AH CHARLIE

Last year I helped a friend move a small shop from one street to another. Same city. Same tools. Still, it was chaos. The fridge didn’t fit the new door. The power sockets were on the wrong wall. The best worker showed up late. And the whole day ran at the speed of the one missing wrench. That’s the boring truth of “migration” in any system. You don’t fail because the plan is bad. You fail because the little mismatches stack up. One odd part. One slow handoff. One weak link.
Crypto teams learn that the hard way. “We’ll just port it” sounds easy. Then you hit the real world: different runtime rules, different tooling, different fee quirks, different node behavior. You can keep the code. You still lose time to glue. That’s where Fogo’s SVM-native idea matters. Not because it’s magic. Because it’s less glue. Less translation. Less “why is this acting weird here?”
SVM-native, in plain words, means the chain is built to run Solana-style programs and the Solana-style runtime model without pretending it’s something else. Think of it like moving your shop into a building that already has the same door size, the same shelf rails, the same power load, the same safety checks. You still have to carry boxes. But you’re not rebuilding the walls as you go. Migration becomes closer to “copy and verify” than “rewrite and pray.”
Most migrations fail on small stuff, not big stuff. One example: account layout and state handling. On SVM, programs talk to accounts in a very specific way. It’s rigid on purpose. Rigid can be annoying. But it also means behavior is easier to predict. If Fogo stays true to that model, dev teams aren’t guessing how state reads, writes, and access rules might change after the move. Less hidden drift. Less time chasing phantom bugs that only appear under load.
Another example: tooling and dev habits. A lot of builders already have pipelines for SVM programs. They have test rigs, audit patterns, monitoring hooks, and battle scars. If the target chain speaks the same “engine language,” teams keep more of that muscle memory. That’s not hype. That’s plain ops math. Every hour not spent re-learning basic deployment flow is an hour spent fixing what actually matters, like edge cases and safety.
Now The Second Part. The One People Ignore Until It Bites Them. The “weakest Link” Problem.
In a distributed network, performance isn’t the top speed of the best validator. It’s the steady speed of the slow ones that still sit on the critical path. If some validators are fast and some are laggy, the chain doesn’t glide. It stutters. Blocks may still land, but latency gets messy. Confirmation feels uneven. Fees can spike for dumb reasons. Users blame the app. Devs blame the chain. Meanwhile it’s the same old story: the network moves like a convoy, not a race car.
Validator variance is the silent killer. It comes from hardware gaps, bad tuning, flaky disks, weak networking, overloaded CPUs, and operators who treat running a node like running a hobby server. On some designs, you can “have decentralization” and still end up with a network that behaves like a group project where half the team forgot the deadline. The spec says one thing. Reality says another.
So what does it mean for Fogo to “solve” validator variance? If we strip the marketing word “solve,” we get a real engineering target: make the system less sensitive to uneven operators, and make performance more uniform under the same rules. Uniform doesn’t mean fast. It means consistent rather than theoretical.
THERE ARE A FEW WAYS AN SVM-NATIVE ARCHITECTURE CAN HELP HERE, IF IMPLEMENTED WITH DISCIPLINE.
First, deterministic execution. Deterministic just means “same input, same output.” Boring, yes. Powerful, also yes. If program execution is deterministic and the runtime rules are strict, validators don’t get to “interpret” transactions differently. Less divergence. Less rework. Less wasted cycles. When nodes agree more easily, the system spends less time resolving conflicts and more time just moving forward.

Second, parallel execution with guardrails. The SVM model is known for parallel processing, but it isn’t free. It depends on knowing which accounts a transaction will touch. That’s like labeling boxes before you load the truck. If you label well, you can load many boxes at once without crushing anything. If you label badly, workers collide and you slow down. An SVM-native chain can enforce those labels and the scheduling logic tightly. That can reduce chaos when the network is busy, which reduces the “some validators handle it, others choke” pattern.

Third, predictable resource costs. This is a big one. If the system can estimate compute and bandwidth costs in a stable way, operators can size hardware and tune nodes with less guesswork. Predictable costs also mean fewer surprise overload events. Overload events are what create variance in the first place. When half the set is fine and the other half is swapping memory like it’s 2009, user experience turns into dice rolls.

