WTF is Vibe Coding?

Who Are The Vibe Coders?

If you haven't heard the term before, you'd be forgiven for thinking its something out of a Cyberpunk novel. In this section, we profile the "vibe coders" – the primary audience who will benefit from context engineering – and analyze their workflow patterns, pain points, and current practices regarding context. Understanding the end-user is crucial for tailoring context engineering solutions that actually solve their problems.

3.1 Profile of Vibe Coders (Tools, Skills, Mindset)

Who are vibe coders? The term "vibe coder" has emerged from developer communities to describe people who code with the aid of AI, often without deep formal expertise, by leveraging a conversational or trial-and-error approach. They might be experienced devs experimenting with new workflows, or novices able to build projects by leaning heavily on AI suggestions.

Common characteristics of vibe coders: - They utilize AI-powered coding tools such as Claude Desktop, Cursor AI editor, Windsurf (Codeium), VS Code with Copilot or chat extensions, ChatGPT or Claude via web/CLI, and other emerging platforms like Zed or replit's Ghostwriter. Many also explore newer AI coding platforms like Lovable, Bolt.new, and Vercel's v0 which promise end-to-end generation of apps from prompts. - They often adopt a rapid iterative workflow: for example, writing a prompt "Create a React component for X", letting AI generate code, then immediately testing or tweaking with another prompt. - Familiarity with code varies. Some vibe coders are seasoned (there are even senior devs who "turned vibe coder" to boost productivity, as per community anecdotes ), but a notable subset are less experienced programmers. These latter rely on AI for things they don't know – essentially learning on the fly. They may not fully understand the underlying code, but they know how to prompt effectively and "steer" the AI. - Vibe coders are typically early adopters, willing to try new plugins, share tips on Reddit or Discord, and continuously refine their setup. They might have 41 multiple AI tools in their arsenal, e.g., using Cursor for heavy lifting but switching to Claude's 100k context window for reading long files, or using VS Code for debugging with Copilot's help.

Mindset: The vibe coder ethos is pragmatic and exploratory. Instead of meticulously planning software architecture upfront, vibe coders jump straight into prototyping with AI – they "vibe" with the code, meaning they go with the flow and adjust course as needed. It's a bit akin to jam sessions in music: they throw ideas (prompts) at the AI and see what comes back, then build on that. This can be empowering (you can accomplish a lot quickly) but also chaotic if not managed.

Crucially, vibe coders are not afraid of making mistakes (or rather, letting the AI make mistakes) because they see it as part of the discovery process. A meme in these communities is embracing failure as learning – e.g., someone might say "I vibe coded an app; it crashed 5 times until the AI and I finally fixed all issues." This mindset means they value speed and creativity over rigorous correctness on the first try. However, it also means they appreciate tools that can rein in the chaos once a prototype needs polishing.

3.2 Workflow Patterns in Vibe Coding

Several typical workflow patterns have been observed among vibe coders:

• Start with a Prompt, then Refine: Often begins with a broad instruction to the AI ("Build me a simple to-do app with Next.js and Supabase"). The AI generates an initial codebase or scaffold. Next, the vibe coder will run it, see issues or additional needs, and iteratively prompt fixes or new features. This "generate -> test -> refine" loop can repeat dozens of times. The key pattern is that context from previous steps (like what code was already generated) ideally should carry into subsequent prompts – which is precisely where context engineering is needed. Without it, the user may find themselves re-describing parts of the app repeatedly.

• Ask-and-Edit Loop: Another pattern in tools like VS Code with Copilot Chat or Cursor is highlighting code and asking the AI to do something with it ("Explain this code", "Optimize this function", "Find bugs in this snippet"). Here the AI's context is anchored on the selected code. Vibe coders often bounce around the codebase doing these micro-interactions. It's a fluid, non-linear progression: fix one thing here, then jump to another file to implement something, then come back. It's non-sequential, driven by curiosity or immediate needs.

• Multiple Agents or Tools: Some vibe coders use different AI assistants in tandem. They might have ChatGPT in a browser for brainstorming or pseudocode, then use Cursor for actual code editing, and maybe GitHub Copilot inline suggestions for minor completions. This pattern emerges because each tool has strengths (e.g., Claude handles larger context, Copilot is quick for inline code, etc.). The challenge is context fragmentation – switching tools can lose context unless the user manually transfers it (copy-pasting code or summaries between tools). This is an area ripe for improvement, potentially by using a centralized context store that all agents can tap into (MCP could be a step in that direction). But currently, vibe coders themselves often serve as the "bridge" of context between tools, which is cumbersome.

• Frequent Resets or Branching: It's common when vibe coding to hit a point where the AI gets stuck or goes in circles. Users then "reset" the conversation or start a new session (in Cursor, starting a new Composer; in ChatGPT, opening a new chat) . They may preserve some important context manually (like copying the important parts of the previous discussion) but drop the rest. This is essentially context loss by design – sometimes starting fresh yields better results if the context became cluttered with wrong turns. The pattern is: iterate -> if stuck, restart fresh but hopefully wiser. A context-engineered approach could alleviate the need for total resets by better managing and pruning context, but the pattern exists currently.

