Numbers are meaningless without trust.
Serving 150M reads/sec is significant only if stale values are below 0.1%.
This requires TTL expiry, CDC invalidations, and synchronous write-path invalidations working together.
Chunking FTW: Switching from linear jobs to chunk-based processing reduced latency and made failures manageable. If a shard fails, retry only the shard—not the entire job. It’s the difference between “restart” and “carry on.”
Hard lesson: a "release train" is a red flag. When teams deploy as one, debugging and rollbacks become political and slow. Split by service, deploy independently. Smaller changes mean smaller risks and faster feature delivery.
🗣🗣People called Bard ‘shit’ at launch, and Gemini barely got any attention.
Now Gemini is getting recognition wherever it can. 💯💥
Life lesson: it’s not about how you start, it’s about how you keep improving and executing over time. Crazy, man
It's funny how everyone says
‘just use GPUs’
but half the battle is picking the right framework.
• PyTorch feels natural, but bloats memory,
• TensorRT screams in prod but takes weeks to tune, and
• JAX makes you feel like a math PhD. Framework choice isn’t just…
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