Software Engineering Oct 6 2025 • 4 min read

From ChatGPT Workflows to Markdown-KV: Quiet Upgrades in Software Engineering

Reading this week’s collection of software engineering blog posts feels a bit like browsing a well-organized, ever-expanding map of our industry’s shifting priorities. The common thread: software engineers are overwhelmingly updating their toolbelts, rethinking the culture of their teams (and their AIs), and, perhaps quietly, sharpening their understanding of foundational tech. What stands out is less the whiz-bang of AI hype and more the considered, sometimes blunt recognition that clever tools are only as good as thoughtful processes, solid documentation, and a culture that’s not allergic to change—or responsibility.

AI Optimization: From SQL to Orchestration

Agoda’s integration of ChatGPT into their CI/CD workflow for SQL stored procedure optimization showcases a real, tangible benefit of placing AI in the loop. Rather than simply porting mundane tasks to LLMs, Agoda uses GPT to generate recommendations, which are then verified by humans with the aid of performance tests. This automation reduced review times and freed developers from repetitive, soul-eroding query-tuning. Yet, as the post notes, it's not a one-and-done: human validation and careful iteration remain critical since logic correctness still isn't LLMs' strong suit (InfoQ).

But it’s not just about optimizing random blobs of SQL. Viren Baraiya's insights on agentic workflow orchestration point to a bigger shift: coordinating not just microservices and APIs but whole swarms of human and AI agents. Platforms like Orkes (descendants of Netflix’s Conductor) promise to wrangle our distributed chaos with scalable rules and compliance baked in. The message is clear—AI-powered orchestration is less a distant future than a work-in-progress, with pragmatic engineering and governance at its core (Software Engineering Daily).

Data Structure Decisions, Not Just for Nostalgia

Speaking of AI optimization, a fascinating study by Improving Agents tested LLM comprehension across 11 data formats when feeding tabular data—spoiler alert: markdown key-value pairs trumped CSV, JSON, even markdown tables. The cost of accuracy? More tokens, hence more dollars. This is real ammunition for anyone rolling their eyes when told “just use JSON, everyone does.” The implication: a two-minute script to reformat data could buy you a measurable accuracy boost in production AI workflows (Improving Agents).

And let’s not forget the roots under our feet: The Eclectic Light Company’s explainer on inode numbers gave us a reminder that sometimes the most persistent tech questions—how files are stitched together on disk—still matter profoundly, especially as we build on top of UNIX-y traditions on new filesystems like APFS. If you ever needed a reason to use ls -i or explain the difference between hard links and symlinks, now you have a fresh anecdote (Eclectic Light Company).

Status Update: The Web’s Quiet Revolution

Buried beneath the sizzle of AI, WebAssembly 3.0 quietly dropped features that will have developers (and compilers) singing: native garbage collection (GC), 64-bit address space, and exception handling. This last bit isn’t merely another spec update but marks Wasm’s final form as a true polyglot runtime—goodbye to clunky, inflated modules and awkward hacks for high-level languages. This sets the stage for a future where your favorite managed language could plausibly (and nimbly) target the browser or the edge, no apologies necessary (LogRocket Blog).

AI Team Dynamics: Culture, Not Just Tools

A cautionary theme emerged, highlighting that “AI-powered” isn’t a silver bullet unless organizations invest in documentation, culture, and iterative improvement. Stack Overflow’s podcast episode reinforced that poor documentation breeds poor outcomes for AI-powered teams: if AI inherits your team’s worst habits, it’ll happily automate them. AI success, they argue, is as much about embracing experimentation and building comfort zones for developers as it is about tooling. The real result: teams that adapt and learn, not just tool up and hope (Stack Overflow).

This point dovetails with InfoQ’s advice for engineers seeking “Staff Plus” career tracks: your influence is measured less in lines of code, more in how you mentor, build culture, and align tech with business outcomes. In our AI-soaked future, the engineer who can bridge the gap between tech, team, and strategy will be the most valuable—no matter how many AI agents you’re wrangling (InfoQ).

The Programming Language Debates (and Non-Debates)

Which brings us to a recurring existential squabble: which language is the future of the agentic AI era? The Phoenix framework’s creator, Chris McCord, made an impassioned (and, by the looks of it, pragmatic) case for Elixir: with cohesive tooling and a virtual machine accidental in its prescience (thanks BEAM!), Elixir—and its developer-first sensibility—may actually be better primed for AI agent development than JavaScript. But, as McCord wryly notes, AI LLMs tend to parrot what’s popular, not what’s optimal—a reminder that the tech community can collectively choose better defaults for its next epoch (The New Stack).

A Quick Glance at the Cost (and Risks) of Scale

If there was a subplot this week, it’s that the price of modern engineering—especially AI at scale—keeps climbing, but culture and risk management are lagging behind. OpenAI’s jaw-dropping annual spend on Datadog (allegedly $170M) and the UK government’s bailout of Jaguar Land Rover (post-security-outsourcing debacle) are cautionary tales about where unchecked automation and scale without real discipline can lead us (The Pragmatic Engineer).

Conclusion: Tune-Ups, Not Takeovers

Software engineering is having a reckoning with both its old habits and new ambitions. The lesson isn’t that AI will automate us all out of relevance (or trouble), but that the greatest leaps come from incremental, sometimes boring, improvements: better workflow tools, thoughtful data formats, smarter orchestration, and—however unfashionable—stronger team culture and mentorship. It’s an upgrade season, not a revolution.