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115 entries
Career
Bookmarks
How to Evaluate a Product Roadmap, for Engineers
A guide for engineers on how to evaluate whether a product leader knows what they're doing — what signals in a roadmap indicate good product thinking vs. cargo-culted process. Useful for engineers interviewing or assessing a new team's product culture.
Goodbye, Data Science
A practitioner's exit essay arguing that 'data science' as a job title is broken — structurally positioned between engineering and analysis in a way that makes it chronically undervalued and poorly defined. Resonated widely in the data community in late 2022.
How to Write a Cold Email
Sriram Krishnan's guide to writing cold emails that get responses — focused on the fundamentals of making a good ask without wasting the recipient's time. Practical advice from someone on both ends of many cold emails.
Build Your Career on Dirty Work
The Dirty Work Theory: work that most people avoid is underpriced, high-impact, and a reliable career accelerator. A practical career heuristic for finding leverage where competition is low.
The CTO Field Guide
The CTO Field Guide is a practical reference for technical leaders — covering hiring, architecture decisions, team structure, and engineering culture. Focused on the operational realities of the CTO role rather than the inspirational.
Navigating Web3 Compensation: A Framework Report
Framework Ventures' 2022 report on compensation structures in Web3 — covering how crypto-native organizations pay differently from traditional companies, the role of token compensation, and what the data shows about salary bands in the space.
InterviewThis: Developer Questions for Prospective Employers
InterviewThis is an open-source list of questions for developers to ask prospective employers — covering engineering culture, team dynamics, technical practices, and company health. Reverses the usual interview direction: candidates evaluating companies.
No Small Plans: Evidence-Based Executive Coaching
No Small Plans is a values-based executive coaching practice using Acceptance and Commitment Training (ACT) for founders and tech leaders. Targets post-exit founders, scaling executives, and leaders seeking deeper alignment between work and values.
Getting into Machine Learning in 2022 (HN Discussion)
Hacker News discussion on the best paths into ML/DL in 2022 — debating resources (fast.ai, Andrew Ng, Bishop), career tracks (researcher vs. engineer vs. data scientist), and prerequisites. A snapshot of community wisdom on the ML learning path before ChatGPT shifted the landscape.
Small Bets: Daniel Vassallo's Indie Entrepreneurship Framework
Daniel Vassallo's Small Bets is a community and framework for indie entrepreneurs built around making many small, reversible bets instead of one big all-in venture. The antidote to survivorship-bias-driven startup thinking.
Programming in the Apocalypse
Mat Duggan's essay on what software engineering looks like when the tech industry's golden decade is over — slower hiring, tighter budgets, and the shift from 'move fast' to 'keep the lights on.' A realistic recalibration for developers who only knew the boom years.
All Roads Lead to Rome: The ML Job Market in 2022
Eric Jang's 2022 essay on the ML job market — titled 'All Roads Lead to Rome,' arguing that different entry points (research labs, industry, startups) all converge on the same destination if you're technically strong. Candid advice from a DeepMind robotics researcher.
Build a Career in Data Science
A practical book by Jacqueline Nolis and Emily Robinson on navigating a data science career, from landing your first job through managing teams and handling workplace politics. More grounded than most career books because both authors have actually worked as practicing data scientists.
ResyMatch: Resume Scanner and Optimizer
ResyMatch scans your resume against a job description and scores it against what ATS systems look for — keyword matching, formatting issues, and language alignment. Helps optimize resumes before submission.
Photofeeler: Profile Photo Testing
Photofeeler gets unbiased ratings on profile photos from real people — shows how your LinkedIn, dating, or social photos score on competence, likability, and influence before you commit to using them.
interviewing.io: Anonymous Technical Mock Interviews
interviewing.io provides anonymous technical mock interviews with engineers from top companies — practice with real interviewers under realistic conditions, with feedback, before your actual loop.
Introduction to Machine Learning Interviews Book
Chip Huyen's free ML interviews book — covers both the process of landing ML roles and the technical depth (math, coding, ML concepts) that hiring loops test. Dual-purpose: career strategy and technical review.
