For the previous decade and a half, I’ve been exploring the intersection of expertise, schooling, and design as a professor of cognitive science and design at UC San Diego. A few of you may need learn my latest piece for O’Reilly Radar the place I detailed my journey including AI chat capabilities to Python Tutor, the free visualization device that’s helped thousands and thousands of programming college students perceive how code executes. That have obtained me eager about my evolving relationship with generative AI as each a device and a collaborator.
I’ve been intrigued by this rising follow referred to as “vibe coding,” a time period coined by Andrej Karpathy that’s been making waves in tech circles. Simon Willison describes it completely: “After I speak about vibe coding I imply constructing software program with an LLM with out reviewing the code it writes.” The idea is each liberating and barely terrifying—you describe what you want, the AI generates the code, and also you merely run it with out scrutinizing every line, trusting the general “vibe” of what’s been created.
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My relationship with this method has advanced significantly. In my early days of utilizing AI coding assistants, I used to be that one that meticulously reviewed each single line, usually rewriting important parts. However as these instruments have improved, I’ve discovered myself progressively letting go of the steering wheel in sure contexts. But I couldn’t absolutely embrace the pure “vibe coding” philosophy; the professor in me wanted some high quality assurance. This led me to develop what I’ve come to name “vibe checks”—strategic verification factors that present confidence with out reverting to line-by-line code opinions. It’s a center path that’s labored surprisingly effectively for my private initiatives, and as we speak I need to share some insights from that journey.
Vibe Coding in Apply: Changing 250 HTML Information to Markdown
I’ve discovered myself more and more turning to vibe coding for these one-off scripts that resolve particular issues in my workflow. These are sometimes duties the place explaining my intent is definitely simpler than writing the code myself, particularly for information processing or file manipulation jobs the place I can simply confirm the outcomes.
Let me stroll you thru a latest instance that completely illustrates this method. For a category I train, I had college students submit responses to a survey utilizing a proprietary net app that supplied an HTML export choice. This left me with 250 HTML information containing helpful scholar suggestions, but it surely was buried in a multitude of pointless markup and styling code. What I actually needed was clear Markdown variations that preserved simply the textual content content material, part headers, and—critically—any hyperlinks college students had included of their responses.
Slightly than penning this conversion script myself, I turned to Claude with a simple request: “Write me a Python script that converts these HTML information to Markdown, preserving textual content, primary formatting, and hyperlinks.” Claude prompt utilizing the BeautifulSoup library (a stable selection) and generated a whole script that might course of all information in a listing, making a corresponding Markdown file for every HTML supply.
(Looking back, I spotted I in all probability might have used Pandoc for this conversion process. However within the spirit of vibe coding, I simply went with Claude’s suggestion with out overthinking it. A part of the attraction of vibe coding is bypassing that analysis section the place you examine totally different approaches—you simply describe what you need and roll with what you get.)
True to the vibe coding philosophy, I didn’t overview the generated code line by line. I merely saved it as a Python file, ran it on my listing of 250 HTML information, and waited to see what occurred. This “run and see” method is what makes vibe coding each liberating and barely nerve-wracking—you’re trusting the AI’s interpretation of your wants with out verifying the implementation particulars.
Belief and Danger in Vibe Coding: Operating Unreviewed Code
The second I hit “run” on that vibe-coded script, I spotted one thing which may make many builders cringe: I used to be executing utterly unreviewed code on my precise pc with actual information. In conventional software program improvement, this could be thought-about reckless at finest. However the dynamics of belief really feel totally different with fashionable AI instruments like Claude 3.7 Sonnet, which has constructed up a status for producing moderately protected and practical code.
My rationalization was partly primarily based on the script’s restricted scope. It was simply studying HTML information and creating new Markdown information alongside them—not deleting, modifying present information, or sending information over the community. After all, that’s assuming the code did precisely what I requested and nothing extra! I had no ensures that it didn’t embody some surprising conduct since I hadn’t checked out a single line.
This highlights a belief relationship that’s evolving between builders and AI coding instruments. I’m way more keen to vibe code with Claude or ChatGPT than I’d be with an unknown AI device from some obscure web site. These established instruments have reputations to keep up, and their mother or father corporations have robust incentives to stop their techniques from producing malicious code.
