A written guide to my DjangoCon US talk on deploying machine learning models with Django.

01 Oct 2025 6:56am GMT

Commoncog by Cedric Chin is probably the most influential resource I've discovered on the Internet in the last couple of years. It's deep. It's grounded. It's deliberate. It's insightful. It's judicious. It's nuanced. It's well written. It's practical. If I were stranded on a desert island and could only receive updates from five websites, Commoncog would be one of them. It's that good.

Consider the series on putting mental models to practice. There's a very compelling argument for limits of scientific knowledge from practical point of view in part two; and right away other types of knowledge are proposed to fill the gap. Some of them are collectively called Naturalistic Decision Making models, elaborated on in part four. Part five is a real treasure - it provides detailed, actionable advice on developing proficiency in expertise-driven decision making. The whole series gives both theoretical framework and practical guidance with a proven track record.

These themes are then echoed in The Secret at the Heart of Continuous Improvement, which covers Plan-Do-Study-Act model - something that you might have heard about before. Nonetheless, Cedric manages to really nail down the importance of how the stages support one another, and the entire model as a whole. He then delivers a knockout punch with revolutionary framing - PDSA provides a methodology for single subject studies. It's the ultimate tool for finding and refining what works for you.

There's also entire series on Cognitive Flexibility Theory, which tries to uncover how people are learning in ill-structured domains - and provides tools to accelerate that learning. One of them is case library, which is explained in depth in How Note Taking Can Help You Become an Expert. It acknowledges the messiness of the real world and builds on the insight that experts are pattern-matching novel situations against all the cases they experienced or heard of, identifying similarities and differences. Becoming an expert is largely about growing this body of cases you can reference.

I'm truly captivated by these ideas. Where I stand right now, I believe that one of the most beneficial learning resources for testers is building a shared case library. I've been modestly contributing to that in this very blog. If this is something you would be interested in and would like to work on together, just send me a message.

Meanwhile, be sure to check out existing Commoncog article base and subscribe to their newsletter or RSS feed.

17 Sep 2025 4:24pm GMT

Python 3.14 introduces improvements to its interactive shell (REPL), bringing a more modern, colorful, and user-friendly environment. The new features make the Python 3.14 REPL a powerful tool for experimentation. Whether you're testing a quick idea, exploring a new library, or debugging a tricky snippet, the REPL gives you instant feedback-no files, no setup, just type and run.

The default CPython REPL intentionally kept things minimal. It was fast, reliable, and available everywhere, but it lacked the richer, more ergonomic features found in tools like IPython or ptpython. That began to change in Python 3.13, when CPython adopted a modern PyREPL-based shell by default, adding multiline editing, better history navigation, and smarter Tab completion.

By the end of this tutorial, you'll understand that:

• In Python 3.14's REPL, autocompletion is on by default. You just need to press Tab in the context of an import statement to see possible completion suggestions.
• The REPL highlights Python syntax in real time if your terminal supports ANSI colors.
• Python 3.14 allows you to customize the color theme with the _colorize.set_theme() experimental API and the PYTHONSTARTUP script.
• You can disable the syntax highlighting by setting NO_COLOR=1 or PYTHON_COLORS=0 in your environment.

Autocompleting module names during import statements makes interactive coding smoother and faster, especially for learning or exploratory tasks. In addition, the colored syntax in the REPL improves readability, making it easier to spot typos and syntax issues.

Get Your Code: Click here to download the free sample code that you'll use to learn about REPL autocompletion and highlighting in Python 3.14.

Take the Quiz: Test your knowledge with our interactive "Python 3.14 Preview: REPL Autocompletion and Highlighting" quiz. You'll receive a score upon completion to help you track your learning progress:

---

Interactive Quiz

Python 3.14 Preview: REPL Autocompletion and Highlighting

Take this quiz to explore Python 3.14's REPL upgrades! Test your knowledge of new autocompletion tools and built-in syntax highlighting.

The Interactive REPL Before Python 3.14

When you first install Python, one of the most immediate ways to try it out is through the interactive REPL (Read-Eval-Print Loop) in your command line or terminal. The REPL is a shell that lets you type Python code, run it instantly, and see the result. It's a quick-start environment perfect for experimentation, learning, and debugging without the overhead of creating and running separate scripts.

For years, the default CPython REPL kept things lightweight and fairly minimal, with very few features. In contrast, tools like IPython, bpython, and ptpython have offered richer interactive experiences.

This landscape has been changing lately. Starting in Python 3.13, the default REPL is based on PyPy's pyrepl, which is written in Python and designed for extensibility and safety. It's also a more capable interactive shell with a modern set of features:

• Color by default: Take advantage of colorized prompts and tracebacks. You can also control this behavior with the PYTHON_COLORS or NO_COLOR environment variables.
• Quick REPL commands: Use exit, quit, help, and clear as commands rather than function calls with parentheses.
• Built‑in help browser: Press F1 to open a help viewer in your pager so you can browse docs for Python, modules, and objects.
• Persistent history browser: Press F2 to open your command history in a pager and keep it across sessions so you can copy and reuse code.
• Multiline editing: Edit and rerun entire code blocks-functions, classes, loops-as a single unit. The block structure is preserved in your history.
• Robust pasting and paste mode: Paste whole scripts or larger code blocks reliably by default. Optionally, you can access paste mode with F3, although direct pasting usually works without issue.
• Smarter Tab completion: Completions update as you type and hide irrelevant suggestions to reduce noise.

In Python 3.14, the REPL has taken another leap forward by including the following improvements:

• Extended autocompletion that now covers module and submodule names in import statements
• Live syntax highlighting that makes your interactive code as readable as in your favorite code editor or IDE

You don't need to perform any extra installation or configuration to start using these new features as long as your terminal supports ANSI color output.

In the sections ahead, you'll explore what these improvements look like in practice, learn how to use them in your workflow, and pick up some troubleshooting tips for when things don't behave quite as expected.

