Building @orangemetrics — AI data products for Fintech 2.0 Open-source dashboards, tools & viz @Bitcoindatalabs | 🎾

Austin
Joined November 2013
On Aug 26, Core Lightning's maintainers told operators : don't shutdown, restart with --offline until the fix. Lightning had never seen that before: one client, switched off on request. A natural experiment. I measured what happened. New paper 🧵
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So "what % runs LND" is the wrong question for resilience. Count isn't resilience: hubs set the blast radius, whatever software they run.
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Saurabh retweeted
⚡️@sorukumar from @Bitcoindatalabs showcasing his project at @tabconf atlanta.
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More new people are contributing to Bitcoin Core in 2026. First-timers, Jan–Sep: • first code merged: 80, up from 42 in 2025 (1.9×) • first PR opened: 254, up from 197 (+29%) 27 new people got code merged in Aug–Sep alone. #TABConf
State of Bitcoin Core — September 2026 • 144 PRs merged (+69% YoY) • Median time-to-merge: 6.9d • 125 human reviewers (-7% YoY) • 424 open PRs · 15 first-time merged contributors • 32.0rc2 out for testing orange-dev.bitcoindatalabs.o… #Bitcoin #BitcoinCore
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LN Full monthly report is up. Follow @Bitcoindatalabs for the report on the 1st of each month in your feed. September notes: 1: Pros added about 75 BTC in channels over 1 BTC. Plebs closed smaller channels, or left the public graph. 2: First monthly capacity gain after five months of declines: +12 BTC. 3: Taproot channels: ~600. Let me know if anything looks off. what else we should add/change.
⚡ Lightning Monthly — September 2026 Baseline: 3,736 BTC across 33,549 channels Net Flow: +12.14 BTC (MoM: +537.34 BTC) • Opened: +5,306 (+1,981.0 BTC) • Closed: -6,971 (-1,968.9 BTC) • Closures: 65% mutual • Med 41d life lightning.bitcoindatalabs.or…
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Saurabh retweeted
if this is not 90% of ur ai portfolio for the next few years, weighted by how close they sit to potus/vpotus, u r doing it wrong
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Hate the game, not the players.
CJ has zero integrity. You don’t take a CEO job lightly. It’s responsibility for thousands of customers and employees, and not one to leave suddenly when you get a bigger offer.
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Skeptical of this. 1: Could be just novelty factor. May be users were tired of getting shown same shit reco. 2: LLM powered reco is probabilistic. Depending on the whole product exp, it could be weakness or strength. 3: In no time, deterministic algo will catch-up with the best out of all sets of LLM reco. Out of this exercise, LLM will give them a couple of more variables for their algo.
Netflix replaced their 15 years old recommendation algorithm with an LLM. It’s called "GenRec" and it completely changes how recommendation algorithms are built. For over a decade, the Netflix recommendation engine was a masterclass in feature engineering. Data scientists built thousands of complex, handcrafted features to figure out what you wanted to watch next. It required bespoke architectures. Massive infrastructure. Constant manual tuning. Netflix threw all of it away. They built GenRec, an LLM-backed ranker. Instead of translating your behavior into complex math, they just turn your watch history and metadata into a natural language sentence. They feed that raw text into a foundation LLM. The AI simply reads your behavior like a story, understands your evolving tastes, and scores the entire catalog in a single forward pass. No manual feature engineering. No complex bespoke architectures. Here is the part that should terrify traditional data scientists. This text-based LLM didn't just match the highly tuned production system Netflix spent years perfecting. It beat it. And it achieved those statistically significant gains using roughly 40x fewer labeled training examples. We are watching a massive paradigm shift in real time. The most complex predictive algorithms in the world are being replaced by models that just know how to read. If Netflix can replace their core product engine with an LLM, what complex system in your business is about to become obsolete?
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Netflix ran GenRec as a large-scale A/B test (roughly 10% of traffic for ~4 weeks). It beat the existing production ranker on key metrics with far less labeled data, but has not fully replaced it. Official sources describe it as an early/promising first step toward an LLM-native system, not a complete production switch.
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got this up and running. let me know if there is any feedback.
