Hi, I'm Onela. That's Joel. And we built Ace, a live AI voice tutor that runs a full lesson start to finish reliably.The trick is LLM is not in charge. If you have shipped a multi-step agent, you know this moment. It's near the demo, then a real user gets in and halfway through the agent decide it's done.Or skip a step, or even loops. The demo never show you that. And the first fix everyone reaches for is prompt this harder, add more holes.But reliability was never a prompting problem. It's a control proble ...

Hi, I'm Ornella, and that's Joel, and we built Ace, a live AI voice tutor. It run on a small model on purpose, and I want to tell you more why that's not a compromise. Quick gut check.That silence on a voice call, that the difference between a tutor and a broken up. When a voice agent pause for even a second, your brain says it's dead. So, when the answer fit a leader of every instant stay, which for the smallest, biggest model.In voice, that instant is actually a backward. Because our budget was never IQ, ...

Hello. We know that the modern B2B selling has evolved significantly. The uncomfortable truth is that by the time the buyer reaches you, the decision is mostly made.They've researched, compared, and probably shortlisted vendors or partners that they want to work with. That may include you or your competitors. This talk is about the architecture that lets GTM teams know the buyer more than they today.My name is Sajjan Khan Akolanu. I have 20-plus years experience across product, technology, and marketing. At ...

AI Adoption & Engineering Challenges - AI adoption primarily enhances individual effectiveness, yet software delivery instability remains a significant concern [6] - Engineering team throughput has not seen the expected gains from AI, as software tends to break more frequently [7] - The research phase for performance optimization remains a "black box," often requiring anywhere from one hour to several weeks of engineering time to investigate [2][10] Agentic Workflow Strategy - Thundra implements a runtime intelligence layer that captures function-level and forensic context to enable autonomous performance optimization [4][28] - Automated workflows are designed to be vendor-neutral regarding compute, model selection, and infrastructure to ensure long-term maintainability [13][14] - The system utilizes GitHub Actions to run weekly analyses, identifying high-ROI (Return on Investment) performance opportunities based on production data [17][18] - To ensure reliability, the agentic process verifies fixes by rerunning tests and measuring impact on specific business flows, such as those invoked 7,000 times per week [20][21] Operational Best Practices - Effective automation requires "context over cleverness," where agents are provided with deep production insights—such as P99 latency thresholds—rather than relying on static code analysis [29][34][43] - To prevent "alert fatigue," the system avoids flooding developers with excessive pull requests, instead surfacing human-friendly reports that prioritize high-impact, low-risk changes [35][39] - Achieving autonomous engineering requires reaching a 80-90% trust threshold, which is significantly higher than the confidence level required for human-assisted AI coding in an IDE [47][48]

AI Infrastructure & Capital Expenditure - Hyperscalers are projected to spend approximately $725 billion on capital expenditure (CapEx) over the coming year [2][13] - Alphabet is expected to report significant CapEx, with projections reaching up to $190 billion, setting a benchmark for other major tech firms [3][13] - Tech companies maintain a "better to overspend than underspend" strategy to avoid losing competitive ground in the AI sector [16] Semiconductor Industry Trends - The semiconductor index has risen over 100% in the last 12 months, despite a recent 20% pullback, creating a "priced to perfection" market environment [21] - Intel has seen a year-to-date stock increase of over 150%, with investors closely watching if it can capture shifting AI data server spending [23] - Taiwan Semiconductor Manufacturing Company (TSMC) experienced a sharp stock decline despite strong earnings and guidance, reflecting high market expectations [20][21] Market Competition & Geopolitics - A debate persists regarding the cost-efficiency of AI models, as Chinese startups like Moonshot and DeepSeek demonstrate the ability to execute tasks at a fraction of the cost of US counterparts [8][9] - Concerns exist regarding the monetization of AI products and the potential for "a race to the bottom" in pricing models [9][11] - Energy availability is emerging as a critical bottleneck for AI development, with China noted for its top-down approach to energy diversification compared to the US [24][25][26] Equity Market & Valuation Risks - SpaceX faces a significant market correction, with its valuation dropping from an initial $1.75 trillion, leading to a loss of roughly $1 trillion in market capitalization [5][28][29] - Investors are exhibiting caution, taking profits from semiconductor stocks like Micron and Intel, which complicates the justification for high valuations in other AI-related IPOs [29] - Uncertainty remains regarding future news flow, potential share unlocks, and the impact of upcoming earnings reports on high-growth tech valuations [31]