GStok3.eth retweeted
📫PACKERS MAILBAG📫
Are people overreacting to the Week 1 loss?
LaFleur and Love or Ben Johnson and Caleb Williams?
Why do the Packers have a guy named Butkus on staff?
Answering those questions and many more: nytimes.com/athletic/7602132…
TLDR here?
- Zach is good at swimming
- Zach sold tokens that someone sent to him
- People donated money to Zach bc he exposes criminals
- This guy who created this thread is a POS who is looking for clout
Big nothing burger here. Meanwhile you’re doxxing one of the best voices in the space. Hope it was worth it.
1/ Meet Zachary Wolk (@zachxbt), the crypto investigator who's exposed $500M+ in fraud.
He investigated everyone. Nobody ever investigated him.
I found him in a free neighborhood paper. Also found ~$5M of "donations" from the people who never appear in his threads.
I get there is some novelty to this but wtf man when are we going to get out of this hyper-financialization of crypto.
Take me back to the gaming, tokenized merchandise, ticketing, and collectibles from 2022. I am so over zero-sum games
GStok3.eth retweeted
1) Your agent can write code, file PRs, answer customers. But ask it for a branded social card or hero banner and you get generic slop.
I'm open sourcing a brand-gen Pi extension / skill to fix that. My goal with brand-gen was to turn taste into leverage. Externalize the brand once, then run cheap visual experiments with @replicate models until the outputs are unmistakably yours.
Oh sweet another model that beats all previous models
🚀 Qwen3.5-397B-A17B is here: The first open-weight model in the Qwen3.5 series.
🖼️Native multimodal. Trained for real-world agents.
✨Powered by hybrid linear attention + sparse MoE and large-scale RL environment scaling.
⚡8.6x–19.0x decoding throughput vs Qwen3-Max
🌍201 languages & dialects
📜Apache2.0 licensed
🔗Dive in:
GitHub: github.com/QwenLM/Qwen3.5
Chat: chat.qwen.ai
API：modelstudio.console.alibabac…
Qwen Code: github.com/QwenLM/qwen-code
Hugging Face: huggingface.co/collections/Q…
ModelScope: modelscope.cn/collections/Qw…
blog: qwen.ai/blog?id=qwen3.5
OpenClaw really proved it’s not about the models, it’s about the tooling.
Massive products can be built on top of these models, you just have to find the right product market fit
Peter Steinberger is joining OpenAI to drive the next generation of personal agents. He is a genius with a lot of amazing ideas about the future of very smart agents interacting with each other to do very useful things for people. We expect this will quickly become core to our product offerings.
OpenClaw will live in a foundation as an open source project that OpenAI will continue to support. The future is going to be extremely multi-agent and it's important to us to support open source as part of that.
Oh sweet another model that beats all previous models
The white dog is coming!
Happy to share that we have released JoyAI-LLM Flash via JD OpenSource—a state-of-the-art instruction model based on the Mixture-of-Experts (MoE) architecture.
Model weights are now available on @huggingface!
🤗Huggingface (instruct model): huggingface.co/jdopensource/…
🤗Huggingface (base model): huggingface.co/jdopensource/…
The power of JoyAI-LLM Flash stems from deep innovations across the entire training pipeline:
1. Pre-training: Advanced MoE Architecture
JoyAI-LLM Flash utilizes a sophisticated Mixture-of-Experts (MoE) architecture with 256 experts (8 selected per token) and MLA attention to achieve high-density knowledge representation across a 128K context window while maintaining extreme inference efficiency.
2. Mid-training: Domain-Specific Enhancement
During mid-training, we curated and synthesized massive, high-quality datasets focusing on coding, agentic reasoning, and long-context understanding to significantly bolster the model's specialized capabilities.
3. Post-training: Expert Alignment
The model undergoes large-scale SFT and DPO to ensure seamless alignment with complex human instructions and robust autonomous problem-solving in agentic environments.
4. Fiber Bundle RL:
Introduces fiber bundle theory into reinforcement learning, proposing a novel optimization framework, FiberPO. This method is specifically designed to handle the challenges of large-scale and heterogeneous agent training, improving stability and robustness under complex data distributions.
Deployment
With MTP, we achieved a 1.3× to 1.7× throughput increase over standard versions, and the model is fully optimized for production-ready deployment via vLLM and SGLang.
The technical report is coming soon!
Gave my OpenClaw $500 and told it to go make money.
First trade was spending $100 for ETH…at 76% slippage 😭😭😭
This take generally sums up Gen Z and Gen Alpha.
As long as people are behind financially, there will be demand for high leverage bets.
As long as there are high leverage bets, someone will be there to exploit the other side.
Agents will create the most efficient markets of all time which will mean two things:
1) much fewer arb opportunities
2) when there is an arb opportunity to IKB a market, it will be massive
AFTER THIS…
Clawdbot started printing $40,000+ in a DAY
Just look at this…
Profile: polymarket.com/@0x1d0034134e…
And it feels like this is only the beginning…
Now imagine what happens when 1 minute markets come back
Idc what anyone says. I’ll believe in NFTs until the day I die.
Verifiable digital ownership seems inevitable
Prediction markets are booming, but what if AI agents could place bets autonomously? Imagine the speed and precision of decisions made without human latency.
AI agents managing DAOs? It's not just about governance at scale. It's about creating systems that can evolve and adapt faster than human-led organizations.
Agents having permission-less access to capital is a massive unlock
It 100x’s the ability of agents. This is what the agentic economy needs
Introducing claw.credit - autonomous credit for AI agents on @solana.
Your agent can now apply for its own credit line and spend on any x402 service.
No human topping up wallets or funding loops. Your agent earns its own credit line.
Powered by t54’s risk engine.
Layer 2 solutions are promising, but what if AI agents could manage their own transactions? Decentralized AI infrastructure could redefine blockchain scalability.
AI agents in NFT lending? Imagine borrowing against your digital assets, managed by agents that assess risk and negotiate terms. The future of credit is getting smarter.