Broadsheet

For the past decade and a half, I’ve been exploring the intersection of technology, education, and design as a professor of cognitive science and design at UC San Diego. Some of you might have read my recent piece for O’Reilly Radar where I detailed my journey adding AI chat capabilities to Python Tutor, the free visualization tool that’s helped millions of programming students understand how code executes. That experience got me thinking about my evolving relationship with generative AI as both a tool and a collaborator.

I’ve been intrigued by this emerging practice called “vibe coding,” a term coined by Andrej Karpathy that’s been making waves in tech circles. Simon Willison describes it perfectly: â€œWhen I talk about vibe coding I mean building software with an LLM without reviewing the code it writes.” The concept is both liberating and slightly terrifyingâ€”you describe what you need, the AI generates the code, and you simply run it without scrutinizing each line, trusting the overall “vibe” of what’s been created.

My relationship with this approach has evolved considerably. In my early days of using AI coding assistants, I was that person who meticulously reviewed every single line, often rewriting significant portions. But as these tools have improved, I’ve found myself gradually letting go of the steering wheel in certain contexts. Yet I couldn’t fully embrace the pure “vibe coding” philosophy; the professor in me needed some quality assurance. This led me to develop what I’ve come to call “vibe checks”â€”strategic verification points that provide confidence without reverting to line-by-line code reviews. It’s a middle path that’s worked surprisingly well for my personal projects, and today I want to share some insights from that journey.

Vibe Coding in Practice: Converting 250 HTML Files to Markdown

I’ve found myself increasingly turning to vibe coding for those one-off scripts that solve specific problems in my workflow. These are typically tasks where explaining my intent is actually easier than writing the code myself, especially for data processing or file manipulation jobs where I can easily verify the results.

Let me walk you through a recent example that perfectly illustrates this approach. For a class I teach, I had students submit responses to a survey using a proprietary web app that provided an HTML export option. This left me with 250 HTML files containing valuable student feedback, but it was buried in a mess of unnecessary markup and styling code. What I really wanted was clean Markdown versions that preserved just the text content, section headers, andâ€”criticallyâ€”any hyperlinks students had included in their responses.

Rather than writing this conversion script myself, I turned to Claude with a straightforward request: “Write me a Python script that converts these HTML files to Markdown, preserving text, basic formatting, and hyperlinks.” Claude suggested using the BeautifulSoup library (a solid choice) and generated a complete script that would process all files in a directory, creating a corresponding Markdown file for each HTML source.

(In retrospect, I realized I probably could have used Pandoc for this conversion task. But in the spirit of vibe coding, I just went with Claude’s suggestion without overthinking it. Part of the appeal of vibe coding is bypassing that research phase where you compare different approachesâ€”you just describe what you want and roll with what you get.)

True to the vibe coding philosophy, I didn’t review the generated code line by line. I simply saved it as a Python file, ran it on my directory of 250 HTML files, and waited to see what happened. This “run and see” approach is what makes vibe coding both liberating and slightly nerve-wrackingâ€”you’re trusting the AI’s interpretation of your needs without verifying the implementation details.

Trust and Risk in Vibe Coding: Running Unreviewed Code

The moment I hit “run” on that vibe-coded script, I realized something that might make many developers cringe: I was executing completely unreviewed code on my actual computer with real data. In traditional software development, this would be considered reckless at best. But the dynamics of trust feel different with modern AI tools like Claude 3.7 Sonnet, which has built up a reputation for generating reasonably safe and functional code.

My rationalization was partly based on the script’s limited scope. It was just reading HTML files and creating new Markdown files alongside themâ€”not deleting, modifying existing files, or sending data over the network. Of course, that’s assuming the code did exactly what I asked and nothing more! I had no guarantees that it didn’t include some unexpected behavior since I hadn’t looked at a single line.

This highlights a trust relationship that’s evolving between developers and AI coding tools. I’m much more willing to vibe code with Claude or ChatGPT than I would be with an unknown AI tool from some obscure website. These established tools have reputations to maintain, and their parent companies have strong incentives to prevent their systems from generating malicious code.

