Gas DAO 價格GAS

I’ve been keeping an eye on Bitget’s push into RWAs, and it’s actually pretty cool. You can now trade big tech names like $AAPLon, $SPYon, $TSLAon, $NVDAon, and $MSFTon fully on-chain with zero gas fees using $USDC There’s also less than 24 hours left in their Onchain Trading Competition 50 where you trade, climb the leaderboard, and earn $AAPLon as rewards. Top spots even get up to 3 $AAPLon (~$680). Feels like a fun way to mix stock trading with on-chain rewards. $BGB $HOLO $SOL

OpenLedger (OPEN) – Deep Analysis Report by INVESTERCLUB; 1. Coin Utility OpenLedger is a purpose-built blockchain for AI, so its native OPEN token powers all core functions of that ecosystem In practice, OPEN is used as gas for on-chain operations (e.g. dataset uploads, model training/inference) and as a payment token in its “Payable AI” economy. Its Proof of Attribution protocol rewards data and model contributors: whenever someone’s data or model improves a project, that contributor automatically earns OPEN. OPEN also enables staking (for network security) and governance (voting on upgrades and grants). In short, OPEN circulates as fees, micropayments, and rewards in a community-driven AI data/model marketplace: e.g. contributors earn OPEN for improving a model, developers spend OPEN to train or deploy models, and token holders vote on protocol change. 2. Hidden Gem Status OpenLedger combines strong fundamentals with early-stage adoption, a mix that some call “hidden gem” territory. Its novel tech (on-chain Datanets and attribution) and large VC backing set it apart. Notably, it raised ~$15M from tier-1 funds (Polychain, HashKey, Borderless, etc) and Phemex highlights it as “one of the first and most advanced blockchains built specifically for the booming AI industry”. This first-mover advantage solves a real problem – fair data compensation – which could translate to undervaluation if AI-as-an-infrastructure grows. On the other hand, OpenLedger is still in “price discovery” mode: a majority of tokens remain locked (≈78% locked until 2026) and early trading has been volatile. In sum, its innovative model and backing give it upside potential, but investors caution that current price trends reflect heavy selling (e.g. ~80% post-listing drop) and an unproven user base. 3. Future Growth Potential OpenLedger’s roadmap and partnerships are aggressive, targeting near-term user growth and long-term ecosystem expansion. Key planned milestones include: Trust Wallet AI Integration (Oct 2025) – A pilot of an AI “co-pilot” for Trust Wallet’s 200M+ users. This will enable natural-language DeFi transactions (e.g. “swap ETH to USDC”), exposing millions to OPEN’s utility. OpenCircle Grant Fund (2026) – A $25 million DAO-controlled fund for AI/Web3 projects, with grants awarded by OPEN-token governance vote. This is intended to spur third-party development on OpenLedger, driving ecosystem growth. Mainnet Launch (TBA) – Transition from private testnet to a public L2 chain. The team has already built the technical foundation: its OP Stack–based testnet (leveraging EigenDA) handled >6 million nodes, 25 million transactions and 20,000 AI models, showing strong developer interest. In addition to these, the platform’s core tech is in place: on-chain Proof-of-Attribution was deployed (May 2025) to transparently log all contributions, and the chain is fully EVM-compatible on Optimism’s OP Stack. These features (high throughput, low fees, familiar tooling) are designed to attract AI/DApp developers. In summary, OpenLedger’s near-term catalysts (wallet integration, rewards, grants) and robust tech foundation (proven testnet, EVM compatibility) suggest significant long-term growth if adoption materializes. 4. Future Price Prediction Price forecasts for OPEN vary widely. Consensus-based models (user-driven polls) suggest modest growth: for example, one aggregate shows OPEN reaching ~$0.95 by 2026 and ~$1.15 by 2030 (roughly a 5% annualized gain). More bullish analysis projects higher targets – on the order of **$2.0 (2026) and $3–4 (2030). assuming sustained demand. Near-term, CCN noted the initial hype produced a 200% surge and posited that “OPEN’s price might rally toward $2” if selling pressure abates. In practice, short-term price action has been volatile (an ~80% pullback from peak), reflecting profit-taking. Technical indicators and unlocking schedules will likely cap near-term upside. Overall, short-term forecasts hinge on exchange listings and airdrop-driven volume; mid-term depends on