Forem: Digit Patrox

I Used Cursor, Windsurf, and Claude Code for 2 Weeks - Here's the One I Kept Opening

Digit Patrox — Tue, 12 May 2026 04:28:20 +0000

A few months ago, AI coding tools felt magical to me.

You type a prompt.
The AI builds the feature.
You feel like software development has changed forever.

Then week two starts.

That’s when the weird stuff happens.

Imports start changing for no reason.
The AI edits files you never touched.
A small bug fix somehow becomes a 14-file refactor.

And suddenly you realize:
the hard part isn’t generating code anymore.

It’s reviewing it.

So I spent the last couple of weeks using Cursor, Windsurf, and Claude Code on actual projects instead of toy demos to figure out which one genuinely helps once the honeymoon phase wears off.

If you've been exploring AI coding assistants, you’ve probably noticed the demos feel much smoother than real production workflows.

Here’s what I noticed.

Cursor vs Windsurf vs Claude Code at a Glance

Feature	Cursor	Windsurf	Claude Code
Best For	Daily product development	Large refactors	Infrastructure & terminal workflows
Biggest Strength	Fast diff review UX	Multi-file context handling	Deep terminal autonomy
Biggest Weakness	Context tunnel vision	“Fixing the fix” loops	Weak frontend workflow
Learning Curve	Low	Medium	High
UI Experience	Excellent	Good	Minimal / CLI-only
Multi-file Reasoning	Strong	Excellent	Strong
Refactoring Ability	Good	Excellent	Medium
Infra / DevOps Tasks	Medium	Medium	Excellent
Frontend Development	Excellent	Good	Weak
Risk Level	Low	Medium-High	Medium
Daily Driver Score	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐

The Reality Nobody Mentions About AI Coding Tools

Most comparisons focus on:

which model is smarter
who generates code faster
benchmark scores

Honestly?
That stopped mattering to me pretty quickly.

What actually matters is:

how much cleanup work the AI creates after generation.

That became my real productivity metric.

Because generating code in 20 seconds means nothing if you spend the next 2 hours fixing subtle architectural mistakes.

Cursor Feels Like the Safest Daily Driver

I kept coming back to Cursor for one simple reason:

It’s the easiest place to reject bad code quickly.

That sounds small until you use these tools every day.

Cursor’s diff UI is genuinely excellent.
The Composer workflow feels lightweight.
Reviewing changes feels fast.

For regular feature work — settings pages, APIs, dashboards, auth flows — it stayed reliable most of the time.

But once the repo gets larger, Cursor develops what I started calling:

"Context Tunnel Vision"

It begins overusing patterns from recently opened files even when they aren't the best fit.

I also noticed:

random import rewrites
unnecessary formatting edits
adjacent-file modifications I never asked for

At some point I realized a good .cursorrules setup is basically mandatory now.

Without constraints, the AI starts inventing architecture decisions on its own.

That becomes even more dangerous once you start building larger AI agent systems where context consistency matters more than raw generation speed.

Windsurf Honestly Impressed Me More Than I Expected

This was the biggest surprise.

Windsurf handled multi-file reasoning better than I expected during larger refactors.

There were moments where it genuinely felt less like autocomplete and more like an actual collaborator.

I tested it during an API migration and it:

updated related types
fixed references
handled dependency changes automatically

For a while it felt incredible.

Then it started spiraling.

The best way I can describe it is:

Windsurf tries too hard to help.

It enters these loops where:

it creates an issue
patches the issue
creates another issue from the patch
edits more files trying to recover

Eventually you stop coding and start supervising.

I once spent nearly two hours reverting changes because the AI completely lost the architectural direction while trying to solve a tiny lint issue.

That was the first time I experienced what I’d call:

AI fatigue

Not coding fatigue.

Reading-AI-thinking fatigue.

A lot of this feels connected to the broader shift toward context engineering instead of simple prompt engineering.

Claude Code Feels Like an AI Sysadmin

Claude Code feels fundamentally different from the IDE tools.

It feels less like an editor and more like:

an autonomous terminal agent

For infrastructure work, it was honestly excellent.

I used it for:

Docker debugging
Terraform fixes
CI/CD issues
shell scripts

And in terminal-heavy workflows, it often outperformed the IDE tools.

