I was debugging a microcontroller recently and was surprised to see that I was limited to 4 breakpoints. That surprised me because I’m usually only used to seeing that kind of limitation when using watchpoints, not breakpoints. (See my youtube video for more on this).
But it makes sense after all — code running on this microcontroller is different than in a process in an OS’s userspace because the code will probably be flashed into some kind of ROM (Read Only Memory) (or EEPROM). Since the code is unwritable at a hardware level, the typical method of inserting software breakpoint instructions isn’t possible. So the alternative is to use the microprocessor’s support for hardware debugging, which occupies hardware resources and is thus finite.
Business models are an interesting topic that lives directly at the boundary between business and technology.
The business model describes an abstract plan for how the business is to function sustainably, and is a full time job to develop in and of itself. Then, it must be implemented in the product using technology.
For example, a typical SaaS business model involves subscription pricing at various intervals (monthly & yearly), with a discount given for the yearly plan in exchange for more money paid up front and longer commitment. There may or may not be a limited time free trial period, or alternatively a limited time guaranteed refund. Furthermore, there may be different pricing tiers that unlock more advanced features.
Ultimately, this is all going to end up as code that models the various pricing tiers, plans, timing deadlines, and enforces security (i.e. making sure the advanced features are only available to pro users). Stripe is a common service used to model arbitrary business models and execute payments processing. They provide client libraries offering data models for concepts like users and plans.
While subscription models have grown in popularity as software shifts to the web, it’s not the only model. The older model of buying discrete software packaged versions still exists, mostly used by vendors of desktop software. In this model, customers pay a larger sum for unlimited use of a specific major version of software. Then they can optionally pay again to upgrade to a newer major version when it’s released. A variant of this exists where there’s no upgrade fee (i.e. “lifetime free updates”). Another model exists called “Rent to own” which is like a subscription, except payments stop at a certain point after which there is free unlimited use.
Whether the software runs on the client or server is another dimension to consider which may influence the business model — server side software is most commonly sold using subscriptions these days.
Client
Server
Subscription
Adobe
SaaS Web Apps
One time payment per version
Dash, Ableton Live
One time payment, lifetime free updates
FL Studio, Tailwind CSS (technically more assets than software)
SaaS Web Apps Lifetime Plans (e.g Roam Research)
Rent to own
Audio plugins via Splice
The business model impacts technical strategy in a few ways.
Branching and release management
Compatibility of document artifacts
Subscriptions, “lifetime free updates”, and “rent to own” are simplest in terms of branching and release management. All users are expected to run the latest software (because it’s either free or automatic, in the case of server side), so development can largely happen on a single main branch which releases are cut from.
“One time payment per version” is more complex because potentially multiple major versions of the software need to be maintained in parallel. Ideally bugfixes in Version 2 would also be applied to V3 and V4 when appropriate, but no new feature development should make its way back to V2 and risk being included in binaries send to users that only paid for V2. For this situation, long term release branches make sense as they provide code isolation, though they require some mechanism to forward bugfixes, etc.
The second aspect is document artifact compatibility. Again in this scenario, subscriptions, “lifetime free updates”, and “rent to own” are simpler in that all users can be assumed to be running on the latest version — or at least can be told to upgrade at no cost.
“One time payment per version” is again more complex because multiple versions of the software exist in the wild, all producing artifacts of slightly different versions. If artifacts may be sent between users of different versions, it creates a mess of trying dealing with all the compatibility issues that may arise.
A major difference between x86 and ARM32 is that while x86 generally1 only offers conditional execution of branch instructions (e.g. BNE) ARM32 offers conditional execution for many more instructions (e.g. ALU instructions, like ADD.EQ- add if not equal). The idea was that these can be used to avoid emitting traditional branching instruction sequences which may suffer from pipeline stalls when a branch is mispredicted — instead, straight line code with equivalent semantics can be emitted.
This was actually removed in ARM64. The official quote:
The A64 instruction set does not include the concept of predicated or conditional execution. Benchmarking shows that modern branch predictors work well enough that predicated execution of instructions does not offer sufficient benefit to justify its significant use of opcode space, and its implementation cost in advanced implementations.
Turns out that the whole pipeline stall problem is generally not a huge issue anymore as branch prediction has gotten so good, while support for the feature still requires allocating valuable instructions bits to encode the conditions. Note that ARM64 still uses 32 bit instructions, so conserving bits is still useful.
What is very interesting is that Intel’s recent APX extensions to x86-64 (whose purpose is to primarily add more general purpose registers) moves closer to this conditional instructions direction.
The performance features introduced so far will have limited impact in workloads that suffer from a large number of conditional branch mispredictions. As out-of-order CPUs continue to become deeper and wider, the cost of mispredictions increasingly dominates performance of such workloads. Branch predictor improvements can mitigate this to a limited extent only as data-dependent branches are fundamentally hard to predict.
