Useful tip to cut time-to-first-token on longer prompts in the API: pre-warm the prompt cache.
Send your system prompt before the user prompt. Claude writes it to the cache, but skips generating any output.
When the real user request lands, it'll hit a warm cache.
Old way of using Claude Code:
Prompt → wait → review → prompt → wait → review → repeat 20 times.
You are the loop. You are the condition check. You are the reason it keeps going.
New way:
/goal all tests pass and lint is clean
Claude works. You do something else. You come back to a result.
Here's exactly how to set it up — and how to write conditions that actually work ↓
Le CEO d’Anthropic quand il a vu cette formation Claude d’1 heure qui a fuité,
où un gars montre littéralement comment il a automatisé tout son business
et gagne 100 000 $ par an en pilote automatique.
Claude Code has 10+ features most people never touch.
Skills. Subagents. Memory. Hooks. MCP servers.
This repo explains when to use each one, and how to set them up properly.
100% Open Source.
Last quarter I rolled out Microsoft Copilot to 4,000 employees.
$30 per seat per month.
$1.4 million annually.
I called it "digital transformation."
The board loved that phrase.
They approved it in eleven minutes.
No one asked what it would actually do.
Including me.
I told everyone it would "10x productivity."
That's not a real number.
But it sounds like one.
HR asked how we'd measure the 10x.
I said we'd "leverage analytics dashboards."
They stopped asking.
Three months later I checked the usage reports.
47 people had opened it.
12 had used it more than once.
One of them was me.
I used it to summarize an email I could have read in 30 seconds.
It took 45 seconds.
Plus the time it took to fix the hallucinations.
But I called it a "pilot success."
Success means the pilot didn't visibly fail.
The CFO asked about ROI.
I showed him a graph.
The graph went up and to the right.
It measured "AI enablement."
I made that metric up.
He nodded approvingly.
We're "AI-enabled" now.
I don't know what that means.
But it's in our investor deck.
A senior developer asked why we didn't use Claude or ChatGPT.
I said we needed "enterprise-grade security."
He asked what that meant.
I said "compliance."
He asked which compliance.
I said "all of them."
He looked skeptical.
I scheduled him for a "career development conversation."
He stopped asking questions.
Microsoft sent a case study team.
They wanted to feature us as a success story.
I told them we "saved 40,000 hours."
I calculated that number by multiplying employees by a number I made up.
They didn't verify it.
They never do.
Now we're on Microsoft's website.
"Global enterprise achieves 40,000 hours of productivity gains with Copilot."
The CEO shared it on LinkedIn.
He got 3,000 likes.
He's never used Copilot.
None of the executives have.
We have an exemption.
"Strategic focus requires minimal digital distraction."
I wrote that policy.
The licenses renew next month.
I'm requesting an expansion.
5,000 more seats.
We haven't used the first 4,000.
But this time we'll "drive adoption."
Adoption means mandatory training.
Training means a 45-minute webinar no one watches.
But completion will be tracked.
Completion is a metric.
Metrics go in dashboards.
Dashboards go in board presentations.
Board presentations get me promoted.
I'll be SVP by Q3.
I still don't know what Copilot does.
But I know what it's for.
It's for showing we're "investing in AI."
Investment means spending.
Spending means commitment.
Commitment means we're serious about the future.
The future is whatever I say it is.
As long as the graph goes up and to the right.
K8s principle#3: The Kubernetes API/API Server. Provides a collection of objects we can use to build and define systems that we want to deploy in code. The objects defined in our code reflect the desired state of our application or the systems we want to deploy in a k8s cluster.
K8s principle #2: Controllers/control loops. These component ensures that the current/running state of the cluster is always in sync with the desired state.
K8s principle #1: Declarative configuration of the desired state. We can configure the desired state of the cluster in a declarative fashion using human-readable code (YAML).
[k8s] Deployments allow us to declaratively deploy applications in a Kubernetes cluster and control our applications in terms of the container image or version of our applications and also scale our application as needed.