lxe

On my local inference box I have a perpetual codex thread open in my llama.cpp checkout that I periodically ask to take a look at currently pending llama.cpp PRs, do some research on latest MTP, Dflash and other prediction or attention optimizations, do research on the latest model quants and finetunes, take a look at localLlama Reddit threads and just do essentially a sweep of the frontier.

Then it rebuilds latest llama.cpp, grabs the PRs it finds relevant to test against, and then it performs a benchmark and finalizes the upgrade and verifies what model, variant, or even a separate finetune that we should be running.

Occasionally, it performs its own optimizations and commits, which then gets superseded by pull requests and merged code that essentially validates the model's own optimization directionality.

Yes, I was also surprised with this narrative because that's exactly how I have treated my drinking years, And I very rarely see this type of honest look back in the community. A lot of self-reflections are either self-flagellating or vilifying alcohol.

I find it very positive for myself to basically look back at any period in my life and be honest about it without re-purposing it into something that it wasn't.

> The network protocol used by Radicle does not give the confidentiality it was expected to give. Anyone who can observe the network path between two nodes can read the data they exchange as the data is sent in plain text.

Is this a... design choice? This feels like too egregious of an omission to be a regular vulnerability here.

Looks like the harness repo is already removed?

I thought it was a pricing mistake. But no, you just have to select the variations. $42,412.00

Related somewhat: ground squirrels in Bay Area park started actively started hunting voles.

https://www.ucdavis.edu/climate/news/carnivorous-feeding-squ...

Oh, fantastic, thank you. If this allows me to drop files onto an iPhone without me having to open an app, that would solve a lot of my issues. I use localsend right now and although I really love it, it still requires me to always keep the app open on the phone.

I know that all of this is super relevant, but it's extremely aspirational, and I can pick apart pretty much every one of these factors on how it doesn't fully hold up when it comes to the reality of production applications.

Is the actual Z.AI ecosystem good enough to replace the main drivers like Codex and Claude? Because it looks like Z Code is just a Codex fork. Just like the Kimi Code one is.

What irks me about this is that the harnesses seem to be just an afterthought here.

Don't get me wrong, I love messing around with installing Pi, getting it hooked up with OpenRouter, and just trying all kinds of different stuff, local models, etc... but when it comes to literally just setting up a productivity environment and trusting my entire machine with it, I just run Codex.

I have heard from anecdotes where people have indeed replaced their main drivers with DeepSek V4 Flash or GLM and state that "it's almost as good as... [claude/gpt]" but I never hear anyone say "yeah, this is the model/harness that I now run on my machine and don't mess with it"

If you haven't been following the current explosion of video gen models, including all the content you see on social media, you should pay attention.

I have no doubt in my mind that right now, in August of 2026, given a couple of weeks, an amateur with a high-end last-gen GPU or somebody with maybe a thousand bucks worth of AI credits, given a good amount of exploration/tinkering to fill the gaps that GenAI cannot currently do, (which is storyboarding, screenwriting, characters, and all the actual creative stuff), can crank out a high quality 1h+ feature film with only minor uncanny artifacts.

The reason why this isn't happening en masse right now is that most creatives are probably not focused on generating feature films, but rather content that's more digestible by social media users.

I would expect to start seeing a LOT of development in the upcoming months as some form of commodification where generated content can be both high quality and tailored to the user.

I'm sorry, what? This just pipes the output to another LLM. You can just use a sane system prompt to do the job.

Yup, I guess I should have added choice 5...

Then who gets prosecuted?

If you structure learning in such a way that makes learning just a means to some end, and overindex on that end being the ultimate goal, that's what you get.

This is a pedagogical problem that AI merely exposed. Educators need to figure out How to make students choose the scenic route instead of having them optimize for the most efficient completion of a task.

Let's say I am "User". I subscribe through a "Third Party" to use "AI Agent" allowing an "LLM" to run.

I want to accomplish some legal non-nefarious task, and run the agent. The agentic loop causes a CFAA-violating behavior.

Who gets prosecuted?

1. User

2. The third party model host with whom I have the account

3. The developer of the harness /agent software

4. The developer of the LLM model

Yup and this is what discovery is looking for / has already found.

Ultimately, I think this is going to come down to proving exactly the extent of what Meta knew, and what they decided to do about it, and how that violated various laws.

I wager there is a foot gun component to this, in which Meta has tried to preemptively, either through gestures of goodwill or public reassurance or PR, institute various think tanks, work groups, and internal studies with an intent to alleviate the public's worry.

Those studies and workgroups actually could end up demonstrating that their products and algorithms and user interfaces, indeed, caused whatever this trial accuses them of doing. And if they didn't act according to what their internal data showed, I bet this is going to be the crux of the trial.

Noted. Will make it worse. Thanks.

What's preventing anyone to just spend some tokens and bring it back and fork Chrome? I think ungoogled Chromium still supports it, right?

This is from pre-ai era

I should modernize this?

To spread suffering

Feedback taken. Will ask ai agents to make improvements.

I made this 6 years ago but it still applies today.

I'm sorry

lol

Is it just me? But every time I use a paradigm of global immutable state, almost always I run into edge cases where practically it falls apart either semantically (mental model explosion) or creates performance issues, whether they stem from architectural problems with the framework itself, or just the way computers operate.

That is definitely not the case. It transcribes everything very accurately, especially all the "ahs" and "uhms".

What's the best local TTS model right now? I'm running parakeet on a mac which transcribes all my uh's and aahs. I'm running whisper on linux/cuda and I by far prefer that one over parakeet.

It's kind of ironic how this doesn't work in Firefox.