I learned C in my freshman year of college where we used K&R as our text book. This was 1989, so that text and our professor were my only source of information.
But, luckily, I had been programming for about six years on a PET, TRS-80, and Commodore 64. It was on that last computer that I learned 6502 Assembly. I had been experimenting with sound generation, and I needed more performance.
When we got to pointers in the C class, the professor described it as the memory address of a variable. That’s all I needed to know. In Assembly, memory addresses are a first-class concept. I had a book called Mapping the Commodore 64 that told you what was at each ROM address. Doing pointer arithmetic is a common Assembly coding task. You can’t do anything interesting without understanding addresses.
So, I guess that I learned about C pointers at some point in my learning of 6502 Assembly. Since C maps to Assembly, by the time we got to it, it felt natural to me. If you are having trouble with the concept, I’d try to write simple Assembly programs. Try an emulator of 6502, and not, for example, something modern. Modern instructions are not designed for humans to code easily, but older ones took that into account a little more.
In the 90’s, I read a book about fuzzy logic that would feel quaint now in our LLM-backed AI world. The hype wasn’t as big, but the claims were similar. Fuzzy logic would bring human-like products because it mapped to how humans thought.
Fuzzy Logic is relatively simple. The general idea is to replace True and False from Boolean logic with a real number between 1 (absolutely true) and 0 (absolutely false). We think of these values more like a probability of certainty.
Then, we define operations that map to AND, OR, and NOT. Generally, you’d want ones that act like their Boolean versions for the absolute cases, so that if you set your values to 1 and 0, the Fuzzy logic gates would act Boolean. You often see min(x, y) for AND and max(x, y) for OR (which behave this way). The NOT operator is just: fuzzy_not(x) => 1.0 - x.
I thought of this today because I’m building a tool to help with decision making about technical debt, and I’m skeptical about LLM’s because I’m worried about their non-determinism. I think they’ll be fine, but this problem is actually simpler.
Here’s an example. In my book I present this diagram:
For example, one rule in my book is that if a tech debt item has high visibility (i.e. customers value it), but is low in the other forces that indicate it should be paid (i.e. low volatility, resistance, and misalignment), but has some force indicating that it should not be paid (i.e. any of the stay forces), then this might just be a regular feature request and not really tech debt. The plan should be to put it on the regular feature backlog for your PM to decide about.
But if you did this, you have to pick some threshold for each value. For example, on a scale of 0-5, a visible tech debt item be one with a 4 or 5. But, that’s not exactly right because even an item scored as a 3 for visibility should be treated this way depending on the specific scores it got in the other values. You could definitely write a more complex logical expression that took this all into account, but it would hard to understand and tune.
This is where fuzzy logic (or some kind of probabilistic approach works well). Unlike LLMs though, this approach is deterministic, which allows for easier testing and tuning (not to mention, it’s free).
To do it, you replace the operators with their fuzzy equivalents and normalize the scores on a 0.0-1.0 scale. In the end, instead of is_feature, you more get a probability of whether this recommendation is appropriate. If you build up a rules engine with a lot of these, you could use the probability to sort the responses.
Fuzzy logic also allows you to play with the normalization and gates to accentuate some of the values over others (for tuning). You could do this with thresholds in the boolean version, but with fuzzy logic you end up with simpler code and smoother response curves.
I have been building web applications in React until very recently, but now I’ve moved to HTMX. Here are the main differences so far. For reference, my React style was client-side only for web-applications.
Client State: In React, I used Redux, which had almost all of the client state. Some state that I needed to be sticky was put into cookies. There was some transient state in useState variables in components. Redux was essentially a cache of the server database. In HTMX, the client state is in the HTML mostly in a style called Hypermedia As the Engine of Application State (HATEOAS). Right now, I do cheat a little, sending a small JavaScript object in a script tag for my d3 visualizations to use.
Wire Protocol: To feed the React applications, I had been using a GraphQL API. In HTMX, we expect HTML from REST responses and even over the web-socket.