What You Give Up When Moving Into Engineering Management
Stack Overflow blog post on what engineers actually give up when they move into management — technical depth, maker's schedule, direct contribution. Honest about the tradeoffs rather than cheerleading the transition.
What Got You Here Won't Get You There (Book Summary)
James Clear's summary of Marshall Goldsmith's book on the behavioral habits that prevent successful people from reaching the next level — the 20 bad habits executives have that are actually making them less effective. Surprising because the habits are things that worked earlier in their careers.
Free Data Engineering Learning Resources
Pipeline Data Engineering Academy's curated list of free data engineering learning resources — covering SQL, Python, Spark, Airflow, dbt, and cloud data platforms. A structured entry point for engineers transitioning into data engineering roles.
A Career Ending Mistake
John Arundel argues the career-ending mistake for software engineers is treating their career as a thing that just happens rather than something to deliberately design. Uses the irony that engineers plan meticulously for computers but rarely apply the same rigor to their own professional trajectory.
hirethePIVOT: Second Career Developer Job Board
hirethePIVOT is a job board for second-career developers — people who switched into software from other fields. The hypothesis: career-changers bring real-world domain expertise, motivation, and maturity that traditional CS grads often lack.
The Unreasonable Effectiveness of One-on-Ones
Ben Kuhn argues that regular one-on-ones are unreasonably effective — not because of domain expertise but because they force priority-setting and provide someone who genuinely cares about your progress. The insight that a non-expert who cares deeply can outperform a domain expert who doesn't is worth sitting with.
On Leaving Facebook
Alex Kotliarskyi's reflection on leaving Meta after 8.5 years — the golden handcuffs, the slow loss of craft satisfaction, and what the transition to Replit taught him about what he actually valued. Honest accounting of how big-tech compensation structures create inertia.
Interviews.AI — Data Science Interview Preparation
A GitHub-hosted book of data science and ML interview preparation material aimed at quantitative candidates facing competitive ML engineer and data scientist interviews. Covers statistics, probability, ML theory, and coding challenges.
Amazon Salary Negotiation Guide
Josh Doody's Fearless Salary Negotiation guide to Amazon job offer negotiation — covering the unique RSU vesting cliff, sign-on bonus structure, and total compensation levers that make Amazon offers different from typical tech companies.
The Community Garden: The Case for Leaving FAANG for Crypto
Paradigm's essay making the case for senior engineers to leave FAANG companies for crypto/web3 in 2021 — arguing that the technology primitives, ownership opportunities, and mission alignment make the career trade-off favorable for the right person.
Free Resources to Get a Blockchain Job — For Non-Coders
A free curriculum guide for non-technical people to learn enough blockchain/web3 to get a job in the space within 6 months. Covers communities, certifications, content creation, and non-engineering roles available in crypto.
A Taxonomy of Software Consultants
Erik Dietrich's taxonomy categorizes software consultants by how they create value — from pure staff augmentation through deep specialization to productized expertise. A useful map for thinking about career trajectories in technical consulting.
The Complete FAANG Preparation Repository
A comprehensive GitHub repository for FAANG interview preparation — DSA problems, technical subject theory (OS, DBMS, networking, OOP), and curated question sets. One of the large open-source interview prep aggregators.
Engineering Manager Resources
Ryan Burgess's curated list of resources for engineering managers — articles, books, podcasts, and tools covering the transition from IC to manager, team building, and technical leadership. A well-maintained starting point for new and experienced EMs.
Salary Negotiation: Make More Money, Be More Valued
Patrick McKenzie's canonical essay on salary negotiation — arguing that employees systematically underprice themselves and that negotiation is always appropriate, always safe, and leaves large amounts of money on the table when skipped. Probably the most widely-cited piece on this topic in tech.
Solitude and Leadership
William Deresiewicz's 2010 West Point address arguing that solitude — time alone with your own thoughts — is the prerequisite for genuine leadership. A sharp critique of multitasking culture and the kind of 'excellent sheep' produced by elite institutions.
