Vibe coding tips and tricks

Here's a summary of the themes expressed in the Hacker News discussion:

The Nature and Definition of "Vibe Coding"

A significant portion of the discussion revolves around what "vibe coding" actually means, with many users expressing skepticism or offering alternative interpretations. The term itself is often seen as unserious or a meme that has been co-opted.

• Skepticism about the term's seriousness: One user noted the concern that "AI is cool and all, but the biggest thing that makes me think that we’re in a bit of a bubble is seeing otherwise conservative organizations take “vibe coding” seriously" (moolcool).
• "Vibe coding" as trusting the AI without review: A more specific definition is offered: "Vibe coding" means you _don't_ look at the code, you look at the front / back end and accept what you see if it meets your expectations visually, and the code doesn't matter in this case, you "see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works." (colesantiago). This definition implies a delegation of trust to the AI's output without rigorous verification.
• Redefinition and dilution of the term: Some feel the term is being expanded beyond its original intent. "Warning Never blindly trust code generated by AI assistants. Always: - Thoroughly review and understand the generated code - Verify all dependencies - Perform necessary security checks." This of course makes sense, but is not vibe coding. (the_af). Likewise, "Bro you’re harshing the vibe" (bn-l) suggests that stringent review processes are antithetical to the spirit of "vibe coding."
• "Vibe coding" as a broader AI assistance: Conversely, some see "vibe coding" as a catch-all for any AI-assisted coding, even with review. "Couldn't agree with this sentiment more. I think it might have something to do with context rot that all LLMs experience now. Like each token used degrades the token after it, regardless of input/output." (burntpineapple) touches on potential LLM limitations that might necessitate a more loosely defined approach.
• "Vibe coding" versus actual coding: A strong sentiment is that if you are reviewing and understanding the code, you are no longer "vibe coding." "Once you step into reviewing & understanding, you're no longer vibe coding you're just...coding." (thewebguyd).
• The term's origin as a meme: Its meme-like origin is highlighted: "Vibe coding" was intended to mean where you don't pay attention to the work your partner creates at all. Where you just lean into their "vibe" and run with it, no arguably how bad it actually is. What you describe already has a name. You even mentioned it yourself. Also calling it "vibe coding" would be a bit redundant. (xyst) and "Vibe coding" was intended to mean where you don't pay attention to the work your partner creates at all. Where you just lean into their "vibe" and run with it, no matter how bad it actually is. What you describe already has a name. You even mentioned it yourself. Also calling it "vibe coding" would be a bit redundant. (9rx) seem to agree here.

The Financial Incentives and Corporate Adoption of AI Trends

Several users point to financial motivations behind the adoption of AI trends by large organizations, suggesting a "gold rush" mentality.

• Monetary gain: "They get paid the more vibe coding occurs on their platform, so of course they have a two-pizza team dedicated to milking the latest trend." (taormina) directly links platform usage to financial incentives.
• Business strategy: "There is massive financial incentive for them to make it happen for AWS. Between selling more bedrock usage or cutting their own headcount." (alfalfasprout) outlines how AI adoption could lead to increased service revenue or cost savings through staff reduction.
• Jumping on trends: The phrase "the page is an interesting display of a very large bureaucratic institution that is extremely worried about being sued, but is still utterly desperate to get in on the AI bubble before it pops" (blibble) suggests that corporate embrace of AI trends, including terms like "vibe coding," is driven by a desire to not be left behind.

The Effectiveness and Pitfalls of Large Prompts and Detailed Specifications

A significant part of the conversation critiques the idea of providing massive, detailed prompts to LLMs, with many users sharing experiences that contradict this approach or highlight its limitations.

