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Darius Garland DIME through the defender's legs 😮‍💨 He finished with 24 PTS, 4 REB, 7 AST and a go-ahead and-1 to seal it for the @LAClippers as they picked up the victory in @emirates NBA Cup West Group B action!

Trade your shielded $ZEC for confidential USDC on Ethereum, without ever unshielding it. No bridge. No custodian. No wrapped ZEC. And the amount never shows up on chain. I connected @zama and @Zcash to make it work. Live on testnet 👇 What it is ZEC Desk is a small trading desk on Zama's FHEVM, on Ethereum Sepolia. On one side there's cUSDC, a dollar token whose balances are encrypted. On the other side there's shielded ZEC on Zcash testnet. You can trade either way. Built on Zama's model Zama's Confidential RFQ is already live on Ethereum mainnet, open to everyone: It runs confidential swaps with encrypted quotes, a sealed-bid pick on chain and a hidden floor. ZEC Desk takes that same model and extends it to native shielded ZEC. Zama's swap settles both legs on one chain, inside its own contract, so a Zcash leg can't plug into it directly. Here that leg lives on Zcash and settles minutes later with a proof, so the mechanics run in my own contract. It's a sketch of how native ZEC could sit next to Zama's swaps one day, with no wrapped token. How a trade works (buying ZEC) 👇 1️⃣ You lock cUSDC in the desk. The amount is encrypted. 2️⃣ Makers send encrypted quotes. Nobody sees anyone else's price, or the minimum you'll accept. 3️⃣ The contract picks the best one under FHE. 4️⃣ The winning maker sends shielded ZEC to your Zcash address, with the order's reference in the memo. 5️⃣ A zero-knowledge proof shows that payment happened, and the contract releases your cUSDC to the maker. If no proof arrives in time, your cUSDC comes back to you. Selling is the same in reverse: the maker locks cUSDC, you pay the ZEC, and the proof releases the cUSDC to you. Why these pieces Each piece does one job. Zcash keeps the payment private. Zama keeps the amounts encrypted and can still compute on them. Seam, the Zcash light client I built last month, connects the two: a contract checks every Zcash block header itself, and a RISC Zero proof shows a shielded payment reached the right receiver. The transaction, the note and the address never touch the EVM chain. The masked amount This was the hard part. Seam's proof used to write the paid amount in the clear, and ZEC amount × price gives away the dollar amount. So the amount is masked. The contract adds an encrypted one-time mask to the encrypted amount and reveals only the sum, which looks random to everyone else. The mask comes from a secret that the side receiving the ZEC hands to the payer. The proof checks that the payment was at least the sum minus the mask, and never states the amount. One-time pads are old, and Zama's RFQ uses one too. Using one to hand a value from FHE to a ZK proof is the part I haven't seen before. If you have, please send it my way. No vault, no signers Seam can read Zcash, but it can't take ZEC from anyone. So the design never needs someone to pay ZEC on someone else's behalf. Whoever pays, pays from their own wallet, and the escrow on the EVM side enforces the deal. The side that paid the ZEC can prove it itself, so nobody depends on the other side's goodwill. Seam does more than this Any EVM app that needs to know a shielded payment happened can use Seam: pay-with-ZEC checkouts, escrows, payouts, cross-chain orders. This desk is one example. Where it stands ↓ Testnet only, unaudited. On 12 November 2026 I ran a Buy end to end: ➡️ Settled with a proof: ➡️ One proof: 2 minutes on a rented RTX 4090, about $0.25 for the whole session ➡️ Gas per Buy on Sepolia: about 7.6M, in six transactions ➡️ A Sell also ran on Sepolia, up to the maker locking its cUSDC; it settles through the same proof step ➡️ Seam's light client is now deployed on Ethereum Sepolia too, next to Base Sepolia Where it can go ↓ → The desk only needs Zama's FHEVM and a cUSDC. Both already run on Polygon, where following Zcash costs a fraction of Ethereum mainnet. → Zama keeps adding chains. On any cheap EVM chain it reaches, the same desk can run, and if it comes to Base, Seam's relay already runs on Base Sepolia. → Zama's roadmap also goes beyond the EVM, to chains like Solana. Seam's verifier would need a port there. → On Ethereum mainnet, following Zcash header by header would cost over 2B gas a day, so batched header proofs are next for Seam. What it can't do yet - Settlement takes minutes, not seconds. Zcash confirmations set the pace, not the GPU. - Right now I'm the only maker, and a maker needs ZEC, cUSDC and a GPU. - Amounts are hidden, but EVM addresses are public. Read more ↓ Seam on Seam: ZEC Desk:

Please help 😭 Last night I secretly met up with my boyfriend away from our families and stayed at my friend's house, and when I woke up this morning, my legs have these swellings like this and I can't go home, please help, what is this, why does it happen, and how does it go away??

