Alonmmusk

At first glance, Fogo looks simple.
It’s a high-performance L1. It uses the Solana Virtual Machine. Faster execution. Familiar tooling. A clean pitch.
But the part that keeps pulling at me isn’t speed. It’s coordination cost.
Because borrowing execution isn’t just a technical choice. It quietly reshapes who needs to coordinate with whom — and why.
I wasn’t sure that mattered at first. If $FOGO runs SVM, developers can port over. Users recognize the environment. Validators understand the performance profile. In theory, this reduces friction.
But reducing technical friction doesn’t eliminate coordination cost. It just moves it somewhere else.
And that shift feels structural.
The Illusion of Frictionless Migration
There’s a common assumption in crypto: shared virtual machines lower migration barriers.
If you already build for SVM, why not deploy on Fogo?
Let’s test that.
Imagine a small DeFi team currently building on Solana. They’re comfortable with SVM. They’ve optimized for parallel execution. They know the tooling quirks. Fogo launches with better throughput under stress and slightly different fee dynamics.
Technically, porting is manageable.
But now the real questions start.
Where is liquidity?
Where are users?
Who are the validators?
What happens during congestion?
Suddenly, the friction isn’t code-level. It’s ecosystem-level.
Coordination cost isn’t about writing smart contracts. It’s about aligning expectations across developers, liquidity providers, and infrastructure operators at the same time.
That alignment is expensive.
Coordination as the Real Bottleneck
High-performance L1s tend to frame constraints as technical — throughput ceilings, latency bounds, validator hardware.
But coordination is slower than execution.
Fogo inherits the SVM model, which means it inherits a set of habits. Developer assumptions. Runtime expectations. Performance trade-offs around parallelism and state management.
That inheritance reduces learning cost. But it also ties Fogo’s fate to an existing mental model.
Here’s the tension:
If @Fogo Official behaves too similarly to Solana, it becomes an execution mirror.
If it diverges meaningfully, it increases coordination cost.
There isn’t an easy middle.
The network needs developers to believe it’s familiar enough to trust. But distinct enough to justify moving capital and attention.
That balance feels fragile.
A Micro Scenario Under Stress
Picture a volatility spike.
A memecoin cycle hits. Transaction volume surges. On Solana, congestion rises but infrastructure providers are battle-tested. Validators know the drill. RPC operators scale.
On Fogo, the technical stack may be capable. Maybe even more performant. But infrastructure coordination is thinner. Fewer validators. Fewer indexers. Fewer fallback RPC endpoints.
Execution speed becomes secondary.
Because during stress, systems don’t fail at their peak theoretical throughput. They fail at their coordination margins.
Who upgrades first?
Who absorbs temporary losses?
Who patches quickly?
A high-performance chain with low coordination depth feels fast — until it doesn’t.
And the market is unforgiving when that happens.
Incentives That Actually Move People
So what would realistically motivate adoption?
It probably won’t be just speed. Not in 2026. Everyone claims speed.
It would need to be one of three things:
Economic asymmetry — meaning materially better fee capture or incentive structures for validators and developers.Liquidity incentives large enough to overcome migration hesitation.A unique application that cannot coordinate efficiently elsewhere.
Otherwise, inertia wins.
Developers are more conservative than they appear on Twitter. They optimize for predictability under pressure. Familiarity is underrated. They will tolerate moderate inefficiency to avoid ecosystem uncertainty.
Users are even more inertia-driven. Liquidity pools create gravity. Capital clusters where other capital already sits.
Liquidity gravity reduces coordination cost for users. Leaving that gravity increases it.
