Bull Meechum

SEC and IRS Whistleblower Extraordinaire
Exposing corruption on a massive scale
“I come from behind the moon, out of the dark, unannounced”

[Bull Meechum @BullMeechum3]

@quipnetwork @cadillion @newscientist @Quantum_Murray @trevorlanting @CathieDWood @ARKInvest @CNBC @zerohedge @HyperTechInvest @EdLudlow @DavidSacks @pmarca

ICYMI

https://t.co/zWmvGfqO3L

[Bull Meechum @BullMeechum3]

@quipnetwork @cadillion @newscientist @Quantum_Murray @trevorlanting @CathieDWood @ARKInvest @CNBC @zerohedge @HyperTechInvest @EdLudlow @DavidSacks @pmarca @CitronResearch @genejchan

QBTS is ABSURDLY cheap

https://t.co/zWmvGfqO3L

[Bull Meechum @BullMeechum3]

@quipnetwork @cadillion @newscientist @Quantum_Murray @trevorlanting @CathieDWood @ARKInvest @CNBC @zerohedge @HyperTechInvest @EdLudlow @DavidSacks @pmarca

ICYMI

https://t.co/zWmvGfqO3L

[Quip Network @quipnetwork]

Quantum computers can mine cryptocurrency at 100x less energy.

@cadillion explains in recent @newscientist article how quantum slashes crypto's energy footprint.

→ https://t.co/oOpYkjttmL

[Bull Meechum @BullMeechum3]

@P3b7_ @TeamOratomic QBTS is both problem and solution today. Testnet live on @quipnetwork is MUCH bigger than most understand

https://t.co/3S4kdd8R26

[Bull Meechum @BullMeechum3]

@P3b7_ @TeamOratomic Read the neutral atom fine print

[Charles Guillemet @P3b7_]

☢️You remember? Google optimized Shor's algorithm. The algorithm that breaks asymmetric cryptography (RSA, elliptic curves) once you have a quantum computer with enough Qubits.

The US government blocked the paper. So Google published a Zero Knowledge proof instead: a mathematical proof that they have the result, without revealing how. Cryptographic sorcery 🧙

But the Internet is sneaky. Someone launched a contest to re-discover the result with AI. The LLM searches a huge space of circuits (each one a candidate optimization of Shor's), and tests whether it beats the previous best. The clever part: they use the ZKP verifier as the reward function. No false positives, and it turns out to be a very efficient signal.

In less than 2 days, the community re-discovered Google's result !!!

🔔15 days later, the LLMs are still improving it. They're already 44% ahead of Google.

Hard to say where this stops, ie. what the true minimum quantum complexity for Shor's is. But we will not close the full gap. You still need a Quantum Computer with a relatively large number of qubits. The only thing that changed is that this number drops a little every day, and it has been dropping for 15 days straight.

[Bull Meechum @BullMeechum3]

@quipnetwork @MichaelKratsios @pmarca @HyperTechInvest @CNBC @CitronResearch @SpaceX @anduriltech Claude says this also has a swarm drone application.
AND a QBTS/Anduril/Davidson is very likely to include @PalantirTech soon

[Bull Meechum @BullMeechum3]

@quipnetwork @MichaelKratsios @pmarca @HyperTechInvest @CNBC @CitronResearch @SpaceX @anduriltech

QBTS satellite placement application

https://t.co/btK16yQGu5

[Bull Meechum @BullMeechum3]

@zenkaixbt @MichaelKratsios @DavidSacks @trevorlanting @Quantum_Murray @Cisco @CNBC @zerohedge @EdLudlow @markets @DanielTNiles @quipnetwork @SemiAnalysis_ @HyperTechInvest More

[Bull Meechum @BullMeechum3]

@zenkaixbt @MichaelKratsios @DavidSacks @trevorlanting @Quantum_Murray @Cisco @CNBC @zerohedge @EdLudlow @markets @DanielTNiles @quipnetwork @SemiAnalysis_ @HyperTechInvest Dr Lanting,
You/your colleagues need to be less “opaque” when talking abt the applications of this amazing technology at investor presentations. Specifically: analog/ digital & AI training. This is profound

[Bull Meechum @BullMeechum3]

@GuangyaHao666 @zhuokaiz These experiments are the next generation of MRAM
On analog quantum annealers
QBTS processors AS memory

https://t.co/6DtOcDpRfv

[Bull Meechum @BullMeechum3]

@GuangyaHao666 @zhuokaiz On QBTS hardware? Los Alamos disagrees w/you

https://t.co/P1JFEV9757

[Zhuokai Zhao @zhuokaiz]

Most memory setups for agentic systems are centralized.