82 Remote-First Companies That Actively Hire
A curated list of 82 companies that are remote-first by design, not remote-tolerant by accident — saved in 2021 when remote work was reshaping hiring. Useful as a reference for companies where remote is a first-class mode of working.
The Ponzi Career
Dror Poleg's essay arguing that knowledge worker careers resemble Ponzi schemes — each generation depends on the next to maintain asset values, and the winners are those who got in early on the right credentials and networks. A sharp structural critique of meritocracy narratives.
Your AI Skills Are Worth Less Than You Think
Ryszard Szopa's contrarian take on the AI skills premium — arguing that ML expertise is overvalued relative to domain knowledge and problem formulation skills, and that most ML work is commodity engineering that will commoditize further. A useful corrective to 2021 ML hype.
Don't End The Week With Nothing
Patrick McKenzie's essay on building career capital through artifact creation — every week should produce something permanent: a blog post, open-source contribution, or piece of writing that compounds over time. Companion to his salary negotiation essay.
Awesome CTO
A curated, opinionated resource list for Chief Technology Officers and engineering leaders, with emphasis on startup contexts. Covers hiring, technical vision, team dynamics, system design, and the transition from individual contributor to exec.
How to Get Promoted
Defmacro's contrarian take on corporate promotions — arguing that performance reviews are social fiction and actual advancement happens through visibility, relationship capital, and strategic positioning rather than merit alone. Cynical but useful.
Build Personal Moats
Erik Torenberg's essay on building personal moats — durable competitive advantages that compound over time by combining rare skills in ways that are hard to replicate. The key: find what's easy for you but hard for others.
Marc Andreessen on Productivity, Scheduling, and Reading Habits
Marc Andreessen on his productivity system — no schedule, reading voraciously and broadly, and operating in a pure reactive mode to maximize serendipity. Counterintuitive for most but coherent for his specific role.
Data Science Interview Questions and Answers
A community-maintained GitHub repo of data science and ML interview questions and answers — covering statistics, machine learning theory, algorithms, and coding. A useful study guide and signal for what interviewers actually test.
Unpopular Opinion — Data Scientists Should Be More End-to-End
Eugene Yan argues that data scientists deliver more value when they own the full problem lifecycle — from identifying the problem through production deployment. Fewer handoffs, better context, faster iteration, and stronger ownership.
Two Books That Turned a 26-Year-Old Programmer Into a Billionaire
An Entrepreneur's Handbook article about the two books that most influenced a young billionaire programmer — likely covering mental models and systems thinking as frameworks for decision-making. The article is paywalled but the premise is evergreen.
Stanford CS 007: Personal Finance for Engineers
Adam Nash's slides from Stanford CS 007 — a practical personal finance course for engineers covering investment theory, tax-advantaged accounts, equity compensation, and real estate. One of the few structured finance curricula aimed specifically at tech workers.
My Younger Self
My Younger Self is a platform collecting career and life advice that experienced professionals wish they had known earlier — interview-format stories organized by career stage and domain. Structured mentorship-at-scale.
What I Learned from Doing 60+ Technical Interviews in 30 Days
Emmanuel Okafor's post-mortem on doing 60+ technical interviews in 30 days — 13 lessons grouped into pre-interview, during-interview, and post-interview phases. One of the most data-rich personal accounts of systematic technical interview practice.
Hiring Without Whiteboards
The Airtable-based Hiring Without Whiteboards list — companies that use practical, work-sample interviews instead of algorithmic puzzles. A valuable reference for engineers who find whiteboard interviews a poor signal for the actual job.
Machine Learning Roadmap
Daniel Bourke's visual roadmap connecting the core concepts of machine learning, what to learn first, and what tools to use — built as a community-oriented guide for self-learners entering the field in 2020.
Awesome GitHub Profile README Templates
A curated collection of GitHub profile README templates — saved when GitHub profile READMEs launched in mid-2020. Reference gallery for designing a developer profile page with stats, badges, and custom layouts.
