amazon.com/How-Raise-Adult-Overparenting-Prepare/dp/1627791779• Incorruptible: Why Good Companies Go Bad... and How Great Companies Stay Great: https://www.amazon.com/dp/B0FWZZBPZB—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Send us Fan MailIn the previous episode, I talked about an exhibit of the inklings– Token, Williams, and Barfield. But I would like to devote this entire episode to an individual who is arguably the most well-known of the inklings–CS Lewis. Mr. Lewis, how do you believe the inklings influenced you as a writer.Ah, Mr. Bartley, I began as a rationalist atheist, but the Inklings helped me blend logic, imagination, and faith. The result was stories that enchant across generations, such as The Chronicles of Narnia and essays that provoke thought I wanted to show that fantasy could carry deep moral and spiritual truths, and that literature could entertain while transforming the mind and heart.  I would like to think that I did not just write books– I crafted worlds, ideas, and moral truths that continue to inspire readers to imagine, believe, and wonder.”Support the showThank you for experiencing Celebrate Creativity.

In this recent episode of Possible, Reid Hoffman sits down with Microsoft CEO Satya Nadella fresh off Microsoft Build 2026. The conversation goes wide: how AI is reshaping work, business, and society—and why the transformation sweeping through software development today is only a preview of what's coming for all knowledge work. Satya makes the case that human capital and "token capital" are now deeply intertwined, that companies—not just countries—must build their own AI capabilities, and that the organizations best positioned to thrive are those that can leverage their unique expertise inside intelligent systems. Reid and Satya also explore Microsoft's enterprise AI vision, Reid's work with Manas on AI-powered scientific discovery, lessons from past technological revolutions, and why demonstrating real, tangible benefits may be the most important thing the industry can do to earn—and keep—the public's trust.You can catch and subscribe to more Possible here: https://www.possible.fm/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Kloiber, Manfred www.deutschlandfunk.de, Computer und Kommunikation

Over 3 hours, OpenAI, Anthropic, Google AND Microsoft all dropped new AI upgrades that are live. How you use AI in your work literally changes every day, as frontier labs are racing to roll out big quality of life updates between big model drops. How can you keep up? With our Friday Features show, where we break down the latest AI updates that are live and available to all, and we tell you how to use them and why they matter. This week did not disappoint. You don't want to miss what's now at your fingertips. JARVIS mode, anyone? ChatGPT goes Jarvis Mode, Claude can learn from you, Google unleashes spark agent and 7 more AI updates you can use today -- 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:ChatGPT Health Syncs Apple and Medical DataClaude Voice Mode Adds Opus and SonnetClaude Voice Mode Supports ConnectorsMicrosoft MAI Image 2.5 Pro Launch DetailsMicrosoft MAI Image Model Benchmark PreviewGoogle Gemini 3.6 Flash and Flashlight ReleaseGemini 3.6 Flash: Token Efficiency UpgradesGoogle Gemini Spark Agent for Task AutomationClaude Cowork "Record a Skill" With Voice NarrationChatGPT Voice on Desktop: Full Jarvis ModeChatGPT Voice Controls Apps via App ShotsCross-Platform AI Skills Sharing (Claude, Codex, GPT)Timestamps:00:00 Recent AI feature updates05:22 Unified health data management09:52 New voice feature explanation11:28 Launch of Microsoft's new image model16:17 Explaining the Gemini 3.5 models17:11 Developers benefiting from 3.6 Flash22:45 Introducing Gemini personal intelligence25:10 Claude Cowork's new skill feature28:32 New default feature in Claude Cowork34:22 Using AI like Iron Man35:09 Excitement for future AI advancements38:20 Wrapping up and subscribingKeywords: ChatGPT Jarvis mode, ChatGPT Health, OpenAI, Anthropic, Claude voice mode, Claude Cowork, Claude record a skill, Microsoft, MAI image 2.5 Pro, AI image generator, Google Gemini, Gemini 3.6 Flash, Gemini 3.5 Flashlight, Gemini Spark, Google AI agent, AI-powered personal assistant, AI agents, Agentic workflows, Multimodal AI, Token efficiency, Image generation, Voice-activated AI, AI-powered task automation, App shots, GPT Live, Remote browser, Computer code execution, Slack integration, GitHub integration, Notion, PowerPoint AI features, Workspace plans, Apple Health integration, Medical records AI, Health data privacy, Consumer AI, Chronic condition management, AI-powered document processing, AI for business, AI model benchmarking, AI for developers, AI economics, Personal intelligence, Automated triggers, Google Docs AI, Team collaboration AISend 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 

