Contents of Authors Content

This is an exhaustive list of videos, talks, blogposts, and resources that inspired me in some point of time.

Technical

• Unlearning toxic behaviors in a code review cultureSandya Sankarram

• How strong techies destroy startups, and how it can be avoidedGadi Evron

• Simplicity MattersRich Hickey

• The Value of Values with Rich HickeyRich Hickey

• Hammock Driven DevelopmentRich Hickey

• Deconstructing the DatabaseRich Hickey

• Simple Made EasyRich Hickey

• Design, Composition, and PerformanceRich Hickey

• Agile Uncertified. Philosophy Over RitualsDave Farley

• Monitoring is Dead. Long Live MonitoringGreg Poirier

• Better Living Through Statistics. Monitoring Doesn’t Have To SuckJamie Wilkinson

• Observability. it’s not just an ops thingCharity Majors

• Observability for emerging infraCharity Majors

• Observability for Emerging Infra. What Got You Here Won’t Get You ThereCharity Majors

• MLOps Tooling Landscape v2Chip Huyen

• ML Systems Design Interview GuidePatrick Halina

• Engineering Large Systems When You’re Not Google Or FacebookChip Huyen

• Erros em Pesquisas EleitoraisRaphael Nishimura

• Kelsey Hightower On Kubernetes & Cloud ComputingKelsey Hightower

• Technical Stories doesn’t workDave Farley

• Why Pull Requests are a bad idea?Dave Farley

• Git Flow is a bad ideaDave Farley

• Continuous Integration vs Feature Branch WorkflowDave Farley

• Why CI is BETTER Than Feature BranchingDave Farley

• Que tal codar como se você não pudesse refatorar?Dev Eficiente

• Acoplamento distribuído - O fundo do poço dos acoplamentosDev Eficiente

• Agile & Scrum Don’t WorkAllen Holub

• The big problem, the big promise of working from homeAnne Hellen Petersen

• It’s time to builda16z Podcast

• Innovation through software development and ITa16z Podcast

• The hidden costs of estimationsMob Programming Podcast

• Responsible Engineers and OutcomesMary Poppendieck

• Mobbing Programming at TeslaJoe Justice

• Why technology still mattersa16z Podcast

• Team Topologies, Software Architecture & ComplexityJames Lewis

• Enabling a world where all software is reliableRobert Ross

• MLOps Anti-PatternsNikhil Muralidhar

• Do we need deep learning for time seriesSheeren Elsayed

• Differential Privacy at the US CensusSimson Garfinkel

• Fast and frugal time series forecastingFotios Petropoulos

• Building the Minerva metrics store @ AirbnbRobert Chang

• Machine Learning, Recommender Systems and the future of AIMichael Jordan

• Fairwashing and the folly of ML solutionismZach Lipton

• Infrastructure for autonomous vehiclesMissy Cummings

• Deep Learning and Time Series Forecasting for Smarter EnergyIgor Gotlibovych

• Matching MarketsAlvin Roth

• Optimal Growth in Two-Sided MarketsGarret Van Ryzin

• Approximately Efficient Two-Sided Combinatorial AuctionsRiccardo Colini-Baldeschi

• Large Matching Markets as Two-Sided Demand SystemsKonrad Menzel

• The death of AgileAllen Holub

• The mechanisms of growthAllen Holub

• CI/CD in 5 minutes - Is it worth the hassle?ByteByteGo

• Boosted embeddings for time seriesRao Karingula

• Detecting driftSam Ackerman

• gRPCRichard Belleville

• SRE for everyone elseSteve McGhee

• Curso Completo de RubyOne Bit Code

• Introduction to Reinforcement LearningDeep Mind

• MLOps Anti-PatternsNikhil Muralidhar

• Building foundational ML Platforms at Capital OneAli Rodell

• The great MLOps Debate - Build x BuyMLOps Community

• Arquitetura funcional em Microservices - 4 anos depoisLucas Cavalcanti

• You are not Facebook - Architecting MLOps for B2B applicationsJacopo Tagliabue

• Contextual Multi-Arm Bandit and its applications to digital experimentsPeterson & Qin

• Practical Machine Learning for Predictive MaintenanceSimon Xu

• I like notebooksJeremy Howard

• NixtlaVerse, bridging the gap between statistics and deep learning for time seriesMax Mergenthaler

• Design PatternsRefactoring Guru

• Practical Machine Learning for Predictive MaintenanceSimon Xu

• Can We Really Use Machine Learning in Safety Critical Systems?Richard Murray

• Can We Really Use Machine Learning in Safety Critical Systems? - PresentationRichard Murray

• Anomaly Detection - Algorithms, Explanations, ApplicationsMicrosoft Research

• Creative Selection - Inside Apple’s Design Process During the Golden Age of Steve JobsKen Kocienda

