faxmeyourcode

Love this. An example of complete and total dominion over the machine. Great quote here too lol

> Prometheus stole fire from the gods and gave it to man. For this he was chained to a rock and tortured for eternity.

Everybody is different, I simply cannot stand the sight of chatgpt styled writing. Give me paragraphs.

I've lobbied to replace our internal tool with a django admin panel. I prototyped it and it showed that it would reduce our code by > 15k lines.

Any internal webapps I need to build like this will 100% be set up with django in the future due to this. I don't need it to be pretty, I just want the UI, database migrations, users, roles, groups, etc for free

One trick to get out of this scenario where you're writing a ton is to ask the model to interview until we're in alignment on what is being built. Claude and open code both have an AskUserQuestionTool which is really nice for this and cuts down on explanation a lot. It becomes an iterative interview and clarifies my thinking significantly.

> LLMs dont care about the story, they just care about the current state of the code

You have to tell it about the backstory. It does not know unless you write about it somewhere and give it as input to the model.

I'm sure the same could be said about tractors when they were coming on the scene.

There was probably initial excitement about not having to manually break the earth, then stories spread about farmers ruining entire crops with one tractor, some farms begin touting 10x more efficiency by running multiple tractors at once, some farmers saying the maintenance burden of a tractor is not worth it compared to feeding/watering their mule, etc.

Fast forward and now gigantic remote controlled combines are dominating thousands of acres of land with the efficiency greater than 100 men with 100 early tractors.

You should check out plain text accounting software Beancount. I've built a skill for this here https://github.com/ouachitalabs/skills/blob/main/beancount/S... and I use it for my custom personal finance agent.

> The TL;DR was that old open source was the cathedral of exclusive developers and groups. Then the Bazaar showed up (which was the Linux Kernel for example) and that freed us from the shackles of the cathedral.

I didn't make it past the tldr lol is this some kind of poisoned data for GPT 6?

I ran a T450s all through college. My late grandmother gifted me the money after high school graduation to buy my first laptop. I'd learned to code on the old gateway pentium 4 (Windows XP) machine with 512 MB of RAM from the factory. I'd resurrected it as a teenager and completely gutted it. I scoured the web endlessly looking for something that was _not_ touchscreen (hard to find at the time), linux-compatible, and rugged enough for the neglect of a college student's backpack.

It was a linux user's dream. I swapped out the spinny HDD with an SSD. It had hotswappable batteries, a spill resistant keyboard with a drain system, and I ran every linux distribution I could get my hands on, eventually settling on the xfce flavor of Ubuntu.

I still have that machine, and will keep it for my son when he's older. I plan to replace the battery and SSD with something more reliable. These days I use an m2 macbook air for portability... but that thinkpad is still something I will treasure for years.

I use claude exclusively at my day job with legacy codebases. I'm not using swarms or fancy multi-agent setups. Just sticking to plan mode, giving enough context, and iterating on a spec before starting the actual build.

I'm also not building webapps. I work in data engineering on a large legacy airflow project, internal python libraries, infrastructure with terraform, etc.

Unfortunately this is a skill issue. And it's a totally different skill than reading and writing code, building solid systems, and general software engineering at large. That is annoying, but where we're currently at.

Assume you're writing code manually, and you personally make a mistake. It's often worthwhile to create a mechanism that prevents that class of mistake from cropping up again. Adding better LSP or refactoring support to your editor, better syntax highlighting, better type checking, etc.

That same exact game of whack a mole now has to be done for you and whatever agent you're building with. Some questions to ask: What caused the hallucination? Did you have the agent lay out its plan before it writes any code? Ask you questions and iterate on a spec before implementation? Have you given it all of the necessary tools, test harnesses, and context it needs to complete a request that you've made to it? How do you automate this so that it's impossible for these pieces to be missing for the next request? Are you using the right model for the task at hand?