They either provide memory only to the orchestrator, or expose one shared pool every agent reads from and writes to. This makes sense because a naive decentralized memory would just mean that each agent has its own isolated context, which goes against the goal of collaboration.

However, centralized memory can hurt multi-agent systems.

It makes every agent have the same context, which blurs the distinct roles each one is supposed to play. The shared pool also makes it computationally expensive — the agents prefill a lot more information that may not be necessary, and this gets worse over time as every update grows this shared repository.

Therefore, we (@GuangyaHao666 ) tried something different, something that's decentralized, but still collaborative.

In our latest paper, DecentMem, the agents still work together the usual way, in whatever agent structure they already have. But we let each agent keep its own private memory instead of pooling everything into a shared repository. And we make the private memory stay collaboration-aware by remembering how a task got solved and who handled each piece, so decentralizing the memory doesn't throw out the coordination signal.

Specifically, each agent's memory has two halves — an exploitation pool of past trajectories it can reuse, and an exploration pool of fresh LLM-generated candidates for things it hasn't seen yet. A lightweight online router reweights the two from stage-wise feedback from a judge, so each agent works out its own exploit/explore balance instead of us hard-coding a schedule.

Theoretically, we model each agent's search as a random walk over a graph of candidate strategies, where the two pools act as two kinds of moves — the exploitation pool is a local walk over strategies the agent already knows, and the exploration pool is a teleport that can jump anywhere in its space through the LLM prior. Under mild assumptions, that combination guarantees no agent ever gets permanently stuck in a local place, since the search can always reach any strategy. We also cast the router as a bandit problem and show it converges toward the right exploit/explore balance at an O(log T) regret rate — about the best rate this kind of online balancing can achieve.

Empirically, across 3 MAS frameworks (AutoGen, DyLAN, AgentNet), 5 backbones (Qwen3-4B/8B/14B, Gemma4-E2B/E4B), and 5 benchmarks spanning math, code, QA, and embodied tasks, DecentMem comes out ahead of the strongest centralized baseline by ~9% on average — up to ~24% in the best case — and the no-memory baseline by ~26%. It also uses up to ~49% fewer tokens, since each agent only touches its own memory instead of the whole shared repository.

We also watched how this plays out as the agents pile up experience, since a memory system should naturally support self-evolution and help the system keep improving. We show that DecentMem helps the agentic system evolve faster than every centralized baseline as it sees more tasks — on DyLAN it reaches strong accuracy roughly 2.5× sooner.

Another interesting result is that the improvement gets bigger when the agent coordination is looser and more free-form.

Going from AutoGen's fixed, scripted workflows to AgentNet's improvised, on-the-fly coordination, the relative gain widens pretty steadily, and on the loosest setup, DecentMem even lands on strategies the shared-pool baselines never reach. Our read is that keeping memory private lets different agents keep chasing different solution paths, while a shared pool drags everyone toward the same stored answers — and that variety pays off most when coordination is loose.

Zooming out, the takeaway may not be that decentralized beats centralized. It's that each agent's memory should be scoped and structured more carefully, and more personalized to that agent, which is something I think most multi-agent systems and memory designs still leave on the table.

📑 Paper: https://t.co/Vv8XdBd8N0

[Bull Meechum @BullMeechum3]

@genejchan @sakacc @IonQ_Inc BTW: you, @NiccoloDeMasi need to lay off the “We’re the CSCO of quantum networking” KookAid

https://t.co/z4kWS8D2RV

[Bull Meechum @BullMeechum3]

@genejchan @sakacc @IonQ_Inc

Fidelity absent gate speed is meaningless.
QCI dual rail:error detection then correct combined w/QBTS on chip control/cryogenics is fastest path to fault tolerant. And 1000x faster

https://t.co/QDaS4LwRjZ

[Bull Meechum @BullMeechum3]

@zenkaixbt @MichaelKratsios @DavidSacks @trevorlanting @Quantum_Murray @Cisco @CNBC @zerohedge @EdLudlow @markets @DanielTNiles @quipnetwork @SemiAnalysis_ @HyperTechInvest QBTS is ridiculously CHEAP.
Not hype. But, based on the very large and rapidly growing addressable market opportunities. $50/sh doesn’t even discount the quantum optimization mkt which they OWN

[Bull Meechum @BullMeechum3]

@1789Capital @pmarca
True. Quantum can help. According to LANL and others. QBTS already in HPC:@fzj_jsc
Hyperscalers next. I believe AWS
Fraction of energy consumption

https://t.co/iJGdQ4Szhw

https://t.co/P1JFEV9757