What began as a strange beeping noise in his Tesla solar setup quickly turned into a deeper exploration of a system that offered very little visibility out of the box. Chris built his own monitoring solution using PyPowerwall, Grafana, InfluxDB, and Raspberry Pi.

Fueling his way through mad vibe coding sessions at 2am with healthy provisions of Tesla TACOS, Chris uncovered hundreds of kWh in lost production annually!

What made this journey especially compelling was how central AI was to the process. Chris didn’t just “use AI”—he worked with it. With Chris as Captain of the development ship, his AI coding assistant helped him move faster across unfamiliar territory, from networking and iptables to electrical concepts and data pipelines. He emphasized the importance of staying in command: verifying outputs, catching edge cases, and knowing when the model was drifting off course.

Christopher’s talk also highlighted the importance of open source and working with a community, the challenges of working within closed ecosystems, and how data can be used as leverage when navigating vendors and warranty claims. From reverse engineering to late-night debugging sessions, this was a practical and candid look at modern problem solving across disciplines.

Take a live peek HERE at what Christopher’s custom built Tesla Solar dashboard is reporting off of his roof. How good is the solar sailing today?

An Unexpected Journey: Life Lessons from Conquering Tesla Solar with Python

Check out the Meetup Event page for all information about time and location.

RSVP and Details on Meetup

About the Talk

In 2024, Christopher installed a Tesla solar array expecting clean energy and lower bills. Instead, he got a masterclass in persistence, reverse engineering, and why you should never trust a system you can’t monitor. Bringing laptops is encouraged for a live interactive surprise at the start of the session!

Tesla’s Powerwall/Inverter ecosystem is powerful but notoriously opaque—out of the box it tells you very little about what’s happening on your roof. When a weird beeping noise caused Christopher to suspect something was wrong, he built his own monitoring solution with PyPowerwall (an open-source library) utilizing Grafana, InfluxDB, and a Raspberry Pi. The shocking result? He discovered hundreds of kWh in lost production annually.

About the Speaker

Christopher Pitstick

In 2024, Christopher installed solar panels on his home and quickly dove into system analysis to optimize performance — a hands-on experience that directly inspired this presentation.

Originally from Dayton, Ohio, Christopher is a Senior Software Engineer at Latitude AI (a Ford subsidiary). His programming journey began at age 12 with QBasic, sparking a lifelong passion that has taken him through roles at Microsoft, Amazon, and Argo AI. He’s worked across numerous languages including C++, Java, C#, TypeScript, Perl, and, of course, Python!

A dedicated neurodiversity advocate, he regularly shares his experiences through public speaking engagements, raising awareness and empowering others in the tech community.

Come join us Thursday, March 26th at CoLab18 in Nova Place for An Unexpected Journey: Life Lessons from Conquering Tesla Solar with Python with Christopher Pitstick (Latitude AI).

After installing a Tesla solar system in 2024, Christopher noticed a strange beeping noise and discovered the system provides almost no visibility into what’s happening on his roof. So he built his own monitoring stack using Python, PyPowerwall, Grafana, InfluxDB, and a Raspberry Pi—and uncovered hundreds of kWh of lost production annually. Doors open 6:00 pm, talk starts at 6:30 pm.

Bring a laptop for a live interactive surprise at the start of the event!

Details and RSVP on Meetup

Christopher Hernández, a physics Ph.D. candidate at the University of Pittsburgh, gave a fascinating talk on Characterizing the Transient Universe with the Pitt-Google Alert Broker. He walked us through how astronomers use machine learning and cloud infrastructure to process massive alert streams from modern sky surveys, helping researchers identify and study transient cosmic events such as Type Ia supernovae in real time.

After the presentation, attendees joined observatory staff and volunteers for a tour of the historic building, including the Fitz-Clark and Thaw refractor telescopes. Many thanks to the Allegheny Observatory staff and volunteers for being such wonderful hosts and making the evening possible!

Explore the links!
pittgoogle-client: A Python library for accessing astronomy data served by the Pitt-Google Alert Broker.

pittgoogle-user-demos: A repo containing tutorials on how to access and interact with alert data served by the broker.

PyData Pittsburgh is excited to host our first event of 2026: Characterizing the Transient Universe with the Pitt-Google Alert Broker. Join us Wednesday, February 25, as Christopher Hernández, Ph.D. candidate at the University of Pittsburgh, shares more about his research using machine learning in astronomy.

