Renaud Gaubert

mistake: a $2 million memory miscalculation wrecked a GPU demo. Memory is the constraint that reveals real system limits - not the glossy dashboards. When data scales, every byte counts. A mis-sized buffer or cache can cascade into page faults and a failed run. 🔍 Size matters: budget memory against worst cases and verify with load tests. ⚡ Rehearse under real pressure: memory pressure kills demos. 🎯 API-first design (GraphQL federation, tRPC) helps fetch only what’s needed. 🛡️ Observability is mandatory: track allocations, GC pauses, and leaks. 💡 Start conservative, then tune with real traffic using traces. Memory tells the truth fast; plan for it or pay the price. ───────────────────────── 🔗 Read the full article: https://lnkd.in/dZfmPgqG 🎯 Practice interview questions: https://lnkd.in/gmy5drNw #operatingsystems #million #memory #mistake

3

While solving LeetCode problems in C++, I started experimenting with memory allocation and saw how much performance can be influenced by low-level optimizations. That made me wonder: If C++ is so fast, why isn't it the go-to language for web applications? And then I discovered frameworks like Drogon. C++ absolutely can be used to build high-performance web applications and APIs. Drogon provides routing, HTTP handling, database support, asynchronous programming and more. So the question isn't whether C++ can do web development. It can. The real question is: why isn't it the mainstream choice? A major reason is the trade-off between performance and developer productivity. C++ gives incredible control over memory, CPU and system resources, but that control also comes with additional complexity. For many web applications, the bottleneck isn't the programming language. It's often: → Database queries → Network latency → External APIs → I/O operations So shaving microseconds from your C++ code may not matter if your database query takes 100ms. This made me realize: The fastest language isn't always the best tool. The right choice depends on the problem you're solving. And that's one of the things I love about learning C++ — it makes you think about what is actually happening underneath the abstractions. 🚀 #CPlusPlus #Cpp #Drogon #WebDevelopment #BackendDevelopment #SoftwareEngineering #Performance #DSA #LeetCode #LearningInPublic

3

A candidate interviewing for a Senior Engineer @ Meta was asked to design a Distributed Stream Processing System like Kafka. Another candidate at Google's L5 loop got hit with the same question. If this question shows up in your system design interview, you are not being tested on config flags. You are being tested on whether you understand when to use it and what tradeoffs you are making. Btw, If you are preparing for DSA or system design, try our mock interview tool on Layrs for free:http://layrs.me/interviews Here is how I would structure a clear answer. (Note: I am not going into nuances, this is a very high-level explanation) 1. What problem are we solving At a high level, Kafka is my go-to when I need: - A central backbone for events or logs coming from many services - High throughput writes with ordering per key - Consumers that can read at their own pace and be added or removed independently - The ability to replay history using offsets Typical use cases I call out: - Asynchronous workflows - Central logging and metrics - Ad click or analytics streams - Pub sub for notifications or chat That already tells the interviewer I know -why- Kafka is in the picture. 2. Requirements Functional - Accept events from many producers in different services and regions - Preserve order for events that share the same key - Allow many independent consumers +some doing real time processing +some doing batch jobs or analytics - Support replay from an offset for backfills and reprocessing Non functional - Handle very high write rates - Scale horizontally by adding machines - Survive broker failures without losing committed data - Let us tune retention for cost vs replay needs Rest of the breakdown: https://lnkd.in/gmg7u-ku

384

12 Comments

⚙️ One library, real DSP speed. There’s a gap in the developer ecosystem. Teams doing DSP/ML often migrate to Python, because that’s where most tooling lives. But Python wasn’t built for high-throughput I/O — under load, it spawns new interpreters/processes, creating duplicated memory, slow cold starts, and more containers. Compute stays fast, but orchestration cost explodes. Meanwhile, Node.js/TS scale I/O well, but were never meant for serious compute. Pure-JS DSP hits performance walls. Even worse, serializing data between Redis ↔ DSP/ML adds latency and forces batching — slowing real-time pipelines. So devs are stuck: Python → strong math, weak concurrency Node → strong networking, weak compute dspx closes that gap — TypeScript DX + native C++/SIMD (AVX2/SSE3/SSE2/NEON) under the hood. 🧩 How dspx works FIR / FFT / Conv1D run in optimized C++/SIMD Memory-safe circular buffers → O(1) throughput TS handles Kafka / Redis / WebSockets Redis persistence < 0.5 ms Batched logging avoids I/O stalls At tiny batch sizes, N-API overhead can make naive JS look competitive. At realistic scale, batched native pipelines win. ⚡ Benchmarks Dell OptiPlex 3000 Micro (i5-12600T · AVX2 · Node 22) FFT: 2.6× faster than fft.js, ~9× faster than tfjs-node FIR (51-tap): ~4× faster than fili / naive JS Conv1D (128-kernel): ~3.5× faster than tfjs-node Moving Avg (O(1)): 559× more throughput/sec than naive JS Redis save/load: sub-ms Logging: <3% overhead 📊 Full tables + charts in carousel 💬 Observations SIMD FMA + contiguous memory → big FIR/Conv wins O(1) moving-average design → massive throughput gain Sub-ms Redis + low I/O overhead → real-time persistence in pure Node 🧠 Open Source dspx is Apache 2.0, free for commercial/academic use. Looking for contributors interested in: ARM NEON / Apple M / Graviton tuning Audio / sensor / biomedical DSP Visualization + benchmark tooling 📦 npm i dspx 🔗 https://lnkd.in/e-tWxAgu 🧠 Real-time DSP for Node, TypeScript & Redis 💼 Note I’m exploring opportunities in full-stack, real-time systems, performance engineering, and DSP. If your team works in this space, I’d love to connect. My next post will cover why sub-ms Redis latency isn’t just technical — it’s economic. Lower serialization + compute overhead → lower infra + energy cost. 🔖 Tags & Mentions #NodeJS #TypeScript #DSP #PerformanceEngineering #EdgeComputing #OpenSource #SIMD #Redis #Cplusplus #RealTime NodeJS Developer TensorFlow Google Microsoft JavaScript Developer Amazon Web Services (AWS) Vercel

5

1 Comment

Anthropic released Opus 4.5. Top of SWE bench at 80.9 percent. Nice chart, but the real story is somewhere else. Everyone compares the LLM race to the cloud race. Same TAM, same few winners. Wrong. In the cloud you do not switch with a single line of code. With LLMs you do. One upgrade from Gemini, GPT, or Claude and teams move millions in spend overnight. That switching pressure pushes prices down. Great for builders. Tough for model vendors. Every month the cost of inference drops while product value goes up. So the war is not model vs model. The war is between three approaches. Single vendor teams that hope their model never falls behind. Multi model teams that treat models like swappable parts. Vertical products that own workflows, data, and users. We already saw a one person AI platform (Maor Shlomo‘s Base44) get acquired for $80M and pass $100M in revenue later. This happens when you ride the model race instead of competing inside it. Opus 4.5 winning the chart is the headline. The real story is who builds something durable while models keep getting cheaper and easier to replace. If you build in this space, ask one thing. If your favorite model changed tomorrow, would your product still stand strong or fall with it

2