AI is accelerating cyberattacks and defense. The 2026 Cost of a Data Breach Report reveals systemic failures in access controls (92% lack proper AI governance) and highlights the need for crypto agility against quantum threats.
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The bottleneck in shipping production AI agents isn't the model; it's the developer loop. Learn how to use sub-agents, skills, and goals to automate your entire development workflow.
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AI products must pass the 'memo test,' requiring verifiable provenance for every claim, reconciling all numbers, and maintaining clear separation between facts and guesses. Accountability requires a human sign-off.
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Learn how to integrate AI agents into event-sourced systems for fraud detection. By building a semantic layer over multiple bounded contexts, you can resolve ambiguous transactions that traditional ML models miss.
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The Exponential Family unifies diverse statistical models—from Gaussian errors to Softmax classification—under one common mathematical form. This framework shows that core ML procedures like inference and learning are fundamentally consistent across all these types of data.
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The lecture establishes Maximum Likelihood Estimation (MLE) as a foundational principle to generalize modeling from continuous regression to discrete classification. It derives Logistic Regression and compares optimization techniques like SGD vs. Newton's method.
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The core of supervised learning is minimizing error using $\text{L}_2$ loss. Optimization relies on Gradient Descent, which scales to massive datasets using Stochastic Gradient Descent (SGD) and mini-batching.
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The course emphasizes understanding the mathematical foundations of ML, covering Supervised, Unsupervised, and Reinforcement Learning. A key focus is optimizing algorithms for hardware compatibility.
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Raphael demonstrates an automated e-ink dashboard using Gemini 3.6 Flash and Nano Banana. The system checks calendars and weather nightly via AntiGravity to provide intelligent daily reminders for family routines.
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Group settings break single-user assumptions, requiring redesigns for memory and security. The focus shifts to guarding the 'action surface' rather than filtering all input data.
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Lucas Palma discusses how Nubank built Skill Vector to vet 2,000+ AI skills, treating them like supply chain dependencies in a regulated environment. The system uses hybrid scanning combining deterministic checks and LLM review.
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Kepler demonstrates how to build verifiable AI for finance by augmenting LLMs with deterministic substrates, ensuring every number is traceable and auditable.
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Synthetic personas use LLMs to simulate human behavior, but they require careful validation. Learn how failure modes like latent confounders and distribution shape loss can mislead results, emphasizing that models are forecasts, not ground truth.
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Off-the-shelf LLMs provide fluent but inaccurate advice—the 'fluent bluff.' The solution requires grounding models in verifiable State-Action-Outcome records, moving finance toward outcome-driven AI.
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Nubank accelerated agent deployment by 20x using simulations. By generating grounded, multi-turn synthetic data instead of relying on slow production traces, teams can rapidly test and derisk AI agents.
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The Kimi K3 model utilizes a complex training pipeline involving multi-tier on-policy distillation across nine specialized domain experts. Key innovations include partial rollout schemes and dynamic resource scheduling.
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Learn how Oracle built an AI skill to enforce database best practices, improving SQL generation from 95% to 100% success rate using automated reviews and iterative refinement.
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AlphaLab is a multi-agent system that automates quant research by managing the entire lifecycle: from natural language problem input to running cluster jobs and statistical tests. Its core value lies in building verifiable evaluation environments.
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Most agent failures are harness failures, not model failures. The system must own state transitions, enforce ordered commits, and use a run receipt to prove actions were visible to the user.
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AI News & Strategy Daily | Nate B Jones
Running out of AI tokens? Learn how 'reused input' dominates token usage in LLMs. We cover 15 rules to optimize your workflow, from editing mistakes to using local frameworks.
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