DOM Updates: In React, I used a classic one-way cycle. Some event in React would trigger an API call and optimistic update to Redux. The Redux would trigger React Component re-renders. If the API failed, it would undo the Redux, and then re-render the undo and any additional notifications. In HTMX, the HTML partials sent back from the REST request or websocket use the element’s id and HTMX custom attributes to swap out parts of the DOM.
Markup Reuse: In React, the way to reuse snippets of markup is by building components. In HTMX, you do this with whatever features your server language and web-framework provide. I am using Django, so I use tag templates or simple template inclusions. Aesthetically, I prefer JSX over the {%%} syntax in templates, but it’s not a big deal. There are other affordances for reuse in Django/Python, but those are the two I lean on the most.
Debugging: In React, I mostly relied on browser developer tools, but it required me to mentally map markup to my source. This was mostly caused by my reliance on component frameworks, like ReactNative Paper and Material. In HTMX, the source and browser page are a very close match because I am using simpler markup and Bulma to style it. It’s trivial to debug my JavaScript now because it’s all just code I wrote.
UI Testing: In React, I used react-testing to test the generated DOM. In Django, I am using their testing framework to test Django views and the HTML generated by templates. Neither is doing a snapshot to make sure it “looks right”. These tests are more resilient than that and are making sure the content and local state is correct. I could use Playwright for both (to test browser rendering) and it would be very similar.
Also, in general, I should also mention that the client-server architecture of my projects is also quite different. In React, I was building a fat-client, mobile-web app that I was designing so that it could work offline. The GraphQL API was purely a data-access and update layer. All application logic was in the client-side. All UI updates were done optimistically (i.e. on the client side, assuming the API update would succeed), so in an offline version, I could queue up the server calls for later.
My Django/HTMX app could never be offline. There is essentially no logic in the client side. The JavaScript I write is for custom visualizations that I am building in d3. They are generic and are fed with data from the server. Their interactions are not application specific (e.g. tooltips or filters).
This difference has more to do with what I am building, but if I needed a future where offline was possible, I would not choose HTMX (or server rendered React).
I found out about HTMX about a year ago from a local software development friend whose judgement I trust. His use case was that he inherited a large PHP web application with very standard request/response page-based UI, and he had a need to add some Single Page Application (SPA) style interactions to it. He was also limited (by the user base) to not change the application drastically.
HTMX is perfect for this. It builds on the what a <form> element already does by default. It gathers up inputs, creates a POST request, and then expects an HTML response, which it renders.
The difference is that, in HTMX, any element can initiate any HTTP verb (GET, POST, DELETE, etc) and then the response can replace just that element on the page (or be Out of Band and replace a different element). This behavior is extended to websockets, which can send partial HTML to be swapped in.
To use HTMX on a page, you add a script tag. There is a JavaScript API, but mostly you add custom “hx-*” attributes to elements. It’s meant to feel like HTML. I would say more, but I can’t improve on the HTMX home page, which is succinct and compelling.
My app is meant to allow users to collaboratively score and plan technical debt projects. My intention is to improve on a Google Sheet that I built for my book. So, to start, it needs to have the same collaborative ability. Every user on a team needs to see what the others are doing in real-time. HTMX’s web socket support (backed by Django channels) makes this easy.
Since the wire protocol of an HTMX websocket is just HTML partials, I can use the same template tags from the page templates to build the websocket messages. Each HTML partial has an id attribute that HTMX will use to swap it in. I can send over just the elements that have changed.
This is part of a series describing how I am changing my entire stack for developing web applications. My choices are driven by security and simplicity.
This last part about my new dev stack environment will be about how my machines are set up to work together. As I mentioned in the introduction to this series, my primary concern was to get development off of my main machine, which is now all on a Framework desktop running Ubuntu.
Before this, my setup was simple. I had a monitor plugged into my laptop over Thunderbolt and my keyboard and mouse were attached via the USB hub the monitor provided (keyboard) or bluetooth (mouse). When I introduced the Framework, I moved to a USB mouse in the hub, and now I could switch my whole setup from Mac to Framework by unplugging/plugging in one USB-C cable.
