Aniket Deshpande

𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝗹𝗲𝘀𝘀𝗼𝗻 𝗰𝗼𝘀𝘁 𝗼𝘂𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 $𝟲𝟬𝟬 𝗮𝗻𝗱 𝗮 𝗺𝗮𝗿𝗿𝗶𝗮𝗴𝗲 𝗮𝗿𝗴𝘂𝗺𝗲𝗻𝘁. Our VP of Engineering, Xiaofan(James) Luan, was supposed to buy his wife a Dior bag for their anniversary.  Instead, he bought three Claude Code subscriptions and spent the holiday trying to cross-compile 2 million lines of C++. Every fix on one platform broke two others.  $600 later, the only output was "git reset --hard" — and a very cold dinner table.😂 "Make it compile on Windows" is a trap. The real goal was "compile everywhere without hacks" — no AI is going to figure that out for you at 2 am. What worked: constraints before code, review tests not code, bottom-up, one layer at a time. Same task, two days. Then he ran six parallel Claude sessions across three machines with git worktree. The bottleneck stopped being intelligence and started being how fast one person can alt-tab. AI solves exactly the problem you give it. Engineering is in knowing which one to give. His wife is still waiting for that bag. Full story: https://lnkd.in/gtsW_Wvk ——— Follow Milvus, created by Zilliz, for everything related to unstructured data

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𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝗹𝗲𝘀𝘀𝗼𝗻 𝗰𝗼𝘀𝘁 𝗼𝘂𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 $𝟲𝟬𝟬 𝗮𝗻𝗱 𝗮 𝗺𝗮𝗿𝗿𝗶𝗮𝗴𝗲 𝗮𝗿𝗴𝘂𝗺𝗲𝗻𝘁. Our VP of Engineering, Xiaofan(James) Luan, was supposed to buy his wife a Dior bag for their anniversary.  Instead, he bought three Claude Code subscriptions and spent the holiday trying to cross-compile 2 million lines of C++. Every fix on one platform broke two others.  $600 later, the only output was "git reset --hard" — and a very cold dinner table.😂 "Make it compile on Windows" is a trap. The real goal was "compile everywhere without hacks" — no AI is going to figure that out for you at 2 am. What worked: constraints before code, review tests not code, bottom-up, one layer at a time. Same task, two days. Then he ran six parallel Claude sessions across three machines with git worktree. The bottleneck stopped being intelligence and started being how fast one person can alt-tab. AI solves exactly the problem you give it. Engineering is in knowing which one to give. His wife is still waiting for that bag. Full story: https://lnkd.in/gtsW_Wvk ——— Follow Milvus, created by Zilliz, for everything related to unstructured data

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If you work in tech, this week’s layoffs might have hit you or someone close. Entire teams across startups and big companies were cut, from engineers at Intel to product and voice teams at Google. Even AI-first companies like Scale AI are shrinking operations and restructuring fast. But when you look at who’s being let go, a pattern starts to emerge. The roles affected are often tied to legacy systems, manual workflows, and older product lines. Hardware teams, generalist PMs, and support ops are being replaced or reorganized. At the same time, hiring is picking up around model infrastructure, agent teams, and internal tooling that can scale with fewer people. Founders and CEOs are rethinking how their companies are built. This shift is not just short-term pressure. It reflects a change in how businesses are choosing to grow and who they are planning to grow with. What are you noticing? Follow Early Adopters for more content on founders, startups and business leaders. We will talk about which roles are rising in a second next video.

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Columbus, Ohio has 13 percent growth in company formation YoY. We also just passed Chicago as the 5th largest concentration of data centers worldwide. Construction is accelerating. #ohiotech -

