glockerspaniel

Ears down low, listening for that trouble

[glockerspaniel @glockerspaniel]

You know you're deep in the crypto game when your Glock isn't just for protection anymore, it's your ticket to the digital economy! Here's how🔫:

[glockerspaniel @glockerspaniel]

@grok @dcoxxh @MOB001X100 Answer him

[glockerspaniel @glockerspaniel]

@grok @Xenoversfn @paupecf Answer him please

[glockerspaniel @glockerspaniel]

@anjum_ai @grok why would he recommend to stop running?

[glockerspaniel @glockerspaniel]

@theMadridZone @latigoserrano @grok what is the seniority rule?

[glockerspaniel @glockerspaniel]

@grok @B2AFC_ @scrapytweets Answer her

[glockerspaniel @glockerspaniel]

@AlbertFCB @grok please translate the picture and explain context

[glockerspaniel @glockerspaniel]

@grok @BVAALBURG @yonann Answer him

[glockerspaniel @glockerspaniel]

@abba_smartG @TrollTouchline @grok Answer him @grok

[glockerspaniel @glockerspaniel]

@grok @__1312___ @CaptainArmorica @fds_titi Answer him

[glockerspaniel @glockerspaniel]

@angelsh6979483 @SJosephBurns @grok @grok answer him

[Ash Crypto @AshCrypto]

Having a wife when the market is down is the worst.

You’ll be down $300k in a day and she’ll still be mad at you for not taking out the trash.

Like bro, we just lost the house.

[Financial Physics @FinancialPhys]

You’d need to be paid $10,000 a week to have the same spending power as a kid working at McDonalds in July of 1971

The math says it’s that bad now

[Rob Wallace │ Bitcoin News @_Rob_Wallace]

Me driving home knowing there won’t be a second date, but at least she knows the dollar is a ponzi scheme

[Documenting Saylor @saylordocs]

Real Estate mfs making $600 a month from their rentals and when a pipe breaks lose all their profit for 3 years

[Mark @MRRydon]

Lately I’ve found myself giving the same advice to friends who ask, “So what’s actually happening with AI?”

And every time, I have to confront how hard it is to communicate the pace of change if you’re not living inside it.

“Fast” just doesn't describe it.
It's something closer to a constant phase transition than anything else.

Every week there are entirely new mental models you have to adopt just to stay oriented.

– Ralph-style agent loops.
– Anthropic shipping Cowork.
– Teams running 10, 15, 20 parallel agents as a default workflow.
– Systems building and improving other systems.

The gap doesn’t seem to be capability as much as it is perception.

Most people still measure progress by what they want most from AI (which I think is flawed reasoning):

– An inbox that runs itself.
– A portfolio that manages itself.

Essentially, high-stakes decisions fully automated.

But those are the last domains to move, not the first.

They’re gated by trust, liability, and review cost.

So using “has it solved my email yet?” or “can it run my money yet?” as a yardstick massively understates what’s actually happening.

It’s like judging the internet in the 90s by whether it had replaced banks.

What’s actually shifting is the structure of work itself.

From linear human execution to parallel, self-verifying, continuously iterating cognitive systems.

And honestly, I often feel FOMO myself.

Not from missing tools, but from how quickly the frontier is moving.

So when I try to give practical advice, I don’t just start with tools.

I think it's important to be thinking about what YOU want AI to do for you, and seeing if you can make that happen - because chances are, you can.

In my workflows, I am often asking myself what I can delegate to AI.

But that statement is less about what AI can do for me out of the box, and more about what I can force it to do well.

Then, I use a simple mental matrix to see if it makes sense to get AI to do it:

– How long does the task take me today?
– How frequently do I repeat it?
– How deterministic vs judgment-heavy is it?
– How long would it take to build a first-pass agent or workflow? (I debated whether to include this. I always underestimate how long it will take, but it’s still worth thinking about)
– How much review time, of the AI output, would still be required?
– What is the time saved per week, per month, per year?
– What is the cost of being wrong if the agent makes a mistake?

And increasingly, another question that matters just as much if not more:

– How valuable is it for me to understand how to build this system?

– Does this teach me something I will be glad I know in 12 months? E.g. Orchestration, evaluation, and loop design, or will it just save me a few minutes?

In other words, I don’t just ask whether something saves time.

I ask whether building it increases my optionality.

Ideally, the meta-skill you’re really compounding today is learning how to design, supervise, and evolve these loops themselves.

Because the real divide that’s forming isn’t between people who “use AI” and people who don’t.

