I do this, and it's a huge quality of life improvement. No so much because of shadowing existing binaries, but for better command auto-complete. For example: I have a bunch of tmux utilities and all start with `,t` which is not a polluted command-name prefix compared to just `t`.
But I'm now facing the problem that LLM agents don't like this, and when I instruct them to run certain tools, they remove the leading comma. It's normally fixed with one extra sentence in the prompt, but still inconvenient.
I think this is something people ignore, and is significant. The only way to get good at coding with LLMs is actually trying to do it. Even if it's inefficient or slower at first. It's just another skill to develop [0].
And it's not really about using all the plugins and features available. In fact, many plugins and features are counter-productive. Just learn how to prompt and steer the LLM better.
I share the same feeling. I waited as much as possible to upgrade to iOS 26 / macOS Tahoe.
Two days ago, I finally upgraded. Liquid Glass is one of the worst things I've ever seen in terms of design. It reminds me of when I personalized old cheap android phones or Linux distros just "to look cool". Cool-looking: yes. Unusable: also yes. Tasteful design: almost absent.
Just the increase of the border-radius in all elements makes it hideous. Apps with a search bar on a scrollable list look like a CSS bug when the search bar is on top of the elements. Neither the search bar nor the element underneath are visible. Although this applies to most transparency effects on Liquid Glass. Neither the elements above nor below the "glass" are visible. And the extra value added is zero.
The thing is, I can still adapt to it, or tweak transparency and contrast. But I've seen elderly relatives struggle just because WhatsApp decided to add the "Meta AI" floating button. I can't imagine what this "inaccessible" UI changes can do.
It is the first time I am trying to skip a macos version. I really hope in macos27 they will fix things. I used to skip every second windows version, so back here we are.
I've been using z.ai models through their coding plan (incredible price/performance ratio), and since GLM-4.7 I'm even more confident with the results it gives me. I use it both with regular claude-code and opencode (more opencode lately, since claude-code is obviously designed to work much better with Anthropic models).
Also notice that this is the "-Flash" version. They were previously at 4.5-Flash (they skipped 4.6-Flash). This is supposed to be equivalent to Haiku. Even on their coding plan docs, they mention this model is supposed to be used for `ANTHROPIC_DEFAULT_HAIKU_MODEL`.
Same, I got 12 months of subscription for $28 total (promo offer), with 5x the usage limits of the $20/month Claude Pro plan. I have only used it with claude code so far.
Not sure about the impact of these, I guess it depends on the context where this engine is used. But there seems to be already exploits for the engine:
A few comments mentioning distillation. If you use claude-code with the z.ai coding plan, I think it quickly becomes obvious they did train on other models. Even the "you're absolutely right" was there. But that's ok. The price/performance ratio is unmatched.
It's a pattern I saw more often with claude code, at least in terms of how frequently it says it (much improved now). But it's true that just this pattern alone is not enough to infer the training methods.
I imagine - and sure hope so - everyone trains on everything else. Distillation - ofc if one has bigger/other models providing true posterior token probabilities in the (0,1) interval (a number between 0 and 1), rather than 1-hot-N targets that are '0 for 200K-sans-this-token, and 1 for the desired output token' - one should use the former instead of the latter. It's amazing how as a simple as straightforward idea should face so much resistance (paper rejected) and from the supposedly most open minded and devoted to knowing (academia) and on the wrong grounds ('will have no impact on industry'; in fact - it's had tremendous impact on industry; better rejection wd have been 'duh it is obvious'). We are not trying to torture the model and the gpu cluster to be learning from 0 - when knowledge is already available. :-)
I don't think that's particularly conclusive for training on other models. Seems plausible to me that the internet data corpus simply converges on this hence multiple models doing this.
I enjoyed the post. I was about to link the "Let Me Speak Freely" paper and "Say What You Mean" response from dottxt, but that's already been posted in the comments.
I'm a huge fan of structured outputs, but also recently started splitting both steps, and I think it has a bunch of upsides normally not discussed:
1. Separate concerns, schema validation errors don't invalidate the whole LLM response. If the only error is in generating schema-compliant tokens (something I've seen frequently), retries are much cheaper.
2. Having the original response as free text AND the structured output has value.
3. In line with point 1, it allows using a more expensive (reasoning) model for free-text generation, then a smaller model like gemini-2.5-flash to convert the outputs to structured text.
