CryptoApe 🔗

Puttin my $$ where my mouth is, picked up 549SOL @ an average of $8.4/SOL. Ill be bookmarking this for the years to come 🤝 Not saying we bottomed, but $SOL in relation to other shitcoins in the top 25 coin market cap & 2022 tax loss harvesting atm seems like a decent bet.

@moonpay Bullish!

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@Sunrise_DeFi @paradex @solana Massive

DIME, the native ecosystem token of @Paradex, is now listed on @Solana. You can now swap and trade DIME against Solana-native assets. If you hold DIME outside Solana, you can move it onto Solana and back through Sunrise.

@stormrae_ai @SecretNetwork Massive!

Stormrae x @SecretNetwork Secret Network's TEE technology is now part of our stack. Your data stays encrypted while we work with it. No leaks. No exposure. Confidential computing is the new standard for AI security.

@stormrae_ai 👀👀👀

First the crown. Now the magic. Merlin.🧙🏻‍♂️

@stormrae_ai Hugee

5 people defeated the AI agent King Arthur and shared the $28,000 prize pool. The story is out and the challenge completed. AI security testing will change completely from here. You missed it? The next one is near. Register now and get ready to win. decrypt.co/360994/stormra…

@stormrae_ai Massive!

We casually broke the world record for public AI red-teaming challenges to date: 14,969 humans trying to outsmart a single autonomous AI. First milestone unlocked. Now we go bigger.

Retail yield in crypto has historically been volatile, directional, and opaque. KASH changes that. It provides institutional-grade exposure to: - Money markets - Private credit yield - Institutional yield strategies Waitlist still open👇

@TradexWhisperer @TradexWhisperer what you think bro?

@TradexWhisperer How does NVDA groq utilizing SRAM vs HBM change this?

$MU "What is the real bottleneck in HBM3E/HBM4 production that the market is not yet pricing in?" The question sounds technical. The answer is actually very simple once you understand one number: HBM consumes 3 to 4 times more wafer per bit than standard DRAM. You do not just build more memory. You build memory that is fundamentally more wafer-hungry than anything that came before it. Every HBM chip stacked on an AI accelerator consumes 3 to 4x the wafer per bit of a conventional DRAM. That means to double HBM output, the industry you need at least 4x more capacity. A new leading-edge fab takes at least 3 to 5 years from groundbreaking to meaningful output. That means the supply constraints visible today do not get resolved in 2026. They do not get resolved in 2027. The runway for elevated pricing and premium margins stretches further than most models on Wall Street currently assume. Every quarter the market expects relief and does not get it is another quarter of pricing power for the producers who already have capacity in the ground. Now add the yield problem and the bull case gets even stronger. HBM yield is not comparable to standard DRAM yield. It is a completely different category of difficulty. In an HBM stack, you take multiple DRAM dies, bond them vertically through thousands of through-silicon vias, and treat the entire stack as a single unit. If any one die in that stack fails, the entire stack fails. You throw the whole thing away. Yield losses multiply across the stack rather than occurring in isolation. A 95% individual die yield sounds impressive until you stack twelve dies and realize your effective stack yield drops to roughly 54%. That wafer consumption number of 3 to 4x suddenly looks conservative once yield losses compound through the stack. This is not a problem that money alone solves. It is a problem that only time, process learning, and engineering discipline solves. Every quarter of yield learning already accumulated by the producers ahead of the curve is a quarter that late movers simply cannot buy back. And the supply constraints do not end with HBM itself. SOCAMM2 is coming. CXL DRAM is coming. Both demand the same leading-edge DRAM process capacity and both serve the datacenter market with growing urgency. As inference workloads scale and memory pooling architectures mature, CXL DRAM becomes a serious incremental revenue stream competing for the same constrained wafer supply. SOCAMM2 brings high-density memory to next-generation server platforms and that ramp adds to the addressable market, not away from it. Then there is the consumer catalyst waiting in the background. When rate cuts arrive and PC and smartphone upgrade cycles accelerate, consumer DRAM demand snaps back hard. And when both datacenter and consumer cycles run simultaneously against a backdrop of structurally constrained wafer supply, the pricing environment becomes something the market has not modeled yet. So when someone asks why HBM supply cannot simply catch up to demand, the honest answer is: because the physics will not allow it, the yield math will not allow it, and the competing memory demands will not allow it. The market prices in a ramp. It has not priced in the ceiling. It has not priced in the yield wall. And it has not priced in what happens when every datacenter memory technology competes for the same wafer supply at the same time. So good luck waiting for 4x more fabs, 4x more packaging plants and 4x more HBM engineers, 95% yield on 12-die stacks, "all of the sudden." Structural Shift

@dieforward @solana 🔥🔥🔥

Submitted Die Forward to the @solana Graveyard Hackathon: 🎮 A mobile roguelite where every run matters — stake to play, survive the crypt, climb the leaderboard. Built with @magaborgs @AudiusProject @tapaborgs ⚡ Here's what we built 🧵👇 dieforward.com

What about $MU & $SNDK ? Samsung & SK Hynix can secure oil, they have the resources & cashflow - this is also a temporary situation, good dip buying opp. imo Only thing I would watch for is $NVDA announcing some sort of solution with Groq for the memory crisis - which can be a bigger issue long term & short term for price action!