THIS IS GENUINELY THE MOST TERRIFYING THING I'VE EVER WATCHED they took the full wiring of a fruit fly, 166,700 neurons, and plugged it into an android hanging from the ceiling the fly it came from has been dead for years 1,000 artificial muscles, a skeleton, no face the fly brain was built for six legs and two wings this body has two legs, two arms and nothing to fly with nobody told it > minute 1, a twitch in the fingers > minute 4, the toes start curling like it's testing what it's attached to > minute 9, the knees lift one after the other, it's trying to walk on air > minute 20, someone walks underneath it and the kicking speeds up, you can hear the cables creak > minute 31, the lights go off and every muscle locks at once it found the rhythm a fly walks with and forced it onto human legs and when the lights went off, it did exactly what a fly does when a shadow passes over it, it froze and got ready to launch then came the part that made the room go quiet > it brought both hands up to its chest > started rubbing them together > slowly, over and over > the way a fly cleans itself there is no code in that body that tells it to do that it's 166,700 neurons doing what they've done for millions of years, inside a body they were never meant to have right now the cables are the only thing holding it up it learned to walk in one afternoon nobody wants to find out what it learns in a month and the next brain they're mapping is a mouse, 70 million neurons, about 400 times bigger than this one

PROMPT SHARE: The biggest AI breakthrough isn’t realism. It’s presence. #AIVideo# #Marketing# #Advertising# The people, movement, noise and unpredictability give the world a sense of life beyond the camera. That’s when generation starts becoming experience. ➜ Image Prompt: Photography & Cinematography Create an ultra-realistic nighttime street portrait with the raw, spontaneous look of early-2000s nightlife photography. Direct on-camera flash illuminates the subject sharply against a much darker city background, with natural grain, slight highlight blowout, deep shadows, imperfect exposure and authentic point-and-shoot character. No fake or plastic skin, no beauty filtering, no CGI or animated appearance. Character & Pose A striking young man with rugged, high-fashion model features, lean athletic build, sharply defined jawline and cheekbones, strong brows, full lips and short dark hair styled wet and tousled with loose curls falling onto his forehead. Natural pores, subtle facial texture and realistic imperfections. He sits casually on the edge of a city curb at night, legs spread asymmetrically, one knee raised toward the camera. One forearm rests loosely across the raised knee while his other hand reaches down beside him toward a small shaggy brown dog partially entering the bottom of the frame. He looks directly into the camera with a serious, confident expression. Outfit Fitted black long-sleeve turtleneck, faded distressed gray-blue jeans with worn areas and small rips, and a black leather belt. Silver pendant necklace, small hoop earring, chunky silver cuff bracelet, dark oversized wristwatch, and a thin bracelet. Styling feels slightly rebellious, understated and naturally lived-in rather than polished. Environment A busy urban nightlife street after dark. Several casually dressed people walk behind him, cropped naturally by the frame, while parked and passing cars create scattered headlights and small pools of distant light. The pavement is gritty and imperfect with curbs, cracks and ordinary street details visible. Keep the background activity believable and incidental—the image should feel like a real moment caught outside a club or late-night gathering, not a staged fashion shoot. Wide horizontal composition, intimate close-range flash photography, authentic real-world energy.