Autocompletion Improvements

Python 3.14 extends the autocompletion logic to recognize import contexts and suggest module or package names accordingly. Now, when you type an import or from ... import statement and press Tab, the REPL will search the import path for available module names matching the partial text.

For example, if you type import and then press Tab twice, you'll get the complete list of currently available modules and packages. If you start typing a module's name like pat and press Tab, the REPL will automatically complete the pathlib name because it matches uniquely with the partial text.

Read the full article at https://realpython.com/python-repl-autocompletion-highlighting/ »

[ Improve Your Python With 🐍 Python Tricks 💌 - Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

17 Sep 2025 2:00pm GMT

In this quiz, you'll test your understanding of Python 3.14 Preview: REPL Autocompletion and Highlighting.

With these skills, you'll be able to take advantage of smarter autocompletion in import statements, real-time syntax highlighting, and even customize or disable the colors to fit your workflow.

[ Improve Your Python With 🐍 Python Tricks 💌 - Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

17 Sep 2025 12:00pm GMT

One thing I wanted to add to this blog for a long time was full text search. Content is steadily increasing and sometimes I have a hard time finding the exact right post to link to when writing a new post. My wife was also complaining that she could never find the post about backups for artists when she wanted to link it. So what a better way to spend a few hours of my vacation than to add full text search functionality.

Usually when adding search to a system people eye ElasticSearch. A monstrosity I had my fair share of "fun" with and something I do not want to touch if I can avoid it. Avid Postgres users might lean on PostgreSQLs excellent full text search and vectorisation options. This blog is running on Django and SQLite and there is no chance I would add either of these two to the stack for "just a blog".

SQLite has an extension for full text search. Technically multiple, but we are using FTS5. Per documentation we need a virtual table and fill it with our data. For the table we add the title of the post and its raw content. A row ID is automatically generated for you and we will use this as a back reference to our post IDs. (Please do not ask me why name and not title. There are good(tm) reasons)

create virtual table search USING fts5(name, content);

The big question is how to get data into the table. We could create an unmanaged model, setup post_save receivers and let Django do its magic. Or we create a few triggers for insert, update and delete in SQLite. I am usually not a fan of this approach, especially with larger codebases and / or teams. But there is a fairly good chance no one but me will ever touch this code.

CREATE TRIGGER post_insert
    AFTER INSERT ON blog_post BEGIN 
    INSERT INTO search (rowid, name, content) 
    VALUES (new.id, new.name, new.content); 
END;

And we obviously want to backfill all existing content.

INSERT INTO search (rowid, name, content) SELECT id, name, content FROM blog_post;

Since rowid is automatically generated we re-use it to set it to the content tables ID. It feels like adding a separate field for that might be cleaner, but I also have not found any suggestion that manually managing row IDs is a bad idea. So here we are, being super efficient, not storing 300 additional integers!

Creating the table and setting up the triggers happens in an empty database migration.

Querying the search index in Django is fairly easy thanks to Django exposing a database connection to run raw SQL queries with. But we are using a raw database connection, so the general guidance and warnings about using params, etc. to prevent SQL injections apply.

cursor.execute("SELECT rowid, rank FROM search WHERE search MATCH %s ORDER BY rank", (query,))

The query returns the rowid which is the ID of our post and a rank. The data is already ordered, no need to further sort it. With Case and When we preserve the order when fetching posts by ID from the database.

ids = [doc[0] for doc in result]
ordering = []

for idx, val in enumerate(ids):
    ordering.append(When(id=val, then=Value(idx)))

return self.published().filter(id__in=ids).order_by(Case(*ordering, output_field=IntegerField()))

I uploaded the model manager and migrations, which will likely make it a bit clearer how things are set up. The triggers need to be created in a separate migration from the one creating the virtual table. I am not yet sure why this is the case, but this was the only way to reliably create the triggers. There is a try / catch blog in the model manager that will make a lot more sense in a second.

Limitations

I think this search works well enough for my blog. But there are some noticeable limitations, especially when you are used to services requiring a distributed cluster to figure out of there is a document with the name "foo".

SQLites FTS really does not like any special characters like >. This is fine. Also do not take this as an invitation to hammer this server with some automated sql injection tools to verify it is actually working as expected. Thanks in advance.

sqlite3.OperationalError: fts5: syntax error near ">"

There are ways to make this work by wrapping > in quotation marks ">" but this means FTS5 will query for an exact match for the string.

The second thing that will be obvious very quickly when misspelling a word is that there is no fuzzy search. backpu will not bring up the article about backup strategies. Neither will back. Word matches only.

There is also no custom weighting of title matches vs content matches.

Improving Search

One option is to use a different tokeniser which will make search behave more like traditional search you know. There is a porter tokeniser implementing the Porter stemming algorithm which will make sure running matches run. There is also a trigram tokeniser indexing three character sequences for partial matching. I still want to run some experiments to see if it is worth using either of them. For some content and use cases it will absolutely be worth it, I cannot imagine building search for e-commerce with them.

Tokenisers support some more options including unicode vs ascii tokenisers and handling diacritics.

Weighting and ranking should be solvable using SQLites bm25() method. I am saying should be solvable because a quick test on my data did not influence the rank at all, even if the content got a 10x boost over the title. There is a fairly good chance this is me messing up, but I do not think this will be a big enough difference to not rollout search v1.

The real fun begins when we install Python with a compile time option to load extensions in SQLite and compile the spellfix1 SQLite extension. This should help with correcting misspelled words and should work with FTS5. I did not spend enough time playing with spellfix, but from its description I think it is doing what you would expect when you ever hand rolled fuzzy search.

I am curious if spellfix will be worth the effort or if it will be easier to query the FTS5 vocabulary table and calculate the Levenshtein distance in memory considering the fairly small dataset of this blog.

