Writing Code Is Easy, Reading It Isn't
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The article 'Writing code is easy, reading it isn't' sparks a discussion on the challenges of reading and understanding code, with some commenters sharing their experiences and others discussing the role of LLMs in code generation and review.
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Even if I use Cursor (or some other equivalent) and review the code I find my mental model of the system is much more lacking. It actually had a net negative on my productivity as it gave me anxiety at going back to the codebase.
If an AI tool could help a user interactively learn the mental model I think that would be a great step in the right direction.
I've contracted some of this understanding of pieces/intellectual work out to Claude code many, many times successfully.
And that's probably the difference between those who are okay with vibe coding and those who aren't. A leader of a company that doesn't care about code quality (elegant code, good tradeoffs, etc) would never have cared if 10 monkeys outputted the code pre-AI or if 10 robot monkeys outputted the code with AI. It's only a developer, of a certain type, that would care to say "pause" in either of those situations.
Out of principal I would not share or build coding tools for these people. They literally did not care all these years about code quality, and the last thing I want to do is enable them on any level.
I don't want to participate on it either, but I'm glad they'll have the chance to make things their way with all the consequences it brings unfiltered.
#1 is easy, #2 requires some investigation, #3 requires studying.
If you're looking at say, banking code - but you know nothing about finance - you may struggle to understand what it's doing. You may want to gain some domain expertise. Being an SME makes reading the related code a heck of a lot easier.
Context comes down to learning the code base. What code calls the part you're looking at? What user actions trigger it? Look at the comments and commit messages - what was the intention? This just takes time and a lot of trawling around, looking for patterns and common elements. User manuals and documentation can also help. This part can't be rushed - it just comes to passing over it again and again and again. If you have access to people very familiar with the code - ask them! They may be able to kick start your intro.
#1 will come naturally with time.
To add to the above; IME, #3 comes first. Study the domain to understand the concepts and their relationships. Read some books/Articles, Watch some Videos, Read Documentation etc. to come up to speed on the terminology/jargon and the general concepts/ideas. Then, in order to understand their mapping to the specific application at hand, sit with the local "guru" (there is always at least one in every group) and pick his/her brain for a few sessions (getting them brown bag lunches works great for this) on the overall architecture of the System. Next sit with testing and use the app as an end-user to understand use-case scenarios which brings all of the above together.
During all the above stages, take copious notes, draw diagrams/graphs/etc. use source code analysis/documentation/browsing/etc. tools eg. doxygen/cscope/opengrok/etc. tools to navigate the codebase and cement understanding. Note also that the above stages are to be done both iteratively and parallelly until you are somewhat comfortable and not necessarily know/understand everything.
With the above in hand, pick one use-case scenario, preferably the most complicated, critical and important one and walk through the code from beginning to end for that path. Remember that you are trying to get the overall picture and hence treat all irrelevant details as blackbox abstractions during initial phases. Over time as you iterate and review the code again and again you can slowly add in the details for a more comprehensive understanding.
Finally, there is no shortcut to the above; it takes time and self-effort. We Humans are natural-born, trial-and-error, continuous-learning problem solvers and so trust to your intelligence and commonsense to find a path to move ahead when stuck at something.
As a bonus you can just send that whole block of code - notes and all - to a colleague if you get stuck. They can read through the code and your thoughts and give feedback.
It's not a joke answer. This entire article is silly. LLMs are great for helping you understanding code.
Especially getting them to generate sequence / flow charts I find is a hack to figure out how everything fits together well.
Claude code is fantastic at quickly tracing through code and building visualisations of how code works together
Code navigation should be instant and effortless. Get good tooling and train muscle memory for it.
When looking at a piece of code, keep asking questions like: what does this return, what are the side effects, what can go wrong, what happens if this goes wrong, where do we exit, can this get stuck, where do we close/save/commit this, what's the input, what if the input is wrong/missing, where are we checking if the input is OK, can this number underflow/overflow, etc
All these questions are there to complete the picture, so that instead of function calls and loops, you are looking at the graph of interconnected "things". It will become natural after some time.
It helps if you read the code with some interest, e.g. if you want to find a bug in an open source project that you have never seen the code for.
To get a good mental model, I'll often get an LLM to generate a few mermaid diagrams to help create a mental model of how everything pieces together
It is indeed difficult to verify a piece of code that is either going to ship as-is, or with the specific modifications your verification identifies. In cryptography engineering the rule of thumb (never followed in practice) is 10x verification cost to x implementation cost. Verification is hard and expensive.
But qualifying agent-generated code isn't the verification problem, in the same way that validating an eBPF program in the kernel isn't solving the halting problem.
That's because the agentic scenario gives us an additional outcome: we can allow the code as-is, we can make modifications to the code, or we can throw out the code and re-prompt --- discarding (many) probably-valid programs in a search for the subset of programs that are easy to validate.
