sothatsit

I remember being marked down for using a ternary in Java for an introductory programming course at uni because the teaching assistant didn’t know what it was. Pretty minor loss of marks but it always annoyed me.

That said, having to do the introduction to programming class at uni despite programming for like a decade in high school was the bigger annoyance.

RLVR generally upweights tokens along the whole thinking trace that led to a correct answer, whether each token was "correct" or not. RLVR doesn't train a model to output an 80% likelihood, it just trains it to produce correct answers, and not to produce incorrect ones.

System One hasn't said how RLCD works, but they do say it is explicitly training models to output "calibrated" probabilities, which makes it distinct from RLVR. This is how they describe it:

> System One models are trained for calibrated decisions: their probabilities are optimized against outcomes to reflect uncertainty.

The probability values don’t really represent confidence in modern LLMs though, especially after RLHF and RLVR.

System One says they use RLCD, Reinforcement Learning for Calibrated Decisions, which presumably has accurate probabilities as an explicit optimisation goal.

Maybe "unusual" or "uncommon" would be better terms here.

I definitely wouldn't put it at 99% of developers, but I'd say it is the norm among developers I know that agents are their primary mode of writing code now (still using IDEs and GitHub to review changes).

Everyone I have talked to who uses coding agents at all now uses them to write almost all their code.

The two people I know who don’t use coding agents work in government, and in a data science company working with government.

I’d say if you work at a tech company and agents aren’t writing the majority of your code, that is weird. But if you work at a traditional company that doesn’t have Claude or Codex subscriptions, there it might be pretty normal to still be writing code by hand.

It could also be interesting by having a practical use.

Does AI make real opinion easier to hear, or fake opinion easier to spread? Even if you believe wholly in manufactured consent, how easy it is to manufacture matters.

There’s quite a few out-of-the-norm assumptions in this.

1. Superhuman AI is inevitable.

2. Writing will become a bottleneck to communicate effectively with it.

3. Higher communication bandwidth would let us keep up with the machines.

4. Therefore with higher bandwidth humans could stay part of AI decision processes.

5. That makes it more “human”, as humans would be kept in-the-loop without becoming a bottleneck to be worked around.

6. If you let superintelligence write into your brain, that lets you merge with the machine.

I remember listening to Andrej Karpathy talk in a podcast about how synthetic data in particular is used to generate more data for pre-training. I see no reasons for that to have changed. I think it is likely a lot of the new data they are paying for contributes to pre-training as well.

I would also be very shocked if they weren't filtering or prioritising existing pre-training data as well, for example to do curriculum learning or to avoid data that degrades performance.

The distinction is between information flowing from people to power (elicitation), vs. it flowing from power to people (persuasion). These are not the same, even if they are closely related.

Claude Cowork is the application aimed at non-developers that gives them a lot of the same functionality. My girlfriend uses it and has gotten quite far in producing her own software.

Labs spend billions hiring experts to generate new data, and better models can better filter existing training data and generate new synthetic data. There’s no reason for that to run out, it’s just expensive.

You could view this as just continually patching a leaky ship. But it seems to work.

Fable is much better at handling nuance. Opus/GPT 5.6 Sol are much more likely to miss the point you are trying to make, emphasise the wrong thing, exaggerate the importance of unimportant details, or introduce contradictions.

That said, Fable is still not a great writer, largely driven by it not knowing what it should exclude, and it still having the usual LLM-isms. But it’s better.