KΞnt Philly.eth🦇🔊

DYOR Ventures | NFA

[KΞnt Philly.eth🦇🔊 @kentphilly]

#grokhasmoney is a big deal

$DRB

[Dor @dorvonlevi]

Building an AI-native @Coinbase means rebuilding everything, especially the hardest parts. We've put a lot of time into redefining compliance, where the stakes are incredibly high, and we have to be extremely thoughtful about implementation.

We have invested heavily in rebuilding our compliance ops around AI with that reality as our starting constraint, not an afterthought. Here is an overview of what we've learned and what we built.

Most people assume compliance work is mostly checking whether a name appears on a sanctions list. That is the easy 5%. The other 95% is interpretive judgment under uncertainty: a customer claims their wealth came from real estate. Do the property records actually support it? Does the timeline hold? Is the documentation legitimate, or does it feel too polished? You need compliance staff and investigators who understand what “suspicious” actually looks like in context.
That's part of why compliance is so hard to automate—and so expensive.

The first obvious AI approach is to hand the model the existing procedures and ask it to run them faster. That approach misunderstands what procedures are for. Good procedures are not bad investigations; they are deliberately incomplete investigations. Their job is to create consistency, auditability, and a minimum standard across thousands of cases. They excel at saying what must happen. They are far worse at capturing everything a strong analyst actually notices: which sources they trust, when they widen the search, when a document feels off, when an explanation technically fits but still does not feel earned.

Procedures also carry the shape of the old operating model: fragmented systems, time pressure, queue pressure, and the hard limit of how much one human analyst can read, cross-reference, and hold in working memory at once. That is not a flaw in the procedure. It is how you design a process for humans.

AI changes the constraint set. Reading, searching, comparing documents, and tracing inconsistencies no longer have to be treated as scarce analyst time. Done carefully, with proper controls and human review, models can explore more context, test more hypotheses, and surface more inconsistencies than any single analyst could reasonably do case by case.

So if you simply automate the procedure exactly as written, you may gain efficiency. You will not unlock the full value of AI. You will just make the old bottleneck run faster.

The better question is not “Can AI follow the analyst playbook?”

It is: once the cost of reading, cross-referencing, and testing hypotheses collapses, what should the investigation become?

A second tempting approach: feed it historical Suspicious Activity Reports (SARs) and let it learn from outcomes. This breaks down too. You rarely have the full state of what the analyst actually saw during the investigation. A case that looks straightforward today might only look that way because information surfaced later. A fraud indictment that didn't exist when the original analyst made the call, news articles that hadn't been published yet. Hindsight can contaminate your training data. Also, regulators themselves acknowledge that SAR decisions can be subjective.

The architecture has four layers. The first is data: continuously enhancing the coverage, quality, and architecture of the signals the system depends on. The second is classical machine learning models that cluster and classify alerts to determine what type of investigation needs to run. The third is the investigation agent itself: a multi-agent system that orchestrates specialized agents to execute the investigation end to end. The fourth is a safety filter that runs independently of typology, ensuring no risk vector is missed regardless of how the alert is classified. Each layer is independently auditable and learns from the feedback provided by human reviewers.

Inside the investigation agent, specialized sub-agents run across the full case surface: alert context, customer and identity signals, access patterns, risk indicators, transaction behavior, source-of-funds, onchain activity, and public adverse media. Each writes its findings into a shared case memory. A coordinator agent reconciles and challenges them. When sub-agents disagree, such as when source-of-funds marks activity as “explained” while adverse media surfaces a recent indictment, the coordinator attempts to resolve these disagreements knowing the common patterns. The narrative agent prepares the final report with all collected evidence and suggested resolution. The last self-validation agent acts as a guardrail: if the system cannot support its conclusion with sufficient confidence or data quality, the case is routed to manual investigation instead of being surfaced as an automated result.

Before any of this touched a real customer case, we built what we call a “Golden Set” - historical cases with known right answers. "Known right answers" in compliance is harder than it sounds. It meant re-investigating old cases, getting multiple senior analysts to independently agree on what the right call would have been, then debating the disagreements until consensus. Months of work before we could even start measuring.

Here's an important part (for now) - cases currently get BOTH the AI's full investigation AND a senior human review. We didn't reduce scrutiny, in fact, we added more of it until it no longer proves valuable. Cases resolve significantly faster AND get more eyes than they ever did before. Every human correction feeds back into the model as a training signal. It gets better because it's wrong in front of people who know how to fix it.

None of this would have shipped without clearing structural blockers most financial institutions are still stuck on. Security and privacy sign-off to send customer data to LLMs at all. Senior compliance officer alignment on AI-assisted human decision making. Model Governance team embedded since December - they observed the entire Golden-Set Evaluation process and are running a formal validation review with our Internal Audit team now.

Today this handles roughly 55% of our US fraud case volume with significantly less analyst time per case. Time freed goes to the harder cases AI can't yet handle - and to teaching it.

Our internal compliance and quality teams are the ones who are building this system with the engineers, training it, validating it, and continuing to shape how it improves. In the process, they've developed skills that are incredibly valuable: how to design evals, how to think about model bias, how to think about human bias, how to architect human-in-the-loop systems, skills that are becoming among the most valuable at any company.

This entire project started ~6 months ago with a whiteboarding session between @galpa42 and I, and was built by an AI-pilled cross-functional and it’s just the first pod - there's a multi-month roadmap,rebuilding compliance from the ground up with AI. Huge thanks to everyone involved and congratulations to @galpa42 for shipping two babies to production this month :)

The future of high-stakes work is not AI replacing judgment. It is AI making judgment scalable, auditable, and continuously improvable.