Good enough

Compared to an ElasticSearch cluster, PostgreSQL and all the other fancy search engines out there this seems very rudimentary and more like a toy. But for this blog it is more than good enough. The fact that something exists is already a huge improvement.

But do not let this simple example make you believe this is all FTS5 is capable of. It is worth quickly scanning the FTS5 documentation to get an idea what is possible, because there is a lot in there including some fun stuff like custom tokenisers, external content references and contentless tables to highlight and snippet functions that could come in very handy in certain situations.

I already toyed around with some of the tokenisers and spellfix, but I will need a bit more time for testing and to actually compare them to ElasticSearch to get an idea how good they actually work and what the pitfalls are. I will likely iterate on search every now and then and see what is feasible to do in SQLite. So far I am more than pleasantly surprised and I already was a fan of SQLite before.

15 Sep 2025 4:58pm GMT

Thoughts on a fun week in Chicago, favorite talks, sprints, and more.

15 Sep 2025 10:56am GMT

The most optimistic vision of generative AI¹ is that it will relieve us of the tedious, repetitive elements of knowledge work so that we can get to work on the really interesting problems that such tedium stands in the way of. Even if you fully believe in this vision, it's hard to deny that today, some tedium is associated with the process of using generative AI itself.

Generative AI also isn't free, and so, as responsible consumers, we need to ask: is it worth it? What's the ROI of genAI, and how can we tell? In this post, I'd like to explore a logical framework for evaluating genAI expenditures, to determine if your organization is getting its money's worth.

Perpetually Proffering Permuted Prompts

I think most LLM users would agree with me that a typical workflow with an LLM rarely involves prompting it only one time and getting a perfectly useful answer that solves the whole problem.

Generative AI best practices, even from the most optimistic vendors all suggest that you should continuously evaluate everything. ChatGPT, which is really the only genAI product with significantly scaled adoption, still says at the bottom of every interaction:

ChatGPT can make mistakes. Check important info.

If we have to "check important info" on every interaction, it stands to reason that even if we think it's useful, some of those checks will find an error. Again, if we think it's useful, presumably the next thing to do is to perturb our prompt somehow, and issue it again, in the hopes that the next invocation will, by dint of either:

1. better luck this time with the stochastic aspect of the inference process,
2. enhanced application of our skill to engineer a better prompt based on the deficiencies of the current inference, or
3. better performance of the model by populating additional context in subsequent chained prompts.

Unfortunately, given the relative lack of reliable methods to re-generate the prompt and receive a better answer², checking the output and re-prompting the model can feel like just kinda futzing around with it. You try, you get a wrong answer, you try a few more times, eventually you get the right answer that you wanted in the first place. It's a somewhat unsatisfying process, but if you get the right answer eventually, it does feel like progress, and you didn't need to use up another human's time.

In fact, the hottest buzzword of the last hype cycle is "agentic". While I have my own feelings about this particular word³, its current practical definition is "a generative AI system which automates the process of re-prompting itself, by having a deterministic program evaluate its outputs for correctness".

A better term for an "agentic" system would be a "self-futzing system".

However, the ability to automate some level of checking and re-prompting does not mean that you can fully delegate tasks to an agentic tool, either. It is, plainly put, not safe. If you leave the AI on its own, you will get terrible results that will at best make for a funny story⁴⁵ and at worst might end up causing serious damage⁶⁷.

Taken together, this all means that for any consequential task that you want to accomplish with genAI, you need an expert human in the loop. The human must be capable of independently doing the job that the genAI system is being asked to accomplish.

When the genAI guesses correctly and produces usable output, some of the human's time will be saved. When the genAI guesses wrong and produces hallucinatory gibberish or even "correct" output that nevertheless fails to account for some unstated but necessary property such as security or scale, some of the human's time will be wasted evaluating it and re-trying it.

Income from Investment in Inference

Let's evaluate an abstract, hypothetical genAI system that can automate some work for our organization. To avoid implicating any specific vendor, let's call the system "Mallory".

Is Mallory worth the money? How can we know?

Logically, there are only two outcomes that might result from using Mallory to do our work.

1. We prompt Mallory to do some work; we check its work, it is correct, and some time is saved.
2. We prompt Mallory to do some work; we check its work, it fails, and we futz around with the result; this time is wasted.

As a logical framework, this makes sense, but ROI is an arithmetical concept, not a logical one. So let's translate this into some terms.

In order to evaluate Mallory, let's define the Futzing Fraction, " FF ", in terms of the following variables:

H

the average amount of time a Human worker would take to do a task, unaided by Mallory

I

the amount of time that Mallory takes to run one Inference⁸

C

the amount of time that a human has to spend Checking Mallory's output for each inference

P

the Probability that Mallory will produce a correct inference for each prompt

W

the average amount of time that it takes for a human to Write one prompt for Mallory

E

since we are normalizing everything to time, rather than money, we do also have to account for the dollar of Mallory as as a product, so we will include the Equivalent amount of human time we could purchase for the marginal cost of one⁹ inference.

As in last week's example of simple ROI arithmetic, we will put our costs in the numerator, and our benefits in the denominator.

The idea here is that for each prompt, the minimum amount of time-equivalent cost possible is W+I+C+E. The user must, at least once, write a prompt, wait for inference to run, then check the output; and, of course, pay any costs to Mallory's vendor.

If the probability of a correct answer is P=13, then they will do this entire process 3 times¹⁰, so we put P in the denominator. Finally, we divide everything by H, because we are trying to determine if we are actually saving any time or money, versus just letting our existing human, who has to be driving this process anyway, do the whole thing.

If the Futzing Fraction evaluates to a number greater than 1, as previously discussed, you are a bozo; you're spending more time futzing with Mallory than getting value out of it.

Figuring out the Fraction is Frustrating

In order to even evaluate the value of the Futzing Fraction though, you have to have a sound method to even get a vague sense of all the terms.