Reflecting on a Year of Making Machine Learning Actually Useful
Shreya Shankar's honest reflection on a year trying to make machine learning actually useful in industry — covering the gap between academic ML and production, the underappreciated role of data work, and why most ML projects fail before the model stage. One of the most cited personal essays in the MLOps space.
HN: 40 Statistics Interview Problems and Answers
A Hacker News thread discussing a list of 40 statistics interview problems — the HN comments add context, caveats, and additional problems to the original post. A snapshot of what statistics knowledge is actually tested in data science interviews.
160 Data Science Interview Questions
Alexey Grigorev's compilation of 160 data science interview questions across statistics, machine learning, SQL, and programming — a broad coverage reference for data science interview preparation, organized by topic.
Your Ultimate Learning Path to Become a Data Scientist in 2020
Analytics Vidhya's structured learning path to become a data scientist in 2020 — a step-by-step curriculum covering statistics, Python, ML algorithms, and tools, with specific resource recommendations at each stage.
Build a Career in Data Science (Manning)
Manning's 'Build a Career in Data Science' book — covers getting a data science job, excelling in the role, and navigating data science careers from both technical and non-technical angles. One of the few books focused on the career side rather than the technical side.
What I Wish I Knew About Data For Startups
Jean-Nicholas Hould's hard-won lessons about building data capabilities at startups — prioritizing tracking over models, avoiding premature data infrastructure, and why startups fail at data for different reasons than large companies.
One Year as a Data Scientist at Stack Overflow
David Robinson's retrospective on his first year as a data scientist at Stack Overflow — what he learned, where the role differed from academic statistics, and why communication matters more than algorithms. A grounded career reflection that aged well.
Data Do's and Don'ts: Lessons from the Front Line
Domino Data Lab's 'Data Do's and Dont's' slides from Data Popup Austin — practical lessons from working data scientists about what actually goes wrong in production ML and analytics projects. A practitioner's guide to avoiding common pitfalls.
Changing Education: How Bootcamps Outperform University
Liz Abinante's argument for how coding bootcamps outperform university education for software development — focusing on applied skills, tight feedback loops, and job-readiness. Written by someone who ran and taught at bootcamps at the height of the coding bootcamp boom.
Bridging the Gap Between Data Science and Engineering
Ryan Orban's slides from a Galvanize talk on bridging the gap between data science and engineering — covering organizational structures, communication patterns, and team design for high-performance data teams. Reflects the friction between DS and SWE roles that defined the mid-2010s.
How to Build a Data Team
A practical guide on building an effective data team — roles, hiring order, organizational structure, and the common mistakes companies make when scaling from one data person to a full team. Shared by Ryan Orban from his Galvanize network.
Online Skills Are Hot, But Will They Land You a Job?
A WSJ report on the gap between online skill acquisition (MOOCs, Coursera, Udacity) and actual employment outcomes — asking whether employers recognize and value credentials from online platforms versus traditional degrees. Saved during the peak MOOC-to-employment hype cycle.
Keys to Understanding: Data Scientist vs. Data Engineer
Domino Data Lab slides on knowing when to hire a data scientist versus a data engineer — clarifying the distinct skills, responsibilities, and organizational needs each role fills. From the Data Popup Seattle conference, when this distinction was still being established.
Warning Signs: Is It Time To Leave Your Startup?
A guide to recognizing when it's time to leave your startup — distinguishing genuine warning signs (runway problems, founder trust breakdown, equity dilution) from normal startup discomfort. The hardest part is separating the signal from the noise when you're emotionally invested.
Software Engineering Daily: Data Science with Jonathan Dinu and Ryan Orban
Ryan Orban and Jonathan Dinu on Software Engineering Daily talking about data science education and the thesis that 'everyone needs a data scientist' — recorded during Galvanize's peak influence on the data science bootcamp market.
Tech Inclusion Conference 2015
Tech Inclusion 2015, a San Francisco conference exploring solutions to the diversity and inclusion gap in tech — hosted at Galvanize. One of the early large-format events treating representation as a systemic engineering problem rather than a PR issue.
