Episodes
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There hasn't been a boom like the AI boom since the .com days. And it may look like a space destined to be controlled by a couple of tech giants. But Ines Montani thinks open source will play an important role in the future of AI. I hope you join us for this excellent conversation about the future of AI and open source.
Episode sponsors
Sentry Error Monitoring, Code TALKPYTHON
Porkbun
Talk Python Courses
Links from the show
Ines Montani on Twitter: @_inesmontani
spaCy: spacy.io
Prodigy App: prodi.gy
Ines' presentation at PyCon Lithuania: youtube.com
LM Studio: lmstudio.ai
Little Bobby Tables: xkcd.com
spaCy and NLP course: talkpython.fm
Use my link to get your .app, .dev, or .foo domain for just $1 right now at Porkbun: talkpython.fm/porkbun
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm
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Do you want to look inside your Django request? How about all of your requests in development and see where they overlap? If that sounds useful, you should check out Kolo. It's a pretty incredible extension for your editor (VS Code at the moment, more editors to come most likely). We have Wilhelm Klopp on to tell us all about it.
Episode sponsors
Sentry Error Monitoring, Code TALKPYTHON
Talk Python Courses
Links from the show
Wil on Twitter: @wilhelmklopp
Kolo: kolo.app
Kolo's info repo: github.com
Kolo Playground: play.kolo.app
Generating tests with Kolo: kolo.app
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm
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So you've created a web app with Python using Flask, Django, FastAPI, or even Emmett. It works great on your machine. How do you get it out to the world? You'll need a production-ready web server. On this episode, we have Giovanni Barillari to tell us about his relatively-new server named Granian. It promises better performance and much better consistency than many of the more well known ones today.
Episode sponsors
Neo4j
Talk Python Courses
Links from the show
New spaCy course: talkpython.fm
Giovanni: @gi0baro
Granian: github.com
Emmett: emmett.sh
Renoir: github.com
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm
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This episode dives into some of the most important data science libraries from the Python space with one of its pioneers: Wes McKinney. He's the creator or co-creator of pandas, Apache Arrow, and Ibis projects and an entrepreneur in this space.
Episode sponsors
Neo4j
Mailtrap
Talk Python Courses
Links from the show
Wes' Website: wesmckinney.com
Pandas: pandas.pydata.org
Apache Arrow: arrow.apache.org
Ibis: ibis-project.org
Python for Data Analysis - Groupby Summary: wesmckinney.com/book
Polars: pola.rs
Dask: dask.org
Sqlglot: sqlglot.com
Pandoc: pandoc.org
Quarto: quarto.org
Evidence framework: evidence.dev
pyscript: pyscript.net
duckdb: duckdb.org
Jupyterlite: jupyter.org
Djangonauts: djangonaut.space
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm
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Do you use Python in an academic setting? Maybe you run a research lab or teach courses using Python. Maybe you're even a student using Python. Whichever it is, you'll find a ton of great advice in this episode. I talk with Keiland Cooper about how he is using Python at his neuroscience lab at the University of California, Irvine.
Episode sponsors
Neo4j
Posit
Talk Python Courses
Links from the show
Keiland's website: kwcooper.xyz
Keiland on Twitter: @kw_cooper
Keiland on Mastodon: @[email protected]
Journal of Open Source Software: joss.readthedocs.io
Avalanche project: avalanche.continualai.org
ContinualAI: continualai.org
Executable Books Project: executablebooks.org
eLife Journal: elifesciences.org
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm
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Do you find yourself or your team building internal apps frequently for your company? Are you familiar with the term "forms over data"? They are super empowering for your org but they can be pretty repetitive and you might find yourself spending more time than you'd like working on them rather than core products and services. I invited Jimmy Chan from Dropbase to tell us about their service who's tagline is "Build internal web apps with just Python." It's a cool service and a fun conversation.
Episode sponsors
Mailtrap
Talk Python Courses
Links from the show
Build internal web apps with just Python.: dropbase.io
Dropbase on Github: github.com
Dropbase @ LinkedIn: linkedin.com
Dropbase on Twitter: twitter.com
Jimmy Chan: linkedin.com
Jimmy on Twitter: twitter.com
Dropbase Docs: docs.dropbase.io
Dropbase: dropbase.io
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm
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We all know that tools like ChatGPT have really empowered developers to tackle bigger problems. Are you using TailwindCSS and need a login page? Try asking Chat "What is the HTML for a login page with the login username, password, and button in its own section in the center of the page?" It will literally give you a first pass version of it. But how far can you push this? Fred Tubiermont may have taken it farther than most. He built a functioning SaaS product with paying customers by only using ChatGPT and Python. It's fascinating to hear his story.
