Some updates on bottleneck timelines: - ABF substrates: 1 year / eg. Ajinomoto (2802) for ABF film, Ibiden, Unimicron, Nan Ya PCB, etc make the substrates - HDI Boards: Over 6 months / Victory Giant (300476), Zhen Ding (4958), Unimicron, Compeq (2313), Meiko (6787) - Multilayer boards: ~6 months / Victory Giant, WUS (002463), $TTMI, Gold Circuit Electronics (2368), - CCL: Over 6 months (severe shortages of materials) / Elite Material (2383), Shengyi (600183), TUC (6274), ITEQ (6213), Nan Ya Plastics (1303), Resonac (4004) Mitsui Kinzoku (5706) / HVLP copper foil. Nittobo / glass fiber/cloth - MLCCs, chip resistors, tantalum capacitors, aluminum electrolytic capacitors: 15-20 weeks to 1 year+ MLCC: Murata (6981), Samsung Electro-Mechanics, TDK (6762), Taiyo Yuden (6976), Yageo (2327), Walsin (2492) Chip Resistors: Yageo, Walsin, KOA (6999), Rohm (6963) and $VSH Tantalum capacitors: Kyocera/AVX, Yageo/KEMET, $VSH again Aluminum electrolytic capacitors: Nippon Chemi-Con (6997) Nichicon (6996) | Timelines sourced from Digitimes, did the honors of adding in related companies. But it's slightly more nuanced since MLCCs and others mix. in commodity types vs. AI DCs. Regardless, just some helpful mapping.

The Q-glass vs. PTFE debate is still raging, but the ground truth is clear: ultra-high layer count AI boards are locked in for the M9 + Q-glass setup. 🧵👇 PTFE materials cause severe processing bottlenecks in drilling and lamination. It’s so problematic that even Shengyi Tech had to alter its PP formulations. Looking into 2027, the volume ramp-up of next-gen LPUs (featuring massive 52-layer designs) will trigger a massive surge in Q-glass demand. PTFE will remain strictly confined to low-layer topologies. #PCB# #Semiconductor# #AI# #SupplyChain# #Hardware#

When AI Automates the Craft You Loved DeepSeek engineer Liu Shengyu’s viral article, "I Had to Bury My Talent in Yesterday", is not simply about losing a job. It captures something more personal: AI may preserve an expert’s value while taking away the part of the work that gave it meaning. Zhihu contributor BBuf sees a warning about craftsmanship, how expertise is formed, and who controls frontier intelligence. 1️⃣ From craftsperson to machine operator Kernel optimization was deeply hands-on. Engineers learned Hopper and Blackwell, arranged shared memory, tuned warps, inspected PTX and SASS, then profiled repeatedly to push throughput higher. The process itself was rewarding. But the role is shifting from weaving the sweater to supervising the knitting machine. Experience still matters. Experts know which designs and failure modes to watch. Yet the activity they loved is no longer what production demands. 2️⃣ Kernels are ideal work for agents An agent can read CUDA, PTX, and SASS, identify stalls, propose an optimization, compile it, run benchmarks, fail, and try again. A human explores only a few paths at once. Fast models paired with tens or hundreds of agents can search far more broadly. BBuf’s conclusion is blunt: whenever work supports repeated, meaningful trial and error, humans may struggle to compete. The same pattern could extend from kernels to algorithms and mathematics. 3️⃣ The expertise paradox If beginners let AI write everything from day one, where will senior judgment come from? Experienced engineers may remain better at directing agents because they understand hardware and hidden trade-offs. But the traditional path for acquiring that intuition may disappear as the underlying craft is automated. The risk is not only fewer jobs. It is losing the apprenticeship process that once created experts. 4️⃣ Intelligence is also a distribution problem If frontier models become the main engine of technical work, who controls the compute behind them? Open weights do not mean everyone possesses frontier intelligence. BBuf argues that unequal access to compute could become unequal access to effective intelligence. The open-versus-closed debate is not merely about licenses. It is also about who can afford to use powerful intelligence at scale. ✅ What may be buried AI may not eliminate experts first. It may turn them into supervisors of systems that perform the craft they once loved. The harder question is whether we can preserve human judgment and the joy of making things while embracing tools that are faster than us. 🔗 Full analysis: #DeepSeek# #CUDA# #AIInfra# #AIAgents# #FutureOfWork# #OpenSourceAI#

Web3 builders in Shenyang, assemble 🙌 Put Your Hands Up Siman Labs Builder Night lands in Shengjing. An exclusive private dinner will kick off at 19:00 on August 9th. There will be no big keynote speeches. We only invite doers and practitioners for casual dinner‑side conversations. Whether you work on public chain ecosystems, cold wallets, overseas marketing, community governance, or you are fundraising and building your own project — this is a space for open‑ended discussions and connecting with like‑minded builders across Northeast China. If you are based in Shenyang and ready for in‑depth exchanges, feel free to DM to apply. Spots are limited. @mocheng88888 @Dachengzi0509 @LoyalSiman @Jolin612

#Shenyang#, a major stronghold in Northeast China that carries 7,200 years of human civilization and 2,300 years of urban history, has witnessed the rise and fall of dynasties under the names "Shengjing" and "Fengtian." Its three #UNESCO# World Heritage sites—the Imperial Palace, Fuling Tomb, and Zhaoling Tomb—tell the story of its profound historical depth. As the capital of Liaoning Province and a transportation hub in Northeast Asia, it not only preserves the cultural roots of "the birthplace of a dynasty" but also writes a new chapter of revitalization as an advanced manufacturing base. In the blend of tradition and modernity, Shenyang continues to radiate its unique urban charm. (Video by Huayun Bilingual Cultural Communication Society of North Minzu University) #ChinaCulture#