How Will Software Engineers Interface with AI in the Future? A breakdown of the three potential ways software engineers will interact with AI coding assistants, ranging from local desktop setups to fully automated software delivery factories. Learn more: speedscale.com #aicoding #devops #techdebate #aiagents

I used to think API traffic was mainly useful for debugging. Then I realized it can tell you how the business actually works. The sequence of calls can reveal workflows, dependencies, and the actions that trigger what happens next. Sometimes the API is the best documentation you have. Read more: speedscale.com/blog/reading-… #Speedscale #APIs #Observability #SoftwareEngineering #TrafficReplay

One security exception is manageable. Thousands of reasonable exceptions can turn your platform into a completely different product. Custom integrations, restricted capabilities, manual processes, compatibility requirements. They all add up, and eventually every upgrade becomes a port. That’s the compounding platform tax. Read more: speedscale.com/blog/the-comp… #Speedscale #Kubernetes #CloudNative #PlatformEngineering #DevOps

Want to know how your app handles a dependency failure? Don’t wait for production to find out. Inject realistic timeouts, errors, and slow responses at the application level and see what actually happens. Break the dependency. Not the whole system. Read more: speedscale.com/blog/applicat… #Speedscale #ChaosEngineering #DevOps #Testing #Resilience

The site is down. So… blame the site? Not so fast. The frontend might just be where a much deeper problem becomes visible — a slow dependency, failed network call, or broken service upstream. Follow the request. Find the real failure. Read more: speedscale.com/blog/drop-the… #Speedscale #Observability #DevOps #APIs #SRE

Telemetry tells you where to look. Traffic lets you recreate what actually happened. Find the signal in your traces and metrics, turn it back into traffic, and replay the request that caused the problem. Because “we found the issue” hits different when you can reproduce it. Read more: speedscale.com/blog/from-tel… #Speedscale #Observability #Telemetry #DevOps #Testing

Chaos testing usually means breaking the infrastructure and seeing what happens. But what happens when the infrastructure stays healthy and the application itself is the thing that behaves badly? That’s the difference between infrastructure chaos and application chaos. One tests failures like network outages or pod crashes, while the other tests how your application handles bad responses, timeouts, retries, and unexpected dependency behavior. You need both to understand how a system actually fails. Because production rarely gives you just one kind of chaos at a time. Read more: speedscale.com/blog/two-kind… #Speedscale #ChaosEngineering #Observability #ResilienceEngineering #DevOps

CPU is at 20%. The service is still painfully slow. That’s because CPU usage doesn’t tell you how long a request spends waiting on a database, network, lock, or dependency. Prometheus shows the CPU isn’t the problem. Proxymock helps replay the request and find where the time actually went. Low CPU doesn’t mean low latency. Read more: speedscale.com/blog/explain-… #Speedscale #Prometheus #Observability #APIs #DevOps

A request succeeds. Everything looks fine. Except your service quietly retries it 4 more times. A rare dependency response triggered a retry path that added 420ms of backoff and extra calls. Loki showed us the pattern. proxymock let us replay the exact request and reproduce it on demand. Rare bugs are a lot easier to fix when you can make them happen on command. Read more: speedscale.com/blog/reproduc… #Speedscale #Observability #APIs #Testing #DevOps

A request times out after 5 seconds, but the dependency’s dashboards are green, and its latency looks normal. So what actually failed? That’s where Hubble helps: it shows what happened to the packets, while proxymock captures and replays the request to prove whether the application behavior changed. In this case, Hubble showed the traffic was being dropped by a network policy before it ever reached the dependency. The important part is knowing what didn't fail. DNS was working, the dependency wasn’t slow, and the application code hadn’t changed; the network was the problem. Read more: speedscale.com/blog/separate… #Speedscale #Observability #Kubernetes #eBPF #NetworkPerformance

Some services are black boxes. No source access, no documentation, and no easy way to know why a request is slow. OpenTelemetry eBPF can give you visibility without changing the application. Then proxymock lets you capture and replay the actual traffic, so you can validate what you’re seeing instead of relying on a trace alone. In one example, a 132ms request turned out to spend ~92% of its time waiting on a downstream dependency. Sometimes you don’t need to see inside the service. You just need to see what’s happening at the boundary. Read more: speedscale.com/blog/observe-… #Speedscale #Observability #OpenTelemetry #eBPF #API

Nothing failed. The response was correct. CPU was mostly idle. And one API request still took ~300ms. The trace showed why: 8 inventory calls, running one after another. Tempo found the bottleneck. proxymock showed what input created it. The fix cut latency to ~80 ms, without changing the response. That’s the difference between “we made it faster” and “we proved it’s faster.” Read more: speedscale.com/blog/diagnose… #speedscale #observability #performance #api #cpu

We've all seen AI generate code in seconds. We've also seen it confidently fix the wrong thing. That "one more prompt" turns into five. Then ten. Suddenly the time you thought you saved is spent untangling a solution that was never pointed at the right problem. The biggest productivity boost for AI might not come from a newer model—it might come from better context. Give an AI agent production traffic and observability data, and it's far more likely to find the right bug before it starts writing code. Maybe the real AI tax isn't computation, it's rework. Read more: speedscale.com/blog/ai-rewor… #AI #SoftwareEngineering #DevOps #Observability #DeveloperTools

A flamegraph can show you where your app is slow. It can't tell you if your fix actually worked. That's where replay comes in—validate changes with real production traffic instead of relying on assumptions. Find it. Replay it. Prove it.   Read more: speedscale.com/blog/flamegra… #speedscale #PerformanceTesting #Observability #DevOps

Why does an HTTP payload sometimes look like complete garbage in eBPF? Because it isn't one buffer. Scatter-gather I/O splits payloads across memory, so capturing traffic means reconstructing data—not just reading bytes. Our latest post dives into the problem (and the fix).  speedscale.com/blog/ebpf-gar… #eBPF #Linux #Observability #DevOps

Most developers know Postman. But is it still the best fit for your workflow? We compared 14 Postman alternatives—from lightweight API clients to enterprise-ready testing platforms—to help you find the right tool for your team. What's your go-to API testing tool? 👇 Read more: speedscale.com/blog/top-14-p… #APITesting #DevOps #DeveloperTools #SoftwareTesting

The question isn't "What's the best model?" It's "What's the best model for *this* request?" That's where routing becomes more important than rankings. speedscale.com/blog/the-best… #Speedscale #LLM #AI #DevOps

We've all had that moment: ✅ The fix is merged. ✅ Tests are green. ❌ Production still breaks. Sometimes the missing piece isn't another test—it's reproducing what actually happened in production. Learn more: speedscale.com/blog/two-conf… #Speedscale #DevOps #Testing #SRE #Prod

AI projects don't just get expensive in production. One of the biggest hidden costs often comes from development, CI pipelines, and repeated testing—where the same prompts generate the same token bill over and over. Instead of paying for identical LLM calls every time you test, there's a smarter approach: capture real interactions once, then replay them throughout development and CI. You keep realistic behavior without racking up unnecessary token costs. If you're building AI-powered applications, this is worth considering before your cloud bill surprises you. Read more: speedscale.com/blog/imaginar… #AI #DeveloperTools #DevOps #CloudComputing #SoftwareEngineering

Kimi K3 defeats Claude Fable at it's own game #speedscale #claude #kimik3 #claudefable #aiagents