Episode sponsors
Mailtrap
Talk Python Courses
Links from the show
Frederick Tubiermont: linkedin.com
The #1 AI Jingle Generator: aijinglemaker.com
Fred's YouTube Channel: youtube.com
AI Coding Club: aicodingclub.com
No Code: saashub.com
Prompt Engineering 101 - Crash Course & Tips: youtube.com
gpt-engineer: github.com
Instant Deployments, Effortless Scale: railway.app
Self-hosting with superpowers.: coolify.io
The newsletter platform built for growth.: beehiiv.com
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm
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What is the state of serverless computing and Python in 2024? What are some of the new tools and best practices? We are lucky to have Tony Sherman who has a lot of practical experience with serverless programming on the show.
Episode sponsors
Sentry Error Monitoring, Code TALKPYTHON
Mailtrap
Talk Python Courses
Links from the show
Tony Sherman on Twitter: twitter.com
Tony Sherman: linkedin.com
PyCon serverless talk: youtube.com
AWS re:Invent talk: youtube.com
Powertools for AWS Lambda: docs.powertools.aws.dev
Pantsbuild: The ergonomic build system: pantsbuild.org
aws-lambda-power-tuning: github.com
import-profiler: github.com
AWS Fargate: aws.amazon.com
Run functions on demand. Scale automatically.: digitalocean.com
Vercel: vercel.com
Deft: deft.com
37 Signals We stand to save $7m over five years from our cloud exit: world.hey.com
The Global Content Delivery Platform That Truly Hops: bunny.net
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm
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We've spoken previously about security and software supply chains and we are back at it this episode. We're diving in again with Charles Coggins. Charles works at a software supply chain company and is on to give us the insiders and defender's perspective on how to keep our Python apps and infrastructure safe.
Episode sponsors
Sentry Error Monitoring, Code TALKPYTHON
Mailtrap
Talk Python Courses
Links from the show
Series: How Malicious Python Code Gains Execution: blog.phylum.io
Pick a Python Lockfile and Improve Security: blog.phylum.io
Bad Beat Poetry: blog.phylum.io
PEP 665 – A file format to list Python dependencies for reproducibility of an application: peps.python.org
PEP 517 – A build-system independent format for source trees: peps.python.org
PEP 518 – Specifying Minimum Build System Requirements for Python Projects: peps.python.org
Lockfiles should be committed on all projects: classic.yarnpkg.com
An Overview of Software Supply Chain Security: tldrsec.com
Typosquatting: docs.phylum.io
Common Attack Pattern Enumeration and Classification: capec.mitre.org
Dependency Confusion: docs.phylum.io
Expired Author Domains: docs.phylum.io
Unverifiable Dependency: docs.phylum.io
Repo Jacking: Hidden Danger in Broken Links: blog.phylum.io
Software Libraries Are Terrifying: medium.com
phylum 0.43.0: pypi.org
linguist: github.com
rich-codex ⚡️📖⚡️: ewels.github.io
Phylum Community Discord: discord.gg
The dream is dead?: mastodon.social
When "Everything" Becomes Too Much: The npm Package Chaos of 2024: socket.dev
pip-tools: github.com
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm
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Do you know what custom GPTs are? They're configurable and shareable chat experiences with a name, logo, custom instructions, conversation starters, access to OpenAI tools, and custom API actions. And, you can build them with Python! Ian Maurer has been doing just that and is here to share his experience building them.
Episode sponsors
Sentry Error Monitoring, Code TALKPYTHON
Neo4j
Talk Python Courses
Links from the show
Ian on Twitter: @imaurer
Mobile Navigation: openai.com
What is a Custom GPT?: imaurer.com
Mobile Navigation: openai.com
FuzzTypes: Pydantic library for auto-correcting types: github.com
pypi-gpt: github.com
marvin: github.com
instructor: github.com
outlines: github.com
llamafile: github.com
llama-cpp-python: github.com
LLM Dataset: llm.datasette.io
Plugin directory: llm.datasette.io
Data exploration at your fingertips.: visidata.org
hottest new programming language is English: twitter.com
OpenAI & other LLM API Pricing Calculator: docsbot.ai
Vector DB Comparison: vdbs.superlinked.com
bpytop: github.com
Source Graph: about.sourcegraph.com
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm
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Interested in data science but you're not quite working in it yet? In software, getting that very first job can truly be the hardest one to land. On this episode, we have Avery Smith from Data Career Jumpstart here to share his advice for getting your first data job.
