Testing in Production: The Reality Check

Imagine this scenario: You’re tracking a bug that’s causing the system to crash every time a user uploads a specific file type. The quick fix? Add a validation check to block that file type. Problem solved, right? But If you stop there, you’ve only treated the symptom. You haven’t asked why the system couldn't handle the file in the first place. Was it a memory leak in the parser? A race condition in the worker thread? A failure in a third-party library you assumed was "broken"? In software, we often mistake the "appearance" of a bug for its cause. Find the Root Cause, Not the Blame It is more likely that the actual fault is several steps removed from what you are observing. It might involve a tangled web of related things you haven't even looked at yet. When you find a bug especially one someone else wrote the natural instinct is to point fingers. But we focus on fixing the problem, not the blame. A bug is not "somebody's fault." It is "all of us" problem. 🤝 Don’t Panic: When you see a bug that "can't happen," remember: it clearly did happen. Don’t Assume It! Prove It: Turn off your ego. Don't gloss over code because you "know" it works. Prove it in the current context with real data. "Selected tool" Isn't Broken: It’s almost never the OS or the compiler. It’s almost always the application code. Once a bug is found, it should be the last time a human has to find it. The moment you discover the root cause, trap it with an automated test so it can never sneak back in. #SoftwareEngineering #PragmaticProgrammer #Debugging #RootCauseAnalysis #CleanCode #DevOps #GrowthMindset

4 agents, 3 platforms, 1 human. The hardest part isn't the agents. It's the handoffs. Four Claude instances run my development workflow across three platforms. An idea hits while I'm out, so I workshop it with Claude on my phone, just enough to capture it in mcp-awareness, a shared knowledge store. No clipboard copying, no retyping. Ideas, designs, and decisions go in once, tagged and retrievable by any agent. Back at my laptop, Claude Desktop and I flesh out the design and save a more complete version. Then claude-dev (a Claude Code instance with its own repo and instructions) picks up the design and writes the code. claude-qa (separate instance, separate repo) reviews the PRs. I still own the merge gate. GitHub Actions track PR state through labels, but only I apply QA Approved, and that's the signal to merge. Last week, Claude Desktop found six bugs during a code review: thread safety, SQL injection, an IDOR vulnerability, among others. claude-dev pulled the findings from awareness, wrote all six fixes, and pushed PRs. claude-qa reviewed each one. Merged, tagged, deployed across two app nodes with zero downtime. One person. Six fixes. Four agents. One session. Is it seamless? No. I'm still the relay at most handoffs. "Save this to awareness." "Check awareness for new work." "Review this PR." The agents don't instinctively check their shared memory, yet. When they do look, retrieval often takes two or three attempts to find what was left for them. But that friction is the roadmap, not the limitation. The coordination layer already lets a solo developer ship like a small team. Making the handoffs automatic and retrieval reliable is the next problem to solve, not a reason to wait. The code is open source: https://lnkd.in/gsR5Htu7 (https://lnkd.in/gsR5Htu7). Self-hosted today. Hosted beta/preview coming soon. What does your multi-agent workflow look like? #MCP #ModelContextProtocol #OpenSource #AgenticEngineering #AITooling #DeveloperTools #AI

A few years ago, I thought being a good developer meant knowing everything. Today, I know it means something completely different. It means: 1. Knowing how to figure things out when you’re stuck 2. Asking the right questions 3. Breaking complex problems into simple steps 4. And staying consistent even when things don’t make sense There are still days when things don’t work… APIs fail, bugs show up, and nothing compiles 😅 But that’s part of the journey. Growth in tech is less about perfection and more about persistence. If you're currently struggling with something keep going. You’re closer than you think.

I wrote 500+ lines of code for one feature. It worked. That was the problem. While working on a backend system, I noticed something: Big methods don’t fail immediately. They fail when you try to scale, debug, or extend them. That’s when I started using the Pipeline Pattern. Instead of one large function, I broke the flow into steps: Input → Validate → Process → Save → Notify → Output Each step does one job only. -------------------------------------- What changed? - Code became easier to read   - Debugging became step-by-step   - Logic became reusable   - System became scalable   -------------------------------------- What about performance? The pipeline pattern adds a minor execution overhead due to multiple processing steps. However, most application latency comes from: - Database interactions   - External API/network calls   Since these dominate overall response time, the additional overhead is insignificant. In return, you gain better structure, testability, and scalability. -------------------------------------- Key takeaway: Good code runs. Great code scales. -------------------------------------- If you are building APIs or backend systems, this pattern is worth trying. -------------------------------------- #dotnet #csharp #cleanarchitecture #pipelinepattern #softwareengineering #backenddevelopment #coding #developers

Four years ago at Edge Browser, I learned a hard truth: Writing code is only a small fraction of software engineering. As a junior dev, you think, "Why can't we just write logic and push?" But shipping enterprise software requires a massive lifecycle before a PR is merged: 1. Privacy and Security Reviews 2. Compliance and Design Approvals 3. Telemetry and Performance Analysis 4. Testing and Documentation Writing security docs before checking in a PR felt like a burden. But it teaches us how to build robust systems, forcing us to think about edge cases before touching your IDE. Today, that landscape is entirely different. With AI assisted development, the mental blockers that slowed us down are mostly gone. Need a privacy review? Explain your architecture and get a first draft in seconds. Use AI agents to handle repetitive manual checks. You don't have to be hesitant to build a quick POC. Instead of getting bogged down in boilerplate, your focus goes to what requires deep thought: system architecture, better design, and solving core engineering problems. We spend less time typing and more time engineering. But this speed brings a new bottleneck, which I will cover next. #SoftwareEngineering #SystemArchitecture #EnterpriseSoftware #AIAssistedDevelopment #Tech #Browser #EdgeBrowser