Fourth, standardization of the validator stack. People hate this topic because it sounds like central control. But standardization isn’t always bad. If the chain’s core software, config expectations, and monitoring patterns are clear, node operators converge toward a known-good setup. You don’t get a zoo of “I compiled it with these flags because I saw a tweet.” Less zoo equals less variance. The chain still needs real decentralization. Sure. But decentralization doesn’t mean letting everyone run random broken builds.

Uniform performance shows up in market structure. Not price. Structure. If confirmations are steady, MEV games are harder to hide behind jitter. If latency is stable, apps can design better flows. If block times are consistent, users stop spamming clicks. The chain becomes usable in a calm way. Minimal drama. That’s the goal.
Now, I’m not going to pretend SVM-native automatically equals seamless migration or uniform performance. Any team can copy an interface and still botch the ops layer. The weak link problem never fully goes away. It gets managed. It gets measured. It gets punished when operators don’t meet standards. If Fogo wants “uniform,” it needs blunt benchmarks, transparent node health metrics, and real incentives that reward steady uptime and low latency, not vibes.
SVM-native is a strategic advantage only if it reduces friction for real builders and reduces variance for real operators. If it becomes a compatibility story without boring reliability work, it’s just another slogan. But if it stays tight on runtime rules, keeps execution predictable, and treats validator variance as a first-class enemy, then yeah… migration can be less painful, and performance can feel consistent instead of lucky.
That’s the bar. Not “fastest chain.” Not “next era.” Just a network that behaves the same on Tuesday as it did on Monday, even when the crowd shows up. That’s rare. And it’s worth taking seriously.
@Fogo Official #fogo $FOGO

@Vanarchain • #Vanar • $VANRY
Last month I did something dumb. I signed up for a “free” AI tool to clean up a voice note. Two taps. Easy. Then it asked for access to my files “to improve results.” I clicked yes… because I was in a rush. Later that week, an ad popped up using words from that private note. Not the full line. Just enough to make my stomach drop. And I remember thinking, well… this is the trade I keep making. I keep renting my own life to systems I don’t control.
AI is not just chat bots and cute filters. Real AI work is data. Health logs. Payroll files. Call center clips. Product plans. Supply chain docs. And most of that data is not meant to be public. So when people say “AI-ready blockchain,” my first question is simple: where does the data live, who can see it, and what stops a leak when a thousand apps start poking it?

A lot of chains were built for open state. Everyone sees everything. That’s fine for token moves. It’s terrible for AI inputs. If your chain can’t handle privacy in a normal way, it’s not “AI-ready.” It’s “AI-curious.” Big diff.
Vanar Chain (VANRY), at least in how the ecosystem talks about it, is pushing the idea that privacy is not a bolt-on. It’s part of the base deal if you want serious apps. When you bring AI into Web3, you’re not just running code. You’re moving raw human data around. If you treat that like public mempool gossip, you’ll get what you deserve. Broken trust. No real firms. No real users.
Think of data like water in a city. The chain is the pipe system. Most pipes in crypto are clear plastic. Anyone can look in and see what’s flowing. That’s “transparent.” Also creepy. For AI use cases, you need pipes that can carry water without the whole street watching. Privacy is not about hiding crime. It’s about not turning daily life into a public feed.
Let’s Talk About What Data Privacy Even Means Here, Without The Fog. On A Chain, Privacy Usually Means One Of A Few Things.
One: you don’t store sensitive data on-chain at all. You store it off-chain, and only store proof or reference on-chain. A “proof” is like a stamped receipt. It says, “This is true,” without showing the whole document. That matters for AI because you often don’t need the full file on-chain. You need to know the file wasn’t changed. You need audit, not exposure.

Two: you use encryption. That’s just scrambling data so only the right key can read it. The chain can hold encrypted blobs, but the public can’t read them. Sounds easy, but it gets messy fast. Who holds keys? Users? Apps? A company? If keys get sloppy, privacy collapses. This is why “AI-ready” is also “ops-ready.” Fancy math won’t save weak key handling.

Three: you use privacy compute. That can mean things like “zero-knowledge proofs.” Simple version: you can prove you followed rules without showing the private inputs. Like proving you’re over 18 without showing your full ID card. For AI, this can extend to proving a model used allowed data, or that output came from a specific model version, without dumping the data itself. Hard to build. But it’s the direction serious systems move in.