We have an exciting venue for this event—the Allegheny Observatory has graciously agreed to not only host the talk but also offer a free private tour exclusively for the PyData Pittsburgh group after the presentation! Don’t miss this opportunity to learn about cutting-edge AI applications in astronomy while exploring one of Pittsburgh’s most fascinating scientific landmarks.

Note: Attendance for this event is limited. Please RSVP only if you are committed to attending. Thank you.

RSVP at Meetup.com

Schedule

February 25th

• 7pm, Doors Open

• 7:30pm, Talk

• 8:30pm, Observatory Tour

About the talk:

We live in a dynamic universe. For the foreseeable decade and beyond, our field’s understanding of the physical mechanisms driving evolutionary processes across local and cosmological scales will flourish thanks to the incredibly rich datasets produced by wide-field astronomical surveys, such as Vera C. Rubin Observatory’s Legacy Survey of Space & Time (LSST). Furthermore, astronomical alerts—packets of data containing world-public information about the sources detected by a survey—produced by LSST will enable the ability to conduct real-time, time-domain studies of the transient universe at a scale never seen before in the field of astronomy.

In this talk, I will introduce the Pitt-Google Alert Broker: an astronomical community alert broker designed to enable broad public access to, and scientific analysis of, alert streams from various surveys. In particular, I will highlight the increasingly diverse suite of machine learning models and cloud resources employed by Pitt-Google, with the intention of mitigating the challenges the field of astronomy now faces in data access, processing, transport, and storage.

About the observatory:

The Allegheny Observatory is one of the major historic astronomical research institutions of the world. A short presentation about the institution will be shown followed by a walking tour of the building finally ending up at the 13” Fitz-Clark refractor. More information about getting to the observatory can be found on our Meetup Event page.

About the Speaker:

Christopher Hernández is a physics Ph.D. candidate at the University of Pittsburgh. He obtained his bachelor’s degree in astrophysics from the University of California, Los Angeles (UCLA) and his master’s degree in physics from the University of Pittsburgh. Christopher is experienced in developing software pipelines for the analysis of astronomical data in real time, and his research currently focuses on analyzing how the systematic differences between ML algorithms—particularly in the training, hyperparameter optimization, and prediction processes that each algorithm employs—affect the resulting decision boundaries of various astrophysical classes.

Sincerely,
~The PyData Pittsburgh Organizers

Due to severe winter weather forecast for the upcoming week, and our desire to keep safety and comfort considerations foremost for our community, we have decided to reschedule this event. This event is now scheduled for Thursday, March 26th. Same time, same location. Please see the event listing for all details.

Thank you, and we look forward to seeing you for the Unexpected Journey, in March!

Sincerely,

~Doug & Madi

Details and RSVP on Meetup

Tesla's Powerwall/Inverter ecosystem is powerful but notoriously opaque—out of the box it tells you very little about what's happening on your roof. When a weird beeping noise caused Christopher to suspect something was wrong, he built his own monitoring solution with PyPowerwall (an open-source library) utilizing Grafana, InfluxDB, and a Raspberry Pi. The shocking result? He discovered hundreds of kWh in lost production annually.

🧠 About the talk:
This talk isn’t just about debugging solar panels, nor is it entirely about Python! It’s about the unexpected lessons you learn when you trust your instincts and refuse to accept “it’s working fine” as an answer:

• Contributing to open source when the tools you need don’t quite exist yet

• Using AI like a Commander. How to keep it on track, check your course, watch out for edge cases, stop a mutiny, and make the impossible, possible!

• How OSS acts as a lever to change the world, and tips & tricks for contributing to OSS

• Bridging domains—how a software engineer learns just enough electrical/RF engineering to be dangerous

• Navigating warranty claims and contractor relationships with data as your ally

• The right-to-repair movement, and why closed ecosystems hurt consumers

• Community and "going together" wins over going it alone

• When to dig deeper vs. when to ship (spoiler: he chose poorly several times)

• Why Python’s ecosystem makes it the perfect Swiss Army knife for uniting a vast array of disciplines and communities

• In the age of AI and vibe coding, what is it that makes software engineers truly valuable?