If you are a business leader, a lot of this is relatively easy to measure. You vaguely know what H is, because you know what your payroll costs, and similarly, you can figure out E with some pretty trivial arithmetic based on Mallory's pricing table. There are endless YouTube channels, spec sheets and benchmarks to give you I. W is probably going to be so small compared to H that it hardly merits consideration¹¹.

But, are you measuring C? If your employees are not checking the outputs of the AI, you're on a path to catastrophe that no ROI calculation can capture, so it had better be greater than zero.

Are you measuring P? How often does the AI get it right on the first try?

Challenges to Computing Checking Costs

In the fraction defined above, the term C is going to be large. Larger than you think.

Measuring P and C with a high degree of precision is probably going to be very hard; possibly unreasonably so, or too expensive¹² to bother with in practice. So you will undoubtedly need to work with estimates and proxy metrics. But you have to be aware that this is a problem domain where your normal method of estimating is going to be extremely vulnerable to inherent cognitive bias, and find ways to measure.

Margins, Money, and Metacognition

First let's discuss cognitive and metacognitive bias.

My favorite cognitive bias is the availability heuristic and a close second is its cousin salience bias. Humans are empirically predisposed towards noticing and remembering things that are more striking, and to overestimate their frequency.

If you are estimating the variables above based on the vibe that you're getting from the experience of using an LLM, you may be overestimating its utility.

Consider a slot machine.

If you put a dollar in to a slot machine, and you lose that dollar, this is an unremarkable event. Expected, even. It doesn't seem interesting. You can repeat this over and over again, a thousand times, and each time it will seem equally unremarkable. If you do it a thousand times, you will probably get gradually more anxious as your sense of your dwindling bank account becomes slowly more salient, but losing one more dollar still seems unremarkable.

If you put a dollar in a slot machine and it gives you a thousand dollars, that will probably seem pretty cool. Interesting. Memorable. You might tell a story about this happening, but you definitely wouldn't really remember any particular time you lost one dollar.

Luckily, when you arrive at a casino with slot machines, you probably know well enough to set a hard budget in the form of some amount of physical currency you will have available to you. The odds are against you, you'll probably lose it all, but any responsible gambler will have an immediate, physical representation of their balance in front of them, so when they have lost it all, they can see that their hands are empty, and can try to resist the "just one more pull" temptation, after hitting that limit.

Now, consider Mallory.

If you put ten minutes into writing a prompt, and Mallory gives a completely off-the-rails, useless answer, and you lose ten minutes, well, that's just what using a computer is like sometimes. Mallory malfunctioned, or hallucinated, but it does that sometimes, everybody knows that. You only wasted ten minutes. It's fine. Not a big deal. Let's try it a few more times. Just ten more minutes. It'll probably work this time.

If you put ten minutes into writing a prompt, and it completes a task that would have otherwise taken you 4 hours, that feels amazing. Like the computer is magic! An absolute endorphin rush.

Very memorable. When it happens, it feels like P=1.

But... did you have a time budget before you started? Did you have a specified N such that "I will give up on Mallory as soon as I have spent N minutes attempting to solve this problem with it"? When the jackpot finally pays out that 4 hours, did you notice that you put 6 hours worth of 10-minute prompt coins into it in?

If you are attempting to use the same sort of heuristic intuition that probably works pretty well for other business leadership decisions, Mallory's slot-machine chat-prompt user interface is practically designed to subvert those sensibilities. Most business activities do not have nearly such an emotionally variable, intermittent reward schedule. They're not going to trick you with this sort of cognitive illusion.

Thus far we have been talking about cognitive bias, but there is a metacognitive bias at play too: while Dunning-Kruger, everybody's favorite metacognitive bias does have some problems with it, the main underlying metacognitive bias is that we tend to believe our own thoughts and perceptions, and it requires active effort to distance ourselves from them, even if we know they might be wrong.

This means you must assume any intuitive estimate of C is going to be biased low; similarly P is going to be biased high. You will forget the time you spent checking, and you will underestimate the number of times you had to re-check.

To avoid this, you will need to decide on a Ulysses pact to provide some inputs to a calculation for these factors that you will not be able to able to fudge if they seem wrong to you.

Problematically Plausible Presentation

Another nasty little cognitive-bias landmine for you to watch out for is the authority bias, for two reasons:

1. People will tend to see Mallory as an unbiased, external authority, and thereby see it as more of an authority than a similarly-situated human¹³.
2. Being an LLM, Mallory will be overconfident in its answers¹⁴.

The nature of LLM training is also such that commonly co-occurring tokens in the training corpus produce higher likelihood of co-occurring in the output; they're just going to be closer together in the vector-space of the weights; that's, like, what training a model is, establishing those relationships.

If you've ever used an heuristic to informally evaluate someone's credibility by listening for industry-specific shibboleths or ways of describing a particular issue, that skill is now useless. Having ingested every industry's expert literature, commonly-occurring phrases will always be present in Mallory's output. Mallory will usually sound like an expert, but then make mistakes at random.¹⁵.

While you might intuitively estimate C by thinking "well, if I asked a person, how could I check that they were correct, and how long would that take?" that estimate will be extremely optimistic, because the heuristic techniques you would use to quickly evaluate incorrect information from other humans will fail with Mallory. You need to go all the way back to primary sources and actually fully verify the output every time, or you will likely fall into one of these traps.

Mallory Mangling Mentorship

So far, I've been describing the effect Mallory will have in the context of an individual attempting to get some work done. If we are considering organization-wide adoption of Mallory, however, we must also consider the impact on team dynamics. There are a number of possible potential side effects that one might consider when looking at, but here I will focus on just one that I have observed.

I have a cohort of friends in the software industry, most of whom are individual contributors. I'm a programmer who likes programming, so are most of my friends, and we are also (sigh), charitably, pretty solidly middle-aged at this point, so we tend to have a lot of experience.

As such, we are often the folks that the team - or, in my case, the community - goes to when less-experienced folks need answers.

