Podcast appearances and mentions of vicki boykis

Forrest Brazeal is concerned about the open source threat from within, Vicki Boykis explains why Redis is forked, John O'Nolan and the Ghost team plan to federate over ActivityPub, Llama 3 is now available for “businesses of all sizes” & nolen writes up questions to ask when you don't want to work.

Forrest Brazeal is concerned about the open source threat from within, Vicki Boykis explains why Redis is forked, John O'Nolan and the Ghost team plan to federate over ActivityPub, Llama 3 is now available for “businesses of all sizes” & nolen writes up questions to ask when you don't want to work.

Forrest Brazeal is concerned about the open source threat from within, Vicki Boykis explains why Redis is forked, John O'Nolan and the Ghost team plan to federate over ActivityPub, Llama 3 is now available for “businesses of all sizes” & nolen writes up questions to ask when you don't want to work.

Machine learning (ML) has reached an exciting phase of development, a phase that Vicki Boykis, Senior ML Engineer at Duo Security* has characterized as the “steam-powered days.” In this episode of Numerically Speaking: The Anaconda Podcast, Vicki talks about the state of the industry and where she sees things heading.   Vicki's discussion with host Peter Wang covers:   The interplay between software engineering and ML, the human element of the development lifecycle (and the lack thereof in social media) and the operationalization and the rise of microservices.   Resources:   Click https://vickiboykis.com to visit Vicki's blog.   Click https://www.amazon.com/Presentation-Self-Everyday-Life/dp/0385094027  to purchase The Presentation of Self in Everyday Life by Erving Goffman, referenced by Vicki.   Click https://www.amazon.com/Broad-Band-Untold-Story-Internet/dp/0735211752  to purchase Broad Band: The Untold Story of the Women Who Made the Internet, also referenced by Vicki.   Click https://jimruttshow.blubrry.net/currents-rob-malda/  to listen to the Jim Rutt/Rob Malda (Slashdot) podcast episode referenced by Peter.   Check out the P2 website https://wordpress.com/p2/   You can find a human-verified transcript of this episode here -  https://know.anaconda.com/rs/387-XNW-688/images/ANACON_Vicki%20Boykis_V2%20%281%29.docx.pdf.    If you enjoyed today's show, please leave a 5-star review. For more information, visit anaconda.com/podcast.   *At the time of the interview, Vicki Boykis was an ML Engineer working on Tumblr at Automattic.  

Today's blog post is "The ghosts in the data", by Vicki Boykis. The post was first published in March 2021 on Vicki's blog. You can find the original post including all the links that are mentioned in the post on https://veekaybee.github.io/2021/03/26/data-ghosts/ We're always looking for great blog posts from the wonderful world of tech and data. Follow us on Twitter @blogcastpod to suggest your favorite posts, or email blogcastpod@gmail.com. We'd also love to hear from folks who want to read for us! Hear ya later :)

rstudio::global 2021 and UseR! 2021 call for abstracts Episode Links This week's curator: Jon Calder (@jonmcalder (https://twitter.com/jonmcalder)) rstudio::global 2021 talks (https://rstudio.com/resources/rstudioglobal-2021/) rstudio::global(2021) %>% summarise() (https://clarewest.github.io/blog/post/rstudio-global-2021-summarise/) 15th Mar: useR2021 Call for Abstracts (https://user2021.r-project.org/participation/call-for-abstracts/) Supplemental Resources Lifelong Learning with R Weekly (rstudio::global 2021) (https://rstudio.com/resources/rstudioglobal-2021/lifelong-learning-with-r-weekly/) Your Public Garden - rstudio::global(2021) Keynote by Vicki Boykis (https://docs.google.com/presentation/d/1RZqE43Y3fWEExGwL3jAZD7uQPDUnt53UZj0GgWo6RtQ/edit#slide=id.g6fe1a5a715_4_4744)

