Nicole: Clamor Within The Silent "Mage," the Voiceless "Angel" #GenshinImpact# #Nicole# Many of the stories Nicole envisions begin with a phrase as old as stories themselves: "Long, long ago..." Perhaps, for some authors, "long ago" is less the threshold of a tale than a refuge from all further questioning. For once a story is consigned to unreachable distance, all the people and events bound up within it may as well have faded into obscurity, leaving no path by which the present might truly follow them back. And yet, in Nicole's thoughts, those stories of "long ago" endure still. Long ago, the lights of many nations filigreed the earth in gleaming gold. Long ago, the daughters of heaven wandered in carefree grace betwixt divine courts and mortal cities. Long ago, the sovereign of ages past, who had descended into deepest darkness, had not yet brought disaster back to his homeland. Long ago, the three radiant moons that hung aloft in the night heavens were still three... The years brook no pause, the wheel spinning onward all the same. Within a cycle emptied of hope, the spark of paradise perished alongside the lives annihilated. Servitors once loyal to their ruler turned traitor for the sake of the lives that had been fashioned. The black dragonlord returned from beyond the stars. The moonlit chariot of the heavens shattered like crystal... The story had not reached its end. Rather, it was those who had once read it, and those who had once listened to its telling, who had long since taken their leave. The angel who once longed to write laughter into the world lost all that had once made laughter possible. In the end, she even lost the voice with which she laughed, and could speak no more. Thus, it seemed that the story Nicole kept within her would never properly find its beginning. She could only go on cradling her many "long agos," roaming the world and watching as all things passed, changed, and were remade by time. That remained so until the day she met a tiny mage in an enormous hat, its extravagant brim tipped up with such pride it seemed almost ready to brush the sky. Compared with the hat she wears now, however, it was still a fair few sizes smaller. The little witch tucked herself down at Nicole's side and listened intently as one story after another unfolded, not stirring until she had finished her drink and emptied her bag of biscuits. "So you really are an angel? And all those stories were real, too?" "Then yay, that works! I'm a mage, and you're an angel, so we match perfectly." The merry little mage stretched out a hand to the angel who had come so far, all the way from those distant "long agos," and warmly invited her along. "Want to join my Hexenzirkel? I only just came up with the name, but it has a real ring to it, don't you think?" "It's going to be the funnest, awesomest, best club in all of Teyvat — I promise!" "..." No answer came from the angel. None could. But the little mage did not wait for one. She caught the angel's hand in both of her own and slipped the last piece of candy into her palm. "There. Now our mages' pact is made." Nicole had no thought of joining then, nor could she have guessed what this little gathering called the "Hexenzirkel" would one day grow into. But at the very least... It did sound like a place with happiness enough to spare.

Beyond the on-court success, Zay’s senior season at Wheeler High School was an emotional roller coaster. His story, on how basketball helped him overcome tragedy and propelled him to the league, is out now on YouTube and JAZZ+ 📺 | Presented by @AFCU

Beyond Reading — AI with Eyes 👁️👁️ AI agents can already read the web. Now, we’re giving them eyes. This week, we demoed Semantic Video Search — powered by the UpRock Network. Not transcript search. Not keyword matching. Real multimodal intelligence across video, audio, frames, timestamps, and context. The demo: We asked UpRock to search across 100+ hours of livestream history. In seconds, it found: → the exact moment → the visual context → the timestamp → the story behind Jesse’s heart-shaped glasses 🕶️ This is what AI search is becoming. Drop in a YouTube video, TikTok, X video, earnings call, competitor demo, or 3-hour conference talk. Don’t just get a summary. Get structured intelligence your AI agent can actually reason over. What makes UpRock different is what’s underneath: 3M+ real devices. 190+ countries. Real phones. Real locations. Ground-truth access. No datacenter proxy games. No synthetic traffic. No stale scrape layer. Just the internet as real users see it. Semantic Video Search is coming soon for UpRock business customers. Building AI agents, research workflows, media intelligence tools, or multimodal search products? Get in touch → ⚡

Meeting the whale brothers of Datawhale in person in Hangzhou was an experience I'll never forget. Meeting actual users/day-1 whale bros, receiving exceptionally detailed questions, and discovering so many of the attendees were teachers - it still makes me a bit sad that my busy schedule cut this portion short. I wanted to take a picture with each individual and hear their story. It gives me so much hope for a future where open source goes beyond technology: a philosophy of sharing applied knowledge freely. The diversity of perspectives, time zones, languages - they become an asset to not only those involved but also all of society. This, to me, is the power and beauty of open source. An elegant, inherently artistic, learning-centric movement - one beyond the constraints of "a person having ordinary skill in the art."

