Watch the live stream: Watch on YouTube About the show Sponsored by Microsoft for Startups Founders Hub. Special guest: Ashley Anderson Ashley #1: PSF security key giveaway for critical package maintainers Giving away 4000 2FA hardware keys Surely a team effort but I found it via @di_codes twitter (Dustin Ingram) links to previous talks on PyPI/supply chain security Interesting idea for helping with supply-chain vulnerabilities At least one dev pulled a critical package in response Previously: I don’t have any critical projects Armin Ronacher has an interesting take Michael #2: PyLeft-Pad via Dan Bader Markus Unterwaditzer was maintaining atomicwrites More on how this relates to a project (Home Assistant) I wonder if PyPI will become immutable once an item is published Brian #3: FastAPI Filter Suggested and created by Arthur Rio “I loved using django-filter with DRF and wanted an equivalent for FastAPI.” - Arthur Add query string filters to your api endpoints and show them in the swagger UI. Supports SQLAlchemy and MongoEngine. Supports operators: gt, gte, in, isnull, it, lte, not/ne, not_in/nin Ashley #4: Tools for building Python extensions in Rust PyO3 pyo3 - Python/Rust FFI bindings nice list of examples people might recognize in the PyO3 README Pydantic V2 will use it for pydantic-core maturin - PEP 621 wheel builder (pyproject.toml) pretty light weight, feels like flit for Rust or python/Rust rust-numpy (+ndarray) for scientific computing setuptools-rust for integrating with existing Python projects using setuptools Rust project and community place high value on good tooling, relatively young language/community with a coherent story from early on Rust macro system allows for really nice ergonomics (writing macros is very hard, using them is very easy) The performance/safety/simplicity tradeoffs Python and Rust make are very different, but both really appeal to me - Michael #5: AutoRegEx via Jason Washburn Enter an english phrase, it’ll try to generate a regex for you You can do the reverse too, explain a regex You must sign in and are limited to 100 queries / [some time frame] Related from Simon Willison: Using GPT-3 to explain how code works Brian #6: Anaconda Acquires PythonAnywhere Suggested by Filip Łajszczak See also Anaconda Acquisition FAQs from PythonAnywhere blog From announcement: “The acquisition comes on the heels of Anaconda’s release of PyScript, an open-source framework running Python applications within the HTML environment. The PythonAnywhere acquisition and the development of PyScript are central to Anaconda’s focus on democratizing Python and data science.” My take: We don’t hear a lot about PA much, even their own blog has had 3 posts in 2022, including the acquisition announcement. Their home page boasts “Python versions 2.7, 3.5, 3.6, 3.7 and 3.8”, although I think they support 3.9 as well, but not 3.10 yet, seems like from the forum. Also, no ASGI, so FastAPI won’t work, for example. Still, I think PA is a cool idea, and I’d like to see it stay around, and stay up to date. Hopefully this acquisition is the shot in the arm it needed. Extras Michael: Python becomes the most sought after for employers hiring (by some metric) Ashley: PEP691 JSON Simple API for PyPI Rich Codex - automatic terminal “screenshots” Joke: Neta is a programmer
Watch the live stream: Watch on YouTube About the show Sponsored by Microsoft for Startups Founders Hub. Brian #1: rich-codex by Phil Ewels suggested by Will McGugan “A GitHub Action / command-line tool which generates screen grab images of a terminal window, containing command outputs or code snippets.” Generate images from commands embedded in markdown files, like README.md, for example. Searches through markdown files for stuff like:  then runs the command, and generates the image. Can be done within a GitHub action Can also send code snippets or json to rich-cli, then generate an image. You can also have commands in a config file, Very easy to use, makes very professional looking images for documentation, that’s always up to date. Michael #2: Pydastic via Roman Right, by Rami Awar Pydastic is an elasticsearch python ORM based on Pydantic. Core Features Simple CRUD operations supported Sessions for simplifying bulk operations (a la SQLAlchemy) Dynamic index support when committing operations More on Elasticsearch here Brian #3: 3 Things to Know Before Building with PyScript by Braden Riggs Package indentation matters Local file access is possible. [HTML_REMOVED] - numpy - pandas - paths: - /views.csv [HTML_REMOVED] DOM manipulation has interesting conventions For buttons, you can include pys-onClick=”your_function” parameter to trigger python functions when clicked. For retrieving user input from within