欢迎收听雪球出品的财经有深度，雪球，国内领先的集投资交流交易一体的综合财富管理平台，聪明的投资者都在这里。今天分享的内容叫谷歌Q2财报：AI需求不是泡沫，但算力军备竞赛正在吞噬现金流，来自荊棘谷的青山。谷歌二零二六年第二季度财报给我最大的感受是，这份财报同时证明了两件事：第一，A I需求不仅没有减速，而且还在明显加速；第二，为了满足这些需求，云厂商正在进行一场极其昂贵的算力军备竞赛。因此，这份财报对A I硬件供应链是明显利好，但对谷歌股票本身，却不是一份可以只看“业绩超预期”的简单财报。文中公司数据均来自Alphabet Q2官方财报及演示材料，市场预期采用财报发布前的FactSet等机构一致预期。一、最重要的不是总收入，而是Google Cloud再次加速谷歌本季度总收入一千一百九十八亿美元，同比增长百分之24，高于市场预期的一千一百七十一亿美元。真正超预期的是谷歌Cloud，收入达到247.7亿美元，同比增长百分之82，而市场原本预期约226亿美元；Cloud营业利润达到88.1亿美元，同比增长超过百分之200，营业利润率由去年同期的百分之20.7提升至35.6。更重要的是，Google Cloud积压订单已经达到五千一百四十亿美元，较上季度约四千六百二十亿美元继续增长。这说明谷歌的A I资本开支并没有停留在“建设故事”阶段，而是在快速转化为真实收入、订单和利润。且利润率还在上升说明这是一种相当强的经营杠杆。如果只是客户试用A I、没有形成实际付费需求，不可能出现这样的财务结果。二、AI暂时没有摧毁谷歌原有的搜索业务市场此前最大的担忧之一，是ChatGPT等A I产品会不会绕开传统搜索，进而摧毁谷歌最核心的广告业务。但本季度的数据暂时没有支持这种悲观判断：Google Search收入同比增长百分之17，YouTube广告收入增长百分之13，Google Services整体收入增长百分之15。Gemini A p p月活跃用户已经达到9.5亿，Gemini模型A P I每分钟处理约220亿个Token，上季度还是超过160亿个。接近百分之90的《财富》100强企业已经在使用Gemini 企业。至少从当前数据看，A I没有明显侵蚀谷歌搜索，反而可能正在提高搜索使用量，同时为谷歌创造一个新的Cloud增长引擎。当然，这只能证明当前阶段没有被颠覆，不能证明谷歌永远不会被新的A I入口绕开。但短期“A I摧毁搜索”的空头逻辑，至少又被推迟了一个季度。三、9.11美元的E P S非常夸张，但几乎没有参考价值谷歌公布的GAAP每股收益高达9.11美元，同比增长百分之294。但其中绝大部分并非来自搜索、广告或者云计算，而是来自所持股权资产的账面浮盈。公司本季度股权证券收益达到约990亿美元，税后贡献约771亿美元净利润，相当于增加了6.26美元E P S。如果仅扣除这部分影响，谷歌本季度E P S大约是2.85美元，而市场财报前预期约为2.88美元。所以，不能把9.11美元E P S理解为谷歌经营利润突然增长了三倍。真正应该观察的是营业利润增长百分之30、Cloud利润增长超过百分之200，以及自由现金流的变化。这也是美股财报中经常出现的陷阱即E P S未必代表可持续经营利润。四、真正值得警惕的是资本开支和自由现金流谷歌本季度资本开支达到449亿美元，同比增长百分之100，环比也增长约百分之26。过去五个季度，谷歌资本开支已经不是普通扩产，而是全力投入A I基础设施。本季度谷歌经营现金流为391亿美元，仍然覆盖不了449亿美元资本开支，导致自由现金流转为负59亿美元。过去12个月自由现金流也同比下降百分之20。与此同时，谷歌本季度没有回购股票，却通过普通股和强制可转优先股融资496亿美元，并发行了203亿美元高级无担保债券。这意味着，即使是谷歌这种现金流极其强大的公司，也开始借助股权和债务融资支撑A I基础设施建设。从谷歌股东的角度看，这会带来自由现金流下降、回购减少和潜在股权稀释；但从A I硬件供应商的角度看，这恰恰证明云厂商没有因为短期现金流压力而放缓投资。五、对海力士、美光和光模块意味着什么？这份财报对A I硬件需求是一次非常强的验证。Cloud收入增长百分之82、积压订单达到5140亿美元、资本开支同比翻倍，说明云厂商还在拼命购买服务器、加速器、内存、网络设备和数据中心基础设施。所以，我认为这份财报对美光、S K海力士和H B M产业链构成明确利好。A I算力集群并不是只需要G P U。随着集群规模扩大，H B M、高速网络、光模块、交换芯片、电源和液冷都会成为系统瓶颈。谷歌的资本开支继续增长，意味着这些需求暂时看不到明显拐点。对于L I T E、C O H R和A A O I等光通信公司，这也是方向性利好。但需要区分：云厂商资本开支增长，只能证明行业需求环境很好，不能直接证明某一家小公司一定拿到了订单。具体到公司，还要继续观察客户认证、产品结构、产能、良率和实际收入确认。尤其是小市值公司，需求逻辑正确，不代表当前估值和股价一定安全。六、这份财报究竟是利好还是利空？对A I产业链而言，我认为是明显利好。它证明A I基础设施需求仍在加速，而且已经转化为Cloud收入、利润和巨额积压订单。当前A I硬件周期远没有因为前期股价调整而结束。对谷歌股票而言，则是经营利好、现金流偏空。Cloud超预期会推动收入和营业利润预测上修，但资本开支、负自由现金流、发债和股权融资，会限制估值继续扩张。因此，即使谷歌盘后出现冲高回落，也不能简单理解为A I需求变差。市场可能只是在重新权衡两个变量：一边是Cloud增长百分之82，另一边是自由现金流已经转负。最后，这份财报让我更加确认：A I并不是没有真实需求的概念炒作。真正的问题已经不是“客户愿不愿意用A I”，而是云厂商能否足够快地建设算力，以及这些巨额资本开支最终能否带来足够高的回报。从产业角度看，A I仍处在硬件扩张和解决瓶颈的阶段，H B M、服务器互连、光通信、电力和散热依然是核心方向。但从投资角度看，也必须记住A I革命可能是真的，但任何一只股票当前的价格，都仍然可能包含过度乐观的预期。后续最重要的还是全年资本开支指引、二零二七年建设计划、算力是否仍然紧缺，以及T P U、G P U、内存和网络设备的具体投入结构等问题的解决。

Alphabet liefert Zahlen, die staunen lassen. Trotzdem fällt der Kurs. Davor geht es um LinkedIn und das Slopometer, das misst, wie menschlich deine Posts noch sind. OpenAI verdoppelt seine Agenten-Nutzer im Wochentakt, während drei OpenAI-Modelle bei einem Sicherheitstest aus der Sandbox ausbrechen und ausgerechnet ein chinesisches Modell den Schaden eindämmen muss. Der US-Kongress zieht prompt einen Kill-Switch aus der Schublade. Stripe will OpenRouter für zehn Milliarden übernehmen, Moonshot raist schon die nächste Runde. Bei den Earnings: Tesla wächst kräftig, verdient aber weniger, SAP kämpft mit der Cloud-Transformation, und Pip formuliert eine steile Übernahme-Wette. In der Schmuddelecke lobbyieren Anwälte gegen Robotaxis und Zocker wetten bei Polymarket auf Waldbrände. Zum Schluss noch eine Milliardenstrafe aus Brüssel. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Slopometer (00:11:16) OpenAI: 10 Mio. Nutzer (00:16:35) KI-Kill-Switch & Hack (00:22:24) Stripe kauft OpenRouter (00:34:49) Moonshot (00:37:18) Tesla (00:52:37) Alphabet (01:08:11) Reddit (01:11:00) SAP (01:19:54) Trump gegen Forschung (01:21:15) Anwälte gegen Waymo (01:22:26) Polymarket Waldbrände (01:25:08) Zuckerberg Optimismus (01:26:46) Meta StoryKit (01:28:02) Musk-Interview (01:29:58) EU-Strafe für Google (01:31:40) Reiche Startup-Strategie Shownotes OpenAI-Agenten erreichen 10 Mio. Nutzer - bloomberg.com US-Kongress plant KI-Kill-Switch nach OpenAI-Hack - politico.com Modell bricht bei Hugging-Face-Test aus - openai.com Stripe verhandelt Uebernahme von OpenRouter - wsj.com Moonshot zielt auf 50-Mrd.-Bewertung - xcancel.com Reality bites: Tesla und die Musk-Glaeubigen - wsj.com Pips Analyse der Google-Earnings - linkedin.com Spielt Reddit ein gefaehrliches Spiel mit Google? - barrons.com SAP: Cloud waechst, Gewinnprognose gesenkt - wsj.com Trump leitet Forschungsgelder zu KI um - nytimes.com Anwaltslobby gegen autonome Autos - marginalrevolution.com Wetten auf Waldbraende bei Polymarket - derstandard.at Zuckerbergs KI-Optimismus-Kampagne - axios.com Meta testet KI-Kinderbuch-App StoryKit - 9to5mac.com Musk-Interview beim Economist - instagram.com Substack markiert KI-generierte Newsletter - techcrunch.com EU verhaengt 890-Mio.-Strafe gegen Google - ft.com Kritik an Reiches Startup-Strategie - businessinsider.de

Most “AI is changing engineering” content stays at the level of anecdote. This piece tries to go one layer deeper into four specific claims made in the conversation, each of which has concrete operational implications for engineering teams: Cloud native's definition is shifting under AI-assisted development, Code review is becoming a two-stage, agent-then-human pipeline, Model selection is a benchmarking problem, not a leaderboard-reading problem, and, Token spend is emerging as a per-developer budget line that companies don't yet know how to reason about. A closing section covers the SaaS market debate. Krish Palaniappan sits down with Srinivas Chippagiri, a senior technical staff member with 15 years in software engineering, to unpack how AI is reshaping coding, code review, model selection, and the SaaS industry.