utility adoption (wallet AI uses, gas fee accrual); long-term (5+ years) predictions range from ~$1–2 up to the low-single-digit range (2050 forecasts have even reached ~$3.06). All models caution that such projections assume successful network growth – actual outcomes will follow real-world usage more than hype. 5. Competitive Edge OpenLedger’s key advantage is its specialization for AI. Unlike general-purpose blockchains, it is engineered as an L2 on Ethereum (OP Stack + EigenDA) optimized for high-throughput, low-cost AI workloads. This means even large datasets and model inferences can be executed cheaply, a hurdle on congested L1 chains. Crucially, OpenLedger’s on-chain attribution is unique: every data contribution, model tweak or inference is immutably linked to its creator, who then automatically receives OPEN rewards. This creates a self-reinforcing, community-driven ecosystem – data becomes a tokenizable asset. By contrast, few other projects offer both an AI marketplace and native incentive layer. Moreover, OpenLedger benefits from strong support: ~$15M in funding from leading VCs, and massive testnet metrics (millions of nodes, transactions and models built) indicate developer interest. Its EVM-compatibility and 51.7% supply allocated for community rewards further encourage participation. In sum, OpenLedger stands out as a first-mover AI blockchain with built-in reward economics, a combination that peers (like Ocean, SingularityNET, etc.) do not fully replicate. 6. Fund Flow Analysis OpenLedger’s launch was marked by an aggressive airdrop/listing campaign. A Binance HODLer airdrop (10 million OPEN tokens) fueled trading, and the token debuted with a 200% price surge as volume spiked�. Even now, trading remains extremely active: 24-hour volume is on the order of $190–190.5 million� against a market cap of ~$196 million, so daily turnover is nearly equal to total market cap. This high volume amidst a ~-9.7% 24h price decline suggests net sell pressure. Bitget’s technical ratings indeed flag daily ACTION: the 1-day chart is rated “Strong Sell”. Inflow versus outflow isn’t precisely public, but the trend implies that early buyers are taking profits. Adding to this, only ~21% of supply is circulating (with the rest locked), so current fund flows come from a limited pool of tokens. Future inflows will depend on unlock schedules (many investor/team tokens vesting in 2026) and on whether new demand (e.g. paying gas/fees with OPEN) emerges. In short, short-term funds have mainly flowed into OPEN via listings and airdrops, and are now partially reversing. One must watch for any large entry of sell orders if locked tokens unlock, as well as any sustained buying (e.g. for AI services) to counterbalance. 7. Market Prediction and Analysis Technically, OPEN’s charts are cautionary. After the initial spike, the token formed a peak ~$1.85 and then fell sharply (now ~$0.90). Short-term indicators are bearish: Bitget’s 4h/1d signals are “Sell/Strong Sell”. Overbought/oversold tools may help time entries; for instance, traders might watch RSI or Bollinger Bands to identify relief points. Socially, sentiment is mixed. Community commentary praises the tech. Tokenomics (will the chain actually consume fees?). Analyses note the strong AI narrative but warn utility (fee burns, adoption) must catch up. On macro grounds, the crypto market is in a moderately bullish cycle (bitcoin ~$115K) and the Fear&Greed index is neutral (~50/100), indicating balanced risk appetite. Key external factors – e.g. any AI regulation, global markets or rate changes – could sway OPEN too. In summary, short-term momentum is cooling off (profit-taking and unlock fears dominate), while sentiment hinges on actual usage pick-up. Traders should monitor broad altcoin trends and AI sector moves: if the AI/crypto theme accelerates, OPEN might see renewed inflows, but if a crypto pullback occurs, it could retrace further. 