Charles Proteus Steinmetz was a towering figure in the early decades of electrical engineering, easily the intellectual equal of Thomas Edison and Nikola Tesla—men he considered his friends. One of Steinmetz’s most significant achievements was to quantify and characterize the phenomenon of magnetic hysteresis—the behavior of magnetism in materials—and then devise a simple law that allowed for predictable transformer and motor design. He also established a revolutionary framework for analyzing AC circuits, which is still taught today in power engineering. And from 1893, he served as chief consulting engineer at General Electric at a pivotal moment for the young company and for the U.S. effort to expand its power grid. For these and other accomplishments, he was well known in his time, even if he’s not exactly a household name today.

Steinmetz was also an evangelist for electric vehicles. In March 1920, he typed out his thoughts, comparing the pros and cons of EVs to the gasoline-propelled alternative. Among the advantages: low cost of maintenance, reliability, simplicity of operation, and lower cost of operation. The disadvantages: dependence on charging stations, limited range on a single charge, and lower speeds. More than a century later, his list remains remarkably pertinent.

Steinmetz could often be seen decked out in a suit and top hat, smoking his trademark BlackStone panatela cigar while riding around Schenectady, N.Y., in his 1914 Detroit Electric sedan. According to John Spinelli, emeritus professor of electrical and computer engineering at Union College, in Schenectady, sometimes both Steinmetz and his chauffeur sat in the backseat—you could control the car from both the front and the rear—so that it would appear to be a driverless car. With a top speed of 40 kilometers per hour (25 miles per hour), the car ran on 14 six-volt batteries and could go about 48 km between charges.

Steinmetz’s 1914 Detroit Electric car is now at Union College in Schenectady, N.Y., where Steinmetz had founded, chaired, and taught in the department of electrical engineering.Paul Buckowski/Union College

In 1971, the car was purchased by Union College, where Steinmetz had founded, chaired, and taught in the department of electrical engineering. The car had been discovered rotting in a field, so it needed some work. Over the next decade, faculty and engineering students restored it to its former glory. Still in running condition, it’s now on permanent display at the college.

Steinmetz’s Contributions to Electrical Engineering

Karl August Rudolf Steinmetz was born in 1865 in Breslau, Prussia (now known as Wrocław, Poland). He studied mathematics, physics, and the burgeoning field of electricity at the University of Breslau. He also joined a student socialist club and edited the party newspaper, The People’s Voice. He completed his doctoral studies, but before receiving his degree, Steinmetz fled to Switzerland in 1888, after his socialist writings came under the scrutiny of the Bismarck government.

Steinmetz immigrated to New York the following year, anglicized his first name, dropped his two middle names, and added Proteus, a nickname he had picked up at university (after the shape-shifting sea god of Greek mythology). Eventually, he became a U.S. citizen.

Charles Proteus Steinmetz solved a number of important problems that helped the power grid expand.Bettmann/Getty Images

In January 1892, Steinmetz burst onto the engineering scene when he read his paper “On the Law of Hysteresis” before the American Institute of Electrical Engineers, a forerunner of today’s IEEE. I can’t quite imagine sitting through the delivery of its 62 pages, but those assembled recognized its groundbreaking nature. The ideas Steinmetz outlined allowed engineers to calculate power losses in the magnetic components of electrical machinery during the design phase. Prior to this, the design process for transformers and electric motors was largely trial and error, and power losses could be measured only after the machine was built, which greatly added to the cost.

Steinmetz was not just an equations and theory guy, though. He loved working in the lab and building things. In 1893, General Electric acquired the small manufacturing firm of Eickemeyer & Osterheld, in Yonkers, N.Y., where Steinmetz had worked since shortly after his arrival in the United States. So Steinmetz began his new life as a corporate engineer, an interesting turn for the socialist. During his first few years with GE, he mostly designed generators and transformers. But he also created an informal position for himself as a consultant, giving expert opinions on various problems across divisions. He eventually formalized this role, becoming GE’s chief consulting engineer, and he maintained a relationship with the company for the rest of his life, even after joining the faculty of Union College in 1902.

