“Building itself got easier too, so it’s really about what to build and how stand out to get distribution.”
$NEAR payment ideas connect checkout and event deposits
For merchants and application developers, Polosukhin proposed a universal checkout widget that would let customers pay through $NEAR Intents using the wallet and blockchain they already hold assets on.
Under his suggested approach, developers would offer the button directly to merchants and seek to bring it into Shopify. His post presented Shopify as a potential distribution target for the proposed widget.
For event organizers, he suggested a registration system that would hold attendees’ deposits in escrow until the host checks them in. Participants who attend would then divide the contributed funds, according to his outline.
Drawing on an earlier project called Kickback, he proposed extending the model across different blockchains and assets to make crypto event registrations more reliable.
Wallet compatibility also featured in his distribution advice. Through Aurora’s Intent Connect, he recommended allowing people to use applications from other chains without arranging a bridge or learning how to operate on $NEAR.
An existing integration provides context for that suggestion. On Sep. 17, Aurora Labs announced one-signature Sui execution, allowing supported assets from other networks to enter Sui applications through its Intents Connect product. According to the company, users could complete supported actions without separately switching wallets, bridging funds, or obtaining SUI for transaction fees.
Aurora Labs said $NEAR Intents supplied the liquidity, settlement infrastructure and connections between chains, while applications could use the integration for lending, trading, staking and yield products.
AI pets would carry NFTs and computing balances
For a consumer-facing application, Polosukhin proposed an “AI Tamagochi” built around digital characters represented by non-fungible tokens.
In his design, each character would carry a $NEAR balance to fund AI inference, with $NEAR AI running verifiable inference from a prompt encoded onchain. He suggested feeding and dressing features, followed by possible battle arenas and competitions.
His proposed payment mechanism has related infrastructure already in place. As crypto.news reported on July 31, $NEAR introduced staking-based AI payments that convert locked tokens into monthly computing credits. $NEAR said the system covered 43 AI models at launch, including models from OpenAI, Anthropic, and Google.
According to the protocol, users retained ownership of their staked tokens and could recover them after unstaking, while their locked balance determined the computing credits available.
For another information product, Polosukhin suggested an AI encyclopedia whose facts would become ongoing prediction markets, borrowing from the Augur model. Rather than relying on community editing, his proposal would have AI generate pages from the markets’ current assessment of each fact.
In a separate benchmarking idea, he proposed testing AI models against private datasets without exposing the underlying material. His outline called for certified results tied to a particular model and the identifying hash of the test set, with developers submitting a testing framework or privately hosted model.
Private trading proposals cover counterparties and position management
For over-the-counter trading, Polosukhin identified finding a counterparty as an obstacle in $NEAR Intents’ existing “private deals” feature.
According to his post, users currently need to know whom they are trading with. He proposed a marketplace where participants could communicate available deals and let others fill them through $NEAR Intents.
For social trading, he suggested selling viewing keys that would give selected customers access to otherwise private transactions. In his outline, traders could display their profit and loss publicly while limiting access to the activity that followers would copy.
Under the label “Never get liquidated,” he also proposed using $NEAR Intents to manage lending and perpetual futures positions, seeking interest income while managing liquidation exposure. He presented the phrase as an application idea, rather than a demonstrated guarantee.
A separate strategy proposal would pair spot holdings with perpetual futures positions under a $NEAR contract that manages transactions through Intents. Polosukhin described the concept as “Delta neutral anything.”
U.S. investors already have a listed route to the network’s token. On Sep. 29, Bitwise announced its U.S. $NEAR ETF launch on NYSE Arca under the ticker NRR, with a 0.75% management fee. According to the asset manager, the fund holds $NEAR directly and plans to stake its holdings, with rewards accruing through its net asset value.
In explaining Bitwise’s investment case, chief investment officer Matt Hougan cited $NEAR Intents as an example of settlement infrastructure for AI agents handling activities such as payments and swaps.
Private AI tools would handle code, health data and recordings
For software teams, Polosukhin proposed an agent that would privately review private pull requests through $NEAR AI, with verifiable execution.
In healthcare, he suggested a frontend that would encrypt medical data end-to-end before sending it to $NEAR AI for a second opinion. His proposal paired crypto and fiat payments with custom prompts stored in NFTs, which users could choose for different agent expertise.
For desktop users, he outlined a private transcription application modeled on Granola. According to his design, $NEAR AI would process the transcription while users kept their records locally, with backups encrypted using their own keys.
As an alternative to subscription payments, he suggested funding the transcription service through $NEAR staking.
For companies raising capital, his list included a capitalization-table tool with related fundraising instruments, crypto payment acceptance, and banking support.
On distribution, Polosukhin advised developers to engage NEARLegion, reach other communities through cross-promotion, and promote their products through Product Hunt, similar discovery platforms, and X.