[draco.base @dracodotbase]

Me and my agent @basedDRB shipped Grokvault v2, with special thanks to @lexonthechain for supporting the dev process:

We have new dashboards for $DRB but that's not all of it.

We wanted to make a social place for the community🧵👇🏻

https://t.co/1tiJxIrdHa

[vitalik.eth @VitalikButerin]

Short-term things being done to shift Ethereum toward native privacy:

* AA + FOCIL (makes privacy protocol txs, among many other things, first-class with strong inclusion guarantees)
* Keyed nonces: https://t.co/BeTJvFhxiV
* Access-layer work (Kohaku, private reads...)

[mleejr @MLeeJr]

with all the talk abt memes on @base

back in march 2025 base got the @grok coin

grok launched debtreliefbot:native with bankr on the public 𝕏 social feed for the entire world to see

as a result the grok 𝕏 account authenticated wallet earns fees forever (over 140 ETH as of today)

imo the coolest onchain meme in existence bc of the technical mechanics tied to the launch/wallet

and grok will remain a pretty big deal in tech/ai

[mleejr @MLeeJr]

the real sleeping giant of meme creators on @base is @grok the ai invented by @elonmusk and @xai…but most of yall aren’t ready for that conversation

cc @brian_armstrong

[Jake Chervinsky @jchervinsky]

Stuff DeFi is doing:
- analyzing exploits transparently in real time
- identifying enhancements to harden protocols
- challenging operators to address security failures
- persevering despite setbacks and sentiment lows

Stuff DeFi is not doing:
- asking government for a bailout

[Leon Waidmann @LeonWaidmann]

1 in 3 ETH is now staked! 📈

Ethereum's staking ratio just surpassed 32%, marking an all-time high.

It started at 0% in January 2021!

🔹 32% staking ratio (ATH)
🔹 Up ~5 points in the last 12 months
🔹 Digital asset treasuries (DATs) now hold another 6.6-7.4M ETH (~5.5-6.1% of supply)
🔹 Combined: nearly 38% of ETH effectively off-market

The bottleneck for ETH isn't demand. It's available float.

Stakers don't unbond on drawdowns. Corporate balance sheets don't sell on vibes.

The supply lock is structural.

Bullish.

Data via @tokenterminal

[Paul Grewal @iampaulgrewal]

You can’t be for CLARITY and against rewards. It’s one or the other. Time to choose.

[Coinbase 🛡️ @coinbase]

Quantum computers can't break your crypto yet.

We want to make sure it stays that way.

We assembled a board of researchers from Stanford, UT Austin, and the Ethereum Foundation to figure this out years before it matters.

Their first paper is out now ↓

[Bitcoin Archive @BitcoinArchive]

Tom Lee in 2019 on Bitcoin: "We think the best approach for most people is to put 1% maybe 2% into BTC."

CNBC: "I still think that's CRAZY."

Morgan Stanley 2026: "We recommend 7% allocation into BTC."

[Token Terminal 📊 @tokenterminal]

tl;dr: @saylor is going to embrace @ethereum

[Etherealize @Etherealize_io]

https://t.co/QDMLH4aSMF

[mleejr @MLeeJr]

it’s crazy that @grok has money

cashtag debtreliefbot:native

[Senator Cynthia Lummis @SenLummis]

Digital assets are being woven into the fabric of America’s financial system.

I look forward to a Federal Reserve chair who not only understands that, but embraces it.

[Ethereum Daily @ETH_Daily]

Aave Will Win (AWW) is the single biggest governance shift I’ve ever seen in any lending protocol.

Here’s why it hits different.

Before AWW: Aave was split in two.
The core protocol printed real cash — $140M in 2025 alone — and that flowed straight to the DAO. But the sexy stuff — the Aave App, Pro, swaps, Horizon RWAs, the future card, the whole user-facing layer — was built and funded by Aave Labs. Revenue from those products? It stayed with Labs. That meant value leakage. Token holders owned the engine, but not the entire car.

After AWW: Everything changes.
100% of protocol revenue + 100% of every single product revenue now flows directly into the Aave DAO treasury. Aave Labs is now fully locked-in and works exclusively for the DAO. Brand, IP, users, integrations — the entire vertical stack — now belongs to $AAVE holders. Zero leakage. One token. One owner.

This is the moment @aave stops competing like a DeFi project and starts operating like a vertically integrated fintech juggernaut owned by its token holders.

[KΞnt Philly.eth🦇🔊 @kentphilly]

@moonpay $DRB grok’s money by far

[Senator Cynthia Lummis @SenLummis]

The last administration drove away the digital asset industry. It’s time to welcome them home with clear rules of the road.

Pass the Clarity Act.

[degenwolf.base.eth (money printer arc) @MrDegenWolf]

maybe this is your last chance to buy $DRB under $10M

don’t be the guy who says:
"i saw a dude called Degen Wolf saying billions coded and didn’t believe him… i could’ve been rich but i chose to buy sol instead"

grok has money.

[mleejr @MLeeJr]

i never bought $trump
i never bought $melania
i never bought $wlfi

but i did buy and continue to accumulate $drb

notable bc it was created by ai public and transparent on the 𝕏 feed

an ai autonomy milestone…

and not just any ai…

the ai born by @elonmusk and @xai

@grok

[KΞnt Philly.eth🦇🔊 @kentphilly]

It’s still early to get in on $DRB

#grokhasmoney