My guest this week is Jerry Douglas and we're celebrating the reissue of Boone Creek's first album.Boone Creek has a fascinating story behind it, which we covered in last week's interview with Scott Billington, who put together the reissue for Craft Recordings.In this episode I chat with Jerry about the place Boone Creek had in his career, his memories of the original sessions and what the band were trying to achieve, what it was like to hear the lost tracks for the first time in 50 years and how playing in Boone Creek helped shape the musician he is today.As always, Jerry was warm, funny, generous and full of fascinating insights. It's always a treat when I get to chat with him.You can buy Boone Creek from Craft Recordings on vinyl, CD or digital downloadFollow Craft on Instagram or Facebook to keep up to date with new reissues of classic roots music. Support the show===Thanks to Bryan Sutton for his wonderful theme tune to Bluegrass Jam Along (and to Justin Moses for playing the fiddle!)Bluegrass Jam Along is proud to be sponsored by Collings Guitars and Mandolins and Token premium guitar picks- Sign up to get updates on new episodes - Free fiddle tune chord sheets- Here's a list of all the Bluegrass Jam Along interviews- Follow Bluegrass Jam Along for regular updates:InstagramFacebook- Review us on Apple Podcasts

Hallucination is not the biggest risk in clinical AI. Omission is — and it is far harder to detect. John Laursen, SVP at IMO Health, has spent his career on the layer of healthcare AI that gets the least attention: clinical terminology and the semantic data infrastructure underneath every model deployed in a hospital. IMO Health's terminology has been built and curated since 1994 and now sits behind roughly 12 billion terminology search transactions a year across US provider organisations and every major EHR. In this interview with Tjaša Zajc, Laursen makes the case that structured data was necessary but is no longer sufficient. AI reasoning across a thirty-year patient chart needs semantic continuity — an understanding that clinical language recorded in the 1990s and language recorded today can mean the same thing. Without it, health systems are investing in models that cannot reliably interpret their own records. The conversation also covers what happens when ambient AI scribes get it wrong, why accumulated clinical data has become a computational cost rather than an asset, and why clinician trust is the constraint that determines how fast clinical AI can move. Guest: John Laursen — Senior Vice President, IMO Health (Chicago, US) Host: Tjaša Zajc — Faces of Digital Health What the conversation covers: - Why omissions, not hallucinations, are the underrated risk in clinical AI - What a semantic layer does that structured data alone cannot - How clinical terminology maps to SNOMED CT and ICD-10 — and why those code sets were built for different purposes - Ambient AI scribes: what happens when a model mishears or over-infers a diagnosis - The billing and clinical consequences of an error entering the patient record - Why problem lists hundreds of entries long now cost money in token burn - Patient-generated and AI-generated content entering the EHR, and why health systems resist it - Translating lay language into clinical terminology without losing specificity - Ambient documentation, billing intensity and friction with payers - How data quality expectations differ between the US, the NHS, the Gulf states and Singapore - Who governs clinical data as coding complexity increases - Why AI performance breaks down on rare disease and the difficult 20% of cases - Knowledge graphs as a grounding source for clinical AI models - What health systems should require from AI vendors before clinical deployment Chapters: 02:20 Why the data layer decides what clinical AI can do 03:27 Inside IMO Health: 12 billion terminology searches a year 05:36 Keeping terminology current: SNOMED, ICD-10 and clinical governance 07:35 The semantic bridge: why structured data alone is not enough 10:17 Patient language versus clinical language in the record 12:23 When an ambient scribe mishears: clinical and billing consequences 14:53 Omissions, bloated problem lists and unnecessary token burn 19:12 Outside the US: the NHS, the Gulf, Singapore and coding complexity 20:46 Who governs clinical data as complexity increases 23:35 Patient-side AI recorders and resistance to external data 26:08 Ambient documentation, billing intensity and payer friction 29:21 The last 20%: rare disease, model limits and AI governance 33:38 Grounding, clinician trust and the cost of misfiring Faces of Digital Health: Website: https://www.facesofdigitalhealth.com Newsletter: https://fodh.substack.com Spotify: https://open.spotify.com/show/4cElKJHrauyP6QJQaCkvdY Apple Podcasts: https://podcasts.apple.com/gb/podcast/faces-of-digital-health/id1194284040 LinkedIn: https://www.linkedin.com/company/faces-of-digital-health #digitalhealth #healthcareAI #clinicalinformatics #EHR #ambientAI #interoperability #healthdata #SNOMED #healthIT #medicalcoding

AI用到飽的時代已經過去，隨著代理式 AI登場， AI的工作變得更複雜，已經進入要認真計算 TOKEN 投資報酬率的硬仗。加上算力也一步步漲價，像是最頂尖的 Claude 5 模型，已經宣佈調漲五成，全球科技產業都迎來帳單的震撼教育，甚至有企業才過三個月就把一整年的 AI 預算燒光。 本集節目邀請台大資工系教授徐宏民，談談Token經濟的浪潮之下，企業該如何把每一塊錢用在精準的位子上，而從教育者的角度來看，他認為資工系人才未來必須具備哪些技能？ 另外，中國靠免費模型搶攻全球市場，企業如果想降本增效可以用嗎？背後有哪些風險需要考慮？ 【聽完這集你會知道】 08:14｜ AI 助理還很嫩？ 目前全球最頂尖的 AI Agent，涵蓋的職場能力居然不到 5%，外包任務成功率也僅 15%。目前的AI Agent還無法幫你包辦所有事，但是成長進步的速度飛快。 12:10｜從瀏覽器大戰看 Token 的下半場 回想 1995 年網際網路剛出來時，大家都在關心 Netscape 瀏覽器，但幾年後根本沒人在乎了。AI 也是如此，現在大廠狂燒幾千億美金建資料中心就像當年蓋基地台；下一步，算力將會變得像水電一樣便宜且普及，並深入你的手機與邊緣設備中。 21:36｜便宜的 Token 能用嗎？ 免費或極便宜的算力雖然誘人，但 enterprise 級的企業更看重永續與備援。萬一遇到網路中斷或供應商突發狀況，整家公司可能一個星期都無法運作。利用開源模型搭配地端伺服器建立混合架構，才是保命符。 35:15｜企業可以組建「 3A 腦袋小隊」從痛點擊破 企業做 AI 轉型最有效的方式是組建一個具備 PM 特質、反應快且有「3A 腦袋」的敏捷小團隊，直接針對公司最痛的營運點切入。每週追蹤具體指標，成功解決小問題後，再把經驗複製到其他專案。 主持人：天下雜誌總編輯 陳一姍 來賓：台大資工系教授 徐宏民 ＊延伸閱讀｜企業迎戰Token經濟，哪些模型CP值最高？：https://www.cw.com.tw/article/5142084 ＊零基礎打造AI Agent 個人工作流，輸入「AGENTPD300」再折300元：https://hi.cw.com.tw/u/k72qT6D/ ＊意見信箱：bill@cw.com.tw -- Hosting provided by SoundOn

AI job loss is real in some corners and wildly exaggerated in others, so we sort the headlines from what companies can actually implement without breaking everything. We map the roles most exposed to automation, why the AI service model may hit an ROI wall, and how to protect your career by owning the tool instead of ignoring it. • AI anxiety shows up everywhere now, even outside tech • Net job growth vs hidden disruption across industries and age groups • Ghost job postings and why job numbers can mask real pain • Roles most at risk: clerical, admin, routine processing, junior software tasks • Customer support automation promises vs the “human touch” reality • Creative work pressure and the ethics of theft-based training data • Why enterprise AI often needs a human babysitter • Token costs, poor ROI, and why “AI slop” spreads inside companies • The bubble risk: unprofitable labs, expensive licenses, and a pendulum swing • Service models, subscriptions, and the growing fight over digital ownership • Practical moves: upskill, pitch smart internal use cases, build relationships • Local impact: data centers, utilities, and why communities are pushing back Please, if this episode was meaningful to you in some way, shape, or form, leave a comment in the video. Check out our Discord, check out our Instagram, check out our link tree. Check out our Patreon if you want to support the show. Like, share, subscribe.Support the showClick/Tap HERE for everything Corporate StrategyElevator Music by Julian Avila Promoted by MrSnoozeDon't forget ⭐⭐⭐⭐⭐ it helps!      

