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⚡️ GMGN Update - ⌨️ Trenches Hotkeys · Quick Buy with Hotkeys: Freely switch between Simple / Combo modes for instant token purchases - Precise targeting with millisecond execution 🎉 Try now: (The demo video is for functional demonstration only. The tokens shown are not intended as investment advice...

45,907 次观看 • 7 个月前 •via X (Twitter)

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Most recent diffusion language model research (that I’ve seen) seems to be using masking as the noising process. It looks like, however, most closed-source models (Google Gemini Diffusion and possibly Inception Labs’ Mercury) use a different noising process, where instead of masking tokens, they replace them with different tokens (either with a random token or a semantically similar token). I wondered how they were getting such high throughput with the latter noising process, since I believed that optimizing inference with KVCache approximation would be more difficult (for various reasons). I visualized this noising process with tiny-diffusion and compared it to normal unmasking, and was very surprised to see how fast the generation “settles” into a reasonable output, and then only slightly refines afterwards, requiring much fewer steps in total. Unmasking (where tokens are never remasked, the typical implementation) is inherently limited in generation speed by the fact that an increase in tokens decoded per step leads to more errors due to the mismatch between individual and marginal token probability distributions we sample from. The token replacement noising process seems to have a much different set of characteristics. Because we sample each token per step, every token makes “progress” towards the final output each iteration (in addition to *potentially* giving other tokens more information in future steps). Generally, masking has outperformed other noising processes, which is probably why most research focused on it (using smaller models). But the paper referred to in the retweet shows that random replacement as a noising process may scale better as model size increases. Big labs might have noticed these results much earlier (due to having drastically more training resources and being able to test larger models), which may explain the discrepancy in the choice of noising process. I’m gonna test this with larger models, since tiny-diffusion only has 10M parameters.

nathan (in sf)

40,440 次观看 • 8 个月前

BREAKING: SpaceXAI has released a major new update for Grok Build (v1.0.14) Grok v1.0.14 is a reliability and workflow update for the Grok CLI. It makes OIDC token refresh proactive, lets PostToolUse hooks send feedback back to the model after tools run, adds per-turn token and cost tracking via grok usage, and cuts Windows downloads by about 70%, with a large set of fixes that tighten sandboxing, subagent handling, hooks, and startup. Features: • OIDC token refresh is now proactive by default for better reliability. • PostToolUse hooks can now provide feedback and context to the model after tool execution. • SDK-registered PostToolUse hooks now provide model-facing feedback. • grok usage now shows persisted per-turn token and cost data. • Retry status in composer and title now shows a short reason for the retry. • Models can now declare a different identifier for each reasoning-effort level instead of always sending the same id. • Prompt suggestions now respect remote configuration and default to the current session model. • Windows CLI downloads are now ~70% smaller using the same compressed sidecars as macOS and Linux. Bug Fixes: • grok inspect now correctly shows Claude bypass locks as advisory rather than enforced. • Subagent sessions no longer leak threads or file descriptors when the parent is busy. • Cold startup no longer performs duplicate remote settings fetches. • Compaction failures due to context size now degrade input instead of retrying identically. • --sandbox strict now restricts writes to ~/.grok/sessions only. • Subagent spawning now waits longer on a busy coordinator and shows clearer retry guidance instead of "unreachable". • Failed task and todo tool calls now appear in the transcript instead of disappearing without a trace. • Composer status row no longer collapses or flashes when using double-Enter to send now. • Session close is no longer delayed by a single slow hook; each SessionEnd hook now has its own timeout. • Hook removal in the extensions modal no longer offers actions that the handler will refuse. • Interjections during a turn are now delivered atomically or not at all. • Subagent tasks no longer get incorrectly cancelled when the parent session is waiting for completion. • Workflow detail view now closes the overlay on X or outside click instead of returning to the run list. • Resuming subagents now succeeds for larger transcripts that still fit the model context with headroom. Performance: • Startup now fetches remote settings only once per boot instead of potentially twice. • First message on large repositories no longer waits on repository status scan. • Large session memory no longer blocks the agent during turn completion or subagent spawning. • Signed-in CLI starts faster by serving remote settings from a local cache on warm boots. Download Grok Build: Update to the latest Alpha release: grok update --alpha Update to the latest Stable release: grok update