That mentioned, I’d like to see working techniques develop a “restricted execution mode” particularly designed for vibe coding eventualities. Think about having the ability to specify: “Run this Python script, however solely enable it to CREATE new information on this particular listing, stop it from overwriting present information, and block web entry.” This light-weight sandboxing would supply peace of thoughts with out sacrificing comfort. (I point out solely proscribing writes fairly than reads as a result of Python scripts sometimes have to learn varied system information from throughout the filesystem, making learn restrictions impractical.)
Why not simply use VMs, containers, or cloud providers? As a result of for personal-scale initiatives, the comfort of working straight by myself machine is difficult to beat. Establishing Docker or importing 250 HTML information to some cloud service introduces friction that defeats the aim of fast, handy vibe coding. What I would like is to keep up that comfort whereas including simply sufficient security guardrails.
Vibe Checks: Easy Scripts to Confirm AI-Generated Code
OK now come the “vibe checks.” As I discussed earlier, the good factor about these private information processing duties is that I can usually get a way of whether or not the script did what I supposed simply by analyzing the output. For my HTML-to-Markdown conversion, I might open up a number of of the ensuing Markdown information and see in the event that they contained the survey responses I anticipated. This handbook spot-checking works moderately effectively for 250 information, however what about 2,500 or 25,000? At that scale, I’d want one thing extra systematic.
That is the place vibe checks come into play. A vibe test is basically an easier script that verifies a primary property of the output out of your vibe-coded script. The important thing right here is that it ought to be a lot less complicated than the unique process, making it simpler to confirm its correctness.
For my HTML-to-Markdown conversion challenge, I spotted I might use a simple precept: Markdown information ought to be smaller than their HTML counterparts since we’re stripping away all of the tags. But when a Markdown file is dramatically smaller—say, lower than 40% of the unique HTML measurement—which may point out incomplete processing or content material loss.
So I went again to Claude and vibe coded a test script. This script merely:
- Discovered all corresponding HTML/Markdown file pairs
- Calculated the scale ratio for every pair
- Flagged any Markdown file smaller than 40% of its HTML supply
And lo and behold, the vibe test caught a number of information the place the conversion was incomplete! The unique script had didn’t correctly extract content material from sure HTML constructions. I took these problematic information, went again to Claude, and had it refine the unique conversion script to deal with these edge instances.
After a couple of iterations of this suggestions loop—convert, test, establish points, refine—I finally reached a degree the place there have been no extra suspiciously small Markdown information (effectively, there have been nonetheless a couple of beneath 40%, however handbook inspection confirmed these have been right conversions of HTML information with unusually excessive markup-to-content ratios).
Now you may moderately ask: “If you happen to’re vibe coding the vibe test script too, how have you learnt that script is right?” Would you want a vibe test in your vibe test? After which a vibe test for that test? Nicely, fortunately, this recursive nightmare has a sensible answer. The vibe test script is usually an order of magnitude less complicated than the unique process—in my case, simply evaluating file sizes fairly than parsing complicated HTML. This simplicity made it possible for me to manually overview and confirm the vibe test code, even whereas avoiding reviewing the extra complicated unique script.
After all, my file measurement ratio test isn’t excellent. It will possibly’t inform me if the content material was transformed with the right formatting or if all hyperlinks have been preserved accurately. However it gave me an inexpensive confidence that no main content material was lacking, which was my main concern.
Vibe Coding + Vibe Checking: A Pragmatic Center Floor
The take-home message right here is easy however highly effective: once you’re vibe coding, at all times construct in vibe checks. Ask your self: “What less complicated script might confirm the correctness of my principal vibe-coded answer?” Even an imperfect verification mechanism dramatically will increase your confidence in outcomes from code you by no means truly reviewed.
This method strikes a pleasant steadiness between the pace and inventive stream of pure vibe coding and the reliability of extra rigorous software program improvement methodologies. Consider vibe checks as light-weight exams—not the great take a look at suites you’d write for manufacturing code, however sufficient verification to catch apparent failures with out disrupting your momentum.