the [HTML_REMOVED] tag document.getElementById(‘input_obj_id’).value can retrieve the input value. And Finally pyscript.write(“output_obj_id”, data) can write output to a tag from within the [HTML_REMOVED] tag. Michael's Pyscript videos Python + pyscript + WebAssembly: Python Web Apps, Running Locally with pyscript Python iOS Web App with pyscript and offline PWAs Michael #4: disnake via Sean Koenig disnake is a modern, easy to use, feature-rich, and async-ready API wrapper for Discord. Features: Modern Pythonic API using async/await syntax Sane rate limit handling that prevents 429 errors Command extension to aid with bot creation Easy to use with an object oriented design Optimized for both speed and memory Quickstart Commands API Extras Michael: Scholarships for upcoming FastAPI + MongoDB live course Humble Bundle for Python 2022 Michael's crazy earbuds (UE Fits) Joke: Better than a wage increase
Watch the live stream: Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training Test & Code Podcast Patreon Supporters Michael #1: Python License tracker by Tom Nijhof/Nyhof Every package depends on other package with sometimes different licenses. Tom made a tool to find out what licenses you all need for a project: PyTest alone needs 4 different licenses for itself and its dependencies. Tensorflow is even worst Brian #2: undataclass Trey Hunner As a teaching aid, and to show how much dataclasses do for you, this is a module and an application that converts dataclasses to normal classes, and fills in all of the dunder methods you need. Example in app: from dataclasses import dataclass @dataclass() class Point: x: float y: float z: float Converts to class Point: __match_args__ = ('x', 'y', 'z') def __init__(self, x: float, y: float, z: float) -> None: self.x = x self.y = y self.z = z def __repr__(self): cls = type(self).__name__ return f'{cls}(x={self.x!r}, y={self.y!r}, z={self.z!r})' def __eq__(self, other): if not isinstance(other, Point): return NotImplemented return (self.x, self.y, self.z) == (other.x, other.y, other.z) Note on NotImplemented: It just means, “I don’t know how to compare this”, and Python will try __eq__ on the other object. If that also raises NotImplemented, a False is returned. The default is the above with @dataclass(frozen=True, slots=True) and adds the methods: fronzen=True gives you implementations of __hash__, __setattr__, __delattr__, __getstate__, __setstate__, Essentially raises exception if you try to change the contents, and makes your objects hashable. slots=True adds the line: __slots__ = (``'``x', '``y``'``, '``z``'``). This disallows adding new attributes to objects at runtime. See Python docs Trey wrote two posts about it: Appreciating Python's match-case by parsing Python code How I made a dataclass remover Turns out, this is a cool example for AST and structural pattern matching. Notes from the “how I made..” article: "I used some tricks I don't usually get to use in Python. I used: Many very hairy **match**-**case** blocks which replaced even hairier if-elif blocks A sentinel object to keep track of a location that needed replacing Python's **textwrap.dedent** utility, which I feel should be more widely known & used slice assignment to inject one list into another The ast module's unparse function to convert an abstract syntax tree into Python code” Michael #3: Qutebrowser via Martin Borus Qutebrowser is a keyboard-focused browser with a minimal GUI." It's Python powered Whats more important - doesn't force you to use it's Vim-based shortcuts, the mouse still works. But you usually don't need it: Because on any page, a keypress on the "f" key will show, you every clickable think and a letter combination to enter to click this. Brian #4: asyncio and web applications A collection of articles Quart is now a Pallets project P G Jones, maintainer of Quart and Hypercorn “Quart, an ASGI re-implementation of the Flask API has joined the Pallets organization. This means that future development will be under the Pallets governance by the Pallets maintainers. Our long term aim is to merge Quart and Flask to bring ASGI support directly to Flask. “When to use Quart?” “Quart is an ASGI framework utilising async IO throughout, whereas Flask is a WSGI framework utilising sync IO. It is therefore best to use Quart if you intend to use async IO (i.e. async/await libraries) and Flask if not. Don't worry if you choose the 'wrong' framework though, as Quart supports sync IO and Flask supports async IO, although less efficiently.” Using async and await, from Flask docs Flask has some support of async/await since Flask 2.0 But it’s still a WSGI application. “Deciding whether you should use Flask, Quart, or something else is ultimately up to ...
