POD, a Base ecosystem token linked to dphn.ai, posted a sharp rebound on Aug. 23, rising 23.7% on the day, according to GMGN tracking data. Its gains over the past three days exceeded 45%, pushing its market capitalization above $264 million. The move came a day after Coinbase added BASECAT, DRB, POD, and GRASS to its asset listing roadmap. The update put fresh attention on several Base-related tokens, with POD recording one of the more notable moves in the group. BlockBeats said token prices remain highly volatile and warned users to be cautious when making investment decisions.

BlockBeats reported on Aug. 22 that HTX market data showed sharp moves across four tokens after a message that Coinbase had added BASECAT, DRB, POD and GRASS to its asset listing roadmap. BASECAT rose more than 270% in 24 hours, lifting its market cap to $32 million. DRB climbed more than 70% to a $14 million market cap. POD gained more than 28% to $235 million, while GRASS advanced more than 7% to $82 million. The report cited HTX data and did not provide additional details.

Coinbase said on its official website that it has updated its asset listing roadmap and added Cluster Protocol (CP), Basecat (BASECAT), DebtReliefBot (DRB), Dolphin (POD) and Grass (GRASS). CP, BASECAT, DRB and POD are Base chain assets, while GRASS is a Solana network asset. Coinbase said being placed on the roadmap does not mean trading is already live. The assets must still meet market-making support and technical infrastructure requirements before trading can begin.

A MarsBit report argues that AI agents are changing storage from a passive persistence layer into part of the execution path itself. As agents continuously observe, reason, call tools, write back results and preserve state, the value of storage is no longer limited to saving data after a task is complete. The report says SSDs are beginning to take on functions tied to model weights, KV cache spillover, indexing, encryption, compression, lifecycle control and long-term memory, pointing to a broader shift toward programmable, functional SSDs. The piece lays out how this transition could play out on both devices and in the cloud. On the edge, SSDs may become the long-lived state layer for personal agents, holding local models, adapters, vector indexes, personal memory and tool traces. In cloud deployments, storage nodes could move closer to the inference path, handling shared prefixes, KV data, adapters, vector search and governance. The report cites Mooncake and NVIDIA CMX as examples of systems where storage is already participating in token production rather than merely holding cold data. It also argues that the rise of agent systems does not diminish HBM. Instead, HBM, HBF, DRAM/CXL and SSDs are likely to be re-tiered by speed, mutability, capacity, cost and governance needs. Existing AI SSD efforts from Phison, Longsys, Maxio and partners are presented as early industrial samples of this shift, where the focus is moving from faster disks for AI workloads to a reallocation of responsibilities across runtime, memory hierarchy, controllers and flash.

Amazon launches AI-powered custom merchandise design via Alexa: users type prompts, get instant AI-generated designs, produced by Merch on Demand and shipped via Prime. The move challenges Redbubble in a $13B+ print-on-demand market.

Major cryptocurrencies traded lower over the past 24 hours, with Bitcoin, Ethereum, XRP, Solana, and Dogecoin all posting losses. Dogecoin saw the sharpest drop among large caps, while several smaller tokens rallied strongly.

A TrendForce research note says the memory hierarchy behind AI systems is being redrawn as the industry shifts from training to inference. High Bandwidth Memory, or HBM, dominated the training era because it solved a bandwidth problem. In inference, the pressure point has changed. Key-value cache growth during prefill and decode is pushing capacity to the forefront, creating room for three different approaches: HBF for larger-capacity memory between HBM and SSD, SSD POD for offloading cache into lower-cost storage tiers, and SRAM for ultra-fast on-chip access in decode-heavy workloads. The report walks through how each technology fits into the stack rather than framing them as direct substitutes. HBF, being developed by SanDisk and SK hynix, is aimed at much larger capacity than HBM but has not reached mass production. NVIDIA’s SSD POD concept, tied to its Dynamo framework and NIXL transport library, is designed to move KV cache from limited GPU memory into CPU RAM, local SSDs, and remote storage without stopping inference. SRAM, used aggressively by companies such as Groq and Cerebras, trades capacity for speed by keeping memory on chip. The piece also points to recent product moves from NVIDIA, Google, SambaNova, Cerebras and Etched as evidence that inference-focused hardware design is accelerating.

Crypto trader Unihax0r lost more than $200,000 after two wallets were drained across Ethereum, Base, and BSC. On-chain analysts believe the incident was likely caused by a private key leak, with evidence pointing to the Telegram trading bot SIGMA.