Catch the video edition live stream on YouTube: youtube.com/watch?v=oKgAsjiJqMs Sponsored by us! Support our work through: Our courses at Talk Python Training Test & Code Podcast Patreon Supporters Special guest: Shari Eskenas Be part of the episode by subscribing and “smashing that bell” over at pythonbytes.fm/youtube Brian #1: pytest 6.2 is out pytester fixture for plugin testing. Like testdir, but a better name, and uses pathlib.Path objects. verbose mode now shows the reason a test was skipped in the terminal line after the SKIPPED, XFAIL, or XPASS Can use monkeypatch as a context manager with .context() and it’s available both in test and fixture functions, but also in helper functions by using pytest.Monkeypatch.context(). import os from contextlib import contextmanager import pytest def test_foo_1(monkeypatch): with monkeypatch.context() as mp: mp.setenv("foo", "bar") assert os.getenv("foo") == "bar" @contextmanager def some_func(): with pytest.MonkeyPatch.context() as mp: mp.setenv("foo", "bar") yield def test_foo_2(): with some_func(): assert os.getenv("foo") == "bar" Lots of other goodies. related: pytest-check, my only released plugin for pytest, was updated to support pytest 6.x Michael #2: SQLite as a file format (like docx) via Jon Bultmeyer An SQLite database file with a defined schema often makes an excellent application file format. Here are a dozen reasons why this is so: Simplified Application Development. No new code is needed for reading or writing the application file. Single-File Documents. An SQLite database is contained in a single file, which is easily copied or moved or attached. High-Level Query Language. SQLite is a complete relational database engine, which means that the application can access content using high-level queries. Accessible Content. Information held in an SQLite database file is accessible using commonly available open-source command-line tools - tools that are installed by default on Mac and Linux systems and that are freely available as a self-contained EXE file on Windows. Cross-Platform. SQLite database files are portable between 32-bit and 64-bit machines and between big-endian and little-endian architectures and between any of the various flavors of Windows and Unix-like operating systems. Atomic Transactions. Writes to an SQLite database are atomic. They either happen completely or not at all, even during system crashes or power failures. Incremental And Continuous Updates. When writing to an SQLite database file, only those parts of the file that actually change are written out to disk. This makes the writing happen faster and saves wear on SSDs. Easily Extensible. As an application grows, new features can be added to an SQLite application file format simply by adding new tables to the schema or by adding new columns to existing tables. Adding columns or tables does not change the meaning of prior queries. Performance. In many cases, an SQLite application file format will be faster than a pile-of-files format or a custom format. Concurrent Use By Multiple Processes. SQLite automatically coordinates concurrent access to the same document from multiple threads and/or processes. Multiple Programming Languages. Though SQLite is itself written in ANSI-C, interfaces exist for just about every other programming language you can think of. Better Applications. If the application file format is an SQLite database, the complete documentation for that file format consists of the database schema, with perhaps a few extra words about what each table and column represents. Shari #3: A Day in Code: Python – A picture book written in code Brian #4: PythonLabs is now hosted by Azure. and “Yes, Barry, there is a PythonLabs” I can’t believe we haven’t covered this in the last 211 episodes. But it seems like good timing now. Now resides at azure.pythonlabs.com By Tim Peters (originally posted on January 6th, 2004 to the PSF members list) Barry (I’m assuming Barry Warsaw) asked the question: “… what /is/ Pythonlabs now? Or /is/ there a Pythonlabs now? I dunno -- Guido owns the domain name which is probably the biggest claim to Pythonlabhood there is.” Tim replies with a very “Yes, Virginia, there is a Santa Clause”-esque answer: Snippets include: Barry, your little friends are wrong. They have been affected by the skepticism of a skeptical age. … Yes, Barry, there is a PythonLabs. It exists as certainly as love and generosity and devotion exist, … Alas! how dreary would be the world if there were no PythonLabs! It would be as dreary as if there were no Barrys. … Not believe in PythonLabs! You might as well not believe in fairies. … Nobody sees PythonLabs, but that is no sign that there is no PythonLabs. … A thousand years from now, Barry, nay 10 times 10,000 years from now, it will continue to make glad the heart of childhood. Michael #5: Extra, extra, extra, extra, extra, extra, hear all about it #1 Numpy version pinning via Grice Just catching up on Ep 208. Note in part 2 about the Numpy issue, folks can pin versions by platform with environment markers: numpy==1.19.3; platform_system == 'Windows' numpy==1.19.4; platform_system == 'Linux' #2 Stylesheet for PySide2 and PyQt5, this time looks like Material Design - via William Jamir Silva #3 Talk Python hits 20M downloads. Python Bytes is almost 6M too. #4 Pyramid 2.0 is coming. #5 Python 3.9.1 is out with 282 changes. Ships as a universal binary (Intel + M1) on macOS. #6 Python + Mac Mini + M1 video #7 Python Steering Council selected Shari #6: OpenMV Extras Brian: agrs, kwargs (quargs), and community a silly discussion on twitter Nocole Carlson: “Huge debate … about whether you say “quargs” or “keyword arguments” for “kwargs”. Obviously “quargs” is correct.” It never occurred to me to say “quargs”, but I like it. Vicki Boykis replied that she’s “… never said anything but “quargs”. This is like a parallel universe.” Some other amusing responses. This reminded me of a conversation I might have over beer at PyCon. or at a booth, or just standing around in the hallway. I miss so much the in person community. I’m grateful that little bits of it are intact on twitter. Joke: Pizza delivery fail Second joke: Why do you many developers use dark mode? Because bugs are attracted to light.