Watch the live stream: Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training Test & Code Podcast Patreon Supporters Special guest: Nick Muoh Brian #1: picologging From a tweet by Anthony Shaw From README.md “early-alpha” stage project with some incomplete features. (cool to be so up front about that) “Picologging is a high-performance logging library for Python. picologging is 4-10x faster than the logging module in the standard library.” “Picologging is designed to be used as a drop-in replacement for applications which already use logging, and supports the same API as the logging module.” Now you’ve definitely got my attention. For many common use cases, it’s just way faster. Sounds great, why not use it? A few limitations listed: process and thread name not captured. Some logging globals not observed: logging.logThreads, logging.logMultiprocessing, logging.logProcesses Logger will always default to the Sys.stderr an...
Watch the live stream: Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training Test & Code Podcast Patreon Supporters Special guest: Gina Häußge, creator & maintainer of OctoPrint Michael #1: beanita Local MongoDB-like database prepared to work with Beanie ODM So, you know Beanie - Pydantic + async + MongoDB And you know Mongita - Mongita is to MongoDB as SQLite is to SQL Beanita lets you use Beanie, but against Mongita rather than a server-based MongoDB server Brian #2: The Good Research Code Handbook Patrick J Mineault “for grad students, postdocs and PIs (principle investigator) who do a lot of programming as part of their research.” lessons setup git, virtual environments, project layout, packaging, cookie cutter style style guides, keeping things clean coding separating concerns, separating pure functions and those with side effects, pythonic-ness testing unit testing, testing with side effects, … (incorrect definition of end-to-end tests, but a good job at covering the other bits) documentation comments, tests, docstrings, README.md, usage docs, tutorials, websites documenting pipelines and projects social aspects various reviews, pairing, open source, community sample project extras testing example good tools to use Gina #3: CadQuery Python lib to do build parametric 3D CAD models Can output STL, STEP, AMF, SVG and some more Uses same geometry kernel as FreeCAD (OpenCascade) Also available: desktop editor, Jupyter extension, CLI Would recommend the Jupyter extension, the app seems a bit behind latest development Jupyter extension is easy to set up on Docker and comes with a nice 3D preview pane Was able to create a basic parametric design of an insert for an assortment box easily Python 3.8+, not yet 3.11, OpenCascade related Michael #4: Textinator Like TextSniper, but in Python Simple MacOS StatusBar / Menu Bar app to automatically detect text in screenshots Built with RUMPS: Ridiculously Uncomplicated macOS Python Statusbar apps Take a screenshot of a region of the screen using ⌘ + ⇧ + 4 (Cmd + Shift + 4). The app will automatically detect any text in the screenshot and copy it to your clipboard. How Textinator Works At startup, Textinator starts a persistent NSMetadataQuery Spotlight query (using the pyobjc Python-to-Objective-C bridge) to detect when a new screenshot is created. When the user creates screenshot, the NSMetadataQuery query is fired and Textinator performs text detection using a Vision VNRecognizeTextRequest call. Brian #5: Handling Concurrency Without Locks "How to not let concurrency cripple your system” Haki Benita “…common concurrency challenges and how to overcome them with minimal locking.” Starts with a Django web app A url shortener that generates a unique short url and stores the result in a database so it doesn’t get re-used. Discussions of collision with two users checking, then storing keys at the same time. locking problems in general utilizing database ability to make sure some items are unique, in this case PostgreSQL updating your code to take advantage of database constraints support to allow you to do less locking within your code Gina #6: TatSu Generates parsers from EBNF grammars (or ANTLR) Can compile the model (similar to regex) for quick reuse or generate python source Many examples provided Active development, Python 3.10+ Extras Michael: Back on 285 we spoke about PEP 690. Now there is a proper blog post about it. Expedited release of Python3.11.0b3 - Due to a known incompatibility with pytest and the previous beta release (Python 3.11.0b2) and after some deliberation, Python release team have decided to do an expedited release of Python 3.11.0b3 so the community can continue testing their packages with pytest and therefore testing the betas as expected. (via Python Weekly) Kagi search via Daniel Hjertholm Not ...
