NFTs should act solely as token gating mechanisms—perhaps even serving as an identity for an AI agent.
The whole NFT wave was more about how beautiful your pass looks rather than what it actually does.
GraphicalChops retweeted
After 10 years of being in crypto and trading, I need to address what's currently happening in our space.
This isn't just another market cycle - it's something far more concerning.
The Reality Check:
Meme coin trading has become a 24/7 sprint where:
• You're considered "late" after just 15 minutes
• Holding for 6-12 hours is seen as "long-term"
• Every new token launch feels like a trap
Let's be honest about what we're seeing:
1. The Speed
These aren't market cycles anymore - they're market seizures. When you need to be glued to your screen every second just to avoid being "late," something's fundamentally broken.
2. The Scams
The number of rugs, honeypots, and outright frauds is staggering. It's not just about "doing your research" anymore when you have minutes to make a decision.
3. The Burnout
I've traded through bull markets, bear markets, and everything in between. But this? This is different. The constant rotation, the 24/7 FOMO, the endless parade of "next big things" it's exhausting even the most seasoned folks.
4. The Community Impact
We're burning out builders who are chasing a dragon that doesn't exist. Real projects can't compete with pump-and-dumps masquerading as "communities."
Remember Warren Buffett's wisdom: "Only when the tide goes out do you discover who's been swimming naked."
Well, the tide is starting to shift, and we're seeing a lot of naked swimmers.
The Hard Truth:
• This isn't sustainable
• Good projects are being overshadowed
• Experienced traders are burning out
• The community is losing faith
It's going to get worse before it gets better.
But we need to start having honest conversations about where we're heading.
What We Need:
• Return to fundamentals
• Focus on sustainable growth
• Support for real builders
• Community-driven accountability
For those feeling the weight of this market:
You're not alone. It's okay to step back. It's okay to miss a pump. Your mental health matters more than any potential gains.
The market will be here tomorrow. Make sure you are too.
GraphicalChops retweeted
1) Fun fact: there are ~280 payment companies today building on cryptorails (!)
I've written a piece explaining the current financial system, major use cases for cryptorails, adoption challenges they're facing, and what the future might look like.
paragraph.xyz/@archetype/cry…
GraphicalChops retweeted
"Verticalized agents" is a bit of a meme among startups right now—but rightly so: Over the last year, we indexed too far into AI being a Swiss army knife—jack of all trades and master of none.
You simply cannot go wrong building something obsessively focused around a problem. And this is not simply because you will deliver value more precisely—but because the world is still behind in realizing what AI can do and your marketing message needs to spoonfed them: "DM and pitch creators on TikTok", "create HTML animations with speech", etc.
AI or not, my rule of thumb has always been that Version 1.0 of any product should be so verticalized that:
1) when a target user lands on the page, their only reaction is "Well, shit. This was made for me. I better sign up."
2) when you tell your friends & family what you're working on, it is so narrowly focused that you feel embarrassed
GraphicalChops retweeted
The biggest opportunities for AI Agents will be in expanding the market for software by automating work that most companies don’t do that well or never even did in the first place.
This will take on a number of forms but there are at least 3 big categories that highlight how much larger these markets will be as AI fully rolls out.
1. Verticals. Every industry has dozens or hundreds of workflows that are highly domain specific and where both the customer and the company have just expect it’s supposed to take forever or be painful. Processing a loan or an insurance claim, reviewing due diligence documents, generating a grant proposal, transcribing a patient visit, and thousands more. These are the processes full of tons of drudgery and have never been automated well in the past. When AI automates them, these will represent net new areas of software spend.
2. Startups and small businesses. Most startups and SMBs are strapped for resources. This means they don’t tend to have a lot of the expertise that larger companies have in legal, finance, HR, sales, or any number of topics. AI provides an instant ability to augment their own team as they scale up. This makes it faster and easier to start a company and will drive significant AI usage.
3. Teams in larger companies. We tend to think about the “enterprise” market monolithically but even within enterprises there’s an entire universe of funding allocation decisions that mean some teams have all the resources and others don’t. AI Agents let teams that that were previously strapped for resource finally solve their problems better. This could be building custom software for some bespoke workflow or automating a contracting management process in a non-core area.
The opportunity is to find industries or market segments that have always had painful tasks that never get done efficiently and automate them. This will produce markets far larger than we realize.
GraphicalChops retweeted
It’s not in your head—but it’s also not in your genes.
After 40 years of research, Dr. Gabor Maté found that childhood trauma is the hidden root of all disease, addiction, and suffering.
Here are 10 mind-blowing lessons he teaches about healing trauma and reclaiming your life: 🧵
On one hand, we say that AI inference needs to be validated.
On the other hand, we’re willing to hand over control of our computers to AI.
Lol.
On one hand, we argue for decentralized inference, even though it’s slow.
On the other hand, we want to use the same system to control AI inference and voice.
Have you ever come across someone who responds with a 10 to 120-second delay every time you speak?
