Vulture's Pick

#bitcoin not compromised. Satoshi declined any communication or interaction with #epstien

@adam3us @TheGuySwann Quantum computing risk for bitcoin is at best is hoax at present and for foreseeable future. AI took more than 50 yrs to be useful

@TheGuySwann if you pretend no error correction is ok (cheat run it 100,000 times to look for the most selected answer in the noise - which obviously doesn't scale!), i think the current world record for quantum computers is 21=7x3. even then some dispute if they cheated in other ways also.

So guys... has anyone successfully factored a randomly chosen 2 digit number with a quantum computer yet? Because every time i go looking, the answer has been "no."

#bitcoin seem to be the best liquidity indicator. It is the first asset to react to liquidity abundance or shortfall.

AI infra controlled by a few giant companies isn't just a pricing problem. It's a single point of failure for anyone building in AI. Political, economic, and energy crisis can unexpectedly take down data centralized centers. And when they do: → GPU access dries up → Compute costs spike → Critical systems go dark → Projects stall → Users lose access Decentralized compute isn't just about cost. It's infrastructure resilience. @ionet CEO Gaurav Sharma breaks it down: supplychainbrain.com/blogs/1-think-…

@sharbel Time series prediction is an impossible task and against physics. Forget wasting time on that

🚨 IMPORTANT: Google quietly open sourced a time-series AI that predicts anything. Sales trends. Market prices. User traffic. Energy demand. Crypto volatility. It's called TimesFM. Here's why it's underrated: → Pre-trained on 100B real-world data points → Zero-shot forecasting, no fine-tuning needed → Outperforms supervised models trained on your specific data → Runs locally. Free. Apache license. Most people are focused on language models. The quietly powerful ones are learning to predict the future.

When evaluating the claim of deep bag-biased Quantum agitpropagandists, it’s always more useful to look at what they are NOT telling you. Recent claims suggest a Quantum computer using neutral atom arrays would require only (!) ~20,000 logical qubits to break Bitcoin. What they tell you is that this is significant because researchers at Caltech recently demonstrated a 6000 (!) logical qubits array. What they do NOT tell you is that those qubits weren’t not entangled, which is a requirement to run Shor’s algorithm. So what is the state of the art in terms of actually entangled logical qubits under the neutral atoms architecture? NINETY SIX (96) qubits. How long did they maintain coherence? 1-2 seconds. How long did the new Oratomic paper suggest the algorithm they designed should run? DAYS! So not only must state of the art in SIMPLE qubits entanglement increase more than 2 orders of magnitude, their ability to maintain coherence across circuit depth must be preserved ONE HUNDRED THOUSAND times longer than they currently do. Suffice to say they have no idea how to even get there but that shouldn’t get in the way of a headline worthy paper!

@pendle_fi @OCRRate Useful information. Great job

In TradFi, the yield curve is the single most-watched indicator in fixed income. A steepening curve says long-end holders are demanding more compensation, and the market expects rates to rise.' Glad to see @OCRRate leveraging Pendle's data to make such signal possible in DeFi.

@iblanco_finance It’s ok. But need not to be that complex.

⚙️ New research! "Learning-to-rank vs. predicting expected returns". One works much better. Lin, Su, and Zhu compare two approaches to building long–short equity portfolios: Standard method: estimate expected returns, then sort stocks. Their method: directly optimize stock rankings. Results: → Learning-to-rank: ~1.5–2.3% monthly excess returns, Sharpe up to 1.2 → Standard return models: 0.35–0.7 Sharpe The reason is clean: when you optimize for returns, you optimize for something you estimate poorly. When you optimize for rankings, you optimize for something more robust, relative ordering. The gains come specifically from better identification of top and bottom stocks, and from better hedging. The method doesn't need better return forecasts; it just uses the available information more efficiently. This is a methodological contribution, not a new signal. But methodology is often where the edge actually lives. 📄 Paper: papers.ssrn.com/sol3/papers.cf… --- → Join the newsletter: ivanblanco.ai/newsletter ---

@Portfolio_Bull With the freebies policies of politicians- this printing won’t stop

🚨BIG BREAKING Indian Govt. to borrow ₹8.2 Trillion via bonds in 1st half of FY26 -27 !