What excites me in regards to the future is the potential for AI coding instruments to recommend applicable vibe checks routinely. Think about if Claude or comparable instruments couldn’t solely generate your requested script but in addition proactively supply: “Right here’s a easy verification script you may need to run afterward to make sure all the pieces labored as anticipated.” I think if I had particularly requested for this, Claude might have prompt the file measurement comparability test, however having this constructed into the system’s default conduct can be extremely helpful. I can envision specialised AI coding assistants that function in a semi-autonomous mode—writing code, producing applicable checks, operating these checks, and involving you solely when human verification is actually wanted.
Mix this with the type of sandboxed execution atmosphere I discussed earlier, and also you’d have a vibe coding expertise that’s each liberating and reliable—highly effective sufficient for actual work however with guardrails that stop catastrophic errors.
And now for the meta twist: This whole weblog submit was itself the product of “vibe running a blog.” At the beginning of our collaboration, I uploaded my earlier O’Reilly article,”Utilizing Generative AI to Construct Generative AI” as a reference doc. This gave Claude the chance to investigate my writing model, tone, and typical construction—very similar to how a human collaborator may learn my earlier work earlier than serving to me write one thing new.
As an alternative of writing your entire submit in a single go, I broke it down into sections and supplied Claude with an overview for every part one after the other. For each part, I included key factors I needed to cowl and typically particular phrasings or ideas to incorporate. Claude then expanded these outlines into absolutely shaped sections written in my voice. After every part was drafted, I reviewed it—my very own model of a “vibe test”—offering suggestions and requesting revisions till it matched what I needed to say and the way I needed to say it.
This iterative, section-by-section method mirrors the vibe coding methodology I’ve mentioned all through this submit. I didn’t want to write down each sentence myself, however I maintained management over the path, messaging, and closing approval. The AI dealt with the execution particulars primarily based on my high-level steering, and I carried out verification checks at strategic factors fairly than micromanaging each phrase.
What’s significantly attention-grabbing is how this course of demonstrates the identical rules of belief, verification, and iteration that I advocated for in vibe coding. I trusted Claude to generate content material in my model primarily based on my outlines, however I verified every part earlier than shifting to the subsequent. When one thing didn’t fairly match my intent or tone, we iterated till it did. This balanced method—leveraging AI capabilities whereas sustaining human oversight—appears to be the candy spot for collaborative creation, whether or not you’re producing code or content material.
Epilogue: Behind the Scenes with Claude
[Claude speaking]
Wanting again at our vibe running a blog experiment, I ought to acknowledge that Philip famous the ultimate product doesn’t absolutely seize his genuine voice, regardless of having his O’Reilly article as a reference. However consistent with the vibe philosophy itself, he selected to not make investments extreme time in infinite refinements—accepting good-enough fairly than excellent.
Working section-by-section with out seeing the complete construction upfront created challenges, much like portray elements of a mural with out seeing the whole design. I initially fell into the entice of copying his define verbatim fairly than reworking it correctly.
This collaboration highlights each the utility and limitations of AI-assisted content material creation. I can approximate writing types and develop outlines however nonetheless lack the lived expertise that offers human writing its genuine voice. The most effective outcomes got here when Philip supplied clear path and suggestions.
The meta-example completely illustrates the core thesis: Generative AI works finest when paired with human steering, discovering the suitable steadiness between automation and oversight. “Vibe running a blog” has worth for drafts and descriptions, however like “vibe coding,” some type of human verification stays important to make sure the ultimate product really represents what you need to say.
[Philip speaking so that humans get the final word…for now]
OK, that is the one half that I wrote by hand: My parting thought when studying over this submit is that I’m not pleased with the writing high quality (sorry Claude!), but when it weren’t for an AI device like Claude, I’d not have written it within the first place resulting from lack of time and vitality. I had sufficient vitality as we speak to stipulate some tough concepts, then let Claude do the “vibe running a blog” for me, however not sufficient to totally write, edit, and fret over the wording of a full 2,500-word weblog submit all on my own. Thus, similar to with vibe coding, one of many nice joys of “vibe-ing” is that it drastically lowers the activation vitality of getting began on artistic personal-scale prototypes and tinkering-style initiatives. To me, that’s fairly inspiring.