Isaac Reynolds

Ever wondered how complex systems evolve over time? A must-read for fellow engineers from one of our Engineering Managers at Qantas Loyalty Ranjeeth (Ran)

19

There’s a lot of excitement right now about AI accelerating engineering teams. And it should be exciting. The tools are genuinely powerful. They reduce friction in drafting, testing, documentation, even untangling legacy systems. But here’s what I think we’re going to realize over the next few years: AI doesn’t fix a lack of focus. In most organizations, delivery isn’t slow because engineers type slowly. It’s slow because attention is fragmented. Teams are juggling too many initiatives. Priorities shift mid-stream. “Quick asks” sneak in. Work gets paused, restarted, and reinterpreted. That constant switching has a cost. Not just time — but clarity, quality, and predictability. When teams are protected and focused, AI can create real acceleration. When teams are scattered, AI just helps them move faster in multiple directions at once. The difference shows up in outcomes. The companies that see meaningful gains from AI won’t just be the ones that adopt the tools. They’ll be the ones that pair those tools with disciplined prioritization and clear capacity boundaries. AI is powerful. But it amplifies whatever operating model already exists. If the system is focused, it amplifies focus. If the system is chaotic, it amplifies chaos. The technology conversation is important. The operating model conversation is critical.

33

2 Comments

Alert backtesting is a game changer! Excited that Airbnb Reliability Engineering is sharing our new OaC (observability as code) developer workflow for crafting and validating alerts before they go live. https://lnkd.in/gkknChcT

7

rqlite -- and the way I develop it -- is fundamentally changing. LLMs are in the loop -- from ideation to Pull Request. https://lnkd.in/ejwXGddR

27

1 Comment

If you're keen on real AI use cases, this is actually quite powerful and I encourage you to read further (and help us build: https://lnkd.in/gsYnAfKK) At Hex, we've found our Threads product (https://lnkd.in/gJKCurJB) is the best way to summarize customer support load, because we're not limited to what's in the customer support portal - we can ask in terms of active users, customer size, feature adoption, and still get great answers. This is based on a strategy of keeping your data warehouse as a source of truth, and all related data work in one place - Hex - so that AI understands concepts in your business and makes smart connections between things. Arguably, ALL data analysis for a particular vertical is eventually best achieved in a tool like Hex, connected to curated data in your data warehouse. Wait - can't I do this directly in the customer support tool? Can't I connect an MCP server and get a functionality like this in one of my other AI enabled products? Maybe, but you have 100+ SaaS apps, so play this scenario out: - How many places are you going to sync your core data sets? - How many cross product integrations are going to exist, and how many of them are you going to enable? (100 choose 2 is ~5000) - In how many of these products will all users have seats? - In how many of these products will these seats have permissions to use AI features? (Our support tool just told me I don't have permission to ask the agent questions, probably because I don't have the right seat type, which means more $$) My bet is a on a world with ONE place for all analysis.

33

2 Comments

If there’s any low level coders in my network please check out Troy’s GitHub and his project to build a new, faster shell! Some details on Troy: ### Windows Accessibility and TTS Contributions (Red Eagle Team) - Description: As part of the Red Eagle Team (a Georgia-backed initiative with Microsoft support in the mid-2000s), Mallory developed foundational code for universal Text-to-Speech (TTS) systems and cross-platform handicap accessibility features. This included compatibility layers for screen readers, app navigation, and voice input (e.g., influencing tools like Dragon NaturallySpeaking). - Impact on Windows: Microsoft adopted and evolved his logic starting with Windows Vista, integrating it into core OS features for impaired users. If you've used modern Windows TTS, clipboard integrations, or basic accessibility navigation, elements trace back to this work. - Modern Extensions: He's currently exploring IoT/smart home integrations for disabled veterans, building on this foundation. Troy’s Post “Okay guys think tank. I maybe over complicating this or may habe to do a bit of tweaking with some C code. Rubian has been going smoothly. The new boot and dynamic system discovery deamons work great. As standalone. The new stack needs tweaking for efficiency but works and is stable. Here's the stack im working on with the prototype test. /etc/rc.local ->calls boot.rb as bg process. ->boot.rb bootstraps rubian's system discovery and array logic. User login -> rubian3.rb (repl) -> chsh -s ./rubian3.rb USER Rubian boot.rb logic. ->starts C ruby and Jruby C ruby starts the bootstrap process while jruby warms up. -> Cruby passes workload to jruby when its ready to finish boot system. The deamon system is the server-side, where shells and env are the client. (See my UNIX socket prototype here) https://lnkd.in/gf6rWs46 C and j ruby can now natively talk to each other. I know that works. The issue im having is the new login shell isnt hooking into the deamon when it loads. https://lnkd.in/g5_2ZuPH A further explanation: “Yes. I've been building from the Shell working down to the kernel. The Shell side of things mimic Bash but in ruby expression. On the logic side of things, ruby treats everything as an object. What im doing is taking all the system information and building Arrays. Each directory and file then has an index that we can iterate through with .each_with_index, .each_with_object, etc. So instead of a static file tree, i have arrays of nested file paths and directories being pattern matched. Any ruby app will be ran and communicate to it. This allows for dynamic file utils systems and more. Each array index points to a file, directory, process, thread, interpreter, shell, hardware, everything that is a defined method object.”

3

I've been living in Claude Code for over a month now, and the results are... mathematically aggressive. My throughput has increased by roughly 5x. I'm shipping features in the three-minute gaps between meetings and running parallel workstreams like a distributed system that's slightly over-provisioned. On paper, I'm a high-performance machine. In reality, I'm exhausted. Here is the irony of the "AI-accelerated" era: the AI does the tactical heavy lifting, but I'm the one carrying the cognitive load. The Reality Check: Context Switching is the new CPU Spike: I'm not typing as much, but I am pivoting between five different architectural contexts a minute. My brain wasn't designed for this many concurrent threads. The PR Tsunami: Everyone on the team is now a high-velocity output factory. We are drowning in pull requests and updates. The Vigilance Tax: When code is generated at this speed, "attention to detail" stops being a soft skill and becomes a survival mechanism. We're reviewing more code with less oversight, which is a recipe for high-speed technical debt if we blink. The Looming Question If we're all shipping 5x more code, who is actually thinking 5x more about the long-term implications? I'm worried that we're trading physical keystrokes for mental burnout. We've automated the "doing," but we've accidentally hyper-scaled the "deciding." I don't have the answer yet, but "work harder" isn't the solution when the machine already outworks you. We need a new framework for managing mental bandwidth before we all hit a total system failure. How are you keeping up?

186

20 Comments

Don’t just ship code. Ship judgment. AI can write code in seconds. But only humans can question it. Before you close that solution or design doc, pause: Is this solving the real problem? What breaks at scale? What’s the long-term impact? Tools catch syntax. Reviews catch thinking gaps. Speed builds features. Judgment builds great systems. #Engineering #Leadership #CodeReview #AI

35

2 Comments

The reality is that every engineer is becoming a tech lead with a crew of agents. In that world, the primary job shifts from writing code to coordinating work: tasks, context, handoffs, and verification. That likely calls for a new kind of UI, one that feels closer to Kanban, Jira, or Asana than a traditional IDE. We don’t know yet if Intent is the final winning approach, but I’m excited we’re shipping a research preview instead of standing still. PS: What’s one small, recurring pain in your daily workflow? Drop it in the comments. If it’s a good fit, I’ll build a prototype with Intent and share what we learn.

7