Shuo LI

In this post, we demonstrate how to integrate Amazon SageMaker HyperPod with Anyscale platform to address critical infrastructure challenges in building and deploying large-scale AI models. The combined solution provides robust infrastructure for...

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If you're a Senior Engineer looking to move up, the next role will likely be a Staff Engineer. And in this guide, Shruti shares tips from her own experience of getting promoted to Staff Engineer at PayPal and Slack. She talks about what Staff Engineers do (and how it's different from Seniors), why you might not be getting promoted, and how to take that next step. https://lnkd.in/g58dnFEG

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I've been discussing this recently with Frances Buontempo in the context of coding assistants. We hear a lot from developers saying that LLM assistants in their fave IDEs are great for generating a lot of the (boring) boilerplate code needed to stand up a non-trivial system. However, most popular IDEs have been able to do exactly this for decades--whether with "wizards", code-block and project templates, or other various forms of meta-programming. The difference is that the "old" way is much less hungry of resources, while simultaneously being much more predictable and reliable at automatically generating the "boring" stuff. Coding assistants are also good at other things, of course, but many of the popular (in my conversations with developers) tasks are those I would much prefer to do myself, such as writing tests.

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You are in a system design interview at Google for the L5 Senior Engineer role, and the interviewer leans in and asks: “Why does Spotify keep playing when I drive into a tunnel with no signal, but YouTube Music often stops or buffers? If you were designing a music streaming system, what different design choices would lead to these two behaviors?” Here is how you break it down. Btw, if you’re preparing for system design/coding interviews, check out our mock interview tool. You can use it for free here: https://lnkd.in/gpCn7t2T We’ve added new features as well:  -Company-specific interviews -In-built interview scheduler  -performance insights & trends — Both apps look like simple music players. Under the hood, they are optimized for very different priorities. [1] Spotify style – Cache first, stream second Idea: The client behaves like a smart offline player that happens to stream. The backend is built to support aggressive prefetching. What happens when you hit play - Client requests the track from a CDN or edge node. - Instead of tiny chunks, it downloads a big buffer ahead of the playhead. - In parallel, it starts pre downloading the next few tracks in the queue. - Data is written to an encrypted cache on disk, not just memory. - Playback reads from that local cache, not directly from the network socket. What this means for tunnels and bad networks - When the network drops, the player already has tens of seconds or entire tracks cached. - Because the next one or two songs are already downloaded, you can be offline for a while and never notice. - Cold start cost is a bit higher. First play might take slightly longer, but then everything feels smooth. - It burns more local storage and possibly more data, because not every prefetched song will be listened to fully. In a design answer, you can mention - Local disk cache with eviction policies (LRU per user, per device). - Background prefetch of N upcoming tracks based on queue. - Download manager that adapts how aggressively it prefetches based on network quality and user settings. - CDN tuned for larger object delivery and range requests. - Explicit offline mode that pins playlists into cache. [2] YouTube Music style – Stream first, cache is minimal Idea: Treat audio like video streams. Cost and bandwidth are optimized first. When you hit play - Player requests audio (and sometimes video) via HLS or DASH-style chunks. - Each chunk is only a few seconds long. - Client keeps a small rolling buffer in memory, not a large queue on disk. - Prefetch of future songs is limited, because video tracks are large and expensive to fetch speculatively. What this means for tunnels and bad networks - If your connection dies, the player only has a few seconds of buffered data. - As soon as those chunks are consumed, playback stalls. - Startup can feel snappy and data usage is controlled, especially for casual listeners. - Works very well on stable networks, feels fragile in spotty coverage.

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Etched just raised ~$500M in a major funding round (valued at ~$5B) to accelerate its custom AI silicon — a solid alternative to Nvidia-centric stacks. https://lnkd.in/dttxqJPJ

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