On the Loop, Not In It — But Code Quality Still Matters
On the Loop, Not In It — But Code Quality Still Matters
Yesterday one of my AI agents wasted 15 minutes chasing a bug that didn’t exist. The function was called transformPayload() — but it didn’t transform anything. It validated. The agent built three layers of transformation logic on top of it before realizing the name was a lie. I’ve seen this pattern dozens of times now. And it’s exactly why I think Kief Morris’s latest piece gets the big picture right but undersells one critical detail.
Technology Evolution Doesn't Move in a Straight Line—It Spirals
Technology Evolution Doesn’t Move in a Straight Line—It Spirals
The Proud Ops Colleague
Years ago, an Ops colleague proudly showed me something new. ClusterSSH—cssh [1]. A tool that opens multiple terminals to multiple machines—at the same time. You type once, it executes everywhere.
Back then, machines still had names. Ops folks knew their history, their specs, their quirks. They could tell you which server had been acting up last Thursday and what firmware it was running. And cssh? It let them follow the runbook consistently across every node. No more SSH-ing into machines one by one, hoping you didn’t forget a step on node 7.
AI coding has quickly developed from an interesting research project to an important tool in the bel
AI coding has quickly developed from an interesting research project to an important tool in the belt of every software developer. Tools like #kiro allow to define subagents, which take on specific responsibilities within the software project and speed up development and improve quality. Nice way to navigate overcrowded context windows.
But where to start? How to identify subagents which can improve the team and subsequently - how to come up with a first version of those agents?
Kiro Subagents: Scaling Development with Specialized AI Agents
Kiro Subagents: Scaling Development with Specialized AI Agents
When you’re building complex software, context management becomes your bottleneck. Your AI agent is juggling frontend components, backend APIs, database schemas, testing frameworks, and documentation—all competing for limited context window space. The result? Diluted focus and suboptimal outputs.
Kiro Subagents solve this architectural challenge by enabling parallel task execution through specialized, autonomous agents that maintain independent context windows.
🏗️ The Architecture: Parallel Contexts, Focused Execution
Subagents operate as independent processes with their own context management. This architectural pattern delivers several technical advantages:
Ah, this is a nice update right on time for some exploration over the upcoming break.
Ah, this is a nice update right on time for some exploration over the upcoming break.
John, Arnd & Inri, we talked about our wish list for Kiro just yesterday. Looks like Santa🎅🏿 - ah, I mean Kiro👻 - is listening and delivering instantly. Let’s do this more often.
🌅 The Dawn of the Renaissance Developer
🌅 The Dawn of the Renaissance Developer
It’s that time of the year. AWS Community gets ready for the event of the year: re:Invent. And Werner publishes his tech predictions [1]. Like every year, a densely packed piece with loads of gems in it. This year Werner came up with 5 major themes, if I didn’t miscount. I covered the first one in my initial post [2].
The second one is about:
𝗧𝗵𝗲 𝗔𝗜 𝗗𝗶𝘀𝗮𝗽𝗽𝗼𝗶𝗻𝘁𝗺𝗲𝗻𝘁 𝗚𝗮𝗽: 𝗔𝗿𝗲 𝗪𝗲 𝗠𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀 𝗼𝗿 𝗝𝘂𝘀𝘁 𝗖𝗵𝗮𝘀𝗶𝗻𝗴 𝗛𝗲𝗮𝗱𝗹𝗶𝗻𝗲𝘀?
𝗧𝗵𝗲 𝗔𝗜 𝗗𝗶𝘀𝗮𝗽𝗽𝗼𝗶𝗻𝘁𝗺𝗲𝗻𝘁 𝗚𝗮𝗽: 𝗔𝗿𝗲 𝗪𝗲 𝗠𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀 𝗼𝗿 𝗝𝘂𝘀𝘁 𝗖𝗵𝗮𝘀𝗶𝗻𝗴 𝗛𝗲𝗮𝗱𝗹𝗶𝗻𝗲𝘀?
There’s been incredible progress in #AI tools for software engineers—new agents, coding assistants, and integrated workflows are launching every week. Yet, when talking to customers, a common question keeps surfacing: are these AI investments really delivering value, or are they still more hype than help?
That’s why I found the latest Tech Lead Journal podcast with Laura Tacho (CTO at DX ) so insightful [1]. The episode dives into real-world research on AI adoption—and tackles the tough questions leaders are facing: • Why “acceptance rates” often mislead organizations • How to move past buzzwords and measure true AI impact (e.g. with DX’s practical AI Measurement Framework) • Which use cases save time today (surprisingly, stack trace analysis outranks code generation!) • Why AI should be treated as a strategic team extension, not a “magic bullet” or a replacement for developers.
This is absolutely brilliant. Go, build your own assistant with Amazon Nova Sonic in less than 24h!
This is absolutely brilliant. Go, build your own assistant with Amazon Nova Sonic in less than 24h!
I love the use case and the presentation in the video, which Tomasz Stachlewski ☁ shared in his post: https://lnkd.in/eabWBQea
What maybe triggers me the most, are those 24h build time (which hopefully also involve some good hours of sleep 😃 ) .
This is a 𝗽𝗮𝗿𝗮𝗱𝗶𝗴𝗺 𝘀𝗵𝗶𝗳𝘁 from
“𝗵𝗮𝘃𝗶𝗻𝗴 𝗮 𝗴𝗿𝗲𝗮𝘁 𝗶𝗱𝗲𝗮, 𝗯𝘂𝘁 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝘀 𝗮 𝗰𝗼𝗺𝗽𝗹𝗲𝘅, 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲 𝗮𝗻𝗱 𝗹𝗼𝗻𝗴 𝗹𝗮𝘀𝘁𝗶𝗻𝗴 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁”
This is absolutely brilliant. Go, build your own assistant with Amazon Nova Sonic!
This is absolutely brilliant. Go, build your own assistant with Amazon Nova Sonic!
I love the use case and the presentation in the video.
What maybe triggers me the most, are those 24h build time (which hopefully also involve some good hours of sleep 😃 ) .
This is a 𝗽𝗮𝗿𝗮𝗱𝗶𝗴𝗺 𝘀𝗵𝗶𝗳𝘁 from
“𝗵𝗮𝘃𝗶𝗻𝗴 𝗮 𝗴𝗿𝗲𝗮𝘁 𝗶𝗱𝗲𝗮, 𝗯𝘂𝘁 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝘀 𝗮 𝗰𝗼𝗺𝗽𝗹𝗲𝘅, 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲 𝗮𝗻𝗱 𝗹𝗼𝗻𝗴 𝗹𝗮𝘀𝘁𝗶𝗻𝗴 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁”
towards
“𝗵𝗮𝘃𝗶𝗻𝗴 𝗮 𝗯𝗿𝗶𝗹𝗹𝗶𝗮𝗻𝘁 𝗶𝗱𝗲𝗮, 𝗮𝗻𝗱 𝗲𝗮𝘀𝘆, 𝗶𝗻𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲 𝗮𝗻𝗱 𝗾𝘂𝗶𝗰𝗸 𝘁𝘂𝗿𝗻 𝗮𝗿𝗼𝘂𝗻𝗱.”
Just uncovered this hidden gem created by Ewa A. Treitz and her AI team.
Just uncovered this hidden gem created by Ewa A. Treitz and her AI team.
Awesome example for the ongoing democratisation of software production in the advent of powerful AI tools.
And - the product itself is a beautiful and super helpful tool to navigate the overload of super interesting sessions at #reinvent 2025.
Checkt it out and let me know your favourite sessions we can’t miss!
#AWSomeVoices #reinvent #aws #kiro