Now, why does Vanar care? Because AI apps are messy. They don’t act like simple swaps. They want big files, repeated access, and rights control. And they bring new risks: model leaks, prompt leaks, data reuse, and silent training on user content. If a chain wants to be a home for AI workflows, it has to make privacy boring. Not magical. Just normal. Minimal drama. Consistent rather than theoretical.
Here’s a real example pattern. A firm wants to run AI on internal docs. They can’t put those docs on a public chain. But they might want a public audit trail that shows: who requested access, when, under what rules, and whether the doc was changed. That’s where an “AI-ready” chain can help. The chain becomes the rulebook and the receipt printer. The data stays protected. The actions stay accountable.
That’s the core: privacy plus audit. If you only have privacy, you can’t prove anything. If you only have audit, you expose everything. AI systems need both. And Vanar’s messaging leans into this combo. Privacy as a guardrail. Audit as a spine.
There’s also a simple market structure reason. AI apps die if users don’t trust them. Not “I like the UI” trust. Real trust. The kind where you will upload a contract draft or a medical report. Most people won’t do that if they think the chain is a glass house. So privacy is not a “feature.” It’s the entry ticket.
But Let’s Not Pretend It’s Free.
Privacy on-chain often fights performance. Encrypting, proving, and controlling access adds cost and delay. AI workflows already cost compute. So the design has to be practical. You don’t want a system that’s secure in theory but unusable in practice. This is where a lot of projects talk big and ship small. The real test is boring stuff: how keys are managed, how permissions are revoked, how logs are stored, how data moves between apps without leaking.
Another issue is privacy can hide bad behavior. That’s a real concern. If everything is private, monitoring fraud gets harder. The answer is not “make everything public.” The answer is good policy layers. Fine-grained access. Selective disclosure. Audit trails that show actions without dumping private content. That balance is the whole game.
The win condition is not “maximum privacy.” It’s “right privacy.” Enough to protect users and firms. Enough to make AI workflows viable. While still giving the ecosystem a way to verify rules were followed. If Vanar Chain (VANRY) can make that feel standard like seatbelts in a car then it’s meaningful. If it stays as a slogan, it won’t matter.
Personal opinion i think data privacy is the difference between AI being a toy and AI being infrastructure. Chains that ignore it will get stuck hosting public, low-stakes apps. Memes, simple games, maybe basic social posts. That’s not an insult. It’s just the limit of open state.
If Vanar Chain (VANRY) wants to be taken seriously in AI-ready talk, it needs to keep doing the unsexy work: privacy primitives that devs can actually use, clear tooling, clear rules, and clean integration paths for off-chain storage and compute. And it needs to be honest about tradeoffs. Latency. Cost. Complexity. No pretending those vanish.

Because the future isn’t “AI on-chain” in some pure form. The future is hybrid. Private data off-chain, verified actions on-chain, and AI models that can prove what they did without spilling the inputs. That’s the architecture that scales trust. That’s what “AI-ready” should mean. If Vanar keeps aiming at that, privacy stops being marketing. It becomes the reason real users show up.

Last month I tried to test an “AI on-chain” demo from a random project. It looked fine… until I asked a simple question: where does the model’s memory live? Not “where is the prompt.” Memory. The bits that make an agent behave like it learned something yesterday. The answer was the same old stack in a new hoodie. Compute off-chain. Memory off-chain. Logic off-chain. The chain was just a receipt printer. That’s the gap Vanar Chain (VANRY) is trying to close. Not with magic. With a design that treats AI apps like they have three hungry needs: fast actions, durable memory, and rules that can be checked. Vanar calls itself an AI-native Layer 1 and builds a “stack” around that idea chain, memory layer, and logic layer working as one system.

