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bayang

@Bayang_BM

LLVM interest Fan of parallel processing Building @simdcompute

Germany Katılım Mart 2017
399 Takip Edilen435 Takipçiler
MCG
MCG@MCGlive·
[Updated] Huge week on MCG! Times in EDT Monday 4/27 12:00 - $REPPO @reppo w/@Jordan_Grollman @rgvrmdya 12:30 - $SIMD @simdcompute w/@bayang_bm Tuesday 4/28 12:00 - $FOLD @clarity_proto w/@333absent333 12:30 - $LIL @Liminalcash w/@0xJackMarsh 1:15 - $LOYAL @loyal_hq w/@candyflipline (@MetaDAOProject) Wednesday 4/29 11:45 - @craftsdev w/@pfo_sac 12:30 - $AOL @americadotfun w/@vesper0x (@MeteoraEco) 1:15 - $DRV @DeriveXYZ w/@ksett13 Thursday 4/30 12:00 - $EITHER @EitherwayAI w/@Depin_Dexter 12:30 - $BLOXX @BloxApi w/@the_davey (@MetaLeX_Labs) 12:50 - $HermesOS @Wayland_Six 1:15 - $LITCOIN @litcoin_AI w/@tekkaadan 2:00 - $ORGO @orgo w/@spncrk (@StreetFDN) Friday 5/1 12:00 - $PEAQ $WOON $ROBOTMONEY @peaq w/@martinelkhouri 12:45 - $OPG @OpenGradient w/@0xDeltaHedged Follow @MCGAlpha for show notes and chart updates.
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MCG
MCG@MCGlive·
Today on MCG: @bayang_bm | @simdcompute Bringing AI and Web3 to "computational fluid dynamics"... a $30B+ legacy industry that hasn't been touched by new technology in 40 years Two technical founders that are building something nobody else is 👇 00:04 - Meet the team 01:54 - Elevator pitch 02:28 - What is CFD? Computational fluid dynamics explained simply 03:11 - Real world example 03:32 - How simulations are traditionally run 05:04 - Who is the user? 08:06 - Vibe coding 08:59 - Target audience breakdown 10:05 - Saving engineers time 12:12 - Go to market approach 13:24 - Real use case example 15:40 - The size of this market 18:31 - Tesla comparison 19:25 - The newest AI approach 23:04 - The token 23:52 - Building the distributed layer 24:46 - Wrap
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Daniel D
Daniel D@drog52869·
@Bayang_BM Great to see this level of commitment from founders ✊
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bayang
bayang@Bayang_BM·
ask each agent to write the conversation from the chat (summarized) in files ~/.shared-memory file, and then ask each agent before running any tool, to read what the other agent is doing. So you have a series of agents working on a shared memory. About lovable (I don't care 😉😂😅) maybe RDMA could work
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Matthieu Cretier
Matthieu Cretier@originalmatth·
Running multiple Codex, Claude Code, and Lovable instances side by side. Moving forward fast. Yes. But the context switching is brutal. Also no one’s even chasing me. It’s a side project. The pain is entirely self-imposed.
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SIMD
SIMD@simdcompute·
First day at Ojin house with the team 🏡 Sam, Adi and Bayang, let’s see where this goes @Ojin_AI
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Marie Hanna Scherf
Marie Hanna Scherf@MarieHScherf·
My brother @zwiebelhelm and I just came back from sf. We grew up in Berlin and are now planning something big there.. If you’re based in Berlin, dm me :)
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Jim Keller
Jim Keller@jimkxa·
Intel won by RISCifying their CISC - still amazed how well x86-64 worked out LPUs is a subset of AI, an accelerator, it's not the RISC @tenstorrent , Trainium, Google TPU are closer. Clean Tensor processor is step one. Then generality, memory and Networking. And of course LInux and Open source software were key for Intel. AI software is a curious swamp tbh
Pushkar Ranade@magicsilicon

NVIDIA GPUs are the modern-day equivalent of the CISC ISA. TPUs, LPUs and other accelerators are the modern-day equivalent of the more elegant RISC ISA. Intel won the RISC vs CISC war of the 1990s. Who will win the AI architecture war of today? 🤔 The most optimal AI computer is yet to be built!

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