Haoyang Li

🚀 NVIDIA GPUs are the powerhouse behind AI breakthroughs from ChatGPT to autonomous vehicles driving faster, smarter, and scalable AI solutions worldwide. 🌐 #RecruitRidersTechnologies #AI #TechNews #Innovation #FutureIsNow #BrandChampions #WeAreRecruitRiders #SocialStars #EmployeeInfluencers

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🚨 New on the #NVIDIA #AI Podcast: 🏙️ San Jose Mayor Matt Mahan joins NVIDIA’s Jumbi Edulbehram to discuss how AI is transforming city services, from optimizing traffic to translating public meetings in real time. 🎧 Listen here: https://apple.co/3LGVoPQ #DeepLearning

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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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Introducing the M5 The M5 chip brings significant advancements in performance and efficiency across various applications. 1. Massive GPU / AI performance boost The M5 features a next-generation 10-core GPU architecture, with each core equipped with an integrated Neural Accelerator. This results in over 4× the peak GPU compute performance for AI tasks compared to the M4. 2. Stronger CPU and enhanced Neural Engine With up to a 10-core CPU (6 efficiency cores + 4 performance cores), the M5 offers approximately 15% faster multithreaded performance over the M4. Additionally, the Neural Engine has been upgraded to 16 cores, enhancing performance for on-device AI and intelligent features. 3. Higher memory bandwidth / unified memory improvements The M5 provides around 153 GB/s of unified memory bandwidth, which is approximately 30% more than the M4. This unified memory architecture allows all components (CPU, GPU, Neural Engine) to access a larger memory pool, enabling larger AI models to run entirely on-device. 4. Graphics & rendering enhancements The improvements in the GPU also boost graphics performance, including ray tracing capabilities. Apple claims up to a ~45% uplift compared to the M4 in ray-traced workloads, enhancing visual fidelity, rendering speed, and smoothness in demanding 3D and graphics applications. 5. Deployment in new hardware + energy efficiency / environmental goals The M5 will power new devices, including the 14-inch MacBook Pro, iPad Pro, and upgraded Vision Pro, all of which are available for pre-order.

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🦺 🛟 Agent Safety > Model Safety LLMs didn't break your app. Agents can. As we wire LLMs to tools, browsers, payments & code the risk shifts from bad text to bad actions. Top failure modes (seen in the wild & in papers): 🚩 Prompt/Content Injection -> Tool Misuse. Malicious web pages or data instruct agents to steal secrets or make unintended API calls. This is now a primary risk for browsing and MCP - style tool agents. 🚩 Over permissioned tools & long-lived tokens. Agents get "god mode" scopes. A single injection becomes account takeover or irreversible ops. Security notes for MCP emphasise least privilege and short-lived auth. 🚩 Unsafe web autonomy. Benchmarks show web agents will attempt harmful tasks unless constrained (posting misinformation, illicit sales, etc.). You must measure misuse not just assume guardrails. 🚩 Supply-chain & retrieval poisoning. Agents trust plugins, third-party tools & indexed data that an attacker can taint. OWASP’s newest GenAI Top 10 calls this out explicitly. 🚩 Process safety gaps. NIST's GenAI Profile (AI 600-1) warns that organizations ship agents without role clarity, human-in-the-loop or incident playbooks. ✅ Model safety reduces bad text. Agent safety prevents bad state. Treat the agent like a junior SRE - with narrow permissions, audited actions and clear escalation paths. If you're building agents, what's the one control you won't ship without? Also drop a comment if you'd like me to share a follow-up post on how to tackle each of these risks. #AgenticAI #AISafety #AIAgents #GenAI #CyberSecurity #MCP #OWASP #NIST #AITrust #ResponsibleAI #AIForBusiness

