F I N K Y

Because “faster” in crypto is a suitcase word. People stuff different things into it—block cadence, confirmation speed, how quickly your swap feels “done,” how the chain behaves when it’s busy—then zip it up and pretend it’s one clean metric. The trick is that you can make almost any chain look heroic if you get to choose what goes inside the suitcase.

Fogo’s headline number usually shows up alongside another number that’s easier to grab onto: 40 milliseconds. The project and launch coverage repeatedly frame Fogo around “40ms blocks,” and then connect that to the “up to 18x faster” claim.

So, is the claim false?

Not in the simple “they made it up” sense. Fogo really does publicly anchor itself to the idea of extremely short block times, and “up to” gives them room to describe a best-case scenario without promising you’ll live in that best case every day.

But is the claim clean? Is it the kind of comparison you can take literally?

That’s where it starts to wobble.

Solana’s own developer documentation talks about slots (the rhythm at which block production happens) being configured around ~400ms, with some fluctuation. If you do the simple math most readers assume they’re being invited to do—40ms versus 400ms—you get 10x, not 18x.

So how do people end up saying 18x?

There are a few perfectly legal ways to land there without ever writing something technically “wrong.” You can compare to the slower end of Solana’s fluctuation range. You can compare a lab-like target to a real-world average. You can quietly shift the meaning of “faster” from “slot cadence” to “confirmation experience.” Or you can just let the “up to” do its job: it signals a maximum, not a typical outcome.

That’s why the claim spreads so well. It’s not pinned to one measurement method, so it’s hard to pin down and disprove.

The more interesting part is how Fogo is trying to make 40ms plausible in the first place.

Most chains run into a boring, unavoidable limit: distance. Consensus is messages flying between validators, and messages have to travel through fiber across continents. You can write the fastest software in the world and still lose to physics if your key nodes are far apart.

Fogo doesn’t pretend that problem doesn’t exist. It leans into a solution that’s blunt: keep consensus-critical validators close to each other.

In Fogo’s architecture materials, validators are grouped into geographic “zones,” and the ideal version of a zone is described as a single data center—close enough that network latency is tiny and predictable. The whole point is to get consensus messages moving fast enough that sub-100ms blocks aren’t a fantasy.

That’s the part most “18x” quotes don’t mention. Fogo isn’t saying “we beat Solana while playing the same game on the same field.” It’s trying to win by shrinking the field.

And that changes what the comparison really means.

If a chain gets low latency by encouraging co-location and tighter operational constraints, you’re not just comparing software quality. You’re comparing tradeoffs.

It’s like comparing two delivery services where one says, “We’re 10x faster,” and then you realize their drivers only operate inside one neighborhood while the other one covers the entire city. The speed can be real. The comparison can still be misleading.

There’s another reason “40ms blocks” doesn’t automatically translate into “you feel 18x faster.”

Users don’t experience block intervals directly. Users experience:

how quickly their transaction gets picked up,whether it gets dropped or delayed under load,how many confirmations they wait before they relax,whether the infrastructure they’re using (RPCs, relayers, validators) becomes the bottleneck.

A chain can print tiny block intervals and still give you a mediocre experience if the network is congested or the transaction path is messy. That’s why serious Solana infrastructure discussions focus on things like confirmation/commitment behavior, not just slot timing.

And Fogo is early enough that the hardest test—stress, volatility spikes, adversarial traffic—hasn’t had years to write the chain’s real reputation yet.

Even in early mainnet coverage, you can see the gap between “block time” and “what’s actually happening on-chain.” Reporting around launch mentioned 40ms blocks, but also cited throughput numbers for early apps that are solid yet nowhere near the kind of “blow everything away” mental image casual readers attach to the slogan.

So here’s the most human, non-hype answer:

If someone hears “18x faster than Solana” and imagines a simple promise—every action you take will settle eighteen times quicker than it does on Solana, under the same conditions, for everyone worldwide—that’s not a fair interpretation. The claim isn’t defined tightly enough for that, and the underlying design choices (zones, co-location, operational constraints) mean it’s not an apples-to-apples race anyway.

If you interpret the claim the way the fine print wants you to—in the best case, with a topology designed around low-latency zones, we can run block intervals far shorter than Solana’s nominal slot cadence—then it’s not inherently false. It’s just narrower than the slogan sounds.

What would actually settle this, in a way that’s worth trusting?