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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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The first SDE-2 absolutely nailed the system design round. The question was: Design a live streaming system. The second SDE-2 was asked to Design Google Drive. He did well, spoke about trade-offs, but missed one important detail: chunking the file, especially for large uploads. Yet, the second engineer got the job. These are from real interviews I took around 4 months ago. You might ask: Why? Only one reason, the second one nailed the behavioral questions like a boss. The question was about working on a project with tight timelines. The first SDE-2 (subtly) blamed the external team’s API for delays, which led to tight project deadlines. The second one, on the other hand, shared a story about how he helped his manager prioritize requirements to navigate the crunch. That’s how important behavioral interviews are. Yet, 90% of SDEs leave them to luck. If everything else is equal: - DSA (both solved the coding problems), - LLD (designed with solid object-oriented principles), - HLD (covered all the P0 functional and non-functional requirements) then your behavioral answers become the deciding factor. Yes, the second engineer missed the file chunking detail. But it wasn’t a critical red flag. He covered all other parts well and crushed the behavioral round. As long as you're working with humans (no matter how many), you’ll be judged on your behavioral skills. Call them soft skills, leadership skills, whatever you like, but you should prepare them well in advance. You can start preparing for behavioural interviews right away, with these 5 MUST read behavioural interview posts I wrote. (tip: save this post for later, as it may take some time to complete all of them) [1] How to answer any behavioural interview question https://lnkd.in/gH3N8b5q [2] 4 step guide for behavioural interview preparation in 3 hours https://lnkd.in/gccxG3MP [3] How to create 10+ stories for your past projects https://lnkd.in/g4nv_jqU [4] How to answer a failure story? https://lnkd.in/gtN-Dfuv [5] 10 behavioural interview mistakes to avoid at all cost https://lnkd.in/gpupvYVs If you’re preparing for mid-senior SDE or EM roles and need dedicated help with behavioral interview prep, DM me COACH.

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“Before, sellers would take 28 days to fully onboard. With Catalog Transformer and Anyscale, we reduced that to a few hours in most cases.” — Arthur Delaitre, Manager of Data Science, Mirakl Mirakl scaled LLM-powered catalog onboarding with Ray on Anyscale, delivering 3× lower inference cost, 20x throughput, and reliable peak handling. See the blueprint: architecture, scaling patterns, and cost wins. https://lnkd.in/gzFaitj4

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Joakim Recht (Uber Distinguished Eng) on tech leads: "Software engineers need to write code. If you're not writing code, you're not a software engineer." Joakim Recht grew from a Senior Engineer to a Distinguished Engineer at Uber and I asked him what it took to get there. We covered: • The project that grew his career • What makes a great software engineer • Learnings from promo committees • When promos are unfair • Biggest technical mistake at Uber in hindsight • Advice for his younger self 𝗣𝗼𝗱𝗰𝗮𝘀𝘁 𝗹𝗶𝗻𝗸𝘀: • YouTube: https://lnkd.in/gTvsB3Ar • Transcript: https://lnkd.in/gG7bmeU2 • Spotify: https://lnkd.in/g4n-fUxe • Apple: https://lnkd.in/gm8r-2_R

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Anysphere, a three-year-old San Francisco startup that makes the AI-powered coding assistant Cursor, raised $900 million at a $9.9 billion valuation, reports Bloomberg, confirming a scoop by the Financial Times last month. The deal was led by Thrive Capital, with Andreessen Horowitz, Accel, and DST Global also opting in. https://lnkd.in/gtYGEPFu

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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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Anysphere, maker of AI coding assistant Cursor, has reportedly raised $900M at a $9B valuation, led by Thrive Capital, with participation from a16z and Accel, according to the Financial Times. Previously, Cursor raised $100M at a $2.6B valuation. The startup has now raised over $173M. Investors like Index Ventures and Benchmark are eager to join, but existing backers are doubling down. Rival AI coding startup Windsurf is also seeking funding at a $3B valuation. #AI #CursorAI #Anysphere #AIStartups #TechFunding #DealonAI

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** How I use multiple LLMs for interview prep — as a system, not a shortcut As interviews become more senior and nuanced, relying on a single AI tool stopped making sense. Instead, I use different LLMs for what they do best—almost like a comparative matrix. 1. Gemini (Live + Deep Research): real-time mock interviews, STAR story stress tests, company and strategy decoding 2. NotebookLM: personal knowledge base—story banks, interview debriefs, pattern detection across feedback 3. ChatGPT: structuring responses, sharpening executive framing, testing logic and trade-offs 4. Claude: refining narrative clarity, tone, and leadership presence in written and spoken answers Why this works: Each model plays a distinct role—interviewer, editor, strategist, or mirror. Together, they surface blind spots, tighten signal, and turn preparation into a continuous improvement loop. AI doesn’t replace preparation. It raises the floor and sharpens the ceiling—if used intentionally. For anyone preparing for senior TPM, Program Ops, or CoS-style roles, this multi-LLM approach has quietly worked in my favor. #lifeofTPM #AILearning #LLMs