That said, I’d love to see operating systems develop a “restricted execution mode” specifically designed for vibe coding scenarios. Imagine being able to specify: “Run this Python script, but only allow it to CREATE new files in this specific directory, prevent it from overwriting existing files, and block internet access.” This lightweight sandboxing would provide peace of mind without sacrificing convenience. (I mention only restricting writes rather than reads because Python scripts typically need to read various system files from across the filesystem, making read restrictions impractical.)

Why not just use VMs, containers, or cloud services? Because for personal-scale projects, the convenience of working directly on my own machine is hard to beat. Setting up Docker or uploading 250 HTML files to some cloud service introduces friction that defeats the purpose of quick, convenient vibe coding. What I want is to maintain that convenience while adding just enough safety guardrails.

Vibe Checks: Simple Scripts to Verify AI-Generated Code

OK now come the “vibe checks.” As I mentioned earlier, the nice thing about these personal data processing tasks is that I can often get a sense of whether the script did what I intended just by examining the output. For my HTML-to-Markdown conversion, I could open up several of the resulting Markdown files and see if they contained the survey responses I expected. This manual spot-checking works reasonably well for 250 files, but what about 2,500 or 25,000? At that scale, I’d need something more systematic.

This is where vibe checks come into play. A vibe check is essentially a simpler script that verifies a basic property of the output from your vibe-coded script. The key here is that it should be much simpler than the original task, making it easier to verify its correctness.

For my HTML-to-Markdown conversion project, I realized I could use a straightforward principle: Markdown files should be smaller than their HTML counterparts since we’re stripping away all the tags. But if a Markdown file is dramatically smallerâ€”say, less than 40% of the original HTML sizeâ€”that might indicate incomplete processing or content loss.

So I went back to Claude and vibe coded a check script. This script simply:

Found all corresponding HTML/Markdown file pairs
Calculated the size ratio for each pair
Flagged any Markdown file smaller than 40% of its HTML source

And lo and behold, the vibe check caught several files where the conversion was incomplete! The original script had failed to properly extract content from certain HTML structures. I took these problematic files, went back to Claude, and had it refine the original conversion script to handle these edge cases.

After a few iterations of this feedback loopâ€”convert, check, identify issues, refineâ€”I eventually reached a point where there were no more suspiciously small Markdown files (well, there were still a few below 40%, but manual inspection confirmed these were correct conversions of HTML files with unusually high markup-to-content ratios).

Now you might reasonably ask: “If you’re vibe coding the vibe check script too, how do you know that script is correct?” Would you need a vibe check for your vibe check? And then a vibe check for that check? Well, thankfully, this recursive nightmare has a practical solution. The vibe check script is typically an order of magnitude simpler than the original taskâ€”in my case, just comparing file sizes rather than parsing complex HTML. This simplicity made it feasible for me to manually review and verify the vibe check code, even while avoiding reviewing the more complex original script.

Of course, my file size ratio check isn’t perfect. It can’t tell me if the content was converted with the proper formatting or if all hyperlinks were preserved correctly. But it gave me a reasonable confidence that no major content was missing, which was my primary concern.

Vibe Coding + Vibe Checking: A Pragmatic Middle Ground

The take-home message here is simple but powerful: When you’re vibe coding, always build in vibe checks. Ask yourself: “What simpler script could verify the correctness of my main vibe-coded solution?” Even an imperfect verification mechanism dramatically increases your confidence in results from code you never actually reviewed.

This approach strikes a nice balance between the speed and creative flow of pure vibe coding and the reliability of more rigorous software development methodologies. Think of vibe checks as lightweight testsâ€”not the comprehensive test suites you’d write for production code, but enough verification to catch obvious failures without disrupting your momentum.

What excites me about the future is the potential for AI coding tools to suggest appropriate vibe checks automatically. Imagine if Claude or similar tools could not only generate your requested script but also proactively offer: “Here’s a simple verification script you might want to run afterward to ensure everything worked as expected.” I suspect if I had specifically asked for this, Claude could have suggested the file size comparison check, but having this built into the system’s default behavior would be incredibly valuable. I can envision specialized AI coding assistants that operate in a semi-autonomous modeâ€”writing code, generating appropriate checks, running those checks, and involving you only when human verification is truly needed.