Yes, this only prevents the callee from mutating it, it can't provide a strong guarantee that the underlying mapping won't be changed upstream (and hence MappingProxyType can't be washable).
There is too much focus on students cheating with AI and not enough on the other side of the equation: teachers.
I've seen assignments that were clearly graded by ChatGPT. The signs are obvious: suggestions that are unrelated to the topic or corrections for points the student actually included. But of course, you can't 100% prove it. It's creating a strange feedback loop: students use an LLM to write the essay, and teachers use an LLM to grade it. It ends up being just one LLM talking to another, with no human intelligence in the middle.
However, we can't just blame the teachers. This requires a systemic rethink, not just personal responsibility. Evaluating students based on this new technology requires time, probably much more time than teachers currently have. If we want teachers to move away from shortcuts and adapt to a new paradigm of grading, that effort needs to be compensated. Otherwise, teachers will inevitably use the same tools as the students to cope with the workload.
Education seemed slow to adapt to the internet and mobile phones, usually treating them as threats rather than tools. Given the current incentive structure and the lack of understanding of how LLMs work, I'm not optimistic this will be solved anytime soon.
I guess the advantage will be for those that know how to use LLMs to learn on their own instead of just as a shortcut. And teachers who can deliver real value beyond what an LLM can provide will (or should) be highly valued.
It is probably a good time to view the root goals of education instead of the markers of success that we have been shooting at for a long time now (worksheets, standardized tests, etc.).
A one hour lecture where students (especially <20 year old kids) need to proactively interject if they don't understand something is a pretty terrible format.
> "Education seemed slow to adapt to the internet and mobile phones, usually treating them as threats rather than tools. Given the current incentive structure and the lack of understanding of how LLMs work"
Good point, it is less like a threat and more like... "how do we shoehorn this into our current processes without adapting them at all? Oh cool now the LLM generates and grades the worksheets for me!".
We might need to adjust to more long term projects, group projects, and move away from lectures. A teacher has 5*60=300 minutes a week with a class of ~26. If you broke the class into groups of 4 - 5 you could spend a significant amount of time with each group and really get a feel for the students beyond what grade the computer gives to their worksheet.
As a teacher, I agree. There's a ton of covert AI grading taking place on college campuses. Some of it by actual permanent faculty, but I suspect most of it by overworked adjuncts and graduate student teaching assistants. I've seen little reporting on this, so it seems to be largely flying under the radar. For now. But it's definitely happening.
Is using AI to support grading such a bad idea? I think that there are probably ways to use it effectively to make grading more efficient and more fair. I'm sure some people are using good AI-supported grading workflows today, and their students are benefiting. But of course there are plenty of ways to get it wrong, and the fact that we're all pretending that it isn't happening is not facilitating the sharing of best practices.
Of course, contemplating the role of AI grading also requires facing the reality of human grading, which is often not pretty. Particularly the relationship between delay and utility in providing students with grading feedback. Rapid feedback enables learning and change, while once feedback is delayed too long, its utility falls to near zero. I suspect this curve actually goes to zero much more quickly than most people think. If AI can help educators get feedback returned to students more quickly, that may be a significant win, even if the feedback isn't quite as good. And reducing grading burden also opens up opportunities for students to directly respond to the critical feedback through resubmission, which is rare today on anything that is human-graded.
And of course, a lot of times university students get the worst of both worlds: feedback that is both unhelpful and delayed. I've been enrolling in English courses at my institution—which are free to me as a faculty member. I turned in a 4-page paper for the one I'm enrolled in now in mid-October. I received a few sentences of written feedback over a month later, and only two days before our next writing assignment was due. I feel lucky to have already learned how to write, somehow. And I hope that my fellow students in the course who are actual undergraduates are getting more useful feedback from the instructor. But in this case, AI would have provided better feedback, and much more quickly.
“It's creating a strange feedback loop: students use an LLM to write the essay, and teachers use an LLM to grade it. It ends up being just one LLM talking to another, with no human intelligence in the middle.”
When I was in high school none of my teachers actually read any of the homework we turned in. They all skimmed it, maybe read the opening and closing paragraph if it was an essay. So I guess the question is if having an ai grade it is better than having a teacher look at it for 15 seconds, because that’s the real alternative.
But I'm now facing the problem that LLM agents don't like this, and when I instruct them to run certain tools, they remove the leading comma. It's normally fixed with one extra sentence in the prompt, but still inconvenient.