AI Engineer

Claws Out: Securing and Building with OpenClaw - Nick Taylor, Pomerium

Stop AI Agent Hallucinations: 5 Techniques + Production Patterns - Elizabeth Fuentes, AWS

AI agent hallucinations and excessive token costs stem from inefficient context management and reliance on probabilistic prompt instructions. Implementing semantic tool selection significantly reduces token waste by filtering relevant tools per query. Transitioning from standard RAG to Graph RAG enables precise, verifi...

The Factory That Dreams: 39 AI Agents, No Framework - Rushabh Doshi, Machinecraft

Machine Craft, a manufacturing firm, successfully preserved decades of institutional knowledge by building "Ira," a decentralized AI agent system. Rather than relying on a single large model, the company deployed 36 specialized agents—including Athena for management and Plutus for pricing—to handle go-to-market operati...

Chat and citations won't save your vertical AI - Atul Ramachandran, Filed Inc

Vertical AI products often fail to deliver on the promise of saving time and money because they rely too heavily on synchronous chat and manual verification via citations. To truly automate complex workflows in sectors like healthcare, legal, and tax, developers must shift from designing for user participation to desig...

State of the Union: Why Local, Why Now — NVIDIA, Osmantic, Roboflow, EXO Labs, @matthew_berman

Every Solo Agent Builder Eventually Reinvents a Worse Version of CI/CD - Sumaiya Shrabony

Building autonomous agent systems requires implementing rigorous operational controls to prevent silent, high-quality failures. As systems scale, solo developers inevitably reinvent CI/CD infrastructure, including regression testing, contract validation, and audit trails. The primary danger lies not in obvious errors, ...

Develop at Idea Velocity - Jeffrey Lee-Chan, Snapchat

OpenCLAW streamlines software development by maintaining persistent context and memory across tasks, allowing agents to handle complex workflows without constant human intervention. This approach leverages multiple agents with work trees for parallelization, while a manager layer provides objective oversight to prevent...

From Writing Code to Designing Systems: How the Developer Role is Changing — Chris Noring, Microsoft

Modern software development has shifted from manual coding to a system-oriented approach where engineers act as architects and orchestrators of AI agents. To maintain control and avoid "AI slop," developers must implement robust guardrails, including AgentsMD files for high-level guidance, specialized skills for repeat...

Design Patterns for AI Trust: Juries, Libraries, and Agent Tiers — Alex Bauer, Upside.tech

Effective go-to-market strategy in the age of AI requires treating agents like human team members, prioritizing "commander's intent" to guide their objectives rather than micromanaging their processes. To prevent hallucinations and ensure reliability, organizations must establish a robust data foundation, utilizing str...

Understanding is the new bottleneck — Geoffrey Litt, Notion

Human understanding of code remains essential for creative participation and avoiding "cognitive debt," even as AI agents increasingly automate software development. Rather than acting merely as correctness verifiers, developers should leverage AI to deepen their conceptual grasp of systems. Three effective strategies ...

Should AI Engineers Still Read Code in 2026? The Z/L Continuum — Alex Volkov, ThursdAI

AI engineering faces a critical tension between the "code is free" philosophy and the necessity of manual code review. While AI-driven development has increased commit volume by 14x, it has simultaneously triggered a 242% rise in incidents per pull request, highlighting the risks of unverified agentic output. The optim...

The Golden Age of AI Engineering — Alexander Embiricos & Romain Huet & Peter Steinberger, OpenAI

AI engineering is undergoing a fundamental transformation, shifting from manual code generation to the orchestration of autonomous, long-running agentic workflows. Rather than replacing engineers, AI tools empower them to focus on high-level problem-solving, design, and strategic judgment. Modern development now center...

Everything we knew about software has changed — Theo Browne, @t3dotgg ​

Think You Can Build a Game with AI? Think Again! - Danielle An & David Hoe, Meta

AI-driven game creation is transforming the industry by removing traditional skill-based barriers, allowing non-coders and non-artists to build games through generative tools. While initial novelty fades, professional-grade results require aesthetic cohesion, effective playtesting, and a clear creative vision. Runtime ...

Your agent is blindfolded — Johan Lajili, Poolside AI

Building an ACP-Compatible Agent Live — Bennet Fenner, Zed

Teaching Coding Agents to do Spreadsheets - Nuno Campos, Witan Labs

Your coding agent doesn't always follow your rules — Talha Sheikh, Checkout.com

Reliability in AI coding agents depends less on raw model capability and more on the implementation of deterministic verification layers. While frontier models demonstrate impressive task completion, they frequently fail at specific requirements, necessitating an enforcement harness to validate outputs. The "Vector" pr...

Running a Chess YouTube Channel entirely by AI — Stephan Steinfurt, TNG

I Run a Fleet of AI Agents Across Three Machines. Here's What Broke. - Kyle Jaejun Lee, KRAFTON

Scaling AI coding agents requires moving beyond simple terminal interactions to a structured, hierarchical organization. Managing multiple agents manually creates a bottleneck where human attention becomes the limiting factor. By implementing a CEO-to-worker hierarchy, agents operate within scoped contexts and approval...