If Fogo cannot create its own gravity well, it remains orbiting another.
The Validator Side of the Equation
There’s another layer.
Running a high-performance chain isn’t cheap. Hardware requirements matter. Bandwidth matters. Operational discipline matters.
If Fogo pushes performance boundaries, validator centralization pressure creeps in. That’s not unique to #Fogo — it’s common across high-throughput L1s — but it sharpens the coordination problem.
Fewer validators means tighter coordination loops. That can increase responsiveness. It can also increase fragility.
There’s a structural assumption embedded here: that validator incentives will align around long-term network stability rather than short-term extraction.
That assumption feels decisive.
Because once coordination thins out — once only a handful of well-capitalized operators dominate — governance dynamics shift quietly.
And reversing that trend later is harder than preventing it early.
Borrowed Execution, Borrowed Expectations
Using SVM creates another subtle effect.
Expectations transfer.
Developers don’t just import code; they import mental benchmarks. They compare performance directly. They compare composability. They compare tooling stability.
Fogo isn’t competing abstractly. It’s compared line-by-line.
That increases pressure.
If Fogo underperforms even slightly in certain scenarios, the narrative forms quickly: “Why not just use Solana?”
If it outperforms meaningfully, then the question becomes: “Why hasn’t liquidity moved yet?”
In both cases, coordination cost dominates.
Execution compatibility reduces migration friction. But it increases comparative pressure.
That trade-off is easy to overlook.
Behavioral Patterns Under Pressure
There’s something else I’ve noticed across ecosystems.
When uncertainty rises, developers cluster around perceived safety. Users cluster around deepest liquidity. Institutions cluster around established compliance narratives.
Coordination compresses inward.
This is why alternative L1s often struggle not during growth cycles — but during contractions.
The real test isn’t onboarding. It’s retention under stress.
If Fogo can coordinate effectively when volatility spikes — if infrastructure actors respond quickly, if incentives hold — then coordination cost becomes manageable.
If not, the borrowed execution layer won’t save it.
Because coordination failures feel like existential risk in crypto markets. Even when they’re temporary.
The Ecosystem Zoom-Out
From a broader view, Fogo sits in an interesting position.
It’s not trying to reinvent execution. It’s trying to optimize it within a known paradigm.
That narrows uncertainty in one dimension and increases it in another.
It reduces developer learning cost but increases ecosystem differentiation cost.
It lowers code friction but raises liquidity gravity challenges.
In that sense, Fogo’s constraint isn’t technical throughput. It’s synchronized belief.
High-performance systems scale transactions easily. They scale trust more slowly.
And trust is a coordination artifact.
The Line That Keeps Coming Back
Here’s the thought I keep circling:
Execution can be copied. Coordination has to be built.
That’s the hidden cost of borrowed architecture.
If Fogo succeeds, it won’t be because SVM runs efficiently. It will be because enough independent actors decide — at roughly the same time — that coordinating around #fogo is worth the risk.
And that decision rarely happens gradually. It happens when incentives line up sharply enough to overcome hesitation.
I’m not fully convinced we know what that trigger looks like yet.
Maybe it’s a breakout application. Maybe a sustained fee advantage. Maybe institutional partnerships that reshape validator composition.
Or maybe coordination simply remains too expensive relative to the benefit.
For now, Fogo feels like a system with technical clarity and social ambiguity.
That isn’t fatal. But it is unresolved.
And coordination, unlike execution, doesn’t scale just because you designed it to.

