While I decide what to do with PEP 832 after polling folks on their opinion, I thought I would write out why I&aposm even bothering with any of this.

I&aposm going to talk from the perspective of VS Code and its Python extensions, but you could just as easily substitute "VS Code" for your editor of choice or even "AI agent" and it wouldn&apost change the problem: it isn&apost necessarily easy for tools like VS Code to know what workflow (tool) you&aposre using and thus where you&aposre putting your (virtual) environment(s) (I&aposm going to say "environment" as a stand-in for virtual environments, conda environments, etc.). Knowing where the environment lives is important in order to know how to run your code (as the environment will have a Python interpreter that you can use), analyze your dependencies (so linting, auto-complete, etc. do the right thing), etc. So having a way to communicate to VS Code where to find the environment is important.

The problems

First time seeing a project

When you first open a project in VS Code, you may have just done a git checkout, so nothing is set up yet. How is VS Code to know what workflow tool you prefer for creating an environment? Do you prefer Hatch? Poetry? uv? No preference? Some custom solution you have just for you (my-tool)? VS Code could guess maybe based on some tool table in pyproject.toml, but that requires having a known list of tools to look for. And while I know some people will say, "just assume uv", I&aposve been doing this long enough to know not everyone uses that tool and it isn&apost guaranteed to be the tool of choice forever (I remember when pipenv was what everyone recommended). Right now there is no way to know what tool a project wants to see used. Same goes for if you have a personal preference when a project has no reason to care. VS Code could have you specify such a preference in your settings or in .vscode/settings.json for a project, but that would then be VS Code-specific and would only work for supported workflow tools that were hard-coded (sorry, my-tool).

What if you ran git checkout , ran your workflow tool to create the environment, and then opened the project in VS Code? There might be an extra hint about which tool was used if VS Code is able to find the environment on its own, but that assumes VS Code even knew where to look for an environment for that hard-coded list of tools. This also has the same issue of needing VS Code to know where various tools put their environments as well as tie the environment back to the project. And that sort of thing is typically an implementation detail, and thus prone to change unexpectedly.

Finding all the environments

Tying into finding the environments, how do you know which environments are meant for the project? Some tools keep environments locally with the project files, but some keep them in a global location. And when that happens you need specific knowledge to map the environments back or hope the tool has a CLI call that returns a list of the environments in a way that never changes.

And then there&aposs the added wrinkle of workflow tools that never tie an environment to one project. Tools like virtualenvwrapper and conda let you name an environment so they can be shared across projects. But then that means VS Code can&apost know what environment to use without asking. And when someone has 700 or more environments (and that number is from actual experience; it isn&apost an exaggeration), it doesn&apost make is easy to know what environments to suggest.

Possible solutions

Let&aposs work backwards from already having an environment to figuring out what workflow tool to run.

The simplest thing is when there&aposs a virtual environment in a .venv directory. It&aposs easy to discover and can thus be quickly used. It doesn&apost help you modify the environment as you don&apost know what workflow tool was used and whether it cares that it is used to make any changes to the environment, but at least you can run code and analyze the environment.

Solving for when an environment is kept outside of a project or if there is more than one can be done with a file that records such details. Right now I&aposm proposing a .python-envs file which is just a newline-delimited list of paths to environments, with the last one being the one to use by default if the user doesn&apost want to have to choose (FYI what&aposs in the PEP as of this writing is out-of-date).

Knowing what workflow tool a project wants you to use is currently suggested to be done by a [workflow] table in pyproject.toml. That would specify how to launch a workflow tool in a server mode implementing the workflow server protocol (WSP, which has a nice connotation for whitespace in grammar definitions). That would let the tool communicate via JSON-RPC over stdin/stdout with VS Code about what environments there are, creating environments, etc. VS Code would provide a way to specify a fallback tool for when a project doesn&apost have a preference, but there currently isn&apost any thinking about how to make specifying a fallback a standard.