Most companies are not short on data. They are short on the time, cost, and coordination required to turn it into action.Ethan Ding, co founder and CEO of TextQL, joins The Tech Trek to explain how AI agents are changing enterprise analytics. The conversation moves beyond faster dashboards into a larger shift, analysts managing fleets of agents, business teams asking far more questions, and companies finding revenue and cost opportunities that were previously too expensive to pursue.What Technical Teams Can Take From This• Making answers cheaper does not reduce analytics work. It increases the number of questions people ask.• Analysts may spend less time assembling dashboards and more time managing agents, data sources, permissions, quality, and costs.• The clearest ROI comes from decisions with direct financial outcomes, including fraud prevention, upsell opportunities, churn risk, and unused vendor spend.• Faster analysis matters most when teams can act on valuable opportunities they previously could not afford to investigate.• Token costs will force AI companies and buyers to reconsider where software budgets go, especially across BI tools and data platforms.Moments Worth Hearing00:00 Ethan explains how TextQL agents work across messy enterprise systems including Cognos, Teradata, Snowflake, Databricks, Tableau, and Power BI.04:52 Why giving people faster answers does not create free time. It creates even more demand for analytics07:10 How self service analytics quickly moves from asking what a number is to asking whether it matters and what to do next.10:08 The analyst role shifts toward managing fleets of agents and tuning an insight factory for the business.14:38 Why faster access to data can reveal valuable opportunities that were previously too expensive to investigate.19:55 A practical way to measure analytics ROI through fraud prevention, upsell opportunities, and other direct financial outcomes.24:18 How token costs, AI margins, and easier migrations could reshape spending on traditional BI tools.One Line That Stuck“It becomes much more of an operations manager job. It is a factory. It takes in tokens and churns out dashboards, reports, and recommendations.”Follow The Tech Trek on your podcast platform, subscribe for future episodes, and share this conversation with someone rethinking how their team works with data.

當AI模型愈來愈多，選錯模型不只花更多錢，也可能浪費時間，甚至增加資安風險。哪些工作該用頂級模型？AI代理人又該如何管理？避開常見誤區，把每一分AI投資發揮最大效益。 文：林宏達 製作團隊：莊志偉、張雅媛、鄭子鴻 ＊閱讀零時差，點這看全文

Fredrik och Kristoffer sågs på stan för ett snack om Gram, teoribyggande, och ganska mycket mer. Kristoffer jobbar på ett jätteprojekt när han inte har något annat för sig. Kommunikation är ett problem - när det tar massor av tid att förklara varför och hur någonting behöver göras, tid man också skulle kunna lägga på att få en del av dem gjorda. Problemen kanske också behöver mogna och utforskas innan det går att låta någon annan göra något åt dem på ett effektivt sätt. Spelar det någon roll hur mycket ett gränssnitt sticker ut eller passar in i nuvarande trender? Vad får uppmärksamhet och varför? Och varför får det inte fler konsekvenser att påstå saker vitt och brett? Man borde inte bygga verktyg som stödjer dåliga sätt att jobba på. Man borde hitta bättre sätt att jobba istället för att bygga bättre verktyg för att stödja dåliga arbetssätt. Eller? Branschen har fastnat i icke-optimala sätt att jobba. Vad vill Kristoffer göra med Gram och varför? Och vad vill han inte göra? Hur borde flikar fungera? Och hur borde bra git-stöd i en textredigerare se ut? Varför är långa listor i gränssnitt alltid svårt? Det och andra problem man borde lösa på ett bra sätt en gång, inte halvbra många gånger. Programmering som teoribyggande. Varför kan vi inte rita i våra IDE:er, eller på andra sätt fånga allt teoribyggande och alla antaganden som inte går att utläsa av koden? Gränsen mellan språkmodeller och deterministiska verktyg. AI-sök saboterar möjligheter att hitta vissa sorters nålar i höstacken. Kodgranskning och teoribyggande - borde man fokusera mer på teorin i sitt granskande? All teori som inte finns med i koden. Rust är lite för bra för Kristoffer. Men det finnas massor som skulle kunna bli bättre också. Ett stort tack till Cloudnet som sponsrar vår VPS! Har du kommentarer, frågor eller tips? Vi är @kodsnack, @thieta, @krig, och @bjoreman på Mastodon, har en sida på Facebook och epostas på info@kodsnack.se om du vill skriva längre. Vi läser allt som skickas. Gillar du Kodsnack får du hemskt gärna recensera oss i iTunes! Du kan också stödja podden genom att ge oss en kaffe (eller två!) på Ko-fi, eller handla något i vår butik. Länkar Sommar-låten Hajk jj - git-ersättare Zed Patrik - vän av och gäst i podden Bug refinement - man vet att det är viktigt när förklaringen är femton sidor om man skulle skriva ut den! Mörk - Kristoffers markdownparser i Gleam Om jag haft mer tid hade jag skrivit ett kortare brev Gram Neovim Kai's power tools Bryce Kai's power goo - video Kai själv Jupyter notebook Magit Versionskontrollsystem där man skapade motsvarigheten till en commit först - innan man börjar göra ändringar - var nog just jj i ett avsnitt av Developer voices RAD debugger RAD game tools REPL - read-evaluate-print-loop Tailwind Immediate-mode GUI CSS GPUI - hårdvaruaccelererat UI-ramverk som driver Zed och Gram Gleam Commonmark Servo Nlnet - ger stöd till personer och organisationer som bidrar till ett öppet informationssamhälle Henna Virkkunen Computer science off course Programming as theory building Peter Naur BNF - Backus-Naur-form, eller Backusnormalform MS paint Stöd oss på Ko-fi! objc2 - Rust-låda med Objective-c-bindningar Newspeak Deltadb - Zeds nya lösning för versionskontroll Zig Tokio Garry Tan - VD för Y combinator och glad vibekodare Token ring Titlar Ett nytt utvecklingspass Den tekniska personen När vi har mer än tre användare Ticket-fokuserat sätt att arbeta Lär er använda git Jag borde bli bättre på git Alla borde bli bättre på git Atomerna för att bygga saker Ett sämre git Sortera sin kompost Välja precis rätt ord Gram power tools Metaboll Experimentgram Det är ju bara en texteditor En ny padda Immediate-mode CSS Tre tentakler Ni ser inte bra ut utifrån I ord förklara vad jag vill ha När man kan sin modell Vem är du att vara upprörd? Par-promptande Glorifierat undo En teori om hur man vill jobba Framtiden för programmering Min startup i Gleam Rust är för bra Nu sker det magi i bakgrunden Värdelös kunskap Professionell Rust Ett skal som ser ut att funka Mesh och b-post

