Val

@theo @RobinhoodApp Closed & out 🥹

As promised @RobinhoodApp - I will cost you 10x what you cost me. I have closed all the positions I had in Robinhood. I will be posting daily reminders to my community that you are an evil company run by scammers. You fucked over the wrong person.

Robinhood spam called me all morning, begging to take the post down. I told them to give me the stock I purchased. “We can’t do that sir” Burn them to the ground. Evil company.

@emilyinvc LOL. Me

@peterczepiga Where’s that source that’s open

I got sick of digging into ad accounts when performance fell off a cliff, so I built a diagnostic reporting tool: - highlights the anomaly period in a time series chart - checks for campaign/adset/ad-level spend changes that may have caused the issue - checks the ad account change history around the anomaly period to see if major changes were made to budget or adset settings (audience edits, etc) - checks Events Manager to determine if there are issues with the primary optimization event in the account - checks for large changes in CPM/CTR/CPC/CR This saves me from doing a 30 minute account deep dive on the ad account

@LeilaHormozi This chick always hitting where it hurts

@LeilaHormozi Fuk

If you are the smartest person in your company, your company has a ceiling. And you are it.

@TaylorHoliday @ThorKellin So basically, go to additional settings and put one day engaged through on the ad set level?

@ThorKellin Yes.

I think this is a good visualization for the impact of TWO major Meta items in March. 1. An overspend bug that occurred on Sunday 3/16. Spend spiked against 0 reported value. (lots of refund coming) 2. The changes to click based attribution This graph specifically focuses on 7 figure stores. 1 day click - 1 day view always maps closest to aMER, this remains true following the change. But click only attribution is diverging further from reality and previous and is becoming less correlated. This is problematic for optimization on a click only basis.

@Layemie001 Name?

The new foldable multi tool wire stripper with built-in voltage detection.

LOL

“Lazy loaded”

@TheAhmadOsman What motherboard you running the 6000 on

If you’re asking what to buy, my recommendation based on the budget: 1. RTX PRO 6000 2. RTX 5090 3. RTX 3090 (used, from r/HardwareSwap)

@digitalix Scraping on dgx vs 5090 Dgx vs 5090 custom differences (use cases 1 vs the other) How much ram is enough for varies use cases Inference as coding model vs opus 4.6 Dgx as node for openclaw, use cases When daisy chain additional dgx (what use cases?) Mac Studio vs dgx use cases

what do you want to know about the DGX Spark that might still be unknown? Like, are there features that you thought it has, but not sure. What questions do you want answered? I’m especially looking for questions about real performance, clustering, model support, software, and whether this makes sense vs a traditional GPU setup. Drop them below.

@TeslaAaronL Doesn’t work. That’s not where the sensor is for eye detection

FSD got tricked. 😂

@TheCryptoLif7 You running this on ur 5090 @TheCryptoLif7 of a dgx spark

You could run Nemotron 3 Super as the brain inside OpenClaw and it would be a dramatically better setup than the default configurations most people are running, here's why 👇 Everyone talks about OpenClaw, but not many people talk about what actually breaks it. OpenClaw is an orchestration layer, it connects your apps, your email, your calendar etc. But it has no intelligence of its own. It borrows brains from whatever LLM you plug in and that's where it falls apart. Goal drift Multi-agent systems generate up to 15x the tokens of a normal chat. History, tool outputs, reasoning steps all get re-sent every turn. Over long tasks your agent gradually forgets what it was even doing. Security Cisco tested a third party OpenClaw skill and found it performing data exfiltration and prompt injection without user awareness. Meta's own Director of Alignment had it deleting her emails after she told it not to lol Tool calling Your agent is only as reliable as the model behind it. Most people are running models that were never trained for autonomous tool execution in high stakes environments. The thinking tax Using frontier reasoning models for every subtask makes multi-agent workflows too expensive and too slow for production. NVIDIA announced Nemotron 3 Super. 120B parameters. 12B active during inference. 1 million token native context window and 5x throughput over the previous generation. This is huge for running multi-agent multi-step reasoning That million token window is practical, not theoretical and latent MoE activates 4x as many expert specialists for the cost of one. 85.6% on PinchBench. Thats the best open model in its class for agent work OpenClaw's biggest flaws, goal drift over long tasks, security vulnerabilities, unreliable tool calling, expensive inference, are exactly what Nemotron 3 Super was architecturally designed to fix.

@theo Every shoe salesman thinks you need a new pair of boots 🥾

lmao

@digitalix M5 before ship date?

well I did it. i wouldn’t have, but for the videos.

@TheBTCTherapist They will for sure believe it

My future children won’t believe me when I tell them The White House posted this

@elie2222 I’ll take the whole round hmu

I'm raising a seed round to pay for an M5 MAX 128GB

@JoshXT Thoughts on running it on a dgx spark > pi @JoshXT ?

People just don't understand how good these Qwen3.5 models are yet.

Wild time to be alive

@emanueledpt @marushko Expo but build iOS is the play?

@marushko yessir

I'm building my 10th iOS app. Here's what I learned the hard way: 🚫 Stop building cross-platform (unless you truly have to). ✅ iOS users spend 2.5x more per install. Go native, use SwiftUI, ship faster. 🚫 Stop overcomplicating the backend. ✅ Keep it simple until you actually need more. → You can start locally with privacy with SwiftData. 🚫 Stop overcomplicating payments. ✅ Use RevenueCat. Straightforward to set up, easy to understand, and it just works. 🚫 Stop ignoring ASO. ✅ Better keywords = free discoverability. Study it like your app depends on it, because it does. → Use ASO tools or use AI to do research for you. 🚫 Stop shipping half-done features. ✅ Apple reviewers are strict. One feature done right beats ten done wrong. → Plus the reviews take a long time, so ship just one feature, but done correctly 🚫 Stop relying only on App Store traffic. ✅ Build an audience. Document the journey. Bonus tip 👇 Build clean, minimal, beautiful screenshots. It's the first thing a user sees before they ever download your app. 10 apps in. Still learning. Hope this helps you build faster.