An alternative I have proposed in the past is having some naming convention for CLI tools and then they return the necessary details. This could still use WSP or come up with some CLI standard that tools implement, but it would help solve the issue of not knowing what fallback to use.

Why I care about any of this

VS Code is used across the experience spectrum: from first-time programmers to senior programmers with decades of experience. One common theme across experience levels is no one likes having to set something up. Another theme is no one likes dealing with environments. As such, I&aposm trying to find as much of a solution as people will agree to that makes getting started as easy as possible and hides environments as much as possible. And I want to do all of this in a way that isn&apost specific to VS Code if it doesn&apost have to be.

On top of that, people often don&apost want to leave VS Code to do any workflow stuff. That makes VS Code a user of the workflow tool rather than a peer. Thus VS Code uses the workflow tools as middleware which they aren&apost usually designed to do. With no tool-to-tool communication standard you end up with bespoke support like the Hatch extension which uses public APIs from the Python environments extension, which means it doesn&apost easily scale to help other tools. It&aposs great for VS Code users that extension exists, but other editors don&apost get that benefit. As well, the extension takes effort and that takes away from what could have been a general solution for any editor.

24 Jun 2026 11:08pm GMT

The path to hiring an Executive Director gained real momentum at DjangoCon US 2024, when Jacob Kaplan-Moss shared a vision for what dedicated resources could mean for the future of Django. In his blog post If We Had $1,000,000, he invited companies and supporters to help get the initiative off the ground. The response from the community was inspiring, and we're proud to see that vision become reality.

24 Jun 2026 7:00pm GMT

Django 6.1 beta 1 is now available. It represents the second stage in the 6.1 release cycle and is an opportunity to try out the changes coming in Django 6.1.

Django 6.1 offers a harmonious mélange of new features and usability improvements, which you can read about in the in-development 6.1 release notes.

Only bugs in new features and regressions from earlier Django versions will be fixed between now and the 6.1 final release. Translations will be updated following the "string freeze", which occurs when the release candidate is issued. The current release schedule calls for a release candidate in about a month, with the final release scheduled roughly two weeks later on August 5.

Early and frequent testing from the community will help minimize the number of bugs in the release. Updates on the release schedule are available on the Django forum.

As with all alpha and beta packages, this release is not for production use. However, if you'd like to try some of the new features or help find and fix bugs (which should be reported to the issue tracker), you can grab a copy of the beta package from our downloads page or on PyPI.

The PGP key ID used for this release is Jacob Walls: 131403F4D16D8DC7

24 Jun 2026 5:00pm GMT

Hi there!

I'm happy to announce version 3.26 of reader, a Python feed reader library.

What's new? #

Here are the highlights since reader 3.24.

Feed autodiscovery #

reader now discovers feeds automatically - instead of searching for feed links, just add the website URL, and the web app will suggest any feeds it finds.

Behind the scenes, this is enabled by the autodiscover plugin, which stores discovered feeds in a feed tag, so you can use it without the web app, or even without reader. For a short related rant about standards, check out this Bluesky thread (there are cats!).

feed autodiscovery

database exports

Database exports #

This is an optional feature that I really wanted in the hosted reader MVP - it should be possible to get all your data out, not just lists of feeds and read / starred articles.

So yeah, now you can download a copy of your entire database from the web app, which means you can always migrate to another reader installation. (If you're using reader locally or self-hosting, the command might be handy for backups.)

Hosted reader status update #

Speaking of, did I tell you I'm working on a hosted version of reader? :D

Background: Why another feed reader web app?, Why not just self-host it?

Public demo #

Another thing I wanted for the MVP was a demo (no login needed):

https://beta.reader.andgravity.com/demo/

Go forth and click all the things! (it's read-only, nothing should break™)

OK, so what now? #

This is what is finished so far:

• multi-user version of the web app
• authentication via email
• infrastructure deployments using pyinfra
• multi-user feed updates
• context-sensitive help
• (new) public demo

So just launch the damn thing already:

• set up a landing page
• give it a good name
• publish a launch announcement + roadmap

Meanwhile, if this sounds like something you'd like to use, get in touch.

