The goal of this page is to make you a more proficient developer. You'll find only resources that I've found truly inspiring, or that have been become timeless classics.
Master](http://www.amazon.com/The-Pragmatic-Programmer-Journeyman-Master/dp/020161622X): hands-on the most inspiring and useful book I've read about programming.
* [Code Complete: A Practical Handbook of Software
Construction](http://www.amazon.com/Code-Complete-Practical-Handbook-Construction/dp/0735619670): a nice addition to The Programatic Programmer, gives you the necessary framework to talk about code.
* [Release It!](http://www.amazon.com/Release-It-Production-Ready-Pragmatic-Programmers/dp/0978739213): this books goes beyond code and gives you best practices for building production-ready software. It will give you about 3 years worth of real-world experience.
* [Scalability Rules: 50 Principles for Scaling Web
* [The Linux Programming Interface: A Linux and UNIX System Programming Handbook](http://www.amazon.com/The-Linux-Programming-Interface-Handbook/dp/1593272200): outside of teaching you almost everything you need to know about Linux, this book will give you insights into how software evolves, and the value of having simple & elegant interfaces.
* [Professional software development](http://mixmastamyk.bitbucket.org/pro_soft_dev/): pretty complete and a good companion to this page. The free chapters are mostly focused on software development processes: design, testing, code writing, etc. - and not so much about tech itself.
* [Practical Advice for New Software Engineers](http://product.hubspot.com/blog/practical-advice-for-new-software-engineers)
* [On Being A Senior Engineer](http://www.kitchensoap.com/2012/10/25/on-being-a-senior-engineer/)
* [Lessons Learned in Software Development](http://henrikwarne.com/2015/04/16/lessons-learned-in-software-development/): one of those articles that give you years of hard-earned lessons, all in one short article. Must read.
* [Write code that is easy to delete, not easy to extend](http://programmingisterrible.com/post/139222674273/write-code-that-is-easy-to-delete-not-easy-to)
* [Safe Operations For High Volume PostgreSQL](https://www.braintreepayments.com/blog/safe-operations-for-high-volume-postgresql/) (this is for PostgreSQL but works great for other db as well).
* [Zero downtime database migrations](https://blog.rainforestqa.com/2014-06-27-zero-downtime-database-migrations/) (code examples are using Rails but this works great for any programming language)
* [Papers we love](https://github.com/papers-we-love/papers-we-love): papers from the computer science community to read and discuss. Can be a good source of inspiration of solving your design problems.
* [My First 10 Minutes On a Server - Primer for Securing Ubuntu](http://www.codelitt.com/blog/my-first-10-minutes-on-a-server-primer-for-securing-ubuntu/)
* [Testing Strategies in a Microservices Architecture](http://martinfowler.com/articles/microservice-testing/) (Martin Fowler) is an awesome resources explaining how to test a service properly.
* [A Quick Puzzle to Test Your Problem Solving](http://www.nytimes.com/interactive/2015/07/03/upshot/a-quick-puzzle-to-test-your-problem-solving.html?_r=0)... and a great way to learn about confirmation bias and why you're mostly writing positive test cases.
* [The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets (No Excuses!)](http://www.joelonsoftware.com/articles/Unicode.html)
* Read the [CLRS](https://mitpress.mit.edu/books/introduction-algorithms). You can watch and download the course on [OCW](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-introduction-to-algorithms-sma-5503-fall-2005/) - there are newer courses as well.
* Or [The Algorithm Design Manual](https://www.amazon.com/Algorithm-Design-Manual-Steven-Skiena/dp/1849967202?ie=UTF8&qid=1297127794&ref_=sr_1_1&sr=8-1)
Let's be honest: algo can be a pretty dry topic. [This quota question](https://www.quora.com/Is-there-a-book-that-teaches-algorithms-data-structures-and-other-computer-science-basics-in-a-fun-way) lists some funnier learning alternative, including:
* [Setting Up a Mac Dev Machine From Zero to Hero With Dotfiles](http://code.tutsplus.com/tutorials/setting-up-a-mac-dev-machine-from-zero-to-hero-with-dotfiles--net-35449)
* [The Infinite Hows](http://www.kitchensoap.com/2014/11/14/the-infinite-hows-or-the-dangers-of-the-five-whys/): this provides a strong criticism of the five whys method.
I highly recommend reading [The Non-Designer's Design Book](http://www.amazon.com/gp/product/0133966151/ref=pd_lpo_sbs_dp_ss_1?pf_rd_p=1944687602&pf_rd_s=lpo-top-stripe-1&pf_rd_t=201&pf_rd_i=0321534042&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=1R7MVQP0BCP7GP9VZGYX). This is a pretty short book that will give you some very actionable design advices.
* If you're working on data, Edward Tufte's [The Visual Display of Quantitative Information](http://www.amazon.com/Visual-Display-Quantitative-Information/dp/0961392142/ref=sr_1_1?ie=UTF8&qid=1458046603&sr=8-1&keywords=tufte) is considered a classic.
* The [Universal Principles of Design](http://www.amazon.com/Universal-Principles-Design-Revised-Updated/dp/1592535879/ref=sr_1_1?ie=UTF8&qid=1458046663&sr=8-1&keywords=universal+principles+of+design) will give you enough vocabulary and concepts to describe design challenges into words.
* [High Scalability](http://highscalability.com/): great blog about system architecture, its weekly review article are packed with numerous insights and interesting technology reviews. Checkout the [all-times favorites](http://highscalability.com/all-time-favorites/).
* [Deep Lessons From Google And EBay On Building Ecosystems Of Microservices](http://highscalability.com/blog/2015/12/1/deep-lessons-from-google-and-ebay-on-building-ecosystems-of.html)
* [Service oriented architecture: scaling the Uber engineering codebase as we grow](https://eng.uber.com/soa/)
* [The Log: What every software engineer should know about real-time data's unifying abstraction](https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying): one of those classical articles that everyone should read.
* At least one dynamic language (Python, Ruby, JavaScript, etc.). Pretty useful for quick one-off automation scripts, and fastest to write for interviews.
* At least one compiled language (Java, C, C++, etc.)
* At least one more recent language to see where the industry is going (as of writing, Go, Swift, Rust, etc.)
* [Design Patterns: Elements of Reusable Object-Oriented Software](http://www.amazon.com/dp/0201633612/): dubbed "the gang of four", this is almost a required reading for any developer. A lot of those are a bit overkill for Python (because everything is an object, and dynamic typing), but the main idea (composition is better than inheritance) definitely is a good philosophy.
* [Patterns of Enterprise Application Architecture](http://www.amazon.com/dp/0321127420/?tag=stackoverfl08-20): learn about how database are used in real world applications. Mike Bayer's SQLAlchemy has been heavily influenced by this book.
* SourceMaking's [Design Patterns](https://sourcemaking.com/design_patterns) seems to be a good web resource too.
* O'Reilly's [How to make mistakes in Python](http://www.oreilly.com/programming/free/files/how-to-make-mistakes-in-python.pdf)
