Salengᵏᵏᵍ

#profitmaxi

[Salengᵏᵏᵍ @l3eChoD]

@snowmoonrising ฟ้าครับ 😉

[Salengᵏᵏᵍ @l3eChoD]

@TaPlot All the best to you.

[Salengᵏᵏᵍ @l3eChoD]

ตรวจร่างกายประจำปี
ิวิตามิน
ิอุปกรณ์วิ่ง (รองเท้า + Garmin)
ค่ายิม
ค่าเวย์โปรตีน

[sad cat, cat club @ktnttnr]

เพื่อนสาย longevity ที่ไม่ไปเที่ยวต่างประเทศใช้เงินไปกับอะไร

- whoop
- เจาะเลือดตรวจตาม whoop
- ค่าสมัครยิม
- lululemon
- อาหารนอกบ้านเกร๋ๆ ฟีล whole foods
- รองเท้าวิ่ง
- นวด

มีไรอีก555555555

[Coinbase 🛡️ @coinbase]

Wrote it all down in case you missed it:

- Shipped @base MCP.
- Hit an ATH in market share in Q1.
- Rewarded Coinbase One members.
- Brought global crypto derivatives to the US.
- Became Hyperliquid's USDC treasury deployer.

Here's everything that happened @Coinbase in May ↓

[Salengᵏᵏᵍ @l3eChoD]

@CoinbaseDuck Next ER will be..👀👀👀

[Brian Armstrong @brian_armstrong]

Big day for our US-based traders, and for Coinbase.

Until now, US users have been locked out of ~80% of global crypto markets (perpetual futures and options). But not anymore!

Coinbase is the first and only regulated platform able to connect US users to global crypto options and perpetual futures (including Deribit options with >$31B in OI).

Thanks to Chairman Selig and @CFTC for making this possible.

[Salengᵏᵏᵍ @l3eChoD]

$COIN is even worse

[Nova @CryptoGirlNova]

Ethereum is definitely one of the most hated assets right now.

[Salengᵏᵏᵍ @l3eChoD]

@ChartsJavi Dead

[shiorichan 🔖 @shiorichanvt]

หนูจะอธิบายให้คนไทยยั���ไงดีคะว่า 28 องศาที่ยุโรปมันร้อนกว่า 40 องศาที่ไทย 😭

กำลังจะตายแล้ว ยีนคนเอเชียก็ช่วยอะไรไม่ได้เลยค่ะ…

[Salengᵏᵏᵍ @l3eChoD]

Neither, it will be $COIN 🤣

[Pumponomics @ThePumponomics]

who’s getting to $100 first?

$hype from $47 or $sol from $85

[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.

[Paul Grewal @iampaulgrewal]

Today Rs and Ds on Senate Banking did impressive work for the American people. Thank you. CLARITY moves on.

[เก่งภาษาอังกฤษแบ��เด็กจบนอก @SoSplendidPM]

อันตรายจากการบริโภควิตามินมากเกิน บทความจากคณะเภสัชฯ ม.มหิดล ออกมาหลายปีแล้วเผื่ออยากลองสังเกตตัวเองกัน

[กัปตันคนเนิร์ด @captainnerd23]

เงินที่ AOT เก็บก้อนนี้ คนละก้อนกับที่รัฐบาลจะเก็บนะครับ

จากข่าวที่ AOT ขึ้นค่าบริการผู้โดยสารขาออกเป็น 1,120 บาท

มีเพื่อนๆด่ากันเยอะว่ารัฐบาลทำแบบนี้ได้ไง คนด่าเรื่องจะเก็บค่าออกนอกประเทศ 1,000 ตั้งเยอะ ยังจะดันทุรังเก็บอีก

อันนี้เป็นคนละส่วนกันนะครับ 1,120 บาท นี่คือเข้ากระเป๋า AOT ส่วนค่าออกนอกประเทศ 1,000 บาท ถ้าเก็บคือเข้ากระเป๋ารัฐบาล

ซึ่งแบบนี้ชิบหายกับกระเป๋าเราหนักมาก เพราะแม้ทั้ง 2 ค่า จะไปเข้าคนละกระเป๋ากัน แต่ก็ออกจากกระเป๋าเราเหมือนกัน

รวยไม่ไหวแล้วโว้ยยยยย

[Salengᵏᵏᵍ @l3eChoD]

ปีก่อนคือ Evo SL ซ้อม ON Clouldboom strike แข่ง ปีนี้คือ Megablast ซ้อม Sky Tokyo แข่ง (ทีม ASICS)

[วิ่งให้เป็นขนม🏃🏼‍♂️🚲💪🏾 @Loverunnn]

เราอยากรู้ว่าแต่ละคนใช้รองเท้าวิ่งอะไรกันคะตอนนี้ เราใช้Ascics 555555

[Paul Grewal @iampaulgrewal]

After months in rooms at the WH and Senate, this much is clear: a lot of the public debate overstated the risks and ignored the substance. This outcome preserves activity-based rewards tied to real participation on crypto platforms and networks, which is what the bank lobby said they wanted.

We've long believed that this issue did not warrant legislative changes. But whatever-- we’re focused on getting a bill done and are satisifed that this language should not be the basis of any objection. Onward.

[สหัสวัต คุ้มคง @Sahassawat_kk]

ก็นี่ไง ทำไมต้องแก้ มาตรา 112
.
ใครก็ฟ้องได้ มันถึงฟ้องกันมั่วซั่ว

[Salengᵏᵏᵍ @l3eChoD]

Speechless... บ้ามาก

Total respect 🔥🔥🔥

[BBC Sport @BBCSport]

WHAT HAVE WE JUST WITNESSED? 🤯

Sabastian Sawe has just become the first person in history to run a sub two-hour marathon in race conditions.

Yomif Kejelcha was also under two hours for second!

[Noppharnach @backaof]

พยายามบอกตัวเองเสมอว่าหงุดหงิดอะไรก็จัดการอารมณ์ตัวเองดีดี อย่าปล่อยให้ตัวเองไปเป็นแบดเดย���ของใครเพิ่มอีก ☺️