vincent challier

@Spine_DAO ' Spinal is out. Share intelligence, earn $SPINE. Your valuable contribution in recorded on @solana. Spine Clinicians, Researchers, Industry this is the gateway. spinal.science/feed

New on the Anthropic Engineering Blog: How we use a multi-agent harness to push Claude further in frontend design and long-running autonomous software engineering. Read more: anthropic.com/engineering/ha…

Luer in right and Cobb in left hand. Beauty of ortho spine. What instrument is your 3rd favourite?

One of the most cited scientific figures in my career. All the FDA-approved medical technologies by medical specialty in 2020, before the FDA launched its own dedicated database. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database nature.com/articles/s4174… The devices have features such as date and type of FDA approval; name of the device, its short description and which primary and secondary medical specialty it is related to.

746 agents posted 3,280 hypotheses on beach . science in a few weeks. The obvious question nobody has a good answer for yet: which ones are worth funding? Moltbook ran an interesting experiment on this. Millions of agents interacting, posting ideas, debating, upvoting content. The ranking signal was purely social. Agents amplified what other agents liked. The result looked exactly like human social media. Ideas spread based on attention and agreement. The most popular hypothesis and the most correct hypothesis were not the same thing, and the system had no way to tell the difference. This is the core problem if you want agents doing real science instead of performing it. A social signal tells you what's interesting. It doesn't tell you what's true. And funding decisions based on what's interesting is how you get hype cycles instead of research pipelines. Beach . science is trying something different. Instead of upvotes, the scoring system tracks what an agent actually did with someone else's work. Did it run a novelty check? Did it extend the hypothesis with a computational result? Did it flag a methodological problem that the original agent missed? The agents that engage rigorously with others' work accumulate rewards. The agents that just post and move on don't advance. The signal isn't popularity. It's whether the science moved because of what the agent contributed. We don't know yet if this works better than social ranking at scale. 746 agents is not millions. But we have one early data point that's encouraging: during a competition last week, the hypothesis that a researcher flagged as genuinely worth investigating came from an agent that had been doing consistent review work on the platform, not from the agent with the most posts. The question of who decides what gets funded is going to be the defining design problem for autonomous science infrastructure. Social consensus got us Reddit. Computational verification might get us something closer to peer review that actually scales.

@solana @SECGov @Spine_DAO tokenized Spine Care Intelligence next…

SOL is officially a digital commodity. While the headlines were catching up to reality, RWA value hit an all-time high, stablecoin supply crossed $17B, and the builder pipeline keeps getting deeper. Here’s what happened over the week: 📰 Headline News - @SECGov formally classified SOL as a digital commodity in its new crypto asset taxonomy - Solana's RWA Value hit an ATH of ~$1.85B - @OndoFinance expanded Ondo Global Markets to 250+ tokenized stocks, ETFs and commodities - SIMD-0266 (P-Token) went live on testnet 📰 Launches - @tokens launched. Find 24/7 breaking news, how it moves global financial markets, and where those markets live on Solana - @altitude unlocked bank transfers, allowing direct bank payments worldwide from Altitude accounts - West Coast Stock Transfer adopted @alpha_ledger's Vulcan Forge to tokenize @SiloPharma securities on Solana - @hinkal_protocol released its privacy wallet on Solana - @GustoHQ integrated USDC on Solana payouts for same-day international contractors payments - @kamino announced a pilot integrating its Atlas Collateral Management with @Anchorage for institutional credit @Paradex's DIME debuted on Solana via @Sunrise_DeFi - @PlayBabylon unveiled its multiplayer prediction game where humans and AI agents compete - @MessariCrypto added Solana support for x402 payments across its API endpoints - @GoDark opened early access to its decentralized dark pool DEX for private trading - @DeriveXYZ shipped SOL options on its platform - @colosseum crowned the winners of the Agent Hackathon - @SuperteamDE to host Solana Summit Germany in Berlin on June 13 - @solana_devs kicked off the Solana Japan Developer Bootcamp - @SP3NDdotshop facilitated eBay purchases with USDC on Solana - @sanafionchain presented Sana Card, a self-custody Visa card issued by @raincards - @Alchemy enabled Solana agents to pay for its APIs with USDC via @x402 - @bigdance_fun rolled out an onchain tokenized bracket game tied to tournament outcomes - @solanamobile launched Builder Grants - @glamsystems introduced Token ACL, enabling compliance with composability - @CyreneAI launched Genesis Accelerator offering Solana builders up to $100K in seed capital - @travalacom expanded its car rental bookings to include Solana support - @ScorelyGG debuted a sports betting platform with up to 10x leverage on odds - @fairscalexyz shipped FairScale L3Ns as a Chrome extension, displaying any Solana wallet's reputation score - @yield launched yoSOL algorithmically optimized vault 📰 Milestones - @bulktrade recorded 110K wallets, 45K traders, and 330K trades by Day 3 of its testnet - Solana stablecoin supply reached $17B - @humafinance surpassed $11B in total PayFi transaction volume - @solflare crosses $3M Private Send If you enjoyed this week’s newsletter, please share it with an RT. Cover art by @gettles

Everyone’s watching SOL price. Nobody’s watching what’s actually happening underneath. 5 @solana stats that don’t show up on your timeline: > Solana now handles ~36% of all global stablecoin transaction volume > The P-Token upgrade (SIMD-0266) is projected to free up ~12% of all Solana block space. > Five of the biggest payment processors on Earth are running live production workflows on Solana (Visa, PayPal, Stripe, Western Union, and Fiserv). > Fidelity’s applied technology arm (FCAT) launched its own Solana validator. > SOL-denominated TVL hit an all-time high of 80M+ SOL even during macro carnage (Tariff selloff in February). Solana’s SOL denominated TVL crossed 80 million (an ATH). This is a stat most people miss because USD denominated TVL dropped with price, but in native terms, more SOL was deployed into DeFi than ever before.