• Overly complex output: Forcing LLMs to adhere to extremely detailed prompts can lead to unnecessarily complex code. "It is mostly because it creates code that is way more complex than it needs to." (nzach) illustrating with an example of a light/dark theme switcher being implemented for a simple web app refactor.
• LLM limitations with large specifications: Users found that detailed, large prompts don't always yield better results and can even degrade performance. "I used to think this was the correct way and based on that was creating some huge prompts for every feature. It took the form of markdown files with hundred of lines, specifying every single detail about the implementation. It seems to be an effective technique at the at the start, but when things get more complex it starts to break down." (nzach) describes this firsthand.
• Context rot and diminishing returns: As the prompt and generated code grow, the LLM's ability to produce working code can decrease. "A lot of information is discovered during development, which causes specs to be outdated or wrong. At that point the LLM context is deeply poisoned, whether from the specs themselves, or from the rest of the codebase. You can try to update the specs or ask for major refactors, but that often introduces new issues. And as the context grows, the chances of producing working code diminish significantly." (imiric) draws parallels to the Waterfall model.
• The need for iterative refinement: A more effective approach for some is to start with simpler prompts and gradually refine them. "After some time I started cutting down on prompt size and things seem to have improved." (nzach) suggests this as a viable strategy.
• Planning models vs. coding models: A nuanced approach suggests using different LLMs for different stages, such as utilizing powerful planning models before switching to code-specific models. "A good next step is to have the model provide a detailed step by step plan to implement the spec. Both steps are best done with a strong planning model like Claude Opus or ChatGPT5, having it write "for my developer", before switching to something like Claude Code." (nestorD) proposes this hybrid strategy.
• The "Followers gonna follow" mindset: Some suggest that the adoption of these potentially flawed methodologies is simply a matter of people following trends. "Followers gonna follow." (esafak) is a concise expression of this sentiment.

The Value of Pseudo-code and Style Transfer in LLM Interactions

A promising and often effective method discussed is leveraging LLMs' strength in "style transfer" by using pseudo-code or natural language descriptions as input.

• Pseudo-code as a driver's seat: This approach keeps the human in control, similar to being a director rather than a passive observer. "The approach I've taken to "vibe coding" is to just write pseudo-code and then ask the LLM to translate. It's a very nice experience because I remain the driver, instead of sitting back and acting like the director of a movie." (danielvaughn) highlights this user-centric benefit.
• LLMs excel at style transfer: The core strength of LLMs in this context is their ability to transform one format or language into another, rather than creating complex logic from scratch. "That's a really powerful approach because LLMs are very very strong at what is basically "style transfer". Much better than they are at writing code from scratch." (jerf) emphasizes this capability.
• Flexibility in input: The pseudo-code can mix natural language, and even snippets of target programming languages, for clarity. "... Notice the mixing of english, python, and rust. I just write what makes sense to me, and I have a very high degree of confidence that the LLM will produce what I want." (danielvaughn) for a Fizz Buzz example.
• "Garbage In, Garbage Out" still applies: While flexible, the quality of the output is highly dependent on the quality of the input specification. "Yep, exactly. "Garbage in, garbage out" still applies." (hardwaregeek).
• Reducing cognitive load by abstracting syntax: The advantage lies in not needing to perfect syntax and trivial language details, freeing up mental energy. "How many programmer hours have been wasted because of trivial coding errors? ... source code requires perfection, whereas pseudo-code takes the pressure off of that last 10%, and IMO that could have significant benefits for cognitive load if not latency." (danielvaughn) articulates this cognitive benefit.
• Language-to-language transfer: This also extends to translating between different programming languages or formats. "Also very good at language-to-language transfer. Not perfect but much better than doing it by hand." (jerf) notes the efficiency gain.

The Importance of Understanding and Reviewing AI-Generated Code

A recurring and strongly emphasized theme is the absolute necessity of thoroughly reviewing and understanding any code produced by AI, regardless of the process used.

• Mandatory review: Several points explicitly state this as a core principle. "Thoroughly review and understand the generated code" is presented as a crucial warning. (the_af, mlhpdx, oeitho).
• AI as a junior developer: A useful analogy is made, comparing the AI to a junior developer who needs oversight. "Sure, sometimes it still does bad things, but I consider it just another junior dev, but with vast knowledge." (toenail) frames the management of AI code.
• The "Catch-22" of AI coding: Requiring thorough understanding seems at odds with the idea of "anyone can code with AI." "It’s a bit of a catch-22 to say “anyone can code with AI” and then make such statements." (mlhpdx) points out this paradox.
• Code obfuscation and complexity: The challenge of understanding complex code, even without AI, is acknowledged, making AI-generated code no exception. "I haven’t felt I thoroughly understood any code after working with C++ and reading the entries in code obfuscation contests." (mlhpdx) highlights past experiences where understanding has been challenging.
• Feedback loop for improvement: Reviewing code provides an opportunity to guide the AI's future output. "Seems to me the result should be that if you aren't sure, your feedback when reviewing the code is that it needs to be more readable. Send it back to the LLM and demand they make it easier to understand." (gs17) suggests a proactive approach to code quality.
• Fundamental coding practice: At its core, reviewing code is just good programming practice that AI assistance doesn't negate. "We do allow LLM agents where I work, but you still need to understand every line of code that you write or generate." (oeitho) reinforces this as a fundamental requirement.

