fcarraldo

Regulation of predatory pricing practices is a primary function of government.

In the title.

Sorry, let me clarify. Regulations should have legal penalties for the corporations who violate them, yes.

What I meant was that we do not need to go from the Wild West straight to “anyone who, as an individual, connects a `bad thing` to an LLM will go to jail”

We don’t even need to jump straight to legal penalties - why not just regulate that these companies can’t build partnerships with organizations in designated risky/regulated industries such as biotech, defense, etc?

Instead of what they are doing now, which appears to be actively courting as many of them as possible for vast sums of money.

it’s satire…

this happens all the time. have you heard of the Sackler family and the opioid crisis?

the difference here is that they’re calling for someone to stop them, which is both weird and unconvincing because these immensely powerful billionaires can in fact make their own decisions

Project 2025 is a great analogy, because of course, they did exactly what was in the plan despite distancing themselves from it during the election.

When people tell you who they are, you should believe them. These guys are all Yud acolytes who think they are the only ones who can prevent Roko’s Basilisk.

pretty sure it’s more Wild West than ever out here. engineers aren’t even reading the code anymore.

no, it’s worse. software users will randomly be flagged to be testers of new code without their knowledge or consent. they don’t have to accept changes in SaaS, they get what you give them and they pay you for the privilege of having their software change in undesirable ways and having to contact support when it breaks.

I want this to be a thing, but a $3000 non-upgradable box is not the way we’re going to get there.

> But they're still around because there are a lot of devs who don't even glance at what the agents are writing for them

But how does automated AI code review help, here? Doesn’t it just reinforce that they don’t need to look at it (or change their habits), because the AI review will catch the issues?

Gemini is utterly useless, but every other modern model would be capable of answering this correctly. If you ran a local coding agent, I wouldn’t be surprised if you could one-shot implement gesture support in Ghostty. It’d definitely configure BTT for you.

Here’s GPT 6’s answer to the prompt “What macOS terminal apps support gestures? Include a reference to the docs on how to enable/configure them.”:

iTerm2 supports configurable three-finger taps and swipes for switching tabs/panes, creating splits, pasting, etc. Set them up under Settings > Pointer > Bindings. Check for conflicting macOS trackpad assignments. [1]

The others are more limited: Ghostty supports macOS lookup/Quick Look gestures [2], while WezTerm lets you bind scroll events—for example, Ctrl+scroll to change font size. [3] Neither is equivalent to iTerm2’s gesture bindings.

For custom gestures without switching terminals, BetterTouchTool can map app-specific trackpad gestures to the terminal’s existing keyboard shortcuts. [4]

[1] https://iterm2.com/documentation-preferences-pointer.html

[2] https://ghostty.org/docs/features#macos

[3] https://wezterm.org/config/mouse.html

[4] https://docs.folivora.ai/docs/trackpad-mouse/magic-mouse-tra...

Tremendous amounts of the Java out there in the training corpus would be based on outdated patterns, especially anything pre-Java 8 or pre-Java 21 (Pattern Matching, Project Loom). I don’t think this negatively impacts the quality of LLM Java code but it sort of calls into question whether this is simply a case of more is better.

My personal experience is that LLMs are quite good at one-shotting complex solutions in both Rust and Go, and tend to be idiomatic.

While I don’t disagree that they should be fighting for open source AI, Sanders is hardly “fighting against all AI”.

https://jacobin.com/2026/07/ai-nationalization-sanders-liber...

I’ve had a good experience with a greenfield project.

The single thing that seems to have helped is that we all agreed to use OpenSpec early on, and to commit the specs alongside the code.

I have no affiliation with OpenSpec and I don’t suspect it’s doing anything unique here, but having the intent develop alongside the code in the repository seems to have ensured that agents have a more holistic view of the project.

It’s a night/day difference when I use an agent against this codebase that integrates its changes using OpenSpec and those that ignore it.

in the US? no.

Not factual

I wish this had non-model comparisons. If Opus 5 is in the top ten, it’s clear that the entire benchmark is somewhere between “Tom Clancy” and “Dan Brown” and about 1,000 new model releases away from Hemingway.

When you see, “Wow, Fable is number one”, you might think it’s a good writer, but that’s not what the benchmark says.

This is extremely impressive for such a young entrepreneur.

I have no reason whatsoever to use this, but it's a cool project!

Nearly 75% of Americans are overweight. (over 30% 'overweight', over 40% 'obese').

Obviously, 75% of Americans do not develop dementia. Do more who are overweight develop dementia than those who are not? Well...it's hard to say when 3/4ths of the population is overweight.

Subscriptions are a very small part of their overall revenue (estimates have been between 5% and 20% based on financial reporting). Enterprise users are charged per-token, and maximal input/output tokens nets them maximal revenue.

I love this theory. "We've invented a new invisible watermark that can detect whether code is LLM written."

The watermark: counting instances of 'load-bearing seam', 'the hard truth', 'and that's the whole point'.

We are solving problems, but we're also creating more problems with those solutions. Greenhouse gases, global warming, oil spills, wars over resources, straining power grids, species extinction, worsening mental health, worsening social cohesion, widening inequality. All are the result of technological progress within our systems of government.

Hedonic adaptation prevents us from asking "is this worth the downsides, or should we go back to how it was?"

Can anyone explain why Cerberus needs to be _fast_ instead of _cheap_?

I don't think I understand why they aren't leveraging the increased speed to do batching to serve more customers at a "normal" tok/s.

Is the limitation, even on cerberus, still that the cache can only serve so many concurrent sessions over time? Is there no scaling advantage? I genuinely do not understand how any of this works.

The assumption is more that, given tools to automate the “less important” parts of important processes, this starts a slippery slope where capitalism will squeeze as many humans as possible out of the job to reduce costs because the “AI is good enough.”

Until, of course, it isn’t - but at that point, the budget is reallocated, there’s no funding, there’s simply nothing we can do except continue to pay this AI contract.

You can, if you have a trusted build graph. I know that “cone-shaped” checkout tools like this are common in monorepo environments, but unfortunately there aren’t any maintained open source implementations that I’m aware of.

Consumer dollars are worth nothing in tech. All of the money is B2B. B2C businesses exist to support, bootstrap, or grow the value (via ads) of B2B businesses.

Vote with your wallet works when you’re talking about local businesses. It doesn’t work when the only factor that determines where the money goes is the executives signing the contracts.

IME, AI does find established patterns more quickly a in monorepo (sometimes the ones you want, sometimes the ones you don't) - but at the cost of an enormous overhead tax you pay on input token cost.

Giving agents pointers to the right patterns, libraries and services helps avoid expensive grep goose chases, but if you're already curating the input you can do the same thing with small repositories.

I work at a monorepo company and any time someone gets to work on a project that necessitates working outside of the monorepo, it's a night-and-day improvement.

Tools, especially open source ones (linters, static analysis, scanning, LSPs, IDEs, etc) are not built for monorepos, and with AI Agents working in the monorepo results in an enormous increase in input tokens as the agents are constantly trying to grep this giant source tree.

I'm sure it's possible that we're doing the monorepo thing wrong, but I'm genuinely curious what the upside is that you're experiencing? Or are these drawbacks unique to our implementation?

found the product manager