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Digests from AI Native Dev
Learn how to build a fully autonomous 'Software Factory' using agent loops and feedback loops. Discover how to achieve 75% PR merge rates without human intervention, while maintaining cost control and model agnosticism.
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AI attackers move at machine speed, finding 0-days by reversing open-source code. To defend, security must automate remediation, implement forensic-grade detection, and use deception (digital twins) to counter the threat.
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The panel discusses securing AI software factories, arguing that agents must be treated as unpredictable actors. Key controls include enforcing boundaries, never letting agents hold credentials, and securing the dynamic supply chain.
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Agentic coding is moving fast, creating massive efficiency but also unprecedented security risks. Experts discuss securing the 'software factory' and the need for new controls like identity and intent analysis.
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Learn how to build a 'software factory' using agent feedback loops. We cover inner, middle, and outer loops, and why clear intent is crucial for self-healing codebases.
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CTO Jacob Lauritzen details how Legora scales with AI agents, discussing custom orchestration tools, agentic bug-fix loops, and why measuring AI adoption by tokens is a flawed metric.
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AI agents are 'blind,' not stupid. The fix is context engineering at the data layer, requiring federation and logical views to unify data from sources like Kafka and Iceberg.
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Tessl Verifiers allow you to write rules for your repository in plain language and automatically check them on every code change. They enforce standards that traditional linters and tests cannot reliably cover.
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The ROI in an AI software factory comes from the system surrounding the agent—context, verification, and feedback loops—not the agent itself. Learn how to measure the full cost of a PR.
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Scaling development with AI requires more than just generating code. Learn how automated code review, Jev classifiers, and robust CI/CD guardrails are essential to manage the flood of AI-generated PRs and maintain system reliability.
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Learn how to manage the exploding costs of AI agents. Experts discuss cache-aware routing, open-weight models, and why only $18 of every $100 spent on AI actually ships to users.
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AI agents lose memory when sessions end. Learn how to build persistent knowledge repositories, implement a 'diary' for decision rationale, and use 'Super Agents' to extract maximum value from AI development.
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Learn how to modernize legacy codebases by treating them like functioning cities, not 'balls of mud.' Discover how to decouple AI workflows from single providers using specialized servers and parallel agent processing.
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The future of software development is moving from writing code to defining specs. The focus must shift to 'context engineering' and building robust 'software factories' to manage AI agents.
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AI agents are democratizing software creation, but failure can occur when product context is missing. The shift requires Product Managers to become 'agent orchestrators' to manage the new wave of non-developer builders.
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Hugging Face's MCP server reveals that out of 10M protocol messages, only 62K are useful tool calls. Learn why stateful protocols are chatty and how Web MCP shifts agent focus from pixels to capabilities.
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Four approaches to agent execution are compared: Docker's microVM sandboxes, Helix ML's centralized compute, Adobe's browser-native agents, and tldraw's canvas collaboration. Where should your AI agent run?
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Marc Brooker argues that the hardest part of software is specification, not code. He details how agentic AI is shifting the focus to rigorous testing, agentic policy, and learning from historical postmortems.
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Coding agents are great at generating code, but verification is the hard part. Learn how to use test oracles, anti test slop, and ADRs to ensure AI-generated code is reliable.
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