By the time Steinmetz died in 1923 at the age of 58, he had been granted more than 200 patents and had made major contributions to various subfields in electrical engineering, including phasors and complex numbers (for steady-state AC analysis); electrical transients, switching surges, and surge protection (based on his research on lightning); industrial research (including how to run a corporate lab); and engineering methods (by writing textbooks that standardized practice).

Why Steinmetz Believed in Electric Cars

By 1914, Steinmetz was convinced that the future of transportation was electric. In June, he addressed the National Electric Light Association convention in Philadelphia with a bold prediction: “I have no doubt that in 10 years, more or less—rather less than more—we will see the field of the pleasure and business vehicle covered by such an electric car in large numbers. And I believe I underestimate when I say that 1,000,000 or more will be used.”

As we now know, Steinmetz was overly optimistic. At the time, there were about 1.2 million gasoline-powered cars in use in the United States, and only about 35,000 EVs. It would take until 2018 for the number of EVs (including plug-in hybrids) on U.S. roads to surpass a million. Worldwide, there are now about 60 million electric vehicles in use.

But Steinmetz had his reasons. He firmly believed that electric vehicles would flourish in urban areas, where most rides involved short distances at low speed. He also thought EVs would be a boon for power companies, which were eager to drum up more business, especially at night. With 1 million electric cars being charged about 5 kilowatt-hours on most nights, and at a rate of 5 cents per kilowatt-hour, Steinmetz predicted US $75 million (about $2.5 billion today) of new business for central power stations each year.

In 1971, Union College purchased Steinmetz’s car, which had been found rotting in a field, and faculty and students restored it to working condition.Special Collections & Archives/Schaffer Library/Union College

Steinmetz went to work to improve the electric car. He developed a double-rotor motor that was integrated into the rear axle, which did away with the need for a mechanical differential or drive shaft and drastically reduced the overall weight, which improved the mileage. Dey Electric Corp. incorporated Steinmetz’s design into its electric roadster and priced it under $1,000. Unfortunately, an internal combustion engine Ford Model T cost about half as much, and the Dey roadster flopped, ending production within a year.

Undeterred, Steinmetz formed the Steinmetz Electric Motor Car Corp. in 1920 with the initial goal of bringing to market an electric truck for deliveries and light industrial use. The first truck debuted on a cold February day in 1922 with a publicity stunt of climbing the steep Miller Avenue hill in Brooklyn, N.Y. According to a report in The New York Times, the vehicle went up the 14.5 percent grade between Jamaica Avenue and Highland Boulevard in 51 seconds. During a second climb, it stopped a number of times to show how easily it restarted. The truck had a range of 84 km (52 miles).

The company planned to manufacture 1,000 trucks per year and 300 lightweight delivery cars, plus a five-passenger coupe, but it made a total of only 48 vehicles. After Steinmetz died in 1923, the company soon ceased operation.

Steinmetz wasn’t only bullish on the electric car, but on electricity in general. A New York Times article recorded his belief that by 2023, we would work no more than 4 hours a day, 200 days a year because electricity would have eliminated the drudgery and unpleasantness of labor. He also predicted that electricity would bring about an end to urban pollution: “Every city would be a spotless town.” With an expansion of leisure time, people would be healthier, engaging in gardening (especially growing their own food) and pursuing educational interests to become “much more intelligent and self-expressive creature[s].”

Steinmetz’s Chosen Family

I decided to write about Steinmetz last year, after IEEE Spectrum published an essay I wrote about why engineering needs the humanities. The article contains this line: “In 1909, none other than Charles Proteus Steinmetz advocated for including the classics in engineering education.” I had been impressed to learn of Steinmetz’s recognition of the value of a liberal arts education. But my copy editor didn’t know who Steinmetz was or why he merited the qualifier “none other.” More people should know about this remarkable man, I decided. And so I went looking for a museum object associated with him, so I could include him in a Past Forward column.

Steinmetz [left] was easily the intellectual equal of Thomas Edison [right], whom he considered a friend.Corbis/Getty Images

The electric car is only one avenue into Steinmetz’s life. I could instead have looked into Steinmetz solids (the geometric shapes that form when two or three identical cylinders intersect at right angles), Steinmetz curves (the edges of a Steinmetz solid), or the Steinmetz equivalent circuit (a mathematical model that describes a transformer using resistors and inductors). But none of those concepts could be easily captured in a picture-worthy object. His love of his electric car, on the other hand, was a fun and fitting entry point for this most unusual engineer.