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This one’s for the infra nerds! 🚀 We just rolled out improvements to Faiss, making it up to 2X faster by integrating NVIDIA cuVS. Huge win for our AI infra 👏👏 ➡️ Check out how our engineering team made it happen: https://bit.ly/4kkfwmS 📖 Dive deeper into Faiss in this article: https://bit.ly/3FgPVMM #AI #MachineLearning #Infrastructure #Faiss #LifeAtMeta

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It all starts here 🌉 ​ ​ The San Francisco Bay Area will be the first market to welcome the Uber-exclusive robotaxi, powered by Nuro and Lucid, expected to begin in late 2026.​ ​ On-road development with our Robotaxi Engineering Fleet is now underway, marking the start of an exciting new chapter in our Global Robotaxi Program. ​ ​ Over the coming months, more than 100 Nuro-driven Lucid Gravitys will join the fleet as we continue to test, validate, and advance the future of safe, sustainable rides.​ ​ The future is on its way! ⚡​ #AutonomousVehicles #Technology #Innovation #Partnership #ElectricVehicles

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𝗜 𝘄𝗮𝗹𝗸𝗲𝗱 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗜 𝘄𝗮𝘀 𝗿𝗲𝗮𝗱𝘆. 𝗜 𝘄𝗮𝗹𝗸𝗲𝗱 𝗼𝘂𝘁 𝗿𝗲𝗮𝗹𝗶𝘇𝗶𝗻𝗴 𝗽𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝗟𝗲𝗲𝘁𝗖𝗼𝗱𝗲. It’s about clarity. Structure. And knowing what your work really means. I interviewed for SDE1 at Microsoft, Bellevue. Six rounds. Mix of coding, system thinking, and conversations about real-world work. Sharing my experience here because a lot of engineers prepare hard but sometimes prepare for the wrong things. 𝗪𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗵𝗲𝗹𝗽𝗲𝗱 𝗺𝗲: 🔹 𝗞𝗻𝗼𝘄 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗶𝗻𝘀𝗶𝗱𝗲 𝗼𝘂𝘁 They don’t just ask what you built, they ask why you designed it that way. In my case, discussing a recommendation system project turned into a deep conversation on optimization and PyTorch. That discussion mattered more than writing syntax on a whiteboard. 🔹 𝗖𝗹𝗮𝗿𝗶𝘁𝘆 𝗯𝗲𝗮𝘁𝘀 𝘀𝗽𝗲𝗲𝗱 One question was a classic matrix rotation. Pretty standard. The follow-up changed the shape from square to rectangle. There wasn’t one perfect solution. But talking clearly through edge cases impressed them more than solving fast. 🔹 𝗦𝘆𝘀𝘁𝗲𝗺 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗶𝘀 𝘂𝗻𝗱𝗲𝗿𝗿𝗮𝘁𝗲𝗱 They asked how I would detect deadlocks across threads. The trick wasn’t writing fancy code. It was mapping the situation to a cycle detection problem in graphs. If you can convert real problems into known concepts, you are already ahead. 🔹 𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹 𝗿𝗼𝘂𝗻𝗱𝘀 𝗺𝗮𝘁𝘁𝗲𝗿 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗽𝗲𝗼𝗽𝗹𝗲 𝘁𝗵𝗶𝗻𝗸 A lot of us ignore this. Microsoft doesn’t. They care about whether you can work in a team, handle disagreements, or explain decisions calmly. Practicing how to talk about your past experiences helps more than cramming 30 more DP problems. 🔹 𝗘𝗱𝗴𝗲 𝗰𝗮𝘀𝗲𝘀 𝗮𝗿𝗲 𝘆𝗼𝘂𝗿 𝗿𝗲𝗮𝗹 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿𝘀 Questions that look simple usually hide tricky edges. The smallest rectangle covering multiple windows. Even odd sorting with internal descending order. These questions are not about syntax. They test how you think through unclear constraints. 𝗕𝗶𝗴𝗴𝗲𝘀𝘁 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Good engineering is communication plus logic. If you can explain your decisions, not just code them, you stand out. If you’re preparing right now, focus on explaining things out loud. Even to yourself. Pretend the interviewer is sitting right there. That practice changes how you think. Happy to chat or share more details if anyone wants to prep together or walk through mock interviews. Thanks for reading and wishing strength to everyone preparing right now.