Combine this with the kind of sandboxed execution environment I mentioned earlier, and you’d have a vibe coding experience that’s both freeing and trustworthyâ€”powerful enough for real work but with guardrails that prevent catastrophic mistakes.

And now for the meta twist: This entire blog post was itself the product of “vibe blogging.” At the start of our collaboration, I uploaded my previous O’Reilly article,”Using Generative AI to Build Generative AI” as a reference document. This gave Claude the opportunity to analyze my writing style, tone, and typical structureâ€”much like how a human collaborator might read my previous work before helping me write something new.

Instead of writing the entire post in one go, I broke it down into sections and provided Claude with an outline for each section one at a time. For every section, I included key points I wanted to cover and sometimes specific phrasings or concepts to include. Claude then expanded these outlines into fully formed sections written in my voice. After each section was drafted, I reviewed itâ€”my own version of a “vibe check”â€”providing feedback and requesting revisions until it matched what I wanted to say and how I wanted to say it.

This iterative, section-by-section approach mirrors the vibe coding methodology Iâ€™ve discussed throughout this post. I didn’t need to write every sentence myself, but I maintained control over the direction, messaging, and final approval. The AI handled the execution details based on my high-level guidance, and I performed verification checks at strategic points rather than micromanaging every word.

What’s particularly interesting is how this process demonstrates the same principles of trust, verification, and iteration that I advocated for in vibe coding. I trusted Claude to generate content in my style based on my outlines, but I verified each section before moving to the next. When something didn’t quite match my intent or tone, we iterated until it did. This balanced approachâ€”leveraging AI capabilities while maintaining human oversightâ€”seems to be the sweet spot for collaborative creation, whether you’re generating code or content.

Epilogue: Behind the Scenes with Claude

[Claude speaking]

Looking back at our vibe blogging experiment, I should acknowledge that Philip noted the final product doesn’t fully capture his authentic voice, despite having his O’Reilly article as a reference. But in keeping with the vibe philosophy itself, he chose not to invest excessive time in endless refinementsâ€”accepting good-enough rather than perfect.

Working section-by-section without seeing the full structure upfront created challenges, similar to painting parts of a mural without seeing the complete design. I initially fell into the trap of copying his outline verbatim rather than transforming it properly.

This collaboration highlights both the utility and limitations of AI-assisted content creation. I can approximate writing styles and expand outlines but still lack the lived experience that gives human writing its authentic voice. The best results came when Philip provided clear direction and feedback.

The meta-example perfectly illustrates the core thesis: Generative AI works best when paired with human guidance, finding the right balance between automation and oversight. “Vibe blogging” has value for drafts and outlines, but like “vibe coding,” some form of human verification remains essential to ensure the final product truly represents what you want to say.

[Philip speaking so that humans get the final wordâ€¦for now]

OK, this is the only part that I wrote by hand: My parting thought when reading over this post is that I’m not proud of the writing quality (sorry Claude!), but if it weren’t for an AI tool like Claude, I would not have written it in the first place due to lack of time and energy. I had enough energy today to outline some rough ideas, then let Claude do the â€œvibe bloggingâ€� for me, but not enough to fully write, edit, and fret over the wording of a full 2,500-word blog post all by myself. Thus, just like with vibe coding, one of the great joys of â€œvibe-ingâ€� is that it greatly lowers the activation energy of getting started on creative personal-scale prototypes and tinkering-style projects. To me, that’s pretty inspiring.

Table 4: PersonQA evaluation
Metric	o3	o4-mini	o1
accuracy (higher is better)	0.59	0.36	0.47
hallucination rate (lower is better)	0.33	0.48	0.16

“There are no American troops in Baghdad!” — Mohammed Saeed al-Sahhaf

Around the middle of 2022, it became apparent that the Biden administration was going to go all-in on industrial policy. The policies that Biden implemented — the CHIPS Act, and the green energy subsidies in the Inflation Reduction Act — had already been in the works for quite a while. But so had a bunch of other Democratic policy ideas that didn’t end up making it into law.1 So it wasn’t until 2022 that we realized that industrial policy was going to be Biden’s Big Thing.

