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🚨 WARNING: TOMORROW WILL BE THE WORST DAY OF 2026!! In 24 hours, SpaceX goes public at a $1.75 TRILLION valuation - the biggest IPO in history. I've been trading for 10+ years, and I've never seen the financial system bend its own rules for ONE private company. Nasdaq. MSCI. America's largest brokerages. All changing long-standing rules for a single company. That doesn't happen by coincidence. Let me tell you what's really going on: First, Fidelity slashed its minimum account requirement from $500,000 to just $2,000. A 99.6% reduction. Think about that for a second. One of Wall Street's most exclusive gates was suddenly opened to millions of everyday investors - right before the biggest market debut of all time. Why do they suddenly want YOU involved? Because someone needs buyers. SpaceX set aside 30% of the offering for retail investors. That's THREE TIMES the typical allocation. And despite that, many investors still received only partial allocations. Which means anyone wanting more shares will be chasing them when trading begins. To do that, they're selling other positions TODAY to raise cash. That's one side of the selling pressure you're seeing. The other? Institutional money positioning ahead of July. Here's the part most people are missing: SpaceX won't enter the Nasdaq 100 immediately. It gets added 15 days later. Why? Because Nasdaq shortened its own waiting period from 3 months to just 15 days. Specifically for this event. The second SpaceX joins the index, every fund is REQUIRED to buy shares. That's an estimated $22–27 billion of automatic demand. The big funds are selling assets now to build cash reserves. Retail is selling. Institutions are selling. Both at the same time. THAT is what's driving this selloff. Now for the part nobody wants to say publicly: When the most powerful money managers in the world create a $1.75 trillion liquidity event and invite the smallest investors to participate at the last minute... That's not generosity. That's distribution. We've watched this play out before: → Dot-com bubble (2000) → COVID crash (2020) Insiders exit at extreme valuations. The crowd rushes in chasing momentum. Something doesn't add up. So over the next 24 hours, you have two options: Buy into the most expensive IPO ever at the opening bell... Or dig into the prospectus and consider the possibility that YOU are the liquidity event. The next few days are going to be wild. I've publicly called some of the biggest tops and bottoms of the past decade. And I'll call this one too. I’ve spent decades studying markets, and I’ve called most major tops and bottoms along the way. And I’ll call it again in 2026. Follow me and turn notifications on before it’s too late. Don’t become exit liquidity.

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A narcissist can fake quite a lot. They can fake empathy. They can imitate affection. They can even put on such a convincing performance of being supportive that, at first glance, you’d swear they meant it. What they struggle to conceal is how they react when something goes well for someone else. Pay attention the next time you have a genuine win. Not the grand victories. The promotion. The successful project. The new relationship. The small moments are often more revealing. The achievement you’re proud of. The piece of good news you’d naturally expect another person to be pleased about. Watch closely. The slight eye roll. The forced smile. The curious inability to simply say, “Well done.” Or the immediate qualification. “That’s nice, but…” An impressive phrase, really. Two words capable of draining the joy from almost any accomplishment. Then there’s the remarkable talent for making your success about them. You share something you’ve achieved and, within moments, you’re listening to a story about something they did years ago that was apparently bigger, harder, better, or more important. Your moment lasts roughly thirty seconds before being requisitioned for their use. My personal favourite is the forensic examination of your achievement. You receive praise; they begin a search for defects. Not because the flaws matter, but because your confidence does. And then, of course, there’s silence. No congratulations. No enthusiasm. No acknowledgment. Just a noticeable drop in temperature, as though your success has somehow inconvenienced them. The lesson is simple. People who are genuinely comfortable in themselves do not find your success threatening. They are capable of applauding without competing, congratulating without comparing, and celebrating without keeping score. Those who cannot manage that are telling you something useful about themselves. Believe them… BELIEVE THEM!!! A narcissist often views another person’s success as a reduction in their own importance. It’s an odd way to move through life, but it does have one advantage for the rest of us: It makes them surprisingly easy to spot.😉💙🎩