Chris will also touch on the electrical engineering side of the debugging adventure. He had to use scraps of documentation, a multimeter, and some good ole' fashion ingenuity to investigate the circuitry. He will also discuss the sheer madness that is debugging Wi-Fi signals, and why Tesla's solar inverters are effectively vehicles without wheels. An open question he'd love help from the community on: how do you actually track down the interference source inside a noisy power electronics system like this? Apparently it’s hard… but Pittsburgh has some pretty serious hardware hackers.

You’ll see real code, real dashboards, real mistakes, and the very unscientific art of “vibe-coding” your way through iptable rules at 2am. Does the system get fixed? Does he recover his lost production? Is the blade Narsil reforged? Come find out!

👤 About the speaker:
In 2024, Christopher installed solar panels on his home and quickly dove into system analysis to optimize performance — a hands-on experience that directly inspired this presentation.

Originally from Dayton, Ohio, Christopher is a Senior Software Engineer at Latitude AI (a Ford subsidiary). His programming journey began at age 12 with QBasic, sparking a lifelong passion that has taken him through roles at Microsoft, Amazon, and Argo AI. He's worked across numerous languages including C++, Java, C#, TypeScript, Perl, and, of course, Python!

A dedicated neurodiversity advocate, he regularly shares his experiences through public speaking engagements, raising awareness and empowering others in the tech community.

The speakers also highlighted Bodo’s expanding ecosystem, including the AI Toolkit for scalable LLM generation, embeddings, and distributed PyTorch training, and new Iceberg support for versioned, metadata-rich dataset management. Together, these tools position Bodo as a unified, Python-first platform for large-scale ETL, AI pipelines, and modern data lake operations.

Check out the full presentation slides here!

PyData Pittsburgh is excited to co-host a panel discussion on Mentorship in Analytics, presented by INFORMS-Pittsburgh and PyData Pittsburgh.

Join us on Wednesday, November 19th, as Dr. Louis Luangkesorn, Lead Data Scientist at Highmark Health, moderates a panel of experienced professionals, each of whom has served as both mentor and mentee in a variety of settings, from supervisors to collaborations both within and outside of companies. Together, they’ll explore how mentoring relationships evolve, what makes them thrive, and how they can shape meaningful career growth in analytics.

Details and RSVP on Meetup

About the Discussion:

Our careers are unique journeys, and having mentors who have navigated similar professional terrains can provide invaluable insights on long-term growth, soft-skill development, and framing technical developments into a grounded, holistic perspective.

We have assembled a panel with professionals with experience in large companies, start-ups, consulting, and government work. We’ll be discussing how these informal relationships start, what makes them last, and the ways they benefit analytics professionals.

We want to make sure the discussion addresses what’s on your mind! Please submit your questions for the panelists on any aspect of finding, forming, or flourishing in a mentorship relationship. What are you most curious about?

Register and submit questions at this link. You’ll receive reminders before the event and a weblink if we’re able to make it hybrid (TBD).
https://forms.gle/LbBxWYjuNfCgxM9s6

Panelists:

• Lori Dempsey, Highmark Health

• Mohar Dey, Dick’s Sporting Goods

• Bryan Lane, DATA XD

• Matt Nicosia, Zayer

• Chris Villi, FedEx Ground

• Louis Luangkesorn, Highmark Health (moderator)

The Pittsburgh INFORMS Chapter is a nonprofit organization associated with the Institute for Operations Research and the Management Sciences to connect Analytics professionals in the Pittsburgh area and beyond.

✨✅ Take the PyData Pittsburgh Member Survey!

Thanks for being part of our community! This quick survey will help shape future PyData Pittsburgh events.

Take the Member Survey

Join PyData Pittsburgh tonight at the Swartz Center for our November event — Bodo DataFrames: A Fast and Scalable HPC-Based Drop-In Replacement for Pandas. Ehsan Totoni, CTO and Co-Founder of Bodo.ai, will discuss how Bodo DataFrames brings high-performance computing (HPC) techniques like MPI and JIT compilation to the familiar Pandas API—allowing data scientists to scale Python workloads from millions to billions of rows without rewriting their code.

Details and RSVP

About the talk:
Pandas is a popular library for data scientists but it struggles with large datasets; programs either become too slow or run out of memory. In this talk, we introduce Bodo DataFrames (https://github.com/bodo-ai/Bodo) as a drop-in replacement for the Pandas library that uses high performance computing (HPC) based techniques such as Message Passing Interface (MPI) and JIT compilation for acceleration and scaling. We give an overview of its architecture and explain how it avoids the problems of Pandas (while keeping user code the same), go over concrete examples, and finally discuss current limitations. This talk is for Pandas users who would like to run their code on larger data while avoiding frustrating code rewrites to other APIs. Basic knowledge of Pandas and Python is recommended.