@zerohedge But how long they can avoid. Ultimately they will have to pay the yield associated with risks because of freebies. Thnx to BJP and Modiji.

RBI Rejects All Bids at T-Bill Sale Amid Rising Borrowing Costs

@pendle_fi @USDai_Official @capmoney_ @Paxos @global_dollar @vaultsfyi But who pays the bill at the end for higher yields than of T- bills

There's a growing sentiment that DeFi yields aren't worth the risk anymore. The safe options underperform T-bills, and anything paying more tends to come attached with hard to stomach risks. Pendle Prime PTs are offering 5–7%+ fixed APY on battle-tested assets, rate locked at purchase, with a defined maturity date and no liquidation risk. The bluechip yield was always there. Most people just weren't looking in the right place...

@Blockworks Watching yields and yields curve structure for underlying and implied both adds further insight into this analysis

1/ Market *signal* is emerging onchain: The implied yields of sUSDe on Pendle behave like a proper forward yield curve: revealing how the market expects funding rates, carry, and BTC itself to evolve before it happens. This is the emergence of an onchain term structure🧵

@DefiLlama It would be good to have a look on this since 2020

Stablecoin APY on Aave vs the Fed Funds Rate vs the median US Bank Deposit Rate. Over the years, stablecoin lending rates have fluctuated above and below the FFR, but bank deposit rates appear to act as a floor for DeFi yield. Chart generated by LlamaAI.

TRON announced the expansion of its AI Fund from $100 million to $1 billion. The fund will target investments in and acquisitions of early-stage companies building core infrastructure for the agentic economy. The fund will prioritize the development and consolidation of agent identity systems, stablecoin-based payment rails, tokenized real-world assets, and developer tooling for autonomous financial systems. The expansion builds on the fund’s thesis, first outlined in 2023, that AI and blockchain would converge, creating demand for programmable, permissionless financial infrastructure. More details from @Cointelegraph 👇 cointelegraph.com/press-releases…

SELF-SERVE LIQUIDITY IS COMING TO EVERY TOKEN ON EARTH. Chainlink's Cross-Chain Interoperability Protocol is entering final security audits. Once live, any token issuer on any chain can integrate cross-chain liquidity without asking permission. Without hiring a team. Without waiting for a partnership announcement. Self-serve. Plug in and go. Think about what that means. A tokenized fund on Ethereum gets instant liquidity access on Avalanche. A stablecoin on Solana settles on Hedera. A tokenized equity on Stellar bridges to Base. No custom integrations. No bridge risk. No middlemen. CCIP turns Chainlink from an oracle network into the liquidity backbone of every chain simultaneously. The final audits are the last step before this goes live. The internet didn't become the internet until HTTP let anyone publish a website without asking permission. CCIP is that moment for cross-chain liquidity.

Post Iraq war the oil jumped 4 times from 30 to 130 and remained at elevated level for 10 yrs. Iran war implications are bigger than of Iraq war. Therefore, oil could easily jump from 60 to 240 and stay at that level for 10 yrs from now. Be prepared. #oilcrisis2026 #IranWar‌

We reject: kings, presidents, and voting. We believe in: rough consensus and running code. —David Clark, 1992

⚡️ NEW: Bitcoin Quantum has launched testnet v0.3 with the first live deployment of BIP 360, a quantum-resistant upgrade for Bitcoin.

@predict_addict Time series forecasting is impossible to achieve. It is predicting future which is against the fundamental laws of physics

People ask me what experienced data scientists should learn in 2026. Here is a shorter version of the full list. * conformal prediction * LLMs * time series forecasting * information theory * signal processing * Kolmogorov’s complexity #machinelearning #datascience

It's mind-blowing that the entire AI revolution is being driven by a single 10-line algorithm.

From AI to ZK. Artificial intelligence is the attack. Zero knowledge is the defense. AI takes advantage of the slightest slip. ZK does not let anything slip. AI decodes all the data. ZK encrypts all the data. AI is the surveillance state. ZK is the sovereign individual.