• The REAL Reason Cyberpunk 2077’s Software FAILED!Dave Farley

• ImageNet Classification with Deep Convolutional Neural NetworksYannic Kilcher

• A DARPA Perspective on Artificial IntelligenceDARPA

• Variational AutoencodersCodeEmporium

• Attention Is All You NeedYannic Kilcher

• LSTM is dead. Long Live TransformersLeo Dirac

• The High Tech Telemetry SystemPatronSpirits

• Machine Learning - Dynamical, Stochastic & Economic PerspectivesMichael I. Jordan

• Personalizing Explainable Recommendations with Multi-objective Contextual BanditsRishabh Mehrotra

• Clearing Matching Markets Efficiently - Informative signals and Match RecommendationsPeng Shi

• Strategies of Two-Sided MarketsMira Mak

• Recommendations in a Marketplace - Part IRishabh Mehrotra

• Recommendations in a Marketplace - Part IIRishabh Mehrotra

• NixtlaVerse, bridging the gap between statistics and deep learning for time seriesMax Mergenthaler

• Deep Learning and Time Series Forecasting for Smarter EnergyIgor Gotlibovych

• Building products for Continous Delivery in Machine LearningAlan Lu

• Science as Amateur Software DevelopmentRichard McElreath

• Some Salient Issues with Saliency ModelsAggregate Intellectual

• LSTM NetworksCodeEmporium

• Recurrent Neural NetworksCodeEmporium

• Differential Privacy and Learning - The Tools, The Results, and The FrontierMicrosoft Research

• Transformer Neural NetworksCodeEmporium

• Generative Adversarial Networks (NIPS 2016 tutorial)Ian Goodfellow

• ImageNet Classification with Deep Convolutional Neural NetworksYannic Kilcher

• XGBoost A Scalable Tree Boosting SystemCarl Mullins

• Transformer - Attention Is All You NeedJoseph Palermo

• Análise de sobrevivência aplicada à mensuração de riscos e a Pesquisa em AtuáriaMáris Caroline Gosmann

• Controversies in Predictive Modeling and Machine LearningFrank Harrell

• Porque Você Nunca Deve Fazer Scaling Antes do Split de Validação em Machine LearningMario Filho

• Diffprivlib - Privacy-preserving machine learning with Scikit-learnNaoise Holohan

• Survival regression with AFT model in XGBoHyunsu (Philip) Cho, Avinash Barnwal

• Deploy your Machine Learning Bots like a boss with CI/CDWilliam Arias

• Ensembling, Blending & StackingAbhishek Thakur

• Deep Learning with DiffPrivacyMakul Rathi

• Privacy-preserving ML with PATEMakul Rathi

• Differential Privacy - Lecture 1Microsoft Research

• Clean Code, Horrible PerformanceCasey Muratori

• Curso Inferencia CausalUFMA

• Deep learning for search and recommendationsKDD

• Deep learning and Medical ApplicationsInstitute for Pure and Applied Mathematics

• Causal Inference BootcampMod-U

• Audio Signal for Machine LearningValerio Velardo

• MLOps Meetup - SRE and ML - Why It MattersML Learners

• Poisoning Web-Scale Training DatasetsNicholas Carlini

• Continuous Integration Deployments of Adaptive Machine Learning ModelsMordechai Worch

• The Challenges of Deploying (many!) ML ModelsJason McCampbell

• Why the 6-month AI Pause is a Bad IdeaYann LeCun and Andrew Ng

• Mastering ChaosInfoQ

• Why Isn’t Functional Programming the Norm?Richard Feldman

• Why Isn’t Functional Programming the Norm?Richard Feldman

• Lawyer Explains Stable Diffusion LawsuitCorridor Crew

• Deep Learning With PyTorchPatrick Loeber

• AI for Full-Self Driving at TeslaAndrej Karpathy

• Forecasting - Principles and PracticeRob Hyndman

• Working Effectively with Legacy CodeMichael Feathers

• How Support Vector Machines workBrandon Roher

• Causality, part 1Bernhard Schölkopf

• Causality, part 2Bernhard Schölkopf

• Generative Adversarial NetworksYannic Kilcher

• Evaluating Classification Models, Part 1 - Weighing False Positives Against False NegativesGreg Gandenberger

• Evaluating Classification Models, Part 2 - The Sufficiency of Precision and RecallGreg Gandenberger

• Evaluating Classification Models, Part 3 - Fβ and Other Weighted Pythagorean Means of Precision and RecallGreg Gandenberger

• Evaluating Classification Models, Part 4 - Weighted Power Means of Precision and RecallGreg Gandenberger