Not more tweets. Not more multipliers.

You’d want to see real, boring evidence over time: does Fogo keep those low block intervals during heavy usage? Does inclusion latency stay tight when blocks fill up? Are reorgs rare and clearly communicated? Do zones expand in a way that doesn’t quietly turn “decentralization” into a small club of co-located operators?

#fogo @Fogo Official $FOGO

When I went looking for Vanar, I expected the usual trail: a token page, a couple of glossy diagrams, maybe a whitepaper that reads like it was written for nobody in particular. What I didn’t expect was how quickly it turns into a very ordinary, very human story about friction—how projects change names, move tokens, publish new promises, and then spend months dealing with the unromantic consequences.

The first solid thing you can grab isn’t a slogan. It’s a set of network parameters. Vanar’s docs tell you exactly how to connect: Chain ID 2040, a public RPC endpoint, a websocket endpoint, and the official explorer. If you’ve ever onboarded a new EVM network, the steps feel familiar—copy, paste, connect, hope the RPC doesn’t time out. (docs.vanarchain.com)

That matters because it separates two categories of crypto projects: the ones that exist mainly as narratives, and the ones that at least have working plumbing. Vanar’s chain is live. There’s an explorer. Blocks tick up. Transactions are there to be inspected. (explorer.vanarchain.com)

And then you see the numbers, and they’re… confrontational. Nearly 194 million transactions. Around 28.6 million wallet addresses. Close to 8.94 million blocks. Those totals aren’t subtle. They’re the sort of figures that make you pause, because you can’t tell at a glance whether you’re looking at genuine scale or a very efficient machine generating activity. Both are possible. The explorer confirms volume; it doesn’t automatically explain the nature of that volume. (explorer.vanarchain.com)

At this point, most projects try to pull you back into the pitch: “AI,” “infrastructure,” “stack,” “future.” Vanar does that too, but if you keep poking around, the tone shifts. You start running into the project’s older life.

Vanar didn’t start as Vanar.

In late 2023, the team announced a clean token migration: TVK becomes VANRY, one-to-one. No complicated ratios. No “rebasing.” Just a swap. It’s written plainly in Vanar’s own announcement. (vanarchain.com) The swap portal repeats the same message, because portals like that exist for one reason: reduce the chance of confusion turning into anger. (swap.vanarchain.com)

If you’ve watched enough of these migrations, you know the emotional arc. A token swap looks tidy in a blog post. In the real world it creates a long tail of problems: people who missed deadlines, people holding tokens in places that don’t support the swap, people who swear they did everything right and still ended up stuck.

That last category is why the story doesn’t stay inside official channels. You can find users on public forums describing confusion around the TVK→VANRY process and trying to troubleshoot what went wrong. It’s not a smoking gun; it’s more like the sound of a support queue spilling out into the open. (reddit.com)

That kind of friction is the opposite of what crypto likes to market. But it’s the part that tells you the most about a project’s maturity. Anyone can write a new roadmap. Not everyone can handle the messy cleanup when users show up late, confused, and suspicious.

So what did Vanar become after the migration?

This is where the story splits into two voices: the marketing voice and the engineering voice.

The marketing voice is about a layered system—Vanar presents itself as an AI-oriented platform with multiple components stacked above the base chain, with language around semantic memory and reasoning modules. (vanarchain.com) The subtext is clear: this isn’t just “another chain,” it’s supposed to be a place where more complex applications can live.

The engineering voice, though, is quieter and more revealing. On GitHub, Vanar’s blockchain client is described as EVM-compatible and explicitly a fork of Geth—Ethereum’s widely used Go client. (github.com)

That one detail changes how you should interpret everything else.

A Geth fork is not a flex. It’s a decision. It’s the project saying: we’re not reinventing the execution model from scratch; we’re taking something proven and modifying it. That can be smart. It also means the core reality of the chain is going to feel Ethereum-shaped: accounts, gas, transactions, familiar tooling—unless the team has gone out of its way to alter fundamentals.

Vanar’s own docs lean into that pragmatism. They make the case for EVM compatibility as a practical choice, not a philosophical one. (docs.vanarchain.com)

So where does the “AI chain” idea fit?

Here’s the honest version: when crypto projects say “AI-native,” they’re often describing one of two things.

Either they’re putting AI compute off-chain and using the chain to anchor results—proofs, attestations, hashes, state commitments.