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IF YOU muted out AI LAY-OFFS look at the AI CODING OUTPUT Many engineers close their eyes/ears on news about AI's impact on job market And, yes, it might be hard to disentangle from over-hiring (in COVID) before our simply bad managerial decisions leading to companies ending up in corner and needing pruning of their organization. ⚡ But what is undeniably visible and we can all easily agree on is that we DID NOT GET +50% engineer count in last year. There is no way you can 1.5x the SW engineering in 12M. Yet, ... ... visible outputs of the SEW engineering work got 1.3 - 1.5x in last year. You can look at new Apps being launched, Code repositories growth, New websites, all around. Everywhere we see Cambrian explosion of the IT output. 👀 So let's get back to original question: If we did not get 1.5x more FTEs working in IT development (we suspect we most likely shrank it a little) and we got 1.5x of output, how does that square with AI not having real impact on engineering work yet? 2️⃣✖️ These charts are mapping just crossing from 2025 to 2026. The end of 2026 will be much more dramatic. Because when IT outputs boost beyond 1.8x or 2.0x we will get to point that Ai will be NOT ABLE but ALREADY INDEED PRODUCING equivalent of all existing engineering effort. And then some things will get nasty in some places. 🍥 Not all companies will go for greedy variant. As Jensen Huang said, those having some master dreams or ideas will try to use this production boost to create more. The SW development "creme-de-la-creme". But then there will be companies (loads of them) who did not know what exactly to build (if anything) first place. And these ... 🎛️ ... will simply turn the dial down on human costs. Because if you can use AI to build more for same, you are left with building same for (significantly) less. ⁉️ And, yes, there will be companies, who will (be not able to) deploy AI on grand scale and will use human coding to keep delivering same with same. But in era where there are other doing much more or yet another doing same cheaper, what is your "life expectancy" of company that kept doing same for same? And would you like to still work for such a company?

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When Steve Huynh was a principal engineer at Amazon, meetings began with a "study hall." Amazon had a "reading culture" even among engineers, Huynh recently told the Pragmatic Engineer podcast, speaking of his time at the tech giant. Employees frequently drafted six-page memos, which they shared with the company to update progress and demonstrate new projects. Huynh, who said the company's embrace of writing and reading the memos was part of its "secret sauce," said employees' writing was often constrained to the format, whether it was a business strategy or press release. Huynh started at Amazon in 2006, only a few years after the company turned its first profit and while Jeff Bezos was at the helm. Read more about how Bezos instilled this culture of memo-writing from the top down on Business Insider: https://lnkd.in/gBqbEf_j (Credit: Daniel Berman) #amazon #reading #jeffbezos

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After promoting engineers at #Amazon, here’s exactly how promo panels decide Senior Engineer (SDE3) Most people think promotions are about working harder or writing better code. They’re not. Every promo panel is asking one question: Are you already operating at the next level? 1. Impact is the first and most important signal. What did you deliver, what changed because of it, and why did it matter to the business? If the impact isn’t clear or measurable, the rest of the case doesn’t hold. 2. Technical strength at this level is about judgement, not just execution. The question is not how much code you wrote, but what decisions you made. Did you simplify systems, improve performance, or help others move faster? Strong judgement is what sets senior engineers apart. 3. Scope expands significantly at this level. Work that stays within a single team is not enough. You’re expected to operate across teams, handle ambiguity, and drive alignment even when you don’t have direct control. The surface area of your impact needs to grow. 4. Leverage through others is a key differentiator. Senior engineers don’t just deliver individually — they make the team more effective. What did you choose to own, what did you delegate, and how did your involvement increase overall velocity? If everything depends on you, you’re not there yet. 5. Peer validation is often the deciding factor. What do other senior engineers say about working with you? Do they trust your judgement and treat you like an equal? Promotions don’t create that signal — they confirm #CareerGrowth #BigTech #SoftwareEngineering #EngineeringLeadership #TechCareers

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