“Every asset will be tokenized,” means the market can grow 10,000x and still have room to grow. - @Bitwise CIO @Matt_Hougan Read the full memo below👇

@Axel_bitblaze69 Are you going to use Claude & Qwen or just Qwen now?

i've been trying to automate my crypto research for months.. burned through probably $1500 in API costs learning what doesn't work then qwen drops this today and i'm like... fuck what i was doing: paying claude API to monitor markets 24/7 checking whale wallets, price alerts, volume spikes, key events and unlocks, the whole thing yes it did worked great but the problem is i was paying per query for stuff that should just... run checking "did this wallet move?" 24 times a day adds up fast but i need that monitoring. can't manually check wallets every hour. what i tried: switched to gemini thinking it'd be cheaper. got rate limited constantly. tried chatgpt API. worked until i hit their usage limits mid-research. tried mixing cheaper models for simple tasks. configuration nightmare. the realization: cloud APIs are designed to monetize every call for simple monitoring, i'm paying premium prices for basic checks then qwen dropped these models today: 0.8B, 2B, 4B, 9B free. run locally. no API key. no per-call charges. i'm reading the specs and it's clicking: "4B model for lightweight agents" "9B closing gap with larger models" wait... i can run these on my laptop? for free? unlimited checks? what this means for my setup: whale monitoring: qwen 4B running locally, checking addresses constantly → $0/month price alerts: 2B model fast enough for real-time checks → $0/month basic research: 9B for pulling on-chain data, comparing metrics → $0/month im testing it now.. downloaded the 4B model. running it locally. set it to watch 5 wallets as a test. response time is solid. checks every minute. no API cost. if this scales to 20 wallets with no slowdown, i just saved $200+/month on monitoring alone. the plan is migrate simple monitoring to local qwen models (2B/4B) keep claude for complex strategy analysis and final content polish basically, free local models for 80% of grunt work, paid API for 20% that needs premium intelligence i'm sharing because i spent months burning money on cloud APIs for tasks that don't need premium models qwen dropped the exact solution today if you're automating anything repetitive (monitoring, alerts, basic research), test these local models first might save you hundreds per month like it's about to save me downloading the models now. will report back on how it actually performs at scale.

Vision creates narratives. Execution creates markets. KAIO COO @odang75 spent over a decade inside global financial institutions and led Venture at @LaserDigital_, Nomura’s digital asset arm. He builds systems that survive regulation, cross-border complexity, and real market stress. This is what we mean by Transforming Institutional Funds Onchain.

@AlasdGem @mayan @portalbridge_ @Sunrise_DeFi @AlasdGem my chad

Swap on @mayan Bridge through @portalbridge_ Follow @Sunrise_DeFi for the next big listing on Solana

@astridwilde1 Sent dm!

if you're a founder that believes in the mission of automating human menial labor this decade, i would love to have you on my cap table just signed our first Fortune 500 company yesterday i want y'all in early before VCs start a bidding war

@Snotty_eth @base @jessepollak @brian_armstrong Its higher for Kelly

These agents are a frontrun they’re not building for the past or even for today, they’re building the future. Those who think this is just hype are mistaken. Zero human companies on @base factoryfloor.dev Fyi @jessepollak @brian_armstrong

Most crypto portfolios are exposed to the same cycles. KASH isn’t. 💰 Targeting secure, predictable yield sourced from real-world credit and funds that are structurally uncorrelated to crypto markets. The waitlist is still open: waitlist.kaio.xyz

@DeepValueBagger $MU if one of my bets for this reason & more

BIG 2026 PREDICTION: EDGE COMPUTING I went deep into the rabbit hole with OpenClaw and setting it up for my personal uses like finance, and health. It costed me nearly $200 for over 2 weeks use. The $80 claude max ran out in a few days, and tokens from openai ran out fast. I figured that xai has the lowest cost by 10x to 15x factor. That's when I digged into a bit more with local model. With my 12GB VRAM RTX, I could run a decent model suprisingly with newer backend like vllm serving openai compatible apis. I ordered by first Nvidia blackwell gb10 128MB VRAM to do more computation. At the pace I'm using with more frequent updates, the only economical way is local inference. Anyways my whole experience convinced me that in 2026 there will be a big shift into edge computing. Openclaw community have been buying up M3 Ultra 128GB $3500 all the way up to 512GB $10K unified RAM (eg it talks directly to the CPU). Apple is selling them like hotcakes. On the nonmac, Nvidia has been licensing to HP, Asus, and Dell. AMD also has a solution with 395+ CPU, and Intel will soon have one. Mark my words, every company will want their own rack server, and every developer will have their own local inference computer. The price reminds me of the the same cost of my first computer 33Mhz 4GB RAM computer. As cost decreases, it will be more accessible for each person. This will be a new growth segment for $APPL $NVDA and $DELL. All have shared very bullish guidances so far.