• Community Feedback Loop: A meta-pattern is vibe coders seeking help from others (on Reddit, etc.) when they hit walls. They'll share prompts, errors, and ask how to prompt better or configure tools. Through this, best practices spread, like "use small, focused prompts" or "when Claude starts rambling, try splitting the task." Many community tips indirectly suggest context strategies (for example, one tip 42 40 might be "remind the AI of what it just did in the next prompt" – essentially user-doing context engineering manually).

A real example from the ClaudeAI subreddit: A user asked, "What is the exact definition of vibe coding?" and one of the linked discussions ("I'm unashamed to say, I have turned into a vibe coder...") describes a workflow where the person uses exclusive git branches for each small task and leverages AI to handle them, merging changes one by one . This indicates a pattern where even traditional practices like branching and committing are adapted to vibe coding – they break tasks into tiny chunks, use AI on each, commit, then proceed. This is almost CI/CD at micro-scale and is a strategy to isolate context and reduce confusion. 43 41

In summary, vibe coding workflows are highly interactive, iterative, and often multi-threaded. This flexibility is their strength but also their Achilles' heel when it comes to keeping context straight. They seldom follow a neat linear plan, so without context engineering, the AI might be acting with incomplete info at any given step.

3.3 Pain Points: Context Loss and Friction

Through observing community discussions and user reports, several pain points keep arising for vibe coders, many of which boil down to context issues:

• AI Forgets Previous Code or Instructions: Perhaps the number one complaint. A user might spend time establishing something with the AI (like writing a helper function or agreeing on an approach), but a dozen messages later, the AI seems oblivious to it and suggests something conflicting or reimplements an existing function. This happens when the context window overflows and earlier content gets pushed out. One Reddit user on r/ChatGPTCoding lamented that vibe coding without saving context leads to "wasting time teaching the AI wtf it wrote earlier" . This frustration is essentially context loss causing redundancy. • 44

• Repetition of Errors: Without memory, the AI can reintroduce bugs or wrong solutions that were already tried. For example, an AI might keep using a variable that was removed, or repeatedly call an API incorrectly even after being corrected once. This loops the user in a cycle of correcting the same thing. It's mentally exhausting and time-consuming.

• Scaling to Larger Projects: Many vibe coders note that while AI works great on a small project, as it grows (files, complexity), the AI "gets confused" or starts producing lots of errors . The context window limitation is one cause; another is that the AI might not maintain a coherent internal model of the project. The result is a steep drop-off in efficiency: what was smooth for 5 files becomes chaotic at 50 files. They experience more "turns" of conversation to get things right, more need to break tasks down, etc. Essentially, the overhead of managing context increases and can cancel out the speed benefits. • 39

• Integration Pain (multiple tools): As mentioned, switching between tools or sessions loses context. If a vibe coder prototypes something in ChatGPT and then wants to move to VS Code for actual coding, they have to manually convey all that context to the new environment. That might involve copy-pasting code or summarizing to the new AI assistant. It's a context handoff problem that's currently clunky. If they don't do it, the new assistant might duplicate work or conflict with what was done.

• Lack of Long-Term Memory Across Sessions: If a coder closes their IDE or ends the day, the next day the AI doesn't remember anything unless provided. So you might have to re-explain the project to the AI. Some tools mitigate this (Cursor can save state in the .cursorrules or project, Windsurf's Memories keep context across sessions ), but many do not. Even those that do, the user must be proactive in setting it up. Without that, every session is like onboarding a new developer from scratch – a clear pain point. • 26

• Hallucination and Incorrect Assumptions: When context is missing, the AI might hallucinate functions or classes that sound plausible but don't exist. Or it might assume something that was true in a generic sense but not in this project. Vibe coders encounter weird outputs that, in hindsight, happened because the AI didn't "know" some specific detail of the project. For example, an AI might invent a UserService class because it assumes one should exist, whereas the project had none. This misleads the user and wastes time. These hallucinations are basically the AI's attempt to fill context gaps with its training data. Ensuring the AI has the actual context eliminates the need for it to make wild guesses.

• Context Overload or Irrelevant Context: Interestingly, giving too much context or the wrong context can also be an issue. If the user dumps a whole wiki or large documentation into the prompt (trying to be thorough), the AI might get bogged down or distracted by irrelevant details. So pain can come from poor context management – either too little (forgetful AI) or too much noise (AI goes off track). Vibe coders sometimes struggle to find the balance, e.g., "Should I paste the entire file? Or just the part I need help with?" If they paste entire codebase parts, they might hit token limits or confuse the model. This indicates a need for smarter context filtering.