DogeDesigner

52,568 次观看 • 1 个月前

Do you want to own part of a AAA game? I know, you hear it all the time. “Triple A game”, you go to play it, it’s crap. This is different, and it’s only possible with Sonic (Sonic) speed, transaction cost, and of-course FeeM. A game that includes talent from Kojima, Ubisoft, EA Sports, Gameloft & more with advisors from NVIDIA. A game that you’ll be able to play on mobile, desktop, and then Xbox and PlayStation (yes really)! YES! A PRETTY BIG DEAL! Before I tell you about the sale, let me at least tell you about this game (being a massive gamer nerd, this excited me), so…. Introducing Animera (Search for Animera): • Fast-paced skill-based PvP in the Nubera galaxy • Compete in real-time space battles for real rewards It will be powered with $STRIKE: • Compete2Earn: win matches, earn tokens • Play2Burn: 5% of $STRIKE used in matches gets burned Oh, and with 8.75% of all game revenue will be used to buy & burn $SWPx, so the SwapX (SwapX) community owns a real stake in this AAA title. Absolutely insane. > Now let me tell you about its beta run quickly: • 16K+ beta signups • 500+ players added weekly • 7.5K+ matches already played • Launching to 500K+ mobile users via Nomina Games > How can you own a piece of Animera? June 5th at 2pm EDT the sale will go live on SwapX, it will go in three phases each lasting 12 hours or until sold out: PHASE 1️⃣: xNFT Holders Early access with exclusive perks and bonuses. These are for xNFT holders only you can get these here on paintswap PHASE 2️⃣ Whitelisted Communities These will be whitelisted from Creo Engine, SFA AGC, derp, and GOGLZ | SONIC 🥽💥. PHASE 3️⃣ Public Round Any remaining allocation will open to the public - only if Phases 1 & 2 don’t sell out. > What is the raise? Token Price & Allocation: • Token: $STRIKE • Currency: USDC • Total tokens for sale: 101.75M Unlock structure: • 50% unlocked at TGE • Remaining 50% claimable in 30 days • Raise cap: Max $100,000 per user, capped at $10,000 per xNFT • Purchase window priority: xNFT holders get early access (see above)! Transparency is key: Why I love working with the team is because transparency is crucial, so I’m going to tell you about its tokenomics, seed, and fully diluted valuation here: Token Symbol: STRIKE Total Supply: 370,000,000 Initial FDV: $1.48M Total Raise: $950,160 Total Initial Unlock: 112,947,501 STRIKE Initial Market Cap (excluding liquidity): $303,790 Token Allocation: • Seed Round: 59.2M tokens (16% allocation), with a 1-month cliff and linear vesting over 9 months. • Private Round: 94.35M tokens (25.5% allocation), with a 1-month cliff and 6-month vesting period. • Crowdsale: 10.75M tokens (2.91% allocation), unlocked 50% at TGE. • xNFT Holders: 10M tokens (2.7% allocation), with a 1-month cliff. • Liquidity: 37M tokens (10% allocation), with no lock or vesting. • Team: 18.5M tokens (5% allocation), with a 6-month cliff and 12-month vesting. • Rewards: 28.6M tokens (8% allocation), vested over 18 months. • Product Growth: 19.6M tokens (5.3% allocation), vested over 24 months. Token Offering: • Seed Round: Priced at $0.0033 per token, raising $195,360 by selling 59.2M tokens. 10% unlocks at TGE, with a 1-month cliff and 9-month vesting. The initial market cap from seed unlock is $234,127. • Private Round: Priced at $0.0037 per token, raising $349,095 for 94.35M tokens. 15% unlocks at TGE, with a 1-month cliff and 6-month vesting. Initial market cap contribution is $262,508. • Crowdsale: Priced at $0.0040 per token, raising $407,000 by selling 10.75M tokens. 50% unlocks at TGE, with no cliff or vesting. Adds $283,790 to the initial market cap. It’s important you had the full information at hand so you can decide whether or not you’d like to participate. I will be, because it’s a low FDV and it looks great. This is not financial advice, I’m helping the team out. Below is real gameplay: Further details: 👇