The Underpriced Truth: Agentic AI Is a Paradigm Shift Centered on Memory 1/ The market will slowly realize: Agentic AI is memory-centric, not compute-centric. The new hardware stack is: ① Memory — HBM / DRAM / NAND ② Parallel compute — GPU / ASIC ③ Coordinator — CPU CPUs stopped doing the heavy lifting a long time ago. This isn't a cycle. It's a paradigm. 🧵👇 2/ First principles Humanity's ultimate pursuit of intelligence has always been two things: Infinite memory + infinite compute. When we say someone is smart, we mean two things: "good memory" + "fast thinking." Machine intelligence is walking the exact same path. 3/ The story the market already understands: HBM LLM inference's decode stage is a textbook memory-bound workload. Every token generated → drag the entire KV cache across memory. Bandwidth too low → expensive GPUs sit idle. That's why every new GPU generation ships with more HBM bandwidth and capacity. 4/ The story the market is missing The "1M context" you keep hearing about? It is not assembled inside the GPU inference cluster. So where is it actually built? 5/ It's built on the traditional servers running the agentic system Those CPU + huge-DRAM servers are quietly doing the heaviest lifting: • loading user long-term & short-term memory • loading the agent's system spec / prompt • loading skill / tool / subagent definitions • compressing the context once it overflows 1M tokens All of this lives in DRAM, not HBM. 6/ Compare this to the previous era In the web / mobile era, we barely stored any user context at all. Only search / recsys / ads kept a small user profile — maybe 1/20, even 1/100 of the data volume an agentic system needs today. That asymmetry is the real overlooked inflection point. 7/ The supply chain is already telling this story Server CPU : DRAM ratio is climbing fast: • Web / Mobile era: 1 core : 4 GB • Agentic AI today: 1 core : 16 GB • Deep agentic future: 1 core : 64 GB and beyond 8/ And it's NOT just "4x more memory" Under agentic workloads, a single CPU serves a fraction of the users it used to. When the entire IT stack migrates to agentic: • CPU count grows several-fold to ~10x • DRAM total grows tens-fold to ~100x That's the part nobody is pricing in. 9/ The conclusion Agentic AI is a paradigm shift centered on storage + parallel compute. The software paradigm changed. The hardware paradigm changed with it. Only those who deeply understand the technology will see it: This isn't a memory cycle. It's a memory paradigm. 10/ Time horizon Given how early we still are on: • user adoption rate • depth of usage per user We are at least 5 years away from the cyclical top of this memory wave. (Zoom out far enough and everything is a cycle — but this one is nowhere near peak.) $MU $DRAM $SNDK

Is crypto being quietly bought out by the people who used to mock it? The numbers tell the story: - $15.12B tokenized stock volume in Q1 2026 - Beat all of H2 2025 combined ($14.84B) - Total RWA tokenized assets hit $26B, up 4x in one year And it's not retail moving this. Look at who's been busy: - @BlackRock brought its $2.4B BUIDL fund onto @Uniswap. Also bought $UNI tokens - Apollo ($700B AUM) tokenized its credit fund across 6 blockchains - @jpmorgan launched $JPMD deposit token on @coinbase's @base - JPMorgan, BofA, Citigroup, Wells Fargo in talks for a joint stablecoin - Fidelity hiring a "DeFi vaults manager" - Morgan Stanley opened crypto trading on E*Trade - @Bitwise just took over Superstate's $267M tokenized fund The migration chain is critical if you don't know: TradFi tokenizes assets → real volume hits DeFi rails → infrastructure protocols capture fees → tokens with revenue start outperforming → narrative shifts from "crypto vs TradFi" to "which chain wins TradFi" Most people will miss this because of anchoring bias. The first time they heard about crypto was FTX or a rug pull. That image stuck even though the market moved on. Even crypto veterans tuned out after hearing "institutions are coming" too many times The size of the prize: - ETF market: $30,000B - Global equities: $110,000B - Bonds: $145,000B - Total tokenized assets today: $26B Larry Fink said it plainly: every stock, every bond will be tokenized. McKinsey forecasts $2,000B by 2030. Standard Chartered says $30,000B by 2034 If you love this market beyond trading memes and ignoring this, you need to look at the bigger picture again. The next cycle won't be won by who shouted the loudest. It will be won by those who positioned in the right sector before TradFi finished building it