Or they’re using “AI” to describe developer tooling and services layered above the chain—SDKs, data systems, orchestration frameworks—things that might be valuable, but aren’t “the blockchain thinking.”

Vanar’s public materials talk about richer data and more structured storage “onchain,” even positioning itself as an alternative to relying on external file layers. (vanarchain.com) That’s a strong claim. Strong claims have a predictable problem: the moment you bring them into a serious review, somebody asks what the chain actually stores, how quickly state grows, who pays for it, and what it does to validator requirements over time.

Those aren’t gotcha questions. They’re basic survival questions.

And they’re the reason so many “bigger than a chain” narratives stall out. Not because the vision is wrong, but because the proof is either unclear, or it lives in proprietary components that can’t be independently evaluated.

The token side of the story is more concrete, but it has its own awkward edges.

Vanar’s whitepaper states a maximum supply of 2.4 billion VANRY, with an initial supply tied to the swap and additional issuance via block rewards until the cap is reached. (cdn.vanarchain.com) Market trackers repeat the cap figure; CoinMarketCap lists a 2.4B max supply, and shows circulating supply near 2.29B at the time of access. (coinmarketcap.com) CoinGecko likewise lists 2.4B. (coingecko.com)

That consistency is good. But it also implies something that isn’t usually said out loud: if most of the cap is already circulating, future “tokenomics excitement” is limited. You don’t get to keep telling the market “wait until supply unlocks” or “wait until emissions start” when the supply story is already largely written.

So the project has to win on something else: actual usage, real developer adoption, applications that generate fees and make staking feel like something other than ceremonial.

Which brings us back to the explorer numbers—the nearly 194 million transactions and 28.6 million addresses. (explorer.vanarchain.com)

Those stats can be read two ways:

If they’re organic, Vanar has traction most mid-tier networks would envy.

If they’re inflated by incentives or automation, they’re a warning sign: the chain can look busy without producing lasting demand.

The uncomfortable truth is you can’t settle that debate with a press release. You settle it by watching patterns: fees, contract activity, repeat users, application diversity, and whether usage persists when nobody’s being paid to click buttons.

That’s why Vanar, to me, reads less like a fairy tale about AI and more like a project trying to push an idea through an unforgiving filter.

The idea is simple to say: blockchains should host more of what applications actually need data, workflows, logic—without falling apart.

The filter is harsh: cost, state growth, decentralization pressures, reliability, developer experience, and the endless reality of migrations and support.

Vanar has cleared one bar that matters: it shipped a live network with public endpoints, a chain ID, and enough on-chain history to be examined. (docs.vanarchain.com) It also carries the baggage that shipped projects carry: token swaps, confused holders, and a past identity that doesn’t disappear just because the logo changed. (vanarchain.com)

If you want a neat conclusion—“it will succeed” or “it will fail”—you won’t get one honestly. What you can say, without pretending, is this:

Vanar is real enough to be judged on execution rather than imagination. And the part of its story that still hasn’t fully proven itself is the part it talks about the most: whether the “AI stack” is a measurable, defensible capability—or a collection of branded layers that sound better than they audit.

#vanar @Vanarchain $VANRY

The first time I watched Fogo do its thing, the setup was almost boring. A team had a program they were sure was “parallel enough.” They weren’t naïve—they knew the SVM model only runs transactions side-by-side when the write-sets don’t overlap. Still, they assumed they’d done the obvious work: split state, avoided obvious bottlenecks, kept the hot paths lean.

Then they turned the dial.
At low load, everything looked clean. Under heavier traffic, it started lying to them—quietly at first. Throughput climbed, then hit a ceiling that didn’t match what the hardware should have been able to do. Latency didn’t drift up gradually; it stabbed upward in sudden spikes and then fell back down like nothing happened. The program didn’t crash. It just behaved like a single-lane road pretending to be a highway.

That’s where Fogo’s personality shows up, and it’s not about the block-time number people like to quote. It’s about what very short blocks and a ruthlessly performance-oriented validator stack do to your excuses. On slower networks, you can hide a lot of bad habits behind “congestion.” Users feel friction, sure, but it’s hard to tell whether the chain is saturated or the app is poorly designed. Blame gets smeared across the whole system, which is convenient for everyone involved.

On a network built to minimize latency, the fog lifts fast. The system becomes annoyingly honest. If your app can’t stay parallel, you see it immediately, and you can usually point at the exact place where parallelism dies: a writable account too many transactions are forced to touch.