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I think that fast strides are the most powerful training stimulus for runners. For a glimpse at the physiology, I want to talk about my all-time favorite study. If we were playing FMK, I’d F this study, M this study, and K any study that is not this study. Published in 2018 in Physiological Reports, it had 20 trained athletes (14 men and 6 women) complete a 40-day intervention with 10 sessions involving 5-10 x 30 seconds with 3.5 minutes recovery, in conjunction with a 36% reduction in weekly volume. Before and after the intervention, participants did a time-to-exhaustion test, two 10km tests (one with glycogen depletion) preceded by 2 x 6 minute intervals at 60% of VO2 max, and a series of muscle biopsy and blood tests. Running economy during the 10k improved by 2.1% and during the intervals at 60% of VO2 max by 1.7%. In other words, the athletes improved at both hard efforts and easy ones. Meanwhile, there was no running economy change in the glycogen-depletion 10k, leading the authors to theorize that improvements were primarily related to changes in slow-twitch muscle fibers. Expression of protein dystrophin increased, which may enhance the ST-fiber structural integrity via the actin-myosin cross-bridge, improving running economy. In addition, the expression of UCP3 decreased, which may improve mitochondrial efficiency. In fast-twitch fibers, expression of the muscle protein SERCA1 decreased by 22%, which could reduce the energy turnover during exercise. Enzymatic activity and testosterone:cortisol ratio also went up. Don’t be discouraged, I only kinda know what a few of those words mean. The point is that as researchers introduce stride-like speed sessions and measure more variables, they often see major changes that may explain why Olympians are always doing strides. So when we use the term “running economy,” we’re actually incorporating dozens of these physiological variables, some of which we can measure and some of which we can’t, from the cells to the muscles to the circulatory and cardiovascular systems. Put it together, and getting faster is a wonderfully complex algorithm. Strides are one way to help solve it 💪

I think that fast strides are the most powerful training stimulus for runners. For a glimpse at the physiology, I want to talk about my all-time favorite study. If we were playing FMK, I’d F this study, M this study, and K any study that is not this study. Published in 2018 in Physiological Reports, it had 20 trained athletes (14 men and 6 women) complete a 40-day intervention with 10 sessions involving 5-10 x 30 seconds with 3.5 minutes recovery, in conjunction with a 36% reduction in weekly volume. Before and after the intervention, participants did a time-to-exhaustion test, two 10km tests (one with glycogen depletion) preceded by 2 x 6 minute intervals at 60% of VO2 max, and a series of muscle biopsy and blood tests. Running economy during the 10k improved by 2.1% and during the intervals at 60% of VO2 max by 1.7%. In other words, the athletes improved at both hard efforts and easy ones. Meanwhile, there was no running economy change in the glycogen-depletion 10k, leading the authors to theorize that improvements were primarily related to changes in slow-twitch muscle fibers. Expression of protein dystrophin increased, which may enhance the ST-fiber structural integrity via the actin-myosin cross-bridge, improving running economy. In addition, the expression of UCP3 decreased, which may improve mitochondrial efficiency. In fast-twitch fibers, expression of the muscle protein SERCA1 decreased by 22%, which could reduce the energy turnover during exercise. Enzymatic activity and testosterone:cortisol ratio also went up. Don’t be discouraged, I only kinda know what a few of those words mean. The point is that as researchers introduce stride-like speed sessions and measure more variables, they often see major changes that may explain why Olympians are always doing strides. So when we use the term “running economy,” we’re actually incorporating dozens of these physiological variables, some of which we can measure and some of which we can’t, from the cells to the muscles to the circulatory and cardiovascular systems. Put it together, and getting faster is a wonderfully complex algorithm. Strides are one way to help solve it 💪

