Why Debugging in Production is Like Searching for a Needle

If you’ve heard of Antithesis and wondered what it actually does, here’s the clean answer in precisely 4 minutes, 19 seconds. Akshay Shah (our Field CTO) walks through a real PR → a failing property → the triage report → and the multiverse debugger (time-travel FTW) to land on the root cause - fully deterministic, perfectly replayable. Ready to test smarter? Chat with us: https://lnkd.in/eEKGg_iM 

Catching exceptions with a debugger and "up" flow is not always the best solution to find an issue. Here is another approach... Last week, I shared a guide on "how to trace errors upward through your code stack".  Which you can check it out here: https://lnkd.in/eHaQQdZc Today, I encountered a situation where this solution was not the best. I got an unexpected exception after changing some things around in the controller. So I put a debugger in the controller and used the catch exception method. As expected, the method stopped at the debugger and then at the exception. While trying to navigate back up the code stack, I realised this is not the best solution for this situation. 🤔 The issue was, after going up about 4-5 steps, I was still inside the Ruby on Rails core libraries. Even later, after about 10 more steps up, still within the core libraries. I could go for a long time without reaching the right place. 🧩 Instead, I minimised the amount of code that might generate the error. The controllers and view templates were stripped to the core, which narrowed down the amount of code that could be causing the issue. Bit by bit, I add back more of the original functionality until the bug appears again. 💡The lesson from today: Tools like a debugger are great, but sometimes you must reduce the haystack size to find the needle. #DevelopmentTips #Debugging #Ruby #Test

From detecting a bug to pushing a fix — it's powered by agents in Linear. ① Product Intelligence triages the issue ② Sentry identifies the root cause ③ Cursor drafts a PR to fix it

We’re starting to see the power of AI agents working across tools. Linear detects and categorizes a new bug, Sentry identifies the root cause, and Cursor ships the fix — all without breaking context. This is how building (and fixing) products should work: fast and automated.

Writing bug reports that actually get fixed? It starts with clarity. Here’s the anatomy of a high-impact bug report that reduces back-and-forth and speeds up resolution.

Day 2 of Array Practice 💻✨ Learning step by step and getting better each day! Today I practiced these array questions: 1️⃣ Find the second largest element in an array 2️⃣ Reverse an array without using built-in functions 3️⃣ Move all zeros to the end of the array 4️⃣ Find the sum of all elements 5️⃣ Check if two arrays are equal Feeling more confident with logic and problem-solving 🙌

Agent performance isn’t just ‘more tools’. Manus shows why context engineering wins: VM sandbox execution, KV-cache discipline, masking actions, and file-based memory.

>>>True mastery of a system isn't achieved by just reading its code, but by systematically debugging it. >>>There's no substitute for stepping through execution, inspecting state, and observing memory allocation to build a deep understanding of complex process. >>>The more you debug, the more you master the system, as we monitor every variable changing state.

Talking in Diagrams: Co-Designing with an LLM A while ago I was using LangChain for orchestration. It was powerful - but the abstractions kept shifting. One minor version bump and everything behaved differently. Prompts grew verbose, debugging opaque. It felt like building on sand. I started wondering: what’s the right level of abstraction for LLM systems? Too high, and you lose control. Too low, and you reinvent plumbing. Somewhere between those extremes, real engineering patterns must exist. When I looked at the OpenAI Agents JS repo, a few minimal examples stood out: Agents-as-Tools, Sequential Pipeline, Planner–Executor. To understand them, I asked ChatGPT to draw them in Mermaid Markdown. That single shift - from text to structured diagrams - changed everything. The model began reasoning visually. I wasn’t just prompting; we were co-designing. What started as frustration with brittle frameworks became a lesson in language: sometimes the clearest way to talk about a system is to draw it. Full post + diagram: https://lnkd.in/gD29FDxh #AgenticAI #SystemDesign #AIArchitecture #LangChain #OpenAI #MermaidJS #GenerativeAI