And this is how the snowball of adoption starts.
More subnets integrated → more routing → more usage → more developers → more eyes on the ecosystem.
$LAYERTAO is making Bittensor easier to actually build on, which is the missing piece most people still overlook $TAO 💎
Everyone loves $TAO and what they are building but the main impediment here is navigating the network both for developers and enthusiasts
Think of it this way.
Everytime you go to a supermarket, the design and order of items in a particular manner makes it easier for the customer to find whatever you are looking for
The $TAO network on the other hand is the complete opposite. While the item (subnet) you are looking for does exist in the supermarket, you might find the bread in the cleaning supplies section, dairy items in the electronics section and so on
This is where $LAYERTAO comes in. It acts as your assistant (Abstraction Layer) to help you find the neccessary item (subnet) based on your needs without you spending any extra time navigating the network
$TAO is here to stay and will be here for a very long time and this is precisely why we need LayerTao to help the user/developer navigate the system
Highly suggest to take a read of the docs to better understand what LayerTao is building
CA: 0xA73Cc56A437718F6da80dd7F5e26a8E24B9F852c
Docs: https://t.co/FZj8hCdkbg
Teams like $LAYERTAO are the reason I got into Web3 advisory in the first place.
Good people, genuine builders, and actually focused on adding value.
When things got ugly and sentiment around $TAO collapsed, most teams disappeared and a lot of people switched up instantly. These guys kept building through it.
That’s why I stayed.
Now it’s already pushed well off the lows and feels like it’s getting back on track toward where it should be.
I’ve spent the morning digging into $LAYERTAO @LayerTao
It’s not an area I’m particularly good at checking, so I ran it all through ChatGPT and it was incredibly positive for such a low cap.
The utility, market fit, etc all seem very well calibrated.
What I am good at analysing, whether price will go up 😅
✅ ~150k mc on ETH
✅ Some quality trench hunters in early such as @ademo9998@WhitePellow@HawkOfCrypto
✅ A great utility that could easily see big adoption.
✅ Under the radar of all big accounts so far.
✅ Team seem highly capable and serious.
I’ve bought a small bag. My $GURU fund has bought a small bag. It’s my intention to buy more.
I think there’s potential for a big runner here but as always anything at this level is risky.
I absolutely think it’s a great entry here and see if it grows with increased momentum and exposure.
CA: 0xA73Cc56A437718F6da80dd7F5e26a8E24B9F852c
NFA DYOR. As always I have bought my own bags.
Today marks the first-ever abstraction that took place on the Bittensor network, powered by the LayerTao Router.
1 API = Every subnet.
LayerTao is building the future of Bittensor by abstracting it today.
I have always been a fan of Bittensor and an OG $TAO holder and that is why I love projects that build around Bittensor. Thus, $LayerTao is one that I have been following for quite some time.
In very simple words, Bittensor has all these different AI subnets being built, but honestly most people have no idea which subnet does what.
Like if you want one thing, maybe Chutes is the best option. Another task might be better handled by a completely different subnet focused on search, inference, images, or something else.
That’s the problem though. The ecosystem is growing fast, but it is becoming harder for normal users to know where to go.
This is why I think LayerTao is actually a pretty interesting concept. You don’t need to learn the whole Bittensor ecosystem or figure out which subnet is best for your task. You just give LayerTao the request and the router figures out where it should go behind the scenes.
If Chutes is the best fit, it routes there. If another subnet is better for that specific task, it routes there instead.
Feels like an abstraction layer for Bittensor in a way. Bringing all the power of the ecosystem to people without needing them to understand all the complexity underneath.