Welcome to another episode of Develomentor. Today's guest is Vicki Boykis.Vicki Boykis is currently a machine learning engineer at Automattic as well as a published writer on topics in the data science space. Vicki received a Bachelors degree in Economics from Penn State and her MBA from Temple. That’s right, she didn’t study computer science in university! She became curious in software engineering at her job at Comcast. She decided to learn on her own with a little help from her peers. Oh, and she took a few online courses from a community college!Vicki has 10+ years’ experience in helping companies across a broad range of industries make their data actionable. Most recently, she has built machine learning data products in Python but she has also worked with R, Tableau, Spark (Scala and Python), and Hadoop. In her spare time, she enjoys reading, running, and mentoring people new to the tech industry.If you are enjoying our content, click here to support us!Episode Summary“Moving into the data science space was almost like a stroke of luck because I had no idea that it even existed.”“I knew that if I did a Coursera course I wouldn’t be able to concentrate on it because I wouldn’t feel like there was enough at stake. But the community college program was for actual credits and you get an actual certificate. It felt a little more structured and a little more real to me.”“For my MBA program what really helped me was taking accounting and finance classes. I started to understand that the people who manage companies and the people who are responsible for budgets think in terms of revenue and costs of projects and opportunity costs.”—Vicki BoykisKey MilestonesHow did Vicki get into economics and financial analysis?Vicki worked at Comcast in a business intelligence role. How did this role allow her to move into data science?In 2016, Vicki went to community college to take some computer science courses. What was it like going back to school after working for more than a few years?Data science engineer vs machine learning engineerHow has having an Econ and MBA degree helped Vicki in her career?These days, Vicki is a manager of data science and engineering. Why did she choose to move into management?How does Vicki approach team building in data science?Vicki is also a writer an an author. In what ways has this helped her career?Additional ResourcesVicki suggests anyone getting into tech should read Hacker News! – https://thehackernews.com/Mike Gualtieri – Tech Analyst at Forrester (#38) – Previous episode mentioned You can find more resources in the show notesTo learn more about our podcast go to https://develomentor.com/To listen to previous episodes go to https://develomentor.com/blog/CONNECT WITH VICKI BOYKISLinkedInTwitterFOLLOW DEVELOMENTORTwitter: @develomentorCONNECT WITH GRANT INGERSOLLLinkedInTwitter

Diana Pojar Staff Data Engineer at Slack April, 2020 blog, twitter, linkedin Tell us a little about your current role: your title, the company you wor... https://staffeng.com/stories/diana-pojar blogtwitterlinkedintechnical leadershipJosh WillsStan BabourineBogdan GazaTravis CrawfordCamille Fournier Lara HoganJosh WillsVicki BoykisDavid GascaJulia GraceHolden KarauJohn AllspawCharity MajorsTheo SchlossnagleJessica Joy KerrSarah CatanzaroOrange Bookmy Goodreads accountReady to read another story?

