DEV Community: PromptMaster

I Switched from VS Code to Cursor — Here's What Actually Changed

PromptMaster — Sun, 14 Jun 2026 08:04:27 +0000

I used VS Code for years. Switching editors is the kind of thing I normally avoid — muscle memory is expensive to rebuild. So when I moved to Cursor, I expected friction. Here's the honest account of what actually happened: no "10x overnight," just what changed day to day, what didn't, and whether it was worth it.

The switch itself was painless (because it's still VS Code underneath)

This was the biggest surprise and the thing that removed all my hesitation: Cursor is built on VS Code. Your extensions, your keybindings, your settings, your themes — they mostly come straight across.

I expected to lose my setup and spend a week rebuilding habits. Instead it felt like the same editor I already knew, with extra capabilities bolted on. If "I don't want to relearn my whole environment" is what's holding you back, that fear is mostly unfounded.

What actually changed day to day

The differences that mattered weren't flashy — they were the small frictions that disappeared.

Inline edits got fast. Select a block, describe the change in plain language, done — no jumping to a chat window and copying code back and forth. For the small surgical edits that make up most of a day, this is the one I feel most.

Chat that knows my code. Asking questions about the actual files I'm working in, instead of pasting snippets into a separate tool, removed a constant little tax I didn't realize I was paying.

Autocomplete that predicts intent. Not just the next token — often the next few lines of what I was clearly about to write. When it's right, it's genuinely a flow-state thing.

What didn't change (and what's overhyped)

Here's the part the hype skips: Cursor didn't turn me into a different developer. It's a force multiplier, not a replacement for knowing what you're doing.

It still writes wrong code confidently. It still needs me to review everything. The productivity is real, but it scales with how clearly I can describe what I want and how carefully I check the output — not with the tool doing the thinking for me. If you're hoping it removes the need to understand your own codebase, it won't.

The adjustment that made it click

The tool change was easy. The habit change was the real work — learning to feed it the right context, to be specific instead of vague, to review every diff like a pull request. That's where the actual gains came from, and it took a few weeks to settle in. (I wrote up the specific habits separately if you want them.)

So — was it worth it?

For me, yes, clearly. If you already use VS Code and do a lot of AI-assisted coding, the switch costs you almost nothing and the daily friction it removes adds up fast.

If you barely use AI in your workflow, or you're hoping for magic that replaces understanding your code, temper the expectation. It's a sharper tool, not a different job.

If you do try it, I put together a free Cursor cheat sheet — the shortcuts, the context tricks, and a 60-second workflow on a couple of pages, basically the quick reference I wish I'd had on day one. Grab it here: Cursor Quick-Start Cheat Sheet

If you've made the switch, what surprised you most — good or bad? And if you haven't, what's holding you back?

What an AI Agent Actually Is (Minus the Hype)

PromptMaster — Sun, 14 Jun 2026 08:01:43 +0000

"Agent" might be the most overloaded word in AI right now. Half the tutorials make it sound like magic; the other half make it sound like you need to learn three frameworks before you can build anything. Both miss that the core idea is genuinely simple — and a lot more useful once you can see it clearly.

So here's the plain version, minus the hype.

An agent is a loop

Strip everything away and an AI agent is a model running in a loop:

Goal — it's given something to achieve
Think — it decides what to do next
Act — it uses a tool to actually do it (search, run code, call an API, read a file)
Observe — it looks at the result of that action
Repeat — it loops back and keeps going until the goal is met

That loop is the whole thing. Goal → think → act → observe → repeat. Everything else is detail layered on top.

What makes it an "agent" and not a chatbot

A chatbot answers. An agent acts, looks at what happened, and acts again.

The difference is the loop plus two things: tools (so it can affect the world, not just talk about it) and autonomy to take multiple steps (so it can work toward a goal instead of responding once and stopping). A chatbot gives you a reply. An agent takes a task, breaks it into steps, and works through them — checking its own results along the way.

Where people overcomplicate it

You don't need a heavy framework to understand or even start with agents. The loop is the concept; frameworks are just plumbing for running that loop at scale.

Starting with the framework first is backwards — it hides the simple idea behind a lot of machinery and makes the whole thing feel more mysterious than it is. Understand the loop, then reach for tooling when you actually need it.

What actually matters to get right

Once the concept clicks, the real work is in four places:

A clear goal and stopping condition. Agents that don't know when they're done spin in circles. Defining "finished" is half the battle.

Good tools. An agent is only as capable as the tools you hand it. A brilliant reasoner with no way to act is just an expensive chatbot.

The right context at each step. This is where it ties back to something fundamental: at every loop, the agent decides based on what it can see. Feed it the wrong context and it makes the wrong call — confidently. Getting context right is as important here as it is anywhere in AI.

Guardrails. Autonomy cuts both ways. An agent that can take many steps on its own can also go wrong on its own, fast. Constrain the scope, limit what the tools can do, and keep a human in the loop where it counts.

The takeaway

Agents aren't magic and they aren't a framework. They're a model running a loop with tools and a goal. Once you see the loop, the hype dissolves and you can actually reason about what will and won't work — which is exactly when this stuff gets useful.