Watch the live stream: Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training Test & Code Podcast Patreon Supporters Brian #1: Polars: Lightning-fast DataFrame library for Rust and Python Suggested by a several listeners “Polars is a blazingly fast DataFrames library implemented in Rust using Apache Arrow Columnar Format as memory model. Lazy | eager execution Multi-threaded SIMD (Single Instruction/Multiple Data) Query optimization Powerful expression API Rust | Python | ...” Python API syntax set up to allow parallel and execution while sidestepping GIL issues, for both lazy and eager use cases. From the docs: Do not kill parallelization The syntax is very functional and pipeline-esque: import polars as pl q = ( pl.scan_csv("iris.csv") .filter(pl.col("sepal_length") > 5) .groupby("species") .agg(pl.all().sum()) ) df = q.collect() Polars User Guide is excellent and looks like it’s entirely written with Python examples. Includes a 30 min intro video from PyData Global 2021 Michael #2: PSF Survey is out Have a look, their page summarizes it better than my bullet points will. Brian #3: Gin Config: a lightweight configuration framework for Python Found through Vincent D. Warmerdam’s excellent intro videos on gin on calmcode.io Quickly make parts of your code configurable through a configuration file with the @gin.configurable decorator. It’s in interesting take on config files. (Example from Vincent) # simulate.py @gin.configurable def simulate(n_samples): ... # config.py simulate.n_samples = 100 You can specify: required settings: def simulate(n_samples=gin.REQUIRED)` blacklisted settings: @gin.configurable(blacklist=["n_samples"]) external configurations (specify values to functions your code is calling) can also references to other functions: dnn.activation_fn = @tf.nn.tanh Documentation suggests that it is especially useful for machine learning. From motivation section: “Modern ML experiments require configuring a dizzying array of hyperparameters, ranging from small details like learning rates or thresholds all the way to parameters affecting the model architecture. Many choices for representing such configuration (proto buffers, tf.HParams, ParameterContainer, ConfigDict) require that model and experiment parameters are duplicated: at least once in the code where they are defined and used, and again when declaring the set of configurable hyperparameters. Gin provides a lightweight dependency injection driven approach to configuring experiments in a reliable and transparent fashion. It allows functions or classes to be annotated as @gin.configurable, which enables setting their parameters via a simple config file using a clear and powerful syntax. This approach reduces configuration maintenance, while making experiment configuration transparent and easily repeatable.” Michael #4: Performance benchmarks for Python 3.11 are amazing via Eduardo Orochena Performance may be the biggest feature of all Python 3.11 has task groups in asyncio fine-grained error locations in tracebacks the self-type to return an instance of their class The "Faster CPython Project" to speed-up the reference implementation. See my interview with Guido and Mark: talkpython.fm/339 Python 3.11 is 10~60% faster than Python 3.10 according to the official figures And a 1.22x speed-up with their standard benchmark suite. Arriving as stable until October Extras Michael: Python 3.10.5 is available (changelog) Raycast (vs Spotlight) e.g. CMD+Space => pypi search: Joke: Why wouldn't you choose a parrot for your next application
Watch the live stream: Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training Test & Code Podcast Patreon Supporters Michael #1: auto-py-to-exe Converts .py to .exe using a simple graphical interface A good candidate to install via pipx For me, just point it at the top level app.py file and click go Can add icons, etc. Got a .app version and CLI version (I think ) Required brew install python-tk to get tkinter on my mac I tested it against my URLify app. Oddly, only ran on Python 3.9 but not 3.10 Brian #2: 8 surprising ways how to use Jupyter Notebook by Aleksandra Płońska, Piotr Płoński Fun romp through ways you can use and abuse notebooks package development web app slides book blog report dashboard REST API Michael #3: piptrends by Tankala Ashok Use piptrends.com for comparing python packages downloads and GitHub Statistics. Whenever doing research which python package, check multiple places to finalize it so thought of putting all those things in a single place. Inspired by npmtends.com. Brian #4: Is it a class or a function? It's a callable! by Trey Hunner It’s kinda hard to tell in Python. Actually, impossible to tell from staring at the calling code. “Of the 69 “built-in functions” listed in the Python Built-In Functions page, only 42 are actually implemented as functions: 26 are classes and 1 (help) is an instance of a callable class. Of the 26 classes among those built-in “functions”, four were actually functions in Python 2 (the now-lazy map, filter, range, and zip) but have since become classes. The Python built-ins and the standard library are both full of maybe-functions-maybe-classes.” len - yep, that’s a function zip - that’s a class reversed, enumerate, range, and filter “functions” are all classes. But callable classes. Cool discussion of callable objects partials, itemgetters, iterators, generators, factory functions … Extras Brian: What’s in which Python - Ned Batchelder brief bullet list of a few memorable changes in versions 2.1 through 3.11 Michael: Orion Browser via Dan Bader PSF 2021 Survey Results are out (full analysis next week) Joke: async problems
