AKKI G

チャートを見たばかりですが、絶対にブルishに見えます。私たちが見たあのポップ？それはただのランダムなノイズではありません—背後には本物のモメンタムがあります。
➡️チャートは$ETH が13%以上上昇し、最近の高値に対抗して強く押していることを示しています。ここで非常に重要なのは、MA60ラインの上でしっかりと保持していることです。これは強いトレンドのための重要なシグナルです。これは単なる急激なポンプとダンプではありません; ボリュームがこの動きを支えており、実際の買い手が参入していることを示しています。
➡️それでは予測はどうですか？ETHの市場センチメントは今非常にポジティブに見えます。テクニカル指標は「買い」および「強い買い」に大きく傾いており、特に移動平均においてです。この種の価格動向は、ポジティブなニュースと強いオンチェーンデータに支えられて、しばしば潜在的なブレイクアウトを示します。もしこのモメンタムが続けば、すぐに過去最高値のテストを見ることができるかもしれません。今日中にかもしれません。

@Dusk #Dusk $DUSK
Privacy conversations in crypto often ignore one uncomfortable truth. Finance does not operate anonymously at scale. It operates through identity, permissions, and accountability. When I look at how Dusk Foundation approaches identity, it becomes clear that the protocol understands this reality deeply.
Dusk does not frame identity as exposure. It frames it as controlled disclosure. Participants can prove who they are, or that they meet certain criteria, without revealing unnecessary personal or commercial information. This distinction is crucial. Institutions need to know they are interacting with compliant counterparties, but they do not need to publish identities on a public ledger forever.
By embedding identity logic into the protocol in a privacy preserving way, Dusk enables regulated activity without creating surveillance infrastructure. That balance is rare. From my perspective, this is where many privacy focused chains fall short. They optimize for anonymity but forget that regulated markets require accountability. Dusk treats identity as a functional layer that enables trust rather than undermining it.

@Dusk #Dusk $DUSK
There is a difference between building markets and building market infrastructure. Many projects chase users first and systems later. Dusk reverses that order. It focuses on infrastructure that markets can rely on once they arrive.
This approach requires patience. Infrastructure rarely attracts excitement early on. Its value becomes obvious only when stress appears. Settlement failures, compliance gaps, and data leaks expose weak foundations quickly. Dusk’s design choices aim to prevent those failures before they happen.
From my perspective, this is a sign of maturity. Instead of asking how fast adoption can happen, Dusk asks how adoption can happen safely. That question changes everything. It influences consensus design, privacy architecture, governance cadence, and validator incentives. Over time, these choices compound into resilience. That is how real financial systems are built.

@Dusk #Dusk $DUSK
The counterparty risk is a silent risk that influences the management of money. Banks are not only concerned with changes in prices, but also with the failure to deliver on their part by other parties. The design of Dusk allows trades to be free to settle under their terms without bringing the disclosures to the public.
Uncertainty reduces when obligations are fulfilled on the blockchain using a high degree of security. There is no longer a need to use middlemen to reconcile trades in a slow and unclear way. They are also in a position to maintain changes in trade secrets. The balance is essential since it is impossible to have banks functioning when all the details are transparent. Dusk allows them to minimize risk at a low profile.
The best thing about this is that it is compatible with current financial logic. Clearance and settlement already tend to reduce counterparty risk. Dusk merely replaces faith in intermediaries by effective implementation. I believe this is when blockchain can really be a superior structure to the legacy systems, and not simply an alternative one.

@Dusk #Dusk $DUSK
The majority of crypto discussion revolves around trading only. The most attention is paid to charts, liquidity and volume. However, real money markets are not concerned with trading. They are about settlement. Settlement is a time when ownership is achieved, debts have been settled and risk has been eliminated. Considering the organization of Dusk Foundation, one can assume that settlement is not a peripheral issue.
In the normal banking setup, it is an expensive and long process to settle and it is divided. Records are checked by a number of middlemen who deal with risk, and ensure that rules are adhered to. Dusk rectifies this by allowing settlements on a public chain. It preserves confidential information and nevertheless makes the deal. It is also possible to complete trades without revealing personal information, and the outcome can be proven and implemented. It is not merely a technical transformation, but it is a massive one regarding the functioning of things.
The main thing is that Dusk does not entirely revolve around rapid trading. It wants to do the right thing. In the regulated markets, it is more significant to do things correctly, rather than fast. A fast and incorrect system will be detrimental to the entire market. A slower yet precise system generates confidence. I believe that this emphasis on actual settlement demonstrates that Dusk is very long term.

@Walrus 🦭/acc #Walrus $WAL
Web3ゲームの最大の障壁となるのは、グラフィックス、ゲームプレイ、または資金ではありません。ゲームが終了したときにデータを保持し続けることが課題です。ゲームは、ゲーム終了後もアクセス可能であるべき大量の情報を生成します。世界の状態、プレイヤーの所有物、進捗履歴、リプレイ、その他の情報は、信頼できる場所に保存されなければなりません。ブロックチェーン上にすべてのデータを保存するコストは高く、遅い一方で、チェーン外に保存するとリスクがあります。
Walrusは、大規模なゲームデータをブロブ形式で保存できるようにすることで、この問題に対処しています。ブロブは予測可能な期間にわたりアクセス可能であり、特定のブロックチェーン上にデータを保持する必要がありません。この仕組みは、ゲームが個々のコントラクトやチェーンを超えて永続的な世界を構築する必要がある場合に、すぐに役立ちます。