AI apps don’t fail on compute. They fail on memory.
Most people think AI is “the model.” In real use, the model is the mouth. The hard part is the brain’s messy desk. An AI agent needs to store notes, fetch them later, and prove what it used. If it can’t, you get a bot that sounds smart but forgets everything. Or worse, a bot that changes its story and you can’t audit why. Vanar Chain (VANRY) describes Neutron as a semantic memory layer that compresses data into “Seeds” that are small enough to store on-chain, while staying searchable and usable for AI-style queries. Think of it like turning a full book into a tight index card… but the index card still knows where the facts are. Not the whole book, but the map to it. If that sounds abstract, picture an insurance workflow. A claim comes in with photos, text, maybe a short video. Classic Web3 can store a hash and point to IPFS or a server. That’s a “trust me bro” pointer if the storage breaks, moves, or gets gated. Vanar’s pitch is: compress what matters into something that can live directly in the chain’s own storage path, so the “memory” doesn’t vanish when a server bill doesn’t get paid. For AI across industries, that memory layer is not a cute feature. It’s the difference between: an agent that can only react right now, and an agent that can keep state, keep evidence, and be checked later. And yes, I’m aware “on-chain data” can get expensive fast. The point isn’t to shove raw files everywhere. The point is to store useful, verifiable, compact memory artifacts that AI systems can read without begging an off-chain database for permission.

“AI logic” needs rules, not vibes
AI agents are great at producing answers. They’re bad at being accountable.
In industry, you don’t just want output. You want guardrails. You want to ask: Did it follow the rule? Did it use allowed inputs? Did it trigger a payment only after checks passed? Vanar Chain (VANRY) frames “Kayon” as an on-chain AI logic engine that can query stored data and apply policy or compliance logic. In plain words: it’s meant to be the rulebook the agent can’t ignore. If Neutron is the memory drawer, Kayon is the clerk that only stamps a form when the boxes are filled. Now connect that to real sectors. In payments (PayFi), an AI agent might route a payout, split revenue, or manage subscriptions. But it should not move money because a prompt said “sounds good.” You want deterministic checks. KYC flags. Limits. Time locks. Proof that certain conditions were met. Vanar positions itself around PayFi and tokenized real-world assets, where automated logic needs to be verifiable and boring in the best way. In supply chain, the AI part might forecast demand and propose orders. The chain part should verify provenance, approvals, and the “why” behind actions. So when someone audits a bad call, you don’t get a shrug. You get a trail. Then In media and gaming, AI tools generate content fast. The ugly issue is ownership and reuse. If an AI model pulls from licensed content, you need proof of rights and revenue splits that run without a studio trusting ten middlemen. An on-chain memory-plus-logic stack can at least support the ledger side: what was used, who owns what, and what gets paid. This is the key in AI-based industry apps, the chain is valuable only when it reduces disputes. Vanar Chain (VANRY) aims at that dispute layer memory, policy, and settlement working together rather than pretending speed alone solves everything.

Industry support is mostly plumbing
When projects say “supports AI across industries,” I usually hear fluff. Real support looks like three boring things: developer compatibility, predictable costs, and a clean path to production. VANRY presents itself as an EVM Layer 1. That matters because EVM is the most used smart contract environment. It lowers the friction for teams who already know Solidity and the existing tooling. Not glamorous. Very practical. Then there’s the “stack” framing: Vanar Chain as the transaction layer, Neutron as memory, Kayon as logic, and other modules in the roadmap. I treat this like a factory line. If each station is separate (random L1 + random storage + random AI service), you get integration debt. Everything breaks at the seams. If the chain stack is designed to fit together, the seams are at least intentional. Across industries, that seam work is where most pilots die. A hospital group might want AI-assisted record handling, but they’ll demand audit logs, strict access controls, and proof that records weren’t tampered with. A bank exploring tokenized assets will demand compliance logic that can be explained to regulators. A logistics firm will demand proof that events happened when they did, without someone editing history. The industries differ, but the plumbing repeats: store evidence, run rules, settle outcomes. Vanar’s approach tries to make that repeatable by default: structured storage for AI-style retrieval, on-chain logic hooks, and a base chain for settlement. If it works as described, it turns “AI + blockchain” from a slide deck into a set of components teams can actually wire into products.

Personal Opinion
I don’t care if a chain calls itself “AI-native.” I care if it reduces the amount of off-chain hand waving. Vanar Chain (VANRY) design focus memory and rules baked into the stack is pointed at a real failure mode in today’s agent apps: they can’t prove what they know, and they can’t prove they followed policy. If Vanar’s Neutron/Kayon pieces deliver real developer ergonomics and real cost control, that’s meaningful. The risk is also plain. AI buzz attracts sloppy builders. And “store more on-chain” can turn into a cost spiral if the compression and query story doesn’t hold up under real workloads. So I’d watch for boring signals: working docs, repeatable demos, teams shipping apps that survive more than one marketing cycle. If you want one clean mental model, it’s this: Vanar is trying to be the place where AI agents can keep memory, follow rules, and settle outcomes without leaning on fragile off-chain glue. That’s not hype. That’s a claim you can test.
@Vanarchain #Vanar $VANRY