In practice, most of what people generate is boring and easy to validate: you know within a couple minutes whether it's the right shape, whether anything sticks out the wrong way, the way a veteran chess player can quickly pattern-match a whole chessboard. When it isn't boring, you read carefully (and expensively), or you just say "no, try again, give me something more boring", or you break your LLM generation into smaller steps that are easier to pattern match and recurse.
What professionals generally don't (I think) do with LLMs is generate large gnarly PRs, all at once, and then do a close-reading of those gnarly PRs. They read PRs that are easy (not gnarly). They reject the gnarly ones, and compensate for gnarliness by approaching the problem at a smaller level of granularity. Or, you know, just write those bits by hand!
nope!
https://sketch.dev/blog/our-first-outage-from-llm-written-co...
(You and I are probably the only 2 people reading this long-dead thread now, and I'm very familiar with the post you just shared --- I've been using Sketch for several months and following along with their Github repo. Sketch is great.)
mostly that -^
but i guess also transitively, that (a) you can even identify whether code is boring and simple in the first place; and (b) you're able to tell the LLM to make it "more boring" in any kind of effective way
i think these kinds of conversations are revealing a really interesting dichotomy among software professionals, around the (recent and LLM-mandatory) concept of inference, and how it relates to their pre-existing mental model of software
some software professionals have a mental model of programming that is just fundamentally incompatible with non-determinism, and for them a tool that's built on inference is delivering way more cost than benefit, and maybe essentially incompatible with how they see what they're doing
whereas the LLM maxis never really thought of their software as deterministic or anything like that in the first place, so for them all of the non-determinism from inference is, like, same as it ever was, and delivers way more benefit than cost
With LLMs, the volume of code has only gotten larger but those same LLMs can help review the code being written. The current code review agents are surprisingly good at catching errors. Better than most reviewers.
We'll soon get to a point where it's no longer necessary to review code, either by the LLM prompter, or by a second reviewer (the volume of generate code will be too great). Instead, we'll need to create new tools and guardrails to ensure that whatever is written is done in a sustainable way.
Something like blowing up the stack or going out of bounds is more obviously a bug, but detecting those will often require inferences from how a code behaves during runtime to identify. LLMs might work for detecting the most basic cases because those appear most often in their data set, but whenever I see people suggest that they're good at reviewing I think it's from people that don't deeply review code.
It’s even worse with offshore devs. They produce a ton of code you have to review every morning.
The real breakthrough would be finding a way to not even do things that don’t need to be done in the first place.
90% of what management thinks it wants gets discarded/completely upended a few days/weeks/months later anyway, so we should have AI agents that just say “nah, actually you won’t need that” to 90% of our requests.
This seems silly to me. In most cases, the least amount of work you can possibly do is logically describe the process you want and the boundaries, and run that logic over the input data. In other words, coding.
The idea that we should, to avoid coding or reading code, come up with a whole new process to keep generated code on track - would almost certainly take more effort than just getting the logical incantations correct the first time.
LLM code is NOT like this at all, but it's like a skilled liar writing something that LOOKS plausible, that's what they're trained to do.
People like you do not have the ability to evaluate the LLM output; it's not the same as reading code that was carefully written at ALL. If you think it's the same, that is only evidence that you can't tell the difference between working code and misleading buggy code.
What you've learned to do is read the intent of code. That's fine when it's been written and tested by a person. It's useless when it comes to evaluating LLM slop.
You're being gaslit.
Obligatory XKCD https://xkcd.com/1172/ "Workflow" reference
High quality code is generally hard to write and easy to read.
Just write simple code
Everyone knows that debugging is twice as hard as writing a program in the first place. So if you're as clever as you can be when you write it, how will you ever debug it?
I guess this means that one should solve the appropriate problems for a given skill level
I use it as a heuristic. If my work is getting more complex, it's a warning sign that I might be doing something wrong or using the wrong approach. If it's getting simpler it means I might be headed in the right direction.
When I was a kid learning programming, I would skim through the whole book teaching Python and type the code using as much keywords as I learned each day, just to boast on my parents and my non-programmers peers about the obfuscated mess that came after.
As I grew I started to contribute to other open-source projects and I came across every kind of unmaitanable spaghetti code, so that I just gave up contribuiting on said project, that is when I gained the consciousness about being zealous on keeping the code as simple as possible so that the next person who comes after me to change the code don't have as much trouble understanding the code, even myself when I revisit the code later.
That altruistic mindset about caring how others read your code, you don't acquire easily unless you get experience how your previous peers did feel.
I guess it’s best to take a look at the code once something works and then see if it can be simplified. A lot of people seem to skip that step.