But frontend work became painful quickly.

The lack of visual feedback slows everything down.
Sometimes it stalls during long operations.
Sometimes it feels brilliant.
Sometimes it feels completely lost.

Using Claude Code feels like giving an AI root access and hoping it makes good decisions.

A lot of this workflow is being shaped by ideas similar to the Model Context Protocol (MCP), where tools and environments become part of the AI workflow itself.

The Real Problem Is Context Debt

The biggest thing I learned from all this:

AI tools don't remove technical debt.

They amplify it.

If your repo already has:

inconsistent naming
weak architecture boundaries
unclear folder structure
random patterns everywhere

the AI absorbs that chaos instantly.

Messy repos create messy AI behavior.

That’s why these tools feel amazing in clean demo projects and much less magical in older production systems.

It’s also why many developers are experimenting with local coding LLMs to gain more control over context windows, latency, and privacy.

So Which One Am I Actually Using?

After all the testing, I still open Cursor the most.

Not because it generates the best code every time.

But because:

it wastes the least amount of my time when things go wrong.

And honestly, that matters more.

My current workflow looks something like this:

Cursor → daily product development
Windsurf → larger refactors and migrations
Claude Code → infrastructure and terminal debugging

Final Thought

AI coding tools are changing software engineering.

But not in the way most people think.

The job is slowly shifting from:

writing code

to:

reviewing machine decisions

And the better these tools become, the more important engineering judgment becomes.

Because eventually the AI will start making architectural decisions for you.

And if you stop paying attention, you won’t notice until production breaks.

LangChain vs LangGraph: Why AI Agents Need Stateful Orchestration

Digit Patrox — Mon, 11 May 2026 05:42:29 +0000

LangChain vs LangGraph: Why AI Agents Need Stateful Orchestration

Most AI agents look impressive in demos.

Then they hit production and break.

APIs timeout. Memory disappears. Tool calls fail. Long workflows lose context halfway through execution. A chatbot that looked “smart” in a YouTube video suddenly becomes unreliable the moment real-world complexity enters the system.

This is why frameworks like LangChain and LangGraph are becoming critical infrastructure for modern AI systems.

We’re moving beyond prompt engineering into something much bigger:

Agent engineering.

The Problem With Most AI Agent Architectures

A lot of AI agents today are basically:

prompt -> LLM -> output

Sometimes developers add:

tools
APIs
retrieval
memory layers

But the architecture is still fundamentally fragile.

That works for:

simple chatbots
short workflows
lightweight copilots
basic RAG pipelines

It does not work reliably for:

autonomous AI systems
enterprise automation
multi-step reasoning
long-running workflows
multi-agent coordination

The moment systems become stateful, complexity explodes.

What Is LangChain?

LangChain is a framework for connecting Large Language Models (LLMs) to:

APIs
tools
vector databases
retrieval pipelines
memory systems
external applications

It became popular because it simplified the “plumbing” around LLM development.

Typical LangChain use cases:

RAG pipelines
AI chatbots
coding assistants
AI search
document Q&A
summarization workflows

A standard LangChain workflow often looks like this:

retriever -> prompt -> llm -> output

This works well for linear tasks.

The issue?

Real AI agents are rarely linear.

The Stateless Wall

Most AI systems eventually hit what I call the Stateless Wall.

Symptoms include:

models forgetting earlier context
retries becoming messy
API failures killing execution
workflows losing coordination
memory becoming inconsistent
server restarts erasing progress

In production environments, this becomes painful very quickly.

Example:

An AI research agent:

searches the web
extracts information
writes summaries
calls APIs
updates databases

If step 4 fails:

should the entire workflow restart?
should the system retry?
should it ask for human approval?
should it checkpoint progress?

Simple chains struggle with this.

What Is LangGraph?

LangGraph is an orchestration framework built on top of LangChain.

Instead of simple linear chains, it introduces:

cyclic workflows
persistent state
retries
branching logic
checkpoints
human-in-the-loop execution

In simple terms:

System	Role
ChatGPT	A conversation
LangChain	A workflow
LangGraph	A decision-making system

Why Graphs Matter

Traditional AI chains usually look like this:

A -> B -> C

But real agents often need:

Think -> Act -> Observe -> Retry -> Decide

That’s a graph, not a chain.

