> For context: GLM 5.1 ran the same task and reached 7.3x. Kimi K2.6 reached 5x. DeepSeek V4 Pro reached 3.3x. The models that stopped early did so because they issued no tool calls for five consecutive rounds, they concluded they couldn’t make further progress and stopped. Qwen3.7-Max didn’t stop.

By this reasoning I could release a model that lacks all the basic optimisations. Have it optimise itself for hours to reach 20x the throughput and then claim that the model is superior to the others?

I am not saying that is what happened here, but the reporting is abysmal.

I would have loved to have this when I was in school and I can imagine it is a great tool for teachers. I am going to let my kids play around with this.

In rereading my comment it sounds like some sort of lame ad, which I can assure you it isn't. I am just excited about it.

I really like this benchmarking. Have you evaluated the judge benchmark somehow? I'd love to setup my own similar benchmark.

I have a very nice grinder: a solis caffissima digital coffee grinder. It is available under a different brand name in the US I think.

I make filter coffee with a very basic earthenware filter holder with melitta high quality yet very normal filters and sometimes I mix it up with an aeropress which offers a different type of taste because of the low acidity way of making coffee. I just drip the coffee into a nice thermos so I can make 4 cups in one go and just pour from the thermos.

My coffee is much nicer than I get in most places, both professional and at homes and it doesn’t cost me a lot in effort, money and, very importantly, workspace footprint.

Espresso machines require a lot of space and maintenance and trouble to make.

Having said all this, I am quite intrigued about all the stories about the negative effects of coffee. I just thought it was about influencing sleep, but I had never thought about the memory and mood effects. I will study this some more in the coming months.

What I'd like is for a proxy or library to provide a truly unified API where it will really let me integrate once and then never have to bother with provider quirks myself.

Also, are you also planning on doing an open-source rug pull like so many projects out there, including litellm?

This post is about exploring code, not documentation. Nobody is going to warn you about the README unless it is super outdated.

Also, kids understand perfectly well that different parents have different rules.

I don’t think the government or Apple should be responsible for protecting you from mopey teenagers by blocking free internet access for everyone just so that it “is fair”. Are you even hearing yourself?

Everything has become so incredibly expensive it just isn't fun to buy anything anymore.

My iPhone 11's FaceID broke a few weeks ago and despite that I think I will just stick with it with today's phone prices.

I've built it earlier and also did a Show HN, now I am going through some of the steps that get recommended to me such as creating Product Hunt launches, etc. But I am struggling a bit with the concept of PH. What is the audience? People into new apps? It all feels a bit desperate to be honest and this app is just a hobby side project, I am not.

So if anyone knows of a good way to get some attention to my useful fun tool, please let me know.

> Add this file to your AI assistant's system prompt or context to help it avoid common AI writing patterns.

So if I put this into my LLM's conversation it is like I am instructing it to put this into its AI assistant's system prompt, so the AI assistant's AI assistant.

The alternative is to say:

"Here is a list of common AI tropes for you to avoid"

All tropes are described for me to understand what that AIs do wrong:

> Overuse of "quietly" and similar adverbs to convey subtle importance or understated power.

But this in fact instructs the assistant to start overusing the word 'quietly' rather than stop overusing it.

This is then counteracted a bit with the 'avoid the following...' but this means the file is full of contradictions.

Instead you'd need to say:

"Don't overuse 'quietly', use ... instead"

So while this is a great idea and list, I feel the execution is muddled by the explanation of what it is. I'd separate the presentation to us the user of assistants and the intended consumer, the actual assistants.

I've had claude rewrite it and put it in this gist:

https://gist.github.com/abuisman/05c766310cae4725914cd414639...

Decoy is 10 MB vs Mockoon's 329 MB and looks and feels differently because it is a native vs cross platform app.

Mockoon seems to have lots of great features such as online mocks and proxies for which you can pay their monthly fee or you could have a lighter weight app that does one thing well natively for a one time fee purchase.

I also notice that Mockoon calls home even when I don't have an account, probably for some sort of tracking. There is no tracking in Decoy.

So overal I think it is about lightness of the applications and UX.