Code Academy as Career Game-Changer (NYT, 2015)
The New York Times profiling coding bootcamps as legitimate career changers in 2015 — a mainstream media validation moment that preceded the eventual credential inflation and quality variance problems that followed. Galvanize was prominently featured.
3 Things About Data Science You Won't Find In Books
Three things about data science that don't appear in textbooks — the communication overhead, the 80% time spent on data wrangling, and the organizational politics of getting models deployed. Classic practitioner wisdom from Galvanize/KDnuggets circa 2015.
Surviving Data Science "at the Speed of Hype"
John Foreman's essay on staying grounded as a data scientist when the field is being hyped beyond recognition — arguing for focusing on decisions and outcomes rather than methods and tools. One of the sharper industry critiques of the 2015 data science gold rush.
Data Scientist Interview Puzzle Questions — Mike Tamir
Mike Tamir's Quora answer on brainstorming and puzzle questions asked in data science interviews. Useful for both candidates prepping for interviews and hiring managers designing them — the best puzzles test statistical intuition, not memorization.
A Data Science Chat with Kevin Novak from Uber
A talk by Kevin Novak, data scientist at Uber, covering how Uber approaches data science in practice — from surge pricing models to driver supply forecasting. An early window into how a hypergrowth tech company used data science operationally.
Must Read Before Attending Any Data Science Interview
Data Science Central's pre-interview reading list covering statistics, machine learning, coding, and business acumen questions asked in 2014 data science interviews. A historical snapshot of what the field considered core practitioner knowledge.
Hadoop, Python, and NoSQL Lead the Pack for Big Data Jobs
InfoWorld's 2014 analysis of job postings showing Hadoop, Python, and NoSQL as the top skills in big data job listings — a snapshot of the technology bets companies were making at the height of the big data boom.
Why You Already ARE a Data Scientist
An argument that data science is a mindset about designing experiments and using tools to answer questions — not a job title or a specific toolset. A pushback against gatekeeping that argued the curiosity and experimental design skills matter more than the specific technologies.
Elusive Data Scientists Driving High Salaries
KDnuggets article on data scientists being scarce and commanding high salaries in 2014 — a snapshot of the early hype cycle, when the Harvard Business Review had recently called data scientist 'the sexiest job of the 21st century.'
Zipfian Academy: Become a Data Scientist in 12 Intense Weeks
KDNuggets profile of Zipfian Academy's 12-week data science bootcamp in San Francisco — one of the first intensive programs designed to train working professionals as data scientists. Captures the moment when data science education was being invented as a category.
5 Mistakes Programmers Make When Starting in Machine Learning
Jason Brownlee's list of five mistakes programmers make when transitioning into machine learning — over-focus on theory, skipping problem definition, ignoring data quality, neglecting model evaluation, and treating ML as a programming challenge. The practitioner's onramp.
Why Soft Skills Matter in Data Science
Data Informed piece arguing that communication, curiosity, and domain knowledge matter as much as technical skills in data science — the overlooked half of the job that makes or breaks whether analysis produces decisions.
Thumbtack Data Scientist Challenges
Thumbtack's public data scientist hiring challenge — real analysis and modeling problems used to evaluate candidates. A window into what applied data science work looked like at a 2014 marketplace startup.
5 Things I've Learned About Data Science
Nicholas Arcolano's reflections on what matters in data science practice — the kind of hard-won lessons about problem framing, communication, and iteration that aren't covered in ML courses.
Questions I'm Asking in Interviews
Julia Evans's list of questions she asks companies during technical interviews — focused on understanding engineering culture, code quality, and whether she'd actually grow there. The kind of due-diligence list that flips the interview dynamic.
How to Be a Programmer: A Short, Comprehensive, and Personal Summary
Robert Read's comprehensive guide to being a programmer — covering debugging, communication, teamwork, and judgment that textbooks leave out. Unusually honest about the human and organizational side of software development.
Who's Training the Next Generation of Data Scientists?