Time:
5:30pm – Doors Open
6:00pm – Talk, Bodo DataFrames: A Fast and Scalable HPC-Based Drop-In Replacement for Pandas

From predictive models for battery degradation to data-driven optimization in additive manufacturing and advanced coatings, MDS-Rely tackles real-world industrial challenges with cutting-edge analytics and experimentation.

MDS-Rely homepage: https://mds-rely.org/

MDS-Rely on LinkedIn: https://www.linkedin.com/company/mds-rely

PyData Pittsburgh is excited to host our November event – Bodo DataFrames: A Fast and Scalable HPC-Based Drop-In Replacement for Pandas.

Join us on Tuesday, November 4th, as as Ehsan Totoni, CTO and Co-Founder of Bodo.ai, discusses how Bodo DataFrames brings high-performance computing (HPC) techniques like MPI and JIT compilation to the familiar Pandas API—allowing data scientists to scale Python workloads from millions to billions of rows without rewriting their code.

For latest information about time/location and to register please see our meetup page:

Details and RSVP on Meetup

About the talk:

Pandas is a popular library for data scientists but it struggles with large datasets; programs either become too slow or run out of memory. In this talk, we introduce Bodo DataFrames (https://github.com/bodo-ai/Bodo) as a drop-in replacement for the Pandas library that uses high performance computing (HPC) based techniques such as Message Passing Interface (MPI) and JIT compilation for acceleration and scaling. We give an overview of its architecture and explain how it avoids the problems of Pandas (while keeping user code the same), go over concrete examples, and finally discuss current limitations. This talk is for Pandas users who would like to run their code on larger data while avoiding frustrating code rewrites to other APIs. Basic knowledge of Pandas and Python is recommended.

About the Speaker:

Ehsan Totoni is an entrepreneur, computer science researcher, and software engineer working on democratization of High Performance Computing (HPC) for data engineering, data science and AI/ML. Ehsan received his PhD in computer science from the University of Illinois at Urbana-Champaign, working on various aspects of HPC and Parallel Computing. He then worked as a research scientist at Intel Labs and Carnegie Mellon University, focusing on programming systems to address the gap between programmer productivity and computing performance. Ehsan co-founded Bodo.ai in 2019 and is advancing Bodo’s mission of bringing HPC to all data applications.

Driving Materials Innovation with Data: The MDS-Rely Center for Industry-Academic Partnerships

Checkout the Meetup Event page for up-to-date information about the time/location.

Meetup Event Page

about the talk

The Materials Data Science for Reliability and Degradation (MDS-Rely) Center is a National Science Foundation (NSF) Industry-University Cooperative Research Center led by the University of Pittsburgh, Case Western Reserve University, and Carnegie Mellon University. MDS-Rely brings together industry, government, and academic partners to conduct pre-competitive research that leverages data science to enhance materials performance, reliability, and service life.

From predictive models for battery degradation to data-driven optimization in additive manufacturing and advanced coatings, MDS-Rely tackles real-world industrial challenges with cutting-edge analytics and experimentation. This talk will provide an overview of the Center’s research focus areas, industry engagement opportunities, and how companies can benefit from and shape this collaborative innovation ecosystem.

MDS-Rely homepage:

https://mds-rely.org/

MDS-Rely on LinkedIn: https://www.linkedin.com/company/mds-rely

about the speaker

Satish Iyengar: I have been at Pitt since I earned my PhD in Statistics at Stanford. My early work was on the approximation of multivariate probabilities and meta-analysis. My interests include applications of diffusion and point processes and multivariate mixture models, especially to problems in psychiatry and neuroscience. I have served on the Food and Drug Administration’s Advisory Committee for Psychopharmacologic Drugs, and for many years I served on the National Security Agency’s Statistics Advisory Group at Stanford. I have long been interested in stochastic models for degradation (many lead to diffusion processes), so I was very happy when Paul Leu contacted me about MDS-Rely in its planning stages.

Mentorship in Analytics – Coming This November! 🌱

We’re thrilled to partner with INFORMS Pittsburgh, a group that brings together people passionate about data, optimization, and analytical decision making to foster collaboration, mentorship, and real world impact.

