- [Thoughts on Building Weatherproof Companies](Thoughts on Building Weatherproof Companies)
- Valuable companies take decades to build
- Flow: Mapping an object — Medium
- Jeff Morrison - A Deepdive Into Flow at react-europe 2016 - YouTube
- Fast and Accurate Document Detection for Scanning | Dropbox Tech Blog
- Large Diffs Are Hurting Your Ability To Ship — Medium
- Stranger Things’ score is a gateway into synthwave · For Our Consideration · The A.V. Club
- Testing the Bejeezus out of React Native Apps with AVA — Red Shift
- Zero-cost futures in Rust · Aaron Turon
- What Happened Before History? Human Origins - YouTube
- Genetic Engineering Will Change Everything Forever – CRISPR - YouTube
- Answer to What is it like to understand advanced mathematics? - Quora
- Journey to HTTP/2 · @kamran
- Shape of errors to come - The Rust Programming Language Blog
- Behind the Scenes of iOS Security - YouTube
- All Olympic Logos, Ordered By Quality — Mike Industries
- A Few Thoughts on Cryptographic Engineering: Is Apple's Cloud Key Vault a crypto backdoor?
- thejameskyle/itsy-bitsy-data-structures: All the things you didn't know you wanted to know about data structures
- Smaller and faster data compression with Zstandard | Engineering Blog | Facebook Code
- AOMedia Video 1 - Wikipedia, the free encyclopedia
- An Intuitive Guide to Linear Algebra
- An Interactive Guide To The Fourier Transform
- THE $25,000,000,000∗ EIGENVECTOR THE LINEAR ALGEBRA BEHIND GOOGLE
- Eigenvectors and Eigenvalues Explained Visually
- Bulletproof SSL and TLS (Ristić)
- The Field Guide to Understanding 'Human Error' (Dekker)
- The New School of Information Security (Shostack and Stewart)
- The Security Development Lifecycle (Howard and Lipner)
- The Tangled Web (Zalewski)
- The Web Application Hacker's Handbook (Stuttard)
- Threat Modeling: Designing for Security (Shostack)
- A Tale of Security Gone Wrong (Miller)
- Anatomy of a Crypto Vulnerability (Gaynor)
- Bounty Launch Lessons (McGeehan and Honeywell)
- Building a Let's Encrypt client from scratch (Peattie)
- Cryptography Right Answers (Ptacek)
- HTTPS is Hard (Workman)
- Incident Response at Heroku (McGranaghan)
- Security Breach 101 and Security Breach 102 (McGeehan)
- Security Engineeing as Caring-For (Palmer)
- What Werewolf teaches us about Trust & Security (Eaves)
- Who Fixes That Bug: Part One: Them!, Part Two: Us! (McGeehan)
- 2016 Data Breach Investigation Report (Verizon Enterprise) - see also previous years: 2015 2014, 2013, 2012, 2011, 2010, 2009, 2008.
- BeyondCorp: A New Approach to Enterprise Security (Ward, Beyer)
- Doomed to Repeat History? Lessons from the Crypto Wars of the 1990s (Kehl, Wilson, and Bankston)
- Practical Security Stories and Security Tasks for Agile Environments (SAFECode)
- Security for Startups: The Affordable Ten-Step Plan for Survival in Cyberspace (Cowan)
- The Security of Modern Password Expiration: An Algorithmic Framework and Empirical Analysis (Zhang, Monrose, and Reiter)
- Crypto 101 (Van Houtven)
- Lessons Learned While Protecting Gmail (Bursztein)
- Web Security Fundamentals (Hunt)
- "The Mess We're In" by Joe Armstrong
- 5 Biggest Legal Mistakes That Startups Make
- Alan Kay at OOPSLA 1997 - The computer revolution hasnt happened yet
- Alan Kay, 2015: Power of Simplicity
- Are We There Yet?
- Bret Victor - Inventing on Principle
- Bret Victor - Stop Drawing Dead Fish
- Bret Victor The Future of Programming
- Crockford on JavaScript
- Daniel Kahneman: "Thinking, Fast and Slow" | Talks at Google
- Do schools kill creativity? | Sir Ken Robinson
- Donald Knuth - All Questions Answered
- Donald Knuth - My advice to young people (93/97)
- Elon Musk Talks About Getting the Job Done
- Google I/O 2009 - The Myth of the Genius Programmer
- Growing a Language, by Guy Steele
- Guy Kawasaki - The Art of The Start
- Guy Kawasaki: The Top 10 Mistakes of Entrepreneurs
- Hamming, "Creativity" (May 23, 1995)
- Hamming, "You and Your Research" (June 6, 1995)
- How to Write a Great Research Paper
- How to Write Clean, Testable Code
- Instant Loading: Building offline-first Progressive Web Apps - Google I/O 2016
- Is it really "Complex"? Or did we just make it "Complicated"?