This sounds like a small detail until you’ve lived inside it. Parallel execution isn’t something you “have.” It’s something you keep, transaction after transaction, by not making unrelated actions collide over the same mutable state. The runtime is basically saying: “Tell me what you will touch. If you touch the same thing as someone else, you’re going to wait your turn.

A lot of teams lose that bargain for reasons that are completely understandable. Centralizing state feels responsible. It’s tidy. It makes invariants easy to reason about. One global config account. One counter. One accumulator. One place where you know the truth lives.

And then traffic arrives, and your one place for the truth becomes the one place everyone has to line up to edit.

You see the same shape across totally different apps. A trading program that insists on touching a global config account on every swap because it’s safer. A lending market that updates the same interest accumulator every time someone borrows, repays, or liquidates, because exactness feels comforting. A game that increments a global match ID counter whenever anyone creates a room, because it’s the simplest way to guarantee uniqueness.

None of these choices scream bad engineering in a code review. They’re the kinds of shortcuts smart teams make when they’re trying to ship. The problem is that SVM-style concurrency doesn’t forgive shortcuts that concentrate writes. If two transactions need the same writable account, they don’t run together. Doesn’t matter how many cores validators have. Doesn’t matter how fast the client is. Doesn’t matter how short the block is. You’ve created a choke point and you’re now paying rent on it.

Fogo amplifies that rent because of how it’s built. It’s not shy about chasing low latency. Its documents talk about zones—validators organized into geographic clusters with a rotating active zone—explicitly to cut round-trip time. It leans on a Firedancer-derived client lineage to push validator performance. It also embraces a fee structure that includes priority fees, which means users can compete to get earlier ordering when things are tight.

Those design decisions aren’t just “infrastructure choices.” They change the emotional experience of using the chain, and they change what users blame when something feels slow.

If you build an app that forces contention, you’ve effectively created a scarce resource inside your own program: access to a handful of hot accounts. Now throw priority fees on top. Suddenly the cost of “bad state layout” isn’t just lower throughput. It’s a visible tax users pay to fight for the single lane you accidentally built.

That’s what makes the phrase parallel execution isn’t free feel less like a slogan and more like a warning label. The chain can be fast, but the app can still be the thing that serializes everything. When that happens, the chain’s speed doesn’t rescue you—it just exposes you faster.

There’s another uncomfortable part people tend to skip: performance-oriented networks often end up taking a position on who gets to be a validator. Reports around Fogo have described a curated validator posture and a willingness to exclude under-provisioned nodes to keep the system tight. You can argue that’s practical—low latency networks don’t want their median participant dragging everyone down. You can also argue it concentrates power. Both can be true. If you care about censorship-resistance or open participation, “curation” isn’t a neutral word.

What’s striking is that Fogo doesn’t look like it’s trying to dodge those trade-offs with vague rhetoric. Its more formal materials describe the token and the system in almost sterile terms: pay for execution, compensate validators, stake to secure the network, inflation that trends down over time, and explicit disclaimers about what token holders do not get (no ownership, no profit claim). It reads like a document written by someone who expects regulators and skeptics to be in the room.

The consequence of that framing is simple: the token’s story is tied to the network being used for execution and staking. Not vibes. Not identity. Not “community.” Execution. If Fogo’s edge is latency, then it needs applications that genuinely care about latency. And those applications, by definition, will care about the ugly details—tail latency, contention, and whether the system stays parallel under real user behavior.

That brings you right back to state layout, because state layout becomes the real differentiator in whether teams can actually extract value from a low-latency SVM chain. It’s the part nobody can hand-wave once the chain is fast enough.

I keep thinking about that original stress test, because it wasn’t dramatic. It was almost mundane. A graph that looked wrong. A program that refused to scale the way its authors expected. And then the slow realization that it wasn’t the network at all—it was a few accounts that everyone had to touch, over and over, because the program’s internal bookkeeping demanded it.

That’s the real story hiding inside Fogo. Not that it’s fast. Plenty of projects chase speed. The story is that it turns speed into a kind of lie detector. If your app is structured in a way that collapses parallelism, you find out immediately. Not months later, after the architecture is fossilized. Not after your users have learned to tolerate a worse experience. Immediately, while the problem is still sitting there in plain sight: a bad state layout making your parallel runtime behave like a single thread.

#Fogo @Fogo Official $FOGO