That's it for now. For more details, see the full changelog.

Want to contribute? Check out the docs and the roadmap.

Learned something new today? Share it with others, it really helps! PyCoder's Weekly HN Bluesky linkedin Twitter

What is reader? #

reader takes care of the core functionality required by a feed reader, so you can focus on what makes yours different.

reader allows you to:

• retrieve, store, and manage Atom, RSS, and JSON feeds
• mark articles as read or important
• add arbitrary tags/metadata to feeds and articles
• filter feeds and articles
• full-text search articles
• get statistics on feed and user activity
• import / export feeds as OPML
• automatically discover feeds in web pages
• write plugins to extend its functionality

...all these with:

• a stable, clearly documented API
• excellent test coverage
• fully typed Python

To find out more, check out the GitHub repo and the docs, or give the tutorial a try.

Why use a feed reader library? #

Have you been unhappy with existing feed readers and wanted to make your own, but:

• never knew where to start?
• it seemed like too much work?
• you don't like writing backend code?

Are you already working with feedparser, but:

• want an easier way to store, filter, sort and search feeds and entries?
• want to get back type-annotated objects instead of dicts?
• want to restrict or deny file-system access?
• want to change the way feeds are retrieved by using Requests?
• want to also support JSON Feed?
• want to support custom information sources?

... while still supporting all the feed types feedparser does?

If you answered yes to any of the above, reader can help.

The reader philosophy #

• reader is a library
• reader is for the long term
• reader is extensible
• reader is stable (within reason)
• reader is simple to use; API matters
• reader features work well together
• reader is tested
• reader is documented
• reader has minimal dependencies

24 Jun 2026 4:48pm GMT

Many of you have probably heard of Wagtail CMS, but not everyone knows that Wagtail, in a nutshell, is a supercharged admin backend for Django. At least that's how I see it, and how I often pitch it to fellow Django developers.

Django comes with its own django.contrib.admin …

Read now

24 Jun 2026 9:38am GMT

As I have discussed in previous posts, "AIs" can make mistakes. In fact, they do make mistakes, and their mistake-making patterns are such that where and how they will make mistakes is both uncertain and constantly changing.

Thus, in any scenario where you want to attempt to make "productive" use of "AI", you must have a system in place for checking every result. Not checking some results; checking every result. If each result might have a consequence for you (and if it didn't have a consequence, why bother automating it?) and you cannot predict in advance which kinds of results will need verification, then verification is always required.

The verification often ends up being just as expensive as doing the work in the first place, which means that if you want your usage of "AI" to be personally profitable, you have to find someone else to externalize the cost of verification onto. This person becomes your adversary, and, if you are successful, your "AI's" victim.

The Ladder-Climber And Their Reverse-Centaur Rungs

One way that this constellation of facts can straightforwardly assemble themselves into a dystopian nightmare is the phenomenon, described by Cory Doctorow, of the reverse centaur. This is when your employer non-consensually turns you into the verification system. The "AI" does the fun part of initially performing the work, and then you do the boring part where you check if the robot is right and clean up its messes, even if everyone already knows that it would, in aggregate, be cheaper for you to do the work in the first place.

Reverse centaurs can be made from any automation, not only "AI" automation. I think that there is a reason that this term happens to have emerged in the "age of AI", though, and not with earlier automation technologies (even those which were considerably more viscerally horrific). That reason is: the wrongness of "AI" output is not merely a technical feature that must be compensated for, it is a generalized externality.

As I mentioned above, if you are responsible for the entirety of the work, both extruding the "AI" output and checking it, it's usually cheaper to have humans do the entirety of the work to begin with. When humans do the writing directly, we can check as we go, and thus verification doesn't need to be as comprehensive.

When "AI" coding advocates say "code review is the bottleneck", what they are observing is that the LLM is still rolling the dice for each PR, and a human is still necessary to verify that each of those rolls is a winner. But calling this process "code review" is a bit of a misnomer; it's not really "code review" in the traditional sense, it's human understanding.