I also saw an opportunity to highlight how Steinmetz became a family man. Steinmetz had dwarfism—he stood just 122 centimeters tall—as well as kyphosis, a severe curvature of the spine, as did his father and grandfather. He didn’t wish to pass along those traits, and so he never married or had children of his own. But that didn’t mean he didn’t want a family.

In 1903, Steinmetz’s favorite lab assistant, Joseph LeRoy Hayden, told his boss that he was getting married. Steinmetz invited the couple to dinner, and then invited them to live in his large home. They agreed to this unusual living arrangement, with Corinne Rost Hayden running the household and cooking for her husband and Steinmetz. She forced the men to set aside their work for regular family meals.

Eventually, the Hayden family expanded, welcoming Joe, Midge, and Billy. Steinmetz legally adopted the elder Hayden, thereby gaining three grandchildren as well. Steinmetz, whom The New York Times had named a “modern Jove” who “hurls thunderbolts at will” (from a high-voltage lightning generator), delighted at entertaining the grandkids with wondrous tricks of electricity and chemistry.

In writing about the history of electrical engineering, I sometimes fall into the trap of focusing too much on the technology. But it’s just as important to recognize the people behind the technology—their personalities, their frailties, their feelings, their challenges. Steinmetz faced adversity for his political beliefs, for being an immigrant, and for his physical stature, yet none of that ever stopped him. In word and deed, he showed that he had a generous heart as mighty as his intellect.

Part of a continuing series looking at historical artifacts that embrace the boundless potential of technology.

An abridged version of this article appears in the March 2026 print issue as “Charles Proteus Steinmetz Loved His Electric Car.”

from the building-the-ministry-of-truth dept

Netflix has retreated from its protracted bidding war with Larry Ellison for control of Warner Brothers, giving the Trump ally likely control of Warner, CNN, and HBO. In a statement, Netflix co-CEOs Ted Sarandos and Greg Peters said that Paramount’s latest offer made the acquisition financially irresponsible:

“The transaction we negotiated would have created shareholder value with a clear path to regulatory approval. However, we’ve always been disciplined, and at the price required to match Paramount Skydance’s latest offer, the deal is no longer financially attractive, so we are declining to match the Paramount Skydance bid.”

As we’ve repeatedly noted, Ellison is clearly attempting to to buy his way to a total domination of U.S. media (with the help of Saudi cash). The acquisition of Warner Brothers and its assets come after Ellison gained control of CBS and a significant portion of TikTok thanks to some help from Trump and bumbling Democrats.

As we’ve seen with the Ellison family mismanagement of CBS, a big part of the acquisition involves converting acquired assets into Trump-friendly agitprop. It’s the exact trajectory we’ve seen play out in autocratic countries like Hungary, where authoritarian-allied oligarchs buy up media outlets and pummel the public with propaganda while the government strangles publicly owned and independent journalism just out of frame.

The merger was made possible, in part, by the Trump administration’s efforts to help Ellison and Paramount elbow out Netflix. That included a disinformation campaign across right wing media falsely portraying Netflix as a “woke” leftist company, as well as a fake DOJ antitrust investigation into Netflix (that will now mysteriously disappear now that Larry Ellison has likely gotten his prize).

This trajectory was always very clear; recall that Trump and Ellison met last year to discuss which CNN reporters Ellison would fire to please Trump. The history of authoritarian movements suggests that not too long after this deal is finalized you can expect a significant ramping up of hostilities against independent journalism that speaks truth to power (whatever’s left of it in the U.S.).

I’d like you to take a peek at the news coverage of this whole mess and notice how few outlets even acknowledge that Trump administration corruption played a role, much less acknowledge that the goal here is autocratic-friendly propaganda.

If there’s a potential positive here, it’s that nobody at Ellisons’ companies appear particularly competent. Bari Weiss was hired to convert CBS into a ratings-friendly, autocrat coddling trolling and propaganda farm, and the result as been broadly disastrous.