At first glance, Mira feels obvious. AI systems hallucinate. They drift. They exaggerate confidence. So you wrap their outputs in cryptographic verification and distribute judgment across multiple independent models. Problem solved.
That was my initial reaction anyway. If reliability is the bottleneck, then verification is the fix.
But the more I think about it, the less this looks like a purely technical problem. It feels like an incentive design problem. And incentives are rarely clean.
$MIRA breaks AI outputs into discrete claims. Instead of trusting one system’s answer, it asks multiple independent models to validate smaller pieces of that answer. Those validations are economically incentivized and settled through blockchain consensus. In theory, truth emerges from distributed alignment.
In practice, throughput starts pressing against truth.
Verification takes time. It takes compute. It takes coordination. And coordination has a cost — not just financially, but behaviorally.
Imagine a trading desk using an AI system to parse breaking geopolitical news. The model generates a summary: sanctions imposed, supply chain impact, projected commodity shifts. Under Mira, that output would be decomposed into claims. Each claim gets validated by other models. Consensus forms. Only then does the desk treat it as reliable.

But markets don’t wait.
If verification adds even a few seconds of delay, the edge narrows. If it adds meaningful cost per query, usage becomes selective. The desk might verify high-impact outputs but skip routine ones. Reliability becomes tiered.
That’s where the tension begins to surface.
Mira assumes that economic incentives can align independent validators toward accuracy. But incentives don’t just reward correctness; they reward speed, volume, and profitability. If validators are paid per claim processed, there is pressure to optimize throughput. If rewards are structured around staking and slashing, participants may minimize risk by converging toward majority signals rather than challenging them.
Truth requires friction. Throughput resists it.
I’m not fully convinced those two forces naturally balance.
There’s also a structural assumption that feels fragile: that independent AI models will be sufficiently diverse in architecture, training data, and bias profiles. If the validating models share similar blind spots — which is likely, given shared data ecosystems — then consensus might amplify systemic bias rather than eliminate it.
Distributed agreement is not the same as independent reasoning.
That line keeps coming back to me.
And then there’s human behavior. Developers under pressure tend to optimize for product velocity. If integrating Mira requires restructuring output flows, decomposing claims, managing verification latency, and handling disputes, many teams will hesitate. Not because they oppose verification. Because complexity compounds.
Developers rarely adopt infrastructure for philosophical reasons. They adopt it when something breaks.
So what would realistically motivate adoption?
Liability is one lever. If AI-generated errors create legal exposure — mispriced assets, incorrect medical summaries, flawed compliance reports — organizations will look for defensible safeguards. Being able to say, “This output was independently verified through decentralized consensus,” has value in courtrooms and boardrooms.
Trust is expensive. Verification is insurance.
But insurance has a premium. And someone pays it.
If @Mira - Trust Layer of AI verification costs are high, usage concentrates in high-stakes domains. Finance. Healthcare. Government. That may be enough. Or it may limit network effects. Lower-stakes applications — content generation, customer service automation — might opt out entirely.
That creates a split ecosystem. Verified AI in critical lanes. Unverified AI everywhere else.
I wonder whether that fragmentation weakens the broader premise.
Zooming out, there’s also ecosystem gravity to consider. AI developers cluster around dominant platforms. Blockchain developers cluster around liquidity and tooling. For Mira to thrive, it has to bridge two gravity wells without being pulled too hard into either.
If it leans too deeply into crypto-native incentives, mainstream AI companies may hesitate. If it abstracts away blockchain complexity entirely, it risks losing the economic backbone that makes decentralized verification meaningful.
Migration friction is real. Teams don’t re-architect systems lightly. Even if Mira’s model is elegant, integration must feel lighter than the risk it mitigates.
There’s another trade-off that’s harder to quantify. Verification increases confidence, but it may reduce adaptability. If every claim requires structured decomposition and validation, AI systems could become less fluid. More procedural. Innovation sometimes thrives in ambiguity. Over-verification might slow experimentation.
Of course, the counterargument is that critical systems shouldn’t rely on improvisation anyway.
Still, I can’t shake the sense that Mira sits at a crossroads between two cultures. AI culture values iteration speed and scaling models quickly. Blockchain culture values consensus, auditability, and adversarial resilience. The incentive design has to reconcile both.
And that reconciliation is delicate.
If rewards are too generous, the system attracts opportunistic validators optimizing yield rather than quality. If rewards are too thin, participation shrinks, and verification centralizes. If slashing is aggressive, validators become risk-averse and align with majority opinions. If slashing is weak, malicious behavior slips through.
Each parameter nudges behavior.
Under pressure, participants respond predictably. They minimize downside. They follow incentives, not ideals. So Mira’s long-term reliability depends less on cryptography and more on whether its economic design nudges participants toward careful disagreement rather than comfortable conformity.