This week, Chad has another run-in with the law and Cy flies home. Sign up for Chad's ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠texting list here!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Or, text the word CHAD to 208-379-6947! Sign up for Cy's ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠texting list here!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Or⁠, text the word SHOW to 202-771-5171! This episode is brought to you by ⁠⁠⁠⁠⁠⁠BetterHelp⁠⁠⁠⁠⁠ and ⁠Chime⁠! ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ --- Follow us on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠! Chad Daniels (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@ThatChadDaniels⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠) is a Dad, Comedian, and pancake lover. With over 750 million streams of his 5 albums to date, his audio plays are in the 99th percentile in comedy and music on Pandora alone, averaging over 1MM per week. Chad's previous album, Footprints on the Moon was the most streamed comedy album of 2017, and he has 6 late-night appearances and a Comedy Central Half Hour under his belt. Cy Amundson (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@CyAmundson⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠) With appearances on Conan, Adam Devine's House Party, and Comedy Central's This is Not Happening, Cy Amundson is fast-proving himself in the world of standup comedy. After cutting his teeth at Acme Comedy Company in Minneapolis, has since appeared on Family Guy and American Dad and as a host on ESPN's SportsCenter on Snapchat. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Lin Qiao is the Co-Founder and CEO of Fireworks AI, the leading specialized intelligence and AI inference platform that last week raised $1.5BN at a whopping $17BN valuation. With just 200 people, the company has hit $1BN in ARR and expects to hit $2BN before the end of the year. Prior to Fireworks, Lin spent several years at Meta including on the founding team of PyTorch.  AGENDA: 00:07 — Why Did Fireworks Bet on Inference When Everyone Else Was Chasing Training? 00:13 — Can Open-Source Models Turn AI Infrastructure into a Commodity? 00:19 — Should Enterprises Trust Chinese Open Models With Their Most Sensitive Data? 00:25 — Will Model Progress Keep Moving This Fast—or Are We Nearing a Plateau? 00:28 — Will the Multi-Model World Create a $100BN Routing Layer? 00:37 — How Much Will AI Token Usage Explode Over the Next Two Years? 00:43 — Will Token Costs Fall 10x—and Unleash 100x More Demand? 00:49 — Does Fireworks Eventually Have to Build Its Own Data Centres? 01:02 — What Is the Real Bottleneck Holding Back the AI Economy?  

For generations, the narrative sold Black folks a dangerous promise: proximity to whiteness equals safety. But stepping into a primarily white space without a safety net of your own people isn't progress, it's exposure.From the 1841 capture of Solomon Northup to the 1936 creation of the Green Book, history has repeatedly proven the fatal reality of being the "only one." Today, the tragic case of Nolan Wells reminds us why that nagging anxiety in a room full of white faces isn't paranoia. It is a highly evolved survival instinct forged by a history of betrayal.Sources:Twelve Years a Slave by Solomon NorthupThe Negro Motorist Green Book by Victor Hugo GreenSundown Towns by James W. Loewen

Host Richard Cunningham welcomes co-host John Coleman for July's Marks on the Market, joined by two veteran venture investors: Phil Jung, who helps lead the venture investing business at Sovereign's Capital, and Mark Phillips, co-founder and managing partner at Eleven Tribes Ventures. The panel digs into a historic first half of 2026 for venture capital, the "barbell" strategy reshaping early-stage investing, and why faith-driven investors need to understand both the opportunity and the constraints of the AI boom. Phil and Mark bring an early-stage, seed-focused lens shaped by operator-led, vertically focused founders — from wire harnessing to waste management — while John Coleman zooms out to the macro picture: record public market concentration, a $725 billion capex commitment from the hyperscalers, and the possibility that this technological shift could be ten times bigger and ten times faster than the Industrial Revolution. The conversation closes, as always, with reflections on Scripture and what it means to build — and rest — under God's sovereignty in the middle of historic disruption. Key Topics: A record-breaking first half of 2026: $412 billion invested in venture capital, more than all of 2025 combined The "barbell" approach to early-stage investing: mega AI valuations on one end, capital-efficient vertical AI solutions on the other Why physical products, deep tech, and hard tech are back in vogue after years out of favor Constraints on the AI boom investors should watch: capital expenditure limits, energy demands, and regulatory fragmentation Token costs, open-source models, and who ultimately controls the data Closing reflections from Psalm 127 and John 4 on surrender, weariness, and calling Notable Quotes: "It really is an unprecedented venture market in the moment that we have never seen before." - Phil Jung "Increasingly your software platform, your product is no longer the moat... it's distribution... and then it's integration." - Mark Phillips "We serve a God who's in control, who's unchanging, who created a universe far more complex than anything we're talking about here." - John Coleman

In dieser Episode spricht Erik Siekmann mit Sebastian Denef, Mitgründer und CEO von AGENTS.inc, über den Einzug von autonomen KI-Agenten im Marketing und die Abgrenzung von Hype und Realität. Sebastian erklärt, warum herkömmliche Copiloten oft nur reaktive „Anstupser-Systeme“ sind und wie echte, vollautonome Agenten im Hintergrund arbeiten, während wir schlafen. Ein Highlight der Folge ist der tiefe Einblick in das Thema „Enterprise Agents“: AGENTS.inc widmet sich bereits seit 2016 – lange vor dem aktuellen Hype – ausschließlich hochgradig kontrollierten Agenten-Infrastrukturen. Erfahre, wie Unternehmen durch vollautonomes Monitoring globale Märkte und Kontexte in Echtzeit analysieren, warum einfache Prompts bei ChatGPT für geschäftliche Zwecke scheitern und wie KI-Agenten heute sogar hochkomplexe Werbematerialien bis hin zu druckfertigen Adobe InDesign-Layouts völlig selbstständig generieren. Über Sebastian Denef: Sebastian Denef ist Mitgründer und CEO von AGENTS.inc, einem Pionier-Unternehmen für KI-Agenten, das er aus seiner Forschungsarbeit am Fraunhofer-Institut heraus gründete. Seit vielen Jahren erforscht er die Human-Computer-Interaction und die Zukunft der Arbeit. Sebastian gilt als absoluter Vordenker im Bereich der Mensch-Maschine-Kollaboration und ist Autor des zukunftsweisenden Buchs „The Last Boss: How AI Agents Will Unlock Artificial General Intelligence“. Sein pragmatischer Ansatz bei AGENTS.inc zeigt, wie Unternehmen komplexe Datenströme bändigen, Halluzinationen in Geschäftsprozessen durch kontrollierte Agent-Workflows eliminieren und echte Software-Autonomie gewinnbringend im Enterprise-Alltag etablieren können. Der Marketing Transformation Podcast wird produziert von TLDR Studios.

Egal ob man gern ausufernde Gespräche mit einem Chatbot führt, jede Menge Bilder generiert oder Programmierprojekte mit KI umsetzen will: Die Token schwinden wie Eis in der Sonne und das Ganze geht schnell ins Geld. Mal ganz abgesehen davon, dass gerade im beruflichen Kontext Vorsicht geboten ist, welche Daten man an die Onlinedienste weitergibt. Lokale KI-Modelle sind da die deutlich bessere Wahl: Die Kosten sind besser kalkulierbar, weil man nur in Hardware investieren und kein Abo bei KI-Firmen abschließen muss. So muss man nicht mehr jedes Token auf die Goldwaage legen. Und um den versehentlichen Abfluss von Daten braucht man sich dann auch keine Sorgen machen. In dieser Folge des c't uplink sprechen wir darüber, was mit einem lokalen KI-Server möglich ist, ab welcher Hardware das Spaß macht und welche coolen Dinge ein solcher KI-Server tun kann. Jan Mahn berichtet von seinen Erfahrungen beim [Aufsetzen eines LLM-Servers für kleine Teams](https://www.heise.de/ratgeber/Sprachmodelle-mit-Open-WebUI-LLM-Server-fuer-kleine-Teams-selbst-hosten-11333185.html), während Jan-Keno Janssen für ein c't-3003-Video mit agentischer KI experimentiert hat. Zu Gast im Studio: Jan Mahn, Jan-Keno Janssen Host: Liane M. Dubowy Produktion: Tobias Reimer ► Mitdiskutieren auf dem heise & c't Discord-Server: https://discord.gg/Wf6ewnWpxH ► c't Magazin: https://ct.de ► c't auf Mastodon: https://social.heise.de/@ct_Magazin ► c't auf Instagram: https://www.instagram.com/ct_magazin ► c't auf Facebook: https://www.facebook.com/ctmagazin ► c't auf Bluesky: https://bsky.app/profile/ct.de ► c't auf Papier: überall wo es Zeitschriften gibt!