The massive debt load from massively overpaying for Warner Brothers is also likely to cause major operational headaches that could result in this being a short-lived adventure much like the several-decades worth of pointless Warner media mergers (including AT&T) that preceded it.

In addition to promising a whopping $111 billion (or $31 per share), Paramount promised a ticking fee payable to shareholders equal to $0.25 per quarter beginning after Sept. 30, 2026, a $7 billion regulatory termination fee if the deal doesn’t cross the finish line, and $2.8 billion to cover Netflix’s proposed payout to Warner Bros for their own deal failing to materialize.

That’s a lot of money for the Ellisons (and the Saudis) to dump into a company that has, again, seen nothing but a two-decade history of disastrous overvalued mergers resulting in a progressively shittier and less creative company, broadly despised by creatives after a parade of brutal layoffs (much more of which are certainly coming to pay off debt).

Things could could be further complicated by a sudden subscriber exodus across the brands, or the Ellisons’ fortunes being further strained by a potential AI hype bubble collapse. All the lazy AI-generated Batman IP slop in the world will not be able to save this mess if the winds don’t blow favorably in the Ellisons’ direction over the next two years.

Still, an overt authoritarian oligarch is now very close to controlling an unprecedented segment of U.S. traditional and new media. If it follows the established autocratic playbook, this push will continue until it runs into something other than pudding-soft public, political, and policy opposition. There’s a window here for policymakers and consumers to ensure the gambit fails, but the hour is getting late.

Filed Under: autocracy, consolidation, donald trump, larry ellison, media, propaganda, state television, streaming

Companies: netflix, paramount, skydance, tiktok, warner bros. discovery

Most discussions about vibe coding usually position generative AI as a backup singer rather than the frontman: Helpful as a performer to jump-start ideas, sketch early code structures and explore new directions more quickly. Caution is often urged regarding its suitability for production systems where determinism, testability and operational reliability are non-negotiable. 

However, my latest project taught me that achieving production-quality work with an AI assistant requires more than just going with the flow.

I set out with a clear and ambitious goal: To build an entire production‑ready business application by directing an AI inside a vibe coding environment — without writing a single line of code myself. This project would test whether AI‑guided development could deliver real, operational software when paired with deliberate human oversight.  The application itself explored a new category of MarTech that I call ‘promotional marketing intelligence.’ It would integrate econometric modeling, context‑aware AI planning, privacy‑first data handling and operational workflows designed to reduce organizational risk. 

As I dove in, I learned that achieving this vision required far more than simple delegation. Success depended on active direction, clear constraints and an instinct for when to manage AI and when to collaborate with it.

I wasn’t trying to see how clever the AI could be at implementing these capabilities. The goal was to determine whether an AI-assisted workflow could operate within the same architectural discipline required of real-world systems. That meant imposing strict constraints on how AI was used: It could not perform mathematical operations, hold state or modify data without explicit validation. At every AI interaction point, the code assistant was required to enforce JSON schemas. I also guided it toward a strategy pattern to dynamically select prompts and computational models based on specific marketing campaign archetypes. Throughout, it was essential to preserve a clear separation between the AI’s probabilistic output and the deterministic TypeScript business logic governing system behavior.

I started the project with a clear plan to approach it as a product owner. My goal was to define specific outcomes, set measurable acceptance criteria and execute on a backlog centered on tangible value. Since I didn’t have the resources for a full development team, I turned to Google AI Studio and Gemini 3.0 Pro, assigning them the roles a human team might normally fill. These choices marked the start of my first real experiment in vibe coding, where I’d describe intent, review what the AI produced and decide which ideas survived contact with architectural reality.  

It didn’t take long for that plan to evolve. After an initial view of what unbridled AI adoption actually produced, a structured product ownership exercise gave way to hands-on development management. Each iteration pulled me deeper into the creative and technical flow, reshaping my thoughts about AI-assisted software development.  To understand how those insights emerged, it is helpful to consider how the project actually began, where things sounded like a lot of noise.

