When researching reward-based products, I have a habit: first, I clarify where the rewards come from, then I examine the numbers on the page. @Bagel_win’s Rewards Center integrates referral commissions, copy trading commissions, and event rewards into its product structure; the event details also introduce badges and VIP tiers. What draws my attention is whether users can clearly understand their participation records and the corresponding rules. But several things shouldn’t be mixed together: trading profits and losses are market outcomes, commissions depend on their specific mechanisms, and event rewards have separate eligibility and distribution criteria. Higher account activity or a higher tier doesn’t automatically mean earning more in reality. If I plan to engage long-term, I first review the reward breakdowns, calculation methods, and claiming conditions before deciding how much time to invest. Especially avoid making trades you hadn’t planned just to earn an extra badge. My understanding is that the value of a Rewards Center should be to make participation transparent—not to make users fixate solely on leveling up. Only when you can clearly distinguish between commissions, event rewards, and trading outcomes can you reasonably determine whether this product is truly right for you.

[What Exactly Changed in This Hash Upgrade?] 🧩 Cryptographic upgrades are often oversimplified with statements like: “We switched to a new hash, so the entire chain is now secure.” 🔧 Looking at Starknet v0.14.3, the official migration notes specify precisely what changed: on mainnet, the OS program hash and OS configuration hash were upgraded from Pedersen to a construction based on BLAKE2s-256. 📌 These two changes have been implemented and are worth noting—but they do not extend to state commitments, contract address derivation, contract storage, or account signatures, nor do they migrate dependencies on Ethereum’s side. I prefer to view this as a construction progress sheet: mark only what has been delivered; keep items still on the roadmap or under research as separate tracking items. Hash-based STARKs provide the foundational proof layer, but all other components must be addressed individually. As of October 25, 2026, Starknet’s quantum resistance preparation focuses on verifiable changes and clearly defined boundaries. Security engineering is built through concrete progress—not by slogans. @Starknet

Many people study prediction markets and only ask, “Am I right?” I think we should also add: At what price and through what method am I prepared to participate? @Bagel_win offers order tools such as market orders, limit orders, and Combo orders. To me, their value lies in turning judgments into concrete operational conditions—not in having more features making it easier to profit. Take this hypothetical: A certain outcome is currently quoted at 0.60, but you’re only willing to consider entering around 0.55. A market order prioritizes execution at the current available price; a limit order lets you set a price condition, but may never fill. These numbers are merely illustrative of the tools, not a recommendation to trade. Especially when news breaks and prices move quickly, “I believe in this outcome” and “I accept this price right now” are two entirely different questions. Beyond directional judgment, you must also consider liquidity, fees, and actual execution. I prefer to clearly define the price I’m willing to pay first, then choose the order type. Tools help express your plan—but they can’t prove your plan is right. Often, reducing impulsive decisions is more useful than adding one more button.

Predictive markets plus AI—what I truly want isn’t just a “buy this” suggestion. In @Bagel_win’s event description, AI market insights and recommendations are mentioned. I prefer to think of it as a research assistant: helping users organize background information, identify key influencing factors, and providing a solid starting point for exploring an entirely unfamiliar market. If I were designing my own usage, I’d ask three consecutive questions: What information does this conclusion rely on? Is there any contradictory evidence? Under which conditions would the original judgment no longer hold? For example, when analyzing a match, team composition, schedule, and last-minute changes could all affect the outcome. Even if the AI’s explanation sounds convincing, I still need to verify its sources and timeliness—just because it reads like an analysis report doesn’t mean it has the latest data. This isn’t to say AI has no value; on the contrary, helping me avoid overlooking a critical variable is already incredibly useful. But organizing information, making judgments, and taking responsibility for outcomes are three distinct tasks. What I hope for in an AI experience is to become a better researcher—not to press the trade button faster.

[Mainnet Demo—How Far Are We from Widespread Adoption?] 🔬 Seeing “post-quantum accounts have transferred funds on Starknet mainnet”? Don’t immediately assume “widespread adoption has arrived.” 🧾 I reviewed the evidence logs from Quantum Hub: An experimental Falcon-512 account by OpenZeppelin did successfully send 0.01 STRK. This demonstrates that the new signature verification logic can function within the existing account model—and there is a verifiable transaction to prove it. ⚠️ But the same documentation clearly states: the code is experimental, un-audited, and intended solely for research and benchmarking. A single successful transfer does not prove production readiness, let alone indicate adoption by wallets, custodians, or everyday users. As of October 25, 2026, what this case highlights for me is Starknet’s account programmability: there is a viable path to replacing authorization methods. My next focus will be on independent audits, official releases, and wallet integrations—not conflating a technical demo with a security guarantee. @Starknet

I’m increasingly realizing that what makes prediction markets interesting isn’t just guessing the right outcome—it’s seeing why different people make the judgments they do. @Bagel_win brings together following users, market discussions, position sharing, and copy trading. If this social design is implemented well, conversations can move beyond a simple “I’m bullish” to “What’s my reasoning, and what would make me change my mind?” For example, in the same match, one person analyzes team lineups, another tracks schedules, and someone else only looks at popularity. Putting these perspectives side by side in one discussion at least reminds us: what we know might only be part of the picture. Of course, revealing your position doesn’t mean your view is correct. Popularity, volume, or high copy counts can’t replace evidence. I’m more likely to pay attention to those who are willing to explain their reasoning—and also willing to adjust their judgment when new information emerges. My understanding is that the most valuable outcome of social features isn’t creating a group where everyone cheers each other on—it’s helping people uncover their blind spots. A prediction community that can openly discuss disagreements and accept being corrected by facts is one worth following long-term.

