John Yang Portfolio
PhD student at Stanford (work began at Princeton) and Research Scientist at Meta; created SWE-bench and SWE-agent, which established "agent-computer interfaces" — a structured set of tools (file viewer, search, edit commands) purpose-built for how a language-model agent, not a human, interacts with a codebase.
What makes it work
A breakdown of the choices that make this portfolio stand out.
A named interface layer designed specifically for agents, not humans
SWE-agent's "agent-computer interface" concept treats the tools an agent uses (not just its reasoning) as a first-class design problem, recognizing that interfaces built for human developers are often the wrong shape for a language model to use reliably.
A benchmark built to resist gaming, then hardened further
SWE-bench Verified and the multimodal/multilingual variants show iterating on a benchmark's rigor over time as agents got better at exploiting the original version's weaknesses, rather than treating the first release as final.
A tight suite of complementary tools around one research question
InterCode, SWE-bench, SWE-agent, and SWE-smith each address a different piece of the same problem — how coding agents should be evaluated and interfaced with — rather than being unrelated one-off projects.
What AI Agent Architects can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
Treat the interface between your agent and its environment as its own design problem, separate from the agent's reasoning — the wrong tool shape can break an otherwise good agent.
If you publish a benchmark, expect it to get gamed, and plan to release harder or corrected versions rather than treating v1 as permanent.
Build a small suite of complementary tools around one specific research question instead of one-off unrelated projects — it makes your body of work read as a coherent research program.
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