Episode sponsors
Sentry Error Monitoring, Code TALKPYTHON
Posit
Talk Python Courses
Links from the show
Avery Smith: www.linkedin.com
Data Career Jumpstart: www.datacareerjumpstart.com
Data Nerd Site: datanerd.tech
Write C# LINQ queries to query data: learn.microsoft.com
A faster way to build and share data apps: streamlit.io
Plotly Dash: dash.plotly.com
Michael's Keynote: State of Python in 2024: youtube.com
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm
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Do you have data that you pull from external sources or is generated and appears at your digital doorstep? I bet that data needs processed, filtered, transformed, distributed, and much more. One of the biggest tools to create these data pipelines with Python is Dagster. And we are fortunate to have Pedram Navid on the show this episode. Pedram is the Head of Data Engineering and DevRel at Dagster Labs. And we're talking data pipelines this week at Talk Python.
Episode sponsors
Talk Python Courses
Posit
Links from the show
Rock Solid Python with Types Course: training.talkpython.fm
Pedram on Twitter: twitter.com
Pedram on LinkedIn: linkedin.com
Ship data pipelines with extraordinary velocity: dagster.io
dagster-open-platform: github.com
The Dagster Master Plan: dagster.io
data load tool (dlt): dlthub.com
DataFrames for the new era: pola.rs
Apache Arrow: arrow.apache.org
DuckDB is a fast in-process analytical database: duckdb.org
Ship trusted data products faster: www.getdbt.com
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm
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Have you ever been wait around for pip to do its thing while installing packages or syncing a virtual environment or through some higher level tool such as pip-tools? Then you'll be very excited to hear about the tool just announced from Astral called uv. It's like pip, but 100x faster. Charlie Marsh from Ruff fame and founder of Astral is here to dive in. Let's go.
Episode sponsors
Neo4j
Talk Python Courses
Links from the show
Charlie Marsh on Twitter: @charliermarsh
Charlie Marsh on Mastodon: @charliermarsh
Astral: astral.sh
uv: github.com
Ruff: github.com
Ruff Rules: docs.astral.sh
When "Everything" Becomes Too Much: The npm Package Chaos of 2024: socket.dev
Talk Python's free Audio AI Course: training.talkpython.fm
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm
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Have you heard of Quart? It's the fully-async version of Flask created by Philip Jones who is working closely with the Flask team on these parallel projects. The TL;DR; version is that if you want to take advantage of async and await and you're using Flask, you want to give Quart a solid look. We've spoken to Philip previously about Quart. This time around here's here to share his top Quart extensions and libraries you can adopt today.
Episode sponsors
Posit
Talk Python Courses
Links from the show
Pallets Team on ExTwitter: @PalletsTeam
Quart Framework: quart.palletsprojects.com
Using Quart Extensions: quart.palletsprojects.com
Quart Tasks: quart-tasks.readthedocs.io
Quart Minify: github.com
Quart Db: github.com
Hypercorn: github.com
Quart-CORS: github.com
Quart-Auth: github.com
Quart-Rate: github.com
Quart-Schma: github.com
Flask-Socket: github.com
Quart-SqlAlchemy: github.com
Flask-Login: github.com
greenback: github.com
secure: github.com
msgspec: jcristharif.com
Server-Sent Events: pgjones.gitlab.io
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm
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Are you interested in contributing to Django? Then there is an amazing mentorship program that helps Python and Django enthusiasts, because contributes and potentially core developers of Django. It's called Djangonauts and their slogan is "where contributors launch." On this episode, we have Sarah Boyce from the Django team and former Djangonaut and now Djangonaut mentor, Tushar Gupta. Not only is this excellent for the Django community, many of other open source communities would do well to keep an eye on how this creative project is working.
Episode sponsors
Neo4j
Posit
Talk Python Courses
Links from the show
Sarah on Mastodon: @[email protected]
Sarah on LinkedIn: linkedin.com
Tushar on Twitter: @tushar5526
Djangonaut Space on Mastodon: @[email protected]
Djangonaut Space on Twitter: @djangonautspace
Djangonaut Space on LinkedIn: linkedin.com
Website: djangonaut.space
Djangonaut Space Launch Video: youtube.com
Sessions: djangonaut.space
Djangonaut Space Interest Form: google.com/forms
Program: github.com
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm
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You've built an awesome set of APIs and you have a wide array of devices and clients using them. Then you need to upgrade an end point or change them in a meaningful way. Now what? That's the conversation I dive into over the next hour with Stanislav Zmiev. We're talking about Versioning APIs.
