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📡 LoRa (MeshCore) module for Flipper One — an off-grid radio messenger The video shows Flipper One sending a public message that's relayed by repeater nodes, reaching users over a long distance. The MeshCore network is built on LoRa technology (868 MHz in the UK) and uses repeaters maintained...

277,726 просмотров • 7 дней назад •via X (Twitter)

Комментарии: 31

Фото профиля punished car leak
punished car leak7 дней назад

шкода тільки що ви підори русняві, рулить якими хохол зрадник шнирь фсб

Фото профиля Alex
Alex7 дней назад

I made a similar module for flipper zero: Happy to collaborate on this initiative!

Фото профиля K
K7 дней назад

When will it be available?

Фото профиля Murdaruss
Murdaruss7 дней назад

Is this the future of group chats 🤔

Фото профиля ESPKVM
ESPKVM7 дней назад

I'm tired of waiting. Release it! ;)

Фото профиля _AG
_AG7 дней назад

is it available for purchase?

Фото профиля Gamer 𝕃𝕏𝕏𝕏𝕍𝕀
Gamer 𝕃𝕏𝕏𝕏𝕍𝕀7 дней назад

Could be dangerous to do. What if it’s a creepy old man or woman luring people into their van? There should be a passkey for messages to go out.

Фото профиля Thionne
Thionne7 дней назад

Videos like this...makes my wallet goes brrrrrrrr

Фото профиля Waren Gonzaga 🐜
Waren Gonzaga 🐜7 дней назад

Nice! I love it.

Фото профиля Oh Wait
Oh Wait7 дней назад

When the internet goes down, the garage invents a backup.

Фото профиля PILOT / SAFETY GEAR
PILOT / SAFETY GEAR7 дней назад

Do you think it has enough range and connection for use in emergency services or rural areas?

Фото профиля Emanuel Perez
Emanuel Perez7 дней назад

cc: @Darbybailey - may be interested

Фото профиля Jazzbox
Jazzbox7 дней назад

some are taking tech into very interisting directions, and some are applying it in break-out ways - it's inspiring ✨️

Фото профиля V4ytel historii
V4ytel historii7 дней назад

Руснява підарасня

Фото профиля Murdaruss
Murdaruss7 дней назад

Medium encryption group chats **

Фото профиля notime.base.eth
notime.base.eth7 дней назад

Want

Фото профиля mikemaz
mikemaz7 дней назад

We need those

Фото профиля d!giD0F
d!giD0F7 дней назад

Wow nice! A cooler sci-fi walkie talkie !

Фото профиля ➡️ NEEDcreations Midwest BTC Summit
➡️ NEEDcreations Midwest BTC Summit7 дней назад

@polycarpweb5

Фото профиля Shoel
Shoel7 дней назад

Great tech

Фото профиля Jamrick
Jamrick7 дней назад

Purchase here 🛒

Фото профиля JamesBlonde
JamesBlonde7 дней назад

too bad LoRa doesn't work for shit

Фото профиля d!giD0F
d!giD0F7 дней назад

Will this also have Spidey Sense for inside the Backrooms?

Фото профиля Elivagar
Elivagar7 дней назад

Trying to fit in, but a red parachute dragging behind spoils the whole game

Фото профиля Greg Pazo
Greg Pazo7 дней назад

Bitcoin transactions?

Фото профиля Diesel_Pan
Diesel_Pan7 дней назад

Back in the day, when the KGB wanted to plant a bug, they did it for free, now you have to pay for it. Capitalism!

Фото профиля Algambra67
Algambra677 дней назад

Офигеть котлета

Фото профиля m
m7 дней назад

Pointless, but okay 🙃

Фото профиля bluelotus99.base.eth 🍀💫
bluelotus99.base.eth 🍀💫7 дней назад

where and when is it available to get my hands on?

Фото профиля ☩ 𝔰𝔦𝔢𝔡𝔢𝔪 𝔟𝔶ł𒀭 ☩
☩ 𝔰𝔦𝔢𝔡𝔢𝔪 𝔟𝔶ł𒀭 ☩7 дней назад

nice ai slop

Фото профиля HouseOfUkiro
HouseOfUkiro7 дней назад

Coupled with @ANNE_p2p …

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As I promised yesterday, I'll briefly explain LoRA training and share a workflow I made so you can do it quickly. First, let me answer a very common question: 'Why train LoRAs when we have such advanced models?' Even though we have incredibly advanced models now (like NBP), we still can't always get them to do specific things we want. Simplest example: the spritesheet LoRA I made the other day. I generated 1000 images with Nano Banana and only 100 were what I wanted. The LoRA I trained using those 100 images gives me nearly 100% consistent results. Second point is cost and speed. With LoRA, we can cut costs by 4-5x. And while doing that, we're generating 4-5x faster. How many images do you need for a good LoRA? This depends on your LoRA's complexity. For example, when I training the spritesheet LoRA, even though I used 100 images, I didn't include buildings in the training data, so this LoRA doesn't work for buildings. So think about your LoRA's use cases and add examples for as many use cases as possible to improve quality. What are paired images and how to train LoRAs for image-editing? When training LoRAs for image editing on fal, we call each edit example paired images - one with _start suffix, one with _end suffix. For example, if you're training a background remove LoRA, the unedited original photo will be your '_start' image. The image with background removed will be the '_end' image. Simply put: images we want to edit or use as reference get _start, target images we want to achieve get '_end'. Important: save both images with the same name. Like image332_start.jpg and image332_end.jpg. This way the system knows which images pair together. What about training LoRAs for models with multiple image inputs? Same logic. We still use _start and _end suffixes, but with one difference. Since there are multiple input images, we can number them: _start, _start1, _start2. Example: start images, 1st image = Woman portrait (image35_start.jpg) 2nd image = Glasses photo (image35_start1.jpg) 3rd image = Hat photo (image35_start2.jpg) Output image = portrait of woman wearing glasses and hat (image35_end.jpg) Can we do more detailed captioning? Yes. Similarly, you can improve training quality by creating a txt file for each set with the caption inside. Example: create image35.txt and write: 'Recreate the image by putting the glasses from the second image and the hat from the third image on the woman in the first image.' What are Steps? How many should I use? What's Learning Rate? Steps determines how many times the model sees and processes your training data (your images). Each step, the model learns a bit more. But as steps increase, so does the risk of overfitting. So there's no real default. But for a simpler LoRA with 20 paired images, 1000 steps is ideal. Here's a metaphor for the Steps and Learning Rate relationship: Imagine you have a balloon. Our goal is to inflate it to the optimal size. Steps = How many times we blow into the balloon Learning rate = How hard we blow each time If we blow too softly, we need to blow many more times. If we blow too hard, we risk popping it quickly and can't reach optimal size. Of course training won't explode, but it won't work as intended because it wasn't trained optimally. Training's done, now what? Once training's complete, you'll have a safetensors file. Every model you train on fal has a LoRA inference endpoint. In that inference, add your safetensors file link to the LoRA url input, and you can use your LoRA. Thanks for the read! The workflow in the video: If I forgot anything, let me know in the replies.

ilker

15,192 просмотров • 8 месяцев назад

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