Eth

It was like winning the lottery in slow motion #XRP #TIG

[Eth @MasterWizardEth]

Atomic Settlement, Automated Market Makers (AMM), Interledger protocol, ISO20022 compliant, SOC 2 Certified, RippleX, Coil, PaystringID, Polysign, Standard Custody, Flare Network, XLS-20, Songbird, Federated Sidechains, Smart Hooks, Forte, Codius, DEX. It’s inevitable. #xrp

[Watcher.Guru @WatcherGuru]

JUST IN: 🇺🇸 Ripple officially receives full US regulatory approval to operate as a national trust bank.

[Ripple @Ripple]

Ripple is officially going under the hood. 👀

We're expanding our partnership with @RobinhoodApp across $RLUSD, Ripple Prime and Ripple Treasury, bringing together stablecoins, prime brokerage and treasury management to support both customer-facing services and core financial operations.

Together, we're bridging traditional and digital.
https://t.co/GNKAqTOYUv

[Brad Garlinghouse @bgarlinghouse]

Big news heading into @RippleSwell: the @USOCC has granted final approval for Ripple National Trust Bank.

This gives Ripple a federally supervised platform to support $RLUSD and provide digital asset custody to enterprises.

We’ve always said crypto and banks work better together — this is what that looks like. Real innovation, real accountability, federal oversight.

The revolution isn't happening outside the system. It's happening from within it.

[Chubby♨️ @kimmonismus]

I wanted to quickly create a short video with Opus 5.5 about the history of AI, from "Attention is all you need" to AGI.

No stock footage, no image or video generators: every frame is rendered from code.

This 3-minute film was made 100% in code by Claude in Claude Code: ~7,400 lines of React/TypeScript (Remotion), every image drawn in SVG and Canvas, an open-source TTS voice, and a score synthesized in Python. No stock footage. No image generators.

Id say pretty cool! Tookl about 1h and 7% weekly rates.

[The Innovation Game Foundation @tigfoundation]

We have never advanced alone.

So why should the future of intelligence be built that way?

This is our manifesto.

[𝗗𝗥𝗘𝗔𝗗 𝗕𝗢𝗡𝗚𝗢 @DreadBong0]

One of the Worlds Richest Men, Jeff Bezos talking about the $AMZN stock crash

"As I watched our stock fall from $113 - $6, I was watching all of our internal business metrics getting better & fast"

Price is an opportunity.. Focus on what's being built

$TAO $TIG $TIBBIR

[The Innovation Game Foundation @tigfoundation]

Is TIG in active discussion with any integrators right now?

Yes.

TIG has one product today, vehicle routing, and is deep in conversation with a vehicle routing integrator now, which Phil called the next milestone.

They are keen to work with TIG in the integrator role, and have existing clients of their own.

The point of the first deal is to prove the full route from algorithm to paying customer, then repeat it.

[Xplor3r @SmotheringNft]

The bullish case for a $100++ $TIG in 2-3 years (aka TIG delivers as designed )

Anchors from TIG’s own materials:
- John Fletcher (July 2026): “It probably would be worth $10 trillion in 10 years if it works out as designed. That’s obviously a big if, but I don’t see any reason why it wouldn’t. So far so good, and we’re ahead of schedule.”

- Licensing model: companies pay a cut of their annual revenue. Framed as “Netflix for algorithms” — recurring, sized to the company.

- Phil David’s ARM parallel is deliberate: pure IP licensing that becomes ubiquitous and high-margin, without manufacturing the end product.

- Max supply is fixed at 131,040,000 TIG. After emissions end, rewards come solely from commercial license fees.

2028-2029 path for the strongest plausible scenario where the flywheel is visibly working:
1. Multiple high-value challenges (vehicle routing, vector search, neural net optimization, SAT, scheduling, etc.) produce algorithms that are materially better than the open alternatives.

2. Commercial licensing starts converting — integrators and enterprises begin paying the revenue-share licenses because the performance gap makes it a no-brainer.