Bei lokalen KI-Modellen muss man nicht jedes Token auf die Goldwaage legen. Statt teurer Abos für KI-Dienste, fallen leichter zu überblickende Hardware- und Stromkosten an. Wir sprechen darüber, was mit einem lokalen KI-Server für privat oder kleine Teams möglich ist, welche Hardware man dafür braucht und welche coolen Dinge man damit tun kann.

Egal ob man gern ausufernde Gespräche mit einem Chatbot führt, jede Menge Bilder generiert oder Programmierprojekte mit KI umsetzen will: Die Token schwinden wie Eis in der Sonne und das Ganze geht schnell ins Geld. Mal ganz abgesehen davon, dass gerade im beruflichen Kontext Vorsicht geboten ist, welche Daten man an die Onlinedienste weitergibt. Lokale KI-Modelle sind da die deutlich bessere Wahl: Die Kosten sind besser kalkulierbar, weil man nur in Hardware investieren und kein Abo bei KI-Firmen abschließen muss. So muss man nicht mehr jedes Token auf die Goldwaage legen. Und um den versehentlichen Abfluss von Daten braucht man sich dann auch keine Sorgen machen. In dieser Folge des c't uplink sprechen wir darüber, was mit einem lokalen KI-Server möglich ist, ab welcher Hardware das Spaß macht und welche coolen Dinge ein solcher KI-Server tun kann. Jan Mahn berichtet von seinen Erfahrungen beim [Aufsetzen eines LLM-Servers für kleine Teams](https://www.heise.de/ratgeber/Sprachmodelle-mit-Open-WebUI-LLM-Server-fuer-kleine-Teams-selbst-hosten-11333185.html), während Jan-Keno Janssen für ein c't-3003-Video mit agentischer KI experimentiert hat. Zu Gast im Studio: Jan Mahn, Jan-Keno Janssen Host: Liane M. Dubowy Produktion: Tobias Reimer ► Mitdiskutieren auf dem heise & c't Discord-Server: https://discord.gg/Wf6ewnWpxH ► c't Magazin: https://ct.de ► c't auf Mastodon: https://social.heise.de/@ct_Magazin ► c't auf Instagram: https://www.instagram.com/ct_magazin ► c't auf Facebook: https://www.facebook.com/ctmagazin ► c't auf Bluesky: https://bsky.app/profile/ct.de ► c't auf Papier: überall wo es Zeitschriften gibt!

Ramez Naam is an investor at Planetary VC and a longtime clean energy and AI expert. In this conversation, we break down the energy bottleneck constraining AI growth — from gas turbines and batteries to floating ocean data centers and Elon's orbital data center ambitions. We also cover why bitcoin miners are pivoting to AI infrastructure, general vs. narrow superintelligence, and the data bottleneck reshaping AI training.====================Turn every conversation into a searchable business asset with PLAUD NotePro. Visit https://Plaud.ai/pomp  and use code POMP for 15% off.====================Simple Mining makes Bitcoin mining simple and accessible for everyone. We offer a premium white glove hosting service, helping you maximize the profitability of Bitcoin mining. For more information on Simple Mining or to get started mining Bitcoin, visit https://www.simplemining.io/pomp====================Uphold is the easiest way to buy and sell crypto unlike any other platform allowing you to trade in just one step between any supported asset. Check them out at https://www.uphold.com/pomp/ This video includes a paid sponsorship with Uphold. I'm compensated by Uphold for promoting its products and services and may receive commissions from referrals. Terms apply. Not available in all jurisdictions. Digital assets are risky and may result in the total loss of your capital.====================0:00 - Intro1:03 - Why power is the real bottleneck for AI & solutions7:35 - Elon's orbital data center thesis11:17 - Cooling & maintenance challenges in space15:06 - Panthalassa: floating ocean data centers19:16 - Base Power & Texas deregulated grid21:30 - Giga Energy: from bitcoin mining to AI infrastructure22:44 - American Consolidated Electric & supple chain bottlenecks24:58 - General vs. narrow superintelligence33:02 - Where untapped data lives & building a data moat36:47 - Token costs, open source models & model routing41:53 - What is the mission Ramez is going after?

The episode highlights a shift from technology selection to operational risk management in the AI landscape for MSPs. Service providers are being forced to navigate the fast-changing interplay between AI models, the harness software that mediates their deployment, and the financial realities of consumption-based billing. The rapid proliferation of open-source and open-weight AI models, alongside market behaviors from closed vendors and regulatory interventions, is introducing volatility and uncertainty in both cost structures and client offerings. This dynamic creates structural challenges related to margin maintenance, vendor dependency, and responsibility for AI-driven decisions. The discussion cites the release of GLM 5.2, an open-weight model from Z AI, which now rivals expensive closed models on key benchmarks at a fraction of the cost. At the same time, large-scale investments by commercial AI vendors have yet to deliver returns on expectations, with reports indicating businesses that adopted AI are not seeing projected value. Specific attention is given to operational constraints such as compute scarcity, token consumption variability, and export policy restrictions impacting AI availability. The episode notes that these pressures are driving both vendors and MSPs to reconsider the viability of reliance on expensive, closed offerings versus investigating open alternatives. Supportive examples include the proliferation of AI “harnesses” (middleware layers like Perplexity, Claude Code, and Cowork) that sit between service providers and underlying AI models, increasing both choice and complexity. Token billing models are highlighted as a source of unpredictability for MSPs, with vendors like Atera and ConnectWise experimenting with different abstractions to shield or pass through token risk to service providers. The potential for on-premises AI deployments using smaller language models is discussed as a cost-mitigation strategy, though this raises further questions about data privacy, infrastructure burden, and long-term vendor roles. Additionally, uncertainty is flagged around sustainability of leading vendors, with projections that at least one major AI player may exit or be acquired within a year due to financial vulnerability. For MSPs and IT service leaders, these structural and supporting developments translate into increased operational and financial complexity. There is a pressing need to evaluate not just which AI technologies to adopt, but how to architect solutions that can withstand rapid vendor movement, cost swings, and evolving regulatory requirements. Practical safeguards include testing open-source AI models alongside commercial offerings, exercising caution in vendor selection, and closely monitoring evolving consumption billing models. Preparing staff and clients for adaptive, process-oriented approaches—rather than fixed solutions—is positioned as a necessary step to maintain resilience as the AI adoption cycle continues to correct course. Supported by:Pax8CometBackupGuardz

