[2021 kcdc] who got dna in my code – intro to genetic algorithms

This post is my live blog from KCDC. For more, see the 2021 KCDC live blog TOC

Speaker: Randall Koutnik

Twitter @rkoutnik


Genetic Algorithm

  • Machine learning
  • Represent problem as an array of things
  • Create a weighting to represent the problem as a number
  • Showed scale of evolving solutions and when processing exceeds laptop processing capacity
  • Keep changing best on best one until that point
  • Fiinite state machines – also like an array of booleans representing choices. Generate possibilities and see which one works. Use fitness function to see which are most successful
  • Get jitter in high score as average goes up because randomness
  • Determine if need perfect answer vs good enough answer


  • Generating counterfeit art
  • Eater game demo where they learn to eat plants
  • Calcu-lords game to determin which cards are best

My take

I learned what a genetic algorithm is And was nice to see some code. The examples were al fun. Hard to take notes on but I enoyed the presentation.

[2021 kcdc] Enterprise Cloud Patterns for Java Devs

This post is my live blog from KCDC. For more, see the 2021 KCDC live blog TOC

Speaker: Pratik Patel (Azul)

Twitter @prpatel



  • Monoliths are not elastic. Cloud is
  • Doesn’t waste money
  • Must design for elasticity
  • We learn to avoid premature optimization/keep it simple stupid. In the cloud, we have to do some optimization
  • Need to design for horizontal scaling (adding more servers/virtual servers)
  • Need entire architecture to be elastic. Else have created a bottleneck
  • Think about cosT, adapabiity/agil/security, time to market

Cloud native review

  • Containerized
  • Dynamically orchestrated (actively schedule containers to optimize resource utiilization)
  • Microservices oriented (try to avoid dependencies between microservices – not possible to avoid, but minimize)

Types of Patterns

  • Design and implementation – covered today
  • Performance and scalabity
  • Data management
  • Availability and reliabliliy

Ambassador Pattern

  • For transitioning legacy apps – could be monolith or SOAP or Java EE or Spring Boot or whatever to microservices
  • The Ambassador is a proxy. It adds monitoring, retry and a protocol adapter (hide SOAP/RMI from clients) and delegates to the legacy app.
  • This lets the callers all the Ambassador and get these cloud capabilities.
  • Implemented as a sidecar (does extra stuff)
  • Use Spring RestTemplate (now called WebClient) or Microprofile Rest client 2.0
  • For end user/callers

Anti-corruption pattern

  • Also for transitioning legacy apps
  • Callers is another service (microservice, cloud function, event driven api)
  • Does more than ambassador.
  • Facade/adapter pattern
  • Firewalls legacy apps
  • Useful for migrating large glegacy app
  • Protocol adapers
  • Legacy unchangeable APIs
  • Compartmentalize legacy app to protect requests coming in and rest of system in case something gos wrong
  • Spring Reg Template or Microprofile Rest client 2.0
  • Must define specific endpoints

Gateway aggregation pattern

  • For when app needs to call multiple services (ex: auth, media)
  • Calls multiple services and returns the results of multiple services to caller in one shot
  • Groups related services into one API call
  • Avoids death star anti-pattern when everything cals everything else
  • Client calls gateway not services directly
  • MUst think about how to handle failures in downstream services
  • Use SprIng AsyncRestTemplate (or web client) or GraphQL (Sprin, Microproile or SmallRye) – note that graphql introduces dependencies. Good to return a bunch of related data.