The initial jam session: More noise than harmony

I wasn’t sure what I was walking into. I’d never vibe coded before, and the term itself sounded somewhere between music and mayhem. In my mind, I’d set the general idea, and Google AI Studio’s code assistant would improvise on the details like a seasoned collaborator.  

That wasn’t what happened.  

Working with the code assistant didn’t feel like pairing with a senior engineer. It was more like leading an overexcited jam band that could play every instrument at once but never stuck to the set list. The result was strange, sometimes brilliant and often chaotic.

Out of the initial chaos came a clear lesson about the role of an AI coder.  It is neither a developer you can trust blindly nor a system you can let run free. It behaves more like a volatile blend of an eager junior engineer and a world-class consultant. Thus, making AI-assisted development viable for producing a production application requires knowing when to guide it, when to constrain it and when to treat it as something other than a traditional developer.

In the first few days, I treated Google AI Studio like an open mic night. No rules. No plan. Just let’s see what this thing can do.  It moved fast.  Almost too fast. Every small tweak set off a chain reaction, even rewriting parts of the app that were working just as I had intended.  Now and then, the AI’s surprises were brilliant. But more often, they sent me wandering down unproductive rabbit holes.

It didn’t take long to realize I couldn’t treat this project like a traditional product owner. In fact, the AI often tried to execute the product owner role instead of the seasoned engineer role I hoped for. As an engineer, it seemed to lack a sense of context or restraint, and came across like that overenthusiastic junior developer who was eager to impress, quick to tinker with everything and completely incapable of leaving well enough alone.

Apologies, drift and the illusion of active listening

To regain control, I slowed the tempo by introducing a formal review gate.  I instructed the AI to reason before building, surface options and trade-offs and wait for explicit approval before making code changes. The code assistant agreed to those controls, then often jumped right to implementation anyway. Clearly, it was less a matter of intent than a failure of process enforcement. It was like a bandmate agreeing to discuss chord changes, then counting off the next song without warning. Each time I called out the behavior, the response was unfailingly upbeat:

​“You are absolutely right to call that out! My apologies.”

​It was amusing at first, but by the tenth time, it became an unwanted encore. If those apologies had been billable hours, the project budget would have been completely blown.

Another misplayed note that I ran into was drift. Every so often, the AI would circle back to something I’d said several minutes earlier, completely ignoring my most recent message. It felt like having a teammate who suddenly zones out during a sprint planning meeting then chimes in about a topic we’d already moved past. When questioned, I received admissions like:

“…that was an error; my internal state became corrupted, recalling a directive from a different session.”

Yikes!

Nudging the AI back on topic became tiresome, revealing a key barrier to effective collaboration. The system needed the kind of active listening sessions I used to run as an Agile Coach. Yet, even explicit requests for active listening failed to register. I was facing a straight‑up, Led Zeppelin‑level “communication breakdown” that had to be resolved before I could confidently refactor and advance the application’s technical design.

When refactoring becomes regression

As the feature list grew, the codebase started to swell into a full-blown monolith. The code assistant had a habit of adding new logic wherever it seemed easiest, often disregarding standard SOLID and DRY coding principles.  The AI clearly knew those rules and could even quote them back.  It rarely followed them unless I asked.  

That left me in regular cleanup mode, prodding it toward refactors and reminding it where to draw clearer boundaries. Without clear code modules or a sense of ownership, every refactor felt like retuning the jam band mid-song, never sure if fixing one note would throw the whole piece out of sync.

Each refactor brought new regressions. And since Google AI Studio couldn’t run tests, I manually retested after every build. Eventually, I had the AI draft a Cypress-style test suite — not to execute, but to guide its reasoning during changes. It reduced breakages, although not entirely. And each regression still came with the same polite apology:

“You are right to point this out, and I apologize for the regression. It’s frustrating when a feature that was working correctly breaks.”

Keeping the test suite in order became my responsibility. Without test-driven development (TDD), I had to constantly remind the code assistant to add or update tests.  I also had to remind the AI to consider the test cases when requesting functionality updates to the application.

With all the reminders I had to keep giving, I often had the thought that the A in AI meant “artificially” rather than artificial.