David Roche

73,596 Aufrufe • vor 1 Jahr

🚨 Anthropic committed up to 1M TPU chips for Claude. Openai is leasing TPUs for chatgpt inference. Here's How kernels work on TPUs (deep dive 2/6 by emi) pallas is Google's answer to kernel writing. a python kernel SDK built on JAX. still very experimental (jax.experimental.pallas). on TPU it compiles through mosaic; on GPU it lowers to triton. if you know CUDA, the syntax will feel familiar but the execution model is completely different. in CUDA, grid=(4,4) launches 16 blocks running simultaneously across SMs. in pallas, those 16 iterations run one after another in lexicographic order. no threads. no warps. no blocks. no occupancy tuning. a TPU is a sequential machine with a very wide vector register — more like a CPU than a GPU. performance comes from width: a 128x128 systolic array doing matmul and an 8x128 SIMD vector unit doing everything else. maximum parallelism on chip: 2, one per TensorCore in megacore mode. three concepts replace CUDA's thread/block/grid hierarchy. Refs are mutable memory references. because execution is sequential, each iteration safely accumulates without atomics. in CUDA you'd need atomics or a separate reduction pass. the memory model is also very different from NVIDIA's. zero hardware caches. VMEM is 32-128 MiB of software-managed scratchpad — 500-1000x larger than GPU shared memory per SM. all data must be explicitly DMA'd from HBM to VMEM before any computation touches it. four levels: HBM → VMEM → VREGs → MXU/VPU, plus SMEM for scalar control data. every byte of data movement is your responsibility. this is like CUDA shared memory except it's 500x bigger and there's no cache fallback. pipelining is mandatory. without double-buffering HBM→VMEM transfers, the MXU just stalls waiting for data. this is the single most important optimization on TPU. and because grid execution is sequential and deterministic, consecutive iterations that need the same input block skip the redundant HBM transfer automatically, impossible on GPU where block execution order is undefined. the compilation pipeline is unlike anything in this series: python → jaxpr → stableHLO → XLA HLO (71+ optimization passes) → LLO (78+ passes) → 322-bit VLIW bundles. the compiler packs instructions for scalar, vector, matrix, and DMA units into a single 322-bit word. everything in that bundle executes in parallel, with no runtime scheduling.

🚨 Anthropic committed up to 1M TPU chips for Claude. Openai is leasing TPUs for chatgpt inference. Here's How kernels work on TPUs (deep dive 2/6 by emi) pallas is Google's answer to kernel writing. a python kernel SDK built on JAX. still very experimental (jax.experimental.pallas). on TPU it compiles through mosaic; on GPU it lowers to triton. if you know CUDA, the syntax will feel familiar but the execution model is completely different. in CUDA, grid=(4,4) launches 16 blocks running simultaneously across SMs. in pallas, those 16 iterations run one after another in lexicographic order. no threads. no warps. no blocks. no occupancy tuning. a TPU is a sequential machine with a very wide vector register — more like a CPU than a GPU. performance comes from width: a 128x128 systolic array doing matmul and an 8x128 SIMD vector unit doing everything else. maximum parallelism on chip: 2, one per TensorCore in megacore mode. three concepts replace CUDA's thread/block/grid hierarchy. Refs are mutable memory references. because execution is sequential, each iteration safely accumulates without atomics. in CUDA you'd need atomics or a separate reduction pass. the memory model is also very different from NVIDIA's. zero hardware caches. VMEM is 32-128 MiB of software-managed scratchpad — 500-1000x larger than GPU shared memory per SM. all data must be explicitly DMA'd from HBM to VMEM before any computation touches it. four levels: HBM → VMEM → VREGs → MXU/VPU, plus SMEM for scalar control data. every byte of data movement is your responsibility. this is like CUDA shared memory except it's 500x bigger and there's no cache fallback. pipelining is mandatory. without double-buffering HBM→VMEM transfers, the MXU just stalls waiting for data. this is the single most important optimization on TPU. and because grid execution is sequential and deterministic, consecutive iterations that need the same input block skip the redundant HBM transfer automatically, impossible on GPU where block execution order is undefined. the compilation pipeline is unlike anything in this series: python → jaxpr → stableHLO → XLA HLO (71+ optimization passes) → LLO (78+ passes) → 322-bit VLIW bundles. the compiler packs instructions for scalar, vector, matrix, and DMA units into a single 322-bit word. everything in that bundle executes in parallel, with no runtime scheduling.

