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The practical problem compounds when users do not set slippage tolerance correctly. Too low a tolerance, and swaps fail unnecessarily during volatile periods. Too high, and a user may accept a far worse rate than the one displayed, losing value to arbitrageurs and liquidity-pool mechanics. This article examines why price differences occur, how Keplr presents slippage controls, what MEV and price impact actually mean, and when a limit order approach or careful tolerance calibration can protect a user’s execution quality.

The quote-to-execution gap is structural, not accidental

When a decentralized exchange quotes a swap price, it is providing a snapshot of the current liquidity pool state at a specific moment. Osmosis, Astroport, and other DEXes on Cosmos-connected chains calculate rates based on the available liquidity in trading pairs and the mathematical model they use—most commonly the constant product formula, where multiplying the quantity of two assets in a pool must equal a constant. That formula ensures that larger trades have worse rates than small ones because the trade moves the pool further from equilibrium.

The quote is not a commitment. Between the moment Keplr displays the rate and the moment a validator includes the transaction in a block, market conditions change. Other traders may execute swaps, altering the pool composition. Gas prices may shift, affecting transaction ordering. Sandwich bots may identify profitable opportunities to transact before and after a user’s swap, extracting value from the gap. The blockchain processes transactions sequentially, and by the time a swap settles, the pool ratios that existed during the quote may no longer exist.

This is why slippage tolerance exists. It is the percentage of price movement a user is willing to accept. If a quote shows 100 ATOM per OSMO and slippage is set to 1 percent, the wallet will execute the swap as long as the user receives at least 99 OSMO. If the actual rate is worse—say, 98 OSMO—the transaction fails to prevent excessive loss. The slippage tolerance is a safeguard, but setting it correctly requires understanding what conditions make slippage large or small.

Price impact versus slippage: why distinguishing them matters

Price impact is the immediate, unavoidable cost of moving the liquidity pool when executing a swap. If a pool has 1 million ATOM and 1 million OSMO, and a user wants to buy 100,000 OSMO, the trade must remove 100,000 OSMO from one side while depositing more ATOM on the other. That large withdrawal makes each remaining OSMO more valuable relative to ATOM, so the user receives a worse rate than the opening price. This is mathematical and inherent to how automated market makers operate. Smaller trades have smaller price impact; larger trades relative to pool size have larger impact.

Slippage, in contrast, includes price impact plus any additional price movement that occurs between quote and execution. If price impact accounts for a 2 percent rate decline, and another 1 percent decline happens because the pool moved during the transaction’s confirmation time, total slippage is 3 percent. Users can calculate expected price impact by knowing the pool composition and trade size, but predicting the additional time-based slippage is harder. Network congestion, transaction ordering, and competing activity all contribute.

Keplr displays an estimated output and often shows the price impact percentage for a proposed swap. That estimate is based on current conditions; the actual impact may differ. Users setting slippage tolerance should account for both components. On quiet chains with deep liquidity, 0.5 percent may be enough. During volatile periods on less liquid pairs, 1 to 3 percent may be more realistic. Setting tolerance too high—above 5 percent on most swaps—often reflects a misunderstanding of what risk the user is actually accepting.

The distinction matters for portfolio decisions. If a user is swapping 50,000 ATOM for OSMO on a pool with 10 million ATOM liquidity, price impact could be substantial. Breaking the trade into smaller chunks across time or using a different route might reduce the total impact. Conversely, a small swap of 500 ATOM on the same pool has minimal impact and can tolerate tighter slippage settings. Keplr’s multi-chain support means users may encounter different pool sizes and liquidity depths depending on which chain and which pair they are trading.

MEV, sandwich attacks, and transaction ordering risk

Maximal extractable value, or MEV, is the profit that can be made by exploiting transaction ordering on a blockchain. In the Cosmos ecosystem, validators and block producers have some power to order transactions. Sophisticated observers can detect pending swaps in the mempool, identify profitable opportunities, and arrange to transact before and after a user’s swap to extract value. This sandwich attack costs the user additional slippage beyond what the pool’s current state would naturally produce.

The threat is real but not universal. Layer-1 Cosmos chains like Cosmos Hub have more direct MEV exposure than chains with encrypted mempools or other privacy protections. Osmosis and other DEX-heavy chains see more bot activity because the profit opportunities are larger. A user swapping 100 ATOM for OSMO on a quiet pair during low-activity periods faces less MEV risk than swapping a large amount of ATOM on the most liquid pair during peak trading hours.

Keplr cannot prevent MEV, but it can help users avoid creating obvious targets. Splitting large swaps into smaller transactions executed across different blocks reduces the appeal to sandwich bots because the profit margin shrinks. Broadcasting transactions through a private mempool or using a relay that does not publicly announce pending transactions can reduce visibility to extractors. Some DEXes on Cosmos chains are beginning to support MEV-resistant mechanisms, though these remain less common than on Ethereum-based systems.

