Yo @MistralAI it would be very nice of you if you could add a function to report bugs in your app.
My keyboard is gone every time I bring app from background and start a new chat. It’s 100% hit, not so hard to reproduce.
damirca retweeted
Elixir is very good at handling many connections without breaking. So you don't need to give it any special attention for good performance. Here a benchmark with a lot of clients posting to the once-campfire repos. Elixir mops the floor with the Rust versions.
Replying to @CaesarDavid_ @dhh
The problem with all of this that keeps getting glossed over is that Elixir is requiring all of this special attention to get it to the numbers it can achieve. I also believe those numbers under represent Elixir's potential but this isn't a "which is fastest" benchmark. It is "which is fastest without knowing how to write in that stack" benchmark.
Replying to @sami3dat
@sami3dat не могу перестать думать об этой машинке как основу для будущего LLM сервера вместо условной студии м5 за 6-7к ebay.de/itm/960000800685
Ну наконец-то!
Meet Mistral Large 4, aka Le Chonk.
• 1T parameters, natively multimodal. 49B active.
It is the best open weights model from US or Europe on aggregated benchmarks.
• State-of-the-art on critical workloads, including cyber defense, manufacturing and finance and it surpasses closed frontier models on visual grounding.
• Forged in Europe end-to-end and is deployable from Europe via our own Mistral Cloud infrastructure.
• Available to all via API today. Working with cybersecurity partners privately.
Open weights release end of October.
Он еще и оптимизировал раст версию, а го и эликсир версию - нет. Если бы он был честным и все 3 версии получили одинаковое время и количество токенов то разница между го эликсиром и растом была бы минимальна. xitter.cf/josevalim/status/21072…
damirca retweeted
Just so we are all on the same page:
* The Rust version has hundreds of commits, with multiple optimizations rounds, over several days. The others have 1 or 2 commits from an initial agentic rewrite
* Based on the available documentation, different things were asked from each attempt. The agents.md from Go mentions the Rust version was the canonical example. The Elixir one mentions it was the Rails one
* While we don’t have the full prompts, it seems they were vague, allowing the agents to make non-language architectural decisions that impact results (such as how closely it should replicate Rails’ drawbacks)
It is actually amazing how quickly we can have full-blown rewrites up and running. But if we want to take conclusions from the data presented, it is important to understand the process behind it.
The agents finished porting Campfire to Laravel and Django too. I'm sure there are optimizations that could be found there too, but the whole point is to get a sense of how frontier agents do with different environments out the box. github.com/basecamp/once-cam…
Вайбы чисто как менеджер по продажам, не знающий про компьютеры ничего, навайбкодил какое-то говно и теперь радуется и все показывает.
Но… это создатель рельс и у него армия хомячков.
Это такое дно просто пиздец.
I've had agents implement and optimize the Campfire web app in Elixir, Go, and Rust. Yes, Ruby is the slowest. I didn't care about that when the pay-off was huge in terms of productivity and developer joy. But if you're no longer reading the code? github.com/basecamp/once-cam…
Readers added context they thought people might want to know
The shown benchmarks use non-equivalent implementations: Elixir routes all DB queries through a single GenServer with Redis for jobs (retained from Rails), while Rust uses in-process queues, omits CSRF tags/fields, and applies other caching changes.
github.com/basecamp/once-…
raw.githubusercontent.com/basecamp/once-…
github.com/basecamp/once-…
github.com/basecamp/once-…
x.com/zachsdaniel1/s…
Потом смотришь видосики всяких мастеров рабочих и прочих чуваков которые делают шумку например. И там просто пизда: под красивым пластиком сэкономлено на всем, чем можно, все ржавеет, все пустое, подвески нет.
Зато дорого богато выглядит, да. Кому что 🤷🏻♂️
Попробовал вот эту модельку на своей b60 huggingface.co/Launch80/Qwen…
65к контекст лимит
Prompt tokens : 70
Output tokens : 500
TTFT : 290.2 ms
Prefill time : 278.6 ms
Prefill speed : 251.3 tok/s
Decode time : 9.571 s
Decode speed : 52.2 tok/s


