Before the advent of "AI", the human understanding was implicit in the process of writing the code in the first place¹, and the code review was a way of diffusing and extending that understanding. Now that the code can be authored with no initial understanding taking place, that cost has not gone away, it has moved.

Human understanding was always the bottleneck.

However, this is taking a collaborative view of a software project, where satisfying the needs and solving the problems of your customers are the goals. We can see that "AI" is a bad tool to satisfy those goals, because all it's doing is converting the first half of the work, that of understanding the code as you write it, to understanding the agent's output as you read it.

What if, instead, we were to take the view that every software company is a Hobbesian nightmare, red in tooth and claw? In this view, the only goal of a software project is for the individual developers to make their promo cycles and get their bonuses. Given that there is only a certain amount of money to go around, this is a zero-sum game where each programmer wants to look more productive than their colleagues.

Pretty much every organization finds it easy to reward "productivity" as expressed by lines of code emitted, but the benefits of doing thorough and thoughtful design, analysis, and code review very difficult to reward. In this world, an LLM is an invaluable tool for the sociopathic ladder-climber, particularly if your legacy organization is still structuring their workflows as if the person prompting the bot is "writing" the code, and then they get to foist off the act of "reviewing" the code onto someone else.

Here, the prompter effectively externalizes the cost of the LLM's failures but internalizes any benefits. The prompter will vibe-code a big feature, so large that the assigned reviewer can't possibly comprehend it all effectively. When this happens, the reviewer will, eventually, be pressured to approve it, even if they can try to spot a few problems along the way. The reviewer has their own work to get back to, after all, the obligation to review the prompter's (read: the bot's) code is a drain on their time that they are not going to get rewarded for.

If this feature is a big success, the prompter gets a promotion. If it causes a big issue, well, the reviewer must not have been careful enough.

This is why LLMs are "good for coding", and also why their biggest promoters keep having outages.

The Generative Gish Galloper

Coding is the biggest "success story" of this type of adversarial communication, but it is by far not the only instance of such a thing. LLMs create a new form of leverage that can turn Brandolini's law from a linear advantage into an exponential one. If you are engaged in a political debate where you want to overwhelm the other side in nonsense, an LLM can generate bullshit faster than it is physically possible for a human being to type, let alone respond thoughtfully. There is an asymmetry to the utility of this weapon as well: only one side of the political spectrum wants to flood the zone and destroy trust in institutions and the concept of truth. There's a good reason that the fascists love it.

Straightforward Spam and Fraud

This is kind of obvious, but LLMs can generate lightly-customized, plausible-looking text much more quickly than any human being. This facilitates their use in fraud, spam, and scams. In a spamming or fraudulent interaction, once again, the costs are externalized onto the victim: the recipient of a spam message has to do all the work of "checking" the LLM's output. Spammers already expect very low hit rates from boilerplate, and if the LLM can increase those percentages from 1% to 5% the technology will pay for itself; they don't need anything like reliable accuracy.

Customer "Support"

If you have any kind of commercial relationship with a company, I probably don't even need to mention this: customer "support" bots are a misery. Everybody knows it at this point. But customer support is usually conceptualized by businesses as an adversarial interaction, because it is a cost center. They maintain internal metrics on time-to-resolution and try to optimize them. Implicitly, this creates a dynamic where the goal of the customer service agent's job is not to solve your problem, but to emit noise that will cause you to think your problem is resolved, or to give up, as fast as possible. Unsurprisingly, LLMs can emit this noise faster than humans can, getting those customers off the phone. But those customers will remember those interactions, and the story outside the TTR metrics is horrible.

Similarly to the situation in software development, LLMs can look very good on paper for customer support, but mostly what they are doing is illuminating the problems with the industry's existing metrics, by turning "winning the metrics battle against the customer" into a more obvious and immediate defeat for the company's long term reputation.

