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I asked Claude to apply a capital cycle analysis to $MU. Here's what it came up with: Net reading: 11 of 14 capital cycle signals are bearish or strongly bearish. The framework reads this as late-cycle, not early/mid-cycle. The two unambiguously bullish signals (equipment lead times, industry concentration) are eroding rather than strengthening. Insights Yielded by Capital Cycle Analysis: 1) "Structural change" rhetoric is itself diagnostic. The capital cycle framework treats coordinated industry-wide CEO claims of regime change as evidence of late-cycle euphoria. The same language was deployed by the same CEOs (Mehrotra at Micron specifically) in 2017–2018 and was wrong. Bayesian base rates argue against accepting the current claims at face value. The previous analysis under-weighted this base-rate evidence. 2) Look at total capital flowing into the supply curve, not just incumbent capex. The structural-change analysis focused on Big Three capex. The capital cycle lens forces aggregation of all capital flowing into memory output: a) Incumbent capex: ~$104B in 2026 across DRAM + NAND; b) CXMT IPO proceeds: ~$4.2B (with state-aligned co-financing many multiples larger); c) YMTC capacity additions (privately financed) d) Substitute technology capital (Cerebras, photonic startups, CXL controller designers) — billions of dollars of equity raised to reduce HBM intensity per dollar of AI compute deployed. When aggregated, total effective supply-side capital formation in 2026 is materially higher than the Big Three capex alone suggests. The supply response is being underestimated. 3) The customer base is doing exactly what late-cycle customers do. Hyperscalers locking in 3–5 year LTAs, pre-ordering 2027 NAND, building strategic inventory — these are not signs of confident long-cycle visibility, they are signs of late-cycle scarcity panic. Historically (DRAM 2017–2018, oil 2008, shipping 2007), customer pre-buying at peak prices is followed by sharp inventory destocking when prices roll over. The structural-change narrative frames LTA penetration as a benefit; the capital cycle frames it as a peak signal. 4) Multiple expansion + earnings expansion = asymmetric downside. The previous analysis flagged the 15x NTM P/E multiple as aggressive (referring to UBS PT raise). The capital cycle framework sharpens this: when both earnings and multiple are at peak, the compound drawdown when either reverts is severe. Memory historically goes from 60% gross margin to negative gross margin and from 10x P/E to <5x P/E. Even a modest reversion to 35% gross margin and 8x P/E from current levels implies a 60–75% equity drawdown for the memory primaries — without any disorderly cycle. 5) Supply lag is real but not unique. The bullish point about EUV/TSV/hybrid bonding lead times is correct but mis-weighted. The capital cycle history of other capital-intensive industries (oil refining, shipbuilding, semiconductor wafer fab) shows that long lead times increase the eventual amplitude of the down-cycle: capital decisions made at peak are not reversible when conditions soften, leading to capacity overhang. Long lead times delay the down-cycle; they do not abolish it. 6) China is the textbook capital-cycle disruptor. In Chancellor's historical case studies (steel, shipbuilding, solar, panels, batteries), state-backed Chinese entrants repeatedly compressed margins of consolidated Western/Korean/Japanese oligopolies once technology gaps narrowed. The U.S. equipment restrictions on China have created the illusion that this dynamic is paused, but the data shows CXMT doubled DRAM share in 18 months and is targeting domestic HBM3. The structural-change analysis appropriately flagged this; the capital cycle framework would weight it heavier as the single most important multi-year risk. 7) Substitute capital formation is its own supply curve. The capital cycle framework treats financing flows into substitutes as a parallel supply expansion. Cerebras' $5.5B IPO, Marvell's $5B Celestial acquisition, the Sandisk/SK hynix HBF JV, and the CXL ecosystem (ALAB, MRVL, MCHP) are collectively financing "HBM intensity reduction." Even if HBM unit demand is met, the value capture per dollar of AI compute is diluted. Capital is flowing in adjacent to the memory primaries to reduce the share of AI spend that ends up in their P&L. 8) The bull case relies disproportionately on demand visibility. The capital cycle warns against demand-anchored theses. The bull case requires AI capex to continue at current levels or accelerate, hyperscaler ROI economics to remain favorable, sovereign AI to scale, and inference workloads not to migrate to non-HBM architectures. Each of these is plausible; the joint probability that all hold through 2028 is materially lower than the headline narrative suggests. 9) Sell-side estimate trajectory is itself a signal. UBS's PT trajectory ($535 → $1,625, a 3x increase in one revision) is historically associated with peak euphoria. Estimate revisions of this magnitude have a poor forward record. The framework would treat the velocity of estimate revisions as a contra-signal. 10) Where the asymmetry sits. The capital cycle framework reframes the risk/reward calculation. Even if the bull thesis is right and earnings hold through 2028, the upside from current levels is modest (multiple expansion has already happened). If the bull thesis is partially wrong — say, 2028 brings 25% peak-to-trough EPS decline rather than 50% — the equity drawdown is still material because multiples will compress simultaneously. The asymmetry is not favourable at current valuations. Bottom line: The structural change thesis was directionally correct but materially overweighted by the original analysis. The capital cycle framework appropriately reweights toward supply-side caution and treats current peak conditions, peak valuations, peak management confidence, and accelerating capital inflows as a coherent set of late-cycle signals. The memory industry has undergone real and beneficial structural change in shape, but the empirical base rate against the "cycle has been abolished" claim is overwhelming. The economic characteristics of memory businesses have improved but have not been transformed into stable, compounding, low-volatility ones — and the next 18–30 months are statistically more likely to mark the end of this up-cycle than a transition to a new regime.