hoeem

21,634 次观看 • 1 年前

bStocks are now live in Debit AI 🟡 Tokenized US equities on BNB Chain, backed 1:1 by shares in regulated custody. NVIDIA (NVDAB), Tesla (TSLAB), Circle (CRCLB), Netflix (NFLXB), SpaceX (SPCXB), plus Apple, Amazon, Alphabet, Coinbase and more. RESEARCH bSTOCKS Ask for performance on any bStock, compare names across the index, or set a routine that watches a position autonomously. The agent works on the same 24/7 clock the tokens trade on. BUY bSTOCKS Swap in and out of bStocks, alongside crypto assets, on BNB chain. USE bSTOCKS AS COLLATERAL Hold bStocks and borrow stablecoins against them instead of selling. Loans via Teller are time based, with no margin calls. Price moves do not trigger liquidation during the term. Debit AI can: → Research and monitor any bStock, around the clock → Spot swaps in and out on BNB Chain → Borrow stablecoins against bStock holdings → Loop equity exposure → Auto rollover before term expiry Debit is non custodial. Assets stay under the holder's control at all times.Each agent runs within limits set: assets, venues, position size, chains, daily spend cap. Use Debit AI via the chat interface on the Debit dashboard, or connect Debit's MCP server to the AI assistant already in use, including Claude, ChatGPT, Perplexity and Gemini. $DEBIT is the utility token of Debit AI. It works as AI usage credits. Spend DEBIT to run the agent: strategy execution, portfolio analysis, monitoring, cross chain routing, and more. Agents consume credits continuously while running. Debit AI: Debit AI App: Debit is not a bank or lender and is not FDIC insured. bStocks are third-party tokenized securities, not issued or guaranteed by Debit, and are not available to US persons. Digital assets are volatile and can lose their full value. Autonomous agents can execute at unfavorable prices or fail to execute. Nothing here is financial, investment, legal or tax advice. Debit is not affiliated with or endorsed by Anthropic, OpenAI, Perplexity or Google. Eligibility and jurisdictional restrictions apply. See Terms of Service.

Teller

59,722 次观看 • 1 个月前

Crypto narratives tend to move in cycles. 2020 was DeFi. 2021 became NFTs. 2023 turned into the AI boom. 2024–2025 were dominated by memecoins and attention tokens. But markets eventually rotate back to something simple: real revenue. That’s why some people are starting to look at iGaming tokens as a potential emerging narrative in 2026. Unlike many hype driven tokens, the iGaming sector already runs large cash flow businesses. Many platforms generate hundreds of millions of dollars in monthly revenue, yet their tokens often trade with far less volume than projects that barely produce revenue at all. In other words, there’s a visible mismatch between actual business activity and token market valuation. One ecosystem that sits right inside this discussion is 1win Token, which already operates as one of the top 10 online casinos globally by scale and user activity. The upcoming $1win Token is designed to connect that existing business with on chain incentives. Its token model includes buybacks and burns funded directly from casino revenue, tying token supply mechanics to real cash flow. There’s also an interesting structural difference compared to previous gaming tokens. For example, $RLB (Rollbit) saw a massive post launch rally, but the product and revenue scale at launch were significantly smaller than what 1win operates today. Another notable point is the launch design: instead of only farming an airdrop, 1win Token plans a public sale model, allowing broader participation from the start. If Web3 narratives are indeed shifting away from pure attention cycles and back toward revenue generating platforms, sectors like iGaming may start attracting more analytical focus and 1win could emerge as the biggest winner.