Back end for front end (BFF)

  • For flexibiity and performance
  • Adapter by client type (ex: mobile, web mobile, web desktop)
  • Returns data in way most practical and efficient for that client
  • Calls microservice and/or legacy app
  • Can be an implementation of a gateway aggregation pattern
  • Make sure not premature optimzation
  • Data can be shaped and size for specific needs
  • Improved performance
  • Could introduce code duplication
  • Are front and back end devs on same team.
  • Sprint AsyncRet Tmplate or Microprofile CompetableFuture or RestClientBuilder
  • Can be different URLs by service type – ex: mobile url (or header sniffing in API gateway)

Gateway Routing

  • Commonly used/misused
  • Calls proper service based on URL
  • Helps minimize change to clients
  • Reducing coupling directly to services
  • Tradeoffs: latency, bottleneck
  • Spring RestController or Microprofile JAXRS. Or cloud pattforms APAI gateway




My take

This was very fast moving, but I found it understandable. I learned a lot. I liked Pratik’s hand drawings of the patterns. It was also interesting that most people were using AWS at a primarily .NET conference

[2021 kcdc] black holes and revelations

This post is my live blog from KCDC. For more, see the 2021 KCDC live blog TOC

Speaker: Sarah Harper

Twitter @sarita1119


Why work gets stuck

  • Work moves through the system until it gets stuck in a phase
  • We like to think of things as a process problem. However, often a behavioral issue (not like major HR leve problems)
  • Reasons: someone out sick, not enough people know how to do something, blame testing
  • Agile black holes!
  • Detrimenta to process and team morale
  • Not obvious what problem is
  • examples: deployment, business requirements, code review, qa
  • Biggest black hole is the backlog

Back hole theory

  • Any work status which acts as a “holding pen” for work items will eventuallly turn into a black hole
  • Once enough are blocked for same reason, all future items will becme blocked
  • It becomes acceptable for things to get blocked there
  • No work appears to be done to outside observer – unclear who working on or what it is up to


  • Queue busts – something wrong and swarm around it. Teams decides to focus on something like a customer service queue.
  • Eceeed WIP limits
  • Blocked column
  • Too much in “done” or “ready” columns
  • Long staging cycle time

Learned Helplessness

  • Exercise: everyone got a piece of paper with three anagrams and people raised their hand as solved
  • One one side, first two not soleable. One other side, first two easy. Then same third one.
  • Taught first group learned helplessness.
  • Trained to give up.
  • I tried X times to change this

Locus of control

  • Exercise: how much control do you have over weather, food you eat, comute, home, projects at wor, team member participation. (Scale of none to all)
  • Think about what under control
  • Don’t blame others for things can control
  • Be alert when someone says can’t/imposssible

Self handicapping

  • Don’t try so can’t fail
  • Fear of looking bad
  • ”give to X because X can do fastest” – setting team up for failure if that person can’t do it.
  • That person becomes busy and a black hole
  • ”You miss 100% o the shots you don’t take”

Somebody else’s problem and the bystander effect

  • Brain edits it out because someone ese’s problem
  • Everyone ignores
  • May or may not actually be someone else’s responsibity
  • Bystander effect – assume someone else will take charge

How spot black holes

  • If have to scroll in a kanan board, have a black hole
  • Not obvious

Visual principles

  • Excercise: think about your kanban board For each of these principles
  • Figure ground – foreground vs background. What see vs ignore. Showed optical illusion of face/vase. Usually digital boards have a white background so white cards disappear into background. Can make a different color to stand out. Also, if something has a date contraint or a high priority, add contrast.
  • Common region – when obects located in same region, we perceive them as being grouped together. If everything looks the same, can’t tell. Change blocked cards to a different color
  • Similarity – thinks that look the same are assumed to be the same.
  • Proximity – things close together appear to be more related than things spaced farther apart. This is more of an effect than similiarity
  • Continuity – Elements on a ine are considered more related. Lines follow the smoothest path.
  • Focal point – watever stands out visually will attract and hold our attention
  • Closure – we try to find recognizable patterns
  • Just noticeabe difference – hard to see Jira dots for how long in state
  • Idea: have blocked work in a different color, in a lane on top. So you can see status blocked in.
  • Avoid too much color

Game: https://getkanban.com/pages/free-version

Escaping back holes

  • Have team talk about problem
  • Visualize future state and take action
  • Use a physical board or customize digital board as much as can
  • FInd an influencer on team that will give good feedback and help team accept idea
  • Queue bust (swarm around problem)
  • Root cause everything

My take

I learned a lot at this session. I like how it was different than a lot of agile talks. I have a bunch of things to take back