Blooms 2 sigma, like many studies that are thrown about, is not as much of a slam dunk as people claim that it is. Have you read the paper or any efforts to replicate?
It's been many months, and I thoroughly prefer my harness inside of nvim as my day to day development environment. Using Claude code or cursor makes me feel very removed from the code.
Exploring code, gathering context and tweaking prompts/giving guidance to the agent are very much enhanced by neovim.
The biggest boon has been the fact that agents make customizing neovim a lot easier. Writing new bindings, config, and even building novel plugins.
Here's a few that I built that fit into my workflow:
- a file picker that renders in your current window (like oil) and uses more intelligent signals for ranking (like frecency) https://github.com/dlants/needle
Recently added support for scripts (like Claude code workflows) and been iterating on the UI for that a bunch.
I also ended up wanting other customized tooling - a more streamlined way to grep, find files and review code that my agent has written. So I wrote a few plugins for that : needle (finder with UI and sorting functions that suit me better), shuck (interactive grepper that has a workflow around refining grep commands) and glean (a review tool that lets you mark parts of the code as seen, leave comments, view diffs commit by commit or collapsed, etc). https://github.com/dlants/dotfiles/tree/main/nvim/lua
These are all in various states of experimental and mostly just for me, but a few of my coworkers and friends have been using magenta and like it.
I guess the hope is that combining two sub-par coding models (xAI's grok + cursor's composer) and combining the data they have access to, they can build something that can compete with OpenAI / Anthropic in the coding space...
I guess I kinda see it... it makes sense from both points of view (xAI needs data + places to run their models, cursor needs to not be reliant on Anthropic/OpenAI).
I think I don't see it working out... I just don't see an Elon company sustaining a culture that leads to a high-quality AI lab, even with the data + compute.
I've been developing an ai coding harness https://github.com/dlants/magenta.nvim for over a year now, and I use it (and cursor and claude code) daily at work.
Fun observation - almost every coding harness (claude code, cursor, codex) uses a find/replace tool as the primary way of interacting with code. This requires the agent to fully type out the code it's trying to edit, including several lines of context around the edit. This is really inefficient, token wise! Why does it work this way? Because the LLMs are really bad at counting lines, or using other ways of describing a unique location in the file.
I've experimented with providing a more robust dsl for text manipulation https://github.com/dlants/magenta.nvim/blob/main/node/tools/... , and I do think it's an improvement over just straight search/replace, but the agents do tend to struggle a lot - editing the wrong line, messing up the selection state, etc... which is probably why the major players haven't adopted something like this yet.
So I feel pretty confident in my assessment of where these models are at!
And also, I fully believe it's big. It's a huge deal! My work is unrecognizable from what it was even 2 years ago. But that's an impact / productivity argument, not an argument about intelligence. Modern programming languages, IDEs, spreadsheets, etc... also made a fundamental shift in what being a software engineer was like, but they were not generally intelligent.
Hey, thanks for responding. You're a very evocative writer!
I do want to push back on some things:
> We treat "cognitive primitives" like object constancy and causality as if they are mystical, hardwired biological modules, but they are essentially just
I don't feel like I treated them as mystical - I cite several studies that define what they are and correlate them to certain structures in the brain that have developed millennia ago. I agree that ultimately they are "just" fitting to patterns in data, but the patterns they fit are really useful, and were fundamental to human intelligence.
My point is that these cognitive primitives are very much useful for reasoning, and especially the sort of reasoning that would allow us to call an intelligence general in any meaningful way.
> This "all-at-once" calculation of relationships is fundamentally more powerful than the biological need to loop signals until they stabilize into a "thought."
The argument I cite is from complexity theory. It's proof that feed-forward networks are mathematically incapable of representing certain kinds of algorithms.
> Furthermore, the obsession with "fragility"—where a model solves quantum mechanics but fails a child’s riddle—is a red herring.
AGI can solve quantum mechanics problems, but verifying that those solutions are correct still (currently) falls to humans. For the time being, we are the only ones who possess the robustness of reasoning we can rely on, and it is exactly because of this that fragility matters!
Thanks for reading, and I really appreciate your comments!
> who feed their produced tokens back as inputs, and whose tuning effectively rewards it for doing this skillfully
Ah, this is a great point, and not something that I considered. I agree that the token feedback does change the complexity, and it seems that there's even a paper by the same authors about this very thing! https://arxiv.org/abs/2310.07923
I'll have to think on how that changes things. I think it does take the wind out of the architecture argument as it's currently stated, or at least makes it a lot more challenging. I'll consider myself a victim of media hype on this, as I was pretty sold on this line of argument after reading this article https://www.wired.com/story/ai-agents-math-doesnt-add-up/ and the paper https://arxiv.org/pdf/2507.07505 ... who brush this off with:
>Can the additional think tokens provide the necessary complexity to correctly
solve a problem of higher complexity? We don't believe so, for two fundamental reasons: one that
the base operation in these reasoning LLMs still carries the complexity discussed above, and the
computation needed to correctly carry out that very step can be one of a higher complexity (ref our
examples above), and secondly, the token budget for reasoning steps is far smaller than what
would be necessary to carry out many complex tasks.
In hindsight, this doesn't really address the challenge.
My immediate next thought is - even solutions up to P can be represented within the model / CoT, do we actually feel like we are moving towards generalized solutions, or that the solution space is navigable through reinforcement learning? I'm genuinely not sure about where I stand on this.
> I don't have an opinion on this, but I'd like to hear more about this take.
$2700/mo is about 1/3 of an engineers' salary (cost to the business of a mid-level engineer in the UK)...
But, there's the time to set all of this up (which admittedly is a one-time investment and would amortize).
And there's the risk of having made a mistake in your backups or recovery system (Will you exercise it? Will you continue to regularly exercise it?).
And they're a 3-person team... is it really worth your limited time/capacity to do this, rather than do something that's likely to attract $3k/mo of new business?
If the folks who wrote the blog see this, please share how much time (how many devs, how many weeks) this took to set up, and how the ongoing maintenance burden shapes up.