I'm also a fan of feature-oriented project structures. I want the unit test file in or next to the code it's testing. For UI projects, similar with React it's about the component or feature not the type of thing. For APIs I will put request handlers with the feature along with models and other abstractions that go together based on what they fulfill, not the type of class they are.
I consider this practice more intuitively discoverable. You go into a directory for "Users" and you will see functionality related to users... this can be profile crud or the endpoint handlers. Security may or may not be a different feature depending on how you grow your app (Users, Roles, Permissions, etc). For that matter, I'd more often rather curate a single app that does what it needs vs. dozens of apps in a singular larger project. I've seen .Net web projects strewn across 60+ applications in two different solutions before. It took literally weeks to do what should take half a day at most.
All for one website/app to get published. WHY?!? I'm not opposed to smaller/micro services where they make sense either. But keep it all as simple as you possibly can. Try to make what you create/use/consume/produce as simple as you can too. Can you easily use/consume/interact with what you make from a system in $NewLanguage without too much headache? I don't like to have to rely on special libraries being available everywhere.
[0] - https://www.laws-of-software.com/laws/kernighan/
Not sure if there is actually data for the assumption to be twice as smart to debug than to write code, but it sounds about right.
And also, if you write the smartest code you can, while you are at your peak, you also won't be able to read it, when you are just a bit tired.
So yes, yes, yes. Just write simple code.
(I also was also initially messed up a bit by teachers filling me up with the idea to aim for clever code)
https://www.manning.com/books/the-programmers-brain
And the best part, most people shallow read all of them and decide the details are needless till they are forced to deal with the details and then their understanding falls apart in front of them
Just like a piece of music being easy to listen to after sweating out the composition.
Sone coding is like solving the puzzle. Once it is written and debugged, you're looking at the solution. The code will readily spoon-feed you the solution again when you revisit it months or years after forgetting everything, possibly even its existence.
Wen code is easy to write but hard to read, you must be writing fluff. Maybe try to steer your career a bit away from that. But do improve your ability to make fluff readable.
You need to both remember the processing done in the code and the structure. The structure will need your working memory in the language center as its n-dimensional and not representable with a closed surface 3D model - at maximum you can do graphs which well dont look like much you will be able to draw from memory.
Remembering state and data needs your whole brain to debug what will happen when you run the code.
All people here telling you they can train this train either an abstract concept like functional programming or work with software in their field which will have a similar scope - you will NOT be able to easily comprehend code written in another style or purpose. Don't be mistaken doing 10 years of mobile apps, ERP or whatever would allow you to follow any C-written code for systems or sth.
Fitting code as an abstraction in your mind is literally neurons growing new pathways - its expensive, no one likes doing it and if you are not in the same field you will try to bend it into your domain or not engage much.
Trying to find general assumptions here will not work - if you could condense n-dim rulesets to some general principles - you would simply do so and refactor the code.
Thinking theres no way to debug abstracted, proveable code you can not fit fully in your working memory is wrong, because you only need to test each function / morphism to be correct, not the whole thing. Its certainly doable to write systems that interconnect 30-40 entities you need NOT remember all at once and still end up with bugfree code because each part is constrained to strict interfaces and types.
That's how rulesets with bigger scopes work - no one designs the aircraft carrier all alone - there's restrictions and interfaces everywhere, not a single engineer knows the whole thing down to the molecules and electrons. Still it works.
With some stakeholders, this is an almost impossible problem; sometimes this is because they lack vision and so their requirements are littered with impossible contradictions; other times, their ego is too big to accommodate any kind of push-back; even if you try to drip-feed the suggestions as gently as possible, they begin to resent you because they start to associate you with negative feelings such as self-doubt.
Schopenhauer explained this phenomena succinctly:
"A man must be still a greenhorn in the ways of the world, if he imagines that he can make himself popular in society by exhibiting intelligence and discernment. With the immense majority of people, such qualities excite hatred and resentment, which are rendered all the harder to bear by the fact that people are obliged to suppress — even from themselves — the real reason of their anger. What actually takes place is this. A man feels and perceives that the person with whom he is conversing is intellectually very much his superior. He thereupon secretly and half unconsciously concludes that his interlocutor must form a proportionately low and limited estimate of his abilities. That is a method of reasoning — an enthymeme — which rouses the bitterest feelings of sullen and rancorous hatred."
This is a really big problem because people who attain management positions are often very good at understanding and then manipulating what other people think about them; this is how they were able to rise to their current ranks. They are exactly the kinds of people who build these reflective mental maps/models of who thinks what about them; and they are good at plotting against those people who they believe may harbor negative thoughts about them.
Really agree with the article, ultimately the typing and thinking speed issues can be solved with AI, but trusting it and auditing what it does seems like a job for humans for the foreseeable, you know, so maybe we avert a classic sci-fi AI apocalypse and whatnot