• t-SNE from ScratchJacob Pieniazek

• Time series for scikit-learn peopleEthan Rosenthal

• Time series for scikit-learn people - Part 1Ethan Rosenthal

• Time series for scikit-learn people - Part 2Ethan Rosenthal

• Time series for scikit-learn people - Part 3Ethan Rosenthal

• Lets Continue Bundling into the DatabaseEthan Rosenthal

• Autoretraining is Easy if You Skip the Hard PartsEthan Rosenthal

Non-Technical

• Why generalists triumph in a specialized worldGary Collins

• The no-nosense guide to simplifying every aspect of your lifeDavid Epstein

• Tesla and the nature of disruptiona16z Podcast

• Inside Apple Software designa16z Podcast

• ‘No Asshole’ Rule: Why Life’s Too Short To Work With ThemAnthony Bourdain

• Steve Jobs on ConsultingSteve Jobs

• The Cues That Make You CharismaticVanessa Van Edwards

• The character traits that drive optimal performanceRich Diviney

• Multiple WIP vs One Piece Flow ExampleHenrik Kniberg

• In praise of maintenance in a world obsessed with innovationThe Art of Manliness

• When to QuitAnnie Duke

• Daniel Ricciardo: Not done with F1F1 Beyond the Grid

• Nicholas Latifi: limits, lessons + leaving F1F1 Beyond the Grid

• Steve Nichols: Engineering domination for McLarenF1 Beyond the Grid

• Valtteri Bottas on Mercedes, Alfa Romeo goals, coffee, and cyclingF1 Beyond the Grid

• Toto Wolff on Hamilton, Rosberg, Lauda and breaking recordsF1 Beyond the Grid

• Chase Carey on his role in shaping F1’s future and guiding the sport through a global pandemicF1 Beyond the Grid

• Sebastian Vettel on leaving Ferrari, life outside F1 and changing goalsF1 Beyond the Grid

• Ross Brawn on Michael Schumacher – the driver, the leader, the manF1 Beyond the Grid

• Nico Rosberg: “I don’t miss the driving, but I miss the winning…”F1 Beyond the Grid

• Toto Wolff: “I run a successful F1 team but thats nothing compared to what other people have achieved…”F1 Beyond the Grid

• Toto Wolff: Falling behind, figthing backF1 Beyond the Grid

• Risk, innovation and winning Toto Wolff

• What caused the Titanic of the skiesMentour Pilot

• A guide to getting of gridGary Collins

• Coachbility is CriticalCode Cubitt

• Deep ResetCarl Newport

• Innovating while scaling: Lessons from AmazonColin Bryar / Bill Carr

• When to quit?Annie Duke

• Confidence and competitionPaul Rabil

• Jocko WillinkJocko Willink

• Grit is good. But quiting can be, tooAnnie Duke

• Avoiding the doomsdayToto Wolff

• Trasforming pain on beauty - This is our most dangerous opponentToto Wolff

• Why grit is an essential element of high performanceMark Webber

• Genio ou burro? Besta ou bestial? Os tecnicos brasileiros!Hoje Sim

• The 6 mindshift you need to rise and thrive at workAliza Knox

• Empathy over engineeringToto Wolff

• So Good They Cannot Ignore YouCal Newport

• Dont Follow Your PassionCal Newport

• Prediction Machines - The Simple economics of AIAvi Goldfarb & Ajay Agrawal

• Frameworks for better decision makingEric Johnson

• How to perform your best under pressureDon Greene

• Grow, adapt, and reinvent yourself through ultralearningScott Young

• Overcome the comfort crisisMichael Easter

• AndragogyLee Rusty Waller

• Advice on Achieving any long-haul dreamSteven Pressfield

• Off-Grid is a lieBushradical

• The future is analogDavid Sax

• 10 Signs You Work At A Dead End CompanyBryan Creely

• Insight: Why Were Not as Self-Aware as We ThinkTasha Eurich

• Meltdown - Why Our Systems Fail and What We Can Do About ItChris Clearfield & András Tilcsik

• A diferença entre o sistema musical do oriente e do ocidenteRafael Bittencourt

• 16 levels of piano compositionNahre Sol

• Getting to Catastrophe: Concentrations, Complexity and CouplingCharles Perrow

• Software Failures, Security, and CyberattacksCharles Perrow

• Competition is for losersPeter Thiel

• Muita retórica, pouca literaturaPaulo Uzai

• Como debater arteVictor Naine

• Porque a beleza também importa?Victor Naine

• Por que a Arte Moderna não é tão ruim?Victor Naine

• Gosto se discute, sim!Victor Naine

• Me criticaram – Mas o que é crítica?Victor Naine

• Arte, técnica e tecnologiaVictor Naine

• Creating High Performance CulturePatty McCord

• Lessons from a Silicon Valley maverick - new ways of working and collaboratingPatty McCord

• One Year Ago I Built an Ecosystem, This HappenedDr. Plants

• Warum haben wir Grenzen?ARTEde

• Disassembling The Greatest F1 Car With The Men Who Made It - The 1988 Prost / Senna McLaren MP4/4JayEmm on Cars