On its own, this is actually pretty great. Answering questions from more junior folks is one of the best parts of a software development job. It's an opportunity to be helpful, mostly just by knowing a thing we already knew. And it's an opportunity to help someone else improve their own agency by giving them knowledge that they can use in the future.

However, generative AI throws a bit of a wrench into the mix.

Let's imagine a scenario where we have 2 developers: Alice, a staff engineer who has a good understanding of the system being built, and Bob, a relatively junior engineer who is still onboarding.

The traditional interaction between Alice and Bob, when Bob has a question, goes like this:

1. Bob gets confused about something in the system being developed, because Bob's understanding of the system is incorrect.
2. Bob formulates a question based on this confusion.
3. Bob asks Alice that question.
4. Alice knows the system, so she gives an answer which accurately reflects the state of the system to Bob.
5. Bob's understanding of the system improves, and thus he will have fewer and better-informed questions going forward.

You can imagine how repeating this simple 5-step process will eventually transform Bob into a senior developer, and then he can start answering questions on his own. Making sufficient time for regularly iterating this loop is the heart of any good mentorship process.

Now, though, with Mallory in the mix, the process now has a new decision point, changing it from a linear sequence to a flow chart.

We begin the same way, with steps 1 and 2. Bob's confused, Bob formulates a question, but then:

3. Bob asks Mallory that question.

Here, our path then diverges into a "happy" path, a "meh" path, and a "sad" path.

The "happy" path proceeds like so:

4. Mallory happens to formulate a correct answer.
5. Bob's understanding of the system improves, and thus he will have fewer and better-informed questions going forward.

Great. Problem solved. We just saved some of Alice's time. But as we learned earlier,

Mallory can make mistakes. When that happens, we will need to check important info. So let's get checking:

4. Mallory happens to formulate an incorrect answer.
5. Bob investigates this answer.
6. Bob realizes that this answer is incorrect because it is inconsistent with some of his prior, correct knowledge of the system, or his investigation.
7. Bob asks Alice the same question; GOTO traditional interaction step 4.

On this path, Bob spent a while futzing around with Mallory, to no particular benefit. This wastes some of Bob's time, but then again, Bob could have ended up on the happy path, so perhaps it was worth the risk; at least Bob wasn't wasting any of Alice's much more valuable time in the process.¹⁶

Notice that beginning at the start of step 4, we must begin allocating all of Bob's time to C, so C already starts getting a bit bigger than if it were just Bob checking Mallory's output specifically on tasks that Bob is doing.

That brings us to the "sad" path.

4. Mallory happens to formulate an incorrect answer.
5. Bob investigates this answer.
6. Bob does not realize that this answer is incorrect because he is unable to recognize any inconsistencies with his existing, incomplete knowledge of the system.
7. Bob integrates Mallory's incorrect information of the system into his mental model.
8. Bob proceeds to make a larger and larger mess of his work, based on an incorrect mental model.
9. Eventually, Bob asks Alice a new, worse question, based on this incorrect understanding.
10. Sadly we cannot return to the happy path at this point, because now Alice must unravel the complex series of confusing misunderstandings that Mallory has unfortunately conveyed to Bob at this point. In the really sad case, Bob actually doesn't believe Alice for a while, because Mallory seems unbiased¹⁷, and Alice has to waste even more time convincing Bob before she can simply explain to him.

Now, we have wasted some of Bob's time, and some of Alice's time. Everything from step 5-10 is C, and as soon as Alice gets involved, we are now adding to C at double real-time. If more team members are pulled in to the investigation, you are now multiplying C by the number of investigators, potentially running at triple or quadruple real time.

But That's Not All

Here I've presented a brief selection reasons why C will be both large, and larger than you expect. To review:

1. Gambling-style mechanics of the user interface will interfere with your own self-monitoring and developing a good estimate.
2. You can't use human heuristics for quickly spotting bad answers.
3. Wrong answers given to junior people who can't evaluate them will waste more time from your more senior employees.

But this is a small selection of ways that Mallory's output can cost you money and time. It's harder to simplistically model second-order effects like this, but there's also a broad range of possibilities for ways that, rather than simply checking and catching errors, an error slips through and starts doing damage. Or ways in which the output isn't exactly wrong, but still sub-optimal in ways which can be difficult to notice in the short term.

For example, you might successfully vibe-code your way to launch a series of applications, successfully "checking" the output along the way, but then discover that the resulting code is unmaintainable garbage that prevents future feature delivery, and needs to be re-written¹⁸. But this kind of intellectual debt isn't even specific to technical debt while coding; it can even affect such apparently genAI-amenable fields as LinkedIn content marketing¹⁹.

Problems with the Prediction of P

C isn't the only challenging term though. P, is just as, if not more important, and just as hard to measure.

LLM marketing materials love to phrase their accuracy in terms of a percentage. Accuracy claims for LLMs in general tend to hover around 70%²⁰. But these scores vary per field, and when you aggregate them across multiple topic areas, they start to trend down. This is exactly why "agentic" approaches for more immediately-verifiable LLM outputs (with checks like "did the code work") got popular in the first place: you need to try more than once.

Independently measured claims about accuracy tend to be quite a bit lower²¹. The field of AI benchmarks is exploding, but it probably goes without saying that LLM vendors game those benchmarks²², because of course every incentive would encourage them to do that. Regardless of what their arbitrary scoring on some benchmark might say, all that matters to your business is whether it is accurate for the problems you are solving, for the way that you use it. Which is not necessarily going to correspond to any benchmark. You will need to measure it for yourself.

With that goal in mind, our formulation of P must be a somewhat harsher standard than "accuracy". It's not merely "was the factual information contained in any generated output accurate", but, "is the output good enough that some given real knowledge-work task is done and the human does not need to issue another prompt"?

Surprisingly Small Space for Slip-Ups

The problem with reporting these things as percentages at all, however, is that our actual definition for P is 1attempts, where attempts for any given attempt, at least, must be an integer greater than or equal to 1.

