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All digests tagged AI Engineering
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AI agents are solving the coding problem, but verification is the new bottleneck. Learn how Anthropic scaled to 30,000 merged changes daily and why multi-layered, zero-trust verification is mandatory for modern software factories.
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Optiver details how its on-prem AI platform handles the immediate, high-stakes P&L feedback loop of trading. Learn about the Inference Gateway, Meta Harness, and Agent Swarm architecture.
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Learn how to build any native Mac App using BAMA, a free set of Claude Code skills. This process guides AI agents through planning, design, and shipping, creating optimized Swift/SwiftUI applications.
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Building an AI code review agent requires more than a simple sandbox setup. Key fixes include model inversion, deep codebase context, autonomous testing, and cost-efficient model routing.
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The AI Engineer role is shifting from writing features to building self-improving loops. This requires building always-on agents that consume telemetry, run in durable sandboxes, and are continuously tested using advanced evaluation systems.
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AI models are not the bottleneck for developer throughput. Analysis of 50,000 engineers shows that individual skill, team environment, and company culture are far more critical drivers than the latest AI model.
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Moshi is an AI recommendation engine for commodity traders that processes structured, unstructured, and private data into a connected N-dimensional graph, moving beyond traditional dashboards to predictive workflows.
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Tenex details how AI is shifting engineering from time-based labor to outcome-based work. They discuss building 'AI-native' processes and the critical challenge of agent deployment in large enterprises.
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AI is drastically changing software development. The focus is shifting from writing code to mastering system architecture, process governance, and the role of the forward-deployed engineer.
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Learn about AutoResearch: an agent-driven loop that automates code proposals and evaluations to optimize ML models. We demonstrate Mini Parameter Golf, a scaled-down challenge for inspecting complex optimization processes.
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Learn the optimal freelance model: combining long-term contracts, project work, and maintenance retainers. Focus on AI automation for SMBs and building proprietary IP.
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AI is making PRs bigger and more numerous, turning CI/Code Review into a bottleneck. Discover agentic validation: an automated system that reviews, autofixes, and auto-merges PRs, keeping your development velocity high.
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AI struggles with production because it spans code, infrastructure, and telemetry. To fix this, a robust agentic harness requires six pillars: Model Orchestration, Causal Reasoning, Context Engineering, and more.
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Bogdan Gaza details the engineering lessons from generating 12 trillion synthetic tokens. He covers scaling from Slurm to a unified Ray/KubeRay/vLLM pipeline on EKS, solving bottlenecks like S3 metadata fetching and cross-cluster scheduling.
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Charlie Holtz shares principles for fast building, advocating for 'orchestras, not factories.' Key strategies include staying near the frontier, creating slop-free zones, and giving agents free-range cloud sandboxes.
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The talk explores AI's role as a GPT, detailing the shift from simple prompting to complex 'software factories.' Key focus areas include agentic safety, data sovereignty, and advanced orchestration using sandboxes.
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The talk details 'harness engineering,' arguing that advanced AI skills must move beyond simple prompting. Techniques include using blind sub-agents, anti-attractors, and hooks to build robust, deterministic, and multi-model compatible systems.
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To become an AI Engineer, focus on a three-tiered skill stack: Python, APIs, RAG, Agents, and deployment tools like Kubernetes. The role is building systems around existing LLMs.
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Master multi-agent coding with Herder. Learn how to set up persistent, customizable workspaces, orchestrate AI agents (Grock, Codeex, Claude), and integrate tools like zoxide and Lazygit into a single, powerful CLI environment.
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AI code generation creates a bottleneck in traditional review processes. The talk explores how rapid agent output strains human maintainers, arguing that review is about aligning mental models, not just quality control.
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