Sponsored by Datadog: pythonbytes.fm/datadog Special guest: Vicki Boykis: @vboykis Michael #1: clize: Turn functions into command-line interfaces via Marcelo Follow up from Typer on episode 164. Features Create command-line interfaces by creating functions and passing them to [clize.run](https://clize.readthedocs.io/en/stable/api.html#clize.run). Enjoy a CLI automatically created from your functions’ parameters. Bring your users familiar --help messages generated from your docstrings. Reuse functionality across multiple commands using decorators. Extend Clize with new parameter behavior. I love how this is pure Python without its own API for the default case Vicki #2: How to cheat at Kaggle AI contests Kaggle is a platform, now owned by Google, that allows data scientists to find data sets, learn data science, and participate in competitions Many people participate in Kaggle competitions to sharpen their data science/modeling skills Recently, a competition that was related to analyzing pet shelter data resulted in a huge controversy Petfinder.my is a platform that helps people find pets to rescue in Malaysia from shelters. In 2019, they announced a collaboration with Kaggle to create a machine learning predictor algorithm of which pets (worldwide) were more likely to be adopted based on the metadata of the descriptions on the site. The total prize offered was $25,000 After several months, a contestant won. He was previously a Kaggle grandmaster, and won $10k. A volunteer, Benjamin Minixhofer, offered to put the algorithm in production, and when he did, he found that there was a huge discrepancy between first and second place Technical Aspects of the controversy: The data they gave asked the contestants to predict the speed at which a pet would be adopted, from 1-5, and included input features like type of animal, breed, coloration, whether the animal was vaccinated, and adoption fee The initial training set had 15k animals and the teams, after a couple months, were then given 4k animals that their algorithms had not seen before as a test of how accurate they were (common machine learning best practice). In a Jupyter notebook Kernel on Kaggle, Minixhofer explains how the winning team cheated First, they individually scraped Petfinder.my to find the answers for the 4k test data Using md5, they created a hash for each unique pet, and looked up the score for each hash from the external dataset - there were 3500 overlaps Did Pandas column manipulation to get at the hidden prediction variable for every 10th pet and replaces the prediction that should have been generated by the algorithm with the actual value Using mostly: obfuscated functions, Pandas, and dictionaries, as well as MD5 hashes Fallout: He was fired from H20.ai Kaggle issued an apology Michael #3: Configuring uWSGI for Production Deployment We run a lot of uWSGI backed services. I’ve spoken in-depth back on Talk Python 215: The software powering Talk Python courses and podcast about this. This is guidance from Bloomberg Engineering’s Structured Products Applications group We chose uWSGI as our host because of its performance and feature set. But, while powerful, uWSGI’s defaults are driven by backward compatibility and are not ideal for new deployments. There is also an official Things to Know doc. Unbit, the developer of uWSGI, has “decided to fix all of the bad defaults (especially for the Python plugin) in the 2.1 branch.” The 2.1 branch is not released yet. Warning, I had trouble with die-on-term and systemctl Settings I’m using: # This option tells uWSGI to fail to start if any parameter # in the configuration file isn’t explicitly understood by uWSGI. strict = true # The master uWSGI process is necessary to gracefully re-spawn # and pre-fork workers, consolidate logs, and manage many other features master = true # uWSGI disables Python threads by default, as described in the Things to Know doc. enable-threads = true # This option will instruct uWSGI to clean up any temporary files or UNIX sockets it created vacuum = true # By default, uWSGI starts in multiple interpreter mode single-interpreter = true # Prevents uWSGI from starting if it is unable to find or load your application module need-app = true # uWSGI provides some functionality which can help identify the workers auto-procname = true procname-prefix = pythonbytes- # Forcefully kill workers after 60 seconds. Without this feature, # a stuck process could stay stuck forever. harakiri = 60 harakiri-verbose = true Vicki #4: Thinc: A functional take on deep learning, compatible with Tensorflow, PyTorch, and MXNet A deep learning library that abstracts away some TF and Pytorch boilerplate, from Explosion Already runs under the covers in SpaCy, an NLP library used for deep learning type checking, particularly helpful for Tensors: PyTorchWrapper and TensorFlowWrapper classes and the intermingling of both Deep support for numpy structures and semantics Assumes you’re going to be using stochastic gradient descent And operates in batches Also cleans up the configuration and hyperparameters Mainly hopes to make it easier and more flexible to do matrix manipulations, using a codebase that already existed but was not customer-facing. Examples and code are all available in notebooks in the GitHub repo Michael #5: pandas-vet via Jacob Deppen A plugin for Flake8 that checks pandas code Starting with pandas can be daunting. The usual internet help sites are littered with different ways to do the same thing and some features that the pandas docs themselves discourage live on in the API. Makes pandas a little more friendly for newcomers by taking some opinionated stances about pandas best practices. The idea to create a linter was sparked by Ania Kapuścińska's talk at PyCascades 2019, "Lint your code responsibly!" Vicki #6: NumPy beginner documentation NumPy is the backbone of numerical computing in Python: Pandas (which I mentioned before), scikit-learn, Tensorflow, and Pytorch, all lean heavily if not directly depend on its core concepts, which include matrix operations through a data structure known as a NumPy array (which is different than a Python list) - ndarray Anne Bonner wrote up new documentation for NumPy that introduces these fundamental concepts to beginners coming to both Python and scientific computing Before, you went directly to the section about arrays and had to search through it find what you wanted. The new guide, which is very nice, includes a step-by-step on how arrays work, how to reshape them, and illustrated guides on basic array operations. Extras: Vicki I write a newsletter, Normcore Tech, about all things tech that I’m not seeing covered in the mainstream tech media. I’ve written before about machine learning, data for NLP, Elon Musk memes, and Nginx. There’s a free version that goes out once a week and paid subscribers get access to one more newsletter per week, but really it’s more about the idea of supporting in-depth writing about tech. vicki.substack.com Michael: pip 20.0 Released - Default to doing a user install (as if --user was passed) when the main site-packages directory is not writeable and user site-packages are enabled, cache wheels built from Git requirements, and more. Homebrew: brew install python@3.8 Joke: An SEO expert walks into a bar, bars, pub, public house, Irish pub, tavern, bartender, beer, liquor, wine, alcohol, spirits...