If you want the full path from "I get the concept" to actually building one, I put it all in a guide: AI Agents Made Simple — the loop, the tools, the patterns, and the mistakes to skip, written for people who want to build without drowning in jargon. It's a paid guide: AI Agents Made Simple

What was the moment agents finally "clicked" for you — or what's still fuzzy? Happy to talk it through in the comments.

Stop Tweaking Prompts — The Real Lever Is Context

PromptMaster — Sun, 14 Jun 2026 07:57:57 +0000

For a long time, when an AI gave me a bad answer, my instinct was to rewrite the prompt. Add a "please." Try "act as a senior engineer." Reword the question for the fifth time. The gains were real but tiny — and I kept hitting the same wall.

The shift that actually moved my results was simple: the model usually isn't being dumb. It's being blind to something I never showed it. Once I started engineering the context instead of polishing the prompt, the quality jumped in a way no amount of wordsmithing ever delivered.

Prompt engineering vs context engineering

These get blurred together, but they're different jobs.

The prompt is your instruction — what you're asking for. The context is everything the model can actually see at the moment it answers: the system setup, any documents you've pulled in, the examples you provided, the prior state of the conversation, and the actual relevant data or code. Context engineering is the discipline of deciding, deliberately, what goes into that window and what stays out.

Prompt engineering optimizes the question. Context engineering optimizes what the model knows when it tries to answer. The second one has a much higher ceiling.

Why context beats wording

Here's the part that took me too long to internalize: a perfect prompt over missing or irrelevant context still fails. A mediocre prompt over the right context usually succeeds.

Most "the AI is so dumb" moments aren't reasoning failures. They're context failures. The model answered the question you asked, using the information it had — and the information it had was incomplete, stale, or buried in noise. You can't fix that by being more polite in the prompt.

The habits that actually help

Show, don't describe. Pasting the actual code, error, or example beats describing it every time. Description is lossy; the real artifact isn't. If you find yourself explaining what your data looks like, just show the data.

Curate, don't dump. More context is not better. Irrelevant context is noise that dilutes the signal and pushes the relevant parts toward the edges of the model's attention. Relevance beats volume. Half the skill is deciding what to leave out.

Structure it. Label your sections. Separate instructions from data so the model can tell which is which. A wall of mixed-together text forces the model to guess at the boundaries; clear structure removes the guessing.

Manage state. In long sessions, what's actually in the window drifts — important details scroll out, old tangents linger. When results start degrading, it's often not the prompt going bad, it's the context going stale. Re-anchor: restate the goal, bring the key facts back in.

You're already doing a version of this

If you use AI coding tools, you've felt this firsthand. The reason adding the right files to context produces dramatically better suggestions than asking against an empty editor is exactly this principle. Same lever, different surface. Context engineering just makes the habit deliberate instead of accidental.

The takeaway

Prompts are the last 10% of polish. Context is the 90% almost nobody optimizes. If your results plateau, stop rewording the question and start asking a different one: what does the model actually need to see to get this right — and am I giving it that, cleanly?

That question has paid me back more than any prompt trick ever has.

If you want to go deeper on this, I wrote a full guide on it: Context Engineering — The Complete Guide, a practical 40-page walkthrough from the core ideas to a 30-day plan for applying them to your own systems. It's a paid guide (no fluff, no filler): Context Engineering — The Complete Guide

How do you decide what makes it into the context window and what gets cut? Curious how other people draw that line.

I've Used Cursor Every Day for Months — These 5 Habits Did More Than Any Setting

PromptMaster — Sat, 13 Jun 2026 10:07:01 +0000

I see a lot of posts hunting for the secret Cursor setting that unlocks 10x productivity. After using it every single day for months on real work, I can tell you: there isn't one.

The gap between "I have AI autocomplete" and "I'm actually faster" isn't a setting. It's a handful of habits. Most people install Cursor, lean on tab-completion, and stop there — which is like buying a car and only ever using first gear. Here are the five habits that did more for me than any config option.

1. Feed it context on purpose

The single biggest upgrade to my results came from giving Cursor the right context instead of hoping it would guess.

When you point it at the exact files, docs, or code that matter — instead of asking a vague question against an empty void — the quality jumps immediately. Garbage context in, garbage suggestion out. Before I ask for anything non-trivial, I take two seconds to add the relevant files to the context. That two seconds saves me three rounds of "no, not like that."

2. Know which surface is for which job

Cursor gives you more than one way to talk to it, and using the wrong one is most people's hidden friction.

Inline edit is for surgical changes: select a block, describe the change, done. Chat is for thinking: when I don't fully know the solution yet and want to reason through a problem. Mixing them up — trying to have a design conversation through inline edits, or making a one-line tweak through a long chat — is slow and frustrating. Match the surface to the task and everything speeds up.

3. Write your rules once, stop repeating yourself

If you find yourself re-typing the same instructions every prompt ("use our naming convention," "no comments," "prefer this pattern"), you're doing manual work the tool can do for you.