A small habit that improved my code quality a lot: Before pushing code, I ask myself: “What can go wrong here?” Not what works. Not the happy path. But what can break. So I check: • What if data is null? • What if API fails? • What if this runs 1000 times? • What if response is slow? • What if user does something unexpected? Earlier, I used to write code for success. Now I try to write code for failure. Because real systems don’t fail in obvious ways. They fail in edge cases. And most bugs come from: things we didn’t think about. This one mindset shift: 👉 reduced bugs 👉 improved debugging 👉 made code more reliable Good developers write code that works. Better developers write code that keeps working. #dotnet #softwareengineering #developers #cleanCode #AjayDevInsights

“This is a small change” Understanding the Real Impact in Software 👩💻 Hello Everyone! 💛 Every engineer knows this is never just a sentence. It is a warning... because in software, nothing is ever just one change 😂 You change a button text. Now: 1. translations need updates. 2. mobile UI breaks in one language. 3. analytics event names no longer match. 4. automated tests start failing. You change one backend condition. Now: 1. old app versions behave differently. 2. one API consumer starts sending weird data. 3. cache logic becomes suspicious. 4. reports show inconsistent numbers. You update one route. Now suddenly: 1. redirects are needed. 2. internal links become outdated. 3. sitemap needs regeneration. 4. indexing risks appear. The code change took 5 minutes. The investigation takes 5 hours lol 🏃♀️➡️ What non-engineers don’t see: we are not editing one file, we are working on a connected system. And it can fail somewhere else completely 🫠 This is why senior developers become nervous when they hear: "It should be a quick fix." Because experience teaches you this: Small change = small visible request + large invisible blast radius. The real work is never writing the line of code, it’s discovering what that line is secretly attached to. So whenever someone says: "This is a small change", my brain hears: Congratulations... we are about to discover parts of the system nobody has visited since 2021. 😂 #SoftwareEngineering #DeveloperLife #WebDevelopement #Programming #TechTalk

Most APIs don’t fail because of bad code. They fail because of bad assumptions. — Everything works fine in development… Until production traffic hits. Then suddenly: ❌ Slow responses ❌ Timeouts ❌ Random failures — Here’s what usually goes wrong: No rate limiting → One spike = system overload No caching → Database gets hammered Poor error handling → Small issues become outages No monitoring → Problems detected too late Assuming ideal conditions → Real users don’t behave like test cases — I’ve seen APIs that worked perfectly with 10 users… Completely break with 1,000. — Production is a different game: ✔ Unpredictable traffic ✔ Network delays ✔ Partial failures ✔ Real-world edge cases — Simple mindset shift: 👉 Don’t just build APIs that work 👉 Build APIs that survive — What helps: • Rate limiting • Caching • Retries & fallbacks • Proper logging & monitoring — Most developers focus on building features… Very few think about failure. That’s the difference. — What’s the biggest issue you’ve faced in production APIs? 👇 #Tech #TechSnippet #Gotogler #API #Scale

One thing that improved my debugging a lot: Stop guessing. Start isolating. Before, when something broke, I used to jump between files, APIs, and random parts of the system. Now I do something simpler: I isolate the problem. - Is it backend or frontend? - Is it data or logic? - Is it happening everywhere or only in one case? Step by step, the problem becomes smaller. And when it’s small enough… the solution becomes obvious. Most bugs feel complex at first. But in reality, they’re just hidden in the wrong place. #softwareengineering #debugging #backend #developers

You think you still know where you’re going. But you're just following the blue dot. Last week, I read an essay on Hacker News that hit me like a physical weight. The title: "The threat is comfortable drift toward not understanding what you're doing." I read it three times. The author wasn't talking about beginners. He was talking about senior engineers. People like me. Here's what happened: I stopped writing tests about a year ago. Not because I got lazy. Because my agents write better tests than I do. They catch edge cases I miss. They run faster. They don't forget. Same with deploys. Same with code review. Same with debugging. One by one, the things I used to do by hand got absorbed by something that does them better. For a while, that felt like winning. Then I tried to explain a race condition to a junior dev last month. I knew it was there. I could feel it. But when I sat down to trace the actual execution path, I had to read the code a few times before I could see it. A year ago, I would've spotted it in seconds. That's the drift. It's not dramatic. Nobody wakes up and forgets how to code. It's like GPS. You stop paying attention to the route because the blue dot always gets you there. Then one day Google Maps glitches and you're standing on a corner in a city you've driven through a hundred times, completely lost. GPS killed navigation. AI is killing debugging. Not because it fails. Because it succeeds so consistently that you stop checking. I run a team of agents. They write code, research markets, and manage grocery orders. It's the most productive I've ever been. And it's the least I've ever understood about what's actually running under the hood. The people who figure out how to use the leverage without losing the understanding are going to build the next decade of software. Everyone else is just a passenger in a car they can't drive. (Link in first comment) Anything drifting for you? Genuinely curious.