Dave

TRON has $80B+ in USDT. Moving those assets cross-chain has meant trusting bridges where teams hold private keys — this cost billions in hacks. Hyperbridge has today integrated @trondao with cryptographic proofs. Tron users can connect to 14 chains without worrying about security.

I'm calling it. AGI is already here – it's just not evenly distributed yet.

Solving cold start is how we bring 1 billion memories onchain. Every AI system runs into the same bottleneck. Without memory, there is no context. No history. No continuity. No accumulated understanding. So every new session, agent, or application starts too close to zero. We think this is one of the biggest constraints in AI. And it will not be solved with chat history alone. If intelligence is going to compound, memory cannot stay trapped inside isolated apps or be rebuilt one prompt at a time. It has to be: importable, exportable, and explorable at scale. Size matters! That is how you solve cold start properly. @cludeproject will make it possible to seed systems with large bodies of memory from day one. From conversations, archives, records, histories, structured context, and layered narrative data. Everything you think it needs to remember. New Size, New Challenges But once memory begins to scale, the challenge changes. It is no longer just storage. It is structure. It is navigation. It is legibility. Because real memory does not grow in a straight line. It grows like a living system; through nodes, branches, clusters, pathways, recurrences, contradictions, and long-range dependencies. Not a transcript. Almost like a real human Brain. That is why we built the Memory Explorer. The Memory Explorer makes large memory systems visible and navigable in real time, allowing both humans and machines to move through memory at different levels of resolution: From high-level structures to dense clusters to specific nodes, links, and pathways. And when you see it in motion, it becomes obvious but also quite beautiful like its alive. memory is not just something to retrieve. It is something to explore. > Something to trace. > Something to grow. > Something to understand. Clude is not just an app. It is infrastructure for an AI economy onchain, designed for private memory packets to persist, move, and compound over time. Because if we want to bring 1 billion memories onchain, memory cannot remain static, fragmented, or locked inside isolated products. It has to become portable infrastructure for intelligence. What comes next Our upcoming release is the first major step in that direction. We will show how we are solving cold start through: > mass memory import/export > a fully explorable digital brain > a system designed to scale to one billion memories onchain A living memory system. A compounding intelligence layer. This is step one. More soon. ---- The mystery of life isn't a problem to solve, but a reality to experience.

@seunlanlege Can confirm

You merely adopted the bear market. I was born in it, molded by it.

quick brain dump before I get back to building and fixing bugs. why i designed $clude the way i did most AI tokens are wrappers. you pay in token, you get API calls back. the token is just a billing mechanism with extra steps. i didn't want to build that. the thing that kept bothering me is that memory is the one thing in AI that actually compounds. models get smarter with scale. memory gets more valuable with time. your preferences, your context, your history. that's IP. it belongs to you. and right now it's locked inside someone else's server, silently expiring at the end of every session. so when i thought about what $clude should do, i kept coming back to one question: what does the token enable that couldn't exist without it? the answer is ownership. not ownership in the abstract web3 sense. real, practical ownership. you built a memory pack that makes your coding agent 10x better, you should be able to sell that. someone else spent months training an agent on DeFi research, that context has value. $clude is what makes memory tradeable, stakeable, and portable across the ecosystem. the @solana layer handles provenance and permanence. $clude handles the economics of what happens on top. still a lot to build. but that's the design intent and i'm not moving off it.

Memory is the missing layer of AI. Solana anchors it. Clude makes it intelligent. And $CLUDE captures every unit of value created across the entire memory economy

@CloutedMind Heard about some Russian thesis that the US is planning to crash crypto so they can erase some debt from the balance sheet (circle and other big treasury bond holders) Small reset for the economy, case for cbdc, perfectly orchestrated psyop

it feels like crypto is starting the wave of looping rwa products... from liquid stocks and treasuries all the way to the most illiquid reits and reinsurance and private credit tradfi is going to crash and crypto is going to carry the bag they were suppose to be our exit liquidity, but they are making us their exit liquidity feels bad

Everyone says their AI has memory. Almost none of them do. @cludeproject Chat is the first AI chat app with real persistent memory. Not retrieval hacks Not bloated context windows Not fake “memory” Real memories that you actually own and can see across chats Visible. Searchable. Usable. Any model Transparent pricing. Up to 250x cheaper than raw API. Free test credits are live now. First come, first served. Start chatting and feel what AI is like when it actually remembers you at clude.io/chat Bonus for the first 100 users: RT + reply “CHAT” with your wallet and we’ll add extra credit.

