Co-Founder of @alpha_insider – building automated trading systems for crypto and stock markets | Coder, investor, and DeFi enthusiast
Miami
Joined December 2025
- Tweets156
- Following138
- Followers24
- Likes1.4K
Phil retweeted
The more left-leaning the policies, the higher the cost to taxpayers. Socialism is not the solution to affordability; it is, in fact, the polar opposite.
Phil retweeted
You're probably copy trading wrong and that's why you lose money.
What’s verified? How bad are the drawdowns? How much of your account is exposed?
This video walks through what to check—and how to start for free with no risk. 👇
Phil retweeted
Frontier labs are advancing the capabilities in AI for biology but preventing the good guys (scientists, academia, non-profits, health agencies,...) to use, study and understand it with widespread but shallow guardrails that the bad guys are bypassing easily (blocked me to proofread this tweet for example 😅).
This will automatically create massive assymetry and risks in the coming years, just like in cybersecurity, especially now that frontier labs are building wet labs (HF cybersecurity scenario for biology when?).
This is why we created Carbon-X, the best open-source DNA model by midtraining Qwen3-14B on 300B tokens of DNA, English and interleaved data. We also used a hybrid tokenizer that tokenizes English with BPE and DNA with a 6-mer tokenizer.
As a result, Carbon-X outperforms all open language models we evaluated on five benchmarks of DNA understanding and generation. This is a 27-point improvement over Qwen3-14B, showing how much a language model can gain from DNA training. It is also API frontier level at 1,800x less cost than GPT 6.1 Sol!
We hope Carbon-X helps researchers explore DNA in new ways. Releasing it openly means anyone can study its capabilities, limitations, and risks. Let's make the AI biology frontier open-source to make it safe and impactful for the whole world (not just a few companies).
Apache 2.0 Model on @huggingface: huggingface.co/HuggingFaceBi…
Phil retweeted
OpenCode is currently under a sustained cyberattack, and mid-incident they ran into the same problem Hugging Face did: the frontier models they were using for defense began refusing to help fight attacking agents because analyzing the attacks started triggering safety guardrails.
Phil retweeted
(1/6) From @GoogleDeepmind’s AlphaGo to AlphaZero, we saw machines find moves that reached beyond what we’d imagined for them. No one taught them those strategies. They emerged from reinforcement learning: playing, failing, and trying again under a single binary task reward for success or failure. Inspired by them, we couldn’t help but wonder: what might a robot discover if we gave it that same freedom to learn?
That question led us to AlphaNIST. Starting tabula rasa, with that same binary reward plus only a simple control cost, a single policy solves all 20 @NIST assembly tasks at a 98.2% success rate in simulation. More surprising still, AlphaNIST can complete 10 different tasks in a row, receiving that reward only once, after all 10 are done.
Just as AlphaGo’s strategies surprised its creators, AlphaNIST discovered dexterous strategies we couldn’t have come up with ourselves.
Paper: arxiv.org/abs/2610.13354
Website: alpha-nist.github.io
You can also get the Grok bot template to easily get your agents to manage your strategy ideas:
api.alphainsider.com/resourc…
Phil retweeted
Technical moats are mostly over at this point. If you’re building something that is meant to be durable, the only moats now are:
• Proprietary data
• Network effects
• A physical component
Phil retweeted
How I built a crypto bot that trades while I sleep:
• Install one AI skill
• Answer a few questions
• Backtest it (v1 failed)
• One prompt to fix it
• Schedule it for 10pm nightly
• Paper trade before going live
No code. Full build in the video.
I have been using the Grok @bot template (found on the API docs) that has all AlphaInsider skills built in. It is impressive on how easy it is to go from idea to fully functional trading strategy. Highly recommend giving these skills a try.
Phil retweeted
Jensen Huang: “Safety is paramount. In a lot of ways, it’s job one. However, safety is an engineering problem... If we’re not confident about the safety of the products — like all companies, like you and I, all the companies here — if you build a product or a service and you’re not confident in its functionality, capability, or safety, then don’t release it.