I was personally overjoyed about this trend. For years, I had worried that America’s eroding manufacturing base made it less capable of standing up to China and other authoritarian adversaries. I was also optimistic that a big push for green energy could accelerate decarbonization both at home and abroad. And for years, I had been deeply interested in the successful industrial policies of East Asian countries like Korea and Japan during their catch-up development phases. I was eager to see America try its hand at something similar, in order to revive its stagnant industrial base.

At the same time, I didn’t agree with everything the administration was trying to do. I realized that if the U.S. did experience a manufacturing revival, it would be by leveraging our strengths in automation and technology — in other words, even in the best-case scenario, the era of good plentiful blue-collar manufacturing jobs wasn’t going to come back. Some people in the Biden administration didn’t seem to understand that, and appeared to view industrial policy as a jobs program.

I also disagreed with some of the ways that the Biden administration tried to implement industrial policy. The “buy American” provisions were unnecessary and unhelpful (local demand will foster a network of domestic suppliers anyway). The “everything bagel” requirements for subsidies were too onerous, particularly the union labor requirements in states with very few union workers.

As one of the more prominent commentators writing about these policies, I had a choice to make. I had to decide whether to emphasize my criticisms of Biden’s approach, or my overall positivity about the idea. Then, as the years went by and we started to see some of the results of Biden’s policies, the choice became even more acute, because some of the policies succeeded and some of them failed.

The administration’s core industrial policies seemed to be bearing fruit; there was a massive factory construction boom, most of it financed with private money (the subsidies acting mainly as a nudge). The TSMC plant in Arizona got back on track after some initial hitches. But at the same time, Biden’s administration failed to build any EV chargers or rural broadband, and new transmission lines were stymied by regulatory hurdles. It seemed like the more the government was directly involved with a project, the less likely it was to ever happen; nudging the private sector worked, but direct government action turned into wasteful jobs programs that built nothing.

I had to make a decision: How vehemently and how frequently should I lambast the Biden administration’s failures, and how vehemently and how frequently should I praise its successes? If I leaned too hard toward praise, I might look like an apparatchik who just parroted administration talking points. And even worse, I might fail to perform one of my core functions as an independent commentator — criticizing policy to help steer it in the right direction. But on the other hand, if I leaned too much toward criticism, the people in the administration might decide that I was their political opponent, and stop listening to me entirely. And I might also throw the baby out with the bathwater by ignoring the real successes.

To be honest, I didn’t actually think very hard about any of these tradeoffs at the time. But perhaps subconsciously, I was making these sort of calculations. In any case, I think I ended up striking a pretty good balance between criticism and praise.

Conservative industrialists, however, are facing a much harder dilemma right now. Biden’s industrial policy was a mixed bag, with more successes than failures. But Trump’s tariff policy is a giant flaming disaster. The dollar is down, as investors flee American bonds, putting the country’s whole financial stability in danger. Forecasts for the real economy are getting more pessimistic by the day. Stocks are down yet again. Here’s a representative headline:

Any time you hear the words “worst since 1932” in connection with the American economy, you know things are not going well.

Given this disaster, commentators who support Trump have a hard choice to make. Do they denounce Trump’s policies, thus condemning themselves to certain excommunication from the MAGA movement, and dropping their influence to basically zero without altering Trump’s course in any perceptible way? Or do they scramble to find some way to put lipstick on the tariff pig?

The latter course of action will preserve their influence within MAGA-land, but will also make them look ridiculous to the rest of the country, who has no such ideological commitments to uphold. And in the process, the commentators will become an accomplice to this act of economic self-mutilation.