Show Notes: (2:06) Nick talked about earning his Ph.D. degree in Psycho-Linguistics from the University of Texas at Austin. (3:58) Nick discussed his Ph.D. dissertation, in which he looked at the role of domain-general decision making processes in human language comprehension. (8:41) Nick talked about his teaching experience in graduate school, as well as why UT-Austin is a perfect place to study linguistics. (12:29) Nick delved into his first job out of school as a Linguistic Associate at Lexicon Branding, a world’s premier naming agency with over 30 years of experience in the business (14:50) Nick talked about his next role as a Data Scientist at Idibon, a dated AI-startup based in Silicon Valley that helped companies understand their language data. (18:55) Nick mentioned his next job as a Senior Data Scientist at CrowdFlower (now known as Figure Eight), a human-in-the-loop ML and AI company based in San Francisco. (23:19) Nick talked about his next Senior Data Scientist role - working on Engagement Lead at Change Healthcare, a healthcare technology company that offers software, analytics, network solutions, and technology-enabled services to help create a stronger, more collaborative healthcare system. (26:28) Nick discussed his transition to become a Senior Data Scientist at Womply, a SaaS-based software that is powered by transaction and online review data for millions of small businesses. (30:08) Nick talked about his current role as a Senior Manager in the Data Science department at Johnson & Johnson’s Health Technology group and shared the challenges of putting AI/ML algorithms into production in the healthcare domain. (34:11) Nick shared his narrative of being a person who can facilitate communication between technical and nontechnical teams in the blog post “Drinks with a businessman” (with an anecdote including his grandfather). (39:27) In reference to “A few thoughts on ML from the perspective of a behavioral scientist,” Nick shared his advice for data scientists who want to develop the core skills including being able to generate and test hypotheses, to think critically about unfamiliar data, and to gauge how much you trust your result. (41:37) Nick shared his findings in his 3-part blog series title “Being a grad student is a lot like being a startup” (Part 1, Part 2, and Part 3). (46:21) In reference to “Data science - the way I see it,” Nick talked about the methodological side of data science. (49:05) Nick discussed how graduate students can leverage their skills to qualify for an industry role. (52:17) Nick reflected on his efforts to stay active in academia while holding an industry job. (54:09) Closing segment. His Contact Info: LinkedIn Twitter Website His Recommended Resources: Stitch Fix Technology Netflix Research Fast Company’s Most Innovative Data Science Companies in 2019 Going Pro in Data Science by Jerry Overton Smart Thinking, Smart Change, and Brain Briefs by Art Markman Data science is different now by Vicki Boykis