Watch the live stream: Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training Test & Code Podcast Patreon Supporters Brian #1: The Python GIL: Past, Present, and Future Bary Warsaw and Paweł Polewicz Michael #2: Announcing the PyOxy Python Runner PyOxy is all of the following: An executable program used for running Python interpreters. A single file and highly portable (C)Python distribution. An alternative python driver providing more control over the interpreter than what python itself provides. A way to make some of PyOxidizer's technology more broadly available without using PyOxidizer. PyOxidizer is often used to generate binaries embedding a Python interpreter and a custom Python application. However, its configuration files support additional functionality, such as the ability to produce Windows MSI installers, macOS application bundles, and more. The pyoxy executable also embeds a copy of the Python standard library and imports it from memory using the oxidized_importer Python extension module. Brian #3: The unreasonable effectiveness of f-strings and re.VERBOSE Michael #4: PyCharm PR Management Really nice but not very discoverable Not covered in the docs, but super useful. Available in pro and free community edition Steps Open a project that has an associated github git repo If the GitHub repo has a PR, you’ll see it in the Pull Requests tab. Browse the PRs, and open them for details There you can see the comments, close or merge it, and more Most importantly, check it out to see how it works Extras Brian: Pandas Tutor: Using Pyodide to Teach Data Science at Scale Michael: Python + pyscript + WebAssembly: Python Web Apps, Running Locally with pyscript video is out And an iOS Python Apps video too Joke: Losing an orm!
Watch the live stream: Watch on YouTube About the show Sponsored: RedHat: Compiler Podcast Special guests Mark Little Ben Cosby Michael #1: libgravatar A library that provides a Python 3 interface to the Gravatar APIs. If you have users and want to show some sort of an image, Gravatar is OK PyPI uses this for example (gravatar, not necessarily this lib) Usage: >>> g = Gravatar('myemailaddress@example.com') >>> g.get_image() 'https://www.gravatar.com/avatar/0bc83cb571cd1c50ba6f3e8a78ef1346' Brian #2: JSON to Pydantic Converter Suggested by Chun Ly, “this awesome JSON to @samuel_colvin's pydantic is so useful. It literally saved me days of work with a complex nested JSON schema.“ “JSON to Pydantic is a tool that lets you convert JSON objects into Pydantic models.” It’s a live site, where you can plop JSON on one the left, and Pydantic models show up on the right. There’s a couple options: Specify every field as Optional Alias camelCase fields as snake_case It’s also an open source project, built with FastAPI, Create React App, and a project called datamodel-code-generator. Mark #3: tailwindcss, tailwindui Not python, but helpful for web UI and open source business model example tailwindcss generates CSS Used on the Lexchart app Benefits of tailwindcss and tailwindui: Just-in-Time makes it fast. Output includes only classes used for the project. Stand on shoulders of design thinking from Steve Schoger and Adam Wathan. See also refactoingui.com. Use in current projects without CSS conflicts. Custom namespace with prefix in tailwind.config.js. Bonus: custom namespace prefixes work with the tailwind plug-ins for VS Code and PyCharm. Works well with template engines like, Chameleon. We use tailwind for our app UI. Toolbar template example. Another example of docs and tutorials being a strategic business asset. Resources tailwindcss.com tailwindlabs on YouTube, great tutorials from Simon at Tailwind Beginner friendly tutorials: Thirus, example of tailwind install methods Michael #4: PEP 690 – Lazy Imports From Itamar Discussion at https://discuss.python.org/t/pep-690-lazy-imports/15474 PEP proposes a feature to transparently defer the execution of imported modules until the moment when an imported object is used. PEP 8 says imports go a the top, that means you pay the full price of importing code This means that importing the main module of a program typically results in an immediate cascade of imports of most or all of the modules that may ever be needed by the program. Lazy imports also mostly eliminate the risk of import cycles or crashes. The implementation in this PEP has already demonstrated startup time improvements up to 70% and memory-use reductions up to 40% on real-world Python CLIs. Brian #5: Two small items pytest-rich Suggested by Brian Skinn Created by Bruno Oliveira as a proof of concept pytest + rich, what’s not to love? Now we just need a maintainer or two or three…. Embedding images in GitHub README Suggested by Henrik Finsberg Video by Anthony Sottile This is WITHOUT putting the image in the repo. Upload or drop an image to an issue comment. Don’t save the comment, just wait for GitHub to upload it to their CDN. GH will add a markdown link in the comment text box with a link to the now uploaded image. Now you can use that image in a README file. You can do the same while editing the README in the online editor. Ben #6: pyotp A library for generating and verifying one-time passwords (OTP). Helpful for implementing multi-factor authentication (MFA) in web applications. Supports HMAC-based one-time passwords (HOTP) and time-based one-time passwords (TOTP). While HOTP delivered via SMS text messages is a common approach to implementing MFA, SMS is not really secure. TOTP using an authenticator app on the user’s device such as Google Authenticator or Microsoft Authenticator is more secure, fairly easy to im...