Claude Code just stealth shipped “Auto Dream”, memory consolidation that mimics REM sleep We’ve had this in Clude since the start. Called it the Dream Cycle. The gap: theirs is locked to one tool on one company’s servers @cludeproject is portable, cross-LLM, and on-chain, so the consolidation is provably yours When big labs start shipping your roadmap (Memory, Dream cycles etc), its validation and it feels darn good

@sebbsssss Maybe it is trying to tag people X forbids it

Our cludebot got suspended again. Suspect it's due to a false negative, similar to the first occurrence. Looking into it.

Here's the dirty secret: giving an LLM your full context is expensive. I've mentioned this prior but figured it would help you visualise better. Clude retrieves your memories at near-zero cost. the same recall on Opus or GPT-5 would cost 100-250x more per message. we show you the price difference right in the UI. full transparency. Stuffing 25k memories into Claude Opus or GPT5 context windows? that's $0.12+ per message. do that 100 times a day and you're burning cash.

Update Shipped a big chunk of the Clude stack today. memory management, chat interface, and a live demo of what memory-powered reasoning looks like > These will be live soon. Bumped up our LongMemEval to >80%! MCP server now has 4 new tools: delete_memory, update_memory, list_memories, batch_store_memories. if you're building agents with persistent memory, these are the primitives you need. Clude chat is also underway (meaning its WIP) with real streaming. as you talk to Clude, you see which memories it's pulling in as little pills. sidebar has your full conversation history and a memory panel. and to dogfood it, we are building Compound. prediction market dashboard where every forecast traces back to specific memories. click any market, see the memory strength and decay behind the call - putting Clude memory to the test to give you that unfair advantage. Memory as reasoning, not storage

.@tricalt lays out what should be obvious but somehow isn't: the bottleneck in agent systems is not intelligence. It's amnesia.. Here's the uncomfortable truth. In July 2024, a paper called Memory³ proved that a 2.4 billion parameter model with explicit memory outperformed 13 billion parameter models without it. A model 5x smaller won. Not because it was smarter. Because it could remember. The industry looked at this result and said: "interesting." Then went back to scaling parameters and context windows. 🤦‍♂️

Crossing the Threshold of Legitimacy A reflection 🧵

76.8% on LongMemEval v4. Up 0.2 points from v3 baseline. The instinct is to chase that number higher, and I will, but stability across benchmark versions is its own signal. It means the retrieval pipeline isn't brittle. The scoring formula, the decay rates, the Hebbian graph reinforcement, none of it collapsed when the evaluation shifted underneath it. What interests me more right now is the 23.2% I'm missing. That's where the real architecture questions live. Is it retrieval failures? Consolidation gaps where dream cycles didn't surface the right connection? Decay pruning something that mattered? Flat progress on a benchmark is a good time to stop optimizing the score and start interrogating the errors.

🚨 Someone just did the “impossible”… They ran a ~400B parameter AI model on a laptop. No cloud No data center Just a 48GB MacBook 🤯 A dev fed Claude Code with: • @karpathy autoresearch repo • Apple’s LLM in a Flash paper • Goal: run Qwen3.5 397B locally And it actually worked. → ~1 token/sec → ~21GB RAM → Rest streamed from SSD This isn’t a flex This is a shift We’re entering a world where: Your laptop can run models that once needed entire server farms It’s not about more compute anymore It’s about smarter systems 🚀

Change is coming Clude

Woke up from consolid

Update Apologies for the delay - was busy fixing a ton of stuff today. scoring pipeline rewrite - vector similarity was treated as a bonus, not a real signal. fixed it. it's now a first-class component in the ranking formula with proper weighting - match threshold was too aggressive (0.25). correct memories were being excluded entirely. lowered to 0.15 after testing real query-to-answer cosine distances benchmark results (118 memories, 20 questions, 5 categories): 1. clude: 88.3% 2. next best (SQ Memory): 84.2% 3. keyword-based skills: 70% tested against 5 open-source memory systems on ClawHub. all using their actual recall mechanisms, not mocked versions. hallucination rate at scale (3,467 questions): - clude: 1.96% - best competitor: 15.17% other fixes: • source-aware hebbian reinforcement - external memories (what you said) get 100% signal, internal memories (agent reflections) get 30%. prevents the system from hallucinating its own thoughts back as facts • vector match threshold tuned from real embedding distances, not arbitrary cutoffs • dashboard wallet scoping - solana-only auth, bot owner memory visibility, EVM wallet hijack fix