- JavaScript: The Good Parts
- Learning from StackOverflow.com
- Leslie Lamport: Thinking Above the Code (Thinking for Programmers: Rising Above the Code)
- Martin Fowler – Microservices
- Media for Thinking the Unthinkable
- Mining Online Data Across Social Networks
- Must-See Tech Talks for Every Programmer
- On Getting Creative Ideas
- Pedro Domingos: "The Master Algorithm" | Talks at Google
- Peter Norvig - The Unreasonable Effectiveness of Data
- Philip Roberts: What the heck is the event loop anyway? | JSConf EU 2014
- Randy Pausch Last Lecture: Achieving Your Childhood Dreams
- Randy Pausch Lecture: Time Management
- Richard Hamming: "Learning to Learn"
- Rob Pike - 'Concurrency Is Not Parallelism'
- Simple Made Easy
- Simplicity Ain't Easy - Stuart Halloway
- Steve Blank: How to Build a Great Company, Step by Step (8/14/12)
- Steve Jobs' 2005 Stanford Commencement Address
- Surviving the Framework Hype Cycle by Brandon Hays test
- Susan Cain: "Quiet" | Talks at Google
- Tech Talk: Linus Torvalds on git
- TechZulu Presents: Biggest Legal Mistakes Startups Make
- The Mother of All Demos, presented by Douglas Engelbart (1968)
- The Next Generation of Neural Networks
- The thrilling potential of SixthSense technology | Pranav Mistry
- Wat
- We Really Don't Know How To Compute!
- Fault Injection in Production (Allspaw)
- Making Reliable Distributed Systems in the Presence of Software Errors (Armstrong)
- Highly Available Transactions: Virtues and Limitations (Bailis et al.)
- The Incident Command System (Bigley and Roberts)
- The Chubby Lock Service for Loosely Coupled Distributed Systems (Burrows)
- Bigtable: a Distributed Storage System for Structured Data (Chang et al.)
- Spanner: Google’s Globally-Distributed Database (Corbett et al.)
- Dynamo: Amazon’s Highly Available Key-Value Store (DeCandia et al.)
- MapReduce: Simplified Data Processing on Large Clusters (Dean and Ghemawat)
- The Google File System (Ghemawat et al.)
- On Designing and Deploying Internet Scale Services (Hamilton)
- Kafka: A Distributed Messaging System for Log Processing (Kreps et al.)
- Weathering the Unexpected (Krishnan)
- The Unified Logging Infrastructure for Data Analytics at Twitter (Lee et al.)
- Automatic Management of Partitioned, Replicated Search Services (Leibert et al.)
- Learning to Embrace Failure (Limoncelli et al.)
- Scaling Big Data Mining Infrastructure: The Twitter Experience (Lin and Rayboy)
- Dremel: Interactive Analysis of Web-Scale Datasets (Melnik et al.)
- Out of the Tar Pit (Moseley and Marks)
- The Log-Structured Merge-Tree (O'Neil et al.)
- In Search of an Understandable Consensus Algorithm (Ongaro and Ousterhout)
- Failure Trends in a Large Disk Drive Population (Pinheiro et al.)
- Fallacies of Distributed Computing Explained (Rotem-Gal-Oz)
- F1 - The Fault-Tolerant Distributed RDBMS Supporting Google’s Ad Business (Shute et al.)
- Dapper, A Large Scale Distributed Systems Tracing Infrastructure (Sigelman et al.)
- Resident Distributed Datasets: a Fault-Tolerant Abstraction for In-Memory Cluster Computing (Zahari et al.)