BitBull

20,972 次观看 • 7 个月前

Native USDC is now live on Aptos! This marks a significant milestone for the Aptos ecosystem, empowering developers and users with access to the world’s largest regulated digital dollar. USDC powers innovative use cases: ✅Build secure apps for peer-to-peer payments, cross-border remittances, RWA settlement, gaming, and more ✅Supercharge DeFi with deep liquidity for digital asset trading and financial services ✅Empower merchants with global, instant, low-cost payment solutions that settle 24/7 Many leading ecosystem apps are expected to support native USDC on Aptos, including: Coinbase 🛡️, Echo Protocol, Petra, Pontem Labs (Liquidswap), Stripe Native USDC is officially issued by Circle and redeemable 1:1 for US dollars. There’s currently a bridged form of USDC in the Aptos ecosystem known as lzUSDC, which is bridged from Ethereum through the AptosBridge built on LayerZero. lzUSDC is not issued by Circle and not redeemable with Circle Mint. Native USDC issued by Circle: Token Name: USDC Token Symbol: USDC Mainnet Address: 0xbae207659db88bea0cbead6da0ed00aac12edcdda169e591cd41c94180b46f3b Testnet Address: 0x69091fbab5f7d635ee7ac5098cf0c1efbe31d68fec0f2cd565e8d168daf52832 Bridged USDC from LayerZero: Token Name: Bridged USDC (LayerZero) Token Symbol: lzUSDC Mainnet Address: 0xf22bede237a07e121b56d91a491eb7bcdfd1f5907926a9e58338f964a01b17fa::asset::USDC Developers can use our step-by-step migration guide for options on migrating bridged USDC to native USDC in their apps: CCTP is coming later this morning: With CCTP launching imminently, leading interoperability providers like Wormhole will enable seamless USDC transfers between Aptos and 9 other blockchains. With the addition of Aptos, USDC is now natively supported on 17 blockchains—with many more expansions planned this year. Start building with USDC on Aptos:

Circle

94,971 次观看 • 1 年前

This prompt will change how you do crypto research. Paste it into DefiLlama's LlamaAI to find trending tokens, assess their fundamentals, check for red flags, and look up what people are saying about them. Find tokens trending in the last 24-48 hours using at least two independent signals: news/narrative mention spikes against baseline, price/volume/mcap movers, and X mention volume with sentiment. Require corroboration across at least two signals per token, and note which signals flagged each one. Return 8-12 candidates, spread across categories rather than one narrative dominating the list. Separate large-cap tokens (top 100 by market cap) from smaller or emerging ones, since "trending" means something different for each. Before pulling any fundamental figures, resolve each candidate's identity precisely: confirm whether it is a chain-native token, a protocol fee/governance token, or a token with no underlying protocol at all. If a symbol has more than one live contract, identify which one the trending signal actually refers to before analyzing anything. If the correct contract cannot be established with confidence, do not publish fundamentals for it: drop it from the ranked table and list it separately as identity-unresolved, with what would resolve it. For each resolved candidate, state in one sentence what the project does, its category, and its chain(s). Assess fundamentals only where they exist: - Price, market cap, fully diluted valuation, and the gap between them - For protocol or chain tokens: TVL, fees, revenue, and price-to-sales / price-to-fees ratios against category peers - 7-day, 30-day, and 90-day price and volume change, checked against TVL/fee change over the same windows, to separate price-driven moves from usage-driven ones - Any unlock cliff in the next 30-90 days and its size relative to circulating supply - Universal Token Rating, if the token has one For tokens with no protocol behind them, skip the fields that do not apply and say so rather than forcing a number in. Assess social activity separately from fundamentals: - Mention volume trend and sentiment direction on X over the past 24-72 hours - Whether a small number of accounts are driving most of the volume, or it is broad-based - Whether social attention is rising faster than, in line with, or behind the fundamental data above Check for red flags before writing a verdict: concentrated holder base, a recent large unlock already sold into the market, known contract or audit issues, and any active exploit or governance dispute. When two sources disagree materially on a red-flag figure, say which one you trust more and why, or mark the figure unresolved-conflicting; never present two contradictory numbers as if either could be picked at random. Write one row per token: name, category, what triggered the trending flag, the fundamental read, the social read, red flags found, and a verdict. Use one of four verdicts, each tied to a rule: fundamentally-backed (usage or fees moving with price), narrative-led (social and price moving, fundamentals flat), unlock-risk (fundamentals fine, but a near-term unlock threatens the float), or unverifiable (identity or data conflict prevents a real read, distinct from identity-unresolved candidates already excluded above). Rank by verdict strength, not by how hard a token is trending. State the source next to every figure. If you chart price changes across the candidate set and one token's move is an order of magnitude larger than the rest, exclude it from that shared chart (or give it a log-scale or separate chart) so the other bars stay readable; keep the number in the table regardless. Close by naming the two or three tokens most worth a deeper look, and for each, the specific data point that would confirm or kill the thesis.

DefiLlama.com

14,280 次观看 • 16 天前