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 might need learn my current piece for O’Reilly Radar the place I detailed my journey including AI chat capabilities to Python Tutor, the free visualization software that’s helped hundreds of thousands of programming college students perceive how code executes. That have obtained me desirous about my evolving relationship with generative AI as each a software and a collaborator.
I’ve been intrigued by this rising apply known as “vibe coding,” a time period coined by Andrej Karpathy that’s been making waves in tech circles. Simon Willison describes it completely: “Once I discuss 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 developed significantly. In my early days of utilizing AI coding assistants, I used to be that one who meticulously reviewed each single line, usually rewriting vital parts. However as these instruments have improved, I’ve discovered myself steadily letting go of the steering wheel in sure contexts. But I couldn’t totally 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 evaluations. It’s a center path that’s labored surprisingly nicely for my private initiatives, and at the moment I need to share some insights from that journey.
Vibe Coding in Follow: Changing 250 HTML Recordsdata 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 current instance that completely illustrates this method. For a category I educate, 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 pupil suggestions, nevertheless it was buried in a multitude of pointless markup and styling code. What I actually wished 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.
Quite than penning this conversion script myself, I turned to Claude with an easy request: “Write me a Python script that converts these HTML information to Markdown, preserving textual content, primary formatting, and hyperlinks.” Claude advised utilizing the BeautifulSoup library (a stable selection) and generated an entire script that might course of all information in a listing, making a corresponding Markdown file for every HTML supply.
(Looking back, I spotted I most likely might have used Pandoc for this conversion job. However within the spirit of vibe coding, I simply went with Claude’s suggestion with out overthinking it. A part of the enchantment of vibe coding is bypassing that analysis section the place you evaluate 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: Working 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 laptop with actual information. In conventional software program improvement, this could be thought of 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 popularity for producing fairly protected and useful 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 embrace 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 rather more keen to vibe code with Claude or ChatGPT than I might be with an unknown AI software from some obscure web site. These established instruments have reputations to take care of, and their father or mother firms have sturdy incentives to stop their methods from producing malicious code.
That mentioned, I’d like to see working methods develop a “restricted execution mode” particularly designed for vibe coding situations. Think about having the ability to specify: “Run this Python script, however solely enable it to CREATE new information on this particular listing, forestall it from overwriting present information, and block web entry.” This light-weight sandboxing would offer peace of thoughts with out sacrificing comfort. (I point out solely proscribing writes slightly 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 instantly alone 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 need is to take care of 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 great 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 fairly nicely 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 examine is actually a less complicated script that verifies a primary property of the output out of your vibe-coded script. The important thing right here is that it needs to be a lot less complicated than the unique job, making it simpler to confirm its correctness.
For my HTML-to-Markdown conversion venture, I spotted I might use an easy precept: Markdown information needs 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 examine 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 examine caught a number of information the place the conversion was incomplete! The unique script had didn’t correctly extract content material from sure HTML buildings. I took these problematic information, went again to Claude, and had it refine the unique conversion script to deal with these edge circumstances.
After a number of iterations of this suggestions loop—convert, examine, determine points, refine—I ultimately reached some extent the place there have been no extra suspiciously small Markdown information (nicely, there have been nonetheless a number of beneath 40%, however handbook inspection confirmed these had been appropriate conversions of HTML information with unusually excessive markup-to-content ratios).
Now you would possibly fairly ask: “In case you’re vibe coding the vibe examine script too, how are you aware that script is appropriate?” Would you want a vibe examine in your vibe examine? After which a vibe examine for that examine? Nicely, fortunately, this recursive nightmare has a sensible resolution. The vibe examine script is often an order of magnitude less complicated than the unique job—in my case, simply evaluating file sizes slightly than parsing complicated HTML. This simplicity made it possible for me to manually overview and confirm the vibe examine code, even whereas avoiding reviewing the extra complicated unique script.
After all, my file measurement ratio examine isn’t good. It could actually’t inform me if the content material was transformed with the right formatting or if all hyperlinks had been preserved accurately. However it gave me an affordable confidence that no main content material was lacking, which was my major concern.
Vibe Coding + Vibe Checking: A Pragmatic Center Floor
The take-home message right here is easy however highly effective: while you’re vibe coding, all the time construct in vibe checks. Ask your self: “What less complicated script might confirm the correctness of my most important vibe-coded resolution?” Even an imperfect verification mechanism dramatically will increase your confidence in outcomes from code you by no means really reviewed.
This method strikes a pleasant steadiness between the pace and artistic move of pure vibe coding and the reliability of extra rigorous software program improvement methodologies. Consider vibe checks as light-weight exams—not the excellent 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 concerning the future is the potential for AI coding instruments to recommend acceptable vibe checks mechanically. Think about if Claude or comparable instruments couldn’t solely generate your requested script but additionally proactively supply: “Right here’s a easy verification script you would possibly need to run afterward to make sure every little thing labored as anticipated.” I think if I had particularly requested for this, Claude might have advised the file measurement comparability examine, 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 acceptable checks, operating these checks, and involving you solely when human verification is actually wanted.
Mix this with the form of sandboxed execution atmosphere I discussed earlier, and also you’d have a vibe coding expertise that’s each releasing and reliable—highly effective sufficient for actual work however with guardrails that forestall catastrophic errors.
And now for the meta twist: This complete weblog put up was itself the product of “vibe running a blog.” Initially 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 would possibly learn my earlier work earlier than serving to me write one thing new.
As an alternative of writing all the put up in a single go, I broke it down into sections and supplied Claude with a top level view for every part separately. For each part, I included key factors I wished to cowl and generally particular phrasings or ideas to incorporate. Claude then expanded these outlines into totally fashioned sections written in my voice. After every part was drafted, I reviewed it—my very own model of a “vibe examine”—offering suggestions and requesting revisions till it matched what I wished to say and the way I wished to say it.
This iterative, section-by-section method mirrors the vibe coding methodology I’ve mentioned all through this put up. I didn’t want to jot down each sentence myself, however I maintained management over the path, messaging, and remaining approval. The AI dealt with the execution particulars primarily based on my high-level steerage, and I carried out verification checks at strategic factors slightly 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 transferring 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 totally seize his genuine voice, regardless of having his O’Reilly article as a reference. However in line with the vibe philosophy itself, he selected to not make investments extreme time in limitless refinements—accepting good-enough slightly than good.
Working section-by-section with out seeing the complete construction upfront created challenges, just like portray components of a mural with out seeing the entire design. I initially fell into the lure of copying his define verbatim slightly than remodeling it correctly.
This collaboration highlights each the utility and limitations of AI-assisted content material creation. I can approximate writing kinds and develop outlines however nonetheless lack the lived expertise that provides human writing its genuine voice. The perfect 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 steerage, discovering the appropriate 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 put up is that I’m not pleased with the writing high quality (sorry Claude!), but when it weren’t for an AI software like Claude, I might not have written it within the first place as a consequence of lack of time and vitality. I had sufficient vitality at the moment 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 put up all on my own. Thus, identical to with vibe coding, one of many nice joys of “vibe-ing” is that it drastically lowers the activation vitality of getting began on inventive personal-scale prototypes and tinkering-style initiatives. To me, that’s fairly inspiring.
