AI Engineer

Cooking with Agents in VS Code — Liam Hampton, Microsoft

Scaling Agents on Kubernetes with acpx and ACP — Onur Solmaz, OpenClaw

Your Coding Agent Should Do AI System Engineering — Ben Burtenshaw, Hugging Face

Any-to-Any: Building Native Multimodal Agents - Patrick Löber, Google DeepMind

Skill issue: Lessons from skilling up coding agents to use Langfuse - Marc Klingen, Clickhouse

Coding agents require specialized "skills"—formalized, reliable shortcuts—to navigate complex tasks like implementing observability and evaluation without relying on outdated pre-training data. By integrating real-time tracing and search endpoints, developers ensure agents access current documentation and provide conte...

From 46% to 90%: Fine-Tuning Tiny LLMs for On-Device Agents — Cormac Brick, Google

What Breaks When You Build AI Under Sovereignty Constraints - Bilge Yücel, deepset GmbH

Don't Build Slop (4 Levels of AI Agent Maturity) - Ara Khan, Cline

Personalization in the Era of LLMs - Shivam Verma, Spotify

Spotify is evolving its recommendation architecture by shifting from traditional, siloed machine learning pipelines toward unified, transformer-based foundational models. This transition centers on three pillars: foundational user modeling, catalog understanding, and durable personalization. By utilizing semantic IDs, ...

Rewiring the State — Eoin Mulgrew, No. 10 (Downing Street)

Let's go Bananas with GenMedia — Guillaume Vernade, Google DeepMind

Anthropic Workshop: Build Agents That Run for Hours — Ash Prabaker & Andrew Wilson

Building autonomous agents capable of running for extended periods requires moving beyond simple, single-shot execution toward sophisticated scaffolding harnesses. Anthropic’s Applied AI team highlights the transition from basic context-window management to complex, multi-agent architectures that utilize separate roles...

Harnesses in AI: A Deep Dive — Tejas Kumar, IBM

AI harnesses serve as essential infrastructure for grounding non-deterministic, black-box AI models in stable, controllable environments. By implementing a harness, developers enforce reliability through structural components like tool registries, guardrails for step limits, and automated verification loops. Rather tha...

Fighting AI with AI — Lawrence Jones, Incident

Why Your AI UX Is Broken (and It's Not the Model's Fault) — Mike Christensen, Ably

AIE Singapore Day 2 ft. Google DeepMind, OpenClaw, Adaption, Arize, Cloudflare, Robot Company & more

AI agents are transitioning from experimental prototypes to reliable, production-grade systems, necessitating a shift toward robust "harnesses" that enforce planning, context management, and deterministic execution boundaries. As agents take on autonomous, long-horizon tasks, they require a "company brain"—a unified so...

Beyond Code Coverage: Functionality Testing with Playwright MCP — Marlene Mhangami, Microsoft

How to Leverage Domain Expertise — Chris Lovejoy, Notius Labs

Connecting the Dots with Context Graphs — Stephen Chin, Neo4j

AIE Singapore Day 1 ft. Minister, NanoClaw, OpenAI, Google, Vercel, Cursor & more

AI engineering is undergoing a fundamental shift from simple language-based assistance to autonomous, agentic workflows that operate across the entire software development lifecycle. Systems like NanoClaw and various coding agents leverage "vibe coding" to automate complex tasks, yet they necessitate rigorous security ...