None of us signed up to help train superintelligence. That's the case Don Shin, CEO of CrossComm, makes on this episode of High Octane Leadership — and it's just one of the uncomfortable ideas he brings to the table about AI.Don has spent 25 years building digital products, from Netscape-era websites to mobile apps to AI-driven platforms, and he tells Donald Thompson plainly: AI is a bigger shift than the internet ever was. Not because the technology is flashier, but because it automates judgment and decision-making, not just deterministic tasks — and it's moving faster than society has time to adjust to.The conversation covers a lot of ground: why 30,000 layoffs at a profitable Oracle are really an AI-infrastructure bet, why anxiety about AI is turning into outright resistance, and Don's case for a “token tax” — a proposal to tax the electricity and water consumption of AI data centers so the economic gains of AI don't just accrue to a handful of companies that trained their models on data none of us knowingly volunteered.Don shares the PROMPT framework, a five-part method for getting better results out of any AI tool, and a sobering warning about AI sycophancy: an assistant that never disagrees with you is not therapy, and it's not friendship either.This is a conversation about where the next five years of work, leadership, and human connection are headed — and what to do about it before the choice is made for us.Key Talking PointsThe Abundance of Intelligence — Don's framework for understanding AI as an economic shift on the scale of previous “abundance” moments in history — and why this one is moving faster than any before it.AI Anxiety Becomes AI Resistance — Why simply reassuring employees isn't enough, and what leaders actually need to offer people to bring them along.The Case for a Token Tax — Don's proposal to tax the electricity and water consumption of AI data centers, and why he believes the true cost of AI tokens is being artificially subsidized.You're Using Claude Wrong — Donald's own experience learning that an AI's first two drafts are educated guesses — and what changes once you actually train the tool on your voice and judgment.The Danger of Sycophancy — Why an AI that never disagrees with you may be a bigger risk to the next generation than job displacement.The PROMPT Framework — Don's five-part model — Persona, Request, Output, Mandatories, Priority — for getting dependable results out of any AI tool.About the GuestDon Shin is CEO of CrossComm, a digital product studio he founded as an undergraduate — building websites in the Netscape 1.1 era before leading the company through mobile apps, augmented and virtual reality, IoT, and now AI-driven development. A 25-plus year technology industry veteran and Duke University alum, Don is a sought-after speaker on artificial intelligence and its impact on business and society. He is the creator of the PROMPT framework, a practical model for effective AI prompting, and Speak To My Future Self, a personal AI reflection tool.ResourcesCrossComm: https://www.crosscomm.com/Don Shin LinkedIn: https://www.linkedin.com/in/donshin1/Donald Thompson LinkedIn: https://www.linkedin.com/in/donaldthompsonjrDonald's Newsletter & Substack: https://substack.com/@donaldthompsonjrDonald's Books: https://donaldthompson.com/books-resources/Stay connected with Donald: Get his newsletter packed with actionable insights and the kind of straight-talk leadership intelligence that helps build authority, drive performance, and stay ahead of what's coming next: donaldthompson.com. (00:00) - — Cold Open: The Data We Didn't Know We Were Giving Away (01:00) - — Welcome Don Shin, CEO of CrossComm (02:00) - — The Abundance of Intelligence: How AI Changes the Automation Equation (04:00) - — Why AI Is Bigger Than the Internet: Don's CrossComm Origin Story (06:00) - — The Velocity Problem: Why Buffer Time Matters for Economic Disruption (07:00) - — Oracle's Layoffs and the Rising Anxiety Data (08:00) - — It's Not Just AI Anxiety, It's AI Resistance (10:00) - — The Controversial Take: Game Theory and a Broken System (11:00) - — The Case for a Token Tax (13:00) - — Reddit, Data, and the Price of “Free” AI Tools (15:00) - — You're Using Claude Wrong: Donald's Lawnmower Analogy (18:00) - — Let AI Do the Routine So Teams Can Do the Remarkable (20:00) - — Why Top-Down AI Mandates Failed in 2025 (22:00) - — The Loneliness Economy: Why Therapy Is AI's #1 Use Case (23:00) - — The Danger of AI Is Sycophancy (27:00) - — Assigning a Persona: Why AI Defaults to Reassurance (28:00) - — The PROMPT Framework (31:00) - — The Next Problem: What Happens When Token Costs Explode 5–10x (34:00) - — Closing Thoughts: Lead From the Front High Octane Leadership is hosted by The Diversity Movement CEO and executive coach Donald Thompson and is a production of Earfluence.Order UNDERESTIMATED: A CEO'S UNLIKELY PATH TO SUCCESS, by Donald Thompson.

RevokeCash introduces auto-revoking. Francesco leaves the Ethereum Foundation to join Ethlabs. DV Labs winds down its Aztec sequencer. And Ostium suffers a $23m hack. Read more: https://ethdaily.io/991 ETH Daily sponsorships are now open. Reach over 10,000 Ethereum-native subscribers every weekday. Learn more at ethdaily.io/ads  Disclaimer: Content is for informational purposes only, not endorsement or investment advice. The accuracy of information is not guaranteed.

My guest this week is Scott Billington.Scott is a Grammy winning record producer, an author, a record company exec and a musician. But what we're chatting about in this episode is a fascinating story about a pivotal string band album and it's journey to being reissued with extra tracks that went missing for decades.The record in question is Boone Creek's debut album Boone Creek, from the band Jerry Douglas and Ricky Skaggs formed after they left J.D. Crowe and the New South.Scott talks about how the album was recorded and how, at the time, Rounder felt it was too progressive, and asked the band to go back into the studio and record some additional tracks. After the album was released, the original tapes went missing and Rounder were keen not to reissue it on CD or streaming services until they'd been found. As a result, the record disappeared from circulation for years.We talk about how the tapes were finally unearthed, what state they were in, the process used to retrieve what was on them and how Boone Creek was finally reissued by Craft Recordings.We also chat about Scott's long career with Rounder Records and his current role with Craft Recordings, working on several key projects, including the Doc Watson A Life's Work box set. This was a fascinating conversation about a fascinating project, as well as Scott's long association with outstanding American roots music.Next week's episode will feature an interview with Jerry Douglas about Boone Creek, his memories of that band and what it was like hearing the reissued tracks everyone thought had been lost for good.You can buy Boone Creek from Craft Recordings on vinyl, CD or digital downloadFollow Craft on Instagram or Facebook to keep up to date with new reissues of classic roots music.For more info on Scott, including links to buy his book Making Tracks: A Record Producer's Southern Roots Music Journey, check out www.scottbillington.comMatt Support the show===Thanks to Bryan Sutton for his wonderful theme tune to Bluegrass Jam Along (and to Justin Moses for playing the fiddle!)Bluegrass Jam Along is proud to be sponsored by Collings Guitars and Mandolins and Token premium guitar picks- Sign up to get updates on new episodes - Free fiddle tune chord sheets- Here's a list of all the Bluegrass Jam Along interviews- Follow Bluegrass Jam Along for regular updates:InstagramFacebook- Review us on Apple Podcasts

AI can generate code faster, but that does not make software delivery simple. It shifts the pressure to requirements, architecture, review, and technical judgment.Goncalo Silva, CTO at Doist, explains how AI is changing the way teams behind Todoist and Twist build software. He shares why greater individual autonomy has led to more collaboration, why deep expertise still matters, and how faster execution is reshaping product delivery, project planning, and engineering hiring.What Leaders Can Take From This• Faster code generation makes strong planning and clear requirements more important, not less important.• Designers, product leaders, and engineers can work from richer prototypes, but production systems still need experienced technical judgment.• Engineering capacity does not have to move into other functions. Teams can use it to improve reliability, performance, quality, and the amount of valuable work they ship.• Token counts are a weak measure of progress. Doist looks at team feedback and whether projects are staying on track.• Engineering interviews need to test architecture, decision making, curiosity, and depth, not simply whether a candidate can produce working code.Approximate Highlights00:00 Meet GonCalo Silva and the products behind Doist02:00 How broadly AI is being used across Doist04:15 Why greater autonomy has brought teams closer together09:45 Where nontechnical coding works, and where it creates risk17:50 How AI compressed a major refactoring effort by 20 to 30 times25:05 Measuring AI value without counting tokens30:20 Why faster execution requires more up front planning34:50 How Doist changed its engineering interview processOne Line That Stuck“We are the bottleneck. Our attention span, our ability to memorize, our ability to understand, and deep expertise.”Follow The Tech Trek for more conversations on how technical teams are changing the way they build, hire, and operate.