Taken in aggregate, if we succeed on the first prompt more often than not, we could end up with a P>12, but combined with the previous observation that you almost always have to prompt it more than once, the practical reality is that P will start at 50% and go down from there.

If we plug in some numbers, trying to be as extremely optimistic as we can, and say that we have a uniform stream of tasks, every one of which can be addressed by Mallory, every one of which:

• we can measure perfectly, with no overhead
• would take a human 45 minutes
• takes Mallory only a single minute to generate a response
• Mallory will require only 1 re-prompt, so "good enough" half the time
• takes a human only 5 minutes to write a prompt for
• takes a human only 5 minutes to check the result of
• has a per-prompt cost of the equivalent of a single second of a human's time

Thought experiments are a dicey basis for reasoning in the face of disagreements, so I have tried to formulate something here that is absolutely, comically, over-the-top stacked in favor of the AI optimist here.

Would that be a profitable? It sure seems like it, given that we are trading off 45 minutes of human time for 1 minute of Mallory-time and 10 minutes of human time. If we ask Python:

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>>> def FF(H, I, C, P, W, E):
...     return (W + I + C + E) / (P * H)
... FF(H=45.0, I=1.0, C=5.0, P=1/2, W=5.0, E=0.01)
...
0.48933333333333334

We get a futzing fraction of about 0.4896. Not bad! Sounds like, at least under these conditions, it would indeed be cost-effective to deploy Mallory. But… realistically, do you reliably get useful, done-with-the-task quality output on the second prompt? Let's bump up the denominator on P just a little bit there, and see how we fare:

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>>> FF(H=45.0, I=1.0, C=5.0, P=1/3, W=5.0, E=0.01)
0.734

Oof. Still cost-effective at 0.734, but not quite as good. Where do we cap out, exactly?

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>>> from itertools import count
... for A in count(start=4):
...     print(A, result := FF(H=45.0, I=1.0, C=5.0, P=1 / A, W=5.0, E=1/60.))
...     if result > 1:
...         break
...
4 0.9792592592592594
5 1.224074074074074
>>>

With this little test, we can see that at our next iteration we are already at 0.9792, and by 5 tries per prompt, even in this absolute fever-dream of an over-optimistic scenario, with a futzing fraction of 1.2240, Mallory is now a net detriment to our bottom line.

Harm to the Humans

We are treating H as functionally constant so far, an average around some hypothetical Gaussian distribution, but the distribution itself can also change over time.

Formally speaking, an increase to H would be good for our fraction. Maybe it would even be a good thing; it could mean we're taking on harder and harder tasks due to the superpowers that Mallory has given us.

But an observed increase to H would probably not be good. An increase could also mean your humans are getting worse at solving problems, because using Mallory has atrophied their skills²³ and sabotaged learning opportunities²⁴²⁵. It could also go up because your senior, experienced people now hate their jobs²⁶.

For some more vulnerable folks, Mallory might just take a shortcut to all these complex interactions and drive them completely insane²⁷ directly. Employees experiencing an intense psychotic episode are famously less productive than those who are not.

This could all be very bad, if our futzing fraction eventually does head north of 1 and you need to reconsider introducing human-only workflows, without Mallory.

Abridging the Artificial Arithmetic (Alliteratively)

To reiterate, I have proposed this fraction:

which shows us positive ROI when FF is less than 1, and negative ROI when it is more than 1.

This model is heavily simplified. A comprehensive measurement program that tests the efficacy of any technology, let alone one as complex and rapidly changing as LLMs, is more complex than could be captured in a single blog post.

Real-world work might be insufficiently uniform to fit into a closed-form solution like this. Perhaps an iterated simulation with variables based on the range of values seem from your team's metrics would give better results.

However, in this post, I want to illustrate that if you are going to try to evaluate an LLM-based tool, you need to at least include some representation of each of these terms somewhere. They are all fundamental to the way the technology works, and if you're not measuring them somehow, then you are flying blind into the genAI storm.

I also hope to show that a lot of existing assumptions about how benefits might be demonstrated, for example with user surveys about general impressions, or by evaluating artificial benchmark scores, are deeply flawed.

Even making what I consider to be wildly, unrealistically optimistic assumptions about these measurements, I hope I've shown:

1. in the numerator, C might be a lot higher than you expect,
2. in the denominator, P might be a lot lower than you expect,
3. repeated use of an LLM might make H go up, but despite the fact that it's in the denominator, that will ultimately be quite bad for your business.

Personally, I don't have all that many concerns about E and I. E is still seeing significant loss-leader pricing, and I might not be coming down as fast as vendors would like us to believe, if the other numbers work out I don't think they make a huge difference. However, there might still be surprises lurking in there, and if you want to rationally evaluate the effectiveness of a model, you need to be able to measure them and incorporate them as well.

In particular, I really want to stress the importance of the influence of LLMs on your team dynamic, as that can cause massive, hidden increases to C. LLMs present opportunities for junior employees to generate an endless stream of chaff that will simultaneously:

• wreck your performance review process by making them look much more productive than they are,
• increase stress and load on senior employees who need to clean up unforeseen messes created by their LLM output,
• and ruin their own opportunities for career development by skipping over learning opportunities.

If you've already deployed LLM tooling without measuring these things and without updating your performance management processes to account for the strange distortions that these tools make possible, your Futzing Fraction may be much, much greater than 1, creating hidden costs and technical debt that your organization will not notice until a lot of damage has already been done.

If you got all the way here, particularly if you're someone who is enthusiastic about these technologies, thank you for reading. I appreciate your attention and I am hopeful that if we can start paying attention to these details, perhaps we can all stop futzing around so much with this stuff and get back to doing real work.

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!

15 Aug 2025 7:51am GMT

My father, also known as "R0ML" once described a methodology for evaluating volume purchases that I think needs to be more popular.

