Teopaskats

[Teopaskats @teopaskats]

@RHdotfun 0xA55e98D3F9fB8D09450522BD1a5A02e8f0F5E885

[Tushar Jain @tushar_jain]

Yesterday we unstaked a large slug of HYPE. We did not unstake to sell.

This is why privacy matters. Our funds are constantly tracked, forcing regular wallet rotations. Institutions need privacy to operate. This is why we’re long $ZAMA and $ZEC. Zama unlocks private defi, Zcash is the leading private store of value chain.

As capital markets continue to move onchain, privacy is paramount.

[Teopaskats @teopaskats]

@BdotFun @RHdotfun 0xA55e98D3F9fB8D09450522BD1a5A02e8f0F5E885

[Teopaskats @teopaskats]

@RHdotfun 0xA55e98D3F9fB8D09450522BD1a5A02e8f0F5E885

[CodecFlow @codecopenflow]

We're hosting Robo Eve on Thursday, 23 July in Dubai.

Join us for a night exploring physical AI: the hidden layer beneath it, simulation, training data, and the people building it. All in one room.

If you're building in robotics or AI, or want to be, don't miss it.

RSVP: https://t.co/SidcvALRnQ

[Teopaskats @teopaskats]

@BdotFun @RHdotfun 0xA55e98D3F9fB8D09450522BD1a5A02e8f0F5E885

[Teopaskats @teopaskats]

@RHdotfun 0xA55e98D3F9fB8D09450522BD1a5A02e8f0F5E885

[Teopaskats @teopaskats]

@0xvsr @RobinhoodCrypto 0xA55e98D3F9fB8D09450522BD1a5A02e8f0F5E885

[Vesper @0xvsr]

We have even more addresses to add to the $FREN airdrop!

Like, repost, address - all are added automatically. We're pioneering Collaborative Launches and Walletless Launches on @RobinhoodCrypto

If you already entered, add another address to double up, Fren 👇

[Teopaskats @teopaskats]

@alexmccurryo 0xA55e98D3F9fB8D09450522BD1a5A02e8f0F5E885

[🐧 @Pentosh1]

If there is one thing I'm great at. It's knowing when to go on offense, and when to go on defense, and capture large parts of trends regardless of what market it is. Equities, or crypto. Especially the time aspect of defense which is where most people struggle. (see all the repeated bottom calls in the bear market on this platform) Right now, in crypto there are a few good long opps out there that should see some significant returns. But I dont think they are wide spread. Each cycle is less.

This past year I sat out crypto, outside of HYPE which luckily did almost a 3x from the original entry, and a few BTC scalps. The same can be said for 2022, where I largely sat out for a year. From above 60k to just under 20k.

There is a time and place for everything, and the time aspect needs a lot more respect and understanding.

I do my best to help people avoid the long, drawn out drawdowns, and try to get in the best trades possible on the long side. of course nobody is perfect, and we all make mistakes. But I do the best I can. And will continue to try to do that. Im looking forward to sharing a little more crypto content again.

[Teopaskats @teopaskats]

@nicoletteduclar Meow

[CodecFlow @codecopenflow]

Your GPU doesn't have to clear the spec sheet.

https://t.co/Gk9GmsRI7f

[Teopaskats @teopaskats]

@game_for_one Indeed!

[CodecFlow @codecopenflow]

There are two ways to make a robot act, and the field hasn't picked one yet.

A VLA takes what the camera sees, what you tell it, and the robot's own state, and maps it straight to an action. A trained reflex, learned from huge robot datasets. π0.5 and SmolVLA work like this. It's fast and simple because almost nothing sits between seeing and moving. The weakness is that it only knows what it learned. Push it far enough outside its training and it can fail, because it carries no model of what happens next.

A world model goes the other way. It learns to predict how the world changes, then uses that learned simulator to test moves and imagine outcomes before the robot commits. Dreamer is built this way, and DayDreamer put it on real robots. The strength is foresight: it can reason over possible futures instead of repeating past actions. The cost is compute and its own mistakes, because every imagined future has to be predicted, and a bad prediction means a bad move.

So the trade is reflex against foresight. They aren't rivals though. World models are increasingly used to generate experience and training data for the action models. The likely future is hybrid, where the imagination teaches the reflex.

[Teopaskats @teopaskats]

@HYPEconomist 😹👌

[Teopaskats @teopaskats]

@lynk0x CHyXDuhfJsvcdRSdSjpRYY1Yz6XAcYBEsDNW8vjtxao7 🚀🚀🙏

[Qubitcoin @qubitcoinx]

An important milestone for the Qubitcoin ecosystem:

As of June 24, 2026, the Qubitcoin Foundation has been officially established in the State of Texas.

This marks an important milestone in the continued development of the Qubitcoin ecosystem.

More to come! Now back to building.

[Teopaskats @teopaskats]

@RichardPtardio CHyXDuhfJsvcdRSdSjpRYY1Yz6XAcYBEsDNW8vjtxao7

[stage @stagedhappen]

DotCode progress update:

Over the past week we've been reworking the execution architecture behind DotCode.

Rather than treating the coding agent as a chatbot that occasionally calls tools, we've rebuilt the runtime around a supervised execution model inspired by the architecture used in today's most capable coding agents.

Several principles have been successfully carried over:
-The agent is treated as an event stream rather than a single response.
-Tool calls, terminal output, filesystem mutations, build logs, and model reasoning all exist as independent events.
-Execution state belongs to the runtime, not the model, and every failure becomes structured context that can be fed back into the agent for repair.

That separation is important, as our coding model should generate software, whilst the runtime should own execution.

Once we adopted that architecture, we started extending it specifically for DotCode:
-Generated projects are now verified before a session can complete.
-Build failures are automatically converted into repair prompts rather than terminating execution.
-Preview servers are health-checked beyond simply detecting an open port, with additional validation around module resolution, runtime imports, and application startup.
-Dependencies are installed by the runtime when required, long-running preview servers are managed independently of the model
-Execution continues even when the underlying model behaves inconsistently.

This matters because DotCode isn't tied to a single frontier model. Different coding models stream differently, expose tools differently, and occasionally report success before a project is actually working.

Instead of assuming perfect model behaviour, DotCode assumes mistakes will happen.

The runtime verifies execution, detects failures, generates repair context, and keeps the engineering session moving until the application reaches a working state.

The execution pipeline is converging on something that looks like this:
Prompt → Sandbox → Agent Event Stream → Tool Execution → Filesystem → Build Verification → Preview Health Checks → Repair Loop → Live Preview → Recoverable Session

The result is a coding environment that becomes progressively less dependent on perfect model behaviour:
-The agent can make mistakes.
-The runtime catches them.
-The session continues.

That's ultimately what DotCode is becoming.

A supervised, private software engineering runtime capable of writing, executing, verifying, repairing, and iterating on software entirely inside Dot's privacy boundary.