Careful disagreement is expensive.
I keep returning to throughput. Not in the blockchain sense alone, but in the cognitive sense. How many claims can realistically be verified per second without diluting scrutiny? As AI systems generate longer, more complex outputs, the number of verifiable units grows. Decomposition scales the surface area of consensus.
More claims mean more coordination.
At scale, the network must decide whether to prioritize volume or depth. Do you verify every small assertion lightly, or fewer assertions rigorously? That decision shapes the character of the protocol.
One sharp thought keeps surfacing: a verification network is only as honest as the incentives that make dishonesty unprofitable.
That sounds obvious. But it’s not trivial to implement. Incentives drift. Markets change. Participants evolve.
I’m also aware that early-stage systems often work beautifully at small scale. Limited participants. High alignment. Shared mission. The stress test comes later, when usage expands and economic stakes increase. Will validators remain independent when large clients depend on certain outcomes? Will economic concentration creep in quietly?
Time will tell.
For now, #Mira feels like an attempt to formalize epistemic responsibility. To say that AI outputs shouldn’t just be plausible; they should be accountable. I respect that instinct. It addresses a real weakness in current AI systems.
But incentive design is unforgiving. Throughput pressures never disappear. And truth, when tied to economics, becomes entangled with profitability.
I’m not dismissing the model. I’m just not ready to assume the equilibrium holds automatically.
It may work. It may bend under scale. The tension between truth and throughput doesn’t resolve itself. It has to be constantly managed.
And that management — economic, behavioral, architectural — might end up being the real product.
For now, the idea sits there. Convincing in principle. Fragile in practice. Quietly waiting for scale to test it.

My first instinct was simple: if Fogo is built for high performance and runs the Solana VM, then faster blocks and smoother execution should just be upside.
More throughput. Lower latency. Fewer hiccups.
But the longer I sit with it, the more the validator layer starts to feel like the quiet constraint. Performance isn’t free. It asks something in return.
If $FOGO pushes hardware requirements upward to sustain speed — more memory, stronger CPUs, tighter network expectations — then validator participation narrows. Not deliberately. Just structurally.
And that’s where the trade-off lives.
Picture a mid-sized infrastructure operator running validators across several chains. They review Fogo’s specs. To stay competitive, they’d need to upgrade machines, maybe colocate in specific data centers to reduce latency variance. It’s doable. But it changes the cost curve. Smaller independent validators might hesitate. Some won’t bother.

Performance improves. Validator diversity might compress.
I’m not saying that’s inevitable. But high-performance systems tend to centralize around operators who can afford precision. The faster the system, the less tolerance it has for uneven infrastructure.
That’s the tension: speed sharpens edges.
There’s a fragile assumption embedded here — that market demand for performance outweighs the long-term value of validator accessibility. That users care more about execution smoothness than about how many independent actors can realistically participate in consensus.
Sometimes that’s true. Traders routing size care about reliability. Applications handling liquidations care about deterministic speed. Under stress, users reward networks that simply work.
But institutions also read decentralization metrics. They don’t want to rely on a validator set that could quietly converge into a handful of industrial operators. Especially if governance power tracks validator weight.
Incentives matter here.
Why would validators join Fogo?
Block rewards, transaction fees, early positioning. If usage grows, being early compounds. There’s optionality in securing a network before it becomes crowded.
But what would prevent movement?
Capital expenditure. Operational uncertainty. The simple fact that running one more high-spec validator is not trivial. Infrastructure teams optimize portfolios. They don’t chase every new L1.
From a developer’s perspective, SVM compatibility lowers friction. But validators don’t experience compatibility the same way developers do. They experience hardware curves, uptime risk, slashing exposure.
And validator coordination shapes everything downstream.
If only well-capitalized operators can maintain top performance, stake may gradually concentrate. That doesn’t mean the network fails. It just means the decentralization profile becomes thinner at the edges.
There’s a behavioral pattern here. Under competitive pressure, validators optimize for yield stability. They prefer chains with predictable issuance and growing activity. A new high-performance L1 has promise, but promise isn’t revenue. Until usage is visible, participation lags.
Which loops back to ecosystem gravity.
Liquidity flows toward execution reliability. Developers deploy where validators are strong. Validators commit where activity is visible. It’s circular.
Fogo’s bet, as I see it, is that performance can initiate that loop. That a smoother execution environment attracts enough application activity to justify validator investment. That hardware intensity doesn’t become a deterrent but a filter — selecting for operators who treat validation as serious infrastructure.
There’s a sharp line here that I keep circling: performance is not neutral; it chooses who can afford to participate.
If @Fogo Official leans hard into speed, it may produce a network that feels institution-ready — stable, predictable, low latency. That could be attractive for trading desks or real-time applications that struggle elsewhere.
But the trade-off is subtle. Accessibility narrows as performance tightens. The validator set may become more professionalized, less hobbyist. Some will argue that’s maturity. Others will see centralization risk.
I’m not fully convinced either way.
There’s also the question of exit dynamics. If validator hardware investments are significant, operators become sticky. High switching costs can strengthen alignment. But they also raise the barrier for new entrants, reinforcing concentration over time.