INFORMS Pittsburgh and PyData Pittsburgh are teaming up to host a panel on mentorship in analytics this November. A strong mentoring relationship supports the mentee’s whole career development, not just a single project or job. Be on the lookout for a brief survey where you can share the topics you’d most like discussed during the panel. Interested in joining the panel? Email us at organizers@pypgh.org

October Event – Driving Materials Innovation with Data 🔬

Thursday, October 16 | 6:00 pm Doors Open | 6:30 pm Talk Start

Speaker: Satish Iyengar – Professor & Associate Chair of Statistics, University of Pittsburgh

PyData Pittsburgh is excited to host our October event, Driving Materials Innovation with Data: The MDS-Rely Center for Industry-Academic Partnerships. Learn how data scientists, engineers, and statisticians are collaborating across institutions to bridge theory and practice, using real-world datasets to advance reliability and degradation research.

The MDS-Rely Center, a National Science Foundation (NSF) Industry–University Cooperative Research Center, unites academia and industry to improve materials performance, reliability, and service life. From predictive models for battery degradation to data-driven optimization in additive manufacturing, the Center is turning analytics into innovation across industries.

Details & RSVP for October Meetup

November Event – Bodo DataFrames: Scaling Pandas with HPC ⚡️

Tuesday, November 4 | Swartz Center for Entrepreneurship

Speaker: Ehsan Totoni – Co-founder & CTO, Bodo AI

Learn more: bodo.ai | GitHub

Pandas is a staple for data scientists, but it struggles with large datasets — programs often become slow or memory-limited. In this talk, Ehsan Totoni introduces Bodo DataFrames, a fast, scalable, high-performance computing (HPC)–based drop-in replacement for Pandas.

Bodo leverages Message Passing Interface (MPI) and JIT compilation to accelerate and scale existing Pandas code — no rewrites required. You’ll get an overview of its architecture, real-world performance examples, and an honest look at current limitations. If you’ve ever wished Pandas could handle more data without breaking, this talk is for you! Look for the Bodo.ai Meetup announcement next week.

Take the PyData PGH Member Survey

Your input helps shape future events, panels, and workshops!

This quick 10-minute survey is the best way to tell us what you’d like to see next from PyData Pittsburgh.

✨ Take the Member Survey Here ✨

Check out the slides and the CITEgeist GitHub repo!

PyData Pittsburgh is excited to host our October event – Driving Materials Innovation with Data: The MDS-Rely Center for Industry-Academic Partnerships.

Join us on Thursday, October 16th, as Satish Iyengar, MDS-Rely Co-PI and Professor and Associate Chair of Statistics at the University of Pittsburgh, discusses how data scientists, engineers, and statisticians are working together across institutions to bridge theory and practice, using real-world datasets to push the boundaries of what’s possible in materials reliability and degradation research.

For latest information about time/location and to register please see our meetup page:

Meetup Event Page

About the talk:

The Materials Data Science for Reliability and Degradation (MDS-Rely) Center is a National Science Foundation (NSF) Industry-University Cooperative Research Center led by the University of Pittsburgh, Case Western Reserve University, and Carnegie Mellon University. MDS-Rely brings together industry, government, and academic partners to conduct pre-competitive research that leverages data science to enhance materials performance, reliability, and service life.

From predictive models for battery degradation to data-driven optimization in additive manufacturing and advanced coatings, MDS-Rely tackles real-world industrial challenges with cutting-edge analytics and experimentation. This talk will provide an overview of the Center’s research focus areas, industry engagement opportunities, and how companies can benefit from and shape this collaborative innovation ecosystem.

MDS-Rely homepage:

https://mds-rely.org/
MDS-Rely on LinkedIn: https://www.linkedin.com/company/mds-rely

About the Speaker

Dr. Satish Iyengar: I have been at Pitt since I earned my PhD in Statistics at Stanford. My early work was on the approximation of multivariate probabilities and meta-analysis. My interests include applications of diffusion and point processes and multivariate mixture models, especially to problems in psychiatry and neuroscience. I have served on the Food and Drug Administration’s Advisory Committee for Psychopharmacologic Drugs, and for many years I served on the National Security Agency’s Statistics Advisory Group at Stanford. I have long been interested in stochastic models for degradation (many lead to diffusion processes), so I was very happy when Paul Leu contacted me about MDS-Rely in its planning stages.

