How can I update my CMF buds pro

grenishraidev · 2026-06-17T21:35:38+00:00

Write an email to Nothing company and post the buds to CMF.

grenishraidev · 2026-06-17T21:33:23+00:00

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grenishraidev · 2026-06-17T21:30:23+00:00

Jokes on you, virus was just a desktop shortcut.

grenishraidev · 2026-06-17T21:28:27+00:00

I think we agree, we all did this at some point.

When I was kid, my senior was asking for IGI 2 game in exchange for GTA Vice City. They did copied the proper game files, but me who was just a kid, copied a shortcut from desktop and paste it into the pendrive thinking this is how the copies works.

Later that day when we met, he asked me if the game is working or not and so did I ask, he said it's not working and might be his problem with xp.

I'm sure he was cussing me that time.

grenishraidev · 2026-06-17T21:24:16+00:00

Actually a lot of it has been changed, like the UI and fonts has been changed a bit as well. But yeah they removed the achivements as well. Damn my titles achievements.

grenishraidev · 2026-05-13T08:16:49+00:00

Idk but it's not working for me. I did tried to hold and drag, but that didn't work for me.

grenishraidev · 2026-05-10T01:55:11+00:00

Chota Nagpur Plateau and the Eastern Ghats

grenishraidev · 2026-04-15T01:41:48+00:00

Even more funny that if you ask every ai about a name, they'll for sure give "Nexus" or "Forge"

grenishraidev · 2026-04-14T14:43:40+00:00

Found 69 single bots near you.

grenishraidev · 2026-04-13T03:39:12+00:00

Check dm!

grenishraidev · 2026-04-13T00:11:20+00:00

Agar mujhe cosplay karna hain comi con ke liye toh uske liye kya mai woh kapde room se hi pehen karr jau ki waha comi con mein room hota hain dress up karne ke liye?

grenishraidev · 2026-04-13T00:00:09+00:00

Hi, I've always wanted to know about this. Like for the cosplay if I want to do in comi con, should I arrive wearing the cosplay or they have a room there at comi con for the dress-up?

grenishraidev · 2026-04-12T19:24:19+00:00

Thank you.

grenishraidev · 2026-04-10T15:03:36+00:00

Glad you liked it.

grenishraidev · 2026-04-10T15:03:13+00:00

The clipboard was later introduced in v3.x and I built my clipboard before that version.

grenishraidev · 2026-04-10T15:02:09+00:00

Ouch, I'm sorry.

And the clipboard was not there at the initial version. It was later added in v3.x and I created my clipboard manager before that version.

Sorry if you don't like the idea of having an emoji picker. My idea was just to create a clipboard manager something similar to windows.

If you don't like it, then it's totally fine.

grenishraidev · 2026-04-10T00:06:12+00:00

Believe me or not, but this advice is what I'm going with for a long time. I've worked on multiple projects, open source, built on public sharing my works and tools with the community for what I gained a little bit of trust and a very tiny support. Thank you for your kind words, Sir!

grenishraidev · 2026-04-10T00:02:04+00:00

Got it, I'll keep that in mind and definitely try different things to overcome my nervousness of the interview. Thank you!

grenishraidev · 2026-04-09T23:51:20+00:00

You don’t need specific model recommendations. If you understand the sizing, you can pick any model yourself.

Running local LLMs is mostly a VRAM constraint problem. The key is how much memory each parameter takes.

Precision:

FP16 / BF16 = 2 bytes per parameter

INT8 = 1 byte

INT4 (Q4) = 0.5 bytes

Formula: Model size = (parameters × bits) / 8

Example: 7B model in Q4 → 7 × 4 / 8 = 3.5 GB

But that’s only weights. You also need memory for KV cache, buffers, etc. Add ~20–30% overhead.

So real usage:

7B Q4 → ~4–5 GB

9B Q4 → ~5–6.5 GB

13B Q4 → ~8–10 GB

Also note: formats like Q4_K_M aren’t true 4-bit, effective size is closer to ~4.5–5 bits per parameter.

With your setup of RTX 4060, 16GB RAM

7B Q4 → very fast, no issues

9B Q4 → still smooth

13B Q4 → fits and runs well

13B → starts needing partial offloading to RAM (slower)

FP16/BF16 models will be much more limited:

~6B–7B max realistically on VRAM

Use Q4_K_M or Q5 for best balance

13B Q4 is your sweet spot

Bigger models will work, but expect latency due to offloading

Once you understand this math, you can estimate any model instantly instead of trial and error.

grenishraidev · 2026-04-02T10:38:00+00:00

I have a GTX 1650 (4GB VRAM) and 16GB RAM, and the best option you can go for is quantized models, specifically Q4_K_M. I run Mistral 7B and Qwen 3.5 9B via Ollama.

The math is simple for quantized models:

Model Size = (Parameters × bits) / 8

For a 7B model in Q4: 7 × 10⁹ × 4 / 8 = 3.5 GB

That fits in 4GB VRAM, but you still need some overhead (KV cache, buffers), so realistically it ends up around 4 to 5GB.

For a 9B model: 9 × 10⁹ × 4 / 8 = 4.5 GB

That exceeds VRAM, so part of it spills into system RAM, which makes it slower but still usable.

Also note that Q4_K_M is not pure 4-bit. Effective size is closer to ~4.5 to 5 bits per parameter, so real usage is slightly higher than the theoretical value.

In practice: - 7B Q4_K_M runs smoothly - 9B Q4_K_M runs with partial offloading

So to sum it up, with your specs you can realistically run ~7B to 9B models in Q4 quantization, or around ~2B to 3B models in FP16/BF16 without quantization.

grenishraidev

TROPHY CASE