How FOMO Built a Social Trading Layer That Tracks Every Balance Change, Across Every Chain

Scaling a global financial social graph means knowing exactly what every trader holds, on every network, in real time. Goldsky Turbo is the infrastructure that makes that possible.

Meet FOMO

Most trading apps show you a chart and a buy button. FOMO is building something different: a global financial social graph where traders build a public identity around their convictions, share theses, earn from their track record, and learn from the people who've been right.

The premise is that financial performance is a form of self-expression — and that the best traders in the world deserve a social layer that reflects it. Call it social discovery for trading. Your portfolio is your proof of work. Your PnL is your reputation.

Founded by Prashan Qazi, FOMO set out to solve a problem that every serious trader intuitively understands: the tools that exist for tracking performance are either too opaque, too fragmented, or too disconnected from the people whose judgment you'd actually want to follow. Prashan came to this from the intersection of consumer product and crypto — the conviction that social mechanics and financial identity belong together, and that nobody had built the thing that proved it yet.

To make that work, FOMO needs to know exactly what every user holds, and exactly how it changes, in real time, across every chain they trade on. That's not a data-fetch problem. That's an infrastructure problem.

The Infrastructure Behind a Financial Identity

A social graph built on financial positions has one hard requirement: it has to be correct.

If your PnL is your identity, a wrong balance means a wrong identity. A missed transfer means a misattributed trade. A delayed update means a user sees yesterday's position while today's market has already moved.

When FOMO was tracking a small number of wallets — the early cohort of power users — standard APIs held up fine. Alchemy webhooks for real-time activity, RPC polling as a fallback when the webhook missed something. Manageable, because the address space was small enough that edge cases didn't compound.

Then FOMO started growing.

The address list expanded from hundreds to thousands to tens of thousands of wallets. They added networks — Solana, Base, BSC, and Monad — with more coming. Roughly 20,000 trades a day, with message volumes in the low eight digits per month. The fragility baked into the old setup started showing up everywhere.

The core problems:

• Reliability gaps — Webhooks missed transactions when the receiving service went down or fell behind. The RPC polling fallback was manual and inconsistent.

• Off-platform blindspots — Activity that happened outside FOMO's app didn't get captured. PnL calculations drifted from reality.

• No delivery guarantees — There was no systematic way to know whether a balance change had been processed or silently dropped.

• No scalability path — Every additional wallet made each of these problems worse. The architecture that held at 500 addresses was breaking at 50,000.

• Fragmented cross-chain logic — EVM and non-EVM chains have meaningfully different data structures. Stitching together a consistent view of user balances across BSC, Base, Monad, and Solana required bespoke handling at every seam.

Something needed to change before they expanded further.

The Meeting in the Modeling Agency

In February 2026, Hemanth from Goldsky was in New York and messaged Prashan directly: "This might be a stretch but I'm in NYC today and tomorrow — happy to swing by for a coffee with my laptop and get into things."

Prashan replied within minutes. FOMO was at 110 Greene Street in Soho.

The building was otherwise entirely occupied by modeling agencies. The doorman did a double take when Hemanth walked in. FOMO's office on the ninth floor looked like the previous tenants hadn't quite finished moving out, or the team hadn't quite finished moving in: boxes stacked against walls, one conference room, no kitchen. A San Francisco hacker house that had somehow landed in Manhattan.

What was supposed to be a short meeting ran well past an hour. The team was sharp — technically precise questions, processing Goldsky's architecture in the time it normally takes to finish the setup slides. By 5:30pm the room was full and still getting fuller. Nobody looked like they were heading home anytime soon.

Within 45 minutes, the conversation had moved past introductions and into a working spec: dynamic tables for managing the watched-address list, Kafka as the sink, how at-least-once delivery would eliminate the fallback polling entirely. That meeting in February to pipelines running in production took roughly three months — Monad first, then BSC and Base, built and validated in sequence.