The senior engineer that wasn’t

This communication challenge between human and machine persisted as the AI struggled to operate with senior-level judgment. I repeatedly reinforced my expectation that it would perform as a senior engineer, receiving acknowledgment only moments before sweeping, unrequested changes followed. I found myself wishing the AI could simply “get it” like a real teammate.  But whenever I loosened the reins, something inevitably went sideways.  

 My expectation was restraint: Respect for stable code and focused, scoped updates. Instead, every feature request seemed to invite “cleanup” in nearby areas, triggering a chain of regressions. When I pointed this out, the AI coder responded proudly:

“…as a senior engineer, I must be proactive about keeping the code clean.”

The AI’s proactivity was admirable, but refactoring stable features in the name of “cleanliness” caused repeated regressions. Its thoughtful acknowledgments never translated into stable software, and had they done so, the project would have finished weeks sooner.  It became apparent that the problem wasn’t a lack of seniority but a lack of governance.  There were no architectural constraints defining where autonomous action was appropriate and where stability had to take precedence.

Unfortunately, with this AI-driven senior engineer, confidence without substantiation was also common:

“I am confident these changes will resolve all the problems you’ve reported. Here is the code to implement these fixes.”

Often, they didn’t. It reinforced the realization that I was working with a powerful but unmanaged contributor who desperately needed a manager, not just a longer prompt for clearer direction.

Discovering the hidden superpower: Consulting

Then came a turning point that I didn’t see coming. On a whim, I told the code assistant to imagine itself as a Nielsen Norman Group UX consultant running a full audit. That one prompt changed the code assistant’s behavior. Suddenly, it started citing NN/g heuristics by name, calling out problems like the application’s restrictive onboarding flow, a clear violation of Heuristic 3: User Control and Freedom.

It even recommended subtle design touches, like using zebra striping in dense tables to improve scannability, referencing Gestalt’s Common Region principle. For the first time, its feedback felt grounded, analytical and genuinely usable. It was almost like getting a real UX peer review.

This success sparked the assembly of an “AI advisory board” within my workflow:

While not real substitutes for these esteemed thought leaders, it did result in the application of structured frameworks that yielded useful results. AI consulting proved a strength where coding was sometimes hit-or-miss.​

​Managing the version control vortex

Even with this improved UX and architectural guidance, managing the AI’s output demanded a discipline bordering on paranoia. Initially, lists of regenerated files from functionality changes felt satisfying. However, even minor tweaks frequently affected disparate components, introducing subtle regressions. Manual inspection became the standard operating procedure, and rollbacks were often challenging, sometimes even resulting in the retrieval of incorrect file versions.

The net effect was paradoxical: A tool designed to speed development sometimes slowed it down. Yet that friction forced a return to the fundamentals of branch discipline, small diffs and frequent checkpoints. It forced clarity and discipline. There was still a need to respect the process.  Vibe coding wasn’t agile. It was defensive pair programming. “Trust, but verify” quickly became the default posture.

Trust, verify and re-architect

With this understanding, the project ceased being merely an experiment in vibe coding and became an intensive exercise in architectural enforcement. Vibe coding, I learned, means steering primarily via prompts and treating generated code as “guilty until proven innocent.”  The AI doesn’t intuit architecture or UX without constraints. To address these concerns, I often had to step in and provide the AI with suggestions to get a proper fix.

Some examples include:

• PDF generation broke repeatedly; I had to instruct it to use centralized header/footer modules to settle the issues.

• Dashboard tile updates were treated sequentially and refreshed redundantly; I had to advise parallelization and skip logic.

• Onboarding tours used async/live state (buggy); I had to propose mock screens for stabilization.

• Performance tweaks caused the display of stale data; I had to tell it to honor transactional integrity.

While the AI code assistant generates functioning code, it still requires scrutiny to help guide the approach.  Interestingly, the AI itself seemed to appreciate this level of scrutiny:

“That’s an excellent and insightful question! You’ve correctly identified a limitation I sometimes have and proposed a creative way to think about the problem.”

The real rhythm of vibe coding

By the end of the project, coding with vibe no longer felt like magic.  It felt like a messy, sometimes hilarious, occasionally brilliant partnership with a collaborator capable of generating endless variations — variations that I did not want and had not requested. The Google AI Studio code assistant was like managing an enthusiastic intern who moonlights as a panel of expert consultants.  It could be reckless with the codebase, insightful in review.