• Coordination in Team Settings: If a vibe coder is working in a small team, sharing context becomes tricky. Each person's AI might have seen different conversations. One dev's vibe-coded function might not come with an explanation for others. Without traditional documentation, other team members (or their AI assistants) might not know why code is the way it is. This is less frequently discussed in communities (since many vibe coders are solo or small-scale), but it's a looming issue as soon as collaboration enters. Context engineering solutions could help by providing common context resources for the whole team's AI agents. •

Overall, these pain points highlight that friction in vibe coding arises when context is lost, incomplete, or mismanaged. This leads to wasted time, frustration, and sometimes giving up on the AI ("I'll just write it myself, it would be faster."). By solving context issues, we address the root of many complaints.

3.4 Current Practices and Gaps in Context Management

What are vibe coders doing today to manage context, and where are the gaps that need filling?

Current ad-hoc practices: - Many have learned to break their work into smaller tasks to fit context windows. They might manually copy relevant code into each prompt. For example, when asking for a bug fix, they'll paste the function code and the error message. This is manual context provision. It works but is tedious; essentially the user is acting as the context engine. The gap is automation. - Using the AI's memory creatively: Users keep the same chat thread going for as long as possible so it retains some memory. They might refrain from starting new sessions unless absolutely necessary. Some folks try to coerce a single conversation to cover an entire project, even if it becomes unwieldy. This often ends badly when the context window is exceeded or the model starts forgetting earlier parts anyway (not to mention potential cost with large contexts). So persistence in one session is a tactic, but limited. - External notes and reminders: Some vibe coders maintain a separate text file or paper notes of important things to remind the AI later. For example, writing down variable names or design decisions so they can be retyped if needed. This is again a manual buffer. - Use of documentation generators: A few use tools like Doxygen or docs auto-generation to create documentation from code which they can show to the AI (like asking "Look at the doc for module X" if they have it). But this is rare in the vibe coding crowd, who typically skip formal docs initially. - Some have adopted the practice of writing unit tests or specs first, which implicitly gives context. Writing tests forces you to specify behavior, which then is context for the AI implementing the code. It's almost like documentation. A user might feed the

test to the AI and say "implement this spec." That's context engineering in a way. The gap is not everyone does this, and it requires testing knowledge.

Tool support currently: - Cursor: supports Notepads and a persistent rules file as mentioned . This is a built-in way to store context. However, it requires the user to populate those with the right info. It's powerful but perhaps underutilized by novices who may not realize they should document stuff there. - Windsurf: has Memories and an index . This likely gives it an edge in keeping context automatically. But windsorf is relatively new; not all vibe coders use it yet. For those who do, some gaps were noted: users reported "cascade errors" and reliability issues, indicating it's not perfect . Also, free-tier limitations have been problematic – meaning not everyone can leverage it fully. - VS Code + Copilot Chat: recently introduced workspace search (#codebase) and mentions as we saw . This is a step toward context management, but again it's user-driven (the user has to explicitly ask for #codebase or #usages). It's not yet an automatic context assembly. - Claude Desktop + MCP: This potentially addresses context by letting Claude connect to local files, etc. . Early adopters are excited (some on Reddit share how they set up Claude with their codebase through MCP). However, setting up MCP servers still requires technical steps (editing config, running Node servers) . That's a barrier for less technical vibe coders. So while the tech exists, adoption is a gap – making it seamless is needed. 18 25 26 45 46 35 23 24 47 48

Gaps / unmet needs:

• Unified Context Store: Currently, context is scattered – some in chat history, some in files, some in the user's head. There's no single place a vibe coder can look and say "here's the knowledge base my AI is using." A connected solution that unifies code, docs, and conversation into one accessible store would fill a gap. Airtable or Google Sheets could be hacked into a knowledge base (the user manually logs key info into a sheet). But a specialized system would be better.

• Onboarding AI mid-project: If you bring an AI in halfway through a project (or switch to a new AI tool), it's tedious to bring it up to speed. Ideally, one could point the AI to a "project context package" and have it instantly knowledgeable. There's no standard for that yet. Perhaps something like an AI-readable README or context manifest could be developed (this could be an area for context engineering standards – a file that summarizes the project's context in machine-readable form).

• Context Expiration / Update: Ensuring context stays current as code changes is hard. If you wrote something in a rules file (like "function X does Y"), but later function X changes to do Z, the context source needs updating or it becomes misleading. Right now, it's on the user to update any documentation or rules. Context engineering would ideally include processes to keep context in sync (like maybe automatically update documentation or summary after each commit). This gap often results in stale comments or rules that confuse the AI.

• Community & Learning: Many vibe coders aren't aware of context management techniques. There's a knowledge gap – people might not know about enabling workspace search in Copilot or that they can use Notion API to feed docs to the AI. They might just struggle and assume the AI is inherently flawed. So an educational gap exists, which Deliverable 2 (the LinkedIn article) will aim to address in an accessible way, and the eBook (Deliverable 3) will cover comprehensively.

To sum up, current vibe coder context management is a patchwork of manual tricks and partially utilized tool features. The gaps present an opportunity: by formalizing and automating context engineering, we can dramatically improve the vibe coding experience, smoothing out the friction points identified above.