Forty years ago, while working for a tiny subsidiary of a gigantic telco, I stumbled through pre-Git source code management and tried to avoid explosively devolving into a mess of conflicts after every merge. Thankfully, modern practices make it possible to work in massive, distributed teams, swarming around a codebase, working independently toward a collective goal. That sounds a lot like what we're heading toward with agents, and here it touches a nerve: nearly everyone in software engineering feels a deep terror as an invasion of agentic systems sweep all before them. Now that Stack Overflow has gone agent-first, what's left for us meatsacks? Shoulder-to-shoulder with the flesh-based cohort most immediately under the pump at a conference called AI Engineer Melbourne, I heard conversations about the future of software engineering working their way through denial, anger, bargaining, and depression, to ... coupon clipping? Now that organisations have been weaned off earlier 'all you can eat' subscription plans and onto 'pay-as-you-go' metered token consumption, they're all in various stages of sticker shock. Several talks at the conference discussed managing token costs, such as AJ Fisher's exploration of 'diffusion' models. Analogous to the diffusers used to generate images, they generate text at lighting speed, making them cheaper to operate while also being less accurate than the pricey and slower “autoregressive” frontier models. Fisher's solution? Use a low-quality model and make it iterate on a problem (that new classic, the Ralph Wiggum loop) until it gets a satisfactory solution. This approach delivers the same result as a full-fat model, for anywhere from one half to one tenth the spend. Google released its DiffusionGemma model, which produces text at prodigious speed, just days after Fisher's talk, giving everyone the ability to try this approach. But some engineers reject AI in 'all the things'. Annie Vella, author of the seminal essay "The Software Engineering Identity Crisis" shared what she's learned about the feelings of grief experienced by a cohort of software engineers, provoked by AI tooling. We've seen the field divide into 'all in' and 'never ever' camps (even in the pages of El Reg), with a broad middle cautiously getting their feet wet. That divide has roots in two styles of work: those who look for outcomes, and those who look for learning, for whom the journey into understanding is the whole point of the exercise. Short circuiting that journey with AI tools makes folks for whom the journey is the reward feel cheated. How do we breach the divide? Annie suggests sensitivity, listening, and openness to change on both sides - highlighting human qualities in the machine age. Kaggle and fast.ai alum Jeremy Howard took a different tack, reminding the audience of the importance of critical thinking - really, a plea to just keep thinking, a refrain we'll be hearing a lot as we struggle to avoid nodding off in the warm bath of machine thoughts. He followed up with a demo of SolveIT, his still-in-beta tool combining some of the best aspects of Python notebooks, Mathematica, Wikipedia, and a chatbot, offering up a counterexample of an environment designed for swimming in the sea of knowledge, rather than floating off into mindless oblivion. Finally, Daniel Rodgers-Pryor's "Fully Automated Luxury Gay Space Engineering" blew my mind with a practical, working vision for AI in the engineering department. Rodgers-Pryor's entire CI/CD pipeline feeds all of its metrics, messages, logs and user feedback into a set of AI agents that quickly identify issues, find the underlying problems, fix them, integrate solutions into the codebase, test them, and push them out to users. What sounds like a recipe for disaster turns out to be a formula for a self-healing, 'anti-fragile' system that improves as the pressure on it increases. More users? Good. More metrics? Great! More messages and logs? Even better. Agents eat all of that data and use it to improve the performance of the overall system. Rodgers-Pryor's "closed feedback loop" reminds me of a 20th century production line worker dipping into the stream of bonbons (or widgets) eyeing a few for quality, then tossing them back into the stream. "This is your job now," he concludes. "How can you can make those feedback loops shorter and tighter?" Software engineers have been forced to absorb more change in the last three years than in the previous thirty, and have every right to be a aggrieved about that. Yet as AJ Fisher, Annie Vella, Jeremy Howard and Daniel Rodgers-Pryor all portrayed in their own ways, adopting AI looks less like rolling over before the dictates of the machine, and more like exploring a whole new world. Like any journey into a new realm, perils and hardships await. Who's to say that's not the price of admission for a once-in-a-lifetime opportunity? ® The author attended AI Engineer Melbourne as a guest of the conference.