Again, speed sharpens edges.
Zooming out, Fogo sits in a competitive landscape where execution environments are converging. SVM compatibility reduces developer retraining. That’s smart. But consensus design and validator economics still differentiate networks.
And consensus is where performance pressure accumulates.
If #fogo finds the balance — fast enough to matter, accessible enough to remain credibly decentralized — it could position itself as a serious infrastructure layer rather than just another execution fork.
If it tilts too far toward raw throughput, it risks narrowing the validator base in ways that only become visible later.
Time makes these trade-offs obvious. Early on, everything looks healthy. Blocks are fast. Metrics look clean. Only gradually does concentration reveal itself, if it does at all.
I’m still unsure which way this bends.
High performance is attractive. No one complains about smoother execution. But performance isn’t just a feature. It’s a structural commitment that shapes who participates and who steps back.
And once that structure hardens, it’s difficult to reverse.
So maybe the real question isn’t whether #Fogo can be fast.
It’s whether it can be fast without quietly choosing its validators for them.
That tension doesn’t resolve quickly. It just sits there, underneath the benchmarks, waiting to show up in the distribution charts.

Globally, the cryptocurrency world is navigating a period of dynamic change marked by heightened regulatory scrutiny, institutional engagement, market volatility, and real-world use cases. After the dramatic rise and corrections of recent years, 2026 may be ushering in a new phase for digital assets — one that’s less explosive in price, but increasing in adoption and integration with traditional finance.
Market Recovery and Price Action
Bitcoin and other major tokens have recently shown renewed life after a period of volatility and investor caution. On February 26, 2026, Bitcoin experienced a notable rebound, climbing approximately 5 % to trade near $68,000, signaling a revival of investor sentiment driven largely by strong inflows into Bitcoin exchange-traded funds (ETFs). This suggests a degree of institutional confidence re-entering the market, even as retail participation remains subdued.
Elsewhere in the market, coupled with Bitcoin’s recovery, other blockchain assets such as altcoins have also rallied in recent sessions, supported by bargain buying and broader market rotation. However, volatility remains notable with occasional downswings — a reflection of macroeconomic influences and shifting risk appetites among traders.
Experts see this dynamic as part of a larger crypto cycle — with some analysts now suggesting that the deepest declines may be nearing their end, especially if traditional markets stabilize. A widely quoted strategist argues that the recent crypto sell-off could be entering its final stages, pointing to historical patterns and sentiment indicators.
Regulation Moves to the Forefront
One of the most transformative trends in 2026 is the increasing regulatory clarity and engagement by governments and financial authorities.
In the United Kingdom, a high-profile call for tighter controls around political crypto donations reflects worries about foreign interference and the anonymous nature of digital assets. Lawmakers urged ministers to consider a temporary ban on such donations ahead of elections, citing gaps in transparency and traceability.
Such discussions are mirrored globally as lawmakers grapple with how to balance innovation and security. While some U.K. authorities focus on political finance risks, other jurisdictions are moving forward with structured regulatory frameworks designed to integrate digital assets more tightly with financial systems.
In contrast, recent approval for a new national trust bank charter for Crypto.com in the U.S. highlights a regulatory environment that, at least in part of the world, is becoming more welcoming to crypto firms operating within traditional financial structures. This conditional approval allows the company to manage client assets and support trade settlement under federal oversight, a significant step toward mainstream acceptance.