Show Notes: (2:06) Nick talked about earning his Ph.D. degree in Psycho-Linguistics from the University of Texas at Austin. (3:58) Nick discussed his Ph.D. dissertation, in which he looked at the role of domain-general decision making processes in human language comprehension. (8:41) Nick talked about his teaching experience in graduate school, as well as why UT-Austin is a perfect place to study linguistics. (12:29) Nick delved into his first job out of school as a Linguistic Associate at Lexicon Branding, a world’s premier naming agency with over 30 years of experience in the business (14:50) Nick talked about his next role as a Data Scientist at Idibon, a dated AI-startup based in Silicon Valley that helped companies understand their language data. (18:55) Nick mentioned his next job as a Senior Data Scientist at CrowdFlower (now known as Figure Eight), a human-in-the-loop ML and AI company based in San Francisco. (23:19) Nick talked about his next Senior Data Scientist role - working on Engagement Lead at Change Healthcare, a healthcare technology company that offers software, analytics, network solutions, and technology-enabled services to help create a stronger, more collaborative healthcare system. (26:28) Nick discussed his transition to become a Senior Data Scientist at Womply, a SaaS-based software that is powered by transaction and online review data for millions of small businesses. (30:08) Nick talked about his current role as a Senior Manager in the Data Science department at Johnson & Johnson’s Health Technology group and shared the challenges of putting AI/ML algorithms into production in the healthcare domain. (34:11) Nick shared his narrative of being a person who can facilitate communication between technical and nontechnical teams in the blog post “Drinks with a businessman” (with an anecdote including his grandfather). (39:27) In reference to “A few thoughts on ML from the perspective of a behavioral scientist,” Nick shared his advice for data scientists who want to develop the core skills including being able to generate and test hypotheses, to think critically about unfamiliar data, and to gauge how much you trust your result. (41:37) Nick shared his findings in his 3-part blog series title “Being a grad student is a lot like being a startup” (Part 1, Part 2, and Part 3). (46:21) In reference to “Data science - the way I see it,” Nick talked about the methodological side of data science. (49:05) Nick discussed how graduate students can leverage their skills to qualify for an industry role. (52:17) Nick reflected on his efforts to stay active in academia while holding an industry job. (54:09) Closing segment. His Contact Info: LinkedIn Twitter Website His Recommended Resources: Stitch Fix Technology Netflix Research Fast Company’s Most Innovative Data Science Companies in 2019 Going Pro in Data Science by Jerry Overton Smart Thinking, Smart Change, and Brain Briefs by Art Markman Data science is different now by Vicki Boykis

Data science, data engineering, data analysis, and machine learning are part of the recent massive growth of Python. But really what is data science? Vicki Boykis helps me understand questions like: No really, what is data science? What does a data pipeline look like? What is it like to do data science, data analysis, data engineering? Can you do analysis on a laptop? How big does data have to be to be considered big? What are the challenges in data science? Does it make sense for software engineers to learn data engineering, data science, pipelines, etc? How could someone start learning data science? Also covered: A type work (analysis) vs B type work (building) data lakes and data swamps predictive models data cleaning development vs experimentation Jupyter Notebooks Kaggle ETL pipelines I learned a lot about the broad field of data science from talking with Vicki. Special Guest: Vicki Boykis.

Hugo speaks with Vicki Boykis about what full-stack end-to-end data science actually is, how it works in a consulting setting across various industries and why it’s so important in developing modern data-driven solutions to business problems. Vicki is a full-stack data scientist and senior manager at CapTech Consulting, working on projects in machine learning and data engineering. They'll also discuss the increasing adoption of data science in the cloud technologies and associated pitfalls, along with how to equip businesses with the skills to maintain the data products you developed for them. All this and more: Hugo is pumped! Links from the show FROM THE INTERVIEW Vicki's Tech Blog Vicki on Twitter CapTech Consulting Vicki's Tweet about Programming Building a Twitter art bot with Python, AWS, and socialist realism art FROM THE SEGMENTS Data Science Best Practices (with Ben Skrainka~15:00) Cross-industry standard process for data mining Fundamentals of Machine Learning for Predictive Data Analytics Statistical Lesson of the Week (with Emily Robinson at ~32:05) Sex Bias in Graduate Admissions: Data from Berkeley (Bickel et al., Science, 1975) Time Series Analysis Tutorial with Python Original music and sounds by The Sticks.