The notes from that session read like a product brief:

• Turbo pipelines with dynamic tables, built for tracking ~150,000 accounts

• FOMO's Kafka consumer to receive balance change events

• One pipeline definition portable across all chains

• Start with Monad, then expand to BSC and Base

How Turbo Works for FOMO

The architecture FOMO landed on is straightforward by design. Goldsky Turbo watches a dynamic list of wallet addresses across each supported chain and emits an event every time a balance changes — native token or ERC-20 — directly into FOMO's Kafka queue. FOMO's consumer picks it up from there.

What makes this work at scale:

• Dynamic address management — FOMO maintains the list of wallets to watch. As new users join and connect their wallets, they get added. The pipeline reads the current list and applies it — no restart, no reconfiguration.

• At-least-once delivery — Every balance change event is delivered to the Kafka topic. If FOMO's consumer falls behind, events queue with backpressure and wait. Nothing gets dropped. There's no polling fallback because there's nothing to fall back from.

• Full balance change coverage — Goldsky tracks native token changes from traces and gas, ERC-20 transfers, and Withdrawal/Deposit events for wrapped tokens. This covers the meaningful majority of all balance change causes — the edge cases the old webhook setup would silently miss.

• A unified access pattern across EVM and non-EVM chains — BSC, Base, Monad, and Solana have different underlying data structures. Goldsky handles those differences internally and delivers a consistent schema across all four. One integration pattern. One consumer. Four chains.

• Backfill from any block — When FOMO needs historical data — to reconstruct a user's PnL history or onboard an existing wallet's balance timeline — Turbo's job mode handles it from any block range. No separate backfill job, no one-off scripts.

• Early Monad access — Goldsky runs the public RPC for Monad at rpc2.monad.xyz and had Monad mainnet data available before public launch. FOMO got access on testnet and built their pipeline before the network went live.

The pipeline is a YAML config — a few transforms that normalize balance changes from traces, logs, and wrapped token events into a single output schema: id, sender, recipient, token_standard, token_address, token_amount. Everything downstream in FOMO's stack reads from that one format.

When a BSC pipeline ran into an issue with ERC-721 transfer signatures decoding against an ERC-20 ABI, the Goldsky team diagnosed and patched it the same afternoon. One SQL condition added to the transform, pipeline redeployed, caught back up within the hour.

What This Unlocks for FOMO

The social graph FOMO is building runs on accurate, real-time position data. Without it, PnL is unreliable. Without reliable PnL, the identity layer doesn't hold. Without a credible identity layer, the social mechanics — following top traders, learning from their theses, earning from your own track record — fall apart.

Turbo is the layer underneath all of that.

"The pipeline definition we wrote for Monad runs on BSC and Base with almost no changes. We get one consistent view of balance changes across all three chains into our Kafka queue — and as we add more networks, the consumer doesn't have to change at all." — Jay, Engineering, FOMO

Monad, BSC, and Base pipelines are live in production. Solana is next, and Elysium — a high-performance chain built for Hyperliquid — is on the roadmap, with early testnet access already lined up through Goldsky before public mainnet launch.

The pipeline definition won't need to change as new chains come online. Address management is already dynamic. The Kafka consumer already knows the schema. Expansion is a matter of spinning up another pipeline and pointing it at the new network.

That's what it looks like when infrastructure is ahead of growth instead of behind it.

👋 Hey, we're Goldsky. We help trading apps, social platforms, and onchain financial products track balance changes and transaction activity across every chain — in real time, at any scale, with at-least-once delivery guaranteed. Teams like Polymarket, Phantom, and Privy have built on Goldsky to ship faster without rebuilding infrastructure from scratch.

If you're building a product where accurate onchain data is load-bearing — for PnL, attribution, or user identity — we'd love to chat.

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How FOMO Tracks Real-Time PnL Across Every Chain Its Traders Use

FOMO is building a global financial social graph — where traders share theses, build reputations, and earn from their conviction. That requires knowing every balance change, on every chain, for every user. At scale, that's not an API problem. It's an infrastructure problem.