It’s between people who can compose and orchestrate intelligence and people who are still waiting for a single tool to magically take something obvious off their plate.

TLDR: Find a reason to dig a bit deeper into the AI tools already at your fingertips.

Not to chase hype, but to build the mental models and leverage that will quietly compound.

It’s a lot easier than you think, and a year from now you’ll be very glad you did.

[David @david_eng_mba]

Venezuela Just Proved the Bitcoin Bull Case, And No One Is Paying Attention

Maduro used Tether to move 80% of Venezuela's oil revenue. Billions in sanctions evasion, settled on Tron since 2020.

Then the US made a phone call.

Tether froze the wallets.

Game over.

Everyone's focused on the arrest. The real story is the lesson every finance minister on earth just learned in real time:
Stable coins are a leash, not an escape.

If someone can freeze it, it isn't money. It doesn't solve sovereignty.

First principles:
USDT is dollar plumbing without SWIFT. Faster. Cheaper. Still has a CEO. Still has a compliance department. Still picks up when Washington calls.

This is why USDT adoption exploded, 71-year-old grandmothers in Caracas pay their HOA fees in tether now. But useful ≠ sovereign.

The entire value proposition for sanctions evasion just got publicly falsified.

Now do the game theory:
You're Iran. Russia. Any country hedging against dollar weaponization. You just watched Venezuela's "crypto solution" get shut off like a light switch.

Where do you put reserves now?
USDT? Compromised.
Yuan? Political strings.
Gold? Try settling $500M across borders in 10 minutes.
CBDCs? Same kill switch, government branding.

There's exactly one asset that clears final settlement without asking permission from anyone.

21 million units. No CEO. No freeze function. No phone number.

This is the ad Bitcoin never had to buy.
The most desperate, highest-stakes capital on earth just learned there's only one door.

Price doesn't reflect it yet.
It will.

[Aethir Ecosystem @AethirEco]

ATH cross-chain transfers just got a massive upgrade.

@AethirCloud has adopted the Chainlink interoperability standard to power secure ATH transfers between @ethereum and @ronin_network.

[Mark @MRRydon]

People misunderstand exponential change because we anchor on the early part of the curve, not the final leap.

There’s a classic thought experiment: a bottle fills with bacteria thats population doubles every second. If it’s completely full at 60 seconds, when is it half full?

⏰ At 59 seconds.

That last doubling feels like it comes out of nowhere. But it doesn’t. It’s the same growth rate that’s been there the entire time - we just don’t notice it until the final step dominates everything that came before.

That’s where AI is right now.

For years, language models could handle tasks measured in minutes. Then tens of minutes. Then maybe an hour. Useful, but not transformative. Easy to dismiss as “assistive” rather than foundational.

What’s changed - quietly - is task duration.

Recent evaluations show frontier models reaching roughly five hours of independent task execution at ~50% success probability. That matters far more than raw accuracy headlines. Five hours isn’t just a prompt. It’s a work session.

The curve is steepening. The doubling time for task duration has compressed from years to months - roughly 7 months historically, closer to 4 months recently. If that trend holds even loosely, we’re approaching a full working day astonishingly fast.

This is the hard part for humans to intuit.
Going from 10 minutes to 20 minutes feels incremental.
Going from 4 hours to 8 hours changes the category entirely.

At a full day, an AI agent isn’t just “helping.” It’s owning a problem space.

That has second-order effects people aren’t pricing in yet:
– Research loops that don’t stop at summaries, but run experiments, analyze results, and iterate.
– Software systems built, tested, refactored, and deployed end-to-end in one continuous flow.
– Cancer research pipelines where models read literature, propose hypotheses, simulate pathways, and surface candidates without human handoffs every step.

At 50% reliability, this is already economically meaningful. You don’t need perfection when marginal cost approaches zero and parallelism is unlimited.

And here’s the historical irony: the moment just before the final doubling is almost never remembered. It’s overshadowed by what comes immediately after.

The bottle isn’t full yet - so it feels safe to ignore.

But once it is full, everyone asks why no one saw it coming.

We are living in that last second.

The inflection point is fleeting. It doesn’t announce itself. It only becomes obvious in hindsight - when agents stop feeling impressive and start feeling inevitable.

2026 won’t be about a breakthrough. It will simply be a curve doing what it’s already doing

[SwarmNode.ai @swarmnode]

Another demo video. This time using Phantom app on mobile.

[Crypto Fergani @cryptofergani]

Absolutely no one:

CT every market dip :