I tried to “feel” chain speed, I did a dumb test. I sent the same tiny swap on two networks while sitting in a noisy cafe, phone on weak Wi-Fi, and I counted in my head. One… two… three… The trade was “done,” but my screen still looked unsure. That gap between done on paper and known in the real world is where most consensus talk gets weird. Fogo (FOGO) starts from a blunt idea: you can’t vote your way around physics. Light in fiber is fast, sure, but not magic-fast. Signals move around ~200,000 km per second in fiber, and real routes bend with cables, peers, and traffic. That’s why cross-ocean round trips can sit around ~70–90 ms, and New York to Tokyo can be ~170 ms on a good day. And most consensus needs more than one message hop. So the “speed” you feel is mostly distance and delay, not some clever math trick. Fogo’s litepaper even says it straight: latency isn’t a nuisance. It’s the base layer. Here’s the part people miss. It’s not just average delay. It’s the slow tail. In plain terms, the whole group moves at the pace of the slowest kid in the line. Fogo calls this out: in big systems, tail latency is the enemy, and the critical path is set by the slowest parts you must wait for. If your validator set is spread all over the world, you’re not building “global speed.” You’re building a global waiting room. That’s why Fogo frames a kind of thesis: a chain that is aware of physical space can be faster than one that pretends space doesn’t matter. So what do you do if you accept the planet as a design rule, not a footnote? Fogo’s answer is zoned, localized consensus. Think of it like running a relay race, but you pick a stadium for each lap. Validators are organized into zones, and only one zone is active in consensus for a given epoch. That sounds like “less decentral,” and yeah, it’s a trade. But it’s a very specific trade: shrink the distance on the critical path, so the quorum can talk fast enough to feel real-time. The inactive zones don’t vanish; they stay connected and keep syncing blocks, but they don’t propose blocks or vote in that epoch. It’s like having a full crew on the ship, but only one shift is steering at a time everyone else is still on deck, watching the map. Zone choice is not hand-wavy either. The litepaper describes different rotation styles. One is epoch-based rotation. Another is “follow-the-sun,” where zones can activate by UTC time, shifting consensus across regions through a 24-hour cycle. If you’ve ever watched how big markets hand off from Asia to Europe to the US, you get the vibe. The point isn’t romance. It’s reducing user-to-quorum distance when it matters. Now, if you only optimize distance, you still lose to the second killer: uneven validator quality. A network can have a fancy protocol, but if half the machines run like old laptops, the chain will act like old laptops. Fogo leans into “performance enforcement,” meaning it tries to cut variance by standardizing on a high-speed validator build and clear ops needs. Again, trade-offs. But it’s honest about what sets real final time: not just what the leader does, but how fast the quorum can receive, check, and respond. This is where Firedancer comes in. Binance Academy notes Fogo integrates Firedancer to push throughput and cut latency. In the litepaper, the mainnet validator is described as “Frankendancer,” a hybrid where Firedancer parts (like networking and block making while leader) run alongside Agave code. If “validator client” sounds abstract, picture it as the engine block of the chain. You can keep the same road rules, but if one car has a lawnmower engine, the traffic flow still suffers. Fogo goes even more “hardware-minded” in how that engine is built. The validator work is split into “tiles,” each pinned to its own CPU core, running tight loops to cut jitter and keep timing steady under load. It’s like a kitchen where every cook has one station and never shares knives. Less bumping, fewer mistakes, faster plates. They also describe tricks like zero-copy flow passing pointers instead of copying data so blocks and tx move through the pipeline without extra lift. And they mention kernel-bypass paths like AF_XDP for fast packet I/O. If you’re not a Linux person, just hear it as: “stop taking the long hallway when there’s a side door.” I like the honesty of starting with physics. Most chains sell you a story about fairness, or purity, or some new vote trick. Fogo is basically saying, “Look, the earth is round, fiber is finite, and slow tails are real. Design from that.” That’s a real frame. But the same frame cuts both ways. Zoned consensus and enforced performance can improve feel and speed, yet they also narrow who can realistically run at the top tier. That’s not evil. It’s just a choice, and choices have edges. If Fogo keeps those edges visible who’s in zones, how rotation works, what the ops bar is then the model is at least legible. And in crypto, legible beats mystical every time.
@Fogo Official #fogo $FOGO #Layer1