Meet FOMO

FOMO is a social trading app built on a direct premise: your financial views are worth sharing, and the best ones should be worth following. Users build identities around their investment theses, share trade ideas with their community, and earn from the track record they build onchain. The result is a social discovery layer for trading — a financial social graph where anyone can learn from top traders, not just institutional analysts behind paywalls.

The team operates out of SoHo in New York. They're young, fast, and deeply technical. Founded to close the gap between public market discourse and actual onchain performance, FOMO gives traders a way to put conviction onchain and let results speak — transparently, verifiably, in real time.

To power PnL calculations as they happen, FOMO needs to know when any user wallet receives or sends any token — native or ERC-20 — on any chain their traders use. That means tracking balance-modifying events across Solana, Base, BSC, and Monad, with Elysium support on the way.

For a few hundred wallets, that's a solved problem. For the scale FOMO operates at now, it isn't.

When APIs Stop Being Enough

Early on, FOMO tracked user balances the way most teams do: a mix of RPC polling and webhooks from established providers. For a small wallet set, that works. Latency is acceptable. The occasional missed event gets papered over with a backfill call. Fine.

Then FOMO grew. Fast.

As the user base expanded, that approach broke down in ways that were hard to patch:

• Reliability gaps at scale: Webhook delivery with RPC polling as a fallback works until you're running tens of thousands of addresses. Missed events and race conditions between the webhook and fallback layers compound into systematic inaccuracies.

• No at-least-once guarantee: If the receiving service went down, events disappeared. No backpressure mechanism, no checkpoint to replay from — just missing data and incorrect PnL.

• Off-platform activity was invisible: Trades and transfers that happened outside FOMO's own surfaces weren't captured. PnL calculations were incomplete by construction.

• Multi-chain coverage was fragmented: Different providers handled different chains with different APIs, different schemas, and different reliability profiles. Stitching them together meant maintaining a matrix of integrations rather than a coherent data layer.

• No real data ownership: The team couldn't replay from a specific block, filter by a dynamic address list, or control the full pipeline. They depended on whatever each provider chose to expose.

The underlying issue wasn't any single provider. It was the model itself — polling and webhooks built for low-volume use cases, running against a product that needed something structurally different.

A Meeting in a Modeling Agency

In February 2026, Hemanth from Goldsky was in New York for a couple of days. He messaged FOMO founder Prashan on a Tuesday morning: he was nearby, happy to swing by with a laptop and get into the weeds in person.

Prashan said yes. Come at 4pm.

The address was 110 Greene Street in SoHo. What Hemanth didn't know until he walked in: every floor of the building was a modeling agency. The doorman gave him the look doormen at modeling agencies reserve for people carrying laptops.

The FOMO office on the ninth floor was a different world. Four or five people in a single room. Boxes stacked against the walls. One conference room. It looked less like a startup office and more like a place where the previous tenants hadn't finished moving out — or these ones hadn't finished moving in. A San Francisco hacker house that had somehow landed in SoHo.

By 5:30, when Hemanth was wrapping up, the room hadn't emptied. More people had arrived.

What stuck wasn't the office. It was the conversation. Things that normally take three meetings to align on — the trade-offs between webhook delivery models, what at-least-once guarantees actually mean in practice, why a pipeline with checkpoints is structurally different from a polling fallback — landed in minutes. FOMO's engineers asked questions that assumed the answers. Within 45 minutes, the architecture was mostly sketched out.

The meeting that was supposed to run 20 minutes ran past an hour. Nobody called time.

The Architecture: Turbo into Kafka

The decision was clear once FOMO understood what Goldsky Turbo does differently.

Turbo pipelines deliver onchain data with at-least-once delivery — if FOMO's Kafka consumer falls behind or their receiving service goes down, messages queue with backpressure and deliver everything once the service recovers. No polling fallback, no window for gaps. The checkpoint and savepoint system means any window of data can be replayed from any block.

FOMO writes balance changes directly into their own Amazon MSK instance. They own the queue. They own the data.

