https://github.com/colbymchenry/codegraph might approach what you’re looking for. I’ve tried tokensave, a reimplementation in rust, and it seems to work ok when it comes to a semantic search over a code base (albeit it seems almost entirely genAI-coded so I’m reluctant to outright recommend it).
looks like they’ve already found one mouse!
Maybe it speeds up when you twist the handle
lol
lmao, even
If anyone is interested in a more thorough and recent write-up of why many of us call DHH fash, this gnome blog post is pretty good: https://blogs.gnome.org/alatiera/2025/11/06/dhh-and-omarchy-midlife-crisis/
I’m starting to think it’s time the AUR introduced namespacing packages by the maintainer’s AUR username. The single namespace was nice while it lasted, but it seems like we’re going to need to get packages based on who’s uploading them anyways…
Federation is still a work in progress IIRC.
But everyone can already selfhost their own instance if all they want is a web-facing git forge.
I have no hope for my car’s Android Auto or LG’s stupid website — but I choose to believe that everyday software will get better as a result of this frustration. We’re already seeing [acts of rebellion] against the current state of macOS and Windows, and I hope the trend will spread across the stack.
I was hopeful, until I clicked on “acts of rebellion” to see that he’s talking about omarchy…
For the unaware: https://マリウス.com/a-word-on-omarchy/
Now, maybe omarchy has gotten much better in the 8 or so months since this critique was first posted?
Haven’t you heard? “React” content is the new meta (/s only half way).
Slay the Spire but with poker hands instead of medieval-fantasy-combat
Oh definitely. I think it’s anthropic who have stated in multiple interviews that they break even on most of their models, it’s just that they keep spending exponentially more to train the next model. They and openai seem to be stuck in an arms race where switching to purely serving existing models to their existing clients just won’t work. I do wonder how accurate that assessment is on their part.
I find it interesting that according to these numbers, if they entirely stopped R&D and marketing, they would just about break even.
Have you tried turning on “developer mode” in lmstudio and looking through it’s logs? No idea about your particular case, but any time generation in LM Studio has failed for me I’ve been able to figure out why and work around it by looking at the logs.
GC enables webpage bloat, in the sense that these bloated designs would be unfeasible to code with manual memory management. I’m not saying they are caused by GC, but that now extra discipline is needed to resist taking the “easy path”. This is the point I’m trying to make with regard to making LLMs code for us; they’ve added incentive to be sloppy because the “black box” result is the same only more trivially obtained. I’m worried about the knock-on effects because I feel like I’ve seen this cycle happen numerous times. And for some reason some places going “all-in on ai” are now either backing off from that approach or shipping buggier software. If you’re not getting worse code from using LLMs, great. Good for you. Having tried again and again to work with these tools myself, I don’t see how to overall gain any actual effectiveness with/from them - shuffle around the effort, sure, but trying to arrive at the same place as without them only faster and/or with less effort? I just don’t see it happen in my attempts. Invariably I come out feeling like I’ve been over promised and simultaneously lost time trying to wrangle hard truths and intentional code out of something designed for the exact opposite. Or that I’ve burnt what used to be my hourly salary in data center costs to save me a few minutes of doldrums.
It’s funny, I get the impression that you’re doing the exact same thing just with the opposite conclusion to mine. I can’t tell if we just have different priorities when it comes to programming, or some other fundamental miscomprehension of what the other is writing. If there is a conclusion I’m already at and guilty of retrofitting into this conversation, it’s that we are collectively, as a species, taking yet another step towards ballooning our energy consumption out of greed and lazyness and I would at least like to be certain it’s partly enabling meaningful progress towards emancipation of the common person, not further proprietary capture of the tools of labor. This is too close to “factory farming so that everyone can eat (dubiously nutritious) pork chops every day for cheap without doing any farm work themselves” for me to just focus on individual luxury or productivity. I don’t understand how the externalities make up for less manual writing of boilerplate, especially when you need to make the thing double-check it’s boilerplate because it can’t reliably one-shot it.
I want to write more but I’m not certain how relevant it would be to the current discussion, so I’ll just wait to see if you’re still interested in continuing this exchange.
I want to agree, but for example GC has enabled webpages that take 3gigs of ram to do the same tasks we could do with 200 megs fifteen years ago. We don’t automatically build more interesting things once the gritty details and boilerplate are automated, and this stochastic automation gives even more room for “bad practices” to creep in and rob us of the gains it is supposed to bring.
Sorry, I misspoke (miswrote?). I meant growing the code through a genetic-algorithm-like process. Though, fundamentally, I don’t think there’s that much difference between applying a selection process on randomized bytes and having an LLM churn on a codebase.
I feel like you’re only considering the time it takes to reach a particular solution when considering what is inefficient - in which case I would agree it’s probably a wash. However, I don’t think an LLM is less energy-hungry than my own body, and I learn by doing, effectively reducing the cost of future coding iterations. I guess if I could run the LLM and surrounding hardware entirely off of solar power I wouldn’t mind nearly as much - though there’s still that part of banging my head against a problem that I believe is crucial for my own growth. I think that, over time and problems/projects, this compounds in a way that letting the LLM figure out the gritty details just won’t.
I think I agree with your last paragraph, though I do wish the LLM was capable of needing less massaging the more it runs. I hope we’ll be able to figure out how to achieve effectively infinite context length so that it doesn’t have to “forget” all of the previous tasks I’ve had it work on.
I really dislike the idea of making the whole program a genetic algorithm - that approach is nice when you don’t have a straightforward approach to employ/enact, but otherwise it feels both overkill and horrendously inefficient.
The next step for my own harness (whenever I get back to working on it) is definitely to look at leveraging structured outputs to help these smaller models iterate towards a longer term goal.
I’ve been pleasantly surprised by Qwen3.6-27b on a Radeon 6700xt (12GB of VRAM) with 32GB of system RAM for it to offload onto (especially when pushing the context window up past 50k). Definitely more of a “compose prompt and hit send -> do something else -> check back after a while to view results” experience than an engaged back-and-forth, but at least compared to previous models I’ve tried running over the past year or two the results are palatable and sometimes even meaningfully useful.
Given the speed I get, I’ve mostly found it useful for doing overviews of a codebase southy some sort of improvement plan suggested at the end. Tool calls work, but I’m still not comfortable letting it code outright (plus, I think I can still code faster than it for now).
I agree with these “inverse” laws as enumerated, and agree with the reasoning given for them. However, how do we enact them? It seems to me that, currently, the incentives are heavily aligned towards the opposite of these three “laws”.
Is it still the “norm” when it concerns less than 20% of the people that make up society? They didn’t say cars shouldn’t exist at all, just that they shouldn’t be the norm.
Not to mention they wrote “busses, trains” before talking about something exclusive to cities.