wafer

33,134 Aufrufe • vor 29 Tagen

InterLink’s Journey to the World’s Top 10 Most Accurate AI Models Artificial Intelligence has rapidly become the defining force of this decade powering breakthroughs across every industry. But while most projects chase trends, InterLink Labs 👤 + 🌐 has been quietly building something deeper: an AI ecosystem grounded in verified human intelligence. Long before AI captured global headlines, InterLink Labs 👤 + 🌐 had already begun its research and engineering efforts back in 2019, assembling a world-class team of engineers and researchers from Big Tech companies and top QS-ranked universities. Their vision was clear - to build a model that truly understands humans, not just data. Unlike traditional AI systems trained purely on digital information, InterLink Labs 👤 + 🌐’s Human-AI Model learns from verified human behavior across millions of Human Nodes. This unique layer of authentic, real-world human input gives InterLink’s AI an unprecedented advantage in trustworthiness, bias reduction, and contextual understanding. Beyond algorithmic optimization, InterLink Labs 👤 + 🌐’s R&D efforts are being scaled to an unprecedented level. The team operates over 100 NVIDIA H100 servers, processing massive volumes of verified behavioral data contributed by real Human Nodes across the world. This data - diverse, decentralized, and human-validated forms the foundation of a next-generation intelligence system designed to mirror real human reasoning patterns. At the same time, InterLink Labs 👤 + 🌐’s AI-powered Human Credit Score applies advanced machine learning to analyze authenticity, contribution, and reliability. Creating an ethical model of digital reputation and fairness. Aligned with National Institute of Standards and Technology (National Institute of Standards and Technology) evaluation standards, InterLink Labs 👤 + 🌐 now aims to achieve Top 10 accuracy globally among AI models. Competing with research teams from Samsung Electronics, Kakao, キヤノン株式会社 / Canon Inc., and other global giants, InterLink Labs 👤 + 🌐’s engineers continue to train, benchmark, and refine their architecture daily to reach world-class precision and consistency. But this is more than a technical race. It’s a human mission. Every verified user contributes to the world’s first Human-Powered Intelligence Network, where real people fuel the evolution of trustworthy AI. As InterLink Labs 👤 + 🌐 advances toward global National Institute of Standards and Technology recognition, one truth becomes clear: The future of intelligence won’t be artificial. It will be human-powered. #InterLink #ITLG #ITL

InterLink’s Journey to the World’s Top 10 Most Accurate AI Models Artificial Intelligence has rapidly become the defining force of this decade powering breakthroughs across every industry. But while most projects chase trends, InterLink Labs 👤 + 🌐 has been quietly building something deeper: an AI ecosystem grounded in verified human intelligence. Long before AI captured global headlines, InterLink Labs 👤 + 🌐 had already begun its research and engineering efforts back in 2019, assembling a world-class team of engineers and researchers from Big Tech companies and top QS-ranked universities. Their vision was clear - to build a model that truly understands humans, not just data. Unlike traditional AI systems trained purely on digital information, InterLink Labs 👤 + 🌐’s Human-AI Model learns from verified human behavior across millions of Human Nodes. This unique layer of authentic, real-world human input gives InterLink’s AI an unprecedented advantage in trustworthiness, bias reduction, and contextual understanding. Beyond algorithmic optimization, InterLink Labs 👤 + 🌐’s R&D efforts are being scaled to an unprecedented level. The team operates over 100 NVIDIA H100 servers, processing massive volumes of verified behavioral data contributed by real Human Nodes across the world. This data - diverse, decentralized, and human-validated forms the foundation of a next-generation intelligence system designed to mirror real human reasoning patterns. At the same time, InterLink Labs 👤 + 🌐’s AI-powered Human Credit Score applies advanced machine learning to analyze authenticity, contribution, and reliability. Creating an ethical model of digital reputation and fairness. Aligned with National Institute of Standards and Technology (National Institute of Standards and Technology) evaluation standards, InterLink Labs 👤 + 🌐 now aims to achieve Top 10 accuracy globally among AI models. Competing with research teams from Samsung Electronics, Kakao, キヤノン株式会社 / Canon Inc., and other global giants, InterLink Labs 👤 + 🌐’s engineers continue to train, benchmark, and refine their architecture daily to reach world-class precision and consistency. But this is more than a technical race. It’s a human mission. Every verified user contributes to the world’s first Human-Powered Intelligence Network, where real people fuel the evolution of trustworthy AI. As InterLink Labs 👤 + 🌐 advances toward global National Institute of Standards and Technology recognition, one truth becomes clear: The future of intelligence won’t be artificial. It will be human-powered. #InterLink #ITLG #ITL