The limit clause isn't official/standard ansi sql, so it's up to the rdbms to implement. Your assumption is true for bigquery (infamously) but not true for things like snowflake, duckdb, etc.

While I agree with the premise of the article, even if it was a bit shallow, this claim made at the beginning is also still true:

> Everyone’s heard the line: “AI will write all the code; engineering as you know it is finished.”

Software engineering pre-LLMs will never, ever come back. Lots of folks are not understanding that. What we're doing at the end of 2025 looks so much different than what we were doing at the end of 2024. Engineering as we knew it a year or two ago will never return.

My boss (great engineer) had been complaining about this with his internal github copilot quality no matter the model or task. Turns out he never cleared the context. It was just the same conversation spread thin across nearly a dozen completely separate repositories because they were all in his massive vscode workspace at once.

This was earlier this year... So I started giving internal presentations on basic context management, best practices, etc after that for our engineering team.

I've run across more and more strudel musicians (developers?) doing a kind of live coding performance art and posting clips on tiktok and reels. It's really entertaining to watch. I've been meaning to dabble in it.

> If Microsoft misappropriates GPL code how exactly is that "stealing" from me, the user, of that code? I'm not deprived in any way, the author is, so I can't make sense of your premise here.

The user in this example is deprived of freedoms 1, 2, and 3 (and probably freedom 0 as well if there are terms on what machines you can run the derivative binary on).

Read more here: https://www.gnu.org/philosophy/free-sw.html

Whether or not the user values these freedoms is another thing entirely. As the software author, licensing your code under the GPL is making a conscious effort to ensure that your software is and always will be free (not just as in beer) software.

There are many misconceptions of the GPL, gnu, and free software movement. I love the idealism of free software and you hit the nail on the head.

Below are the four freedoms for those who are interested. Straight from the horse's mouth: https://www.gnu.org/philosophy/free-sw.html

    The freedom to run the program as you wish, for any purpose (freedom 0).

    The freedom to study how the program works, and change it so it does your computing as you wish (freedom 1). Access to the source code is a precondition for this.

    The freedom to redistribute copies so you can help others (freedom 2).

    The freedom to distribute copies of your modified versions to others (freedom 3). By doing this you can give the whole community a chance to benefit from your changes. Access to the source code is a precondition for this.

This landing page is vibe coded and littered with mistakes/typos (per 1,000 tokens), outdated models (Gemini 1.5?), the security link at the bottom of the page is an href=#, and I can see the "dashboard" without logging in or signing up.

> Message Privacy: Your API requests are processed and immediately forwarded. We never store or log conversation content.

> Minimal Data: We only store your email and usage records. Nothing else. Your data stays yours.

Source: trust me bro.

I think they're referring to this study on LLM poisoning in the pretraining step: https://arxiv.org/abs/2510.07192 (related article: https://www.anthropic.com/research/small-samples-poison)

I'll admit I'm out of my element when discussing this stuff. Maybe somebody more plugged into the research can enlighten.

Point 2 is very often overlooked. Building products that are worse than the baseline chatgpt website is very common.

Yes, it's still a terrible UX. Anybody claiming otherwise is using Apple only, which still has trouble (albeit a bit less than mixed ecosystems), or stockholm syndrome.

Copilot as a harness for the model is generic enough to work with every model. Claude sonnet, haiku, and opus are trained with and specifically for Claude code in mind.

Also, as a heavy user of both, there are small paper cuts that seriously add up with copilot. Things that are missing like sub agents. The options and requests for feedback that cc can give (interactive picker style instead of prompt based). Most annoyingly commands running in a new integrated vscode terminal instance and immediately mistakenly "finishing" even though execution has just begun.

It's just a better harness than copilot. You should give it a shot for a while and see how you like it! I'm not saying its the best for everybody. At the end of the day these issues turn into something like the old vi/emacs wars.

Not sponsored, just a heavy user of both. Claude code is not allowed at work, so we use copilot. I purchased cc for my side projects and pay for the $125/m plan for now.