Last month I tried to “automate” a simple crypto habit. Move a small balance each week, swap a slice into a stablecoin, then log it. I wired a bot to a wallet, typed a few rules, and hit run.
It worked… once.
Next run, fees jumped, the swap path changed, the bot stalled mid-step, and my “automation” turned into me staring at a screen at 2 a.m. asking one dumb thing: why does this feel so brittle?
That night is why I pay attention when a chain says it’s built for AI-driven automation, not as a bolt-on, but as a core idea. Vanar Chain (VANRY) is pushing that angle: a stack where data, memory, logic, and action sit close together. Vanar’s own material frames it as an AI-powered Layer 1 aimed at PayFi and real-world assets, with built-in support for AI work and data tools. I’m not here to cheerlead. I’m here to poke holes, for real.
Automation on-chain is not magic. It’s just steps that run when you’re asleep. A smart contract is like a vending machine: you put value in, it follows fixed buttons, it drops the snack. The weak part is not the vending machine. It’s the messy world around it: bad prices, stale data, edge cases, and humans who change their mind.
AI-driven automation tries to handle that mess. Not by making code “alive,” but by picking better routes when facts change. The AI part is like a sharp intern skimming your notes fast, then saying, “I think this is the next move.” The chain part is the strict manager who only allows moves if rules are met.
Most automation today is split. Funds and rules live on-chain. The “brain” lives off-chain on a server, watching prices and firing calls. That split is a risk. Servers go down. Keys leak. The bot updates, and now you don’t even know what you’re running.
Vanar’s bet is that you can pull more “memory” and “context” closer to the chain. Their Neutron layer is described as turning files into compact, on-chain “Seeds” that apps and agents can use. instead of leaving your app’s memory in a private database, you try to pin the important parts into the network rules.
Why does that matter? Because an agent needs context to act safely. Past actions. User limits. A list of approved payees. If that context lives in a private box, you’re trusting whoever runs the box. If it lives on-chain, you’re trusting the same rules that secure the rest of the system.
Vanar also leans on a stack idea: base chain, then memory, then “reasoning,” then automation and app layers. I treat “reasoning” as a plain flow chart. It’s not mind. It’s structured choice. “If the bill is due and the balance is fine, then pay. If the fee is high, wait.”
Now the part that decides if any of this is useful: execution. In crypto, thinking is cheap. Doing is the danger. Once funds move, there’s no undo button. So automation without guardrails is just speed-running your own mistakes.
This is why Vanar points to Flows as the bridge from decisions to actions, with controlled execution and guardrails. I like that framing because it admits the real problem: you can’t let an agent free-run on money and then act shocked when it burns you.
Good automation needs friction. Limits, delays, and checks. Like the child lock on a cabinet. Annoying when you’re in a rush. Useful when you’re wrong. A flow system can encode rules like: only spend up to X per day, only swap if slip is under Y, pause if the data is old, require a second key for big moves. Boring. And that’s the point.
If Vanar’s target is PayFi payments that happen often, small, and dull then automation has to be dull too. The chain should act like plumbing, not fireworks. The test is simple: does it reduce panic at the worst moment?
AI-driven automation fails when three basics are weak: data quality, key safety, and clear blame. Data quality is “are we looking at the right facts?” Key safety is “who can sign?” Clear blame is “can we trace what happened and why?” If a platform can’t answer those, it’s just new paint on old risk.
Vanar’s design pulling context on-chain and making action paths more explicit could help with data and tracing. It still has to prove key safety in real use. Keys are where dreams die. Agents love keys. Hackers love them more.
I don’t buy the “AI chain” label on its own. I also don’t dismiss it. If Vanar can make automation feel less like my 2 a.m. mess less brittle, less server-heavy, more rule-driven then it earns attention the hard way. Not by hype. By fewer broken nights.
Until then, treat it like any other stack: interesting design, real promise, real risk. Watch what gets built. Watch what breaks. And watch whether the boring stuff limits, logs, safe defaults shows up first. That’s usually where the truth is.
@Vanarchain #Vanar $VANRY