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A deterministic action-level attestation architecture for AI mediation was developed and validated in discussions with Microsoft's engineering team. The author seeks investors or partners for the software architecture.
The author introduces 'KnowledgeOS', a prototype control plane designed to govern local coding agents by managing task lifecycles, preventing state drift, and ensuring execution evidence. They are seeking architectural critique on whether this OS-like abstraction is necessary or if it constitutes over-engineering for agent workflows.
A software engineer shares insights on learning software architecture, emphasizing the primacy of social and incentive structures over code, with examples from rust-analyzer and scientific code.
This analysis challenges the reflexive insertion of AI into all enterprise workflows, suggesting that deterministic systems often require traditional software rather than probabilistic models. It argues for a strategic approach to distinguish where AI creates leverage versus where established architectures remain superior.
The author reflects on rebuilding a Kubernetes dashboard tool, arguing that while 'vibe-coding' with AI accelerates feature development, it often leads to architectural bloat and technical debt without human oversight.
A technical guide and reference implementation for building autonomous 'Hermes' agents using an Auto-think and Auto-build architecture to research, plan, code, and verify tasks without human intervention.
A new book by Gigi Sayfan guides readers on building multi-agent AI systems from scratch using Python, MCP, and A2A protocols, focusing on custom orchestration rather than third-party frameworks.
A curated GitHub repository listing over 300 engineering articles from major tech companies, covering topics like AI, data engineering, and system scalability.
This article summarizes Karpathy’s core points at the Sequoia Ascent conference, highlighting that AI is a paradigm shift restructuring workflows rather than merely an acceleration tool. It introduces the concept of a "jagged edge" for model capabilities based on verifiability and economic viability, and predicts that future software will evolve into an agent-native architecture where LLMs serve as the logic layer and traditional code functions as sensors and actuators.
Martin Kleppmann discusses how the fundamentals of building large, distributed systems have evolved over the past decade in light of the updated second edition of his book "Designing Data-Intensive Applications."
Blog post argues that good software architecture should be self-evident and frictionless, advocating Netflix/Spotify-style “paved road” patterns over coercive governance boards or embedded architects.