I worked at a company like this, I was an intern with wide eyes seeing the migration to git via bitbucket in the year ... 2018? What a sight to see.

That company had its own data center, tape archives, etc. It had been running largely the same way continuously since the 90s. When I left for a better job, the company had split into two camps. The old curmudgeonly on-prem activists and the over-optimistic cloud native AWS/GCP certified evangelist with no real experience in the cloud (because they worked at a company with no cloud presence). I'm humble enough to admit that I was part of the second camp and I didn't know shit, I was cargo culting.

This migration is still not complete as far as I'm aware. Hopefully the teams that resisted this long and never left for the cloud get to settle in for another decade of on-prem superiority lol.

Absolutely, it's wildly fun to read the outputs of even a little tiny 0.8M model trained on CPU. And now I've actually got a much better understanding of the transformer architecture after playing around with it for a day. This repo is probably going to spawn some new folks to try out ideas which will turn into new researchers in the field, no doubt.

This weekend I just cracked into nanoGPT (https://github.com/karpathy/nanoGPT), an older but fabulous learning exercise where you build and train a crappy shakespeare GPT with ~0.8M parameters on a cpu. Results are about what you'd expect from that, they suck, but you can start to feel the magic, especially if you're not a deep learning professional and you just want to poke around and hack on it.

I started writing up a blog post on my weekend with nanoGPT but it's not done yet... Would have been great to link to here lol oh well

My side project - https://macrosforhumans.com - is a traditional mobile macro tracker with first class support for voice (and soon image and text blob) inputs for your recipes, ingredients, measurements, units, etc. Kind of a neat project that may never make it too far off the ground considering I am not a mobile dev but it's been fun to build so far with the help of claude code. It's built with flutter and a fastapi backend.

In the AI macro food logging world, there's really only Cal AI which estimates macros based on an image. I use cronometer personally, and it's super annoying to have to type everything in manually, so it makes sense why folks reach for something like Cal AI. However, the problem with something like Cal AI is accuracy. It's at best a guess based on the image. Macros for humans tries to be more of a traditional weigh your food, log it, etc kind of app, while updating the main interface for how users input that info into something more friendly.

I set myself a hard deadline to present a live demo at a local showcase/pitch event thing at the end of the month. I bet the procrastination will kick in hard enough to get the backend hosted with a proper database and a bit more UI polish running on my phone. :-)

Here's a really early demo video I recorded a few weeks ago. I had just spoken the recipe on the left and when I stop recording you can see my backend streams the objects out as they're parsed from the LLM https://www.youtube.com/watch?v=K4wElkvJR7I

Pointers in and of themselves are not difficult to learn on their own, but when you're learning them alongside your first programming language, it's just adds to the difficulty I think.

I think a lot of noobs learning C struggle with pointers especially because there are no good error messages besides "segmentation fault" :D

This is some of the cleanest, modern looking, beautiful C code I've seen in a while. I know it's not the kernel, and there's probably good reasons for lots of #ifdef conditionals, random underscored types, etc in bigger projects, but this is actually a great learning piece to teach folks the beauty of C.

I've also never seen tests written this way in C. Great work.

C was the first programming language I learned when I was still in middle/high school, raising the family PC out of the grave by installing free software - which I learned was mostly built in C. I never had many options for coursework in compsci until I was in college, where we did data structures and algorithms in C++, so I had a leg up as I'd already understood pointers. :-)

Happy to see C appreciated for what it is, a very clean and nice/simple language if you stay away from some of the nuts and bolts. Of course, the accessibility of the underlying nuts and bolts is one of the reasons for using C, so there's a balance.

It's a symptom of a regression of society as a whole. Twenty years ago things were different. Not just the vibes, but the net hope for humanity and short/medium/long term forecasts of the health of the united states were all much higher among the average joe.

The ship is sinking.