And that distinction matters enormously in production systems.

The Restaurant Analogy

Imagine a restaurant.

LangChain

LangChain is the waiter:

takes requests
connects tools
delivers outputs

LangGraph

LangGraph is the kitchen manager:

coordinates timing
manages retries
tracks memory
handles failures
pauses for approvals
reroutes workflows

If the oven breaks:

LangChain often fails the request.
LangGraph reroutes execution.

Minimal LangGraph Example

from langgraph.graph import StateGraph

workflow = StateGraph(MyStateSchema)

workflow.add_node("planner", planner_function)
workflow.add_node("tool", tool_function)

workflow.add_edge("planner", "tool")
workflow.add_edge("tool", "planner")

app = workflow.compile()

The key difference is this line:

workflow.add_edge("tool", "planner")

That creates a cycle.

The system can:

retry
self-correct
evaluate outputs
continue iterating

instead of permanently failing after one bad step.

What Is Stateful Orchestration?

Stateful orchestration means:

preserving execution state
maintaining memory
storing workflow history
checkpointing progress
recovering after failures

Without state:

every request becomes isolated
workflows become brittle
agents lose continuity

This is one of the biggest shifts happening in AI infrastructure right now.

LangChain vs LangGraph

Feature	LangChain	LangGraph
Workflow Type	Linear Chains	Stateful Graphs
Memory	Basic	Persistent
Loops	Manual	Native
Retries	Limited	Built-In
Human Approval	Not Native	Supported
Best Use Case	RAG / Chatbots	AI Agents

Why Enterprises Need Stateful AI

Enterprise AI systems cannot rely on stateless prompts.

A banking AI system must:

survive downtime
maintain audit logs
support human approval
recover from failures
preserve workflow history

A healthcare AI system cannot simply “forget” context halfway through execution.

This is why orchestration frameworks are becoming core infrastructure for enterprise AI.

Prompt Engineering vs Agent Engineering

The industry is moving away from:

prompt engineering

toward:

orchestration engineering
agent engineering
reliability engineering

The challenge is no longer:

“How do I write the perfect prompt?”

The challenge is:

“How do I build AI systems that survive failure?”

That’s a completely different engineering problem.

Why This Matters for the Future of AI

Modern AI systems increasingly require:

memory
persistence
retries
observability
human approval
orchestration layers

This is why tools like:

LangGraph
CrewAI
Temporal
AutoGen
OpenAI Agents
n8n

are becoming increasingly important.

The next generation of AI applications will not be defined by prompts alone.

They’ll be defined by:

reliability
orchestration
state management
recoverability

Final Thoughts

The first wave of AI apps was built on prompts.

The next wave is being built on orchestration.

And long-term competitive advantage probably won’t come from having the “smartest prompt.”

It will come from building AI systems that:

remember
recover
adapt
coordinate
operate reliably over time

ai #machinelearning #python #llm #langchain #aiagents #generativeai #programming

Forem: Digit Patrox

I Used Cursor, Windsurf, and Claude Code for 2 Weeks - Here's the One I Kept Opening

Cursor vs Windsurf vs Claude Code at a Glance

The Reality Nobody Mentions About AI Coding Tools

Cursor Feels Like the Safest Daily Driver

Windsurf Honestly Impressed Me More Than I Expected

Claude Code Feels Like an AI Sysadmin

The Real Problem Is Context Debt

So Which One Am I Actually Using?

Final Thought

Related reading

LangChain vs LangGraph: Why AI Agents Need Stateful Orchestration

LangChain vs LangGraph: Why AI Agents Need Stateful Orchestration

The Problem With Most AI Agent Architectures

What Is LangChain?

The Stateless Wall

What Is LangGraph?

Why Graphs Matter

The Restaurant Analogy

LangChain

LangGraph

Minimal LangGraph Example

What Is Stateful Orchestration?

LangChain vs LangGraph

Why Enterprises Need Stateful AI

Prompt Engineering vs Agent Engineering

Why This Matters for the Future of AI

Final Thoughts

Related Reading

ai #machinelearning #python #llm #langchain #aiagents #generativeai #programming