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Important ingredient for long-term productivity: having a reliable activity to clear your mind. For me it's running or weightlifting. For first half hour, I am still thinking about the problem of the day; by an hour I'm a blank slate. Hard problems somehow feel far simpler after.
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ML bugs are so much trickier than bugs in traditional software because rather than getting an error, you get degraded performance (and it's not obvious a priori what ideal performance is). So ML debugging works by continual sanity checking, e.g. comparing to various baselines.
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Biggest mental shift while switching from classic to scientific programming: explicit “for” loops are now extremely expensive. Instead, you express as much as you can by chaining hyperoptimized lower-level “for” loop primitives, eg matrix multiplies. Fun & new way of thinking.
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Worst part about using an internal-only library: can't search online for documentation/usage & usually not fully polished. Best part about using an internal-only library: someone at your company knows every corner of it & any change is on the table. Not an easy tradeoff.
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One very simple technique which helps me move faster: when printf debugging, print out full log messages rather than just bare quantities: e.g. print(f"Reached speed: {velocity=}") rather than print(velocity). More typing but saves puzzling out meaning of each line at runtime.
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For example, a few years ago we made the painful decision to shut down our robotics project. It was producing great results, but we'd discovered we could move even faster in data-rich virtual domains. Most of that team started working on code, which led to Codex & Copilot.
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The health of an organization’s vision is better measured by its ability to cancel the right projects than to start new ones. Saying yes to a new project is the easy part, what’s hard is making tough & visionary decisions in the service of focus. Superpower if you can do it.
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Security is the art of sufficiently raising the cost of attack such that you either (a) will yield a net negative return on investment to adversaries, or (b) are a much less attractive target than alternatives.
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Management technique we learned early on: short-term machine learning deadlines can be set based on inputs (e.g. high-quality execution on a set of experiments) but not outputs (e.g. reaching some level of performance). Science does not bend easily to the wishes of managers.https://twitter.com/hardmaru/status/1524184314304659456 …
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Well deserved. Congrats
@ilyasut!https://twitter.com/sama/status/1524045306207297537 …Thanks. Twitter will use this to make your timeline better. UndoUndo -
Trick to avoid all timezone bugs: represent times as Unix timestamps (i.e. a number) rather than your language's Time or Date object. Additionally makes time arithmetic much clearer, and is especially friendly when building an API where remote machines will parse the timestamp.
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Such an indescribable feeling when one variant is showing great improvement relative to the baseline... and then upon more careful inspection you realize you had a bug and both runs were actually identical except for different seeds.
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I fell in love with programming right before college. I remember surprise & delight when I realized that college was prepared to give me credits for getting better at my newfound hobby. Even today I am amazed that it’s possible to be employed to program. The joy has not faded.
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The file system makes for a great key-value store but a poor general database — writing and reading a key is very fast, but it's slow to list (with 100k+ entries), and many tools choke on giant directories. Still, sometimes can be a great decision to use it to back your app.
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Modern feature that has most changed my use of Python: dataclasses. Not just useful for data structures — they let you separate state from implementation of any class. Also dataclasses tend to be more useful by default, since they come with repr, eq, etc.
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First rule of system administration — a service tends towards one of three states: manually watched, monitored, or broken.
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A software skill people not often appreciated outside security: probing the emergent semantics of a complex system. First understood this at Stripe while integrating various financial backends. Turns out to be extremely useful for building systems for training neural networks.
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A small taste of the kinds of content that you will be able to produce personally in the not-too-distant future:https://twitter.com/phillip_isola/status/1519840707372060672 …
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Any technology comes with benefits and commensurate risks. AI is unique only in the unprecedented magnitude we expect from both. Navigating these risks & distributing these benefits is daunting a priori, but each day of progress & experience makes the puzzle a bit more clear.
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"A photo of an artificial neural network growing in a garden":pic.twitter.com/ueTsnY9OQR
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