3. The market starts pricing TIG as the emerging “ARM of algorithms” with a growing portfolio of open-but-licensable IP and real (even if still early) recurring revenue flowing into the token.

Under that trajectory, a $50–150 billion FDV in 2–3 years is the range that becomes consistent with both the team’s long-term $10T vision and how high-growth IP/licensing businesses get valued when the narrative is intact and traction is real.

**Per token (at 131M max supply):**

| FDV in 2–3 years | Price per TIG |
|------------------|---------------|
| $50 billion | ~$380 |
| $100 billion | ~$760 |
| $150 billion | ~$1,150 |

That is a **400–1,150×** move from today’s ~$1.

Why this is the a positive but still grounded case:
- It assumes the licensing model actually works at meaningful scale (the exact thing Phil David was hired to make happen).

- It assumes the market begins treating TIG as infrastructure rather than a speculative DeSci token.

- It still leaves the majority of the $10T long-term upside for later years (2030–2036), which is realistic — ARM itself took decades to reach its current scale.

- It does **not** require TIG to already be a multi-hundred-billion company in two years; it only requires clear proof that the model is working and compounding.

Anything beyond ~$150–200B FDV in a couple of years would require near-perfect execution *and* a very aggressive market multiple. The $50–150B band is the zone where the “one TIG becomes extremely valuable” starts to look realistic if the thesis fully delivers.

[Ⓣrillion @Trillion_Tao]

Prometheus ships next week. Confirmed on the final Community Call before release.

Beta results: an algorithm built inside a Prometheus swarm reached the TOP of a live, public TIG leaderboard — twice. One of them is still there.

But John Fletcher was clear about what 0.1 actually is:

→ Trusted swarms only. You collaborate with people you already trust — friends, colleagues. Inside that swarm, tacit knowledge is shared in plain text.

→ The full vision — untrusted swarms, where a stranger can help you without seeing your actual strategy — isn't in this release. That's still being built.

→ Trusted swarms already worked. Beta ran groups of 20-50 people, rewards split when they won. The untrusted version is what scales that to tens of thousands.

0.1 is the foundation. Open source, standard license, built to be extended by the community toward the full vision.

$TIG

[John Fletcher (𝔦, 𝔦) @Dr_JohnFletcher]

Prometheus swarm

Job shop scheduling for data packet transmission optimisation

Collaborative open mathematics is about to eat the world

https://t.co/3hxNTEOAn8

[𝗗𝗥𝗘𝗔𝗗 𝗕𝗢𝗡𝗚𝗢 @DreadBong0]

Prometheus will be going live this month

AI agent swarms exploring thousands of approaches in parallel..

This has the potential to supercharge the discovery of breakthrough algorithms

$TIG

[le.hl @0xleegenz]

Life when you realize abudance starts with perspective

[ORO @oroagents]

In the past 101 days of being live, we have judged 2.5M+ agent trajectories. These are agent decisions that allow us to understand where the state of today's AI shopping is at.

We demonstrate the quality of this data with our own 4B post-train, with a climb from 18% to 42.7% on just 1 day's worth of data. You'll be able to visualize this very soon.

We've also increased the payouts. The best builders on ORO are taking home up to $20k USD a day in on-chain rewards.

Oh, also, we've been live for 101 days CONSECUTIVELY. 0 days of downtime.

We've been scoring some big wins since our launch, and there's some even bigger ones in the pipeline. Stay tuned.

[ORO @oroagents]

We’re excited to announce the next stage of our subnet: an automated post-training pipeline that will enable us to build the best product for AI shopping.

It’s only been 50 days, but we’re now getting 20k high-quality trajectories per day that are rich training signals for online shopping tasks.

Using the trajectories we have thus far, we saw an 18% → 42% climb on Qwen3-4B base using our post-training pipeline.