7. Anthropic - Applied AI Architect（Startups 团队） - 角色: 帮助创业公司构建 Claude 应用的技术架构师 - 要求: 有 LLM 应用开发经验，能与创始人对话 - 中国讨论度: 低 - 🔗 8. Stripe - Staff Full Stack Engineer - 角色: 创业公司级别的早期员工，设定技术方向 - 薪资: 高端（未公开具体数字） - 中国讨论度: 低 - 🔗 --- 校招/实习信息差 9. Cloudflare - 2026 年招聘 1,111 名实习生 - 背景: 从原来 60 人项目暴增至 1,111 人 - 目标: "培养下一代技术领袖" - 中国讨论度: 低 - 🔗 10. - 2026 校招（New Grads） - 岗位: 计算机视觉 / 算法工程师 - 地点: San Jose, CA - 公司: 自动驾驶上市公司 - 中国讨论度: 低 - 🔗 11. Databricks - 2026 New Grad + 实习 - 岗位: 多个 AI/ML 研究、工程岗位 - 地点: SF / NYC / Mountain View / Berlin / Amsterdam / Bengaluru - 中国讨论度: 低 - 🔗 12. IBM - 2026 年校招扩招 3 倍 - 背景: IBM 人力资源官表示将"重写每一个岗位" - 原因: AI 能做入门级工作，但需要能驾驭 AI 的新人 - 中国讨论度: 极低 - 🔗 13. Google DeepMind - Student Researcher Program - 项目: 付费研究实习，覆盖 BS/MS/PhD - 团队: DeepMind / Google Research / 其他 AI 团队 - 中国讨论度: 低 - 🔗

Türkiye's Ethereum ecosystem: where crisis meets innovation. 🇹🇷 - One of the highest crypto adoption rates globally - Lira devaluation → crypto as financial necessity - Government restrictions? Young, tech-savvy population didn't slow down - Grassroots ecosystem: web3 startups, university clubs, devs, academia → economic pressure meets passion for education, inclusion, global connectivity Full overview:

Everybody keeps asking me why I dropped out of nursing and went into Tech/Ai, instead of just becoming a nurse or going into pre-med and get rich. But you only live once. Why should I chase money instead of chasing my dreams? I haven’t received a single penny from my parents since I turned 18, which made my life much harder. But I chose to do so, cuz I knew it would make me realize what I’m good at, how to manage money, and how to build a life on my own. The things is this - we learn about life through difficulties. So you need to step out of your comfort zone and put yourself on an unstable path . That’s when you start creating a new path on your own. That’s what changes your life, not home-sweet-home. So.. chase your dream! #startups# #SF# #tech# +pic of me in college/working for UNSA(United Nation of Student Association)

I’ve left Google DeepMind. The last two years have been an incredible whirlwind. A couple years ago, I joined a small startup called Codeium. There, I got to ship Windsurf, train SWE-1 (a frontier agentic coding model), go to DeepMind in the $2.4B acquisition. Now, I decided to leave the acquisition money and DeepMind. I’m grateful to the mentors, teammates, and friends I worked with along the way. At Windsurf, thanks to @_mohansolo and Douglas Chen, I got to see what a fast moving startup that ships relentlessly and builds for the future looks like. I learned from @thenickmoy how excellent research leadership can drive outsized innovation. At DeepMind, I got to push the frontier of agentic coding, be part of the amazing team that shipped Antigravity and contributed to Gemini 3. DeepMind is a rare place: deeply curious people, exceptional research taste, and access to enormous compute and Google-scale infrastructure. A few things that I learned: 1. Finding the right hill to climb. Now more than ever, there are a multitude of directions to push the frontier in AI research. It’s easy to optimize for the wrong benchmark or capability. You should step back regularly to question if you are climbing the right hill, and adjust course often. 2. The secret to being a fast-moving team. Moving quickly is not just about working hard and long hours. It requires making concrete bets about where the world will be in 6 months, aligning around them, and cutting everything else. This was our journey from the Codeium Extension → Windsurf IDE → SWE-1 → Antigravity → Antigravity CLI 3. Silicon Valley is small. Since the split of Windsurf to DeepMind and Cognition, many of my colleagues have gone to other exciting places - Thinking Machines, OpenAI, xAI, Cursor, fast-moving startups, or started their own companies. I’m grateful to have worked with so many talented, hungry people whose stories are not yet finished. So what’s next? We are living in one of the most exciting and powerful times in human history. Just like we transformed software engineering, soon every industry, every unit of work will be radically transformed, democratized, accelerated. With this comes new challenges, and new doors of frontier research to be opened. More soon.

