cudam

@cudam321

10h

cudam

@cudam321

11h

cc @unrlzdtrade @AlexAuroraDev @ilblackdragon @rhea_finance @near_intents x.com/i/article/2110…

John Gillen - WartimeEthereum.eth

@BitcoinJesusETH

Nov 8

John Gillen - WartimeEthereum.eth

@BitcoinJesusETH

Oct 24

The market still prices $NEAR like an L1 that missed its moment. It's 74% below its 2022 high

@Bitwise prices it as the chain AI agents run on, with a $155 base case for 2030. That's about 30x from $5.26

BlackRock's research paper "The Machine-Native Economy" describes the same outlook. Agents that find, buy, and pay for compute on their own

In the paper's words, agents "query real-time marketplace APIs to compare available capacity by price, performance, latency, location, and hardware specialization," then pay "on a per-use, per-model-token, or per-job basis"

NEAR already runs two pieces of that:

1. NEAR Intents: you say what you want and solvers route it across chains. $33.99B in volume to date, $4.84B in the last 30 days (DefiLlama), and its fee share has bought back NEAR since February
2. NEAR AI Cloud: private inference inside secure hardware, now a provider on OpenRouter, and running private sessions in Brave's Leo assistant

When I asked Matt Hougan on @MilkRoadDaily whether Bitwise's call was absurd, he said NEAR could 40x or 50x, and that "doesn't strike me as absurd if it actually executes on this vision"

His framing: Intents alone roughly justifies today's price, and the AI goal comes "like a moonshot for free"

For context, $155 a NEAR works out to about a $200B market cap. That's still below ETH's $298B today

Wall Street is already buying in. Bitwise's spot NEAR ETF launched Sept 29 and took in $35.5M on day one, which Hougan called "a Bitcoin ETF style success"

It's important to say that these price predictions are ambitious, and not necessarily going to play out in NEAR's favor. Ryan Rasmussen said their model assumes "a very high likelihood that this doesn't work out," and the bear case in the report is $1.63

If agents end up buying their own compute, the chain built around that job looks cheap at a $6B market cap

It's a risky bet, but the upside is a huge moonshot.

Full disclosure: $NEAR is a position in my Milk Road PRO portfolio. Our PRO team was early to $UNI (+54.8%), $AERO (+93.7%), and $SOL (+39.1%)

Try Milk Road PRO for $1 for 7 days, link in bio: @BitcoinJesusETH

Illia (root.near) (🇺🇦, ⋈)

@ilblackdragon

Nov 7

am.will

@LLMJunky

Nov 6

Deepseek 4.1 Flash 1017 is the Opus 5.5 of the Open Weights world.

It's just so clearly better than everything else. If you can run it, it's the defacto choice.

David Hoffman

@TrustlessState

Nov 6

Haseeb Qureshi ＞|＜

@hosseeb

Nov 6

NEAR.com is now the most liquid venue to buy and sell PEARL spot.

Non-custodial, listed permissionlessly, tradeable in just a few clicks.

I love this because it's the exact same strategy that challenger exchanges use. Binance used to list everything first, then Hyperliquid. Now NEAR.

(Dragonfly is invested in both PEARL and NEAR)

David Hoffman

@TrustlessState

Nov 6

fiskantes ⭐️🩸

@Fiskantes

Nov 6

I just tried to buy some Pearl coins via @NEARProtocol and holy shit that was the smoothest on-chain experience I ever had

It takes few clicks from having no NEAR wallet / receiving a gift via one of your exiting crypto wallets, to buying such an "inaccessible" asset like PRL

(note: we are long NEAR and PRL, but its my first time trying it out personally smh)

Illia (root.near) (🇺🇦, ⋈)

@ilblackdragon

Nov 5

International Cyber Digest

@IntCyberDigest

Nov 5

The bug is like a joke.

On Alpine Linux, type 257 to 1024 characters of anything and you get root. 💀

NEAR Protocol

@NEARProtocol

Nov 5

Illia (root.near) (🇺🇦, ⋈)

@ilblackdragon

Nov 5

Releasing Delta League: edit LLM weights for benchmarking and glory! Join at deltaleague(.)ai

Earlier this week, the idea of editing weights directly with an LLM got a lot of attention —
but it was shunted by limitations in the demonstrated approach.

Delta League is an open e to submit deltas to model weights and get them scored on a hidden test set. Auto-research and hillclimb to make smaller models more performant.

The broader vision for Delta League is creating a platform for open AI research. Beyond just editing weights, this approach offers verifiable evaluation and an open collaborative space for everyone to continue improving Pareto of open source models.

In the coming days, we will extend this platform to support editing model architectures beyond just weights and custom evaluation sets to understand how models perform in specific domains and for businesses.