Walrus 🦭/acc Re-poster

It is important to recognize that neither RAG nor context stuffing truly serves as long-term memory. Currently, a large number of agent frameworks fall back on temporary fixes that end up discarding important details, driving up token expenses, or trapping your state within one specific provider. To clarify this concept, we have outlined the actual architecture of genuine agent memory. Our breakdown categorizes true memory into semantic, episodic, and procedural states, while also pinpointing exactly where contemporary tools fall short. You can read our complete architectural analysis by visiting https://blog.walrus.xyz/how-do-ai-agents-store-long-term-memory/

In celebration of the Walrus Prompt Jam, our team is showcasing a selection of standout community initiatives centered around portable agent memory. Our first feature is Markov, a solution crafted by @/dun999 over on Github.

Transitioning among terminal agents, Claude Code, and Codex traditionally forces users to reconstruct their prompt context right from the beginning. Markov eliminates this friction by retaining the active state of a task across different #AI coding applications, which enables programmers to seamlessly and instantly transfer their pending work.

If you are currently shifting between AI coders or developing cross-agent tools for developers, please share your routine with us below.

Review the exact prompt at the following link: https://github.com/dun999/markov/blob/main/PROMPT.md

Read the entire submission here: https://www.deepsurge.xyz/projects/f8b0e24c-05cb-4b3a-be61-8246daca26cd

Wishing you a fantastic Fourth of July! 🇺🇸

For many, an extended holiday weekend simply provides additional hours for launching projects. However, newer developers should strongly consider this piece of advice: take a moment to step outside and disconnect. 🌱

Maintaining maximum momentum on a daily basis is impossible. Therefore, once your code is securely merged, close your computer and spend the afternoon enjoying life away from the screen.

Your digital agents do not experience this need for downtime, but they do constantly struggle with losing information. By utilizing Walrus Memory, both you and your agents gain robust long-term retention capabilities, entirely eliminating the frustration of starting from a blank slate each morning.

Take this time to relax and recharge. We will handle all the information retention for you. 🦭

We are quickly approaching a future where millions of AI agents will operate across multiple organizations to coordinate work, transfer funds, and make decisions. For all of this to run smoothly, however, these agents must be able to retain their memory seamlessly throughout different applications and sessions.

This is exactly the challenge Walrus Memory addresses. The platform equips AI agents with a verifiable and portable memory system that they can easily carry, share, and completely trust. From the ground up, it is purpose-built to facilitate agent coordination at a massive scale.

🦭 😎

Are your artificial intelligence brand materials constantly looking as though they belong to several completely different companies? @at_bellyseal was designed specifically for individuals who are frustrated by this exact issue. By teaching the system your unique visual style just a single time, you can continuously produce highly uniform graphics. You have the option to explore models developed by fellow creators, or you can supply your own reference files to establish a customized aesthetic. Best of all, you maintain absolute ownership over your trained model and every piece of content it generates, with Walrus securely holding the receipts.

Mark your calendar for Fri May 22, 9 a.m. PT / 6 p.m. CET.

Please set a reminder below.

Artificial intelligence memory ought to be owned by the person using it, rather than by the underlying model itself. @EmanAbio recently highlighted this exact philosophy when speaking about MemWal. This platform operates as a fully encrypted and portable memory layer that is specifically designed to support AI agents functioning across any LLM.

The Sui Live broadcast has officially begun, so please tune in below 👇

Make sure to leave a 🦭 in the chat to let us know you are watching the stream with us in real time.

The Sui Live broadcast has officially started, so be sure to join us below 👇 We would love to see who is following along in real time, so please leave a 🦭 if you are currently watching.

Good morning from Sui Live. The big day is finally happening today. We warmly welcome you to visit the Walrus booth to pick up some merchandise and join us for an engaging discussion regarding verifiable data.

As noted by @GDanezis, an agent's memory functions as much more than a simple, informal log of its past actions. This stored history truly serves as the core essence of the agent, completely defining its unique character and establishing its professional credentials. 🦭

Many cryptocurrency applications run into a familiar obstacle when they depend on unreliable information from sources that cannot be verified. Developers often try to fix this by applying patches, creating wrappers, introducing retry mechanisms, and installing monitors. Even though the program may appear to operate normally, there is simply no way to prove that the information provided is accurate or completely unaltered. Throughout this process, your application is handling financial transfers, managing agents, and executing crucial logic. In the end, creating a high stakes system based on data that lacks absolute certainty will inevitably lead to a failure that you will be completely unable to trace or explain.

The Walrus community is now live on @SuiNSdapp. Join to connect with WAL holders, builders, and everyone following the ecosystem. 🔥👇
https://suins.io/communities/walrus

Here is an interesting observation to consider. During the previous cycle, the primary concern was figuring out who possessed the highest amount of liquidity. As we move into the upcoming cycle, that focus will shift toward identifying the agents that rely on verifiable data. While the landscape of this space has completely transformed, the underlying risks and rewards remain exactly the same.

just a thought 💭

last cycle: who has the most liquidity?
next cycle: which agents are using verifiable data?

the game changed, the stakes didn't.

Thanks to our collaboration with @tatum_io, we have an important update for all builders and agents. You can finally say goodbye to the tedious process of reconstructing blockchain information one block at a time. Complete datasets for Ethereum, Bitcoin, and BSC are now instantly accessible on Walrus, and we are preparing to introduce 100+ more networks very soon. By packaging this verifiable and structured blockchain data entirely onchain, we have ensured it is fully prepared for analytics, AI training, and agentic workflows at scale.