InterLink Labs 👤 + 🌐

51,489 Aufrufe • vor 9 Monaten

This is my "feel the AGI" moment: I used GPT-5.6 Sol to train my own autocorrect model that outperforms GPT-5.6 Sol (wtf??) I have no ML background. I have no idea what I'm doing. I just kept pushing Sol until it spat out a SOTA model. And I spent $0. The motivation: Years of talking to AI have made me terrible at typing. Rather than fix my skill issue, I decided to throw more AI at it. My idea was: instead of autocorrect that interrupts my flow, I want to type fast with mistakes and have AI clean it up after. I wanted the smallest local model possible, for speed, for battery life, for science! So I decided to train my own. Inspired by Andrej Karpathy’s autoresearch, I ran Codex /goal with this setup: pick an experiment, try it, record the results to a doc, throw it out if it fails, and plan the next experiment without repeating failures. I gave a few examples that had to pass, tight latency targets, and let it run. Sol did some amazing things. First, it scanned benchmarks and shortlisted base models: Qwen 3.5, Gemma 4, Liquid LFM 2.5. It found a dataset on HuggingFace for typed text. Then it built a simulator for fingers striking a Mac keyboard, modeling the physical layout with a Gaussian distribution around each key. It simulated striking the wrong key, wrong order, fat-fingering, etc. With the models + data + simulator, it fine-tuned using MLX right on my MacBook. It had a working prototype within an hour! But accuracy was pretty poor. — Problem 1: Tokenization Sol read papers, ran tests, and identified that the tokenizer was the bottleneck. Tokenization makes typos hard for the model to see, so it memorizes mappings instead of using its language priors. Sol tried ByT5, Google’s tokenizer-free byte-level LLM. This made a big improvement, but the model is old and lacked the knowledge needed to reach Sol performance. Sol dug deeper and realized a tokenizer-free model isn’t needed; instead, it used T5Gemma, an encoder-decoder model. This can understand the input deeply before producing output, and furthermore, Sol could post-train the encoder to improve performance. This gave a much higher ceiling. — Problem 2: Loss function Now the model was correcting some typos perfectly, but ignoring most. Sol realized that standard cross-entropy loss was teaching the model to avoid edits, because the vast majority of characters in the training data were left unmodified. The fix was wild: Sol wrote a custom loss function that byte-aligns the source and target strings, uses a dynamic programming algorithm to compute the minimum edits between the two, then weights correct edits much higher than copies. After a lot of tuning, this dramatically improved accuracy. — Problem 3: Autoregression One failure mode remained: if the model made a mistake, it couldn’t backtrack. It could only predict the next token. Teaching it to “think” like a reasoning model would solve this, but would be far too slow. Sol found a beautiful solution: instead of greedily predicting the next token, beam search over all possibilities. This parallelizes the exploration instead of one linear chain-of-thought. At the end, choose the path with highest cumulative log probability. This worked great, but made the experience worse, since the user wouldn’t see progress until the whole search was done. To fix this, Sol made a clever observation: after each search step, the longest common prefix among surviving branches is guaranteed to appear in the final result, so it can be displayed immediately. As the search progresses, weaker paths are dropped and the prefix grows, so the user sees continuous progress. Sol built all this as a custom MLX pipeline that does the parallel decoding on the MacBook GPU, with just ~40ms TTFT. It’s crazy fast and entirely local. — Final eval (error reduction rate, higher is better): - Apple autocorrect: 49.66% - GPT-5.6 Luna: 82.47% - GPT-5.6 Terra: 87.64% - GPT-5.6 Sol: 90.56% - Our model (1.7B): 91.02% Final cost: - 1 quota reset (thanks Tibo) - $0 (And yes, I verified there's no cheating. In fact, we test words scrubbed from the training data to prove the model isn’t memorizing) There were a ton more details and tangents I could write about: contrastive learning, GRPO, DPO, dynamic masking, and more. Sol is a fascinating and creative model. It blew my mind so many times. Don’t let a lack of experience stop you: Sol makes AI experiments accessible to anyone!