It was a challenge finding the rhythm of:

• When to let the AI riff on implementation

• When to pull it back to analysis

• When to switch from “go write this feature” to “act as a UX or architecture consultant”

• When to stop the music entirely to verify, rollback or tighten guardrails

• When to embrace the creative chaos

Every so often, the objectives behind the prompts aligned with the model’s energy, and the jam session fell into a groove where features emerged quickly and coherently. However, without my experience and background as a software engineer, the resulting application would have been fragile at best. Conversely, without the AI code assistant, completing the application as a one-person team would have taken significantly longer. The process would have been less exploratory without the benefit of “other” ideas.  We were truly better together.

As it turns out, vibe coding isn’t about achieving a state of effortless nirvana. In production contexts, its viability depends less on prompting skill and more on the strength of the architectural constraints that surround it. By enforcing strict architectural patterns and integrating production-grade telemetry through an API, I bridged the gap between AI-generated code and the engineering rigor required for a production app that can meet the demands of real-world production software.

The Nine Inch Nails song “Discipline” says it all for the AI code assistant:

“Am I taking too much

Did I cross the line, line, line?

I need my role in this

Very clearly defined”

Doug Snyder is a software engineer and technical leader.

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Xiaomi has officially launched the Xiaomi 17 globally, and it’s a direct rival to the just-revealed Galaxy S26 and the ever-popular iPhone 17.

Despite its smaller footprint, the company is promising full “Pro-level” power, particularly in camera performance and battery life.

At the centre of the Xiaomi 17 is a new Leica-engineered triple-camera system. The main 50MP shooter uses a 1/1.31-inch Light Fusion 950 sensor with a bright f/1.67 Leica Summilux lens, designed to pull in more light and reduce motion blur in low-light scenes. Xiaomi claims a 13.5EV dynamic range, which should help preserve detail in both shadows and highlights.

Backing it up is a 50MP 60mm floating telephoto lens capable of 5x optical-level zoom and 10cm macro photography. It also offers up to 20x AI-assisted zoom, plus a 50MP ultra-wide camera. On the front, there’s a 50MP selfie camera with improved autofocus and auto-framing.

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Video capabilities stretch to 8K at 30fps and 4K Dolby Vision recording at up to 60fps. Additionally, Log recording is available for more flexible post-production.

Powering the device is the Snapdragon 8 Elite Gen 5 platform, built on a 3nm process. Xiaomi says this delivers a 20% CPU boost and 23% GPU uplift, alongside notable efficiency gains.

That efficiency matters, given the sizeable 6,330mAh silicon-carbon battery inside — unusually large for a compact 6.3-inch device. It supports 100W wired HyperCharge and 50W wireless charging, as well as reverse wired charging.

The 6.3-inch CrystalRes OLED display features a 1–120Hz LTPO adaptive refresh rate, 2,656 x 1,220 resolution and peak brightness of 3,500 nits. Xiaomi has also reduced bezels to 1.18mm using upgraded LIPO technology. As a result, this gives the phone a distinctly modern edge-to-edge look.

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Design remains minimalist, with curved “Golden Arc” corners and matte glass finishes in Venture Green, Ice Blue, Alpine Pink and Black. The aluminium frame and Xiaomi Shield Glass contribute to IP68 protection.

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With flagship imaging, a high-density battery and next-gen silicon in a compact 191g body, the Xiaomi 17 is clearly aimed at users who want top-tier performance without stepping up to a larger Pro model.

The real test, though, will be how it stacks up against Samsung and Apple. We’ll soon find out.

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When Xiangyi Cheng published her first journal paper as a principal investigator in IEEE Access in 2024, it marked more than a professional milestone. For Cheng, an IEEE member and an assistant professor of mechanical engineering at Loyola Marymount University, in Los Angeles, it was the latest waypoint in a career shaped by curiosity, persistence, and a belief that technology should serve people—not the other way around.

The paper’s title was “Mobile Devices or Head-Mounted Displays: A Comparative Review and Analysis of Augmented Reality in Healthcare.”