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docs(day-2): Aother talk

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---
title: Tech is broken and AI won't fix it
weight: 5
tags:
- rejekts
---
<!-- {{% button href="https://youtu.be/rkteV6Mzjfs" style="warning" icon="video" %}}Watch talk on YouTube{{% /button %}} -->
## Recruitment & Staffing
> The background for this talk is a very bad experience in this sector
- "We want someone opinionated" -> No you don't
### We don't know how to recruit engineers
- How many rounds of interviews?
- Technical tests?
- Not allways show your real worlds skills
- Many different kinds that usually don't fit (telling a junior)
- Other fields don't do this
- Is there an accurate feedback loop?
- The anwer to the qustion "how was the interview process" usually depends if there is an result and the kind of result
- Many just try to follow fang
- Plot twist you are not fang, you are a small company
- Mostly tests resilience and not really technical skills
### We're not very good at training for the future
- Many companies "only recruit senior engineers"
- Junior engineers are sometimes viewed as a cost center
- But they are important:
- Force seniors to explain stuff better
- Force collobaration
- Are a bit like the usual clueless customer
## Teamwork
- Depending on the team size and skillset the requirements for leadership change (Lead Engineer vs Engineering Manager)
- Don't hang up on stuff like "this is our team's senior/junior engineer" - everyone servers a role that changes depending on context (everyone is )
### Agile is broken
- The core of agile is about
- Individuals&Interaction > Process&Tools
- Working Software > Documentation
- Customer collaboration > Contract negotiation
- Responing to Change > Following a plan
- Agile never planned for all of the meetings
- If you have to have standups: Keep them short with rules
## Bias
- Everyone has biases and uncontious biases
- Example: The doctor test "I went to the doctor" -> Most people assume a man
- AI results in the same stereotype "Draw a doctor" -> While man with glasses
- Reality usually contradicts these biases
## Coding
- In the old days we copied from stackoverflow, now we let ai do the copying
- If GenAI uses LLM it bases everything on past code, will we reach a point where noting is new?
## What can we do
- Be an empathetic human (by gut feel or just by asking others)
- Develop engineers
- Never say something is shit
- Treat AI as an unreliable colleague (never assume it's allways right or wrong)

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Yes that is a negative day.
Why? Because the numbering of the days is based on KubeCon instead of the trip.
Why? Ask the sleep deprived version of me who started his trip to London at 3am...
But this is the first day of Cloud Native Rejekts and the first time of me attending this event.
This is the first day of Cloud Native Rejekts and the first time of me attending this event.
## Talk recommendations
- The Cluster API Migration Retrospective: Live migrating hundreds of clusters to Cluster API
- CRD Data Architecture for Multi-Cluster Kubernetes
- Handling large scale migrations: [The Cluster API Migration Retrospective: Live migrating hundreds of clusters to Cluster API](../02_clusterapi)
- Handling lrage number of clusters: [CRD Data Architecture for Multi-Cluster Kubernetes](../04_multicluster-crd)
- How to hire, manage and develop engineers: [Tech is broken and AI won't fix it](../05_broken-tech)