This is my "feel the AGI" moment: I used GPT-5.6 Sol to train my own autocorrect model that outperforms GPT-5.6 Sol (wtf??) I have no ML background. I have no idea what I'm doing. I just kept pushing Sol until it spat out a SOTA model. And I spent $0. The motivation: Years of talking to AI have made me terrible at typing. Rather than fix my skill issue, I decided to throw more AI at it. My idea was: instead of autocorrect that interrupts my flow, I want to type fast with mistakes and have AI clean it up after. I wanted the smallest local model possible, for speed, for battery life, for science! So I decided to train my own. Inspired by Andrej Karpathy’s autoresearch, I ran Codex /goal with this setup: pick an experiment, try it, record the results to a doc, throw it out if it fails, and plan the next experiment without repeating failures. I gave a few examples that had to pass, tight latency targets, and let it run. Sol did some amazing things. First, it scanned benchmarks and shortlisted base models: Qwen 3.5, Gemma 4, Liquid LFM 2.5. It found a dataset on HuggingFace for typed text. Then it built a simulator for fingers striking a Mac keyboard, modeling the physical layout with a Gaussian distribution around each key. It simulated striking the wrong key, wrong order, fat-fingering, etc. With the models + data + simulator, it fine-tuned using MLX right on my MacBook. It had a working prototype within an hour! But accuracy was pretty poor. — Problem 1: Tokenization Sol read papers, ran tests, and identified that the tokenizer was the bottleneck. Tokenization makes typos hard for the model to see, so it memorizes mappings instead of using its language priors. Sol tried ByT5, Google’s tokenizer-free byte-level LLM. This made a big improvement, but the model is old and lacked the knowledge needed to reach Sol performance. Sol dug deeper and realized a tokenizer-free model isn’t needed; instead, it used T5Gemma, an encoder-decoder model. This can understand the input deeply before producing output, and furthermore, Sol could post-train the encoder to improve performance. This gave a much higher ceiling. — Problem 2: Loss function Now the model was correcting some typos perfectly, but ignoring most. Sol realized that standard cross-entropy loss was teaching the model to avoid edits, because the vast majority of characters in the training data were left unmodified. The fix was wild: Sol wrote a custom loss function that byte-aligns the source and target strings, uses a dynamic programming algorithm to compute the minimum edits between the two, then weights correct edits much higher than copies. After a lot of tuning, this dramatically improved accuracy. — Problem 3: Autoregression One failure mode remained: if the model made a mistake, it couldn’t backtrack. It could only predict the next token. Teaching it to “think” like a reasoning model would solve this, but would be far too slow. Sol found a beautiful solution: instead of greedily predicting the next token, beam search over all possibilities. This parallelizes the exploration instead of one linear chain-of-thought. At the end, choose the path with highest cumulative log probability. This worked great, but made the experience worse, since the user wouldn’t see progress until the whole search was done. To fix this, Sol made a clever observation: after each search step, the longest common prefix among surviving branches is guaranteed to appear in the final result, so it can be displayed immediately. As the search progresses, weaker paths are dropped and the prefix grows, so the user sees continuous progress. Sol built all this as a custom MLX pipeline that does the parallel decoding on the MacBook GPU, with just ~40ms TTFT. It’s crazy fast and entirely local. — Final eval (error reduction rate, higher is better): - Apple autocorrect: 49.66% - GPT-5.6 Luna: 82.47% - GPT-5.6 Terra: 87.64% - GPT-5.6 Sol: 90.56% - Our model (1.7B): 91.02% Final cost: - 1 quota reset (thanks Tibo) - $0 (And yes, I verified there's no cheating. In fact, we test words scrubbed from the training data to prove the model isn’t memorizing) There were a ton more details and tangents I could write about: contrastive learning, GRPO, DPO, dynamic masking, and more. Sol is a fascinating and creative model. It blew my mind so many times. Don’t let a lack of experience stop you: Sol makes AI experiments accessible to anyone!