@Walrus 🦭/acc #Walrus $WAL

Data marketplaces have been promised in the Web3 years ago, yet very few of them actually work. It is not that people desire to sell data, the instruments to accomplish it fail to perform. When a buyer is unable to obtain reliably the data he or she paid and when a seller cannot regulate the duration of its existence, the market collapses since no one believes it.
Walrus resolves this by offering big data sets predictable guarantees. This is what a data market should work out of theory.
Prior to Walrus, the majority of Web3 data markets possessed a weak set of rules. The databases were kept off chain whilst on chain contracts transacted with money and tokens. Buyers were forced to believe that a connection would not fail. The sellers could not be sure that the data could not leak. When either of the two guesses did not work, the market became untrustworthy.
Walrus alters the foundation of the system by maintaining the availability of the data independent of the regulations of the market.
Practically, a data provider puts a dataset into Walrus and indicates a duration of retention. The dataset is indicated in the market contract and the license is established. The buyers receive an actual connection to the data, rather than a promise. The buyers can also depend on downloading the data as long as the time window is open.
This renders the sale of data binding rather than a wish.
This is something that sellers are relieved of. They do no longer need to maintain their own servers and are no longer concerned with uptime. Storage layer is concerned with the data storage online. Sellers do not have to spend time managing a server, but they can concentrate on putting good data, setting prices and writing licenses.
For buyers, the trust changes. They put their faith in storage system rather than the server of the seller. This reduces the conflicts and makes markets more transparent. In the case of missing data within the agreed time, the loss is evident and quantifiable, but not opinionated.
The other main advantage is that it has an ability to control the time data is available. Not every dataset is meant to be permanent. Others can only be useful in a research project, a market moment or an event. Walrus allows the sellers to fix an availability period that is accommodative to the real value. That makes them low on costs and short on long term liability.
This also helps other tools. Data analytics tools, auditors, and researchers can refer to the same datasets without renegotiating provided the rules allow them to. Information is no longer confined to a file that is secured by proprietary APIs.
Walrus is not a marketplace. It does not establish prices, imposes no licenses or determine access policies. It only ensures that in case a market claims that it will provide information, that commitment is technically sound. This makes markets dynamic and reliable.
Migration freedom is also obtained by data markets. The datasets on Walrus remain alive in case a market ceases to function or evolves. That data can be utilized in new markets without necessarily beginning afresh. This continuity is critical towards creating sustainable data economies.
I believe that Web3 data markets did not work due to the unreliability of storage. You cannot sell what you cannot deliver. The delivery layer is fixed by Walrus, allowing the remaining stack to finally be used.
Walrus transforms data markets into reality infrastructure by providing datasets with a stable house, with distinct life times. That displacement is not particularly loud, but it is the basis of any real on chain data economy.

$WAL @Walrus 🦭/acc #Walrus

Another issue that is common to AI projects based on Web3 is the distance between the model location and the data storage location. Outputs are enormous in the form of training sets, embeddings, logs and others. They do not lend themselves to being placed on a chain but numerous AI processes still require verifiable reference, predictable access, and common visibility across tools.
Walrus bridges this divide by storing data blobs with obvious availability guarantees, and not pressurizing those datasets to execution layers that were not intended to support them. This is significant to AI teams who desire transparent, verifiable, and reproducible model workflows without using a central cloud storage.
Prior to Walrus, majority of Web3 AI projects took one of two directions. They stored datasets either fully out of the chain in their own privately built infrastructure, which was difficult to verify and collaborate with, or attempted to push references onchain and guessed that the underlying data sets would be available in other places. These two solutions were weak. In case the storage was modified or lost access, the onchain references were no more.
Walrus alters this and becomes a long-lasting availability layer of the real data. In reality, collections of training data, inference records, model snapshots and evaluation results may be stored as blobs on Walrus and accessed with smart contracts, agents or offchain computer systems. The information does not have to be held onchain, but its presence is no longer a coincidence. It is imposed through infrastructure.
The process is made simpler. Data is generated or curated. It is kept on Walrus and has a clear availability period. That data are referenced by models or agents through hash or pointer. Other people can audit, reproduce or extend results. This also permits teamwork without coercion to centralization. A dataset can be freely shared by teams or chosen selectively but the access remains predictable during the duration of a project. A verification of results is possible by external researchers without special permissions. The same data can be retrieved by the agents in various environment with high reliability.
Cost is a major factor here. AI data are massive and it is not cheap to store them indefinitely. Walrus allows teams to decide on the duration of datasets. Artifacts used in training that are only important in a particular research cycle should not be eternal. Assessment collections attached to published outcomes can be stored more. This flexibility maintains the costs as per value.
Other large advantages include reproducibility. Reproduction of results is one of the most difficult issues in AI. Data disappears. Versions drift. Context is lost. Storing datasets and logs on Walrus creates a consistent baseline by teams. Any experiment can be repeated with inputs the same with any data provided.
This also improves trust. According to what the models were trained on can be inspected by the users and those who work with them rather than just by assertions. Walrus does not render models trustworthy in and of itself, but eliminates one of the largest verification barriers.
Noticeably, Walrus does not compel AI teams to a certain compute or execution stack. It makes no assumption of the location of training or how the inference is carried out. It merely guarantees that the information such processes rely on is not lost. This impartiality enables AI processes to develop without re-implementing assumptions of storage.
It is not just the effect of individual projects. Shared datasets are coordination points as increasing numbers of AI systems begin to interact onchain. The ability to have a common and trusted place of storage and reference of those datasets minimizes duplication and fragmentation.
I believe that Web3 native AI will not be scalable when the data is completely centralized or perilously short lived. It requires some middle ground on which big data can reside safely without congesting execution environments. Walrus is that layer, though it is not a future concept only in theory but in practice as well.
It is not a vision in the air. It is a practical enhancement that eliminates friction in actual working processes. This is what AI teams in Web3 require at this point in time.