Episode sponsors
Neo4j
Sentry Error Monitoring, Code TALKPYTHON
Talk Python Courses
Links from the show
Stanislav Zmiev: github.com
Monite: monite.com
Cadwyn: github.com
Stripe API Versioning: stripe.com
API Versioning NOtes: github.com
FastAPI-Versioning: github.com
Flask-Rebar: readthedocs.io
Django Rest Framework Versioning: django-rest-framework.org
pytest-fixture-classes: github.com
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm
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Building web UIs in Python has always been in interesting proposition. On one end, we have a the full web design story with artisanal HTML and CSS. On another end there are several Python platforms that aim to the bring RAD, rapid app development, style of building with Python. Those can be great, and I've covered a couple of them, but they usually reach a limit on what they can do or how they integrate with the larger web ecosystem. On this episode, we have Samuel Colvin to share his latest exciting project FastUI. With FastUI, you build responsive web applications using React without writing a single line of JavaScript, or touching npm. Yet designers and other tools can focus on React front-ends for a professional SPA like app experience.
Episode sponsors
bright data
Sentry Error Monitoring, Code TALKPYTHON
Talk Python Courses
Links from the show
Samuel on Mastodon: fosstodon.org
Samuel on X: x.com
FastUI: github.com
FastUI Demos: fastui-demo.onrender.com
FastAPI: fastapi.tiangolo.com
Pydantic: pydantic.dev
How Did REST Come To Mean The Opposite of REST Article: htmx.org
Tailwind UI: tailwindui.com
Dropbase: dropbase.io
Anvil: anvil.works
Flutter code example: github.com
ReactJS code example: github.com
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm
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So you've created a Python-based open source project and it's started to take off. You're getting contributors, lots of buzz in the podcast space, and more. But you have that day job working on Java. How do you make the transition from popular hobby project to full time job? After all, you are giving away your open source project for free, right? Well, on this episode, I have put together an amazing panel of guests who all have done exactly this: Turned their project into full time work and even companies in some cases. We have Samuel Colvin, Gina Häußge, Sebastián Ramírez, Charlie Marsh, Will McGugan and Eric Holscher on to share their stories.
Episode sponsors
Basedash
Sentry Error Monitoring, Code TALKPYTHON
Talk Python Courses
Links from the show
Will McGugan: @willmcgugan
Charlie Marsh: @charliermarsh@hachyderm
Sebastián Ramírez: @tiangolo
Samuel Colvin: @samuel_colvin
Gina on Mastodon: chaos.social/@foosel
Eric Holscher: @ericholscher
Pydantic: pydantic.dev
Astral (makes of Ruff): astral.sh
Octoprint: octoprint.org
Read the Docs: readthedocs.com
FastAPI: fastapi.tiangolo.com
Textual (makes of Rich): textualize.io
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm
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It's an exciting time for the capabilities of Python. We have the Faster CPython initiative going strong, the recent async work, the adoption of typing and on this episode we discuss a new isolation and parallelization capability coming to Python through sub-interpreters. We have Eric Snow who spearheaded the work to get them added to Python 3.12 and is working on the Python API for 3.13 along with Anthony Shaw who has been pushing the boundaries of what you can already do with subinterpreters.
Episode sponsors
Pybites PDM
Sentry Error Monitoring, Code TALKPYTHON
Talk Python Courses
Links from the show
Guests
Anthony Shaw: @[email protected]
Eric Snow: @[email protected]
PEP 684 – A Per-Interpreter GIL: peps.python.org
PEP 734 – Multiple Interpreters in the Stdlib: peps.python.org
Running Python Parallel Applications with Sub Interpreters: fosstodon.org
pytest subinterpreters: fosstodon.org
Long-Term Vision for a Parallel Python Programming Model?: fosstodon.org
Hypercorn Server: github.com
msgspec: jcristharif.com
Dill package: pypi.org
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm
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Why is Python so popular? There is plenty of room for debate on this but one solid reason is it's easy to adopt, easy to use, and caters to people who are not quite developers/data scientists but need to do some computing. Do you know where there largest untapped set of that group hang out? Excel. That's why it's super exciting that Python is now going to be built directly into Excel. Just go into a cell and type =PY and you're off writing full Python 3 code that is backed by a lite Anaconda distribution of Python. And we have Dr. Sarah Kaiser here to give us the rundown on Python in Excel.
Episode sponsors
Posit
Pybites PDM
Talk Python Courses
Links from the show
Sarah's website: sckaiser.com
Sarah on Mastodon: @[email protected]
Get started with Python in Excel: microsoft.com
Python in SQL Server: microsoft.com
8 of the Biggest Excel Mistakes of All Time: blog.hurree.co
Security and Python in Excel: microsoft.com
Episode transcripts: talkpython.fm
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