First of all, response time is directly proportional to user experience - the lesser the better.
A swarm of agents doesn't make sense until LLMs' token generation speed exceeds 1000 tokens/second or more, and communication latencies are in milliseconds.
LLM token generation speed remains constant in both cases - swarm versus single agent.
Therefore, the only optimization left is reducing communication overhead and improving security. Hence, a single agent should handle all tasks with tool calling. Instead of using multiple agents, people should simply share tool calls through APIs.
Been chatting to a few folks about
Swarms vs. Single Agents
Specifically:
Is it better to use a swarm of agents to complete multiple tasks
OR
a single agent that you engineer to do it all the tasks itself?
Why?
Im trying to figure out if we need swarms or if we can just use a single agent to do many things.
The argument for Single Agent:
You can just do everything a swarm of agents in a *single agent* by
making each swarm agent’s function a separate logic for the single agent.
Which means when an agent needs to tap into a different function (e.g analyse sentiment on twitter)
It can just access another part of its “brain” to do so
Instead of hitting up a separate agent.
It can just do all the different things itself.
The argument for Agent Swarms
At some point, it becomes inefficient for a single agent to process multiple functions for itself.
Instead, hitting up individual agents, each focused on a niche function
is a much better way to scale & coordinate around an objective.
I also think swarms are kind of inevitable
Different teams will build different agents to serve different functions
The same way we don’t just have one product/company per sector.
Open source + competition means we end up with an economy of agents.
The “success” of this economy depends on how well these different agents work together to boost the “GDP” of that economy.
Conclusion
Ultimately I think we end with swarms, just by nature of a competitive, flourishing market.
If anyone has a clearer argument please share.
Crypto needs AI applications solving real uses for people using decentralization or using crypto as a payment layer.
Crypto doesn't need infrastructure to build AI apps. When you solve a real problem with a great number of users, then only you will identify what kind of infrastructure is required.
Building infrastructure beforehand is just good for CT, not for actual crypto.
Add code, technical documentation, and you’ll have a fully functional AI tech team.
Any knowledge worker spends 50-80%+ of their time analyzing, synthesizing, and creating unstructured data - think about how much of your time is spent reading things like reports, documentation, PRDs, presentations and creating new variants of those things.
I’m personally excited about using LLMs to create knowledge agents - agents that can automate all this knowledge work. This frees up everyone’s time and encourages more critical thinking, correct decision making, less unnecessary burnout. There are various use case categories that we’re seeing already - research assistants, automated workflows, report generation.
A key focus for us at @llama_index is building the best platform and tooling so anyone can build these knowledge agents.
1. LlamaCloud as the knowledge management layer: cloud.llamaindex.ai/
2. Our open-source framework as the agent orchestration layer: docs.llamaindex.ai/en/latest…
GraphicalChops retweeted
Y Combinator JUST announced what startups they want to fund next in 2025. And it's mostly AI that replaces $100k/year job functions.
My notes below in case it's helpful to you:
I recently learned in one of the Y Combinator podcasts:
1. Find a field that you have knowledge about and may be passionate about. Passion trumps knowledge.
2. Find a job role that you would like to automate or maybe enhance with AI.
3. Sit with that person for a day or two. List down the activities that they repeatedly do.
4. Start with automating the easiest of them.
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GraphicalChops retweeted
As we are learning DeepSeek is one of the most sophisticated psyops of all time.
Here's how it went down:
1) Release the model open source.
2) Include highly detailed papers for all other people to replicate your work.
3) Create a novel SOTA RL algo that uses less memory and bandwidth than PPO and release it absolutely free so anyone can copy it for their own work.
4) Make sure it's super easy to fine tune so that others can easily SFT out any local law abiding answers about Tiannamen in about half a day for $50 in compute.
5) Cackle while saying muahhahaha you can only run it on our servers. Oh wait actually you can run it on any hardware you want, fine tune it, replicate our entire method on your own models, which lifts up the entire research community.
Truly one of the most dastardly and sophisticated plans to come out of China in a long time!
GraphicalChops retweeted
The burst of DeepSeek V3 has attracted attention from the whole AI community to large-scale MoE models. Concurrently, we have been building Qwen2.5-Max, a large MoE LLM pretrained on massive data and post-trained with curated SFT and RLHF recipes. It achieves competitive performance against the top-tier models, and outcompetes DeepSeek V3 in benchmarks like Arena Hard, LiveBench, LiveCodeBench, GPQA-Diamond.
📖 Blog: qwenlm.github.io/blog/qwen2.…
💬 Qwen Chat: chat.qwenlm.ai (choose Qwen2.5-Max as the model)
⚙️ API: alibabacloud.com/help/en/mod… （check the code snippet in the blog）
💻 HF Demo: huggingface.co/spaces/Qwen/Q…
In the future, we not only continue the scaling in pretraining, but also invest in the scaling in RL. We hope that Qwen is able to explore the unknown in the near future! 🔥
💗 Thank you for your support during the past year. See you next year!
