yesterday, i merged 109 code changes, or pull requests (prs), at metaview. it was a heads-down day, broken up by lunch, a bike ride and a few strong cups of tea. however, when the 100th pr went in at about 6pm, i paused a little bit and asked myself, ‘what is my job?’ when i started coding professionally, i'd have been stretched to do a pr a day. and although prs are an awful measure of productivity (almost up there with counting the emails someone sends), going from one a day to 109 in a day s engineering

My first 100 PR day

at metaview, we build ai agents for recruiting: sourcing, application review, screening, reports and more. they all run on one shared harness. it didn't start that way. our earliest agent ran on aws step functions, and after that each one was built differently: one agent on langgraph, another on the openai responses api, others on hand-rolled loops. every new product meant solving the same problems again: sandboxes, sessions, streaming, observability, model access. that was partly deliberate: t engineering

How we built one harness for every Metaview agent

how we used cursor + claude opus 4.5 with a deterministic benchmark harness and a shared markdown file to cut p95 react frame time by ~76%. engineering

Improving React page performance with AI agents.

at metaview, we build ai agents for recruiting. our stack runs on aws lambdas for most backend tasks, langsmith for agentic workflows, postgres on aws, datadog for logs and rum, and slack for error notifications - both automated alerts and issues flagged by our customer success team. like most teams running ai agents in production, we quickly discovered that observability is hard. when something breaks in a traditional web service, you look at the logs, find the error, and fix it. when somethin engineering

How we cut engineering triage time by 80% with AI.

the five operating principles that decide how metaview ships, supports, and hires. own end-to-end. radical transparency. work backwards from the customer. optimize for rate of learning. drive to clarity. recruiting engineering

Metaview's operating principles.

in this post, we share the creation process of metaview wrapped, as an example of how metaview experiments with ideas, and how we ‘do things that don’t scale’. at metaview, we help companies run amazing interviews. we achieve this in a number of ways including training interviewers with automated interview shadowing and coaching them with personalized, contextual feedback. to do that, we are building a saas product, at the cutting edge of conversational ai. the product operations team at metav engineering

Metaview Wrapped.

how metaview gets the structural simplicity of a monolith and the scaling independence of micro-services with a serverless architecture. engineering

The Serverless Monolith.

how we built 60fps transcript-audio sync in our react web app in april 2022, using refs and direct dom mutation to bypass react's render cycle. engineering

Engineering

My first 100 PR day.

How we built one harness for every Metaview agent.

Improving React page performance with AI agents.

How we cut engineering triage time by 80% with AI.

Metaview's operating principles.

Metaview Wrapped.

The Serverless Monolith.

Syncing a transcript with audio in React.

Explore more

Get our latest updates. Subscribe to our newsletter.