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archetype: chapter
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archetype: chapter
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title: Day -1
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title: Day -1
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weight: 3
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weight: 2
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---
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TODO:
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TODO:
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---
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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://youtu.be/rkteV6Mzjfs" style="warning" icon="video" %}}Watch talk on YouTube{{% /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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---
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title: Geographically Distributed Clusters: Resilient Distributed Compute on the Edge
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weight: 6
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tags:
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- rejekts
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---
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<!-- {{% button href="https://youtu.be/rkteV6Mzjfs" style="warning" icon="video" %}}Watch talk on YouTube{{% /button %}} -->
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## Background: The state of cloud in mauritius
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- Cloud native is more cloud naive
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- Government treated cloud as bad for a while
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- People know AWS but not the cloud native ecosystem
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- Bad uplinks due to sea-cables that tend to get broken from time to time
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- Only one local cloud service provider and the big providers are "an ocean away"
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## The Solution
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- Idea: Use multiple homelabs across the island as availability zones or multi-cloud
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- Goal: Orchestrate everything through kubernetes
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- Plan: 3 Homelabs with at least 3 Nodes each that join one big cluster
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- Tech: Longhorn Storage, Tailscale for connectivity
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- Converns: Latency, Power-Cuts, Bandwidth, IP-Rotation
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- Prod use: A startup wanted to use this for their workload and needed
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- Tailscale exit nodes for external services
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- GPU nodes for AI-Workload
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## Q&A
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- How is ingress handled (by me)?
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- Migrate the control plane to the cloud provider and use their static IPs.
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- Ingress allways starts at the cloud and routes over
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- Why tailscale?
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- Fairly reliable
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- Pretty simple
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- Handles routing
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- How are you planning on scaling this setup?
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- More friends aka more homelab locations
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- Utilize Tailscale
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- How are you handling image distribution?
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- Bandwitdh is not that limited (200 Down)
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- They just host their own registry for stuff
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- What about the neighboring islands? -> Cool ideas
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- How big is your local cloud community? -> 15 People at smaller meetups and 1600 at the yearly dev meetup
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- How do you handle security in your setup?
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- This is not the primary concern for the government
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- Most locals banks/insurers have inhouse servers or stuff on aws
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- Most of the time security is an afterthought
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- What kind of hardware are you running on and how do you aquire it?
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- The second hand market is not really a thing (or rather expensive).
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- They usually just import stuff themselves
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- Most Nodes are Dell Optiplex or Lenovo Thinkcenters
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- How does longhorn perform over the 200mbit connection? -> Surprisingly good
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- Is Starlink available? -> No and the government does things linke "let's shut down socialmedia before the election"
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---
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---
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archetype: chapter
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archetype: chapter
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title: Day -2
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title: Day -2
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weight: 2
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weight: 1
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---
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---
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This is the first day of Cloud Native Rejekts and the first time of me attending this event.
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Yes that is a negative day.
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Why? Because the numbering of the days is based on KubeCon instead of the trip.
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Why? Ask the sleep deprived version of me who started his trip to London at 3am...
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But this is the first day of Cloud Native Rejekts and the first time of me attending this event.
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## Talk recommendations
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## Talk recommendations
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- Handling large scale migrations: [The Cluster API Migration Retrospective: Live migrating hundreds of clusters to Cluster API](../02_clusterapi)
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- The Cluster API Migration Retrospective: Live migrating hundreds of clusters to Cluster API
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- Handling lrage number of clusters: [CRD Data Architecture for Multi-Cluster Kubernetes](../04_multicluster-crd)
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- CRD Data Architecture for Multi-Cluster Kubernetes
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- How to hire, manage and develop engineers: [Tech is broken and AI won't fix it](../05_broken-tech)
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---
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archetype: chapter
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title: Day -3
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weight: 1
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---
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Yes that is a negative day.
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Why? Because the numbering of the days is based on KubeCon instead of the trip.
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Why? Ask the sleep deprived version of me who started his trip to London at 2am...
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## What h
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---
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---
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archetype: chapter
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archetype: chapter
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title: Day 0
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title: Day 0
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weight: 4
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weight: 3
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---
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---
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TODO:
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TODO:
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@ -1,7 +1,7 @@
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---
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---
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archetype: chapter
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archetype: chapter
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title: Day 1
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title: Day 1
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weight: 5
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weight: 4
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---
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---
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TODO:
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TODO:
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---
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---
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archetype: chapter
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archetype: chapter
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title: Day 2
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title: Day 2
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weight: 6
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weight: 5
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---
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---
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TODO:
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TODO:
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---
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---
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archetype: chapter
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archetype: chapter
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title: Day 3
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title: Day 3
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weight: 7
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weight: 6
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---
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---
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TODO:
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TODO:
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---
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---
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archetype: chapter
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archetype: chapter
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title: Lessons Learned
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title: Lessons Learned
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weight: 8
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weight: 7
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---
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---
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TODO:
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TODO:
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