The pipeline tracks every event category that modifies a user's balance:

• Native token balance changes — derived from traces and gas, covering 100% of balance change causes

• ERC-20 Transfer events — standard token movements across DEX swaps, contract interactions, and direct transfers

• Wrapped token Deposit/Withdrawal events — for tokens like WETH that modify balances without emitting a Transfer

FOMO maintains a dynamic address table — currently tracking [X] wallets in production — and Turbo filters events against that list in real time. As users join and connect wallets, the address table updates and the pipeline captures them without redeployment or manual intervention.

The path from chain tip to FOMO's Kafka topic runs through dedicated static IPs, with pipeline workers sized to handle message volume across all active chains simultaneously.

One Access Pattern Across Four Chains

One of the less obvious advantages of running on Turbo: FOMO gets a unified access pattern across EVM and non-EVM networks.

On EVM chains — Base, BSC, Monad — the pipeline reads from traces and logs. On Solana, the backing datastore is architecturally different and the schema reflects that, but the interface FOMO's team builds against is consistent enough that they don't maintain four separate integration models. When FOMO adds a chain, they're extending an existing pattern. Not rebuilding from scratch.

The current chain lineup:

• Monad — live in production; the first chain onboarded through Turbo. Goldsky runs the public RPC at rpc2.monad.xyz and had private mainnet data available before public launch, so FOMO's pipeline was running at launch rather than waiting for public RPC access to stabilize.

• Base — live in production

• BSC — live in production

• Solana — in evaluation; Turbo's solana_native_balances and solana_token_balances datasets include amount_before and amount_after in schema, with latency improvements on the roadmap

• Elysium — Hyperliquid's high-performance chain, coming soon; FOMO already has testnet access ahead of mainnet launch

Early access to Monad's private mainnet is the same pattern repeating on Elysium. FOMO doesn't wait for the ecosystem to catch up — they're already running when the chain goes live.

Real-Time PnL at Scale

The production setup runs pipelines on Monad, Base, and BSC simultaneously, writing to FOMO's MSK instance with sub-second delivery. At roughly 20,000 trades per day, monthly message counts reach into the tens of millions across traces and transactions combined.

A few specific capabilities that matter for FOMO's use case:

• Dynamic address filtering: The pipeline filters against a live wallet table, so new users get captured in real time without pipeline changes or redeployments

• Full replay from any block: Job mode scopes historical backfills to any block range — essential when a user connects a wallet with existing trade history that needs retroactive PnL calculation

• Incident recovery: When a BSC pipeline error surfaced in May 2026 from a type casting edge case with receipt_l1_fee, Goldsky diagnosed and patched it within hours; the pipeline caught up from the lag point without manual replay

• Transparent cost structure: Message-based pricing scales with volume — FOMO ran entirely on the free tier through staging, moved to Scale for production, and is finalizing an enterprise agreement as full production volume comes online

From first conversation to the Monad pipeline running in staging took roughly two weeks, with BSC and Base following in parallel the week after.

The Foundation Under a Social Trading Network

FOMO's product works because the financial data underneath it is accurate and complete. A trader's reputation on a social graph is only as trustworthy as the PnL it's built on. A missed transfer, a delayed balance update, a gap during a volatile session — those aren't edge cases. They're the kind of errors that erode trust in the product itself.

That's why the infrastructure choice matters. Turbo's at-least-once delivery means the data layer doesn't have holes. The dynamic address table means the pipeline grows with FOMO's user base without engineering overhead. The unified access pattern across EVM and non-EVM chains means adding Elysium — or any future network — is an extension, not a rebuild.

Goldsky handles the indexing. FOMO handles what only they can build: the social layer, the reputation system, the discovery surface that makes top traders worth following. That division is what lets a small, fast team ship the product they're actually building.

👋 Hey, we're Goldsky. We help social trading apps, wallets, and onchain financial products like FOMO track every balance-modifying event in real time — across EVM and non-EVM chains — with at-least-once delivery into your own infrastructure. Teams like Phantom and Privy use the same pipeline architecture to power payments and wallet activity at scale.

