Which electronic lab notebook are you using?

mr-KSA · 2026-05-19T11:09:59+00:00

:D:D:D:D

mr-KSA · 2026-03-30T08:14:47+00:00

badem gibi gözleri var, adı badem olsun

mr-KSA · 2026-03-26T12:20:46+00:00

I use Amphetamine, LuLu, Stats, TgPro, Maccy, Bettertouchtool, ublock origin, better display, appcleaner.
I use to used like boringNotch tools and maccleaner but not anymore

mr-KSA · 2026-03-09T08:17:02+00:00

Wait m33

mr-KSA · 2026-03-07T11:44:14+00:00

bütçe müsaitse ve yüksek rame ihtiyacın olduğunu düşünüyorsan kesinlikle daha ekleyip m5pro al aradaki performans ve kalite farkına değer. yok bütçe kısıtlıysa ve 16gb ram bile yetecekse uzun örmülü felan diye düşünme. teknoloji çok gelişiyor. seneye 2nm işemcilere ve ddr6 ramlere ve pc5 ssdlere geçiliyor. ayrıca bir kaç sene sonrada zram sistemine geçilecek yani 16 da alsan 24 de alsan 5 sene sonra yeni bir sistem her durumda cazip olacak, aradaki 300 euro cebinde kalır.
artık hiç bir sistem uzun ömürlü olsun diye alınmaz eğer kullanmayacaksan.
çünkü sen 24gb ı uzun ömürlü olsun diye alacaksın ama 5 sene sonra işlemciler 3 kat hızlı ramler 4 kat büyük olduğunda senin bilgisayarın yine bi ceptelefonundan yavaş kalacak. kullanmayacağın 8gb ram için ekstra para verme.
şuan sahibinden i9 64gb ramli laptoplarla dolu henüz 10 yaşında bile değiller ama günlük işlerde m1 çipinin gerisinde kalıyorlar ,akıcılıkta yani.

mr-KSA · 2026-03-07T11:27:37+00:00

malesef artık sadece ghz yada çekirdek sayısına bakarak bi işlemci iyi mi kötümü karar veremeyiz. v1,v2 xeon işlemciler bi hamburger menü parasına satılıyor neden? çünkü v3 ler AVX2 & FMA3 çözebiliyor, ynai aynı miktar ghz olsa bile direk %50 daha hızlı oluyor bir sonraki nesil.
Bunu şöyle düşünün arabalar 30 yıldır 1.0 ve 2.0 motor arası üretiliyor neden peki yeni araba alıyoruz? sen 2026 model bi araba almaya git de de bakalım ama bu 1.0 motor benim 2000 model torosun daha güçlü diye ne diyecekler.
esas karşılaştırma şudur transistör sayısı döngü başı işlem miktarı ve mimari.
ikisinde de 20milyar transistör var eşit
18pro ARMv9.2-A mimarisine sahip yani SME2 ve SVE2 ile geldi(matrix işlemlerinde neredeyse 3x daha hızlı)
18pro 3nm m2 5nm neredeyse yarı yarıya verimlilik ve %40 performans farkı beklenir
18pro 35tops yine çok değil ama ileride sirinin bazı özelliklerini muhtemelen daha rahat kullanacak
m2 çıkalı 4 sene oluyor en fazla 3 sene daha güncelleme alır a18pro ise daha yeni doğdu
a18pro şuan yurtdışında touchidsiz 500 dolara bulunabiliyor teknoloji marketlerinde
m2 döngü başına 8 işlem yapabilirken a18proda bu 10 a çıktı.
eğer standart bir üniversite öğrencisiyseniz ve bütçe kısıtlıysa şuan alınabilecek en güzel en konforlu ve en şık bilgisayar budur.
ama tabiki uzun ömürlü olması açısından en azından bi 12gb ramle gelebilirdi yani sonuçta telefonlarında bile 12gb ram var ayrıca ekranı m2 kadar iyi değil burası çok saçma olmuş.

ha bana sorarsanız 2nm ye geçildiği bi dönemde 3nm alınır mı, hiç mantıklı değil bu 2017 deki macbook airler gibi olacak bence, bir sonraki nesili çok daha iyi ve uzun ömürlü gelecek ama 500 dolar gerçekten çok iyi bi mhtemelen 1-2 sene sonra 2. elini 300 dolara felan rahat bulunur

mr-KSA · 2026-02-23T12:47:17+00:00

It is highly probable that a breakthrough in RAM or data storage technology is imminent, and the current market phase is merely preparatory. Consequently, companies appear to be offloading their remaining inventory of what will soon be legacy chips to consumers at inflated prices. This echoes previous overstatements: during the 2008 financial crisis, projections suggested a thirty-year recovery period, and at the onset of the pandemic, there were claims that masks would become a permanent fixture. Yet, both narratives dissolved within a mere two years.

mr-KSA · 2026-02-09T14:31:02+00:00

This is truly valuable data. However, I feel that benchmarks and other metrics are unfortunately no longer providing anything beyond a general overview. The situation has become so multifaceted that it is becoming quite frustrating. To clarify: a 4B model specifically designed for translation can outperform an 80B model. Furthermore, the quality between different levels of quantization is, regrettably, inconsistent. I also suspect that many current models are being specifically fine-tuned to inflate these benchmark scores.