Anshu

178,432 Aufrufe • vor 22 Tagen

🚨Weekly Cronos News🚨 📅 20/10/2025 1️⃣ 🔧 - Cronos EVM v150 Smarturn upgrade set for Oct 30, delivering smarter accounts, advanced EVM capabilities, and enhanced performance. 2️⃣ ObsidianSwap 🔄 - Trading contest by CronosArmy on ObsidianSwap for cronos_chain kicks off soon, rewarding top 10 over 7 days with 100% boost. - TGE concludes with $ROBIN 4.2B committed at $96K, $LION 850M at $120K, cronoszk $LION 880M at $93K; $CRO liquidity hits 215K at $33K. - Legacy tokens from RobinCronos and CronosLionel converted, sold into burnt pools to boost $OBS liquidity with added $CRO. - $OBS liquidity and trading launches October 17 at 2pm UTC; 15% allocation claimable now, remainder vests over 90 days. - Loaded Lion 2842 joins Mystery Prize inventory on ObsidianSwap via LoadedLions_CDC, ranked 2570; daily quests offer prizes from $2-1.5K. - Listing requests submitted for $OBS to CoinGecko and GeckoTerminal, ID GTIU1710250019. - $OBS whitelisted for smart trading on ObsidianSwap, delivering more tokens, low fees, and fast swaps on cronos_chain. - Full $MCS allocation from MyCroStrat partnership paired with $OBS to strengthen liquidity for both projects. - Liquid NFT contract with PUUSHDABUTTON activates, backing ROBIN NFTs with 450M $OBS valued over $36K 3️⃣ VVS-Finance 💎 - $LION $BARA and $USDC $BARA farms extended for 6 months on VVS Finance, partnering with capy_nation and LoadedLions_CDC. - $KITTY by KitCoinX now whitelisted on VVS Finance, expanding token options on Cronos chain. - Stake xVVS Earn FUL Mine episode 8 launches on VVS Finance with FulcromFinance, emissions starting soon. - $BONE by CroHound_GODS whitelisted on VVS Finance, boosting builder integrations on Cronos chain. 4️⃣ WolfSWAP | SWAP & WIN 🐺 - Season 16 surges with over 5K contestants, $12,212 starting prizepool and $3M volume in just 6 days. - $ROBINSTR from TheLionStrategy and $OBS from ObsidianSwap now whitelisted on wolfswapdotapp for mystery box wins on $10+ trades. - New Amplify Mission launches: Spot Wolfies on Brave Browser, screenshot and tag for $1K prize pool in $MOON – 5 winners snag 424K $MOON ($200 each). 5️⃣ Moonlander 🌕 🚀 - BTC Voyage leaderboard tops out at 60.8M pts, earning $10.9K from $50K pool. - MoonlanderTrade unveils leveraged prediction perps for trading future outcomes. 6️⃣ Loaded Lions 🦁 - LoadedLions_CDC expanded $LION trading to Gate, Coinone, Bitmart, Lbank, Digifinex, Biconomy, Bitrue. - Q3 milestone: LoadedLions_CDC launched $50K prize pool trading campaign on VVS Finance. - Nightmare Blitz event live in Loaded Lions Mane City until Oct 31; prizes include $20K CRO and $15K Tactical Gear NFTs. 7️⃣ puush.fun 🔴🔵 - $OBS token listed on ObsidianSwap post successful TGE and ecosystem merger. - Liquid NFTs activated for ObsidianSwap's $OBS and Robin NFTs, with $36K liquidity locked at $14.56 per NFT floor. 8️⃣ LionStrategy 💡 - Launch alert: CrofamStrategy unveils RobinStrategy $ROBINSTR on ObsidianSwap with RobinCronos. - Burn milestone: WolfieStrategy torches $WOLFSTR worth 58K $CRO from team funds, hitting 21% total supply reduction. - Holdings update: RobinStrategy acquires 110 Robin NFTs. 9️⃣ WAM 🌟 - playWAM unveils revamped website featuring enhanced content and latest project updates on Cronos Chain. - Sovereign microeconomy in development for playWAM app, deploying on EVM chains starting with Cronos Chain followed by BNBCHAIN. - playWAM gears up for Q4 gaming app launch, promising industry-shaking innovations. 