• 13 Rules of Being RelentlessTim Grover

• Why discipline equals freedomJocko Willink

• Deliberate practiceSprouts

• I Landed A Rocket Like SpaceX - Scout FBPS Space

• GarduinoPractical Engineering

• Formula 1 Car Development And ProductionCars Garage

• VIVALDI // Juditha triumphans, RV 644: Air Armatae face et anguibus by JupiterAlpha Classics

• HANDEL // Flavio, Re de Longobardi, HWV 16: Aria “Rompo i lacci, e frango i dardi” by Eva ZaïcikAlpha Classics

• Motivação para estudar - Você tem brio?Clóvis de Barros Filho

• Pare de usar metas Waterfall: Como OKR pode ajudá-lo a focar em valor Felipe Castro

• NoEstimatesAllen Holub

• The Problem of HFT - Collected Writings on High Frequency TradingHaim Bodek

• When Genius Failed: The Rise and Fall of Long-Term Capital Management Roger Lowenstein

• Flash Boys: A Wall Street RevoltMichael Lewis

• The Big Short - Inside the Doomsday MachineMichael Lewis

• Average Is Over - Powering America Beyond the Age of the Great StagnationTayler Cowen

• The Great Stagnation - How America Ate All The Low-Hanging Fruit of Modern HistoryTayler Cowen

• The Quants - How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed ItScott Patterson

• Dark Pools - The Rise of the Machine Traders and the Rigging of the U.S. Stock MarketScott Patterson

• Inside the Black Box - A Simple Guide to Quantitative and High-Frequency TradingRishi K. Narang & Manoj Narang

• My Life as a Quant - Reflections on Physics and FinanceEmanuel Derman

• Valuation - Métricas de Valor e Avaliação de Empresas Alexandre Assaf Neto

• The Smartest Guys in the Room: The Amazing Rise and Scandalous Fall of EnronBethany McLean & Peter Elkind

• Giving a F*ck about what really mattersMark Manson

• F*ck yes or noMark Manson

• Adaptability is the new stabilityReid Hoffman

• Escape the happiness trapRuss Harris

• Andy Cowell - The man leading Mercedes fominant F1 engine divisionMotor Sport Magazine Podcast

• 13 Rules of Being RelentlessTim Grover

• The Complacent ClassTyler Cowen

• Is Average Over in an Age of Great Stagnation?Tyler Cowen

• Why specializing early doesnt always mean career successDavid Epstein

• Meses Coloridos - promoção de saúde ou de doença?Luis Correia

• Discomfort Is The Price Of Admission To A Meaningful LifeSusan David

• Extreme Ownership - How U.S. Navy SEALs Lead and WinJocko Willink

• The Design Behind GTA 3Rockstar

• The Visual Effects CrisisRoyal Ocean Film Society

• On failureSteve Jobs

• Agile EstimationMike Cohn

• Hardware, Software and HeuristicsRichard Feynman

• How Complex Systems FailRichard Cook

• Managing ComplexityRichard Millan

• Barbarians at the gateEmpowerment High Performance Center

• Resilience In Complex Adaptive SystemsRichard Cook

• O Ceticismo Empírico dos GregosOswaldo Porchat de Assis Pereira

• Empirismo e Ceticismo - David HumeRoberto Bolzani Filho

• Is Average Over in an Age of Great Stagnation?Tyler Cowen

• Advice to Aimless, Excited ProgrammersJames Hague

• Produto ReativoChristiano Milfont

• How a Formula 1 Race Car WorksAnimagraff

• Michael ShellenbergerMichael Shellenberger

• Robots are racistMike Solana

• SF Politics, Tech & Media fights, Tik Tok policy, and AI warsMoment of Zen

• The game theory of an AI pauseTyler Cowen

• Most Technologies Arent RacesAstral Codex Ten

• Por que deixamos de escrever? E por que devemos voltar a escrever?Rodrigo Gurgel

• Artificial Intelligence as exit strategy from the age of acute existential riskArturo Macias

• Why Managers Matter as Applied OrganizationNicolai J. Foss && Peter G. Klein

• Accountability is Not BlameKent Beck

• Rise of the AI Doomsday CultDaniel Jeffries

• AI Doomsaying Is Circumstantially RidiculousThe Sinistral

• Stop Reading the News: A Manifesto for a Happier, Calmer and Wiser Life Rolf Dobelli & Caroline Waight

• How Machine Learning is Transforming the Video Games IndustryAI in Games

• Even The Politicians Thought the Open Letter Made No Sense In The Senate Hearing on AIPirate Wires

• Experimental Evidence on the Productivity Effects of Generative Artificial IntelligenceShakked Noy & Whitney Zhang