That’s a very obvious thing to do. You pace yourself until you are confident you’re releasing something that the market would appreciate. The market forces are already there. We don’t need any new laws. We don’t need new regulations.”
It's in every AI labs' best interest to make their models (products) harmless to their customers. If a model harms someone, the courts should handle this rather than stifle innovation preemptively.
Last month I wrote about how we can build a positive and safe future for everyone: meta.com/thefutureisforevery…
Every lab has the responsibility and incentive to move at the pace required to train its models safely, and the ability to take its own actions to ensure that happens.
The reality is:
- People won't want to use agents that are misaligned with them and that don't do what they ask, so labs have a strong natural incentive to make their models more aligned.
There is a lot of debate about slowing progress on capabilities until alignment catches up. My view is that trust and alignment are quickly becoming the most important capabilities that will differentiate agents and models. Any lab that doesn't focus on alignment will fall behind.
- Labs face significant liability if their models cause harm, so they have a strong incentive to prevent this as well.
Meta delayed shipping Muse for several months to focus on safety and security. We didn't call for everyone else to do this before we would. We just did it as part of our day-to-day work because it was clearly the right thing for people and for us. I'm proud of the security foundations we've built.
- Engaging independent evaluators and advisors is industry best practice. MSL already does this today in several areas because it helps produce better work. Other labs can just do this too. In general, it would be helpful for there to be a larger and more diverse ecosystem of evaluators.
- Committing the significant majority of compute towards serving people rather than racing towards recursive self-improvement is one of the best ways to ensure we develop this technology safely. Meta has made this commitment and other labs can do this as well.
I believe the key to building a positive future for everyone is maintaining the right balance of power. This is within our power to do.
Any idea you have for a trading strategy can now be implemented easily with this skill. No coding experience is needed.
Phil retweeted
Dario has written that we need to “pace the frontier,” and Sam has agreed. People may be surprised by my response: go ahead.
You guys are the frontier. By any reasonable metric — market share, revenue growth, model capability — the two of you have a duopoly on frontier intelligence. You’ve also claimed the lead is widening because of recursive self-improvement.
I don’t see what you see in the lab. If the unreleased models are scary enough that you think you should slow down, I support your decision to be responsible.
But stop pretending you need anyone else’s permission. Stop pretending antitrust law has to be suspended so you can form a cartel. Stop pretending you need a regulatory approval process that supersedes product liability. Stop pretending METR is independent when it is intertwined with Anthropic’s investors and staff. Stop pretending you need those same evaluators to police competitors who aren’t even at the frontier.
Most of all, stop pretending the motivation to slow down is purely altruistic. You face massive product-liability exposure if your products enable a truly damaging cyberattack. The market already punishes models that behave in unpredictable or unauthorized ways. After the Hugging Face episode, it is simply good business for OpenAI and Anthropic to trade some raw power for reliability and predictability. Call it alignment if you want. It is also just giving customers what they want.
Pacing the frontier would also create breathing room for a more intelligent conversation about regulation than Bernie Sanders’ “shut it all down.” China is very unlikely to join a global agreement, as you know, and that has to be taken into account as well.
So go ahead and pace the frontier. You are the ones setting it. The easiest way not to build superintelligence is for you to agree not to build it. Demanding your preferred regulatory framework as the price of that will look like blackmail of the public and the political system. So just do it.
If you do, you’ll buy goodwill for the next conversation. If you don’t, we’ll know this was just another bid for regulatory capture — or an election-season psyop.
Phil retweeted
KYC needs to be completely abolished rather than reformed
Like many of my Twitter mutuals reading this, I'm a ZK technologist with business instinct and understand the excitement around "verifiable credentials and zero-knowledge proofs of limited identity data" as a clear improvement to the status quo...
...however, it's important not to let opportunity distract you from the real issue at hand: KYC is draconian and needs to be completely abolished rather than reformed.
Your obvious self-interest shows when you advocate for more-advanced gatekeeping technology rather than the policy changes that address the root cause
Phil retweeted
Lighter is becoming more open and easier to build on!
Developers can now use our shared trading core to create their own interfaces, apps, and trading experiences without starting from scratch, for free.
github.com/elliottech/lighte…