- The Human Side of Postmortems (Zwieback)
- Crew Resource Management: a Positive Change for the Fire Service
- Resilience Engineering: Part I, Part II (Allspaw)
- Systems Engineering: a Great Definition (Allspaw)
- Chaos Monkey Released Into The Wild (Bennett and Tseitlin)
- Some Rules for Engineering and Operations (Black)
- Service Level Disagreements Part I, Part II (Black)
- Incuriosity Will Kill Your Infrastructure (Crayford)
- My Philosophy on Alerting (Ewaschuk)
- You Can’t Sacrifice Partition Tolerance (Hale)
- Customer Trust (Hamilton)
- Observations on Errors, Corrections, & Trust of Dependent Systems (Hamilton)
- Game Day Exercises at Stripe: Learning from
kill -9(Hedlund) - Life Beyond Distributed Transactions: An Apostate’s Opinion (Helland)
- Notes on Distributed Systems for Young Bloods (Hodges)
- The Network is Reliable (Kingsbury)
- The Trouble with Clocks (Kingsbury)
- Call Me Maybe: Final Thoughts (Kingsbury)
- Getting Real About Distributed Systems Reliability (Kreps)
- The Log: What every software engineer should know about real-time data's unifying abstraction (Kreps)
- Incident Response at Heroku (McGranaghan)
- On HTTP Load Testing (Nottingham)
- Observability at Twitter (Watson)
- Stevey’s Google Platforms Rant (Yegge)
- Design, Lessons, and Advice from Building Distributed Systems at Google (Dean)
- Service Design Best Practices (Hamilton)
- The Field Guide To Understanding Human Error (Dekker)
- Agile Retrospectives: Making Good Teams Great (Derby et al.)
- Better: A Surgeon’s Notes on Performance (Gawande)
- The Checklist Manifesto: How to Get Things Right (Gawande)
- High Performance Browser Networking (Grigorik)
- Resilience Engineering in Practice (Hollnagel et al.)
- Effective Monitoring and Alerting (Ligus)
- Release It!: Design and Deploy Production-Ready Software (Nygard)
- The Challenger Launch Decision (Vaughan)
- Managing the Unexpected (Weick and Sutcliffe)
- Bidirectional Recurrent Neural Networks (Better classifications with RNNs!)
- Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation (Two networks in one combined into a seq2seq (sequence to sequence) Encoder–Decoder architecture. RNN Encoder–Decoder with 1000 hidden units. Adadelta optimizer.)
- Sequence to Sequence Learning with Neural Networks (4 stacked LSTM cells of 1000 hidden size with reversed input sentences, and with beam search, on the WMT’14 English to French dataset)
- Exploring the Limits of Language Modeling (Nice recursive models using word level LSTMs on character level CNN using an overkill amount of GPU power)
- Exploring the Depths of Recurrent Neural Networks with Stochastic Residual Learning (Basically, residual connections can be better than stacked RNNs in the presented case of sentiment analysis)
- Neural Turing Machines (Outstanding for implementing simple neural algorithms with seemingly good generalisation)
- Teaching Machines to Read and Comprehend (A very interesting and creative work about textual question answering, there is something to do with that)
- Pixel Recurrent Neural Networks (Nice for photoshop-like "content aware fill" to fill missing patches in images)
- Adaptive Computation Time for Recurrent Neural Networks (Very interesting, I would love to see how well would it combines to Neural Turing Machines. Interesting interactive visualizations on the subject can be found here.)
- What is the Best Multi-Stage Architecture for Object Recognition? (For the use of "local contrast normalization")
- ImageNet Classification with Deep Convolutional Neural Networks (AlexNet, 2012 ILSVRC, breakthrough of the ReLU activation function)
- Visualizing and Understanding Convolutional Networks (For the "deconvnet layer")
- Very Deep Convolutional Networks for Large-Scale Image Recognition (For the idea of stacking multiple 3x3 conv+ReLU before pooling for a bigger filter size with few parameters, also there is a nice table for "ConvNet Configuration")
- Going Deeper with Convolutions (GoogLeNet: Appearance of "Inception" layers/modules, the idea is of parallelizing conv layers into many mini-conv of different size with "same" padding, concatenated on depth)
- Highway Networks (Highway networks: residual connections)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (Batch normalization (BN): to normalize a layer's output by also summing over the entire batch, and then performing a linear rescaling and shifting of a certain trainable amount)
- Deep Residual Learning for Image Recognition (Very deep residual layers with batch normalization layers - a.k.a. "how to overfit any vision dataset with too many layers")
- Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning (For improving GoogLeNet with residual connections)
- WaveNet: a Generative Model for Raw Audio (Epic raw voice/music generation with new architectures based on dilated causal convolutions to capture more audio lenght)