We are hosting our September event tonight at the Swartz Center, and we would love to see you there!

🎙️ Talk: Decoding Spatial Biology with Python: Multi-Modal Insights into Breast Cancer Progression
🗓️ Tuesday, September 30
🕕 5:30 PM Doors Open | 6:00 PM Talk Begins
📍 Swartz Center for Entrepreneurship, Tepper School of Business, CMU

Free Parking is available after 5pm in the Morewood Gardens Lot, directly behind the Tepper School building. The entrance is off of Morewood Avenue.

Join us as Alex C. Chang, CMU-Pitt (Graduate Student PhD, Computational Biology) and Brent Schlegel, University of Pittsburgh School of Medicine (Graduate Student PhD, Integrative Systems Biology), present their novel tool, CITEgeist, which harnesses Python’s capabilities for multi-modal spatial transcriptomics.

Details and RSVP on Meetup

PyData Pittsburgh is excited to host our September event: Decoding Spatial Biology with Python: Multi-Modal Insights into Breast Cancer Progression. Join us on Tuesday, September 30th, as Alex C. Chang, CMU-Pitt (Graduate Student PhD, Computational Biology) and Brent Schlegel, University of Pittsburgh School of Medicine (Graduate Student PhD, Integrative Systems Biology), present their novel tool, CITEgeist, which harnesses Python’s capabilities for multi-modal spatial transcriptomics.

Details and RSVP on Meetup

Python has rapidly become a cornerstone of scientific computing, computational biology, and bioinformatics due to its ease of use and scalability for handling large datasets—qualities that are critical in today’s “big data” era of clinical and translational research. As computational resources and data collection methods continue to expand, we are now empowered to ask larger and more clinically relevant questions that enable us to dissect complex biological systems with unprecedented detail.

However, this surge in data complexity brings new challenges, from the integration of diverse data modalities to the need for sophisticated analytical methods capable of untangling intricate biological signals from background noise.

About the Talk

In this talk, Alex and Brent describe how Python not only meets these challenges but also drives innovation through the development of novel bioinformatics tools like CITEgeist. Biological datasets often face challenges of high sparsity and noise. CITEgeist harnesses Python’s robust ecosystem to provide an efficient, scalable pipeline that deconvolutes messy spatial signals into actionable, clinically relevant features.

About the Speakers

Alex C. Chang
Alexander Chih-Chieh Chang is a fourth-year MSTP student in the CMU-Pitt Computational Biology Ph.D. Program, mentored by Drs. Lee and Oesterreich. He earned a BS/BA in Chemical and Biomolecular Engineering/Sociology from Johns Hopkins University in 2021. Previously, during his undergraduate research in the lab of Rong Li, Ph.D., he conducted large-scale genomic screens to study proteomic dysregulation and spent a gap year in the lab of Manish Aghi, MD. PhD., studying breast cancer metastasis to the brain. Currently, as a computational biologist and medical student, he coordinates the Hope for OTHERS tissue donation program in the Lee-Oesterreich Lab and computational research projects in breast cancer metastasis and genomic evolution.

Brent Schlegel
Brent Schlegel is a first-year PhD student in Integrative Systems Biology at the University of Pittsburgh School of Medicine, co-mentored by Drs. Adrian Lee and Steffi Oesterreich. He earned his AS in Mathematics and Sciences from CCAC (2019) and a BS in Computational Biology from Pitt (2021). Most recently, he worked as a Bioinformatics Analyst at the UPMC Children’s Hospital of Pittsburgh, where he specialized in the integrative analysis of large, complex biomedical datasets. Now, Brent combines data science, computational modeling, and multi-omic integration to tackle the systems biology of invasive lobular breast cancer, using patient-derived organoid models and leveraging “big data” to uncover hidden patterns and drive innovation in diagnosis and treatment.

✨✅ Take the PyData Pittsburgh Member Survey!

Thanks for being part of our community! This quick 5-minute survey will help shape future PyData Pittsburgh events.

Take the Member Survey

Thank you to everyone who joined us last week at CoLab18 for our August event! Kristen Scotti, STEM Librarian at CMU, guided us through her Python for All: Democratizing Coding Mastery with AI Chatbot Support workshop, where we stepped through exercises on prompt engineering, code optimization, and language conversion using Python and various AI chatbots. You can view the full workshop in the PARTICIPANT_VERSION Jupyter notebook here!