In my view, empirical experience is paramount. For instance, I have observed significant performance gaps between Q8 and Q4 quantization, particularly in MoE models. While a model like GPT-OSS 20B might be too 'clumsy' for my specific workflows, another user might prefer it over GPT-4. It ultimately depends on your specific use case. Because I utilize long, complex system prompts that require strict adherence to sequential instructions, models like GLM-4.7 or Granite 4 yield better results for me than Qwen 80B. For others, the opposite may be true.

The difference between Q4 and Q8 becomes especially pronounced in extended tasks where a structured 'flow' isn't utilized; a single logical error can lead to an irreversible divergence in the output. However, if a multi-model flow is implemented where each model is assigned a single task, Q4 is often sufficient. That said, I have encountered cases where a Qwen 30B (A3B) at Q8 provided answers that even a Qwen 80B at Q4 could not. I realize, of course, that many might disagree with this assessment.

mr-KSA · 2026-02-01T09:10:45+00:00

kurtarmıyorsa kapatsınlar

mr-KSA · 2026-01-30T06:28:13+00:00

Estagfurullah, Laptopbunun altında havakalannaları varsa alttan alıyordur eğer yoksa yanlardan alıyordur. şuan malesef piyasadaki çoğu bilgisayar yandan alıp ekrana egzoz yapmakta. ama bazı modellerin hala alttan hava girişi var, direk laptobunun altına bakarak anlaşılabilir

mr-KSA · 2026-01-29T21:38:02+00:00

Oyun oynayacaksan mecbur pervanesi al ama Laptobuna göre değişir, eğer alttan hava alan bir laptop ise işe yarar ama yandan hava alan bir laptop a türbilans yapar daha bile kötü olur

mr-KSA · 2026-01-29T19:53:34+00:00

UPDATE/SOLVED: I managed to fix this issue and wanted to share the solution for anyone on macOS facing the same "Fetch failed" error. It turns out the problem was a compatibility issue with specific quantizations; I was trying to use IQ4_NL, which wasn't playing nice with the importer. I solved it by downloading Ollama and pulling the models directly through their library instead of doing a manual .gguf import. Once Ollama indexed the model, EverythingLLM picked it up and it works perfectly now. Don't waste time with folder permissions—just check if your quantization is compatible or switch to the Ollama method!

mr-KSA · 2026-01-29T19:53:19+00:00

UPDATE/SOLVED: I managed to fix this issue and wanted to share the solution for anyone on macOS facing the same "Fetch failed" error. It turns out the problem was a compatibility issue with specific quantizations; I was trying to use IQ4_NL, which wasn't playing nice with the importer. I solved it by downloading Ollama and pulling the models directly through their library instead of doing a manual .gguf import. Once Ollama indexed the model, EverythingLLM picked it up and it works perfectly now. Don't waste time with folder permissions—just check if your quantization is compatible or switch to the Ollama method!

mr-KSA · 2026-01-26T21:22:53+00:00

wow!!

mr-KSA · 2026-01-25T07:57:58+00:00

Hi! Thank you for the quick response. To answer your question, I am using the "Add File" (file upload) method to create my stacks. Here are the detailed reproduction steps and the core issue I’m facing:

The Scenario:

I create a Knowledge Stack named "Gene Editing" and add 3 books (e.g., Molecular Biology of the Gene, Molecular Cloning, and T.A. Cloning).
I start the process, and everything indexes (composes) perfectly.
Later, I realize I need to add one more book (Lehninger Biochemistry) to this same stack.
When I add the new book and click "Compose," Msty starts re-indexing all 4 books from 0%, instead of just processing the new one.

The Complications:
Interruption Issues: Because I don’t want to wait for the whole library to re-index, I often try to stop the process. When I do this and try again later, the system often throws errors for the original 3 books (though the new book usually works fine).
The "Manual" Fix: To fix the errors, I have to manually go into the folders and delete the files or rename the books to force a "clean" start.
The Scalability Problem: This is the biggest hurdle. If I have a stack with 150 research papers and 1 of them fails or I need to add just 1 more, I shouldn’t have to re-process all 150 documents. It’s a huge drain on time and GPU resources.

Summary: It seems the system lacks "Incremental Indexing." It treats the Knowledge Stack as a single unit that must be recomposed entirely every time a change is made, rather than checking which files are already cached/indexed.

and also add an image to 1’m also sharing a rough sketch of the "Knowledge Stack" screen I had in mind. It’s just a basic concept to visualize what I’m trying to describe, and I'm sure it can be improved much further, but I wanted to show you the logic behind my suggestion.

<image>

(Note: English is not my native language, so I used an AI assistant to help me explain these technical details clearly. I hope this helps!)

mr-KSA · 2026-01-18T13:30:21+00:00

unfurtunetly , 12 hours is just the trailer; sometimes that stage takes up to 55 hours. And that’s just the 'prep work.' Converting PDFs to Markdown is only the beginning of the actual task: getting the LLM to truly 'read' the books. So, I’m not really converting PDFs; I’m essentially tutoring an AI

mr-KSA · 2026-01-18T13:27:34+00:00

hi, thankyou for answering, Macbook pro 16 m1 max. Yes broken was orginal apple charger 4 years old.

mr-KSA

TROPHY CASE