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https://twitter.com/lxe
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Previous HN thread:
I've been iterating on this writing clarity prompt. The term we came up with was "Asyndetic parataxis with referential ellipsis and repeated rhetorical closure." It doesn't fully describe the exact issue I have with modern AI writing, but it's close.
Also the distillation attacks and other distillation methods that gather chain of thought information from frontier models are also used to generate vast amounts of data in order to generate synthetic datasets to post-train what I believe is the vast majority of models that are being released.
If you look at any information about how these models are trained, the details about how the data has been synthesized, where the agentic traces come from that are used in post-training, etc., they almost never tell you about exactly what model was used to generate them.
The moat is custom inference hardware, lower power, higher throughput, etc.
Perplexity looks bad and unsloth just did some good quants but they don’t perform as good on my asymmetric old dual 3090 box.
However, actual quality evals show literally zero degradation in “hard math” questions against full hosted model at a cost of 1.5x - 2x CoT tokens. I expect other quants to suffer from this too. This behavior seems to decrease as precision increases. I also suspect it’s the specific tuning a what experts I’m picking.
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Occasionally, it performs its own optimizations and commits, which then gets superseded by pull requests and merged code that essentially validates the model's own optimization directionality.
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.
Is this a... design choice? This feels like too egregious of an omission to be a regular vulnerability here.
https://www.ucdavis.edu/climate/news/carnivorous-feeding-squ...
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"
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.
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.
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