Dec 29, 2025

Agentic Al in SW Development: Evolving Patterns & Protocols • Bhuvaneswari Subramani • GOTO 2025

Bhuvaneswari Subramani details the "Agentic Shift" in AI by presenting an evolutionary journey through seven foundational system design patterns. The talk progresses from simple conversational clients to sophisticated, multi-agent systems, covering key patterns like Retrieval-Augmented Generation (RAG), Self-Correcting RAG, and the Model Context Protocol (MCP), explaining how each pattern adds new layers of context, action, and autonomy.

Dec 29, 2025

Cybersecurity Trends in 2026: Shadow AI, Quantum & Deepfakes

Explore Jeff Crume's cybersecurity predictions for 2026 and beyond, detailing the dual impact of AI in security, the rise of autonomous AI agents, the futility of deepfake detection, and the critical importance of post-quantum cryptography and passkeys for future defense.

Dec 26, 2025

How Claude Code Works - Jared Zoneraich, PromptLayer

An unofficial deep dive into the architecture of modern coding agents like Claude Code. Jared Zoneraich of PromptLayer explains the shift towards simpler, model-centric designs, detailing the core components like the master loop, tool calling (especially `bash`), and context management strategies. The talk also contrasts Claude's philosophy with other agents like Codex, AMP, and Cursor, offering practical takeaways for building your own AI agents.

Dec 26, 2025

Learning Python Programming • Fabrizio Romano & Naomi Ceder

Fabrizio Romano, author of "Learning Python Programming," discusses the evolution of his book with Naomi Ceder. Key topics include the strategic shift from GUIs to CLIs, the evolving perspective on Python's type hinting, and the dual role of AI as a powerful tool and a potential threat to junior developer growth. Fabrizio emphasizes the importance of fundamental skills, mentorship, and the human element in the age of AI.

Dec 23, 2025

What are we scaling?

A critical analysis of AI progress, arguing that short AGI timelines are unlikely given the current reliance on pre-baking skills via reinforcement learning. The author contends that true AGI requires on-the-job, continual learning—a capability current models lack. The modest economic impact of AI is presented not as a diffusion lag but as direct evidence of this capability gap. The future of AI will be a gradual, competitive race to solve continual learning, not a sudden takeoff.

Dec 23, 2025

Continual System Prompt Learning for Code Agents – Aparna Dhinakaran, Arize

The talk by Aparna Dhinakaran introduces "system prompt learning" as an efficient alternative to traditional Reinforcement Learning for improving large language model-based coding agents. By leveraging LLM-as-a-judge evaluations to generate English feedback and explanations for code failures, agents can automatically refine their system prompts and rules. This method, demonstrated on Claude and Klein, significantly boosts performance on benchmarks like SWEBench with minimal data, highlighting the critical role of high-quality evaluation prompts.