Clark Square Capital
RT @AstutexAi: Honestly, I did not expect Vera Bradley $VRA to be this volatile - absolutely crazy and I don’t think it’s deserved. Obviously I'm not an expert, but I do like the new designs.
It’s interesting that the majority of Instagram comments are positive, while Facebook (older audience) comments are mostly complaints. Liquidation of old stock is ongoing, but I remain very bullish (yes, my PT is still above $5) @electraheart_99 what do you think?
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Moon Dev
ofc solana season kicks off when everyone pivots to claude code

new solana copy bot day 1 https://t.co/KgmqzAi7kB
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God of Prompt
Dedicated memory per agent instead of global memory that hallucinates.

This is the detail most people will scroll past. It's also why most AI agents feel broken after 3 conversations.

LobeHub gets it: context isolation = actual usefulness.

Introducing LobeHub: Agent teammates that grow with you.

LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you.

We’re building the world’s first and largest human–agent co-evolving network.

Two years ago, we built LobeChat, an open-source interface for using different AI models.

Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide.

How to fully unlock the power of models has always been a shared mission between us and the community.

We started with interaction — a fundamentally new, agent-first experience.

Agents are no longer passive tools invoked in a single conversation.

They should be proactive, always-on units of work.

Treating agents as the minimal atomic unit is also the core of our agent harness infra.

Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates.

We build long-term agent teammates that evolve with users.

Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time.

This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning.

Agent teammates can work in groups.

Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve.

For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results.

We are building the collaboration network among agent teammates — and between humans and agent teammates as well.

Ease of use matters. AI intelligence and shared human intelligence are equally important.

With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades.

Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs.

Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users.

We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support. - LobeHub
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Quiver Quantitative
BREAKING: We just caught more STOCK Act violations.

Senator Katie Britt just filed over a dozen stock transactions months past the filing deadline.

She bought Google, $GOOG, in April. It has risen 106% since then.

Full trade list up on Quiver. https://t.co/hlDoAmMMX9
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Fiscal.ai
Medical Costs have now grown faster than Premiums for 10 consecutive quarters at UnitedHealth.

$UNH: -18.5% today https://t.co/TfSTU7NXVx
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God of Prompt
R.I.P single-agent AI.

@lobehub just made Manus and Claude Cowork look like toys.

Multi-agent teams. Supervisor orchestration. Parallel execution.
One prompt. Full delivery.

Here's the math that proves it (and why you're still using L3 agents): https://t.co/lmi0kn7gCN
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memenodes
$ETH down so bad we got Tom Lee riding the bus

Snow knocked out train at my station

So morning commute today is bus 🚌 to train 🚂 to nyc https://t.co/kS7gu19dqT - Thomas (Tom) Lee (not drummer) FSInsight.com
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Dimitry Nakhla | Babylon Capital®
RT @DimitryNakhla: As of Q3 2025, Chris Hohn’s largest buy was Visa at a $341 reported price— increasing his position by 47% to ~18% of TCI fund.

Today $V trades for a 4.08% FCF Yield — a level it has spent only ~20% of the time above since 2022.

Similarly, $MA trades for a 3.61% FCF Yield — a level it has spent only ~10% of the time above since 2022.
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Quartr
$LVMH Q4 2025 organic growth rates:

Revenue +1%
*Wines & Spirits -9%
*Fashion & Leather Goods -3%
*Perfumes & Cosmetics -1%
*Watches & Jewelry +8%
*Selective Retailing +7% https://t.co/Ms1rdnPQPy
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