We looked at the jobs pages of 910 early-stage startups from the top accelerator programs, analyzing who they want to hire and for how much. Explore at our latest drop:

Most people think Burning Man is just about EDM, naked people, and giant art cars. But the thing that really shocked me was the Temple that gets burned on the final night. The Temple isn’t built by the organizers. Hundreds of strangers spend months — sometimes half a year — in warehouses cutting wood, designing structures, fundraising, transporting materials, and then surviving brutal heat and dust storms in the desert, working 10 to 16 hours a day to build it together. And in the end, they burn the whole thing down. What’s even crazier is that many people have been doing this for 10 or 20 years. That’s when I realized: Temple Crew isn’t really a volunteer group. It’s more like a civilization experiment. Because in modern society, almost everything revolves around: money, efficiency, valuation, growth, traffic. But the Temple is the complete opposite: * massive effort * low efficiency * no commercial return * and total destruction at the end And somehow, that’s exactly why it becomes the most meaningful part of Burning Man. People leave behind: photos of loved ones, letters to ex-partners, stories of failed startups, farewell notes, even ashes of family members. Then on Temple Burn night, something strange happens. Tens of thousands of people suddenly become silent. No screaming. No partying. No music. And a lot of people cry. Because what’s really burning isn’t the wood. It’s grief. Regret. Pressure. Old versions of themselves. That’s also why I think the most respected burners are not the people with the biggest RVs or the most money. It’s the people who, at 3AM when something goes wrong, everyone instinctively trusts to help. Now I finally understand why so many people from Silicon Valley, AI, and crypto keep returning to Burning Man every year. Because the desert offers something that modern society is quietly losing: real community, real trust, and large-scale human collaboration that isn’t driven by money.

Startup city rankings for Seed + Series A + Series B. US only, ranking is by total capital invested into Carta startups. So this obviously ignores the $100B foundation model rounds. But the Ai share for the Bay still hovers close to 50%. Where does your ecosystem land?

100+ places to launch your startup: 1. Product Hunt 2. BetaList 3. TrustMRR 4. Uneed 5. TinyLaunch 6. Indie Hackers 7. Hacker News 8. Tiny Startup 9. PeerPush 10. SideProjectors 11. DevHunt 12. Launching Next 13. Microlaunch 14. Launch Directories 15. StartupBase 16. ShowMeBestAI 17. Trendy Startups 18. Software Advice 19. There's an AI for that 20. AlternativeTo 21. OpenAlternative 22. SaaSHub 23. Toolfolio 24. LibHunt 25. SaaS Genius 26. FoundrList 27. Stacker News 28. PitchWall 29. API List 30. MakerPad 31. Dan Recommends 32. Startup Buffer 33. AppSumo 34. SEO Wins 35. RocketHub 36. StackSocial 37. SaaS Mantra 38. SaaS Warrior 39. LTD Hunt 40. KEN Moo 41. Prime Club 42. SaaSZilla 43. Fazier 44. Peerlist 45. Next Gen Tools 46. Sustainability Softwares 47. Saas Baba 48. PromptZone 49. Futurepedia 50. Toolkitly 51. LaunchIgniter 52. Firsto 53. Indie Tools 54. Manta 55. Indie Deals 56. PayOnceUseForever 57. Slocco 58. ToolFame 59. GPTStore 60. AlterOpen 61. SaaS Gallery 62. Aura Plus Plus 63. That AI Collection 64. BasedTools 65. SaaS Pirate 66. Product Canyon 67. Deal Mirror 68. Dealify 69. Goodfirms 70. AI Agent Store 71. BroUseAI 72. Altern 73. BestWebDesignTools 74. MadGenius 75. BotsFloor 76. AIDir Wiki 77. Look AI Tools 78. The AI Generation 79. Waild World 80. Wavel 81. Indie Products 82. Invent List 83. Hack the Prompt 84. Startup Heroes 85. AI Marketing Directory 86. RankYourAI 87. EarlyHunt 88. Tekpon 89. Dokey AI 90. Appscribed 91. Open Tools 92. SEOFAI 93. Startups FYI 94. AI Tool Trek 95. Powerusers 96. AI Parabellum 97. Serchen 98. RobinGood 99. Affiliate Watch 100. IndieHunt 101. Reviano 102. Nocode List 103. Software World 104. AIxploria 105. Ctrlalt 106. AI Hunter 107. Public APIs