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Merry Christmas! 🎄Your Mai-san might be a little past her date... Still willing to accept delivery? 圣诞快乐啊🎄我的意思是…能陪我嘛？（圣诞过期菜，还有人吃吗） #cosplay# #longlegs# #mai#

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:

#プロンプト# ビリヤードしたくなる写真 以下にプロンプトを書いたので ぜひ使ってみてください Prompt: Generate a 16:9 horizontal photorealistic image with a film-like, CCD flash snapshot aesthetic. If a reference image is provided, use the woman in the reference image as the identity reference and prioritize preserving her facial identity, very small face-to-body ratio, fashion-model proportions, long legs, high waist, slim waistline, body silhouette, and overall impression. If no reference image is provided, generate an original fictional adult Japanese woman in her early 20s, clearly adult, with a very small face, long legs, a high waist, a slim but feminine fashion-model figure, long black hair, fair skin, and a refined natural beauty. The setting is a slightly old Japanese billiards hall that feels like it was built around the late Showa to early Heisei era. The place should look modest, worn, and nostalgic, with an everyday atmosphere: aged walls, old chairs, scoreboards, signs, dim indoor lighting, and the slightly gritty charm of an old neighborhood pool hall. Show the woman playing billiards, captured from a side view. She is leaning forward seriously while aiming at a ball with a cue stick, focused and concentrated. The image should feel like a real candid moment rather than a studio portrait. Her outfit: a white tight ribbed tank top loose silhouette indigo-blue denim jeans, worn low on the hips the waistband of her Calvin Klein underwear is intentionally visible above the jeans as a natural styling element, clearly readable as a visible waistband rather than something that is only accidentally showing simple silver accessories such as a necklace, bracelet, and rings well-done nails Composition and pose: keep the image horizontal 16:9 capture her from the side, showing her body extended over the pool table emphasize her small face, long legs, high waist, and model-like proportions make the cueing posture believable and natural her expression should be serious and focused the scene should feel like a documentary-style flash snapshot, not a polished commercial fashion shoot Visual style: photorealistic film-like texture CCD flash look slightly harsh direct flash, mild grain, natural imperfections nostalgic Japanese retro indoor atmosphere Important: The visible Calvin Klein waistband should look like an intentional styling choice Preserve a strong sense of realism, candidness, and everyday atmosphere Avoid making it look like a glamour studio portrait The woman must look clearly adult

President Lucilla's photos are finally here 🔥 I cosplayed Lucilla during the 2nd Anniversary event, and honestly, I love her even more after this shoot ✨ Calm, reliable, and always quietly watching over everyone—she's such an amazing character 🥹 (And yes... those long legs are incredible 🙈) Hope you all enjoy these photos ❤️ #WutheringWaves# #Lucilla# #Cosplay# #鳴潮#

今日はストッキングの日らしいです✨ ストッキングといえばスーツかな？ tailored office suit outfit, fitted charcoal gray blazer with sharp waist silhouette, single breasted design, crisp white dress shirt underneath, slim black necktie, high waist pencil skirt, sheer black pantyhose with glossy nylon texture, elegant long legs silhouette, polished black leather pumps, minimal silver accessories, sophisticated business fashion aesthetic, clean monochrome palette, modern corporate elegance, refined feminine suit styling