Stablecoins and Payments Innovation
Stablecoins — digital currencies designed to maintain a stable value — continue to evolve. A pound-pegged stablecoin pilot led by fintech company Revolut in the UK exemplifies how digital assets are increasingly seen as tools for payments and settlement, not merely speculative tokens. The experiment explores use cases in payments, wholesale settlement, and crypto trading, although participation from major traditional banks remains limited.
Meanwhile, Circle Internet Group — the issuer of the widely used stablecoin USDC — reported strong earnings driven by rising demand for stablecoin use, even during periods of crypto price weakness. Investors reacted positively to Circle’s financial results, and the stablecoin’s circulation expanded significantly, reflecting confidence in this form of digital money amid uncertain markets.
Institutional Adoption and Exchange Developments
Institutional engagement continues to influence crypto’s trajectory. Exchange giants such as Binance are actively positioning themselves for regulatory compliance and expansion, including establishing a European base in Greece. With application progress under the EU’s Markets in Crypto Assets (MiCA) framework, this move highlights a broader industry push to operate within recognized legal boundaries and attract professional capital.
Similarly, Bitcoin-backed ETFs and spot crypto funds are garnering interest from institutional investors seeking regulated exposure to digital assets. This trend is seen as a key driver behind recent price rebounds and could shape how capital flows into crypto over the long term.
Crime, Fraud and Security Concerns
Not all developments are positive. Cryptocurrency’s pseudonymous nature continues to attract illicit flows, with recent reporting alleging that terrorist groups acquired $1.7 billion using Binance accounts tied to Iran — a reminder of the ongoing challenges regulators face in policing digital asset markets.
On the consumer side, dozens of individuals continue to fall victim to scams, including a recent high-value fraud case in India where a small business owner lost over ₹5.5 lakh after transferring funds to a fraudulent crypto platform. These incidents underscore the importance of education and vigilance in digital finance adoption.
The Future Landscape: Innovation and Integration
Beyond market moves and regulatory debates, the broader crypto ecosystem is evolving in technological and economic terms. Industry research and reports highlight several forces likely to shape 2026 and beyond:
Tokenization of real-world assets — blockchain’s ability to represent traditional assets digitally — is expected to gain momentum, potentially revolutionizing how securities, real estate, and even commodities are traded.DeFi (decentralized finance) and Web3 technologies continue advancing, introducing new financial products that operate outside traditional intermediaries.Institutional demand for blockchain infrastructure is increasing, not just for investment purposes but for settlement, identity services, and cross-border payments.
These trends suggest that even if token prices are choppy, the underlying technology and market infrastructure are maturing — setting the stage for broader adoption across industries and financial systems.
Conclusion: Crypto’s Inflection Point
In early 2026, cryptocurrency markets are far from settled. Price volatility, regulatory responses, fraud risks, and institutional engagement are all converging to reshape the landscape. What’s clear is that crypto is increasingly moving beyond a purely speculative asset class toward a broader infrastructure layer for digital finance.
As governments refine their approaches, and as institutions and innovators continue to build and invest, the future of cryptocurrency may well be defined not by price headlines but by integration, regulation, and real-world utility.