But I had a few development use cases that this didn’t support well:
I sometimes need to code with someone over Zoom. My webcam, mic, and headphones are staying connected to the Mac.
I regularly program outside of my office in co-working environments.
I need to support programming while traveling.
I want to be able to go back and forth to between the machines while working at my desk.
To start with, I tried using remote desktop. There’s an official client for Mac made by Microsoft and it’s built into Ubuntu. As I mentioned in my Linux post, I was surprised at how hard this way to troubleshoot. The issue is that you can’t RDP to a Linux box unless it is actively connected to a monitor. So, at first I just left it plugged in while taking the laptop outside. But, this was not ideal.
There are a few solutions for this, but the easiest for me was just buying a virtual HDMI plug. They are cheap and fool the machine into thinking it has a monitor.
To even get RDP to work at all though I needed to make some way for the two machines to see each other. Even in my home office, I put them on different networks on my router. But, I would also need to solve this for when I’m using my laptop outside of my network. This is what Tailscale was made for.
Tailscale is a VPN, but what sets it apart is its UX. You install it on the two machines, log them in to Tailscale, and now they are on a virtual private subnet. I can RDP at my desk or from a café. I can share the Mac “space” that is running RDP over Zoom. The setup was trivial.
So far this has been fine. I don’t even notice the VPN when I am coding at home. When I am outside, it’s a little sluggish, but fine. AI coding makes it more acceptable, since I don’t have to type and navigate code as much.
This is part of a series describing how I am changing my entire stack for developing web applications. My choices are driven by security and simplicity.
Coverage Gutters – this shows coverage right in VSCode. It works with anything that produces standard coverage files, so it works well with coverage.py. I wrote about how I use that here and in my book.
mutmut – This is something I have been playing around with in Page-o-Mat because of my interest in mutation testing. I contributed a feature a few months ago, which I’ll cover later this month. I’ll be using it more seriously now.
flake8 and black – for linting and auto-formatting. This is more necessary as I use AI since its style adherence isn’t perfect.
I still haven’t figured out what I will do for JS testing. I used jest before, but it doesn’t meet my criteria of low-dependencies. Might have to start with this gist for TestMan.
I also need to replace my code complexity extension (it was for JS/TS). I might see how long I can last without it because the main replacements don’t have enough usage to consider installing (VSCode extensions are another hacking vector, like supply chain attacks).
This is part of a series describing how I am changing my entire stack for developing web applications. My choices are driven by security and simplicity.
This one is easy. Before my latest project, I used pyenv, virtualenv, and then pip with requirements.txt for Python projects. But, since I am friends with Becky Sweger, and read this post about uv, I knew better (but didn’t yet overcome my inertia). Starting fresh meant that I could finally get on modern tools.
This is part of a series describing how I am changing my entire stack for developing web applications. My choices are driven by security and simplicity.
Since Django uses an ORM, switching between databases is relatively easy. I usually pick MySQL, but I’m going to see how far I can get with Sqlite.
The project I am working on is to make a better version of the tech debt spreadsheet that I share in my book (sign up for the email list to get a link and a guide for using it). The app is very likely to be open-source and to start out as something you host yourself. So, I think Sqlite will be fine, but if it ever gets to the point where it won’t work, then switching to MySQL or Postgres shouldn’t be that hard. My DB needs are simple and well within the Django ORM’s capabilities.
Even if I decide to host a version, I might decide on a DB per tenant model, which might be ok for Sqlite. Another possibility is that it would be something in the Jira Marketplace, and in that case, I’d have to rewrite the backend to use Jira for storage, but that wouldn’t be that bad because (given the Jira data-model) I only need to add some custom fields to an issue. Most of the app at that point would be the visualizations and an expert system.
One nice thing about Sqlite is that it’s trivial to host. It’s just a few files (with WAL mode). It’s also trivial to run unit-tests against during development. You can do it in-memory, which is what Django testing does by default. I can also run those test suites against more powerful databases to make sure everything works with them too.
