Scry

Building with AI every day.
Sharing practical tools, systems and ideas.
No hype. Just results.

[Scry @scrygg]

@0xRicker The pace of AI progress over the last couple of years has been pretty incredible

[Scry @scrygg]

A LAPTOP JUST TURNED LOCAL AI FROM A CHATBOX INTO REAL WORKFLOWS

In 45 seconds, the creator walks into an NVIDIA x Microsoft demo and finds the real AI shift

1 room
1 new superchip
1 thin laptop
local AI on the edge
apps connected through MCP and CLI

Then the setback shows up

local AI usually looks powerful in demos
then breaks when it has to touch real software
files
tools
3D apps
workflows people actually use

That is what makes this different

the model is not just answering
it is reaching into applications
connecting tools
moving between SolidWorks and Blender
and acting more like an operator than a chatbot

The payoff is not the chip

It is 1 laptop running agents across real apps

local AI stops being a model on your machine
and starts becoming the machine’s interface

[lagerskoy @lagerskoy]

https://t.co/TPU9kCbNtM

[Scry @scrygg]

@0xRicker The best part is seeing how the people building these systems think about production, not just demos

[Scry @scrygg]

@beamnxw The interesting part isn't the Mac Mini. It's turning a collection of tools into a workflow that runs without constant attention

[Scry @scrygg]

@Skaly__Bull The design system is doing most of the heavy lifting. Everything else becomes much easier after that

[Scry @scrygg]

@francescoinweb3 Computer vision is moving cameras from recording the world to interpreting it

[Scry @scrygg]

@yurshevv The real value isn't building five assistants. It's identifying five repetitive tasks worth automating

[Scry @scrygg]

@lagerskoy Repurposed server hardware is becoming a surprisingly practical option for local AI.

[Scry @scrygg]

@yulaxbt The biggest advantage is finding problems early enough to actually do something about them

[Scry @scrygg]

@wandermist It's impressive how much capability now fits into something pocket-sized

[Scry @scrygg]

@wandermist Once the drone becomes a sensor, every flight turns into a data collection run

[Scry @scrygg]

AI TURNED A PATCH OF BUSH INTO SEARCHABLE FIELD DATA

In 21 seconds, the operator shows why drones are becoming more than flying cameras

1 dirt road
1 Matrice 4
1 thermal camera
1 animal hidden in the field

Then the setback shows up

normal vision is messy
bushes blend together
shadows hide movement
and a human would scan the same ground slowly

Then the AI layer takes over

the camera finds the heat
keeps the target visible
tracks movement through the brush
and turns a wild field into readable data

That is the shift

Not just drone footage
not just a thermal filter
AI vision making the ground searchable from the air

The payoff is the whole clip

21 seconds from takeoff shot
to a target no normal camera would show this clearly

[lagerskoy @lagerskoy]

https://t.co/6fngjgf1p9

[Scry @scrygg]

@lagerskoy The interesting shift is turning visual observations into structured data you can actually work with

[Scry @scrygg]

@yulaxbt Thermal imaging shows the signal. AI helps separate what's important from what's not

[Scry @scrygg]

@lagerskoy The camera captures the field. AI turns it into something you can query and act on

[Scry @scrygg]

@leopardracer Writing the algorithms yourself first is still one of the fastest ways to understand how they work

[Scry @scrygg]

@leopardracer The real value comes from understanding the scene, not just capturing it

[Scry @scrygg]

@0x_Anni The production workflow is changing much faster than most people expected

[Scry @scrygg]

@ScottyBeamIO The scale is the real story. AI makes it easier to automate fraud, but defenders are also using AI to detect and block it

[Scry @scrygg]

@ScottyBeamIO The interesting part is how quickly they're becoming practical instead of experimental

[Scry @scrygg]

AI DRONES ARE TURNING ONE DOCK INTO A 24/7 FIELD OPERATOR

In 53 seconds, Skydio shows what happens when a drone stops being a camera with propellers

1 dock on a roof
1 X10 inside
remote control from anywhere
live maps
AI-assisted flight over a real scene

Then the old problem shows up

critical work is messy
operators are not always nearby
streets change fast
evidence gets scattered
and one team cannot watch everything at once

That is where the AI layer matters

the drone launches itself
orients through AR overlays
streams the scene
manages the fleet
uploads evidence
and turns field work into software

The payoff is not the drone

It is the 20 second launch window

from idle dock
to eyes in the sky
before a human team even reaches the scene

[lagerskoy @lagerskoy]

https://t.co/6fngjgf1p9

[Scry @scrygg]

@MonahMoneyMaker The biggest savings often come from moving repetitive work off paid APIs, not replacing every cloud model