CIO.com coverage of who was training the next generation of data scientists in late 2013 — pointing to Berkeley's data science program and Zipfian Academy as the two leading sources. Ryan saved this while attending Zipfian, literally one of the people being trained.
Nate Silver on Finding a Mentor, Teaching Yourself Statistics, and Not Settling
HBR interview with Nate Silver on finding mentors, self-teaching statistics, and not settling for work below your potential. Advice from the FiveThirtyEight founder at the peak of his post-2012-election fame.
Uber Challenges on Talentbuddy
A set of Uber-branded coding challenges on Talentbuddy, an early technical hiring platform. Saved in September 2013 — likely for interview practice while at Zipfian Academy, targeting data engineering or software engineering roles at Uber.
Machine Learning Skills for Jobs (2013)
A Quora thread on what skills machine learning jobs required in 2013 — the answer set reflects the early data science job market before the role fragmented into ML engineer, data scientist, and AI researcher specializations. A snapshot of what practitioners thought mattered at the time.
The Data Science Mindset
Zipfian Academy's post on the data science mindset — the cognitive habits and intellectual approach that distinguish effective data scientists from people who merely know the tools. Published by one of the first data science bootcamps when the profession was still being defined.
INFORMS Narrows Big Data Skills Gap
INFORMS (the operations research professional society) launching continuing education courses to address the big data skills gap in 2013 — a telling sign that demand for analytics talent had outpaced formal education pipelines. The gap was real, but the institutional response came well after the bootcamp ecosystem had already mobilized.
Hilary Mason Joins Accel as Data Scientist in Residence
TechCrunch covering Hilary Mason leaving Bitly to become Accel Partners' first Data Scientist in Residence — a signal that VC firms were beginning to treat data science as a strategic capability for evaluating and supporting portfolio companies, not just a product skill.
How to Better Compete with Other Data Scientists
AnalyticBridge post on differentiation strategies for data scientists in an increasingly crowded field circa 2013. Covers specialization, communication skills, and building a public track record as ways to stand out beyond pure technical competence.
What Every Computer Science Major Should Know
Matt Might's canonical essay on what a CS graduate should know across breadth and depth — from formal theory to systems to software engineering practice. A widely-shared framework for thinking about CS education and self-directed learning gaps.
So You Want to Be a Freelancer...
Samuel Mullen's practical guide to starting as a freelance developer — covering how to find clients, price your work, manage cash flow, and protect yourself legally. Written from hard experience, not theory.
First Targeted Ads, Now Data Scientists Think They Can Change the World
GigaOm piece on a wave of data scientists pivoting from ad targeting to social good applications — healthcare, education, poverty prediction. A 2013 snapshot of the idealism that accompanied the data science boom and the question of whether these techniques could address harder problems.
A Taxonomy of Data Science
Hilary Mason and Chris Wiggins' 2010 taxonomy of data science roles and skills, organized around the OSEMN framework: Obtain, Scrub, Explore, Model, iNterpret. One of the earliest attempts to define what data science actually comprises as a discipline.
ABC: Always Be Coding
David Byttow's Medium post arguing that the best way to get a software engineering job at top companies is to code constantly — side projects, open source, and deliberate practice rather than cramming interview prep. The ABC (Always Be Coding) philosophy.
Career Advice: How Do I Become a Data Scientist?
Quora's canonical 2013 answer on becoming a data scientist — one of the most-read career guides in the early data science field. A snapshot of what skills and background paths were considered credible entry points before formal data science degrees existed.
Smart Guy Productivity Pitfalls
Tom Forsyth's (Book of Hook) essay on the specific productivity failure modes that affect intelligent people — analysis paralysis, over-engineering, and the tendency to confuse thinking with doing. A direct counterpoint to the 'just work harder' productivity genre.
Coding for Interviews — Book Recommendations
Coding for Interviews was a weekly newsletter and book-recommendation site for software engineering interview preparation — curating the canonical algorithm and data structure books used in technical interviews at top tech companies.
Why 'Do What You Love' Is Terrible Advice
Jeff Haden's Inc.com argument against 'do what you love' as career advice — because passion doesn't precede competence, it follows it. The better advice is to get very good at something, and love tends to develop from mastery.