"Education"

In 2026 it is sadly a fact of life that students cheat all the time using "AI", and that this cheating is very successful, in that the teachers find it very hard to detect.

LLMs are great for cheating on schoolwork because the student is externalizing the work of the checking onto the teachers, who are often starting at a disadvantage to begin with, at least in the US.

My view is that this is happening because of a divergence in the way that students vs. teachers (or, more accurately, "the broader educational system") view grading.

When a student is asked to write an essay, the teachers see the effort as both intrinsically worthwhile for the student, as well as useful as a pedagogical tool to evaluate and react to the student's progress. The student, by contrast, sees a stumbling block designed to knock them off the path to success and into a permanent underclass. It is no wonder that the student sees "AI" as useful to their own goals and has no compunction about deploying it.

There is a bitter irony that the ability to understand the inherent value of actually writing the essay on their own is the sort of thing that students can really only learn by writing a bunch of essays. There's no way that I can think of which makes the benefit legible as long as a shortcut is available.

The net effect here is a downward spiral, where the already-wobbling educational system is sustaining an attack that it doesn't have the resources to recover from. The individual students' attacks against their teachers and their schools' grading systems might appear to momentarily succeed, but they will win the battle and lose the war.

Spamming "For Good"?

Usually when we talk about someone unilaterally choosing to enter into an adversarial relationship, that's an "attack" and for good reasons we have a negative impression of the attacker. However, I would be remiss if I did not point out that there are some cases where the relationship was already adversarial; just because you're the attacker doesn't mean that you are evil.

For example we might imagine use-cases like automatically filing appeals for prior authorizations against health insurance. It's relatively well-known at this point that the main way for-profit insurers maintain their margins is by denying claims right up to the line of the policies themselves being fraud, so using a spamming tool to fight them might be entirely justifiable² in that case.

Similarly, using an LLM could be justified in a fight against a company refusing to honor a warranty. One could imagine using an LLM to immediately generate replies and escalations.

However, even in imagined cases like these, the underlying problem is that the insurers and the vendors already have a tremendous amount of structural power, so it is more likely that they will have the advantage in deploying a communications weapon like an LLM, as well as enacting policies to simply ignore any LLM-based communication that you might submit. Worse, if these strategies were to become widespread, they might provide an excuse to reject any communications by feeding them into an unreliable "LLM detector" and issuing an automated "computer says no" even to hand-written correspondence.

It is also worth stressing that these cases are imagined, as compared to the very real coworker-abuse, spam, scam, fraud, and disinformation campaigns being waged in real life today.

Therefore, while legitimate uses might exist, it's hard to imagine that there's anywhere they would be genuinely valuable and sustainable. In the best case "AI" will provide a temporary advantage for underdogs that will provoke an arms race which the resource-advantaged adversaries will win in the long run, in the worst case the arms race itself will cement permanent structural change that will make things worse.

"Search" By Stealing

Most of the adversarial utility of "AI" is on the "write" side, since write-amplification is more obviously aggressive than reading. But the "read" side of LLMs - summarization and question-answering - can be a form of attack as well.

To begin with, the act of reading itself is currently enormously destructive, but that's arguably not a fundamental aspect of this technology. They could set reasonable rate-limits and respect things like robots.txt, as search engines have for decades now. They could also refrain from committing criminal levels of copyright infringement. But, today, using "AI" tools does suborn this sort of out-of-control crawling.

More insidiously, consider the scenario described in this YouTube video. The LTT Bros decided to try Linux again, and in the course of so doing, they had problems. When trying to solve these problems, they were faced with a choice: they could consult Reddit, or they could ask an LLM. Asking an LLM would "gaslight the heck out of" them, but they still found it preferable, because they would at least get an answer without getting yelled at.

