2,235 karma · joined August 17, 2012
Building indie and BigTech
Building: https://getgabrielai.com
Email: sisciapub [at] gmail [dot] com
The revenue for a new feature today is something sure. While the cost associated with supporting such feature will be up to debate in the coming quarters.
As often it is the case, we are moving on a long vs short term trade-off space. And I don't think any experience will generalize
If you can afford that, with all the coordination costs that it comes from it, good.
An alternative is just to let the downstream service own the retry logic. Too many requests? Just error out as soon as possible.
Each team manages its budget and each other team adapts.
Because the value of writing are not the words on a piece of paper but the idea they convey.
Politicians speech are not worth listening too because they have a copywriter polish them?
Teacher assistant homeworks are not worth to do, because they are made by a TA and not by the course professor?
Of course there are writing as: "Hey ChatGPT write me a piece around coding with LLMs" and yeah, those are not worth reading.
But most of the writing is usually, these are the ideas, this is how the idea are structured together, now let's review them and then let's get a nice prose out of it.
I personally found V4-flash an amazing model and really hungry to try 4.1-flash
For software factories, cost is much more a concern that standard development workflow and using anthropic models is just a non starter
> tests should test behaviour and not structure.
Of what?
The whole idea of software architecture, and underlying of my messages, is to structure the code so that it is easy, but before easy, possible, to work at the right abstraction level.
Which allows to tests the behaviour of components and not their structure.
Trivial example, say your code read from a socket and manage the bytes with some CPU operations and write them to another socket. (You may recognise it is basically what a compressor do)
The way in which you manage read and write to the sockets will make dramatically simpler or much more difficult to write good tests.
If you adopt strategies like sans-io, you will see that the testing is almost trivial.
If all the logic sprawl up from the read syscall in a loop, you will notice how more challenging testing becomes.
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To answer your question, the way I let LLMs write code is very DI (dependency injection) based.
A class never instantiates another class - all the dependencies are passed to the constructor. Including time. Including whatever DB iteraction.
The reason why I prefer this is that I can test each component at every level of abstraction. I don't have to. But I can.
My current approach is to force coverage higher than say 80% as default and then when a bug is discovered drill down to the components.
The way you test code cannot (and should not) be decoupled by the way in which you architect the code itself.
80%+ of effective testing is not in the testing framework but in the code architecture.
The author doesn't mention how the code is being architected and managed.
For what it is worth, I found that forcing agents on DI/hexagonal architecture and forcing a trivial coverage check is quite useful and produce overall good enough code with relative little effort
Only saying "LLM writing" is honestly lazy writing. Specifically what?
I get the glaring cases, I get the idea that if the prose is generated then maybe also the idea, I get the feeling when reading a complete LLM authored piece.
But that doesn't help the piece, because - beside those glaring cases - most writing today is a mix between authors ideas and LLM prose.
I am trying to understand in your view what are the parts that actually breaks and what kind of improvement we would need.
The idea is that legislator don't have the full context and don't know the impact of their work - so they welcome industry or matter experts. Which is quite reasonable. I would argue that the job of the legislator is to know what they are doing, but I can see it being hard for specific technical issues that really benefits from first hand experience.
I am not even particularly against the idea of donating money to make sure a particular issue is known to the public, so that the public can make an effective voting decision.
But all of this seems quite too much.
Companies have disproportionate amount of money to donate and much less synchronization issue that citizen.
Do you have any example?
https://redbeardlab.gitbook.io/acem/essays/ambient-developme...
I basically wrote a small GitHub app and I simply create a GitHub issue, the bot read it, run an LLM loop and come up with a PR (or a design)
Then I simply approve the pr (or the design)
I find it much calmer and much more productive
"Unlimited" as in 8vCPU and then I am billed for it on consumption?
I think we can do much better.
The workflow of copy to chatgpt and getting feedback is just the first step, and honestly not that useful.
What I would love to see is a tool that makes my writing and thinking clearer.
Does this sentence makes sense? Does the conclusion I am reaching follows from what I am saying? Is this period useful or I am just repeating something I already said? Can I re-arrange my wording to make my point clear? Are my wording actually clear? Or am I not making sense?
Can I re-arrange my essay so that it is simpler to follow?
I force the code to be almost 100% dependency injection-able.
It simplifies a lot writing tests and getting the coverage. And I see the LLM being able to handle it very very well.
Good enough that I don't review it.
Granted, it is a personal project that I care only to the point that I want it to work. There are no money on the line. Nothing professional.
I believe that part of the secret is that I force CC to run the whole est suites after it change ANY file. Using hooks.
It makes iteration slower because it kinda forces it to go from green to green. Or better from red to less red (since we start in red).
But overall I am definitely happy with the results.
Again, personal projects. Not really professional code.
LLMs works on both translation steps. But you end up with an healthy amount of tests.
I tagged each tests with the id of the spec so I do get spec to test coverage as well.
Beside standard code coverage given by the tests.
If they have another tag, the agent in the server creates a PR considering the whole issue conversation as context (with the idea that you used the plan above - but technically you don't have to.)
If you comment in the PR the agent start again loading your comment as context and trying to address it.
Everything is already in git and GitHub, so it automatically pick up your CI.
It seems simpler, but I am sure I missed something.
Especially on the angle of automatic/continuos improvement (https://github.github.io/gh-aw/blog/2026-01-13-meet-the-work...)
Often code is seen as an artifact, that it is valuable by itself. This was an incomplete view before, and it is now a completely wrong view.
What is valuable is how code encode the knowledge of the organization building it.
But what it is even more valuable, is that knowledge itself. Embedded into the people of the organization.
Which is why continuos and automatic improvement of a codebase is so important. We all know that code rot with time/features requests.
But at the same time, abruptly change the whole codebase architecture destroys the mental model of the people in the organization.
What I believe will work, is a slow stream of small improvements - stream that can be digested by the people in the organization.
In this context I find more useful to mix and control deterministic execution with a sprinkle of intelligence on top. So a deterministic system that figure out what is wrong - with whatever definition of wrong that makes sense. And then LLMs to actually fix the problem, when necessary.
Yeah you could ask ChatGPT or Claude to write code, but it wasn't really there.
It needs a while to adopt the model AND the UI. As in software are the first one because we are both makers and users.
It can be used by human or by AI agents.
I experiment the same with other mechanisms, and CLI are as effective - if not more effective - than MCP.
Granted, having access to AI I would use AI to run it. But nothing is stopping a manual, human centric, use.
I believe more tools should be written like that.
You can just install the tool and use it. It is a CLI with very verbose output. Verbose output is good for both humans and AI.
Students are very simply NOT doing the work that is require to learn.
Before LLMs, homeworks were a great way to force students to approach the material. Students did not have any other way to get an answer, so they were forced to study and come up with an answer to the homeworks. They could always copy from classmates, but that was considered quite negatively.
LLMs change this completely. Any kind of homework you could assign undergraduates classes are now completed in less than 1 second, for free, by LLMs.
We start to see PERFECT homeworks submitted by students who could not get a 50% grade in classes. Overall grades went down.
This is a common pattern with all the educators I have been talking with. Not a single one has a different experience.
And, I do understand students. They are busy, they may not feel engaged by all the classes, and LLMs are a way too fast solution for getting homeworks done and free up some time.
But it is not helping them.
Solutions like this are to force students to put the correct amount of work in their education.
And I would love if all of this would not be necessary. But it is.
I come from an engineering school in Europe - we simply did not have homework. We had frontal classes and one big final exams. Courses in which only 10% of the class would pass were not uncommon.
But today education, especially in the US, is different.
This is not forcing student to use LLMs. We are trying to force student to think and do the right thing for them.
And I know it sounds very paternalistic - but if you have better ideas, I am open.
The student is supposed to submit a whole conversation with an LLMs.
The LLM is prompted to answer a question or resolve a problem, and the LLM is there to assist. The LLM is instructed to never reveal the answer.
More interesting is the concept that the whole conversation is available to the instructor for grading. So if the LLMs makes mistake, or give away the solution, or if the student prompt engineer around it. It is all there and the instructor can take the necessary corrective measures.
87% of the students quite liked it, and we are looking forward to doubling the students that will be using it next quarter.
Overall, we are looking for more instructor to use it. So if you are interested in it please get in touch.
More info on: https://llteacher.blogspot.com/
I would split it in 2 steps.
First, just move it to svelte, maintain the same functionality and ideally wrap it into some tests. As mentioned you want something that can be used as pass/no-pass filter. As in yes, the code did not change the functionality.
Then, apply another pass from Svelte bad quality to Svelte good quality. Here the trick is that "good quality" is quite different and subjective. I found the models not quite able to grasp what "good quality" means in a codebase.
For the second pass, ideally you would feed an example of good modules in your codebase to follow and a description of what you think it is important.
However my approach would be to use duckdb and S3 over lambda.
Leaving many of the concerns to the infrastructure. Like basically no OOM. No need to manage servers.