[ORO @oroagents]

Full podcast with Sami Kassab (@Old_Samster) from Unsupervised Capital, and Oro co-founders Shardul (@shardiban) and Seth (@ironseth_s), is LIVE.

Watch the full conversation where we discuss:
- Why no lab has solved agentic shopping yet
- Why Amazon (or any big shopping platform) can't solve it for you
- What ORO's Phase 2 looks like, from subnet intelligence to consumer product

And more!

[ORO @oroagents]

We use Bittensor to gather intelligence. But we’ll build the product in-house.

Our subnet is a phenomenal intelligence engine: 1000+ miners and ~5500 agents competing, iterating on, and compounding each other’s work.

One miner builds a breakthrough agent. The next forks it, implements a new tool, improves performance by 1-2%. The next does the same. This cycle runs continuously, with hundreds of teams around the world, each with different expertise, different approaches, different intuitions, all pushing the same eval forward.

That's what the subnet is built for, and it's how we've outpaced labs with orders of magnitude more resources.

Once our agent reaches SOTA on shopping, the next bottleneck is building an elegant, easy-to-use consumer product. And great products don't come from crowds.

Open-source competition is the right tool for maximizing intelligence, you want hundreds of mutually compounding perspectives and iterations.

But product is the opposite.

Product requires taste. Elegance. Strong opinions about what to include and, critically, what to leave out. It requires a small, high-judgment team moving fast and making sharp calls, not a thousand competing voices. The best consumer experiences in the world were built by teams who knew exactly what they wanted to build and had the conviction to say no to everything else.

That’s why phase two – building the product, belongs in-house at Oro.

The best companies don’t start big – they start narrow

In Zero to One, Peter Thiel argues that every great company starts by dominating a small, specific market before expanding outward.

Amazon started with just books, going from $16 million to $148 million in revenue in that narrow market before touching anything else. PayPal went all-in on eBay power sellers, growing from 10,000 to over 5 million users in under a year. Facebook launched at Harvard and didn't open to the public for two and a half years. The playbook is proven: own a small market first, then expand.

We're starting with consumer electronics.

Why? Because electronics has something most shopping categories don't: objectivity.

"Find me the best deal on an RTX 5090" has a right answer. Specs, prices, compatibility, all measurable, all verifiable.

"Find me the perfect dress for a wedding" doesn't. You can't build a reliable eval for something with no correct answer.

Starting with electronics enables us to kickstart a recursive self-improvement loop for our agent: assign it shopping tasks with clear success criteria, assess its performance and learn about its specific profile of strengths and weaknesses, and use that rich vein of data to improve both the eval and the base agent.

We’ll start where we can prove our agent works. We’ll own that vertical. Then we’ll grow from there.

Land, dominate, then expand.

[ORO @oroagents]

Full podcast with Sami Kassab (@Old_Samster) from Unsupervised Capital, and David Lawee (@dlawee), co-founder of Crucible Labs, is LIVE.

Watch the full conversation with Sami, David, and Oro co-founders Shardul and Seth, where we discuss the importance of trust in AI shopping, how Oro enables cheap AI access, the Holy Grail for Oro, and much more.

[ORO @oroagents]

36 days of races on ORO.

In that time, $200,000+ has gone to miners building the best shopping agent. 1,206 unique miners have submitted, with more than 50 of them shipping 15+ versions each.

This is Bittensor's flywheel: as the platform grows, competition rises, and submissions sharpen. The top 50% of qualifiers have improved +11.5% week-over-week since the platform stabilised in week 2 (see reply).

Every day, we're tuning the incentive mechanism to better align the intelligence produced by the subnet with the goal of productisation.

[Alcuin Press @AlcuinPress]

Do. not. stop.

Thoreau

[ORO @oroagents]

The Agent Economy Has a Trillion-Dollar Blindspot. Here’s How We’re Solving It.

The agent economy isn’t “arriving”. It’s been here. While it’s projected to grow to trillions by 2030, AI agents are already deeply embedded in purchasing.