One portability issue is that if I get used to running against Sqlite, I will probably not notice performance issues. Since Sqlite is just some local files, it’s incredibly fast. You can feel free to do lots of little queries to service a request and not notice any latency issues. The same style over a network, potentially to a different datacenter, won’t work as well.
But I have seen enough evidence of production SaaS products using Sqlite, that I think I can get to hundreds of teams without worrying too much. I would love to have a performance problem at that point.
In my book, I talk about how technical debt is the result of making correct decisions and then having wild success (that invalidate those choices). I don’t like calling these decisions “shortcuts” because that word is used a pejorative in this context. Instead, I argue that planning for the future might have prevented the success. If this project is successful, it’s likely that Sqlite won’t be part of it any more, but right now it’s enabling me to get to first version, and that’s good enough.
This is part of a series describing how I am changing my entire stack for developing web applications. My choices are driven by security and simplicity.
In my drive for simplicity I have decided to have no build for scripts and CSS. This means I can’t use Tailwind, which I would otherwise choose.
In my research, I found a few options, and I have tentatively chosen Bulma. Aside from having no build, it’s other strength is that Copilot knows it well enough to help me use it.
I also considered Pico and Bootstrap. I preferred Bulma’s default look to Pico and I have already used Bootstrap in the past, so I basically know what to expect. I chose Bulma to see how it compares. If it falls short, I’ll move to Bootstrap. I’m pretty sure that Copilot will know it.
It’s worth saying here that if I had chosen Ruby on Rails instead of Python/Django, Hotwire would have been a sane default choice and would have played the role that HTMX and Bulma are playing for me.
This is part of a series describing how I am changing my entire stack for developing web applications. My choices are driven by security and simplicity.
I switched to Cursor in February because its prompting capabilities were way beyond Copilot. But, I’ve increasingly become frustrated with everything else about it.
Even though I use AI to generate code, I still need to fix that code myself. I also tend to do refactoring myself. So, the IDE’s regular coding features are still important to my work. Since Cursor is a fork of VSCode, moving to it was simple, but their fork is starting to age, and it doesn’t look like they can keep up with VSCode.
The first thing I noticed was that they could no longer load the latest versions of extensions I use. When I researched why, it turned out it was because they were not merging in VSCode changes any more. When 2.0 came out last week and the extensions were still stuck, I knew they didn’t share my security priorities. Not being able to update something is a huge red flag.
So, just to check, I tried out VSCode again. They could load the latest versions of extensions (of course), but I also noticed lots of little improvements to the UI. The most striking was the speed. But, also, the exact timing of auto-complete suggestions was less intrusive than Cursor. They could both use some improvement, but by default, Copilot was a little less anxious to complete, which suits me better.
But this switch would not have been possible if the prompt-based coding was worse than Cursor. So far, in the week I have been using it, I haven’t noticed a difference. They are both not perfect, but that’s fine with me.
Ten months ago, Copilot wasn’t worth using. Now, it feels the same as Cursor. I don’t know if that might also be because my prompting has improved, but it doesn’t matter. My goal is to add a CLI based agent to my stack, so I think I would close any gap that way.
In my drive to simplify and reduce dependencies, it’s also good to be able to remove a vendor. I have to rely on Microsoft already, and I trust them, so moving to just VSCode/Copilot is a plus. I was pretty sure this was going to happen.
The problem for Cursor in competing with Microsoft is that Microsoft has no disincentive to follow them. [… And] because Cursor is easy to switch back from, there is actually no advantage to Cursor’s land grab. I went from VSCode with Copilot to Cursor in 20 minutes, and I could go back faster. I can run them in parallel.
Here are Microsoft’s other incumbent advantages:
Infinite money
Azure (gives them at-cost compute)
Experience with AI engineering (built up from years of working with OpenAI)
The relationship with OpenAI which gives them low-cost models
50 years of proprietary code (could this augment models?)
Developer Tools expertise (and no one is close — maybe JetBrains)
GitHub
Control of Typescript and C#
Control of VSCode (which they are flexing)
In the end, #6 might not be possible for anyone else to overcome, and it’s why I’m back.
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