If you are a hardcore fan, you might know that he has already described this concept publicly in a talk at OSCON in 2005, among other places, but it has never found its way to the public Internet, so I'm giving it a home here, and in the process, appropriating some of his words.¹

Let's say you're running a circus. The circus has many clowns. Ten thousand clowns, to be precise. They require bright red clown noses. Therefore, you must acquire a significant volume of clown noses. An enterprise licensing agreement for clown noses, if you will.

If the nose plays, it can really make the act. In order to make sure you're getting quality noses, you go with a quality vendor. You select a vendor who can supply noses for $100 each, at retail.

Do you want to buy retail? Ten thousand clowns, ten thousand noses, one hundred dollars: that's a million bucks worth of noses, so it's worth your while to get a good deal.

As a conscientious executive, you go to the golf course with your favorite clown accessories vendor and negotiate yourself a 50% discount, with a commitment to buy all ten thousand noses.

Is this a good deal? Should you take it?

To determine this, we will use an analytical tool called R0ML's Ratio (RR).

The ratio has 2 terms:

1. the Full Undiscounted Retail List Price of Units Used (FURLPoUU), which can of course be computed by the individual retail list price of a single unit (in our case, $100) multiplied by the number of units used
2. the Total Price of the Entire Enterprise Volume Licensing Agreement (TPotEEVLA), which in our case is $500,000.

It is expressed as:

RR = TPotEEVLA FURLPoUU

Crucially, you must be able to compute the number of units used in order to complete this ratio. If, as expected, every single clown wears their nose at least once during the period of the license agreement, then our Units Used is 10,000, our FURLPoUU is $1,000,000 and our TPotEEVLA is $500,000, which makes our RR 0.5.

Congratulations. If R0ML's Ratio is less than 1, it's a good deal. Proceed.

But… maybe the nose doesn't play. Not every clown's costume is an exact clone of the traditional, stereotypical image of a clown. Many are avant-garde. Perhaps this plentiful proboscis pledge was premature. Here, I must quote the originator of this theoretical framework directly:

What if the wheeze doesn't please?

What if the schnozz gives some pause?

In other words: what if some clowns don't wear their noses?

If we were to do this deal, and then ask around afterwards to find out that only 200 of our 10,000 clowns were to use their noses, then FURLPoUU comes out to 200 * $100, for a total of $20,000. In that scenario, RR is 25, which you may observe is substantially greater than 1.

If you do a deal where R0ML's ratio is greater than 1, then you are the bozo.

I apologize if I have belabored this point. As R0ML expressed in the email we exchanged about this many years ago,

I do not mind if you blog about it - and I don't mind getting the credit - although one would think it would be obvious.

And yeah, one would think this would be obvious? But I have belabored it because many discounted enterprise volume purchasing agreements still fail the R0ML's Ratio Bozo Test.²

In the case of clown noses, if you pay the discounted price, at least you get to keep the nose; maybe lightly-used clown noses have some resale value. But in software licensing or SaaS deals, once you've purchased the "discounted" software or service, once you have provisioned the "seats", the money is gone, and if your employees don't use it, then no value for your organization will ever result.

Measuring number of units used is very important. Without this number, you have no idea if you are a bozo or not.

It is often better to give your individual employees a corporate card and allow them to make arbitrary individual purchases of software licenses and SaaS tools, with minimal expense-reporting overhead; this will always keep R0ML's Ratio at 1.0, and thus, you will never be a bozo.

It is always better to do that the first time you are purchasing a new software tool, because the first time making such a purchase you (almost by definition) have no information about "units used" yet. You have no idea - you cannot have any idea - if you are a bozo or not.

If you don't know who the bozo is, it's probably you.

Acknowledgments

Thank you for reading, and especially thank you to my patrons who are supporting my writing on this blog. Of course, extra thanks to dad for, like, having this idea and doing most of the work here beyond my transcription. If you like my dad's ideas and you'd like to post more of them, or you'd like to support my various open-source endeavors, you can support my work as a sponsor!

09 Aug 2025 4:41am GMT

What's a good maximum line length for your coding standard?

This is, of course, a trick question. By posing it as a question, I have created the misleading impression that it is a question, but Black has selected the correct number for you; it's 88 which is obviously very lucky.

Thanks for reading my blog.

OK, OK. Clearly, there's more to it than that. This is an age-old debate on the level of "tabs versus spaces". So contentious, in fact, that even the famously opinionated Black does in fact let you change it.

Ancient History

One argument that certain silly people¹ like to make is "why are we wrapping at 80 characters like we are using 80 character teletypes, it's the 2020s! I have an ultrawide monitor!". The implication here is that the width of 80-character terminals is an antiquated relic, based entirely around the hardware limitations of a bygone era, and modern displays can put tons of stuff on one line, so why not use that capability?

This feels intuitively true, given the huge disparity between ancient times and now: on my own display, I can comfortably fit about 350 characters on a line. What a shame, to have so much room for so many characters in each line, and to waste it all on blank space!

But... is that true?

I stretched out my editor window all the way to measure that '350' number, but I did not continue editing at that window width. In order to have a more comfortable editing experience, I switched back into writeroom mode, a mode which emulates a considerably more writerly application, which limits each line length to 92 characters, regardless of frame width.

You've probably noticed this too. Almost all sites that display prose of any kind limit their width, even on very wide screens.

As silly as that tiny little ribbon of text running down the middle of your monitor might look with a full-screened stereotypical news site or blog, if you full-screen a site that doesn't set that width-limit, although it makes sense that you can now use all that space up, it will look extremely, almost unreadably bad.

Blogging software does not set a column width limit on your text because of some 80-character-wide accident of history in the form of a hardware terminal.

Similarly, if you really try to use that screen real estate to its fullest for coding, and start editing 200-300 character lines, you'll quickly notice it starts to feel just a bit weird and confusing. It gets surprisingly easy to lose your place. Rhetorically the "80 characters is just because of dinosaur technology! Use all those ultrawide pixels!" talking point is quite popular, but practically people usually just want a few more characters worth of breathing room, maxing out at 100 characters, far narrower than even the most svelte widescreen.