#JaneStreet10AMDump #MarketRebound #STBinancePreTGE #BitcoinGoogleSearchesSurge #Binance $BTC $ETH $BNB

最初は、$FOGO はただのスピードプレイだと思っていました。
高性能L1。
ソラナ仮想マシン。
並列実行。親しみやすいツール。
それは効率のアップグレードのように聞こえました。クリーンなブロックスペース。おそらく混雑が少なくなるでしょう。戦略的な変化よりも技術的な洗練。
しかし、これに座っている時間が長くなるほど、これがパフォーマンスの物語のようには感じられなくなります。
それは封じ込めの物語のように感じます。
具体的には、Fogoがインセンティブを十分に長く維持できるかどうか、それが耐久性のあるものに固まるまで。
活動を引き寄せることは一つのことですが、それを漏れ出させないことはまったく別のことです。

カッタック、2026年2月24日 — インドの暗号エコシステムに持続的な影響を及ぼす可能性がある展開として、オリッサ高等裁判所は当局に暗号通貨の法的地位を明らかにするよう求め、デジタル資産取引に関連するとされる凍結された銀行口座に関してバランギル地区の警察署長（SP）を召喚しました。
この件は、地方警察によって凍結された銀行口座を持つ個人が提出した複数の請願の公聴会中に裁判所に持ち込まれました。請願者によれば、口座は取引または送金の疑いでブロックされました。彼らは、このような行動には明確な法的裏付けが欠けており、インドにはまだ暗号通貨の地位を定義する包括的な法律がないと主張しています。

派手で目立つ感じではなく、静かに。実際に何をしているのか、そしてなぜその道を選んだのかを理解しようとしています。
$FOGO はレイヤー1のブロックチェーンです。しかし、そのフレーズだけではもはやあまり言うことがありません。L1はたくさんあります。皆がスピードを主張し、皆がスケールを主張しています。しばらくすると、あなたは言葉に反応するのをやめ、下の構造を見るようになります。
Fogoで際立っているのは、Solanaバーチャルマシン — SVMを使用していることです。そして、そこから物事が興味深くなります。

私は最初の反応が軽視的だったことを認めます。

別のレイヤー1。また別のパフォーマンス主張。すでに構造的に混雑している市場でスペースを切り開こうとする別の試み。

しかし、

は仮想マシンを再設計しようとしているわけではありません。それはSolana仮想マシンに寄り添っています。それはフレームを少し変えます。新しい実行ロジックを発明するのではありません。すでに速く動けることが証明されたものに倍加しています。

それでも、単独の速度はもはや希少ではありません。注意が希少です。流動性が希少です。開発者の焦点が希少です。したがって、実際の質問はFogoが取引を迅速に実行できるかどうかではありません。実行効率が自らの重力を生み出せるかどうかです。

それは決定から始まるように感じます。

大きな声ではありません。ただ、すべての後に続くものを静かに形作る技術的な選択です：それはSolana仮想マシンを使用します。

最初は、それはスキップするはずの詳細のように聞こえます。実行環境。仮想マシン。インフラストラクチャ言語。しかし、そこで一時停止すると、この一つの決定が全体のトーンを定義することが明らかになります。
なぜなら、仮想マシンは単なるソフトウェアではないからです。それは計算がどのように動作すべきかに関する一連の前提です。
そして、Solana仮想マシンは非常に特定の何かを前提としています：トランザクションは列に並ぶ必要がありません。

最初の反応は通常、速度についてです。スループット。ベンチマーク。そのようなこと。
しかし、それをしばらくの間考えていると、より興味深い部分は生のパフォーマンスではないように感じます。最初にSolana仮想マシンを使用するという決定です。
ネットワークについては、通常、実行する環境によって多くのことが分かります。仮想マシンは単なる技術的な詳細ではありません。開発者の考え方に影響を与えます。プログラムの挙動に影響を与えます。構築する際に自然に感じることに影響を与えます。