Initially this sounds great. But it also means that you want to extract knowledge from a community, while mechanically eliding any values or norms that the community may want to impart as part of offering that knowledge. As someone who spent many years in a community tech support role, this is worrying. Many requests for support are people asking how to do things that will momentarily solve a superficial problem but create a long-term reliability problem or even an immediate security risk, that the question-asker doesn't want to hear about. Consider the question "I'm tired of entering my password so much, how do I make it so my laptop unlocks automatically". An obsequious chatbot will helpfully tell you how to do this without pushback.

But, this is also a sort of ethically murky area. The Linux community is somewhat famously, for many years now, a toxic cesspool of general hostility, misogyny, etc. It is certainly a good thing that people can get access to this knowledge without subjecting themselves to abuse. But it also means that the people with the power and the privilege to change the community for the better can just quietly withdraw, rather than fixing the problems. It also means that the positive elements of culture cannot be transmitted, and people will have no opportunity to learn about unknown unknowns.

In this case, the "adversarial" communication is with society. The thing that using an LLM for search lets you do is withdraw from society and avoid forming any personal connections. There are some personal connections which are painful and annoying, and so that can feel like a momentary balm. But the need to make connections in general is, like, the concept of society itself.

Who Am I Hurting?

LLMs are good at adversarial communication. They are so good at it, relative to their other benefits, that they will tend to make communications adversarial if you are not remaining vigilant about the possibility that it might do so. My request to you, dear reader, if you are going to use such tools, is to always ask yourself, "who might I be hurting, if I use an LLM for this?"

If you're using an "AI", who is its adversary? If you haven't given it one yet, who might the "AI" turn into an adversary? Who might you overwhelm with an asymmetric amount of output, or, if you're receiving information and not sending it, who are you taking that information from without consulting?

Figure out the answers to these questions and conduct yourself accordingly; the answer might be "yourself".

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you've read here and you'd like to read more of it, or you'd like to support my various open-source endeavors, you can support my work as a sponsor!

23 Jun 2026 8:06pm GMT

The third article in the series, still on conditions. The previous installment was about their shape - merging ifs, factoring shared decisions, dropping checks that earn nothing. This one reaches for the other lever: the algebra of the conditions themselves - not a textbook tour, just the handful of transformations I lean on in everyday code.

23 Jun 2026 12:00pm GMT

Codecov's unreliability breaking CI on my open source projects has been a constant source of frustration for me for years. I have found a way to enforce coverage over a whole GitHub Actions build matrix that doesn't rely on third-party services.

09 Jun 2026 12:00am GMT

Let's say you're writing a Python library.

In this library, you have some collection of state that represents "options" or "configuration" for a bunch of operations. Such a set of options is a bundle of potentially ever-increasing complexity. Thus, you will want it to have an extremely minimal compatibility surface, with a very carefully chosen public interface, that is either small, or perhaps nothing at all. Such an object conveys state and might have some private behavior, but all you want consumers to be able to do is build it in very constrained, specific ways, and then pass it along as a parameter to your own APIs.

By way of example, imagine that you're wrapping a library that handles shipping physical packages.

There are a zillion ways to do it ship a package. There are different carriers who can ship it for you. There's air freight, and ground freight, and sea freight. There's overnight shipping. There's the option to require a signature. There's package tracking and certified mail. Suffice it to say, lots of stuff.

If you are starting out to implement such a library, you might need an object called something like ShippingOptions that encapsulates some of this. At the core of your library you might have a function like this:

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async def shipPackage(
        how: ShippingOptions,
        where: Address,
    ) -> ShippingStatus:
    ...

If you are starting out implementing such a library, you know that you're going to get the initial implementation of ShippingOptions wrong; or, at the very least, if not "wrong", then "incomplete". You should not want to commit to an expansive public API with a ton of different attributes until you really understand the problem domain pretty well.

Yet, ShippingOptions is absolutely vital to the rest of your library. You'll need to construct it and pass it to various methods like estimateShippingCost and shipPackage. So you're not going to want a ton of complexity and churn as you evolve it to be more complex.

Worse yet, this object has to hold a ton of state. It's got attributes, maybe even quite complex internal attributes that relate to different shipping services.