The Search for Practical, Reproducible AI Coding Workflows

Many users are actively experimenting and seeking reproducible methods for integrating AI into their development process, moving beyond anecdotal anecdotes to find practical benefits.

• Seeking real-world examples: There's a desire to see AI used for interesting projects beyond generic "top 10" lists. "I was trying to find some youtuber working on some interesting project using AI to get a feel for how useful it could be but didn't have much luck..." (zppln) expresses this need for practical demonstrations.
• Personal accountability and testing: Some users record their own AI coding experiments to track progress and maintain accountability. "I recorded myself trying it out to port some old apps of mine using Claude code as a first time user of it. I'm not even a youtuber and make these to keep myself accountable, so it's not that fun to watch, but it might be in the direction of your query:" (mchinen) offers a personal example.
• Iterative development and limitations: Current AI agents are not yet fully autonomous for complex tasks; human intervention is still critical. "Show me a long-running agent that can get within 90% of its goal, then I'll be convinced. But right now we barely even have the tools to properly evaluate such agents." (danielvaughn) points to the current limitations of autonomous AI agents.
• Contextual awareness and API documentation: Handling API documentation effectively within prompts is a key challenge for AI. "The biggest issue I've had with vibe coding, by far, is the lack-of and/or outdated documentation for specific APIs. I now spend time gathering as much documentation as possible and inserting it within the prompt as a <documentation> tag, or as a cursor rule." (EcommerceFlow) describes a practical workaround.
• Tools for context management: Projects and tools are emerging to help manage context for LLMs, including integrating documentation into IDEs. "There are tools like context7. Apple is also starting to put markdown files summarizing/detailing APIs for inclusion in LLM context automatically and have shipped these inside Xcode" (wahnfrieden) provides examples of this developing ecosystem.
• The ultimate goal: reducing mundane errors: Even with ongoing experimentation, the potential to reduce wasted hours on trivial coding errors remains a key motivator. "Not to shift the goal post but my intuition has shifted recently as to what I’d consider a “trivial” problem. API details, off-by-one errors, and other issues like that are what I’d lump into that category." (danielvaughn) articulates this focus on cognitive load.

Skepticism and Alternatives to "Vibe Coding"

Some users express a strong preference for traditional coding practices or a more structured approach to AI assistance, viewing "vibe coding" as a step backward or a misdirection.

• Learning frameworks vs. wrestling with agents: The sentiment that investing in learning core tools is more productive than relying on fickle AI is articulated. "Everytime I see these tips and tricks, it reinforces my viewpoint thag it would be more productive to actually learn the abstractions of your framework and your tooling. Instead of wrestling with a capricious agent." (skydhash) advocates for foundational knowledge.
• Focus on complexity reduction and automation: The true benefits of AI should be in automating repetitive tasks and reducing system complexity, not in replacing core thinking. "- A reduction of complexity in your system. - Offloading trivial and repetitive work to automated systems (testing, formatting, and code analysis) - A good information system (documentation, clear tickets, good commits,…) Then you can focus on thinking, instead of typing or generating copious amounts of code." (skydhash) outlines a more productive AI integration.
• "Vibe coding" as writing bad code: For some, "vibe coding" is simply a euphemism for producing erroneous code without understanding. "I really don’t see how vibe coding has any place here. It’s just writing bad code without knowing anything it does." (oceanhaiyang) offers a blunt assessment.
• Suggestions to avoid "vibe coding": Direct advice is given to steer clear of the practice. "Best vibe coding tip: Don't." (Netaro) and My list: 1. Don't. 2. Don't do it. 3. Seriously, don't. (zb3) reflect a strong opposition.
• AI as a fuzzy compiler or translator: A useful mental model for LLMs is that of a "fuzzy compiler" that translates specifications, emphasizing the need for clear inputs. "My mental model for LLMs is that they’re a fuzzy compiler of sorts. Any kind of specification whether that’s BNF or a carefully written prompt will get “translated”. But if you don’t have anything to translate it won’t output anything good." (hardwaregeek) highlights the input-output relationship.
• "Semantic Diffusion" as a more professional term: A more technical term exists in the ML community for this type of interaction. "ML pros call it "Semantic Diffusion", with a smirk, I assume." (DaiPlusPlus) offers an alternative jargon.