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How FOMO Built a Global Social Trading Graph on Infrastructure That Scales With Them

From a few hundred tracked wallets to tens of millions of balance events across four networks — without rebuilding their stack every time they add a chain.

Metadata

Metrics to be confirmed with FOMO's team before publishing.

Meet FOMO

Most trading apps show you what to buy. FOMO shows you who to follow.

Founded by Prashan and his co-founders, FOMO is a global financial social graph — a place where traders build a public identity around their financial views, share investment theses, grow a personal brand, and earn from the audience they attract. Think of it as the social discovery layer that crypto trading has been missing: a place where the best traders become teachers, and where their track record is onchain and verifiable rather than a screenshot on Twitter.

The product sits at the intersection of social and financial infrastructure. Every follow, every shared thesis, every executed trade feeds a graph that surfaces the people worth learning from. That graph only works if the underlying financial data is accurate, real-time, and complete — which means FOMO needs to know, at any given moment, exactly what every tracked wallet holds and how those balances have changed.

That accuracy requirement is what drove every infrastructure decision Prashan's team made. When you're computing PnL for a social graph where traders build their reputations on their numbers, a missed balance event isn't a minor data gap — it's a wrong number shown to every follower. At a few hundred tracked wallets, that's a manageable problem. At the scale FOMO operates today, it's an existential one.

The Problem: Webhooks and APIs Stop Scaling Before You Expect Them To

When FOMO was early, their data stack matched their scale. They used a combination of RPC polling and webhooks to track user balances and compute trading PnL. For a small address set, this worked fine. APIs are ergonomic. Off-the-shelf webhook providers are fast to set up. You don't need infrastructure you don't need yet.

But FOMO grew. The architecture didn't grow with it.

• Missed events: When FOMO's receiving service went down or fell behind, webhook events were lost. Recovering those gaps required a separate polling mechanism that added complexity without guaranteeing completeness.

• Off-platform blindspots: When a user's wallet was active outside FOMO — a swap on another app, a transfer from a hardware wallet — the balance change went undetected. PnL figures drifted out of sync.

• No Monad support: When FOMO decided to expand to Monad, existing providers hadn't caught up. The five endpoints they needed — getTokenBalances, getAssetTransfers, getBlockNumber, getTransaction, address activity webhooks — had no Monad mainnet support. They were blocked before they started.

• Fragmented multi-chain coverage: Each new chain meant a new provider evaluation, a new integration, a new set of quirks to manage. A patchwork of providers across four networks wasn't sustainable, and Solana and EVM chains have fundamentally different data structures that compounded the complexity.

• No data ownership: When something broke, FOMO depended on provider status pages and support queues. The data didn't live in their systems. The reliability wasn't theirs to control.

The underlying issue: APIs designed for small wallet sets don't carry the reliability or completeness guarantees that a social trading app needs when the tracked-address count scales by orders of magnitude. At ~20,000 trades per day and a monthly message volume in the low eight-digit range across all chains, FOMO had outgrown the category of infrastructure they'd started with.

A 15-Minute Meeting That Ran an Hour

In November 2025, Goldsky connected with FOMO's team to explore whether Goldsky could replace their existing Monad dependency. Prashan's team came in knowing exactly what they needed: five specific endpoints with no Monad equivalent, the precise behavior they were getting from their existing webhooks, and the failure modes they were trying to avoid. The conversation moved fast.

Three months later, in February 2026, Hemanth from Goldsky was in New York and suggested something simpler than a call: he'd walk over.

The address FOMO sent back was 110 Greene Street in SoHo — a building occupied almost entirely by modeling agencies. The doorman gave Hemanth a look that said this wasn't the usual visitor profile. He rode the elevator up anyway.

