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After you get an agent working, you’ll want to get it to run cheaply and quickly.
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I’ve administered a lot of system design interviews over my career, and I’ve never had anyone use shuffle-sharding.
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Exponential backoff with jitter is an effective retry strategy that helps avoid thundering herds, but most implementations only start applying jitter after the initial request fails.
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There’s a subtlety in building AI agents that took me an embarrassingly long time to appreciate: your tool responses are prompts!
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When comparing REST and GraphQL, it’s not just about payload size, flexibility, or tooling.
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A few months ago Stripe announced their upcoming Order Intents API at Stripe Sessions.
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Networks are unreliable.
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I have some thoughts on the Google Cloud’s latest global outage!
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Webhooks are surprisingly hard to process correctly.
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Designing great resolver functions in GraphQL is quite unintuitive.
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I’ve written about TypeScript enums in the past, and back then I encouraged the use of enums with string values instead of numeric ones due to the improved type safety and debuggability you get.
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I typically describe my career as being the result of a lot of luck.
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I’ve been extremely fortunate over the course of my career, and I’ve been able to learn from some truly incredible people working in software.
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A common mistake in system design interviews is to underestimate a low but steady write rate.
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Autofilling form fields has become an increasingly common and practical application of generative AI.
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One of the powerful design patterns that can be employed within a GraphQL schema is the concept of collection lookups.