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UID:pretalx-juliacon-2026-TBHR8T@pretalx.com
DTSTART;TZID=CET:20260812T164500
DTEND;TZID=CET:20260812T170000
DESCRIPTION:Static analysis for Julia is still an underdeveloped domain. Th
 e linting ecosystem is looking especially barren – practically one packa
 ge is used for writing and running linting rules\, [StaticLint](https://gi
 thub.com/julia-vscode/StaticLint.jl)\, even though it is difficult to exte
 nd with new rules or features. [ReLint](https://github.com/RelationalAI-os
 s/ReLint.jl) was built to address some of StaticLint's limitations\, impro
 ving upon extensibility and interoperability. However\, it too suffers fro
 m shortcomings\, especially in terms of flexibility. [Argus](https://githu
 b.com/iuliadmtru/Argus.jl)\, mainly a pattern matching framework for Julia
  syntax\, offers a powerful and expressive language for writing code patte
 rns and linting rules. This presentation shows the result of combining ReL
 int and Argus into a new version of ReLint that provides a built-in set of
  rules\, a DSL for extending the default set with custom rules and CI/CD i
 ntegration.
DTSTAMP:20260502T100436Z
LOCATION:Room 1
SUMMARY:ReLint.jl and Argus.jl are merging into a powerful Julia linter - I
 ulia Dumitru\, Alexandre Bergel
URL:https://pretalx.com/juliacon-2026/talk/TBHR8T/
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UID:pretalx-juliacon-2026-L3QPFT@pretalx.com
DTSTART;TZID=CET:20260813T103000
DTEND;TZID=CET:20260813T110000
DESCRIPTION:**"Practical Artificial Intelligence in Julia: Build Neural Net
 works\, Genetic Algorithms\, and Neuroevolution From Scratch"** is a new b
 ook published by APress and Springer. \n\nThe book is divided into three p
 arts:\n\n- _Neural networks_ are a technique inspired by a simplification 
 of neurons in the brain. Neural networks are useful for identifying comple
 x patterns\, classifying data\, and predicting outcomes.\n- _Genetic Algor
 ithms_ are a computational metaphor for the biological evolution of specie
 s\, inspired by the Darwinian principles. Genetic algorithms are useful fo
 r finding near-optimal solutions to complex\, large-scale\, and non-linear
  optimization and search problems.\n- _Neuroevolution_ is a combination of
  the two previous parts: a genetic algorithm evolves a neural network. Neu
 ral networks produced by Neuroevolution can solve complex problems without
  being trained using gradient-based methods.\n\nThis talk gives a highligh
 t of these techniques and will demonstrate several applications using the 
 Julia REPL\, in particular:\n\n- evolution of an artificial organism able 
 to walk and climb\;\n- building an artificial player for a Mario Bros-like
  game\;\n\nThe talk aims to showcase innovative machine learning technique
 s and applications within the Julia ecosystem.
DTSTAMP:20260502T100436Z
LOCATION:Room 2
SUMMARY:Neural Networks\, Genetic Algorithms\, and Neuroevolution - Alexand
 re Bergel
URL:https://pretalx.com/juliacon-2026/talk/L3QPFT/
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