How buffered writes boost performance in CockroachDB

Make just saved me hours of pointless debugging time by adding this small feature. They quietly added a new feature: "Run with existing data." If you’ve ever built scenarios in Make, you know the pain. You tweak something small, a filter, a mapping, a module, and now you have to: - Go back to your source app - Manually trigger the webhook again - Wait for the data to come through - Then finally, run your scenario All that just to test one change. Now you can simply re-run your scenario using data from a previous run. - Pick a past execution, hit run, done. - Same data, no new webhook, no wasted time. Perfect for debugging, iterating, or checking fixes without rebuilding test payloads. Maybe there was a workaround before. If so, I missed it. But this update makes building in Make feel 10x smoother. Small feature. Massive quality-of-life upgrade.

The Internet Is Quietly Returning to RSS — and I’m All In. You know how every big platform eventually decides what you should or shouldn’t see? Yeah, that’s why RSS never really died — it just went stealth mode. Lately, I’ve been building a systematic RSS feed infrastructure for one of the architecture companies I’m working with — something that doesn’t just aggregate but actually understands content structure. Here’s the play: 🧠 Each source feed gets parsed and normalized (yeah, that normalization logic is still the trickiest part — but that’s where the fun is). 🧩 The data is restructured into a unified schema — so it’s readable, scalable, and extendable. ⚡ Hosted on my VPS using FastAPI for speed, modularity, and control. 🏗️ Future updates? The system will automatically post, sync, and trigger content updates like clockwork. Why bother? Because maintaining your own RSS pipeline is like owning your own media infrastructure — algorithm-free, control-rich, and future-proof. I’m sharing this because I feel more developers and creators should own their distribution layers. Don’t let platforms dictate visibility — build your own visibility protocol. Future updates on this feed system coming soon. Until then, I’ll be deep in the code trenches. ⚙️

The Rise of Reactive Backends That Adapt in Real Time Imagine a world where your backend doesn’t just respond to requests — it reacts to changes in real time. Where your systems don’t wait for clients to ask for data… they push updates the instant something changes. Welcome to the age of Reactive Backends — a silent revolution that’s redefining how modern web apps are built. For decades, our web systems have relied on a simple model: Client asks → Server responds. It worked fine… until real-time experiences became the new normal. Google Docs (instant document sync) Instagram Live (real-time comments and reactions) Stock trading platforms (data updates every millisecond) The traditional backend simply can’t keep up with these expectations anymore. A Reactive Backend is not just faster — it’s smarter. reacts immediately. Here’s what sets it apart: Event-driven → It responds to changes, not requests. Stream-based → Continuous data flow instead of static responses. Scalable by design → Built to handle massive concurrent users. Imagine you’re https://lnkd.in/gJpFuhZa

The Rise of Reactive Backends That Adapt in Real Time Imagine a world where your backend doesn’t just respond to requests — it reacts to changes in real time. Where your systems don’t wait for clients to ask for data… they push updates the instant something changes. Welcome to the age of Reactive Backends — a silent revolution that’s redefining how modern web apps are built. For decades, our web systems have relied on a simple model: Client asks → Server responds. It worked fine… until real-time experiences became the new normal. Google Docs (instant document sync) Instagram Live (real-time comments and reactions) Stock trading platforms (data updates every millisecond) The traditional backend simply can’t keep up with these expectations anymore. A Reactive Backend is not just faster — it’s smarter. reacts immediately. Here’s what sets it apart: Event-driven → It responds to changes, not requests. Stream-based → Continuous data flow instead of static responses. Scalable by design → Built to handle massive concurrent users. Imagine you’re https://lnkd.in/gJpFuhZa

⚙️ Boosting Go Performance with sync.Pool When performance tuning Go applications, one of the most underrated tools in the standard library is the sync.Pool — a powerful mechanism for reusing objects and reducing pressure on the garbage collector. 💡 What is sync.Pool? It’s a pool of temporary objects that can be reused instead of being constantly allocated and freed. When you need an object, you take it from the pool. When you’re done, you put it back. This simple pattern can drastically cut down heap allocations, reducing GC cycles and improving latency. 🔍 When to use it: For short-lived, reusable objects like bytes.Buffer, slices, or temporary structs. In high-throughput services (e.g. APIs, message processing). 🚫 When not to use it: For objects that hold external resources (files, DB connections, etc.). For data that must persist between requests. By minimizing allocations and reusing memory, sync.Pool helps Go apps achieve lower latency and smoother GC performance — especially in systems under heavy load. #Golang #Performance #BackendEngineering #GoLangTips #MemoryManagement #SoftwareOptimization

Still using `console.log` to debug objects? You're working blind. That messy text dump is a waste of time. Use `console.table()` on your arrays/objects for a clean, sortable view. Just be warned: it can choke on massive datasets. Are modern dev tools too basic, or are we just too lazy to learn them? #GaboTips

Still debugging objects with `console.log`? You're basically coding blind. Everyone thinks more logs mean more clarity. Nope. It just creates noise. Try `console.table()` on your next array of objects for a clean, sortable grid. Just don't use it on massive data unless you love browser freezes. Are modern dev tools making us smarter, or just better at hiding our messes? #GaboTips

APIs are the core structures of modern applications, running anything from mobile apps to huge, distributed systems. However, as traffic grows, performance bottlenecks can rapidly morph into frustrated users and potential lost opportunities. That’s where improving APIs comes in. Things like pagination (to prevent over-fetching of data), caching (to limit redundant calls), async logging (to remove delay in response), connection pooling (to reuse expensive connections), and payload compression (to compress the size of transferred information) all help. An optimized API doesn’t only run more quickly; it scales better, costs less in operation, and creates a better experience or flow for users. As developers, the time to devote to optimizing APIs is not purely related to speed, but rather to building reliable, resilient systems that respond well as traffic grows. 👉 Subscribe to my newsletter, where I tackle system design and data structures and algorithms problems in depth- https://lnkd.in/grqVsyCS

LLM Gateway Update: Now Smarter Rate Limits, Budget Controls 🚀 We’ve always supported rate limiting per user, per key, per model, and even on custom metadata. The catch was that rate limits could only be applied to a single header key. For example: x-tfy-metadata: {"environment": "prod"} → you could set a limit for environment=prod. But in many real-world applications, one end app might be serving multiple clients. One of our users wanted the ability to prevent a single client from abusing the system without affecting others. So, we added support for rate limits per metadata key. For instance, if you send x-tfy-metadata: {"customer_name": "name1"}, you can now set limits specifically for that customer_name and even visualize usage per client in the UI. The filter rule can now be something like rule-name-{metadata.customer_name}. (Pinned comment has more details if you want to try this out.)

I want to show you how fast the CLode editor UI can process multi-agent edits via MCP server. Here I am running a performance test to determine how fast should it be for humans to watch the changes that 3 agents could make to a codebase. After working with the AI to push it to ~400 calls per seconds, I stopped there on optimization. Findings were that we only need to show 4 to 9 agent changes per second across 3 panels. Anything faster was too fast for humans to view. Keep in mind that we would not usually want multiple developer agents editing in the same folders. After this performance testing, I was able to add another view that was paired down for watching the agents work across separate folders/git branches, create pull requests, and do merges.