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Development

Featured in Development

Accelerating Performance by Incrementally Integrating Rust into Existing Codebase

Lily Mara explains how to avoid high-risk software rewrites through incremental FFI refactoring. She shares how engineering teams can replace Python bottlenecks with Rust via PyO3, demonstrating how to achieve dramatic function-level speedups, seamless integration testing, and meaningful infrastructure cost savings without microservice overhead.

All in development

Architecture & Design

Featured in Architecture & Design

Implementing Durable Workflows on Postgres Without an External Orchestrator

Postgres can serve as the durable state store and coordination layer for workflows, eliminating the need for an external orchestrator. SKIP LOCKED enables concurrent work processing, primary-key checkpoints enforce idempotency, and leases support crash recovery. Workflow sleeps and human approvals can also be persisted as database state and survive restarts.

All in architecture-design

AI Infrastructure

Featured in AI, ML & Data Engineering

Decision Models in Agentic Architectures: From Production to Agent Skills

Alex Porcelli discusses the critical gap in enterprise AI: non-deterministic output and lack of accountability in high-stakes decisions. He shares how integrating DMN decision models with LLMs, agent skills, and NeMo guardrails creates auditable, deterministic agentic architectures - allowing business leaders to own decision logic while engineers maintain robust architectural governance.

All in ai-ml-data-eng

Culture & Methods

Featured in Culture & Methods

Lead Without a Ladder: How I Climbed Into Engineering Leadership

Pauline Jepp explains how systems thinking, rock climbing, and flocking behaviors apply to engineering leadership. She shares strategies for balancing team autonomy with alignment, supporting invisible work like mentorship, and navigating transitions from hands-on engineer to engineering leader while maintaining organizational trust and psychological safety.

All in culture-methods

DevOps

Featured in DevOps

Platform Engineering in the Age of AI

The panelists explain how platform teams adapt to support AI-assisted engineering, highlighting which capabilities belong in the platform. They discuss trade-offs between standardization and developer autonomy, while sharing strategies to manage AI tooling, security guardrails, and shifting workflows.

All in devops

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AI, ML & Data Engineering

When Spec-Driven Development Pays off

AI coding assistants have become a core part of software development. AI-generated code has shown productivity gains, but it's also contributing to security weaknesses and familiar bug patterns. In this article, author Nitin Garg highlights the bottleneck has moved from code generation to code verification, and how to detect & mitigate it when the AI-generated behavior diverges from the intent.

Nitin Garg
on Sep 10, 2026
AI, ML & Data Engineering

CodeGuardian: a Model Context Protocol Server for AI-Assisted Code Quality Analysis and Security Scanning

CodeGuardian is an MCP server that extends AI coding assistants with comprehensive code quality and security analysis capabilities. By implementing eleven specialized tools, CodeGuardian enables developers to access enterprise-grade analysis directly through their AI assistant, eliminating context-switching and reducing friction in adopting secure coding practices.

Madhvesh Kumar Deepika Singh
on Apr 28, 2026
Development

Stateful Continuation for AI Agents: Why Transport Layers Now Matter

Agent workflows make transport a first-order concern. Multi-turn, tool-heavy loops amplify overhead that is negligible in single-turn LLM use. Stateful continuation cuts overhead dramatically. Caching context server-side can reduce client-sent data by 80%+ and improve execution time by 15–29% .

Anirudh Mendiratta
on Apr 08, 2026

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