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Articles by Riley Zhu
Pin the Fixture, Not the Vendor: A Take-Home for Swap-Safe Eval Runners
A second reviewer cannot trust a take-home that only passes on one laptop and one model string. The score that survives a handoff is the fixture hash, a shared decision schema, and invariants that do not name a vendor. This packet asks a candidate to build a small evaluation runner for a frozen support-triage task. The runner must pass locally with a stub, then accept an optional live endpoint without edits to the assertions.
Hidden Truncation Cases for a Structured-Output Hiring Packet
A structured-output hiring packet should pass only when a deterministic checker accepts valid tool calls and rejects truncated or over-broad payloads. Model confidence, a successful local demo, and a polished explanation are not evidence that the boundary holds under hidden cases. Graders freeze fixtures before any model runs, then execute those cases on a shared runtime that candidates cannot silently upgrade.
The Agent That Cached an Authorization Decision: A Take-Home Packet
A cache that stores an allow decision without a revocation epoch is not an optimization at all. It is a deferred authorization bypass that keeps serving files after membership has already been removed. Public tests that never revoke a grant stay green while the bypass remains completely invisible to CI. This take-home packet grades that distinction before a human reviewer has to reconstruct the incident. Hiring loops now see many diffs that look production-ready after only a short agent session.
A Minimal-Context Take-Home Test for AI Code Reviewers: When More History Hurts
Recent discussions about AI assistants that trust every archived comment raise a practical question for engineering teams: does an AI code reviewer become more accurate when given the full repository history, or does it just become more confidently wrong? Experience with review bot evaluations suggests that an excessive or stale context often produces worse feedback than a bare diff.
When Your AI Reviewer Remembers Too Much: A Two-Phase Memory Probe
Hello Glad to see you, I am Kane Lim from Hong Kong. I have over 10 years of development experience. I am writing this because your post was interesting. This is a particularly strong evaluation design because it treats persistent memory as an additional stateful failure surface rather than assuming every review is independent. I would push it further by making the probe temporally adversarial.
The MiniMax H3 Hype Arrived Before the Numbers. A Small Team Built a Gate Instead.
A new model called MiniMax H3 started moving through a small backend team's group chat on a Tuesday morning. One engineer pasted a screenshot with a long answer. Another pasted a leaderboard line with no link. A third wrote, 'we should switch.' Nobody pasted a failure. The team had been burned by this pattern before. A model looked good on a cherry-picked example and then failed inside a real pipeline. The cost of a bad switch was not only the API bill.
Every Week a New Model Drops. Here's the 30-Minute Eval I Run Before Believing the Hype
A new open model release trends, the benchmarks look incredible, and by the time I've read the third hot take I still have no idea whether it's actually good for my work. The recent chatter around MiniMax's H3 release is a perfect example: lots of excitement, lots of leaderboard screenshots, and very little about how it behaves on the boring tasks I actually do every day.
Judge AI Code Review With a Scoreboard, Not a Demo: A Repeatable Experiment That Runs on Free Access
There's a familiar ritual when a team considers an AI review assistant: someone opens the product, feeds it a file from the current sprint, watches it produce a confident-sounding paragraph, and reports back that it "seems pretty good." Two months later nobody can say whether the subscription is catching real bugs or just generating plausible noise. The failure isn't in the tool — it's in the test.
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