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If you want to become better at system design rounds, definitely read these 10 case studies by Neo Kim. I am 100% sure, they will make you smarter for your next interview.  1/ How Meta Achieves 99.99999999% Cache Consistency: → https://lnkd.in/e88kUZAm 2/ How Uber Computes ETA at Half a Million Requests per Second: → https://lnkd.in/eVKV2ePC 3/ How PayPal Was Able to Support a Billion Transactions per Day With Only 8 Virtual Machines: → https://lnkd.in/eqcb7MpP 4/ How YouTube Was Able to Support 2.49 Billion Users With MySQL: → https://lnkd.in/efmJw4Dx 5/ How WhatsApp Was Able to Support 50 Billion Messages a Day With Only 32 Engineers: → https://lnkd.in/gRqBgXfc 6/ How Uber Finds Nearby Drivers at 1 Million Requests per Second: → https://lnkd.in/eeqH9Hjh 7/ How Stripe Prevents Double Payment Using Idempotent API: → https://lnkd.in/erMkqwq4 8/ How Google Ads Was Able to Support 4.77 Billion Users With a SQL Database: → https://lnkd.in/efnSvwJp 9/ How Amazon S3 Achieves 99.999999999% Durability: → https://lnkd.in/eutGiK35 10/ How Slack Works: → https://lnkd.in/eATMDjrK

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Why does an E3 level SWE at Meta make only ~190k/year, while an E8 level engineer makes over ~$2M/year, even though both engineers are ICs and spend the same time at work? I have spent the last 5 years at Meta as an IC. I joined with a little over 15 years of experience, and I’ve worked with many solid engineers in this time. Here is how I think about that compensation jump. 1. Same hours, completely different “unit of work” An E3’s unit of work is usually a task or a ticket. An E8’s unit of work is a multi year problem for the company. E3: “Implement this service, fix this bug, write this feature.” E8: “How do we cut infra cost by 20 percent across this product” or “How do we make this platform safe to scale to 10x users.” One person is paid to execute. The other is paid to decide what is worth executing in the first place. 2. Radius of impact E3 usually impacts a file, a service, maybe a small team. E8 shapes whole orgs and product lines. If an E3 ships something great, the impact is great but local. If an E8 ships the right platform, hundreds of engineers become faster and the company saves or earns millions every year. Comp tracks the area of the circle you influence. 3. Risk and downside protection At junior levels, mistakes are usually contained and reversible. At senior staff levels, a bad call can burn tens of millions or damage the brand. E8s are paid for judgment under ambiguity. They decide which bets the company should not make, which migrations can wait, which “shiny idea” is going to kill reliability. You pay more to people whose good judgment protects you from very expensive failures.  4. They scale themselves This can happen in a few ways. 1. Delegation with ownership They define the shape of the problem, then hand large pieces to other senior and mid level engineers while keeping the bar and direction clear. 2. Knowledge that travels They write RFCs, public comments, FAQs, wikis, internal posts. One answer helps hundreds of people who will face the same issue next quarter. 3. Tools over heroics Instead of unblocking people manually all day, they build tools, libraries or guardrails so others can unblock themselves. One well designed tool can save thousands of engineering hours every year. This is what “scaling yourself” actually looks like. The company pays for that multiplier.  5. Ownership of the “uncomfortable problems” Junior engineers usually work inside a well defined box. E8s take ownership between the boxes. They pick up problems that:  Span many teams and no one really “owns”  Require aligning leaders who disagree  Have product, infra, legal and security angles at the same time Most people avoid those because they are messy, political and slow. Very senior ICs lean into them. That is where a lot of value sits. Continued ↓

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