Setting project rules once means Cursor carries your conventions into every suggestion automatically. I went from constantly correcting style to barely thinking about it. This is the habit people skip most often, and it compounds the hardest.

4. Small and specific beats big and vague

"Fix this" is a coin flip. "This function returns null when the input array is empty, but it should return an empty array instead" gets it right the first time.

The instinct is to throw a huge request at the AI and hope. In practice, the narrower and more specific my ask, the better the result — and counterintuitively, a series of small precise prompts is faster than one big vague one, because I'm not spending the saved time untangling a wrong guess.

5. Review every diff like it's a pull request

This is the one that keeps you out of trouble. The speed is real, but the responsibility for the code is still entirely yours.

I read every change Cursor proposes the same way I'd review a teammate's PR — before accepting, not after something breaks. Accepting diffs blindly is how subtle bugs sneak into a codebase at high speed. The habit of reviewing keeps the velocity without the regret.

The pattern underneath

Notice what all five have in common: none of them are about the tool. They're about how clearly you communicate intent and how carefully you check the output.

Cursor doesn't make you faster. It's a multiplier on how clearly you can describe what you want — and a multiplier amplifies whatever you feed it, sloppy or sharp. The habits are just the discipline of feeding it sharp.

If you want a quick reference for this, I put together a free Cursor cheat sheet — the shortcuts, the context tricks, and a 60-second workflow, condensed onto a few pages you can keep open next to your editor. Grab it here: Cursor Quick-Start Cheat Sheet

Which habit took you the longest to build? Curious if other daily Cursor users landed on the same five or a different set entirely.

I Reach for Cursor 90% of the Time — Here's the 10% Where Claude Code Wins

PromptMaster — Sat, 13 Jun 2026 09:54:09 +0000

Most of the "Cursor vs Claude Code" takes I read are framed wrong. It's not a cage match. They're not competing for the same job — they're good at different jobs, and once that clicked for me, both got more useful.

After months of leaning on both for actual day-to-day work (not demos, not toy repos), I've settled into a pretty stable split: Cursor handles about 90% of my coding, and Claude Code handles the 10% that actually moves the needle. Here's where I draw the line, and the rule of thumb that decides it.

The 90%: why Cursor owns my day

Most coding isn't dramatic. It's small, local, iterative work: tweak this function, rename that, fix the bug in the file I'm already staring at, ask "what does this block do" without breaking focus.

That's exactly Cursor's home turf. It lives inside the editor, so I never leave my flow. Inline edits, fast completions, quick questions about the code in front of me — all without context-switching. When the work is local and I want to stay in the loop keystroke by keystroke, an in-editor copilot is the right tool. It keeps me fast and in context, which is most of what a normal coding day actually is.

The 10%: where I close the editor and open Claude Code

Then there's the other kind of task — the one where I don't want to babysit every edit.

Claude Code is terminal-native and agentic. Instead of sitting beside me suggesting the next line, it works more like something I hand a well-described task to and let run across the whole project. That changes what it's good for:

Codebase-wide refactors that touch a dozen files at once
"Understand this whole repo and do X" type tasks, where the work depends on grasping how everything connects
Jobs I want to delegate and step away from, rather than steer line by line

The mental model that finally made it stick for me: Cursor is a copilot sitting next to you. Claude Code is more like handing a ticket to a capable teammate and checking the result. Different relationship, different jobs.

How I actually decide

When a task lands, I run it through three quick questions:

Am I editing, or delegating? Editing → Cursor. Delegating → Claude Code.
Is the change local or systemic? One or two files I'm looking at → Cursor. Something that ripples across the whole project → Claude Code.
Do I need to stay in the loop, or do I want to write a clear instruction and walk away? In the loop → Cursor. Walk away → Claude Code.

That's it. No agonizing, no tribalism. The tool follows the shape of the task.

A couple of things that took me a while to learn

Don't fight the tool. Reaching for Claude Code to change two lines is overkill; trying to refactor 30 files inside the editor is misery. Most of my early friction was just using the wrong one for the moment.

Clear instructions matter way more with the agentic tool. A vague prompt to a copilot costs you a bad suggestion you ignore. A vague prompt to an agentic tool costs you a whole run in the wrong direction. The 10% tasks reward you for spending an extra minute describing what "done" looks like before you hit enter.

Keep your codebase readable. Agentic tools do noticeably better on code that's already well-structured. The cleaner the project, the more you can safely hand off.

The real skill

The productivity unlock was never "master Cursor" or "master Claude Code." It was developing the intuition for which one to reach for — and being willing to switch mid-task when the shape of the work changes.

That intuition is the whole game. Once you stop treating these as rivals and start treating them as two different tools on the same belt, both get sharper.

If you're getting into Claude Code, I put together a free Claude Code cheat sheet — the commands and workflows I actually use day to day, condensed onto a quick reference so you don't have to dig through docs mid-task. You can grab it here: Claude Code Cheat Sheet

What's your split? Curious whether other people land on a similar 90/10 line or draw it somewhere completely different.