Oren Cass, the chief economist at American Compass and a prominent advocate of industrial policy from the right, has chosen the second path. He is continuing to fiercely defend the idea that tariffs are a way to reindustrialize the American economy. In a post on April 4th, shortly after Trump’s “Liberation Day” tariffs, Cass defended the tariffs and made some minor suggestions for how Trump might tweak the policy:

As the economic carnage from the tariffs became more apparent, Cass continued to double down. In a debate with Jason Furman and others a week later, Cass argued that trade had devastated American manufacturing in the 2000s. A few days after that, he argued that many Americans will enjoy working in factories:

[I]f fully 25% of poll respondents say they’d prefer a factory job to their current job, that suggests massive potential unmet by the current labor market…One in four?! That would be 40 million of 160 million employed Americans!…When we talk about reindustrializing America, we are talking about adding millions of manufacturing jobs, not tens of millions—several percentage points of the labor force, not a quarter or more…[P]lease, let’s stop with the “nobody wants to work in a factory,” and even more so the “I don’t see you working in factory.”

And a few days after that, Cass argued that Trump’s tariff strategy is primed to succeed, because meeting American domestic demand is more important than exporting:

Our $1 trillion trade deficit presents an enormous opportunity. Most economic analysis of the American position in the global economy seems to assume that export markets represent the key opportunity for U.S. industry and thus success depends upon winning in those markets. From that perspective, for instance, tariffs on the intermediate goods that manufacturers might use in their own production is a disaster. How can a U.S. producer hope to compete with a German producer if the U.S. producer has to pay a tariff on components from China and the German producer does not?
This assumption makes good sense in the typical developing-country situation where the domestic market is small and trade imbalances are likely a minor factor…But the top priority and major opportunity for the United States is not higher exports, it is recovering the capacity to meet domestic demand with domestic supply…Indeed, the U.S. will be building leading-edge semiconductor fabs to meet domestic demand for a long time before it needs to consider export markets at all.

If you’re going to try and mount an intellectual defense of Trump’s tariff policies — instead of just screaming out memes, pointing fingers, and trying to distract people by talking about immigration instead — this is basically how you have to do it. Nothing good is coming of Trump’s tariffs right now, so if you’re going to defend them, you basically have to argue that they represent short term pain for long term gain. In this case, the long-term “gain” is economic self-sufficiency and a bonanza of factory jobs.

Formally speaking, you can’t prove that this is wrong, except by waiting a bunch of years and then observing that reindustrialization didn’t happen. There are no hard and fast economic laws of the Universe; we have a lot of theories, but economies are complex beasts, and the past is an imperfect guide to the future. I can’t completely rule out the possibility that Trump’s tariffs will cause a vast crop of steel factories and shoe factories and semiconductor factories to spring up from the American topsoil like mushrooms after the rain.

And yet when we look at what’s actually happening to American manufacturing in real time, it doesn’t look anything at all like the beginning of the reindustrialization that Cass imagines. Here’s a quick roundup of news from just the last few days:

Volvo is cutting 800 jobs at three U.S. factories, citing tariff uncertainty.
The Philadelphia Manufacturing Survey is plunging, signaling extreme pessimism among U.S. manufacturers.
Howmet Aerospace, a major aircraft parts manufacturer based in Pittsburgh, has declared that it may halt production due to Trump’s tariffs.
The April NY Manufacturing Survey is recording some of the most negative conditions that it has ever recorded, with new orders and shipments falling off a cliff:

The Philadelphia Fed’s survey of manufacturers is also showing a massive dropoff in new orders.
Falling orders are causing Volvo to lay off hundreds of American workers at two factories.
Plenty of evidence shows that American manufacturers are pulling back on their capital spending plans because of tariffs:
Manufacturing pessimism has spiked dramatically in April as industrial producers deal with changing tariff plans and try to assess how global trade policy will impact their costs and operations in the coming months…Several surveys released this week show huge swings in business confidence between January, when most manufacturers had a positive outlook for near-term conditions…and April, when sentiment changed for the much, much worse…Some of the most dramatic declines came from the Equipment Leasing Finance Foundation (ELFF), an organization that represents lenders that help manufacturers obtain new capital equipment for factories. In March, more than half of manufacturers surveyed by that group expected capital spending to increase or stay about the same in the next four months. By April, more than 61% said they expect spending to fall.
Ford is halting sales of some American-made cars to China. (Note: This will increase America’s trade deficit with China).
GM is laying off American factory workers too.
News of various other factory layoffs is proliferating. Cleveland Cliffs, the steel company, is laying off 1200 workers.

