vishvananda

It compiles about 7x faster both at -O0 and -O3. My optimization is not quite on par but it is really close. Compiled code is about 5% slower.

I do some pretty insane agentic work running constantly. I’m currently burning through multiple $200 accounts every week. I regularly spend $10-$20k worth of tokens a month. Most of it has been going to my experimental c++ compiler project

The reason people aren’t freaking out is because most people are using heavily subsidized subscriptions.

I tried the cheapest provider on openrouter and burned through $50 in a few days. Quality was ok, seems slightly above Luna quality perhaps? But that $50 is 1/4 of my codex subscription where I could have burned that many tokens or more using Astra within my weekly reset.

This won’t last forever but as long as the frontier labs are subsidizing this heavily the open models won’t matter.

From harness? Because people expect a squishy set of things from a harness that is different from what I end up building. I end up with a rigid internal structure that the harness uses in-turn (tests with clear error messages, tools, etc.) and a matched rigid external structure that drives the turn tracking progress and deterministically handles the overall progress. You could call that whole thing a harness but that makes the definition muddy and hard to talk about. So scaffold or skeleton seems more appropriate. The harness constrains the agent. The matched endoskeleton and exoskeleton gives it structure.

I’m definitely on the deterministic code train as well. All of my success for long running tasks has been around wrapping the agentic harness (cc, codex-cli, etc.) in a deterministic workflow with deterministic gates. We need a name for this outer layer. In my mind that is the true harness because it constrains the agents failure mode. I think flow engineering has been proposed. Maybe it’s the agentic exoskeleton?

I think there is a flow in most organizations from:

llm -> prompt -> result

llm -> prompt + prompt encoded as skill -> result

llm -> prompt + deterministic code encoded as skill -> result

I do think prompting to generate code early can shortcut that path to deterministic code, but we're still essentially embedding deterministic code in a non-deterministic wrapper. There is a missing layer of determinism in many cases that actually make long-horizon tasks successful. We need deterministic code outside the non-deterministic boundary via an agentic loop or framework. This puts us in a place where the non-deterministic decision making is sandwiched in between layers of determinism:

deterministic agentic flows -> non-deterministic decision making -> deterministic tools

This has been a very powerful pattern in my experiments and it gets even stronger when the agents are building their own determinism via tools like auto-researcher.

This is actually a very interesting moment to potentially overcome network effects, because more and more code is going to be written by agents. If a crdt approach is measurably better for merging by agent swarms then there is incentive to make the switch. It also much easier to get an agent to change its workflow than a human. The only tricky part is how much git usage is in the training set so some careful thought would need to be given to create a compatibility layer in the tooling to help agents along.

I am by no means an AI skeptic. It is possible to encode all sorts of things into instructions, but I don’t think the future of programming is every individual constructing and managing artisan prompts. There are surely some new paradigms to be discovered here. A code locking interface seems like an interesting one to explore. I’m sure there are others.

I'm really baffled why the coding interfaces have not implemented a locking feature for some code. It seems like an obvious feature to be able to select a section of your code and tell the agent not to modify it. This could remove a whole class of problems where the agent tries to change tests to match the code or removes key functionality.

One could even imagine going a step further and having a confidence level associated with different parts of the code, that would help the LLM concentrate changes on the areas that you're less sure about.