Whoa! I remember the first time a swap failed mid‑flight and ate my gas. Seriously? Yeah. My gut sank, and I swore I’d never sign another blind tx again. Initially I thought the problem was just bad UX, but then I dug into how mempools, gas estimation, and MEV interact, and things got messy fast.
Here’s the thing. Transaction simulation is not a nicety anymore. It’s a rudimentary safety check that every advanced Web3 wallet should run before it asks you to confirm. Short simple sims catch obvious reverts. Medium‑complex sims catch slippage, approvals, and gas misestimates. Long, end‑to‑end sims that include stateful mempool checks and bundle dry‑runs can catch front‑running and sandwich risks before they happen, though they cost more to run and require smarter infra.
Okay, so check this out—wallets that simulate only with eth_call are still playing catch‑up. Hmm… eth_call gives you a static view against a node’s current state, but it ignores the mempool and concurrent state changes. On one hand, eth_call is fast and cheap; on the other hand, it lies sometimes—especially with high‑latency RPCs or heavily negative slippage paths. Something felt off about relying on that alone.
I’ll be honest: I’ve used wallets that promised “simulation” and then returned garbage results. Why? Two reasons. First, they used default RPCs which were throttled or cached. Second, they didn’t model MEV pressure—so a trade that looked safe in simulation got eaten by a sandwich bot seconds later. My instinct said: you need local mempool awareness and optional relay proofing. Actually, wait—let me rephrase that: you need layered simulation that escalates from cheap node calls to mempool or bundle dry‑runs as risk increases.
What good transaction simulation looks like
Short version: simulate multiple times, under multiple conditions. Medium answer: run a basic eth_call for quick checks, then run a stateful trace to capture internal calls and revert reasons, and finally run a mempool/bundle dry‑run for high‑value or sensitive txs. Long answer: combine RPC calls with locally cached state, gas oracle adjustments, MEV relay dry‑runs (e.g., Flashbots style), and heuristics for slippage and approval risks so the wallet can present a single risk score that actually means something.
On the technical side, here’s how that breaks down. Step one: replicate the exact calldata, value, and gas settings and call eth_call on a non‑stateful node. Step two: attach a trace or debug_traceTransaction where possible to see internal calls and token transfers. Step three: query fast mempool nodes or private relays to estimate whether the tx would be frontrun or sandwiched under current conditions. Step four: present deterministic outputs to the user—expected received amount, worst‑case slippage, revert reason, and an MEV flag. These pieces together reduce surprises.
Not every tx needs step three. For a small token tip, it’s overkill. But for swaps, leverage operations, or contract interactions that move big balances, the added latency is worth it. On the flip side, if you force heavy simulations for every click, users will hate the wait. Balance matters. (Oh, and by the way… caching results for a short TTL helps a lot.)
Multi‑chain adds new wrinkles
Different chains, different failure modes. Short note: gas estimation on BSC is different than on Optimism. Medium thought: L2s have different finality and challenge windows, so simulations must account for reorg windows and sequencer behavior. Long and messy: cross‑chain transactions, like using a bridge, require you to simulate each leg separately and model the bridging protocol’s timelocks, relayer availability, and slippage across both asset pools, which is often overlooked and very very important.
I live in the US DeFi scene, so I watch both mainnet congestion events and regional infra outages. Sometimes an RPC from a popular provider gets slow in Silicon Valley and everything blows up for east‑coast users. Your wallet should have smart failover: multiple curated RPCs per chain, quick synthetic checks, and user‑transparent toggles to prefer safety (more simulation) or speed (fewer checks). This is why good wallets allow advanced users to tweak simulation depth.