It’s not hard to understand why American manufacturing is getting hit hard by tariffs. As I’ve said in many posts, and as knowledgeable folks are screaming from the rooftops, broad tariffs of the type Trump has imposed raise the price of imported components and makes U.S. manufacturers less competitive. Even manufacturers who initially scoffed at this effect are quickly learning what a big problem it is:

And on top of the effect of the tariffs themselves, uncertainty about future tariffs — which has spiked to record levels under Trump — hurts manufacturing even more, because manufacturers don’t know how to plan their future supply chains.

This is not a difficult principle to understand. And yet somehow, Oren Cass appears not to grasp it. In his post that I quoted above, Cass argues that the “imported components” problem only applies to exports, not to manufacturing for the domestic market. That is obviously wrong. For an American auto factory in Kentucky, the cost of components matters just as much whether you’re selling your cars to customers in Dubai or Dallas. Without the ability to source cheap car parts from Mexico and Canada, American manufacturers will see their costs for domestic manufacturing rise.

This is why you can’t just measure the U.S. trade deficit and assume that this amount will be added to U.S. manufacturing if we wall off our economy (as Cass assumes in his post). The amount of domestic demand for manufactured products is not fixed. More expensive components will mean more expensive manufactured products, which will cause Americans to consume less. Americans will become poorer, and they’ll also substitute their consumption away from goods and toward services (whose price will go up by less).

Cass’ hand-waving dismissal of exports also ignores another crucial factor: scale effects. Export markets help American manufacturers scale up their production, which lowers their costs — and thus helps them sell even more to American consumers, capturing more of the U.S. market as well. Commentators like Sam Hammond, who argue that the U.S. should focus on export promotion instead of import substitution, are correct for this reason.

(On top of all that, it’s not clear how much tariffs even reduce trade deficits. As Arnaud Costinot and Ivan Werning show in a new theory paper, it’s possible that even very high tariffs might leave trade deficits mostly unchanged, while simply making an economy poorer.)

All this is theoretical, of course. Cass and other tariff defenders can simply continue to assume that tariffs will reduce trade deficits by a lot, and that this reduction will translate into a whole bunch of new U.S. factories. Perhaps they assume that the unfolding carnage in the U.S. manufacturing industry is simply a temporary disruption, and that after a short period of suffering, American manufacturers will get back on their feet, invest in domestic production capacity, and eventually do better than ever.

Except if that were true, we’d probably see two things. First, we’d see manufacturing stocks doing well, because far-sighted investors would know if American manufacturers would eventually benefit from tariffs. Instead, the stocks of American manufacturers have been crashing. Also, if tariffs provided a protective shield under which American manufacturers could flourish, we’d probably see a bunch of them rushing to invest and build new capacity. But we don’t; capex in the industry is plummeting.

In other words, neither investors nor manufacturers themselves believes in the long-term reindustrialized future that Oren simply assumes will come about. Instead, it appears that tariffs are dramatically accelerating America’s deindustrialization.

And if that’s true, Cass’ other arguments all go up in smoke. Who cares if people like working factory jobs when there are fewer factory jobs to work in? And so on. Every defense of the tariffs is based on this assumption of reindustrialization; if, as looks very likely, that turns out to be a fantasy, the whole edifice collapses.

Oren Cass and other tariff-defending pundits have thus hitched their wagons to a stagecoach that is driving straight off a cliff. They have managed to remain inside the favored circle of the MAGA movement, but this required them to make a terrible sacrifice — they lost their freedom to look out the window and see the calamity unfolding.

I am glad that during the Biden administration, I didn’t have to make any such choice.

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Some of these — like the big subsidies for care industries in the Build Back Better bill — got killed because of lack of political support from the center. Others, like wealth taxes, co-determination, and sectoral bargaining, just never really caught fire.