Nicolai Ort
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72 lines
2.6 KiB
Markdown
72 lines
2.6 KiB
Markdown
---
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title: Tech is broken and AI won't fix it
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weight: 5
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tags:
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- rejekts
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---
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{{% button href="https://www.youtube.com/watch?v=CAPtQnH4rPY" style="warning" icon="video" %}}Watch talk on YouTube{{% /button %}}
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<!-- {{% button href="https://docs.google.com/presentation/d/1nEK0CVC_yQgIDqwsdh-PRihB6dc9RyT-" style="tip" icon="person-chalkboard" %}}Slides{{% /button %}} -->
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## Recruitment & Staffing
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> The background for this talk is a very bad experience in this sector
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- "We want someone opinionated" -> No you don't
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### We don't know how to recruit engineers
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- How many rounds of interviews?
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- Technical tests?
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- Not allways show your real worlds skills
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- Many different kinds that usually don't fit (telling a junior)
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- Other fields don't do this
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- Is there an accurate feedback loop?
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- The anwer to the qustion "how was the interview process" usually depends if there is an result and the kind of result
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- Many just try to follow fang
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- Plot twist you are not fang, you are a small company
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- Mostly tests resilience and not really technical skills
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### We're not very good at training for the future
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- Many companies "only recruit senior engineers"
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- Junior engineers are sometimes viewed as a cost center
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- But they are important:
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- Force seniors to explain stuff better
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- Force collobaration
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- Are a bit like the usual clueless customer
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## Teamwork
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- Depending on the team size and skillset the requirements for leadership change (Lead Engineer vs Engineering Manager)
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- 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 )
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### Agile is broken
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- The core of agile is about
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- Individuals&Interaction > Process&Tools
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- Working Software > Documentation
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- Customer collaboration > Contract negotiation
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- Responing to Change > Following a plan
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- Agile never planned for all of the meetings
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- If you have to have standups: Keep them short with rules
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## Bias
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- Everyone has biases and uncontious biases
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- Example: The doctor test "I went to the doctor" -> Most people assume a man
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- AI results in the same stereotype "Draw a doctor" -> While man with glasses
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- Reality usually contradicts these biases
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## Coding
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- In the old days we copied from stackoverflow, now we let ai do the copying
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- If GenAI uses LLM it bases everything on past code, will we reach a point where noting is new?
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## What can we do
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- Be an empathetic human (by gut feel or just by asking others)
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- Develop engineers
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- Never say something is shit
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- Treat AI as an unreliable colleague (never assume it's allways right or wrong)
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