Junior vibe coder:
- installs Claude Code, Antigravity, Codex, or Cursor
- picks an app idea
- sends one big prompt
- starts the build
- spends the rest of the day debugging AI slop
Senior vibe coder:
- configures preferences and memory
- adds agent skills and project rules
- wires up the necessary connectors and MCP tools
- tests the agent first
- explores the product idea with it
- writes a PRD
- plans the tasks and assigns focused pieces
- builds a roughly 70% functional MVP
- works through failures until the product really works
Same tools. Different preparation.
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Junior vibe coder:
- installs Claude Code, Antigravity, Codex, or Cursor
- selects an app concept
- sends one large prompt
- begins the build
- spends the rest of the day fixing AI-generated mess
Senior vibe coder:
- sets preferences and memory
- adds agent skills and project rules
- connects required connectors and MCP tools
- tests the agent first
- explores the idea collaboratively
- writes a product requirements document
- breaks work into focused pieces
- produces a roughly 70% functional MVP
- iterates through failures until the product works reliably
Same tools. Different preparation.
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A 1M-token context window can still fill up with the wrong context.
After each task, choose deliberately:
Continue when the next step needs the same working context.
Rewind when an approach failed.
Start fresh when the next task has a different goal.
Compact when you need continuity but the conversation has become long.
Delegate when a task will produce lots of intermediate output.
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Beginner vibe coder:
- installs a coding agent
- decides on an idea
- asks for the whole app
- starts generating
- spends the next few days untangling the result
More experienced builder:
- sets preferences and project memory
- adds useful rules and skills
- connects the required services
- tests the environment
- explores the idea with the agent
- writes the product requirements
- divides implementation into smaller tasks
- builds the core MVP
- tests and fixes each flow until it works
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7 ways to get more out of Claude Code:
1. Package repeated work as Skills.
2. Save project knowledge between sessions.
3. Automate predictable sequences.
4. Connect the external services the task needs.
5. Maintain a task list in the repo.
6. Give independent work to focused agents.
7. Separate parallel tasks into clear workspaces.
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People keep saying there's no point learning programming because AI can generate the code. I think that's bad advice.
The people who benefit most will know how to tell a computer what they want and how to evaluate what it returns.
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Clear written instructions matter. You have to explain the result you want without leaving critical gaps.
Computer science knowledge matters too. It helps you spot faulty logic, question an architecture, and test the cases a polished interface can hide.
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A harness gives the agent tools and limits. A loop exposes errors and lets it try again. A routine starts that loop when needed.
Start with the test, a retry limit, and a person who can review the outcome.
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Think in loops, not isolated prompts.
Instead of one request and one answer, set up a routine where the agent takes a task, makes a change, tests it, sees the result, and continues until there's evidence it worked.
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A good Claude Code walkthrough can teach more in half an hour than hours of disconnected tips.
Look for the full workflow: how the creator frames a task, supplies context, lets the agent work, checks the result, and decides what to do next.
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The useful lesson from multi-agent demos isn't “run 100 agents.”
It's how the work is broken down, how context is passed between tasks, how the cost stays bounded, and how the final result is checked.
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An agent setup becomes straightforward when you see six layers built in sequence:
First, install Claude Code.
Next, write a CLAUDE.md.
Then save recurring corrections as memory.
After that, make Skills.
Use focused subagents.
Finally, schedule routines.
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I remember my first time on a linux server.
It felt like navigating a spaceship or being in a plane cockpit.
I was confused about SSH keys, what command to run, and why I needed the -i flag.
I was also scared of waking up to a huge cloud bill.
That's why I made a mini course showing the steps I follow.
Comment "SERVER" and I will send you the link
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Don't let “done” be the last line. Ask the agent to demonstrate a working result: passing tests, a successful build, or a screenshot compared with the intended design.
Then have a reviewer with fresh context inspect the actual diff.
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Always use plan mode. Get the plan right before asking it to implement.
One session can develop the approach. A second can challenge it from a staff engineer's perspective.
When implementation starts going off course, pause and revise the plan.
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I think it's time to retire the term "vibe coding" and to stop saying "I had Claude/Codex build XYZ" (I still keep saying it)
At this point, when you say "I built XYZ" it's understood that you didn't write the code yourself.
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Revolut: Europe's most valuable startup ($200B) just showed the world its AI strategy.
At the start of this year Revolut relied on frontier labs. Its 2,000 engineers used Claude Code and Codex.
Then Revolut switched its default to an open-source model called GLM.
The results:
- The bill is roughly one-tenth
- Engineers get unlimited tokens
- Coding time is down 60% and lead time is down 70%
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Automation consultants charge $15K for what Claude Code now does in 2 hours.
Here's the exact process:
Here’s the exact process:
Step 1: Discovery (20 min)
Share your tools, org chart, and biggest bottlenecks. Claude asks a few questions and maps where time is actually being lost.
Step 2: Workflow Mapping (15 min)
Describe the daily work in plain English. Claude turns it into a clear process map and flags the manual or repetitive steps.
Step 3: Opportunity Audit (10 min)
It ranks the best automation opportunities by impact and difficulty.
Step 4: Architecture Design (20 min)
Claude designs how the pieces should connect and where agents vs. simple automations make sense.
Step 5: Build
It writes and iterates on the actual workflows.
Step 6: The output
A clean, usable system with process maps and clear next actions — not just a list of ideas.
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OH MAN: Anthropic's Claude Code engineer Addy Osmani is warning about a brutal AI paradox: the more coding you hand to agents, the more you risk losing the very skills needed to supervise them.
He calls it "cognitive surrender." AI-generated code looks clean, tests pass, and engineers start approving work they no longer fully understand.
"When you're producing more than you can stand behind, you're no longer reviewing. You're hoping."
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