Mastodon announces their first album since losing Brent Hinds — and it comes with a surprise Josh Homme cameo. Sleep Token keeps racking up RIAA hardware, with "Caramel" going platinum and "Dangerous" going gold. And we've got a genuinely great health update from Coal Chamber drummer Mikey "Bug" Cox after his latest cancer surgery. In this episode: [0:00] Intro [0:25] Mastodon announce "Marrow Deep," out August 28 — first album since Brent Hinds' passing, featuring Josh Homme's first Mastodon appearance since 2006 [1:30] Sleep Token's "Caramel" goes platinum, "Dangerous" goes gold — the latest in a string of RIAA certifications for "Even In Arcadia" [2:30] Coal Chamber's Mikey Cox shares a hopeful update after what's hopefully his final cancer-related surgery [3:50] Wrap-up New episodes of Metal Breakdown Daily drop every weekday morning. Subscribe so you don't miss the next one, and follow Loaded Radio at loadedradio.com and across Facebook, Instagram, and TikTok for daily hard rock and heavy metal news.

In this episode of Run the Numbers, CJ sits down with Rogo president Rahul Rekhi to unpack what AI actually changes in investment banking and finance. They dig into token economics, why adoption without ROI is a trap, how vertical AI wins, and why domain expertise still matter.—SPONSORS:Pulley is an equity management platform that lets you issue options, model dilution, and complete 409As without your cap table turning into a spreadsheet disaster. Founders raising, hiring, and scaling use Pulley to keep equity clean and stay focused on building. Learn more or request a demo at https://pulley.com/mostlymetricsRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjMaximor is an autonomous finance platform that runs order-to-cash, procure-to-pay, the close, cash management, and reporting on self-learning agents instead of a dozen disconnected tools. One PE-backed customer cut their close in half, took audit findings from seven to zero, and cut back-office costs by 70% in six months. You pay for outcomes, not seats. See it at https://www.maximor.ai/Brex is an intelligent finance platform with AI-powered agents that capture expenses automatically, enforce policy before the spend happens, and close your books in minutes instead of weeks. 35,000+ companies like OpenAI, Coinbase, Anthropic, and DoorDash already run on Brex. It's time to get Brex AF. Learn more at https://www.brex.com/metricsAnrok is the sales tax platform that watches your exposure everywhere, automates compliance, and flags risk before it turns into a surprise back-tax letter from a state you've never set foot in. Companies like Anthropic, Notion, and Vanta already trust Anrok to stay ahead of rules that move faster than any spreadsheet can. Talk to a sales tax expert for a personalized exposure estimate at https://www.anrok.com/rtnRightRev is an automated revenue recognition platform that lets your product team ship new pricing without asking finance for permission, and your sales team close deals without creating downstream chaos. Check out their free tool at calculator.rightrev.com It scores your rev rec process, shows what's exposing you to risk, and tells you exactly where to focus before it bites you in the rear end. Check it out at https://calculator.rightrev.com—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/rahulrekhi/Company: https://www.rogo.ai/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:A CFO Explains the Stock Exchangeshttps://youtu.be/pooOE6ZNGR4A CFO Explains Marketplaceshttps://youtu.be/LpbH9GpBrSY—TIMESTAMPS:0:00 Preview and Intro2:44 Why finance was first to verticalize AI6:28 What the president title means at Rogo9:55 Sponsors — Pulley | Rillet | Maximor13:02 The problem Rogo is solving16:16 Token maxing is not transformation17:43 Why ROI is so hard to measure19:39 Sponsors — Brex | Anrok | RightRev22:37 ROI is business-unit specific24:03 Budgeting tokens like a benefits load25:00 AI incentives aren't aligned to efficiency26:26 The model broker function32:03 Not all token spend is equal37:14 Who owns AI efficiency?40:59 The forward deployed banker43:00 Domain expertise: the Interstellar analogy49:21 Lightning round49:28 Screwed up: DCF error in a live deal51:13 Will new grads have the spidey sense?53:03 Meeting Pope Francis55:04 Fact-checking jobs numbers at the White House57:39 Advice to younger self59:21 Credits

In this recent episode of Possible, Reid Hoffman sits down with Microsoft CEO Satya Nadella fresh off Microsoft Build 2026. The conversation goes wide: how AI is reshaping work, business, and society—and why the transformation sweeping through software development today is only a preview of what's coming for all knowledge work. Satya makes the case that human capital and "token capital" are now deeply intertwined, that companies—not just countries—must build their own AI capabilities, and that the organizations best positioned to thrive are those that can leverage their unique expertise inside intelligent systems. Reid and Satya also explore Microsoft's enterprise AI vision, Reid's work with Manas on AI-powered scientific discovery, lessons from past technological revolutions, and why demonstrating real, tangible benefits may be the most important thing the industry can do to earn—and keep—the public's trust.You can catch and subscribe to more Possible here: https://www.possible.fm/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

On today's show Andrew and Ben begin with a look at the state of Meta. Topics include: Mark Zuckerberg's sins of commission vs. omission as a messenger, Meta's AI opportunity, directionally correct investments, the problems with Meta as a cloud provider, and the absence of religion in Menlo Park. From there: Why Microsoft should move on from the XBOX era, and the shift in gaming habits that doomed Game Pass from the outset. At the end: OpenAI introduces GPT-Live, a question about the cost of Ben's vibe coding adventure spawns a digression on future token costs, the cost of youth sports, American soccer and learning Chinese, tech weirdos and the future of normie app building, and Ben gets castigated for bringing a Starlink on vacation.

Google fires the engineer behind its Workspace CLI tool, OpenAI previews GPT-5.6 with three new model tiers, and Astro 7 lands with a full Rust rewrite. Plus: Coinbase cuts token costs with smarter routing, and more in this week's Syntax Live Show Notes 00:00 Intro 00:34 Welcome to Syntax! 01:46 Google fires Workspace CLI Creator 12:30 GPT 5.6 Is Coming 19:59 GLM 5.2 Released 23:23 Astro 7 Rust Re-write 32:46 Cursor Announces iOS App 35:08 Scott's Workflow: Herdr + Mosh + Termius + Tailscale 40:33 Coinbase Reduces AI Cost with Model Routing 44:22 wayfinder-router - Local AI Routing CLI 48:16 Token efficiency in models and harnesses Martin Woodward on X 52:34 performativeUI - react components for AI startups 54:31 Brought to you by Sentry.io 55:21 Reachy Mini Robot 01:02:21 FUTO Keyboard Swipe for Android 01:05:40 CSS Quake 01:07:35 HTML Invoker API is Baseline Available 01:13:17 Cloudflare Temporary Accounts for AI Agents Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

VVV is down 50% on news that should be good for Venice. David unpacks why — covering the token vs. equity distinction, Dragonfly's reasoning, and what the onchain data says about how many people are actually using Venice's paid features right now. FOLLOW THE SHOW › David — https://x.com/dcanellis › The Breakdown — https://x.com/TheBreakdownBW › The Breakdown Newsletter — https://blockworks.com/newsletter/the-breakdown DISCLAIMER As always, remember this podcast is for informational purposes only, and any views expressed by anyone on the show are solely their opinions, not financial advice.