So maybe those 80 character terminals are holding us back a little bit, but... wait a second. Why were the terminals 80 characters wide in the first place?

Ancienter History

As this lovely Software Engineering Stack Exchange post summarizes, terminals were probably 80 characters because teletypes were 80 characters, and teletypes were probably 80 characters because punch cards were 80 characters, and punch cards were probably 80 characters because that's just about how many typewritten characters fit onto one line of a US-Letter piece of paper.

Even before typewriters, consider the average newspaper: why do we call a regularly-occurring featured article in a newspaper a "column"? Because broadsheet papers were too wide to have only a single column; they would always be broken into multiple! Far more aggressive than 80 characters, columns in newspapers typically have 30 characters per line.

The first newspaper printing machines were custom designed and could have used whatever width they wanted, so why standardize on something so narrow?³

Science!

There has been a surprising amount of scientific research around this issue, but in brief, there's a reason here rooted in human physiology: when you read a block of text, you are not consciously moving your eyes from word to word like you're dragging a mouse cursor, repositioning continuously. Human eyes reading text move in quick bursts of rotation called "saccades". In order to quickly and accurately move from one line of text to another, the start of the next line needs to be clearly visible in the reader's peripheral vision in order for them to accurately target it. This limits the angle of rotation that the reader can perform in a single saccade, and, thus, the length of a line that they can comfortably read without hunting around for the start of the next line every time they get to the end.

So, 80 (or 88) characters isn't too unreasonable for a limit. It's longer than 30 characters, that's for sure!

But, surely that's not all, or this wouldn't be so contentious in the first place?

Caveats

The screen is wide, though.

The ultrawide aficionados do have a point, even if it's not really the simple one about "old terminals" they originally thought. Our modern wide-screen displays are criminally underutilized, particularly for text. Even adding in the big chunky file, class, and method tree browser over on the left and the source code preview on the right, a brief survey of a Google Image search for "vs code" shows a lot of editors open with huge, blank areas on the right side of the window.

Big screens are super useful as they allow us to leverage our spatial memories to keep more relevant code around and simply glance around as we think, rather than navigate interactively. But it only works if you remember to do it.

Newspapers allowed us to read a ton of information in one sitting with minimum shuffling by packing in as much as 6 columns of text. You could read a column to the bottom of the page, back to the top, and down again, several times.

Similarly, books fill both of their opposed pages with text at the same time, doubling the amount of stuff you can read at once before needing to turn the page.

You may notice that reading text in a book, even in an ebook app, is more comfortable than reading a ton of text by scrolling around in a web browser. That's because our eyes are built for saccades, and repeatedly tracking the continuous smooth motion of the page as it scrolls to a stop, then re-targeting the new fixed location to start saccading around from, is literally more physically strenuous on your eye's muscles!

There's a reason that the codex was a big technological innovation over the scroll. This is a regression!

Today, the right thing to do here is to make use of horizontally split panes in your text editor or IDE, and just make a bit of conscious effort to set up the appropriate code on screen for the problem you're working on. However, this is a potential area for different IDEs to really differentiate themselves, and build multi-column continuous-code-reading layouts that allow for buffers to wrap and be navigable newspaper-style.

Similar, modern CSS has shockingly good support for multi-column layouts, and it's a shame that true multi-column, page-turning layouts are so rare. If I ever figure out a way to deploy this here that isn't horribly clunky and fighting modern platform conventions like "scrolling horizontally is substantially more annoying and inconsistent than scrolling vertically" maybe I will experiment with such a layout on this blog one day. Until then… just make the browser window narrower so other useful stuff can be in the other parts of the screen, I guess.

Code Isn't Prose

But, I digress. While I think that columnar layouts for reading prose are an interesting thing more people should experiment with, code isn't prose.

The metric used for ideal line width, which you may have noticed if you clicked through some of those Wikipedia links earlier, is not "character cells in your editor window", it is characters per line, or "CPL".

With an optimal CPL somewhere between 45 and 95, a code-line-width of somewhere around 90 might actually be the best idea, because whitespace uses up your line-width budget. In a typical object-oriented Python program², most of your code ends up indented by at least 8 spaces: 4 for the class scope, 4 for the method scope. Most likely a lot of it is 12, because any interesting code will have at least one conditional or loop. So, by the time you're done wasting all that horizontal space, a max line length of 90 actually looks more like a maximum of 78... right about that sweet spot from the US-Letter page in the typewriter that we started with.

What about soft-wrap?

In principle, source code is structured information, whose presentation could be fully decoupled from its serialized representation. Everyone could configure their preferred line width appropriate to their custom preferences and the specific physiological characteristics of their eyes, and the code could be formatted according to the language it was expressed in, and "hard wrapping" could be a silly antiquated thing.

The problem with this argument is the same as the argument against "but tabs are semantic indentation", to wit: nope, no it isn't. What "in principle" means in the previous paragraph is actually "in a fantasy world which we do not inhabit". I'd love it if editors treated code this way and we had a rich history and tradition of structured manipulations rather than typing in strings of symbols to construct source code textually. But that is not the world we live in. Hard wrapping is unfortunately necessary to integrate with diff tools.

So what's the optimal line width?

The exact, specific number here is still ultimately a matter of personal preference.

Hopefully, understanding the long history, science, and underlying physical constraints can lead you to select a contextually appropriate value for your own purposes that will balance ease of reading, integration with the relevant tools in your ecosystem, diff size, presentation in the editors and IDEs that your contributors tend to use, reasonable display in web contexts, on presentation slides, and so on.

But - and this is important - counterpoint:

No it isn't, you don't need to select an optimal width, because it's already been selected for you. It is 88.

Acknowledgments

Thank you for reading, and especially 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!

08 Aug 2025 5:37am GMT

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