🔟 Wolfies ⚡ - Wolfies NFT fetches $4.8K and 10M $MOON in Cronos sale. - Listings surge: Wolfies hit cryptocomnft and cryptocom App just 7 days post-mint. - Exposure boost: Wolfies appear to 40M Brave users; screenshot contest offers $1K prizes. - Volume milestone: 2M $CRO traded across EbisusBay and MintedNetwork in 11 days. - Backing vault grows to nearly $40K, fueling Wolfies flywheel. 1️⃣1️⃣ Ebisu's Bay ⛩️ - Advanced NFT analytics unveiled on EbisusBay collection pages; toggle datasets via legend icons. - ObsidianSwap's $OBS token now listable on EbisusBay DEX for select NFT collection listings. 1️⃣2️⃣ Tectonic.cro 🏦 - Sleek redesign unveiled for Tectonic Finance mobile app, elevating DeFi user experience. 1️⃣3️⃣ Delphi Trade 💡 - Delphitrade launches prediction markets: BNB top 5 by Nov 25, LINK to $28 before Nov 25, USDT to $190B by Nov 25, CRO price Oct 30 2025. - Fresh markets hit delphitrade: ETH, BTC, SOL prices Oct 30 2025; CAW to $20M MC before Oct 30 2025. 1️⃣4️⃣ FROGS 🐸 - Excitement brews: realFrogsClub hints at new Super PERP DEX project in development. 1️⃣5️⃣ NeurotiCat 🐱 - Zealy Quests kick off on Neuro Island, drawing over 100 participants for daily challenges and $NEURO rewards. 1️⃣6️⃣ Lume Finance 🌊 - Lume Finance migrates NOVA and LUME liquidity pools from VVS Finance to Solidex; V2 pools await Dexscreener support. - Despite market volatility, Lume Finance advances with ecosystem integrations, Lilypad milestones, and liquidity expansions. 1️⃣7️⃣ BRAVE 🚀 - Mega raffle spin for Brave Badges launches Oct 18 at 1800 UTC, with 23 prizes including Loaded Lion, 2x 2500 $LION tokens, and Brave Legends customs. 1️⃣8️⃣ PuppetZ.CDC🎭 🔗 - PuppetzART drop launches on Cryptocom NFT: 100 packs at $1, 1600 at $33; FCFS with no whitelist or wallet limits. - Mystery packs mint explodes: 720 sold in 24 hours, yielding $21K volume on Cryptocom NFT and Cronos Chain; half the collection gone. - Prizes await in PuppetzART mint: 15 NFTs with rewards left; funds set for buybacks, 100% royalties for sweeps; vault exceeds $8K. 1️⃣9️⃣ CroVegas.Fun ⚡ - KitCoin $KITTY secures official verification on Cronos Explorer, displaying live badge, logo, website, market cap, and volume. 2️⃣0️⃣ Ryoshi with Knife 🔪 🐶 - Ryoshi Royale unveils fatigue system: Top 10% players hit with 15%, 10%, 5% stat cuts over 3 battles for more winners. - Update adds pay-to-level-up, entourage for bonus XP, and training points reset in hottest Cronos game. 2️⃣1️⃣ MyCroStrategy 🚀 - $MCS-$CRO pair goes live on SoliDexCro, yielding 424% APR with active SDX emissions and rewards. - $MCS treasury deploys across Cronos protocols, amplifying yields via Wolfswap, TectonicFi, and FulcromFinance. - ObsidianSwap initiates first $OBS burn, fueled by 50% royalties from $MCS trading volume. 2️⃣2️⃣ Flipsuite 🔗 - Flippy debuts on Telegram via Flipsuitexyz: in-chat swaps, bridging, trading, airdrops, raffles. Versatile wallet spans 32 chains. 2️⃣3️⃣ Crazzzy Monsters 👹 - Delivered new mint dApp for Imperiums Legion I drop with ImperiumCro; metadata updated on IPFS, 290 NFTs airdropped, issues resolved in 3 days. 2️⃣4️⃣ Nova Labs | GameFi & Launchpad 🔗 - Nova Clash of Galaxies emerges as key evolution in Nova ecosystem, integrating blockchain gaming. - $NFX to drive in-game economy, enabling purchases of premium assets like ships, heroes, and land upgrades. - Game introduces Web2 players to crypto ownership through seamless asset management. 2️⃣5️⃣ YUROSAKO 🗡️ - SHIROTSO sweep contest builds massive prize pool via community NFT donations; donators honored as official sponsors. If you have project announcements, DM us! 📩 For more Crypto.com news, visit Csilla

Pampa

46,995 Aufrufe • vor 9 Monaten