The office matched the energy of the company. Four or five people in a room, boxes stacked against walls, gear piled up like the previous tenants hadn't fully moved out — or FOMO hadn't fully moved in. No fancy kitchen, no polished conference room. It looked more like a San Francisco hacker house than a Series A headquarters. Hemanth arrived around 5pm for what was supposed to be a 15-minute conversation. By 6:30, when he left, there were more people in the office than when he'd arrived.

The meeting ran an hour. FOMO's team asked questions that normally take multiple conversations to reach. They understood the tradeoffs between webhooks and streaming pipelines almost immediately — the kind of sharp, architectural thinking that comes from founders who've already lived through the failure modes. Within 45 minutes, both teams had mapped out a complete architecture: Turbo pipelines writing balance change events to a Kafka queue, dynamic address tables keeping the tracked wallet list current without pipeline redeploys.

The notes from that session captured it plainly: FOMO was using webhooks and RPC polling to track user balances and compute trading PnL. Neither approach was resilient enough, and both missed off-platform activity. The fix was Turbo into Kafka, with dynamic tables managing the tracked address list. That was the moment the project became real.

The Architecture: Turbo Into Kafka, Chain by Chain

The core of what FOMO built is straightforward. Turbo pipelines stream every balance-modifying event across their tracked wallets into a Kafka queue. FOMO's consumer picks events off the queue and updates PnL in real time. When the receiving service goes down, the queue holds everything and delivers it when FOMO comes back online — at-least-once delivery, backed by checkpoints and savepoints, with the ability to rewind to any block in history.

The tracked address list lives in Turbo's dynamic tables. When a new user joins and adds a wallet, it enters the table. The pipeline picks it up without any redeploy.

Getting the event coverage right took iteration. Balance changes on EVM chains come from more sources than Transfer events alone:

• Native token balances — tracked through traces and gas from transactions, covering 100% of ETH/BNB/MON movement

• ERC-20 transfers — via the standard Transfer event

• Wrapped token balance changes — through Deposit and Withdrawal events that don't emit a Transfer

• Custom token types — handled case-by-case for tokens using non-standard balance mechanics

The YAML pipeline config that Goldsky's team drafted, revised, and deployed for Monad became the template for every chain that followed. Moving from Monad to BSC to Base didn't mean rebuilding anything — just standing up the next pipeline against the appropriate chain dataset. That's the cross-chain design advantage that matters here. FOMO writes one consumer, one Kafka-based PnL computation layer, one data model. The differences between EVM and non-EVM chains are handled inside the Turbo configuration, not in FOMO's application code.

Chain by Chain: The Production Rollout

Monad — The Starting Point

Monad was the reason the conversation started. When existing providers had no Monad mainnet support, Goldsky did — including rpc2.monad.xyz, the public Monad RPC that Goldsky runs directly. By March 2026, FOMO's Monad pipeline was running in staging. By early April, their engineering team had a Kafka consumer ready for end-to-end testing. By May, the Monad pipeline was live in production.

BSC and Base — Scaling the Template

BSC and Base came online in parallel in May 2026. At production scale, BSC surfaced one unexpected edge case: a permissive topic hash check that matched ERC-721 transfer signatures alongside ERC-20s. When a contract within the tracked address set emitted a non-standard transfer, the pipeline panicked on a type cast and throughput dropped to zero.

Goldsky's team was in the logs within minutes. They diagnosed the root cause — a SPLIT_INDEX check on topic length excluded the non-ERC-20 transfers, and a separate fix addressed receipt_l1_fee field handling specific to BSC. The pipeline was processing again the same day. Goldsky caught up the hours of blocks that had queued during the outage manually, so FOMO's consumer received every event in order without a gap.

That incident is worth noting not because it was severe — it wasn't — but because of how it resolved. FOMO didn't have to file a ticket and wait. The root cause came with an explanation of why it hadn't surfaced earlier. The fix was precise.

Solana — On the Roadmap

FOMO wants Solana coverage too. Turbo's Solana support uses a dedicated backing datastore with solana_native_balances and solana_token_balances datasets that carry amount_before and amount_after directly in the schema — simpler to consume than trace-based EVM balance reconstruction. Current p50 latency runs at roughly 2–3 seconds from chain tip.