【Change the lock, don’t rebuild the whole building】 🔐 If changing a door lock required construction across the entire building, how many people would need to coordinate for a security upgrade? 🧩 This is precisely how I understand Starknet’s native account abstraction: accounts themselves are smart contracts, and “what counts as a valid signature” can be defined within the account’s verification logic. In the future, when adopting a new post-quantum signature verifier, developers can create new account implementations without forcing a network-wide hard fork for every signature type. As cryptographic schemes evolve, allowing accounts to update their rules provides a flexible path for adaptation. 🔍 But flexibility doesn’t mean automatic completion. Upgrading old accounts, how wallets sign, how keys are stored, and how recovery permissions are configured—all require someone to deliver and audit the product. As of October 25, 2026, what I value is this adaptable mechanism, not the conclusion that “all wallets are quantum-safe.” Being able to change the lock is just the beginning; the final step is ensuring users can safely and reliably use it. @Starknet

🙏 Twitter Creator Earnings | Top 40 Publicly Shared Earnings from the Chinese Community Thank you to all the teachers for publicly sharing your earnings—each post is an excellent reference, and I’ve learned a lot from them. 🥇 Suoha Ge @sh11668 | $10,116.70 🥈 Hong Kong Lao Lei @btcmos | $4,292.60 🥉 Shao Allen @AlanShao111 | $2,660.29 4️⃣ PaoPao Sees the World @paopaoksj | $1,491.71 5️⃣ Xing Yu @YxzRainy | $1,419.69 6️⃣ Sha Po Lang @wolfyxbt | $1,228.04 7️⃣ Kin @KinGao476942 | $1,160.09 8️⃣ Alchemy Uncle @AirdropAlchemis | $1,007.81 9️⃣ Qi Hang @qihang_zeng6688 | $852.80 🔟 Yang Wei @HenryLin511 | $843.52 11 | Prism @PrismChemAI | $813.10 12 | Brother Chen @chennan789 | $646.74 13 | Bitker @bqk002 | $602.00 14 | Kaka @xKaka1456 | $597.24 15 | The Real Bird Uncle @niaoshu | $568.27 16 | Sky @skybytee | $563.27 17 | Mars @mars_bnb | $555.52 18 | Tim @Timbazp | $519.85 19 | Su Jing @sujing110 | $519.03 20 | Never-Ending韭菜 @alpinekaka | ~$470 21 | Xiao Yao Zi @Xiaoyzi_Web3 | $458.80 22 | Tu Ya @14256617zgq | $440.00 23 | Jiao Jiao @jiao_newlife | $432.08 24 | Crypto Dai Tu @daitu0822 | $409.45 25 | Zi Yan @0xCzyan | $394.19 26 | Ji Zhengguang @Jizhengguang | $349.04 27 | Cat Brother @yqgyx123 | $347.27 28 | Wang Xiao Er @wangxer233 | $254 29 | jusir @bigjusir | $247.88 30 | Katherine @Katherine1198 | $239.00 31 | PPP @Web3454354 | $235 32 | Quan Ge @WeiquanZ68597 | $233.21 33 | Anis @Yonkxx | $224.56 34 | Old Wang’s Comeback Story @wang__god | $213.87 35 | He Bao Dan @egg_ehh | $200 36 | Russell @Russell3402 | ~$200 37 | goDavi @corndogjpn2 | $190.93 38 | Michelle @michelle_z21331 | $180.45 39 | Wang Wu Ming @Bottlecap8699 | $137.93 40 | This Is a Good Thing @s6629z | $118 📊 Total for 40 creators: ~$36,434 | Median: $464 | 8 creators earned over $1,000 I still have a long way to go—I’ll keep writing diligently and humbly learning from everyone 🙏 If I missed any teachers, please let me know in the comments—I’ll add them right away. See you next time on 11/6.

I used to plan AI infra the way I plan a road trip: pick a route, hope traffic behaves, and find out about the tolls later. @CottoniaAI 's Copilot changed that conversation for me. The flow is almost uncomfortably simple. It asks for the things that actually drive cost and compute—application type, expected daily requests, context length, and the model you're running—then comes back with a projected compute footprint, optimization opportunities, and a cost check before you commit. No dashboard archaeology. No guessing whether a long-context RAG pipeline needs the same shape as a bursty coding assistant. What I appreciate most is the design philosophy underneath it: infrastructure should behave like an active optimization partner, not a passive resource pool. Cottonia's AI-aware scheduling and layered context management are aimed squarely at the waste I see in real workloads—repeated context, redundant token input, inference steps that add latency without adding value. The claim of 20–50% reduction in repeated token input in high-frequency scenarios is the kind of number I actually want to test against my own traffic. My one honest note: a Copilot recommendation is only as good as the inputs you give it. If you under-specify request volume or context density, the plan will look cleaner than reality. But that's also the point—Cottonia turns infrastructure planning into a feedback loop rather than a one-time spreadsheet. If you're building AI products and still sizing infra by vibe, this is worth a look. #Cottonia @CottoniaAI

The most important thing when engaging with Kaito—don’t FUD. I posted three tweets criticizing the Axis project, essentially advising against participating in their launch due to poor market conditions at the time. Despite this, none of those three tweets were counted in the final tally. Yet, Axis still distributed an airdrop of over 1,280 USDT. Final reminder: You can share neutral opinions, but if you’re earning from creator rewards, never FUD a project! Don’t eat at someone’s table and then smash their dishes!