Tomaš Dvořák - "Game Boy Tune" - Machinarium Soundtrack - "Mark's intro" - "Recap of first year, part 1" - "Scott Heiferman excerpt" - "Vicki Boykis excerpt" - "Jessamyn West excerpt" - "Courtney Maum excerpt" - "Eric Zimmerman excerpt" - "Andrew Beccone excerpt" - "Roger Anderson excerpt" - "Andy Rehfeldt excerpt" - "Janelle Shane excerpt" - "Zaire Dinzey-Flores excerpt" - "Cheyenne Hohman excerpt" - "College student excerpt" - "Nir Eyal excerpt" - "Kirby Ferguson excerpt" - "Steven Levy excerpt" - "Mark reads Botnik's Harry Potter - excerpt" - "Ken Freedman excerpt" - "Jace Clayton excerpt" - "Jonathan Taplin excerpt" - "Scott Williams rec" - "Gabriel Weinberg excerpt" - "Christopher Potter excerpt" - "Botnik's Bob Mankoff and Jamie Brew excerpt" - "Matt Klinman excerpt" - "Yong Zhao excerpt" - "Recap of first year, part 2" - "Irwin Chusid excerpt" - "Kimzilla excerpt" - "Mathew Ingram excerpt" - "Alex George excerpt" - "Dylan Curran excerpt" - "Henry Lowengard (aka Webhamster Henry) excerpt" - "Catherine Price excerpt" - "Len Sherman excerpt" - "Corey Pein excerpt" - "Anya Kamenetz excerpt" - "David Sax excerpt" - "Felix Salmon excerpt" - "Meredith Broussard excerpt" - "Andrew Keen excerpt" - "Brett Frischmann excerpt" - "John Keating excerpt" - "Siva Vaidhyanathan excerpt" - "Mobile Steam Unit excerpt" - "Jaron Lanier excerpt" - "Paul Ford excerpt" - "Dr. Robert Epstein excerpt" - "Matt Warwick excerpt" - "James Bridle excerpt" - "Ali Latifi excerpt" Recap of the first year! Episode 50 of Techtonic, finishing the first year of the show, with a clip from every guest so far. https://www.wfmu.org/playlists/shows/81296

Tomaš Dvořák - "Game Boy Tune" - Machinarium Soundtrack - "Mark's intro" - "Recap of first year, part 1" - "Scott Heiferman excerpt" - "Vicki Boykis excerpt" - "Jessamyn West excerpt" - "Courtney Maum excerpt" - "Eric Zimmerman excerpt" - "Andrew Beccone excerpt" - "Roger Anderson excerpt" - "Andy Rehfeldt excerpt" - "Janelle Shane excerpt" - "Zaire Dinzey-Flores excerpt" - "Cheyenne Hohman excerpt" - "College student excerpt" - "Nir Eyal excerpt" - "Kirby Ferguson excerpt" - "Steven Levy excerpt" - "Mark reads Botnik's Harry Potter - excerpt" - "Ken Freedman excerpt" - "Jace Clayton excerpt" - "Jonathan Taplin excerpt" - "Scott Williams rec" - "Gabriel Weinberg excerpt" - "Christopher Potter excerpt" - "Botnik's Bob Mankoff and Jamie Brew excerpt" - "Matt Klinman excerpt" - "Yong Zhao excerpt" - "Recap of first year, part 2" - "Irwin Chusid excerpt" - "Kimzilla excerpt" - "Mathew Ingram excerpt" - "Alex George excerpt" - "Dylan Curran excerpt" - "Henry Lowengard (aka Webhamster Henry) excerpt" - "Catherine Price excerpt" - "Len Sherman excerpt" - "Corey Pein excerpt" - "Anya Kamenetz excerpt" - "David Sax excerpt" - "Felix Salmon excerpt" - "Meredith Broussard excerpt" - "Andrew Keen excerpt" - "Brett Frischmann excerpt" - "John Keating excerpt" - "Siva Vaidhyanathan excerpt" - "Mobile Steam Unit excerpt" - "Jaron Lanier excerpt" - "Paul Ford excerpt" - "Dr. Robert Epstein excerpt" - "Matt Warwick excerpt" - "James Bridle excerpt" - "Ali Latifi excerpt" Recap of the first year! Episode 50 of Techtonic, finishing the first year of the show, with a clip from every guest so far. http://www.wfmu.org/playlists/shows/81296