8. Best Trade Plan by Trader Level; Beginners (low-risk): Use conservative, “set-and-forget” tactics. Consider HODLing a core position rather than frequent trades. If trading, buy near strong support levels (e.g. previous lows around ~$0.85–0.90) and set stop-loss orders just below (to cap losses at ~5–10%). Always set take-profit targets ~10–20% above entry (aim a risk:reward ≥1:2). For example, a newbie might buy at $0.88 with a stop at $0.84 and target $1.00. Avoid leverage entirely; use small position sizes (1–2% of account) and prioritize capital protection. In essence, think long-term (participating in ecosystem growth) and use basic technical levels with strict stops. Intermediate (swing/position traders): Trade the mid-term cycles. Look for swing-trading setups around the 3–5 day cycles noted post-listing. Enter on pullbacks after short-term rallies (e.g. buy dips on 4h or daily charts), and exit near resistance or after news catalysts. Use indicators (moving averages, RSI) to confirm entry/exit. For instance, if OPEN breaks above a short-term resistance, wait for a retest then buy; target the next resistance (~10–15% higher) before selling. You can employ moderate leverage (up to 2x) if comfortable. Always combine a stop-loss (e.g. just below the support you bought) with a take-profit (e.g. at next Fibonacci or prior swing high). This might mean a stop at –5% and target +10% for a 1:2 risk/reward. Advanced/Pro (high-frequency and hedging): Leverage technical tools and market instruments. Experienced traders might scalp tiny moves on low timeframes or use 2–5x margin positions, but must manage risk tightly. Employ trailing stops to lock profits: for example, set a 10% trailing stop so gains auto-protect as price rises. Take profits in stages (“rule of thirds”): sell 1/3 at a 1:1 reward, 1/3 at 1:2, and leave 1/3 on with a trailing stop. Use OCO orders (combine stop-loss and take-profit) to automate exits. Seasoned traders will also hedge: for instance, short small OPEN amounts if macro risk spikes, or use correlated positions (like shorting the overall AI token sector) as insurance. Always account for volatility – widen stops in choppy markets (e.g. use ATR-based sizing). Finally, advanced plans consider token unlocks: one might scale out before large unlock events (late 2026) or use options/futures to hedge those supply increases. $OPEN

OpenLedger’s $OPEN token is designed as an AI-blockchain infrastructure layer where contributors, developers, and validators are rewarded fairly through a “Proof of Attribution” mechanism. With a capped supply of 1 billion tokens and only a portion currently circulating, the token sits at the intersection of AI and decentralized networks. Its role covers gas fees, governance, model deployment, and rewarding participants, making it central to the project’s ecosystem. Key Drivers (Pros) That Could Push $OPEN Higher Growing Demand for AI Infrastructure: As AI becomes more essential, transparent systems that ensure data attribution and contributor rewards will be in demand. OpenLedger’s model could solve this. Controlled Tokenomics: With only part of its supply circulating, scarcity could push value upward if adoption grows. Ecosystem Growth: Developer incentives and community-driven adoption can fuel real-world use cases, driving usage of $OPEN. AI + Web3 Macro Tailwinds: Interest in combining AI with blockchain creates favorable conditions for infrastructure tokens like $OPEN. Community and Governance Strength: Strong participation and transparent governance could attract long-term users and contributors. Risks (Cons) That Could Limit Growth Intense Competition: Other AI-blockchain projects may capture market share. Regulatory Challenges: Data privacy and attribution could bring scrutiny. Technical Hurdles: Scaling inference, cost, and reliability may be challenging. Token Unlock Pressure: Large unlocks over time may create sell pressure. Adoption Pace: Without sufficient developer and user growth, utility demand may lag. $OPEN Price Predictions by 2026 Scenario What Happens Possible Price Range Bullish Strong adoption, many AI models deployed, enterprise use, healthy governance, managed unlocks. $3 – $6 Moderate (Base Case) Moderate adoption, steady ecosystem growth, balanced unlocks and demand. $1 – $2.50 Bearish Weak adoption, high competition, technical or regulatory setbacks, heavy token selling. $0.20 – $0.80 Most Likely Outcome By 2026, the most realistic case is moderate adoption, with $OPEN,trading in the $1.50 – $3.00 range if execution remains steady and usage grows. However, should the project secure strong partnerships and achieve large-scale AI adoption, the bullish scenario could play out. Conclusion $OPEN is positioned at the convergence of AI and blockchain, offering transparency, attribution, and fair rewards for contributors. Its capped supply and infrastructure role give it solid fundamentals, but competition, regulation, and execution risks remain. If adoption accelerates and demand for AI attribution grows, $OPEN could establish itself as a leading infrastructure token by 2026.