Watch the live stream: Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training Test & Code Podcast Patreon Supporters Brian #1:distinctipy “distinctipy is a lightweight python package providing functions to generate colours that are visually distinct from one another.” Small, focused tool, but really cool. Say you need to plot a dynamic number of lines. Why not let distinctipy pick colors for you that will be distinct? Also can display the color swatches. Some example palettes here: https://github.com/alan-turing-institute/distinctipy/tree/main/examples from distinctipy import distinctipy # number of colours to generate N = 36 # generate N visually distinct colours colors = distinctipy.get_colors(N) # display the colours distinctipy.color_swatch(colors) Michael #2: Soda SQL Soda SQL is a free, open-source command-line tool. It utilizes user-defined input to prepare SQL queries that run tests on dataset in a data source to find invalid, missing, or unexpected data. Looks good for data pipelines and other CI/CD work! Daniel #3: Python in Nature There’s a review article from Sept 2020 on array programming with NumPy in the research journal Nature. For reference, in grad school we had a fancy paper on quantum entanglement that got rejected from Nature Communications, a sub-journal to Nature. Nature is hard to get into. List of authors includes Travis Oliphant who started NumPy. Covers NumPy as the foundation, building up to specialized libraries like QuTiP for quantum computing. If you search “Python” on their site, many papers come up. Interesting to see their take on publishing software work. Brian #4: Supercharging GitHub Actions with Job Summaries From a tweet by Simon Willison and an article: GH Actions job summaries Also, Ned Batchelder is using it for Coverage reports “You can now output and group custom Markdown content on the Actions run summary page.” “Custom Markdown content can be used for a variety of creative purposes, such as: Aggregating and displaying test results Generating reports Custom output independent of logs” Coverage.py example: - name: "Create summary" run: | echo '### Total coverage: ${{ env.total }}%' >> $GITHUB_STEP_SUMMARY echo '[${{ env.url }}](${{ env.url }})' >> $GITHUB_STEP_SUMMARY Michael #5:Language Summit is write up out via Itamar, by Alex Waygood Python without the GIL: A talk by Sam Gross Reaching a per-interpreter GIL: A talk by Eric Snow The "Faster CPython" project: 3.12 and beyond: A talk by Mark Shannon WebAssembly: Python in the browser and beyond: A talk by Christian Heimes F-strings in the grammar: A talk by Pablo Galindo Salgado Cinder Async Optimisations: A talk by Itamar Ostricher The issue and PR backlog: A talk by Irit Katriel The path forward for immortal objects: A talk by Eddie Elizondo and Eric Snow Lightning talks, featuring short presentations by Carl Meyer, Thomas Wouters, Kevin Modzelewski, Samuel Colvin and Larry Hastings Daniel #6:AllSpice is Git for EEs Software engineers have Git/SVN/Mercurial/etc None of the other engineering disciplines (mechanical, electrical, optical, etc), have it nearly as good. Altium has their Vault and “365,” but there’s nothing with a Git-like UX. Supports version history, diffs, all the things you expect. Even self-hosting and a Gov Cloud version. “Bring your workflow to the 21st century, finally.” Extras Brian: Will McGugan talks about Rich, Textual, and Textualize on Test & Code 188 Also 3 other episodes since last week. (I have a backlog I’m working through.) Michael: Power On-Xbox Documentary | Full Movie The 4 Reasons To Branch with Git - Illustrated Examples with Python A Python spotting - via Jason Pecor 2022 StackOverflow Developer Survey is live, via Brian TextSniper macOS App PandasTutor on webassembly Daniel: I know Adafruit’s a household name, shout-out to...