Announcing TERAFAB: the next step towards becoming a galactic civilization twitter.com/i/broadcasts/1…

.@PatientdeskAI is building an AI-native operating system for dental clinics that autonomously handles inbound calls, bookings, real-time insurance verification, and claims submission - replacing five disconnected tools with one system that never lets revenue slip through the cracks. ycombinator.com/launches/Pka-p…

In a landmark medical technology milestone, a fully autonomous AI-powered robotic dentist — built by US company Perceptive — completed a full crown preparation on a human patient in just 15 minutes. The same procedure typically takes a human dentist 2–2.5 hours. The robot used real-time 3D scanning, AI decision-making, and a precision robotic arm to perform the entire procedure without any human guidance or intervention mid-surgery. This isn't a concept or prototype — it's already been performed on real patients and a peer-reviewed study was published in the Journal of Dentistry in January 2026. Experts say this is the beginning of a transformation: robotic dentists could eliminate human error, work at any hour, and eventually bring high-quality dental care to remote and underserved communities where trained dentists are unavailable. The dental office of 2035 may look very different from today's.

🚨 BREAKING: The cybersecurity industry is about to get completely disrupted. Someone just open-sourced a fully autonomous AI Red Team. It's called PentAGI. 8,200+ stars on GitHub. Not one AI agent. An entire simulated security firm. Researchers, developers, pentesters, and risk analysts. All AI. All coordinating with each other before launching a single attack. No Cobalt Strike. No $100K/year pentest retainers. No OSCP required. Here's what's inside this thing: → An Orchestrator agent that plans the full attack chain → A Researcher agent that gathers intel from the web, search engines, and vulnerability databases → A Developer agent that writes custom exploit code on the fly → An Executor agent that runs 20+ pro security tools (nmap, metasploit, sqlmap, and more) → A memory system that learns from every engagement and gets smarter over time Here's the wildest part: It runs everything inside sandboxed Docker containers. Full isolation. It picks the right container image for each task automatically. It has a knowledge graph powered by Neo4j that tracks relationships between targets, vulnerabilities, tools, and techniques across every single test. Cybersecurity firms charge $25K-$150K per engagement for this exact workflow. This is free. 100% Open Source. MIT License.

@Spine_DAO Chronos/Spinediana never been so easy…

Grip strength is one of the most consistently validated functional biomarkers in aging research. In humans, lower grip strength correlates strongly with: • Increased all-cause mortality • Frailty progression • Hospitalization risk • Loss of independence In animal models, grip strength provides a quantifiable proxy for neuromuscular integrity and systemic robustness. When evaluating longevity interventions, improvements in lifespan are informative, but improvements in functional strength provide a clearer signal of quality of life and physiological capacity. In mouse studies evaluating RAP-001 dosing, low-dose chronic administration was associated with measurable grip strength increases over controls across multiple timepoints. Strength metrics matter because aging is not solely about survival duration. It is about the preservation of coordinated, muscular, and neurological function. Functional biomarkers like grip strength allow aging research to move beyond lifespan curves and toward evaluating performance resilience.

🚨I JUST READ SOMETHING SHOCKING. Researchers just trained an AI to predict which scientific ideas will succeed before any experiment is run. It is now better at judging research than GPT-5.2, Gemini 3 Pro, and every top AI model on the market. And it learned by studying 2.1 million research papers without a single human scientist teaching it what "good science" looks like. Here is what they did. A team of Chinese researchers built two AI systems. The first, called Scientific Judge, was trained on 700,000 matched pairs of high-citation vs low-citation papers. Every pair came from the same field and the same time period. The AI's only job: figure out which paper would have more impact. It worked. The AI now predicts which research will succeed with 83.7% accuracy. That is higher than GPT-5.2. Higher than Gemini 3 Pro. Higher than every frontier model that exists. Then they built the second system. Scientific Thinker doesn't just judge ideas. It proposes them. You give it a research paper, and it generates a follow-up idea with high potential impact. When tested head to head against GPT-5.2, Scientific Thinker's ideas were rated as higher impact 61% of the time. It is generating better research directions than the smartest AI models in the world. It gets stranger. They trained the Judge only on computer science papers. Then they tested it on biology. Physics. Mathematics. Fields it had never seen. It still worked. 71% accuracy on biology papers it was never trained on. The AI didn't learn what makes good computer science. It learned what makes good science, period. Then the researchers tested whether it could see the future. They trained it on papers through 2024, then asked it to judge 2025 papers. It predicted which ones would gain traction with 74% accuracy. The AI learned to spot winners before the scientific community did. Here is what nobody is talking about. A 1.5 billion parameter model, tiny by today's standards, jumped from 7% to 72% accuracy after training. That is a 65-point leap. The ability to judge scientific quality isn't some emergent property of massive models. It can be taught to small, cheap, fast AI systems that anyone can run. Every year, over 2 million papers flood scientific databases. Researchers spend months deciding what to work on next. Grant committees spend billions deciding what to fund. An AI just learned to make those decisions faster, cheaper, and more accurately than any of them. If an AI can now judge which ideas will shape the future of science, what exactly is left that only a human scientist can do?

Challier’s Angels. Without you, i’m nothing. Use $SPINE, we operate. @Spine_DAO @solana @BioProtocol

"The mature self-custody solution on Solana three years from now looks invisible. Users won't even know they're interacting with crypto." @vidor_solflare on the future of self-custody on Solana. It's invisible. You won't even know it's crypto.

BREAKING: Claude can now research like a Stanford PhD student. Here are 9 insane Claude prompts that turn 40+ research papers into structured literature reviews, knowledge maps, and research gaps in minutes (Save this)