AI Drug Discovery Is Becoming a Bottleneck Trade I get excited when an industry starts going through a real regime change. AI drug discovery (“AIDD”) increasingly looks like one of those moments. Two things are happening: 1/ Upstream: the frontier AI labs are piling in. 2/ Downstream: The supply chain is clearly moving. Supply-chain checks suggest the upstream picks-and-shovels of discovery and early preclinical R&D are starting to feel the increase in experimental volume. DNA → protein → assays → sequencing → automation → preclinical testing Names across that stack include $TWST , @GenScript , $ILMN , $TXG , lab-automation vendors and CROs. $TWST expects triple-digit percentage growth in AI-enabled drug-discovery orders in FY26, and another year of triple-digit order growth in FY27. @GenScript's AIDD business doubled YoY in 1H26. Its current platform advertises industrial-scale validation of 4,000+ designs/day, with integrated sequence-to-data workflows. Our channel checks suggest the ramp is moving even faster: roughly 8,000 designs/day currently, with a path toward ~16,000/day by YE26. --- Why does AI drive more wet-lab demand? 1/ AI makes hypothesis generation almost free → way more shots on goal. The bottleneck is moving from expert-driven design to biological validation. 2/ AI models need continuous experimental feedback — and both good and bad data are useful. Traditionally, only the highest-conviction A+ candidates might get pushed into expensive validation. With AI, even the B/C candidates can be valuable because failed experiments generate training data. @GenScript has said its sequence-to-binding workflow can return data in 4–7 days, and that faster cycle times matter because AI models depend on continuous experimental feedback. @Anthropic is a clean example. @claudeai designed 1,320 protein binders. @adaptyvbio converted those digital sequences into DNA, expressed the proteins and tested binding. Only 354 actually bound. And the 966 failures are not wasted. They are useful negative labels: what does not express, what does not bind, what has poor affinity. Those results help train the next model iteration. 3/ Wet labs are no longer just making drugs. They are making training data. $TWST / @GenScript increasingly look like biological data foundries. $TWST explicitly talks about generating model-ready data from AI-designed sequences. In some workflows, the customer may care less about receiving the physical protein than about getting structured experimental results back into the model. Traditional drug discovery asks: “Does candidate X work?” AI drug discovery also asks: “What can this experiment teach the model?” --- TAM of AIDD If AI is simply a better R&D tool, the relevant spending pool is the $300–400B of annual global pharma R&D. If AI meaningfully increases the number of viable drug programs, the opportunity is larger because it expands downstream demand for DNA synthesis, protein production, assays, and preclinical work. Near term, we can also size demand from AI-company spending. If Anthropic reaches $80B of ARR in 2026 and spends just 1% on AIDD, that alone would imply ~$800M of annual investment. --- Trade setup This is a trade that could have long legs. It’s hard to really stop working until PhaseI/II results (2028+) It smells a lot like the bottleneck trade we just saw in semis: GPUs → HBM → networking → power/cooling. In biology, It basically follows the drug discovery process downstream: AI models → designs → DNA/protein → assays → preclinical capacity. After the upstream picks-and-shovels, animal testing could become the next bottleneck. Monkey prices are already near prior highs and CRO capacity is tight. AIDD pushing more candidates into preclinical development would only add demand. ?? But clinical trials are still the bottleneck? This is the biggest pushback I keep coming back to. No matter how fast discovery becomes, drugs still need to go through preclinical → Phase I → Phase II → Phase III → approval. You still need patients, time and capital. But that doesn’t mean the bottleneck trade won’t work. More viable candidates — especially with higher success rates — still means more demand throughout the development process. And who knows: clinical trials themselves may eventually be optimized by AI. ?? What breaks the trade? Near term, the picks-and-shovels trade breaks if experimental budgets stop growing, AI-generated designs don’t translate into useful wet-lab hits, or capacity catches up too quickly. Longer term, the thesis breaks if AI drugs look great in discovery / Phase I but fail at normal rates in Phase II/III. That is why Phase II matters so much. ?? Milestones Late 2026–2027: first Isomorphic-designed drugs enter human trials; more AI-native programs move into IND-enabling work / tox. 2028–2030: clinical trial results start telling us whether AI-designed drugs actually perform better than conventional drugs. Calling all the "bottleneck bros". :) @jukan05 @zephyr_z9 @aleabitoreddit @ParadisLabs +++ More comprehensive analysis:

trying to desensitise my cat to being picked up, so i fed him one of those tuna cream tubes while carrying him to associate me with nice things. an hour later i walk back into the room and he body slams me and starts climbing my legs

$TSLA just gave the bulls some ammunition. Better-than-expected deliveries, strong Friday momentum and earnings ahead. If buyers follow through Monday, I could see this move getting legs.