🗞️ This Week in Bitcoin Core (Sep 14–20) • Spotlight: PR #34861 (importdescriptors) • +4 new contributors • 34 PRs merged, 622 reviews on GitHub • 20 research threads on Delving & lists Full debrief & charts: twib.bitcoindatalabs.org/ #Bitcoin #BitcoinCore
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go fuck yourself @sama claiming that you solved navier stokes because 10000 agents ran in circles for 88hours on a multimillion dollar gpu cluster to formalize in lean a blowup case under controlled external forcing is pure scientific vulgarity the clay mathematics institute millennium prize does not ask m whether you can artificially force a singularity in a fluid by injecting an ad hoc smooth external forcing term f(x,t) to twist the vortex until it breaks the real problem questions the fundamental stability and global smooth existence for 3dimensional incompressible euler & navier stokes equations under natural conservation laws and viscous dissipation alone using a mathematical loophole on forced equations to parade a century old victory is a major conceptual scam Altman technically & epistemologically what you present as an agi breakthrough is nothing more than bruteforce combinatorial autoformalization the ai did not understand fluid mechanics it simply navigated a continuous search space previously mapped out and constrained by the monumental work of human mathematicians like tristan buckmaster/ levent alpöge / diego córdoba or tarek elgindi coordinating 10000 agents to check the logical consistency of a 100 page proof via lean is a software engineering feat and computational parallelization triumph not an intrinsic scientific discovery it is the victory of the compute bulldozer over abstract human intuition repackaged for the public as a higher mathematical consciousness to this theoretical imposture you add a disgusting ethical and industrial cynicism taking advantage of private codex sessions and informal preprints from academic researchers to siphon their research leads and then trying to redact or erase the contribution of levent alpöge under the pretext that he works at rival anthropic is intellectual serfdom openai behaves like a feudal lord of silicon appropriating the cognitive subsistence of independent scholars threatening their careers behind closed doors if they protest and turning community academic labor into a privatized pressrelease this entire staged event serves a desperate financial agenda in a pre ipo panic facing the slowdown of scaling laws and growing investor skepticism over the profitability of foundational models openai needs to manufacture an artificial sputnik moment claiming to solve a millennium prize without immediately submitting the proof to traditional peer review means using the prestige of fundamental mathematics as cheap marketing fuel to inflate a delusional valuation!!! real science is not a clout chase on social media or a compute spike spent to rob the clay mathematics institute it is a quest for elegance physical truth and universal rigor to decode reality true artificial intelligence will not emerge from hostile corporate takeover of academic work hidden behind computational bruteforce but from architectures capable of generating new conceptual paradigms by masquerading constrained formalization as the collapse of physics greatest mysteries you did not solve navier stokes you only proved how far silicon valley will go to prostitute scientific integrity for capitalist spectacle
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.
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Spent the weekend in the grant thread. Same debate, over and over. We boiled the whole debate down to four points. Been thinking about this for months. A map of projects × contributors × grants; that’s what we’re building, and I think it helps.
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one of my seniors (now CEO of a public fintech) had a pet line on growing in tech: it’s not the people who only do the work. it’s the folks who create work for others. sounds counter until you sit with it. when you’re deep in the work and actually thinking about it, you see the gaps. so you don’t just finish the assigned task. you end up creating new ones. same idea for the ecosystem. the job of a new devs isn’t only to contribute to what exists. it’s also to create new projects. i see why it may not be happen. 1. lag. you show up, spend months learning the terrain, contribute to existing work, and only then have enough context to propose something new. 2. most new contributors are fresh grads/young guns. what they usually don’t bring is experience from outside the ecosystem. the thing that helps you see and propose work that isn’t already on the board.
We wrote about this in our Year Four Report, but I’ve been thinking about it a lot lately: Are we still bottlenecked by the number of Bitcoin developers, or increasingly by the ecosystem’s capacity to absorb new contributors? What projects or areas in Bitcoin development do you think are genuinely under-resourced today? Where would adding capable new contributors actually be valuable? Looking for input from maintainers, contributors and funders.
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💯 right now bitcoin needs folks who make it useful and push adoption more than folks who improve the tech xitter.cf/TheBlueMatt/status/209…
While there’s always more to be done on Bitcoin protocol engineering, the thing we all want to see is not The Best Bitcoin Protocol, but Bitcoin adoption. That requires protocol engineers, yes, but more than that it requires founders working to take on the challenge of building useful stuff people want to use Bitcoin for. This has always been one of the biggest challenges in Bitcoin - how do you solve real problems for people who don’t have Bitcoin to begin with? Sure, if everyone had Bitcoin suddenly lots of global financial transactions would be cheaper and easier, but that’s not the world we live in. Meanwhile, actual real-world adoption of Bitcoin continues to grow incredibly slowly. Grow, yes, but not fast enough to sustain a traditional startup trajectory. In the mean time, we need more folks like @tando_me doing good work to bridge Bitcoin to local financial rails all over the world. And founders who are driven by mission, not necessarily the biggest short-term return (cause they’re already in AI, obviously).
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