To address this growing performance issue, we significantly expand the conditional instruction set of x86, which was first introduced with the Intel® Pentium® Pro in the form of CMOV/SET instructions. These instructions are used quite extensively by today’s compilers, but they are too limited for broader use of if-conversion (a compiler optimizationthat replaces branches with conditional instructions).”
…including support for conditional loads and stores which is apparently tricky with modern out of order and superscalar architectures.
I’ve managed to make writing fun again and have been publishing a lot on my blog lately.
It’s due to three reasons:
1 – Having explicit content categories creates the freedom to post things that aren’t highly effort intensive deep dives.
By introducing explicit post categories (i.e. Micropost, Lab Notes, Essay, Deep Dive, etc) I feel much more free to post without the expectation that every post needs to be a time consuming, deeply researched technical post (which are the kind of posts that are popular).
I don’t have time for those lately, but that doesn’t mean I can’t write or post anything! Explicitly tagging something as a micropost or lab notes takes a lot of the pressure off, and makes me much more willing to write and publish.
2 – I stopped sharing on Twitter.
The curse of growing an audience is that posting to that audience has increasing weight as the audience grows, which creates stress and friction about posting. What if I post something that people don’t like and I lose a bunch of followers? What if I’m straight up wrong? What if I want to share something but don’t have time to fully polish it and people judge me? What if I post too often?
It’s also distracting to share on Twitter, and very difficult to not monitor notifications afterwards.
Furthermore, writing on Twitter was actually difficult in that it took extra energy to abide by the character limits, or fit things into threads otherwise. Writing freeform of my own blog is easier in this regard.
Not posting on Twitter removes a nontrivial amount of friction and stress that used to prevent me from sharing.
3 – Using WordPress (i.e. not a static site generator) removes a ton of friction.
The fact that I can click buttons in a web interface, write and post as easily as sending a tweet makes all the difference.
It’s so nice being able to change the website without coding. For instance, I just added a “Popular posts” feature in the sidebar in the last 5 minutes. Turns out Jetpack already has the feature included and I just had to enable it. I don’t even want to imagine what it would have taken to implement that by hand or with a static site generator.
Though not as fast a static site, the blog loads fast enough and I’m more than happy to take the performance hit.
It’s also awesome that I can quickly edit posts to fix typos, even from my phone with the WordPress app. I do this very often.
(bonus reason: It’s also due to the realization that posts don’t have to be long, can be written in one sitting, and don’t have to be absolutely perfect!)
When going to programming meetups, conferences, or starting a new job, it can be extremely easy to feel dumb around other engineers there. Sometimes these engineers truly are geniuses, but many times they’re not as “smart” as your immediate imposter syndrome is leading you to believe.
My advice, especially for beginners, is to remember that these people are probably talking about a domain they are extremely familiar with, and have spent probably 1000x the time with than you. You probably have stumbled upon to a topic that they are specifically knowledgeable about, which gives the impression that they’re ultra smart (and you’re ultra dumb, for not knowing anything about it).
The key is to remember that this impressive depth of knowledge is likely limited in breadth. If you were talking about your domain, you’d might know a lot more than them.
It’s also easy to feel dumb when watching a conference talk. My trick here is to remember that the reason this person can speak so confidently about such a technical topic is because, again, they’ve spent 1000x more time on it than you have. They’re had their face pressed directly against this problem for a long time, which is why they know all this and can talk about it.
This comes from my experience:
Entering the programming world (feeling dumb at hacker club in college)
Enter the infosec world (feeling dumb at infosec conferences)
Going to conferences not directly in my expertise (i.e. the LLVM Dev Mtg)
Entering the audio programming world (feeling dumb at audio conferences)
This might an interesting metric to compare programming languages: distance between application programming and standard library programming.
In general, standard library programming is more difficult than average application programming for any programming language. Or at the very least “different” in some way — language features used, programming style required, etc. That difference might vary depending on the language.
Mainly, I’m thinking about C++, where standard library programming requires expertise in C++ template meta-programming (TMP) (i.e. to implement std::variant), which is effectively an entirely different programming language, existing in the C++ type system. While many application developers may also be competent in TMP, there are many that aren’t (including myself). It’s possible to be a very productive C++ programmer, and STL user, without being an expert at implementing generic libraries using TMP.
Given this, my impression that C++ has a relatively high distance between application programming and stdlib programming.
Python of course also has some distance here. I’m not qualified to speak on it, but I can imagine it also involved more obscure language feature that do not occur often in normal app development. I would guess that this distance is less than C++.
Lastly, C is an interesting language to consider because it offers such a spartan feature set that there aren’t particularly that many more language features available to be used. (I’m probably wrong here and there are obscure things I’m not aware of, including symbol versioning for compatibility). But in general, my assumption would be that C has a some limited distance, given it’s restrained feature set.
Ultimately, this metric is probably impossible to quantify and may have limited value, but is something I find intriguing anyway if it were to exist.
The retort “What could go wrong” is one of my big pet peeves.