𝐔𝐒𝐃𝐦 𝐚𝐧𝐝 𝐌𝐞𝐠𝐚𝐄𝐓𝐇: 𝐖𝐡𝐚𝐭 𝐈𝐬 𝐓𝐡𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞? TL;DR USDm is issued on Ethena’s USDtb rails and backed primarily by tokenized U.S. Treasuries (BlackRock BUIDL via Securitize). The reserve yield is programmatically routed to cover MegaETH sequencer OPEX, so gas can be priced at cost while keeping fees stable as throughput scales. Integration is deep across wallets, paymasters, and apps on @megaeth_labs. USDT0 and cUSD remain first-class assets. ------ 📌 𝐓𝐡𝐞 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐒𝐭𝐚𝐜𝐤 1. Economic layer → Reserves: USDm v1 uses the USDtb reserve model: ~target 90% BUIDL (tokenized short-duration Treasuries) held via Securitize, plus liquid stables for redemptions. It has 24/7 atomic swaps between USDtb and BUIDL, which tighten settlement and transparency. → Yield source: BUIDL’s T-bill yield accrues on reserves such that yield is earmarked to fund sequencer costs on MegaETH. 2. Issuance → Issuer rails: Ethena provides the stablecoin stack (contracts, treasury operations, reserve disclosures). USDm adapts its collateral mix over time. → Compliance/custody: USDtb has a clear path toward compliance (GENIUS Act notes with Anchorage) and institutional integrations. 3. Execution & Settlement → Chain integration: MegaETH bakes USDm into paymasters, wallets, DEX routes, oracles, and app services. Gas can be paid cheaply while USDT0 and cUSD remain supported routes. → Sequencer OPEX link: Reserve yield flows to a funding sink that offsets the L2’s sequencer costs. This inverts the usual fee-margin model and lets MegaETH run the sequencer at cost. 📌 𝐌𝐞𝐜𝐡𝐚𝐧𝐢𝐜𝐬 1. Reserve backing and mint/redeem → Backing: USDm v1 is economically equivalent to USDtb units sitting on BUIDL, with a liquidity sleeve in stablecoins to support redemptions. Reserve composition is adjustable by policy. → Mint/redeem path: On issuance, assets routed through venues become USDtb exposure. Atomic swap rails allow moving between USDtb and BUIDL 24/7, improving settlement finality and liquidity management. Redemptions unwind the path in reverse. 2. Yield routing to L2 costs • Computation: Net portfolio yield of reserves → USDm Reserve Yield Account → periodic transfer to Sequencer OPEX bucket. Gas prices can then track data and compute costs rather than a markup. Why? As data costs change and throughput scales, fee volatility doesn’t need to be pushed to users to protect L2 margins. 3. Onchain, Ethena’s USDtb/USDe contracts provide ERC-20 interfaces and policy hooks. MegaETH then integrates paymaster support and router/oracle paths for USDm. ------ 📌 𝐈𝐧𝐭𝐨 𝐌𝐞𝐠𝐚𝐄𝐓𝐇 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 MegaETH’s execution architecture is heterogeneous: ✔️ One active sequencer ✔️ Full nodes, ✔️ Replica nodes (apply diffs without re-execution), and ✔️ Prover nodes. It targets ~10 ms latency and 100k+ TPS, secured by Ethereum and paired with EigenDA for data availability. The USDm design complements this by de-linking fee revenue from user surcharges and pinning it to reserve yield. ------ 📌 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐑𝐨𝐥𝐞𝐬 1. MegaETH (MegaLabs): → Integrates USDm into wallets, paymasters, gas accounting, indexers, and routing. → Maintains a fee policy that prices gas at cost, funded by USDm yield. → Preserves competing stablecoin routes (USDT0, cUSD) to avoid lock-in. 2. Ethena → Operates the stablecoin stack and reserve policy for USDtb. Provides issuance, risk, disclosures, and protocol governance. 3. Securitize / BlackRock → BUIDL custody/transfer, investor compliance, and tokenized fund operations that underpin USDtb’s reserve quality and liquidity rails. ------ 📌 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞𝐬 → Fee Stability at Scale: Yield covers sequencer OPEX as usage rises, so MegaETH avoids hiking fees to defend margins. → Institutional Reserve quality: BUIDL and Securitize add a clear operational framework and integrations across CeFi/DeFi. → Adaptable Reserve Policy: Ethena’s stack allows shifting collateral mix (e.g., to include USDe exposure) if conditions change. ------ 📌 𝐑𝐢𝐬𝐤𝐬 𝐚𝐧𝐝 𝐎𝐩𝐞𝐧 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 (𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐏𝐨𝐥𝐢𝐜𝐲) → Reserve yield vs. OPEX mismatch: If short-rate yields compress or DA costs rise, yield may not fully cover sequencer costs, forcing policy changes or supplemental revenue. → Counterparty/compliance dependencies: Reliance on BUIDL/Securitize and associated investor frameworks introduces offchain and regulatory dependencies. → Smart-contract surface: USDtb, paymasters, routers, and OPEX routing add integration risk. I expect audits and management. → Liquidity pathing: Deep liquidity for USDT0 and cUSD will coexist. This means USDm needs competitive venue support to minimize routing slippage in practice. → Governance clarity: Who will manage reserve-mix changes for USDm, and how OPEX distributions are parameterized? This matters a lot to users and integrators. You can also check a simplified explanation of USDm, written by @St1t3h: