brunooliv

The problem here is not the vendors. The "problem" is that design mattered when producing code was a truly manual effort. Writing things down, aligning specs, design, contracts, all of that would mold what could come next. Now ANY design from amazing to terrible can be regenerated and generated dozens of times over for literal single digit dollars.

Cost of tokens is NOT a problem unless you have a codebase with hundreds of person-years of effort. Not many people do.

It’s obvious: they train on prompts and store data when using through their official API. And for third party it’s just… not good. That’s it.

"There will be consequences"

And, so what? Software as an engineering discipline has long lacked standardization and regulation to be on par with other engineering disciplines, and the fact that code and all its surrounding ecosystems are not "visibile" or "malleable" makes this extremely hard.

You can use terraform and yaml to define infrastructure that literally spins up machines _somewhere_ in the internet. With all its issues, bugs and associated consequences mostly being ignored.

I just don't understand the difficulty in KNOWING what needs to be done: NO LLM usage in university/grad/high schools, NO LLM usage in the first 3 years of your professional career.

Once the basics are solidly grasped, then they can use it at will.

The problem has never been about wisdom, knowledge or LLMs writing good, bad, maintainable or terrible code. It has always been about the skill level of people using it AND on the fact that people start off-loading basic things to these models that they wouldn't before.

If you have the knowledge and "suffered" through experience to learn the fundamentals, than not using LLMs becomes more deterimental than beneficial.

You just CAN NOT skip the trial by fire of learning and absorbing knowledge on your own. That's all.

Click on the implementation notes and it tries to open a README.md that 404s....

I keep chipping away at my running app, RunCoach!

I've recently managed to bridge the biggest gap I've had and, while it seems to be something that is NOT yet great from a UI/UX perspective (you need an account with intervals.icu to use it), I can now send workouts with structured splits, pace targets and reps to my Garmin watch, which effectively means I can replace using a paid app with my own to get, essentially, the same benefits.

I did put a previous version to the test, by running a plan generated with it to run my first ever trail race in May (a 22Km race with over 1100m elevation successfully completed) and since then I've kept revamping plan adaptation, workout generation and watch integration and it's now sitting in a very sweet spot for me, I might actually cancel my paid app subscriptions after running a few more hundred kms to really test it! runcoach.fly.dev

Genuine question: can these posts be paid to hype the open source models? If yes, what would be the purpose?

On my work tasks, FastAPI Python and Springboot Java on a modern SaaS product, the only open model that can do tasks well and efficiently is Qwen3.7-Max.

In all my experiments, both GLM-5.2 and Kimi are busy grepping around the codebase for ALMOST 70-80K tokens before writing anything and when they do it typically breaks the code… it feels to me that these models are good but only when you write out a super detailed spec of the task just like it was done a year ago… Qwen3.7 just… does it

Qwen is so much better than GLM or Kimi that this makes me genuinely excited!!

I hope you're right and I want you to be right, but, even seeing the current hype around local models, etc... and open-source models, I think the industry is currently under a big confusion where they see the benchmarks of things like Kimi, GLM, Qwen, they play with it via opencode, and they think like: "Wow this is pretty good, I want to deploy this". But they don't understand how the KV cache grows over time and can take almost as much memory as needed for a 30B param model, they dont understand that a quantized model WILL NOT be the same as a full precision one, and they surely don't see the engineering work needed to serve inference to even tens of customers at a decent quality and latency level.

The biggest moat of these giant labs and models is increasingly shifting towards deployment capabilities and (debatably) having better (proprietary) harnesses.

The models themselves can be impressive on benchmarks, but unless they can be served reliably to customers either at scale, hosted somewhere, or even on edge with predictable latency and memory usage, then frontier will always be leading.

This is really insane to me.

There's nothing practical about open-source models yet that makes them even remotely comparable to closed frontier models.

All the hype around GLM, Qwen, now Kimi.... Are people really this naive that they believe these reports or, more worringly, are people NOT using these models and seeing the HUGE gap that still exists?

Take a task, any medium-sized task, decently scoped that you'd trust to give to Sonnet to finish without a hitch. Now give it to ANY open-source frontier model and watch them struggle and go in circles while failing tool calls and randomly assuming things.

Open-source is and has been amazing but its so hard to deploy reliably and at scale and there's still big problems in the underlying models with instruction following and tool calling that makes it basically unusable for production workloads at a decent price point...

So on one hand it states:

Delivery is becoming a sequence of checks, not a ritual The same thing happens when it is time to deliver.

First, I invoke my /definition-of-done skill. It checks whether the implementation covers what was stated in the PR and in the plan. It checks tests and the other validations I care about. It tells me when something looks unusual, broken, or missing. When everything looks good, I invoke another skill, /pr-check-release. That checks the remote PR, updates labels, removes [WIP], adds [RFC], updates the description, and prepares the change for review. If one day passes and nobody on the team reviews the PR, we merge it. The agent also tracks that condition for me, so I can run /pr-merge-dev, and it takes care of the process: merge the PR, delete the remote branch, delete the local branch, and pull dev back into a fresh state. None of these steps is particularly difficult. That is exactly the point.

But then:

I still care about all of those things. Probably more than most people.

Obviously the author has irreversibly became AI-pilled and the day API costs balloon or APIs are down, what work will the author do?

I love using AI but please read the diffs and process them with your human brains and eyes. Spin up your containers manually, test the app, MANUALLY. Talk to real users face to face.

Outsourcing the grunt work is fine, but there's a fine line between that and becoming a button-presser.

I only wish Opus 4.6 from earlier this year at a faster inference speed. Since Opus 4.6 things have been so much messier and the overall push for more agency isn’t really panning out for agent assisted development as much as they would like

Thanks for saying this! I completely agree with everything you said!

There’s far, far too many people who confuse code quality for speed of development and start treating code quality as the product for customer base in the hundreds and active customers in the dozens and for most features to be basically unused.

The reality is that tech debt as a concept these days is hardly real: to be in debt means previous decisions or a previous implementation makes current work extremely hard or impossible, but, the truth is that the human factors such as knowing what to build, team collaboration and even speaking to customers matter far more and can get you “in debt” so so much faster than code alone. At least in your typical SaaS company.

If you ship code in a way that you let tech debt pile up to the point that customers notice it, you have an organisational problem, not code issues per se.

The fact that a lot of people don’t get this is really baffling to me.

Side question: I've always been a recreational runner, running 3/4x a week, completed a few half marathons, and recently decided to _also_ go to the gym to do strength training as it has a lot of benefits for runners too. Should I consider/take creatine, is it useful?