unoti

> Why vibe code with a language that has human convenience and ergonomics in view?

Recently I've been preparing a series that teaches how to use AI to assist with coding, and in preparation for that there's this thing I've coded several times in several different languages. In the process of that, I've observed something that's frankly bizarre: I get a 100% different experience doing it in Python vs C#. In C#, the agent gets tripped up in doing all kinds of infrastructure and overengineering blind alleys. But it doesn't do that when I use Python, Go, or Elixir.

My theory is that there are certain habits and patterns that the agents engage with that are influenced by the ecosystem, and the code that it typically reads in those languages. This can have a big impact on whether you're achieving your goals with the activity, either positive or negative.

> I often try running ideas past chat gpt. It's futile, almost everything is a great idea and possible. I'd love it to tell me I'm a moron from time to time.

Here's how to make it do that. Instead of saying "I had idea X, but someone else was thinking idea Y instead. what do you think" tell it "One of my people had idea X, and another had idea Y. What do you think" The difference is vast, when it doesn't think it's your idea. Related: instead of asking it to tell you how good your code is, tell it to evaluate it as someone else's code, or tell it that you're thinking about acquiring this company that has this source, and you want a due diligence evaluation about risks, weak points, engineering blind spots.

> Please just use Docker in a microVM or whatever. It's 0% slower and 100% more mature.

Wasm has different characteristics than docker containers and as a result can target different use cases and situations. For example, Imagine needing plugins for game mods or an actor system, where you need hundreds of them or thousands, with low latency startup times and low memory footprints and low overheads. This is something you can do sanely with wasm but not with containers. So containers are great for lots of things but not every conceivable thing, there’s still a place for wasm.

> Yes, I want to play in easy mode. Why would I want to play in hard mode?

Working alone can be much easier than managing others in a team. But also, working in a team can be far more effective if you can figure out how to pull it off.

It's much the same as working with agents. Working alone, without the agents, it's easier to make exactly what you want happen. But working with agents, you can get a lot more done a lot faster-- if you can figure out how to make it happen. This is why you might want hard mode.

> If everyone you’re managing is completely transparent and immediately tells you stuff, you’re playing in easy mode

So much this. There are many managers who are effective at managing people who do not need management.

> effective management requires that you're able to trust that the person tells you when they've hit a snag or anything else you may need to know

This is what we shoot for, yes, but many of the most interesting war stories involve times when people should have been telling you about snags but weren't-- either because they didn't realize they were spinning their wheels, or because they were hoping they'd somehow magically pull off the win before the due date, or innumerable other variations on the theme. People are most definitely not reliable about telling you things they should have told you.

> if you feel you have to review every line of code anyone on the team writes...

Somebody has to review the code, and step back and think about it. Not necessarily the manager, but someone does.

> I'm not sure that having the patience to work with something with a very inconsistent performance and that frequently lies is an extension of existing development skills.

If you’ve be been tasked with leadership of an engineering effort involving multiple engineers and stakeholders you know that this is in fact a crucial part of the role the more senior you get. It is much the same with people: know their limitations, show them a path to success, help them overcome their limitations by laying down the right abstractions and giving them the right coaching, make it easier to do the right thing. Most of the same approaches apply. When we do these things with people it’s called leadership or management. With agents, it’s context engineering.

> I can see why someone might say there's overlap between RL and SFT (or semi-supervised FT), but how is "traditional" SFT considered RL? What is not RL then? Are they saying all supervised learning is a subset of RL, or only if it's fine tuning?

Sutton and Barto define reinforcement learning as "learning what to do- how to map situations to actions-- so as to maximize a numerical reward signal". This is from their textbook on the topic.

That's a pretty broad definition. But the general formulation of RL involves a state of the world and the ability to take different actions given that state. In the context of an LLM, the state could be what has been said so far, and the action could be what token to produce next.

But as you noted, if you take such a broad definition of RL, tons of machine learning is also RL. When people talk about RL they usually mean the more specific thing of letting a model go try things and then be corrected based on the observations of how that turned out.

Supervised learning defines success by matching the labels. Unsupervised learning is about optimizing a known math function (for example, predicting the likelihood that words would appear near each other). Reinforcement learning would maximize a reward function that may not be directly known by the model, and it learns to optimize it by trying things and observing the results and getting a reward/penalty.

> Having spent a couple of weeks on Claude Code recently, I arrived to the conclusion that the net value for me from agentic AI is actually negative. > For me it’s meant a huge increase in productivity, at least 3X. > How do we reconcile these two comments? I think that's a core question of the industry right now.

Every success story with AI coding involves giving the agent enough context to succeed on a task that it can see a path to success on. And every story where it fails is a situation where it had not enough context to see a path to success on. Think about what happens with a junior software engineer: you give them a task and they either succeed or fail. If they succeed wildly, you give them a more challenging task. If they fail, you give them more guidance, more coaching, and less challenging tasks with more personal intervention from you to break it down into achievable steps.

As models and tooling becomes more advanced, the place where that balance lies shifts. The trick is to ride that sweet spot of task breakdown and guidance and supervision.

I imagine there’s actually combinatorial power in there though. If we imagine embedding something with only 2 dimensions x and y, we can actually encode an unlimited number of concepts because we can imagine distinct separate clusters or neighborhoods spread out over a large 2d map. It’s of course much more possible with more dimensions.