# Flash Sniper Introducing the revamped **Flash Sniper**, a game-changing tool in the NFT trading world. It's now fully automated, combining the previous Listener and Sniper tools into one. NFT traders can run it autonomously with preset gas settings for speedy acquisitions. NFT traders can now leave the autonomous **Flash Sniper** running without worrying about missing out on an NFT snipe (automated gas settings will play a more important role here). It is important to note that there is a difference between a *Snipe* and a *Buy*. A *Snipe* involves a tool running an autonomous process to purchase an NFT within a set parameter. A *Buy* is a manual purchase triggered by an action and usually includes a delay from time of human observation to time of trigger action. ### Problem New NFT listings on secondary market trading platforms are seen by a multiple prospective NFT buyers simultaneously. When there are multiple traders attempting to buy the same NFT, only the fastest trader with the highest gas setting will secure the purchase. ### Solution Mispriced new NFT listings are bought by a trader that combines the fastest detection tool along with the highest gas setting simultaneously. In an evolving NFT market, **Flash Sniper** contains autonomous sniping parameters with preset gas settings to increase the probability of the purchase. ### Use cases 1. Ability to run an unattended autonomous Snipe within set parameters 2. Increase the probability of securing a target NFT with preset gas settings ### **Access** **Flash Sniper** will be available via the Telegram Bot and v1.0 DApp at a later date. Requires Genesis, Alpha, or Founder Key. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://flashtools.gitbook.io/flashtools-nft-toolkit/flashtools-telegram-toolkit/flash-sniper.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.