The practical mitigation is calibration: set slippage tolerance high enough to execute during actual market conditions, but not so high that MEV and front-running become negligible concerns. On stable pairs during normal conditions, 0.5 to 1 percent is often appropriate. On volatile or less liquid pairs, 1 to 2 percent may be needed. Anything above 3 percent should trigger a question: is this pair actually liquid enough for the trade size, or should the user break it into smaller chunks?

How to set slippage tolerance correctly in Keplr swaps

Most Keplr swap interfaces present slippage tolerance as a setting, sometimes with preset options such as 0.5 percent, 1 percent, and 3 percent, plus a custom input field. The wallet may also warn users if they select unusually high values. To use this control effectively, a user should first understand the trade size relative to pool liquidity. Keplr’s interface should show the pool composition or at least an indicator of how large the trade is relative to available liquidity.

Next, assess market conditions. If multiple assets are moving sharply, slippage across the ecosystem will be wider because many users are executing swaps simultaneously and pools are moving rapidly. In these periods, a tolerance of 1 to 2 percent is more realistic than 0.5 percent. If the network is quiet and the pair is liquid, tighter tolerance is defensible. Check recent block times and transaction activity; rapid blocks indicate low congestion, while slower times suggest that MEV competition may be higher.

For routine swaps under normal conditions, a starting point of 1 percent is reasonable for most Cosmos assets. If a swap fails with that tolerance, the user can retry with 1.5 or 2 percent, but should also check whether the pool is liquid enough or whether splitting the trade makes sense. Repeatedly increasing slippage without investigating the underlying reason is a sign of either illiquid trading pairs or excessively large trades for the available liquidity.

Keplr also shows the minimum output amount based on slippage tolerance. If a quote shows 100 OSMO output with 1 percent slippage, the minimum received is 99 OSMO. Users should verify this calculation before executing, especially for large trades where the actual difference in fiat value matters. Some users prefer to note this number and monitor the transaction result to see whether they received the minimum or a better rate, building intuition for when slippage is working as expected versus when something else is degrading execution.

Limit orders and alternative execution strategies

Not all swaps on Cosmos chains use the slippage-tolerance model. Some DEXes and protocols support limit orders, which specify a minimum price the user is willing to accept without a time-constrained execution guarantee. A limit order to swap 1000 ATOM for OSMO at a rate of no worse than 1 ATOM per 1.05 OSMO will sit in the order book until the pool rate improves to that level or a market maker fills it. This removes the MEV and sandwich risk because the user is not executing immediately; the downside is that the order may not fill immediately or at all if liquidity never reaches that price.

Limit orders are more common on centralized services, but some Cosmos protocols and DEXes are beginning to support them. Before using a limit order, verify how long it will remain active, whether there is a fee to place or cancel it, and whether the fill mechanism is transparent. A limit order that sits for days consumes user attention and may miss a window where the target price was briefly available. Some protocols charge cancellation fees, making frequent limit order adjustments expensive.

Alternatively, a user can execute multiple smaller swaps instead of one large swap, spacing them across time or different blocks. This reduces price impact per swap and makes sandwich attacks less profitable. Keplr’s ease of re-execution makes this practical. A user swapping 10,000 ATOM can split it into five 2,000 ATOM swaps executed minutes apart, potentially achieving better average execution than one large 10,000 ATOM swap with high slippage. The trade-off is higher total transaction fees and more operator effort.

Another approach is to use intermediate pairs. If swapping ATOM to OSMO directly has poor liquidity, a user could swap ATOM to JUNO and then JUNO to OSMO if those pairs are deeper. Keplr can handle multi-step routes, and some DEXes automatically route through the best path. The downside is additional fees and additional MEV exposure at each step. This strategy is most useful when a direct pair is illiquid or when the intermediate asset is significantly more liquid.

Reading and verifying slippage during execution

When executing a swap in Keplr, the wallet displays several pieces of information: the quoted output amount, the price impact percentage, the estimated gas fee, and the minimum received amount (calculated from slippage tolerance). Users should verify each before signing. If any number looks unexpectedly large, confirming the asset names, decimals, and pool composition is worth the extra minute. A displayed output of “123456789” without careful attention to decimal places can represent vastly different fiat values depending on the asset.

Keplr’s interface should also indicate which DEX or routing protocol is being used. Osmosis, Astroport, and others may offer different rates for the same pair. Some wallets automatically select the best route; others let users choose. Understanding which source is quoting the swap helps contextualize whether slippage expectations are appropriate. A swap routed through Osmosis on the Cosmos Hub chain will have different liquidity and MEV characteristics than the same swap on the Evmos chain through a different DEX.

After executing a swap, users can verify the actual settlement by checking the transaction hash and viewing the on-chain receipt. Keplr provides transaction history; clicking into a swap should show the input amount, output amount, and the actual rate achieved. Comparing this to the quote and the set slippage tolerance helps users understand their actual execution quality. Over multiple swaps, patterns emerge: if slippage is consistently tighter than the set tolerance, it may be safe to reduce future tolerances. If swaps regularly hit the slippage limit, the user may need higher tolerances or a reassessment of trade size relative to liquidity.

