标签：#gui

The agent harness I work on has an Electron GUI that shares a renderer with a web shell. Last night it broke twice in one evening. The second break was caused by the first fix. Both were silent. The first one I could explain. The second one was the interesting one, because it exposed something the first fix's test suite could not see — and the fix was a guard that checks reality instead of checking the guard's own arithmetic. Failure one: the light-theme regression. The React shell used CSS custom properties for theming, but a chunk of the migration hardcoded dark-palette hexes directly in component CSS. In light mode the UI looked wrong: dark text on light cards, bad contrast, the exact shape of a half-finished theme refactor. The fix was to route everything through theme variables (the release shipped that as v0.2.84). Straightforward. Failure two: the fix had a hole, and the hole was invisible. After the theme-variable fix landed, a second round of breakage showed up: the task-form background rendered transparent, file-tab hover was dead, badge font sizes and radii were wrong. Nothing threw. No console error, no crash, no failing test. The cause: the fix consumed four variables — --fs-small , --radius-sm , --bg-1 , --bg-hover — that did not exist in tokens.css . A bare var(--x) with no fallback is not an error. At computed-value time the declaration becomes invalid at computed-value time , and the property is treated as if it were never specified. The element just falls back to the default — transparent background, no hover style, default font metrics. The failure mode of an undefined CSS variable is silence. This is the part I want to keep: the bug was not a wrong value. It was a value that was never there, consumed as if it were. The tests passed because the tests asserted behavior, and the behavior was "whatever the browser does with an invalid declaration". The guard that checks definedness. The fix was a guard, not just a value: a static test that walks ever

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What tok/s really measures, how concurrency changes it, and why a single-user benchmark is not the whole story for local LLM performance. A Few Moments Later… How Fast Is "Fast"? Every interface in the world of local AI eventually shows you that dreaded spinner, and on the wrong setup it sits there long enough that your brain supplies the meme: "A few moments later…" That pause is a number wearing a disguise. Somewhere inside your machine, the model is grinding out tokens — fragments of words — and the only question that matters is how many of them it produces per second. Tokens per second (tok/s) is the universal speedometer of local LLMs, quoted in every benchmark and every GPU review. But it is also one of the most misleading numbers in the field, because the same model can measure 45 tok/s or 793 tok/s depending on how you test it. This guide explains what the number actually means, why it moves so dramatically, and how to read a benchmark without fooling yourself. What a Token Actually Is Before speed makes sense, the unit has to. Models do not read words; they read tokens, which are chunks of text roughly three-quarters of a character on average in English. The word "calculator" might be one token or three, depending on the tokenizer, and this is not idle trivia — it is the reason the same prompt can cost a different amount across providers, as the Token Counter Calculator shows in practice. Because tokens are the unit of both billing and speed, "tokens per second" is the single number that connects all three corners of the local AI decision: how fast the model answers (tok/s), how big the model is (parameters), and what it costs to run (hardware amortized over time). A model doing 50 tok/s reads roughly 100-150 words per second — comfortably faster than you can read. A model stuck at 5 tok/s feels like a slow internet connection in 1998. The Single-User Number Is Not the Whole Story Here is the trap: most consumer benchmarks report tok/s at one user, one requ

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