clj-refactor sayid.el cider-hydra clomacs helm-cider squiggly-clojure cider-eval-sexp-fu kaocha-runner emidje

• Immutable time-series compute
• Kafka as aligned with core Clojure properties
• Use as a data-oriented programming framework
• consume - transform - compute
• Use simulation as core to the system
• Update brokers, groups, topics to simulate load
• Sente for websockets passing data from topic updates to browser
• operator-io/shortchut

• paradigms: functional, logic, imperative, meta, parallel
• languages: Scheme, Python, Clojure, C, Kotlin, C#, Ruby
• book: Programming Clojure https://pragprog.com/book/shcloj3/programming-clojure-third-edition
• book: Seven More Languages https://pragprog.com/book/7lang/seven-more-languages-in-seven-weeks

• Kafka
• Generative tests
• Clojure spec

• https://github.com/sgepigon/piggy
• Covers some of the core cases for breaking changes
• Producer and consumer contracts
• Spec protocol:
• https://clojure.org/guides/spec
• https://github.com/clojure/spec-alpha2/wiki/Differences-from-spec.alpha

Provides the ability to see if the input args are more restrictive (subset) or the return more expansive (super).

• https://github.com/jdhollis
• dependency resolution for deployment order
• manage state
• environments/{dev,stage,prod}
• tools (state)
• https://docs.datomic.com/cloud/ions/ions-tutorial.html
• ions/{.bin/{dev-relase, version, codebuild-release}}

• Setup has modules for each of the environment
• datomic_copute and datomic_storage is manual
• arbitrary comans with nullresource
• change detected as md5 hash for code or configuration change
• release region, env, group

• https://itrevolution.com/the-unicorn-project/
• https://www.amazon.com/Unicorn-Project-Developers-Disruption-Thriving-ebook/dp/B07QT9QR41
• performance
• using Datomic
• migration from SQL

• https://clojars.org/cnuernber/libpython-clj
• Numpy from Clojure vectors
• with semantics

(def np (py/module-import "numpy"))

• https://github.com/oakmac/record-linking-talk
• Create a test bed of [User] for name, record_id, ssn, address
• Create (patient-ids-match? [user1 user2])
• Establish a deterministic algorithm
• Create match and unmatch probabilities (Fellegi-Sunter)
• clojure.spec for evaluating data transformations
• Create match probability for

  {
   :match-prob .8
   :unmatch-prob .1
  }

  (def default-match [e1 e2]
    "Default check is pure equality"
    (= e1 e2))
  (def name-match [n1 n2]
    "Split phonetically and match based on phonemes"
    (double-metaphone n1 n2))

• Create a distribution for the thresholding
• Put questionables into a DLQ
• Use ML for finding the match weights for each of match and unmatch probabilities

• https://docs.datomic.com/on-prem/best-practices.html
• uses spec for testing entities

• Clojure and AWS
• Lambdas
• AWS CloudFormation

• Types from the physical world
• Lua for embedded systems
• Various hardware options: Ada, Arduino
• Fennel

• use 20 minutes for the expected prototype
• experiment early
• check for assumptions early
• have a next step forward
• real enough to feel
• favor observable vs. conjecture
• tractable vs. intractable problem statements

• http://probcomp.csail.mit.edu/news/
• https://www.youtube.com/playlist?list=PL_PW0E_Tf2qvXBEpl10Y39RULTN-ExzZQ
• https://insights.stackoverflow.com/survey/2019
• https://github.com/probcomp/metaprob

The goal here would be to determine the relationships between

Example: MgrIdiot predicts CareerSat

Generate answers for any of the virtual types of developer given KnowsClojure.

• count model (for columns count both then )
• conditional count model
• bayesian

(simulate count-model {} 1)