Right now, today, you need to add something so you can have "no rush", "standard" and "expedited" options. You can't just put off implementing that indefinitely until you can come up with the perfect shape. What to do?

The tool you want here is the opaque data type design pattern. C is lousy with such things (FILE, pthread_*_t, fd_set, etc). A typedef in a header file can easily achieve this.

But in Python, if you expose a dataclass - or any class, really - even if you keep all your fields private, the constructor is still, inherently, public. You can make it raise an exception or something, but your type checker still won't help your users; it'll still look like it's a normal class.

Luckily, Python typing provides a tool for this: typing.NewType.

Let's review our requirements:

1. We need a type that our client code can use in its type annotations; it needs to be public.
2. They need to be able to consruct it somehow, even if they shouldn't be able to see its attributes or its internal constructor arguments.
3. To express high-level things (like "ship fast") that should stay supported as we add more nuanced and complex configurations in the future (like "ship with the fastest possible option provided by the lowest-cost carrier that supports signature verification").

In order to solve these problems respectively, we will use:

1. a public NewType, which gives us our public name...
2. which wraps a private class with entirely private attributes, to give us an actual data structure, while not exposing the constructor,
3. a set of public constructor functions, which returns our NewType.

When we put that all together, it looks like this:

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from dataclasses import dataclass
from typing import Literal, NewType

@dataclass
class _RealShipOpts:
    _speed: Literal["fast", "normal", "slow"]

ShippingOptions = NewType("ShippingOptions", _RealShipOpts)

def shipFast() -> ShippingOptions:
    return ShippingOptions(_RealShipOpts("fast"))

def shipNormal() -> ShippingOptions:
    return ShippingOptions(_RealShipOpts("normal"))

def shipSlow() -> ShippingOptions:
    return ShippingOptions(_RealShipOpts("slow"))

As a snapshot in time, this is not all that interesting; we could have just exposed _RealShipOpts as a public class and saved ourselves some time. The fact that this exposes a constructor that takes a string is not a big deal for the present moment. For an initial quick and dirty implementation, we can just do checks like if options._speed == "fast" in our shipping and estimation code.

However, the main thing we are doing here is preserving our flexibility to evolve the related APIs into the future, so let's see how we might do that. For example, let's allow the shipping options to contain a concrete and specific carrier and freight method:

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from dataclasses import dataclass
from enum import Enum, auto
from typing import NewType

class Carrier(Enum):
    FedEx = auto()
    USPS = auto()
    DHL = auto()
    UPS = auto()

class Conveyance(Enum):
    air = auto()
    truck = auto()
    train = auto()

@dataclass
class _RealShipOpts:
    _carrier: Carrier
    _freight: Conveyance

ShippingOptions = NewType("ShippingOptions", _RealShipOpts)

def shipFast() -> ShippingOptions:
    return ShippingOptions(_RealShipOpts(Carrier.FedEx, Conveyance.air))

def shipNormal() -> ShippingOptions:
    return ShippingOptions(_RealShipOpts(Carrier.UPS, Conveyance.truck))

def shipSlow() -> ShippingOptions:
    return ShippingOptions(_RealShipOpts(Carrier.USPS, Conveyance.train))

def shippingDetailed(
    carrier: Carrier, conveyance: Conveyance
) -> ShippingOptions:
    return ShippingOptions(_RealShipOpts(carrier, conveyance))

As a NewType, our public ShippingOptions type doesn't have a constructor. Since _RealShipOpts is private, and all its attributes are private, we can completely remove the old versions.

Anything within our shipping library can still access the private variables on ShippingOptions; as a NewType, it's the same type as its base at runtime, so it presents minimal¹ overhead.

Clients outside our shipping library can still call all of our public constructors: shipFast, shipNormal, and shipSlow all still work with the same (as far as calling code knows) signature and behavior.

If you need to build and convey some state within your public API, while avoiding breakages associated with compatibility churn, hopefully this technique can help you do that!

Acknowledgments

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22 May 2026 12:33am GMT