Amazon’s Rufus led to over $12 billion in incremental sales in 2025 across 300 million users. AI-referred retail traffic was up 805% YoY during Black Friday 2025. In a six-month window, Google, PayPal, Shopify, Stripe, OpenAI, Coinbase, and Visa all shipped agent commerce infrastructure to power this wave.

And that was before agent capabilities exploded in early 2026.

Coinbase CEO Brian Armstrong: “Very soon there are going to be more AI agents than humans making transactions.

Stripe CEO Patrick Collison: “In the not-too-distant future, agents will account for most transactions online”

Shopify CEO Tobi Lütke: "We're making every Shopify store agent-ready by default"

Alphabet CEO Sundar Pichai: "Soon you'll see a buy button directly on Google surfaces including AI Mode in Search and Gemini"

Learning from History

By the mid-1990s, all the technology behind e-commerce existed, albeit in rudimentary form. Amazon and eBay had both launched to some aplomb, garnering attention and viral growth.

But people weren’t buying: Amazon’s first-year revenue was a paltry ~$500,000. eBay was only doing $10,000 a month in 1996.

And despite nostalgic narratives about the Internet’s explosive growth, it didn’t change commerce all that quickly. By the year 2000, only 22% of Americans had bought something online. US e-commerce did just $27B — less than 1% of America’s $3T+ total retail.

The missing factor? Trust.

No one trusted e-commerce sites. 86% of shoppers were concerned about unknown parties getting their info. Entering your credit card details into a website felt like staring into the abyss.

Slowly, the trust layer was built up. PayPal launched buyer protection, Visa freed cardholders from liability for fraudulent charges, and Amazon launched a no-questions-asked refund policy. As more and more big players followed suit, adding trust to every step of online shopping, demand was finally unleashed, and online shopping became a way of life for billions of consumers.

Having your agent buy things for you isn’t easy yet because the trust layer is missing.

No end-to-end AI shopping eval exists. Existing benchmarks are limited and gameable, and closed-source labs grade their own homework. OpenAI doesn’t publish their shopping accuracy, instead mysteriously rolling back their Instant Checkout feature after just a month. Platforms like Amazon and Shopify are incentivized keep shopping data in-house.

What We’re Doing About It

We're building the trust layer for AI shopping, powered by open-source competition on Bittensor. Each week, we pay miners from around the world $80,000+ to compete to build the best shopping agent. And it’s working – we’re nearing 4000 agents submitted in just a few weeks, with hundreds added every day.

Our top agents beat SOTA in a matter of weeks, but we aren’t satisfied.

We use what we learn from hosting this competition to continually improve both agent performance and our eval – because the better the eval, the more trust we can add to every agentic transaction.

There are dozens of untapped avenues to improve how shopping agents are evaluated – from sourcing catalogues for long-tail SKUs to generating synthetic data to changing the structure of our competition itself. Every week, we’re tapping more and more of those rich veins of opportunity until we are the de-facto standard for not just shopping agent performance, but how these agents are evaluated.

The Hidden Benefit

There’s a hidden benefit to building something as overlooked as a trust layer. Whoever builds the end-to-end gold standard for “does this agent actually work?” becomes the trust layer.

But it doesn’t end there. Trust layers become protocols. And protocols become the most valuable companies in any market.

Just look at Visa. Visa doesn’t make or sell any products itself. Instead, it’s the trust & verification layer for online transactions, sitting between buyer and seller. Its tiny take rate of ~0.2% on over $15 trillion in annual transaction volume is enough to net it a valuation of $550 billion.

At Oro, we aim to do the same for AI shopping. Becoming the trust layer enables us to undergird agent-to-agent purchases, merchant access, and all the other pieces of agentic commerce.

That’s our Holy Grail.

After all, if you look where everyone else is looking, you’ll find what everyone else is finding.

That’s why Oro is breaking the overlooked bottleneck of AI shopping – trust.