Okay, here’s a pattern I keep seeing: wallets that pretend “multi‑chain” by hooking RPCs into a single tab with no chain‑specific logic. That bugs me. Each chain has nuances: gas token mechanics, opcodes supported, native token decimals, and EIP rollout differences. Simulate against node versions that match the chain’s majority clients. If you don’t, some txs will behave like ghosts—working sometimes and failing in production.
dApp integration: where simulation meets UX
Developers want frictionless flows. Users want safety. These are conflicting priorities. Hmm… my first instinct is to side with users, but I also get why dApps push for fewer preflight checks to reduce latency. Initially I thought we could just hide complex simulation behind “advanced” toggles, but then realized that certain integrations should mandate deeper sims automatically—like any tx that transfers >X ETH or uses an approval for a sensitive contract. On one hand you want speed, though actually, a small slowdown that prevents a thousand dollar loss is fine.
Good dApp integration patterns: inject a preflight simulation API that the dApp can call with the tx payload, and let the wallet return a structured response: pass/fail, revert details, gas burn estimate, and MEV risk. This avoids the dApp guessing state or overpromising. The wallet should also expose UI primitives for the dApp to show simulation results inline, rather than forcing users to jump contexts. That improves conversion and reduces disputes.
There’s also an increasingly popular pattern where wallets act as the aggregator for simulations across chains and relays, then sign and optionally submit through private relays. This is what advanced users expect. For example, you might simulate a swap on Polygon and then submit via a relay that minimizes sandwich risk. It’s not trivial to build, but the UX payoff is big.
MEV protection: simulation isn’t sunshine without it
MEV is ugly business. Short reality: if you don’t model it, you get exploited. Medium dig: simulation that includes mempool state and private relay dry‑runs can flag transactions that are likely to be extracted. Longer thinking: true MEV defense needs combined strategies—slippage caps, route randomization, time‑delays, private relays, and bundling. Simulation alone won’t stop bots; it tells you which txs are high risk so you can apply mitigation.
Flashbots and similar relays let you submit bundles directly to block builders rather than broadcast to the public mempool. Wallets that simulate a bundle and then submit it privately can materially reduce sandwich risk. But—there’s a tradeoff: private submissions can add latency and dependence on relays that may change policy. Still, for high value txs it’s often the right trade.
Here’s a nuance: not all MEV is malicious. Liquidation bots, arbitrage that tightens spreads—some of that benefits the ecosystem. Simulation should categorize MEV types when possible and let users decide their risk appetite. I’m biased, but a wallet that gives users choice here wins trust.
How Rabby approaches these problems
I’ve tried a lot of wallets. rabby stood out because it layers simulation and gives clear, actionable output. They run preflight checks that go beyond eth_call, they surface revert reasons, and they offer MEV risk indicators in the signing flow. That kind of transparency matters when you’re managing multiple chains and a diverse token set.
Rabby also provides dev‑friendly hooks so dApps can call preflight simulation endpoints and render user‑facing warnings without reinventing the wheel. That lowers friction and improves safety across the whole stack. I’m not saying it’s perfect—no product is—but the direction matters.
FAQ
What exactly can simulation catch?
Simulations can detect immediate reverts, gas underestimates, incorrect calldata, slippage errors in swaps, underpriced approvals, and some predicted MEV scenarios when you include mempool state. They can’t perfectly predict future state changes but they reduce a lot of common surprises.
Does simulation add a lot of latency?
Basic eth_call sims are near‑instant. Traces and mempool/bundle dry‑runs add time—milliseconds to a few seconds depending on infra. Good wallets make this configurable: quick for low‑risk txs, deeper for high‑risk ones.
Can simulation prevent all MEV?
No. Simulation flags risk and enables mitigation like private relay submission or adjusted parameters. It’s a tool, not a silver bullet. Use it with other mitigations.
Alright—so what’s the takeaway? Use a wallet that treats simulation as core infra, not an optional addon. Short checks are fine for casual taps. Medium checks should be default for swaps and approvals. Deep checks should be available for big moves. I’m not 100% sure we’ve solved every edge case, but this stacked‑simulation approach reduces the dumb losses I kept making early on. Try not to learn the hard way like I did—get a wallet that simulates thoughtfully, and if you care about MEV, use private submissions when it matters. Somethin’ to file away for your next trade.