For FOMO's real-time PnL use case, that's borderline. Rather than overpromise, Goldsky was direct: the datasets are production-ready, latency improvements are on the roadmap for one to two quarters out, and FOMO will get both when the time is right. The decision is whether to run Solana now at current latency or wait for the faster version. Either way, the pipeline template is already built.

The Elyisum Advantage

There's a compounding benefit to being early that shows up at the edges: access.

FOMO is expanding to Elyisum, a high-performance chain built for Hyperliquid. Through their relationship with Goldsky, they got access to testnet data before Elyisum's mainnet publicly launched. For a social trading app where the competitive edge is knowing which traders to surface first and which chains to support before the crowd arrives, pre-launch infrastructure access is a distribution advantage — not a minor convenience. The teams who build on a chain from day one shape how it gets used.

That pattern holds across Goldsky's network partnerships. Early customers get early access. At the infrastructure layer, that compounds into product advantages that are hard to replicate after the fact.

What FOMO Has Now

The stack FOMO runs today is structurally different from where they started — not just more reliable, but built on properties their previous setup couldn't offer.

• Data ownership: Every balance event flows into FOMO's own Kafka infrastructure. The data lives in their systems, not in a third-party API that can change pricing, deprecate endpoints, or go down without warning.

• At-least-once delivery: The pipeline doesn't drop events when FOMO's consumer falls behind. Events queue and deliver when the consumer recovers. No separate backfill mechanism required.

• Complete balance coverage: Native balances, ERC-20 transfers, wrapped token deposits and withdrawals — every event that modifies a wallet's balance is captured, not just the standard transfer case.

• A unified cross-chain access pattern: One data model, one consumer architecture, four chains. Solana fits the same pattern when it comes online. So does whatever chain comes after that.

• Backfill on demand: When FOMO needs historical data — to reconstruct PnL for a new user, to cover a chain they didn't track from the start — they can replay from any block without negotiating with a provider or paying for a one-time pull.

The dynamic address tables mean the tracked wallet count can grow without any pipeline changes. The architecture FOMO built for 150,000 accounts doesn't need to be replaced when that number climbs by an order of magnitude. When Prashan's team decides to expand the program, the pipelines won't need to change.

"[PLACEHOLDER — quote from Prashan or Jay on what Turbo changed for their team. Ideally something specific about reliability, data completeness, or the cross-chain access pattern.]" — [Name, Title], FOMO

The Bigger Picture

FOMO's product works because trust in the data is total. A social trading graph where PnL figures are occasionally wrong, where off-platform activity goes missing, or where one chain's coverage lags another isn't a product people build a financial identity around.

The infrastructure problem FOMO solved isn't unique to social trading apps. Portfolio managers, tax tools, DeFi dashboards, institutional custody products — any application that tracks wallet activity at scale hits the same ceiling. APIs that work at small scale become reliability liabilities at large scale, and the rebuild cost is high enough that teams wait too long to address it.

Turbo's design addresses this directly. The pipeline architecture is the same whether FOMO is tracking 10,000 wallets or 10 million. Kafka-backed delivery keeps consumer complexity inside FOMO's systems, not scattered across multiple provider integrations with different failure modes. Each new network is a pipeline config, not a vendor evaluation.

FOMO came to Goldsky because their existing stack couldn't follow them to Monad. They stayed because the infrastructure grows with them — chain by chain, wallet by wallet, without rebuilding anything.

👋 Hey, we're Goldsky. We help consumer crypto teams like FOMO track wallet balances and onchain activity across every network they support — in real time, at scale, with data that lives in their own infrastructure.

If you're building a social trading app, portfolio tracker, or any product where accurate balance data is load-bearing, we'd love to chat. Teams like Phantom, Privy, and Polymarket build on Goldsky today.

Sign up for free (no credit cards or forms to fill) or instantly schedule a call with our team.