Secret Ingredient for Success
NYT Sunday Review piece on the role of self-awareness in success — arguing that high achievers distinguish themselves not by talent but by how they process failure and feedback. The 'secret ingredient' is the ability to learn from experience rather than defend against it.
The Single Most Unfair Advantage a Person Can Get
Ivan Mazour's blog post arguing that public speaking ability is the single most unfair competitive advantage — it multiplies every other capability, yet most people avoid developing it systematically. A sharp take on asymmetric skill investment.
Aaron Swartz: howtoget
Aaron Swartz's notes on how to get what you want — a short, frank guide to the mechanics of creating opportunities, from reaching out cold to the right people to learning by doing. Characteristically blunt.
The Mathematical Hacker
Evan Miller's essay arguing that programmers who invest in mathematical fluency gain compounding advantages — because math enables them to evaluate methods rather than just apply them, and to work with uncertainty and probability naturally. The case for quantitative education in software.
How to Code a Life
A BuzzFeed essay framing life decisions using programming metaphors — an early example of using software concepts to explain life choices to a general audience. Part of the 2012 wave of coding-as-metaphor content.
Don't Waste Your Time in Crappy Startup Jobs
Michael O. Church's argument that most startup jobs are bad deals for engineers — low salary, equity that rarely pays out, and false promises of learning opportunities. Counterintuitive at the peak of startup mania in 2012.
What a Hacker Learns After a Year in Marketing
A software engineer reflects on a year spent working in marketing, describing what surprised them about how products get positioned and sold. The key lesson: engineers often build for other engineers, while marketing forces you to think about the actual customer's frame of reference.
Get That Job at Google
Steve Yegge's definitive guide on how to prepare for and pass Google software engineering interviews, written in 2008 and widely read through the 2010s. The advice is brutally practical: most candidates fail because they stopped practicing algorithms and data structures after college.
So You Call Yourself a Data Scientist?
VentureBeat's 2012 examination of what 'data scientist' actually means — a job title proliferating faster than the field had consensus on its definition. Published at the height of the 'sexiest job of the 21st century' hype cycle.
Freelancing: A 6-Month Retrospective
Mike Rooney's honest retrospective on his first six months of freelancing as a software engineer in 2012 — what worked, what didn't, and the unexpected realities of self-directed work. Useful as a primary document from the early independent-worker wave before 'digital nomad' became a cliché.
Do What You Love
Michael Abrash's post on joining Valve and the 'do what you love' principle — a meditation on how career satisfaction relates to craft mastery rather than passion alone. Written just as Abrash joined Valve from Intel after years in graphics programming.
From Programming to Business: Lesson 0
A blog post on transitioning from engineering to business thinking — the 'Lesson 0' framing signals it's about unlearning as much as learning. The core shift: from solving specified problems correctly to defining which problems are worth solving at all.
Don't Become Anything, Especially Not a Programmer
Zed Shaw's contrarian post arguing that 'learning to code' for career reasons is a trap — programming should be learned because you love it, not as a get-rich-quick path. A direct rebuke of the 'anyone can be a programmer in 12 weeks' message gaining traction at the time.
Why I Declined an Offer to Work at Instagram
A Quora answer (surfaced by TechCrunch) from someone who declined an Instagram job offer because they wanted to work on a 'platform for all human knowledge' instead. A 2012 moment capturing the idealism around mission-driven work vs. joining a rocket ship.
Three Tips to Succeed as a Programmer
Facebook Engineering's post on three principles for succeeding as a programmer — written at a moment when Facebook was the most visible example of what high-performing engineering culture could produce. Likely covers ownership, breadth vs depth, and learning from code review.
Entrepreneurship's dirty little secret
Alan Gleeson's contrarian take that entrepreneurship is oversold as a career path — arguing that actively discouraging unfit founders would produce better outcomes than the blanket glorification of starting a company. An uncomfortable but useful corrective to startup culture's hero narrative.