AI can write a recipe in seconds. But ask a robot to pick up a cup without dropping it, and things get interesting.
That’s the difference between knowing something and being able to do it.
Robots need practice too. They need examples of how to pick things up, move around objects, and react when things don’t go as planned. Getting that data can be costly, which makes projects like @axisrobotics worth watching.
Axis gives people a way to help through robot tasks they can control in a browser. You don’t need to own a robot to play a part in teaching machines how to interact with our world.
With Epoch 2 potentially being the final campaign epoch, now’s a good time to get involved instead of waiting until it’s over.
Explore Axis and start here 👇
https://t.co/jMduKMaclo
Adani group’s play here is pure vertical integration. Gautam Adani already committed $100 billion to build 5 GW of green-energy AI data centres by 2035.
Now, they are moving to own the hardware layer. By partnering with Jabil, they combine their energy footprint with precision manufacturing. Generating the green power, building the data centres, and manufacturing the liquid-cooled AI racks that go inside them creates a highly defensible ecosystem.
This is a definitive milestone for India's push to become a global AI hardware exporter rather than just a consumer. https://t.co/er7SQGBfrw
This Is One Of The Most Anticipated iGaming Token Launches.
@1winToken | $1WIN is coming in at a $30M FDV, with 130M tokens (13% of the 1B total supply) allocated to the public sale at $0.03 per token.
I like how the pricing and token allocation are out in the open before the sale. Everyone knows the entry price, the total supply, and how much is being offered to the public.
Now, I'm interested in how @1win connects the token to its existing ecosystem and build demand beyond the launch.
The countdown is on, connect your Solana wallets and get ready.
One thing I’ve realised while working with AI systems:
A lot of the complexity isn’t in the model itself. It’s everything you have to build around it.
More context handling. More retrieval. More orchestration. More moving parts.
SubQ Snap is interesting because it’s tackling the decision layer directly - with a 384K multimodal context window and a 61.6 score on the Hugging Face Decision Index.
If a model can handle more of that work directly, developers get to build simpler systems.
And honestly, simpler AI systems are usually better AI systems.