Cosmos ecosystem liquidity variation and chain-specific considerations

Liquidity depth varies significantly across Cosmos-connected chains. Osmosis, as the primary Cosmos DEX, typically has deep liquidity for major pairs like ATOM, OSMO, and popular Cosmos assets. Evmos, Juno, and other chains may have shallower liquidity pools for the same assets. A user swapping on Osmosis often faces tighter spreads and lower price impact than the same swap on a less liquid Cosmos chain. Keplr supports multiple chains, so users may not realize they are on a lower-liquidity network unless they pay attention to which chain is selected.

Staking tokens and newer assets typically have lower liquidity than established pairs. Swapping 1000 units of a newly launched token can have dramatic price impact even if the token’s fiat market cap seems substantial. Conversely, swapping between liquid staking derivatives and their underlying assets may have tighter spreads on specific DEXes designed for that pair. Users unfamiliar with a trading pair should start with small test swaps to gauge actual slippage before committing large amounts.

Cross-chain swaps using IBC (Inter-Blockchain Communication) introduce additional variables. Routing through IBC relayers and bridging protocols may add latency and additional fees. Slippage tolerance must account for the entire path, not just the final swap. Keplr handles much of this transparently, but users should understand that a “cross-chain swap” involves multiple confirmations across different blockchains, each with its own risk of delay or reorg. For this reason, cross-chain swaps may require higher slippage tolerance or patience with longer confirmation times than single-chain swaps.

For comprehensive details on Keplr’s swap mechanics, security features, and multi-chain capabilities, users can read more about wallet features and best practices. Understanding these mechanics helps users make informed decisions about when and how to execute trades across the Cosmos ecosystem without suffering unnecessary losses to slippage or MEV.

Optimizing for execution quality over time

Users who execute frequent swaps should track their execution quality metrics: the actual slippage achieved versus the set tolerance, the frequency of failed swaps due to tight tolerance, and the average price impact per pair. These metrics reveal whether current settings are sustainable or whether adjustments are needed. If a user consistently fails swaps on a particular pair with 0.5 percent tolerance, accepting 1 percent may be more realistic. Conversely, if actual slippage rarely approaches the set tolerance, tightening it could reduce MEV exposure.

The cost of a failed swap—the gas fee spent without any asset swap occurring—should also factor into tolerance decisions. On some Cosmos chains, failing a swap due to tight slippage may cost 0.1 to 0.5 ATOM in gas. If retrying the swap several times before succeeding, the cumulative gas cost could exceed the amount saved by tight slippage tolerance. A user should factor in expected gas cost when deciding whether 0.5 percent versus 1 percent tolerance is worth the risk of retries.

Finally, consolidating swaps can improve execution. Instead of executing ten small swaps across the day, batching them into two or three larger swaps may reduce total gas costs and, if executed during less volatile periods, reduce overall slippage exposure. Keplr’s ease of execution makes frequent trading convenient, but conviction about price movement and patience with timing often produce better results than reactive, rapid execution.

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شعار روز جهانی مهندسی بهداشت حرفه ای و ایمنی کار 2025

The 2025 World Day for Safety and Health at Work will focus on the impacts of digitalization and artificial intelligence (AI) on workers’ safety and health.

This theme will examine various new technologies through an occupational safety and health lens, including:

Advanced robots
Artificial intelligence (AI) and machine learning
Exoskeletons
Unmanned aerial vehicles (UAVs)
Internet of Things (IoT)
Virtual and Augmented Reality
The campaign will also shed light on new work practices, such as automation of tasks, big data analysis, smart digital systems and workers management through AI.

New technologies have also given rise to new types of work, such as digital platform work and remote work/hybrid work/telework, which will be further examined.

The ILO will produce a report and other promotional materials examining these critical issues, looking at how the digital transformation of work may support safe and healthy working environments and what governments, employers and workers and other stakeholders are already doing to respond to these emerging challenges.

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Target

The software is very useful for ergonomists and for all professionals in the area of occupational safety and health. It’s also very good for educational purposes.

Ergonomic Tools

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دانلود پاورپوینت ارزیابی ارگونومیکی پوسچر به روش OWAS

OWAS identifies the most common work postures for the back (4 postures), arms (3 postures) and legs (7 postures), and the weight of the load handled (3 categories). Whole body posture is described by these body parts with a four digit-code. These 252 postures have been classified to four action categories indicating needs for ergonomic changes. The observations are made as “snapshots” and sampling has usually been with constant time intervals.

this method was developed in Finland in a steel industry companly, Ovako Oy, in 1973 to describe the workload in the overhauling of iron smelting ovens (Karhu 1977). A portable computer system for coding and analysis of OWAS has been developed (Kivi 1991).

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