HANDS ON That new AI-juiced Siri that Apple rolled out last week at WWDC was supposed to set a new paradigm for on-device AI. But don't believe the hype coming out of Tim Cook's final big event. After a week-long test drive, it seems like Apple just crammed Google AI Overviews on top of the most useful parts of its various operating systems and made the whole ecosystem more cumbersome to use. But hey, it has more AIs! I’ve been running the iOS and macOS 27 developer betas since they were made available on June 8, and I was blessed by the waitlist gods with access to the new version of Siri a few days after that. There are definitely some useful new features: Siri now carries on actual conversations, which makes it far more useful than the ask, get a response, we’re-done-here flow of the old Siri that left no room for clarifying questions or follow ups. Siri is now able to find things on my device more easily too – at least on my M1 MacBook. My iPhone 15 Pro has been telling me it’s still re-indexing my device after the update for more than a week, but I was still able to use it to conduct web searches and find some things on my phone – it's possible this message itself was an error. The dedicated Siri app is also nice in its own way, as it shows a record of every conversation I’ve had with the new Apple Intelligence front end for later review, but that comes with a caveat, too. Even the most brief questions – the overnight weather forecast, for example – is now stored in perpetuity, cluttering up the list of chats we’ve had until I manually delete it. The only apparent alternative is setting an expiration window for past chats and losing records of the more useful conversations we’ve had. Who turned out my Spotlight? Those are small inconveniences, however, compared to my biggest gripe with Siri AI: It’s completely ruined Spotlight. I’ve come to rely on Apple’s embedded search/launcher feature almost exclusively for digging up apps that I don’t keep a shortcut for, and on my iPhone, it’s the main method I use to kick off a web search because it's so simple. Swipe down from the center of the screen, type what I want to search for, and tap on the item that points to my query as a Google search in Safari. Swipe, type, and a tap and I’m perusing a search result page. Not anymore. The new Siri-first interface that presumes that if you’re searching for anything but an app or file, you must want Siri to feed you a few links of Apple Intelligence’s choosing. Getting to a web search from a Spotlight query now requires multiple taps: Type your query, tap “Show Results” (careful: hitting enter will trigger Siri to craft a response, eliminating the possibility of seeing any actual Spotlight content), tap on “Show More” next to the list of Siri-surfaced web results, scroll down until you see Search Google (or whatever engine you have set as your default), then tap that. Maybe I’m being a grumpy old journalist who likes things the way they used to be, the transformation of Spotlight into a Siri interface seems like intentional degradation of a basic feature in order to front-load an AI that in my experience so far is largely an inconvenience. Overall, the experience reminds me of Google’s much-maligned and often wrong AI Overviews, which push actual search results down the page in favor of force-fed info from Google Gemini. There's a logical reason for the similarity. At the end of 2025, Apple replaced its former AI chief John Giannandrea, formerly Google's SVP of search and AI, in a bid to right the Siri ship. Taking his place was another Google alum with even closer ties to The Chocolate Factory’s AI strategy, Amar Subramanya, who spent 16 years there, including a turn as the head of Gemini engineering. Subramanya, now Apple’s VP of AI, now reports directly to Apple's SVP of software engineering, Craig Federighi, who himself has assumed responsibility for Apple’s machine learning initiatives, including the construction of Apple foundation models. As we learned at WWDC last week, Apple has leaned heavily on a partnership with Google to build its foundation models, and it appears Subramanya has brought some of that Google AI ethos with him as well. So, what’s the alternative to the new AI bloat in iOS 27? Siri can still be turned off entirely in the Settings app, so there’s that, but I’ve decided to take another tack and use one of Apple’s other AI features to get what I want. As the iMaker mentioned at WWDC, you can now create shortcuts (tiny scripts that automate basic tasks) by making a natural language request to Siri. In my case, I asked it to build a shortcut I could drop on my home screen to do a Google search with whatever text I input. It works perfectly, and is available to duplicate on your own iDevice should you see fit. Again, this is a developer beta, so it’s entirely possible that Apple will wise up and stop burying basic Spotlight search functionality before its 27 series of OSes release to the public this fall. We asked Apple if the change was intentional, but didn’t hear back. ®