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An aspiring AI engineer seeks advice from senior professionals on which portfolio project best demonstrates production skills, proposing ideas like LLM routing services, agent systems with guardrails, and local inference microservices, while asking for feedback on common gaps in portfolios.
Stanford has released a free 67-minute course on building AI agents from scratch, covering both coding and no-code methods.
This article elaborates on the key concepts in AI engineering, emphasizing that prompts are the core of engineering aspects such as context, tool integration, loops, evaluation, and graphs, and explains the application of these terms in AI Agent development.
A tweet from @mattpocockuk encourages sharing completion certificates for a course, with a reply from someone seeking to hire course graduates for AI engineering roles at the University of Illinois Chicago.
The team at g2i_ai is organizing the AI Engineer Code Summit in San Francisco this November, bringing together experts in AI-powered software development.
The author shares insights from building an agent harness for GPT-3.5 Turbo, emphasizing that code-based verification and guardrails are crucial for reliable AI agent performance.
This article organizes 13 attention mechanisms in AI by the bottleneck they solve, covering KV cache reduction, attention patterns, compute efficiency, and serving efficiency to help AI engineers understand and apply these techniques.
The tweet celebrates the success of 'The LLM Engineer’s Handbook' as the best-selling book from Packt Publishing in 2025 and teases an upcoming second book.
Kent C. Dodds announces his attendance at the AI Coding Summit in Singapore, detailing the event's focus on AI-transformed software development with talks, workshops, and speakers.
Φ-Bench is a benchmark designed to evaluate large language models on their ability to engineer and optimize the LLM infrastructure stack, covering tasks from kernel optimization to end-to-end system design.
The article explains why AI demos are not suitable for production use and outlines key engineering practices needed to build reliable AI systems.
The author encourages followers to comment "CONCEPT" for a private roadmap emphasizing that core AI concepts remain stable despite frequent tool updates.
The post inquires about current workflows for vibecoding and AI engineering, inviting comparison of tools like Codex, Claude Code, and n8n to optimize the development process from idea to software.
The author shares a postmortem on building a production phone-based AI voice agent, revealing that most engineering time was consumed by telephony infrastructure, turn detection, observability, and failure handling rather than core LLM behavior. They suggest using managed platforms like Vapi, Retell, or Dasha from the start to focus engineering effort on business logic.
Issue 306 of the deararchitects newsletter curates recent tech reads including the 'Agentic Enterprise' book, context engineering for production-grade AI, Spotify Engineering's Claude Code token optimization, and Netflix's ArchUnit scaling with Nebula ArchRules.
The author argues that learning robotics is more advantageous than AI engineering in the next 12-24 months due to market saturation in AI, with robotics offering unique opportunities for building a competitive moat.
Andrew Ng has shared an AI Engineering Skills map for software engineering fundamentals in agentic coding, highlighting that while agents can generate code, developers must manage critical decisions like data, architecture, security, and production.
An Anthropic engineer explains that instead of writing prompts, they build loops, and demonstrates how the Claude team creates self-prompting loops in a free 42-minute video.
Benchmarking four AI-agent architectures showed that verification-based design achieved 100% success, highlighting that architectural choices matter more than raw step budgets for performance.
This article discusses the relationship between Graph Engineers and Loop Engineers, emphasizing that a Loop is the smallest Graph, and references research from 'Nature Machine Intelligence' to analyze the applicable scenarios of multi-agent systems.