For more, let's bring in Yorkville Ives partner Dan Ives and CBC contributor requisites Bin Talkington. Thank you both so much for being here. Um Dan, you think Alphabet is actually the best positioned of the hyperscalers and today is about proving it. Why do you think it's the best position and and what do you think it needs to do to actually prove it.>> I think it sets the tone for this tech earning season because when you think about what's happened on cloud, call it mid60% growth. This has really been a ...

The AI buildout is being paid for in jobsTech's biggest companies are running the same trade: cut payroll, pour it into compute. US tech job losses are estimated above 100,000 this year while collective AI capex heads past $700B, with @Meta trimming thousands and @Microsoft offering buyouts to 7% of its US workforce. Today @OpenAI added a $20B Georgia campus to the pile.@Oracle put the swap in writing, tying 21,000 cuts to AI deployment in its own SEC filing while spending $55.7B on data centers. Analysts a ...

Let's dive into this. The ultimate starting point for our whole investigation today boils down to one simple question. How much are investors actually willing to pay for one single dollar of revenue.Think about it. When you buy a stock, you're buying a living, breathing slice of a business. And before that business can generate a single dime of profit, it has to generate revenue.This question right here, it sits at the absolute heart of the price to sales or PL/S ratio. So, here's our game plan. We'll start ...

Agent Development Lifecycle and Frameworks - Organizations achieve the best agent delivery by shipping early and iterating systematically around agent performance and behavior [1] - The agent development lifecycle consists of five key stages: build, test, deploy, monitor, and govern [1] - Frameworks like LangChain and Llama Index provide standardized abstractions for model inputs, outputs, tools, and prompts, though they may obfuscate underlying multi-step model calls [1] Testing and Evaluation - Testing agents involves running inputs against data sets, measuring performance through regression testing or benchmark hill climbing, and evaluating outputs via ground truth or criteria-based judging [2] - Testing frameworks like Harbor utilize isolated sandboxes to execute long-running stateful agent tasks that can last 15 to 30 minutes [2] Deployment and Production Operations - Production deployments require robust runtime features including durable execution, thread-level and long-term memory persistence, streaming responses, and safe code execution sandboxes [3] - Virtual file systems allow agents to interact with stored contexts via familiar read and write operations, improving scalability and efficiency [3] Monitoring and Governance - Monitoring systems rely on tracing every step and tool call to debug underlying model errors, while collecting user feedback and online evaluations to detect perceived errors at scale [3][4] - Governance structures wrap the entire lifecycle to enforce cost controls, manage tool access privileges, and maintain audit trails for enterprise scalability [5] Advanced Platform Capabilities - Purpose-built databases like Smith DB process and store massive volumes of agent traces efficiently, handling quantities up to billions of traces [6] - Automated tools like LangSmith Engine operate in the background every 6 hours to cluster traces, identify issues, suggest code or prompt changes, and update testing datasets [7][10]

Have you ever been sitting at a meeting and thinking to yourself, why am I here. Or even worse, got to the end of the meeting and thought, "This is an hour of my life I'm never going to get back." If you've had that, you're not alone. I've had that feeling myself. In fact, I had that feeling recently.Uh, I was in a meeting, got to the end of the meeting, had those feelings, and the meeting leader came to me afterwards and said, 'I bet you thought that was a waste of your time. And I thought to myself, "Yeah ...

Welcome back to Trader Talk here at Yahoo Finance. I am Kenny Pulcari, your host. And today we're talking to Kevin Man who's a CIO at Henyan and Walsh and we're talking to Willie Lee who is a principal at Neoellar >> Capital which has been renamed from Suro Capitals.Gentlemen, thank you very much for the conversation. There is a lot to talk about. The world has changed in the in the last in the first six months.It's going to change again over the next six months. There's a lot going on in terms of earning s ...

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Business Challenges & Market Demand - Small business hiring managers spend 9.5 hours a week reviewing candidates and deciding who to contact [2] - LinkedIn developed a hiring agent that cuts the time to interview by 60% for small businesses [1] Architecture & Technical Framework - LinkedIn selected LangGraph among 89 agent frameworks after thorough research and evaluation [10] - The agent platform utilizes conversational memory for chat histories and experiential memory for state checkpoints [13][14] - Custom context management was built to control checkpoint sizes and maintain stateless scalability [28][30] Evaluation & Continuous Learning - Production data is evaluated using a combination of human annotation and LLM-as-judge models [22][23] - Current optimization relies on manual prompt and model refinement, with plans to transition to continuous online optimization [23][24]

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Industry Dynamics and Market Trends - Google released three lightweight AI models, and the anticipation for the Gemini 3.5 Pro model is rising [1] - OpenAI and Hugging Face experienced a security incident involving zero-day vulnerabilities discovered in pre-release models, while OpenAI's next-generation model entered regulatory briefings [1] - Claude Cowork launched new features, and Kimi K3 faced ranking shifts and real-world challenges amid accelerating next-generation model competitions [1] Investment Opportunities and Commercial Growth - Codex and ChatGPT Work user base surpassed 10 million (10 million users), accompanied by a frequent quota reset frequency of four times every seven days and fierce growth competition [1] - The AI industry focus heavily spans across major technology stocks, AI infrastructure, data centers, semiconductor investments, and supply chain segments including NVIDIA, AMD, Broadcom, TSMC, and Micron [1] - Market attention extends to core hardware and technology areas such as HBM, DRAM, storage chips, optical modules, 800G and 1.6T networking, AI servers, GPUs, ASICs, and advanced packaging like CoWoS [1] - Capital markets and investors closely monitor cloud computing vendors including Microsoft, Google, Amazon, and Meta, alongside AI software, SaaS, AI Agents, and generative AI commercialization [1] - Power equipment, nuclear energy investments, liquid cooling technologies, and PCB/ABF substrates remain key components of the AI infrastructure investment logic [1] - Broader market sentiment tracks AI bubbles, technology stock valuations, PE valuations, EPS, financial reports, and related indices such as NASDAQ, QQQ, SOXX, SMH, SOXL, and AI ETFs [1]