It’s often used in response to a failure of a complex system or operation. Sometimes the system had clearly poor design, making it warranted. But more often these comments reek of hindsight bias and carry an arrogance — as if the speaker could have easily avoided the failure if they were the one in charge.
It’s possible to construct that kind of retort for almost anything if you try hard enough:
“Flying massive metal tubes around in the sky filled with hundreds of people, what could go wrong?”
“Cementing metal wires into the mouths of children, what could go wrong?”
“Shooting lasers into peoples’ eyes, what would go wrong?”
But if you did so, you’d actually be wrong a lot — because there are many complex systems that function correctly for most users, most of the time. They function because many people have poured blood, sweat, and human ingenuity into them to make them reliable. And it’s often not intuitive that they can work.
Even if sometimes people are truly negligent and deserve it, I don’t find the phrase of net benefit to the culture. I consider it harmful because it ups the consequences of failure — a necessity for innovation — in exchange for cheap virtue signaling from bystanders who often have no experience in the domain.
So rather than assuming incompetence, let’s all be a bit more charitable. The world is complex and less intuitive than it looks.
A common misconception I long held is that core devs of a product must be the top product experts.
The reality is that, as a core dev
there isn’t enough time to be both a dev and a user
knowledge of the implementation taints you and can prevent you from seeing the product clearly
it’s extremely difficult to focus deep on the details, and also view the product from a high level, holistically
Yes, you will have the absolute expertise on certain product behaviors or possibilities. But almost certainly never for the whole product at once; just the part you’ve been working on lately, where the knowledge is most fresh.
This is why it’s so important to surround yourself with “power users” — those that are untainted and unburdened by the implementation, and can use their full mental power to absorb and innovate on the product simply as a product.
These are often the people that find the most interesting uses and abuses of systems, that the core devs weren’t even aware of.
This can happen for any kind of product, including and especially programming languages. Many of the interesting programming “patterns” are created not by the developers of the language, but by “power users”.[citation needed]
Here are a few reflections after building two separate online audiences with 1000+ followers (comfort and offlinemark).
Audiences require active maintenance .
If you don’t actively maintain the audience by regularly posting content, the actual effective audience size declines over time (despite the concrete number staying the same) as audience members become inactive on the platform.
They grow in fits in bursts.
In my experience, the audience growth was not linear, but occurred in fits and bursts of about a hundred followers for major events or releases. Sometimes things just pop off. For offlinemark there were a few major events that got me several hundreds of followers each time. Plus, these tweets sometimes get retweeted and have second lives on the internet, getting me a bunch of new followers for free.
Demand paging blog post
/proc/self/mem blog post
Git tweet about referencing commits by commit message
Thread about forking being unsafe in real-time contexts due to page faults
A few other tweets.
Audiences grow within some specific context where you’ve established reputation, and do not engage with content that’s outside that context.
offlinemark is within the technical context and other kinds of content like about my music or other thinking is not engaged with nearly as much.
Releasing content to a large audience can be very distracting.
It’s extremely difficult to have content being going viral and not be glued to your notifications.
What if you’re wrong in a major way? What if you’re getting cancelled somehow for something you said?
At the very least, it’s an incredible amount of dopamine and affirmation that takes immense self control to not bask in.
That’s a personal goal of mine. I’m not sure why, but I think it comes from spending a long time in computer security and feeling unsatisfied with just breaking “low level” software — I want to be able to build it too.
But what does “pro” mean? What does “systems programmer” even mean?
Here’s what comes to mind:
Little skill
Some skill
Practical experience with
Fluent in a systems programming language. Able to write idiomatic code.
x
Understanding of modern systems programming concepts — e.g. smart pointers.
x
Understanding of operating system and hardware/software interface
x
Competent with C/C++ build concepts — e.g. headers, source, compilation, linking
x
Able to engage professionally in open source
x
Competent with git — e.g. can rebase, rewrite history and prepare perfect patch series (no stray newlines, commits build on each other, etc)
x
Understanding of multithreaded programming and concurrency. Able to write idiomatic multithreaded code.
x
Skilled with development tools — e.g. debugger
x
Competent with performance profiling
x
Understanding of how to model data with a static type system — e.g. static vs dynamic dispatch, polymorphism, OOP concepts, reference vs value semantics.
x
Understanding of I/O programming, esp. async I/O.
x
Understanding of different OS platforms — e.g Linux, Windows, macOS
x
Experience with a variety of architectures
x
Antigoals:
Become highly skilled in C++ template meta-programming
Milestones
2014 — First professional Linux kernel experience (internship)
2016 — Landed a job working on symbolic execution engine (Trail of Bits)
2019 — First professional C++ work (Trail of Bits / osquery)
2019 — Landed commit in lldb
2020 — Landed commit in Linux kernel
2021 — Landed a full time C++ job (Ableton)
2021 — Built significant git competence (Ableton)
2023 — Built significant Linux kernel & hardware / software interface knowledge at work (Ableton)
2023 — Built knowledge of data modeling with static type system (Ableton)