We are proud to share that Bitdeer AI has been ranked in the Bronze Tier SemiAnalysis GPU Cloud ClusterMAX™ Rating in its April 2026 report, standing out among more than 80 neocloud providers 🎉. SemiAnalysis looks beyond surface-level pricing, evaluating the technical pillars that determine true business value: 🔹 Reliability & Support 🔹 High-Performance Networking & Storage 🔹 Orchestration Excellence 🔹 Total Cost of Ownership (TCO) As AI workloads scale in complexity, raw hardware is only half the story - operational efficiency and architectural integrity are the only way forward. This recognition validates our commitment to building enterprise-grade AI infrastructure ready for the most demanding training and inference workloads. Thank you, @SemiAnalysis_ team, for the deep dive and the recognition. We are already hard at work on our next phase of optimizations, further refining our cloud capabilities and O&M excellence to stay ahead of the curve. 🔗 Read the full SemiAnalysis breakdown: #BitdeerAICloud# #SemiAnalysis# #AIInfrastructure# #GPUCloud# #neocloud# #CloudComputing#

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?

Some quick thoughts on Intel's EMIB-T packaging for the new 2H27 Google TPU (Humufish). Based on my industry checks: 【How to read EMIB-T's 90% yield?】 1. Given Intel's track record running EMIB in mass production, hitting 90% technology validation yield on EMIB-T (still under development) is a very positive but reasonable data point. 2. Intel benchmarks EMIB production/assembly yield against FCBGA. Industry FCBGA yield today is generally above 98%. 3. On yield, getting from 90% to 98% is harder than getting from project kickoff to 90%. And technology validation yield ≠ final production yield, especially with some Humufish specs still unfinalized. So long-term, I'm positive on Intel's advanced packaging story. Near to mid-term, I'm staying cautious on how they get there. 【From 90% to 98%. Looks like just a few points. Does Google care? Absolutely】 1. Google recently asked TSMC how much it could save by placing wafer orders for Humufish's main compute die (designed in-house by Google) directly, rather than routing them through MediaTek. 2. Google and MediaTek have run a semi-COT model since day one (8t). MediaTek's mark-up sits mostly on the parts it designs itself, so whether Google places the wafer orders for main compute die directly isn't a key swing factor for MediaTek's earnings trajectory. 3. But Google even probing whether it can squeeze out the pass-through mark-up on wafer orders tells you something: Google has shifted from easygoing buyer to hard-nosed on cost. The reason is simple: to take on Nvidia head-on, cost is Google's edge, which makes EMIB-T production yield Google's problem to solve. For context, TSMC's yield target on 5.5-reticle CoWoS in 2026 also starts at 98%. 【TSMC's position】 1. My understanding is TSMC is still working out how much advanced-node capacity to allocate to Humufish in 2H27, for two reasons: (1) it still wants the back-end packaging orders, though looks unlikely for now, and that's by design on Google's part; and (2) it's still gauging actual back-end output from EMIB-T, to avoid misallocating scarce advanced-node capacity. 2. Humufish's effective back-end output hinges on both EMIB-T and substrates, and both need to be tracked together. 3. On the Humufish semi-COT model, TSMC also prefers MediaTek to place the wafer orders for the main compute die. Beyond the close working relationship, the key point is MediaTek is TSMC's third-largest advanced-node customer in 2025. If TPU orders shift, MediaTek's scale makes it a natural buffer for TSMC to rebalance its wafer allocation mix.

Alliance Entertainment $AENT: Physical Media Is Back, and the Numbers Show It Alliance Entertainment just reported a standout Q3 FY2026, with revenue climbing 21% year over year to $258.2M and net income rising 25% to $2.3M, driven by broad-based growth across core segments: • Vinyl sales up 15% to $99M • CD sales up 90% to $39M • Collectibles revenue up 48% • Gaming revenue up 12% to $33M But the investor story is much bigger than just one quarter: • Year-to-date net income up 78% to $16.6M • Year-to-date Adjusted EBITDA up 47% to $35.7M For investors watching the entertainment and media space, $AENT is showing that collectors value physical ownership. Vinyl, CDs, movies, gaming, and collectibles are translating into real revenue growth, and stronger earnings. The company is evolving beyond distribution into platform opportunities with Alliance Authentic™ and Endstate Authentic, creating new revenue streams that further diversify the business. With 340,000+ SKUs, 35,000+ retail and e-commerce storefronts served, improving operating leverage, and growing demand for collectible physical media, $AENT is positioning itself as a public-market play on the resurgence of entertainment ownership. Read the full Q3 FY2026 results: $AENT $AMZN $MGM $FNKO $SONY $DIS $PARA $WBD $NTDOY