The electoral college is, without question, an inseparable friend to those who believe that every American across our vast country deserves to be heard.
We must fight to defend it from the far Left.
This whole thing sucks, and I wish none of it had happened. It’s a really sad day!
But I want to make one thing clear: we’ll keep fighting for open-source AI, harder than ever. This is too important to slow down now. Let’s go!
@vladtenev The most efficient way is to not make a trading strategy idea at all and instead have data sources an agent can focus on and come up with their own ideas. By doing this, adding agent routines to come up with new ideas every day becomes possible.
If Circle collapses due to rehypothecation (buying things other than treasuries), isn't this also a problem with DeFi? I'm all for DeFi but stablecoins could easily be taken down the wrong path. Blockchain native assets like Ethereum and Bitcoin are really the only way to fully not trust intermediaries.
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.
@bot I can finally automate the spam text messages I get and get a digest of group chats. At some point I pretty much just stay on Grok bot. I see what they mean by the everything app.
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. 👇
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: https://t.co/RRc1VLSrGT
Socialism is just a bill with a bow on top because nothing is truly “free.” Someone always pays for it through higher taxes, more debt, and fewer opportunities. That someone is you.
@ClementDelangue@netflix@OpenAI@huggingface This will lower their credibility neutral stance on topics for new documentaries they make. Unfortunate they made you and your team look like you guys didn't know how to fix the problem.
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.
(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: https://t.co/HZBYgG7SWK
Website: https://t.co/VyUKiYFI2y
@octi_zhang@GoogleDeepMind Humans intervention will be during the planing and final product phase (taste) of the production line. Robots should be able to come up with their own creative ways to solve problems without human guidance. @GoogleDeepMind has the right approach.
Want a strategy that trades on who wins the Super Bowl? Or McDonald's ice cream machines?
If you can describe it, Grok can build it. No Python. No code.
Then a trading bot runs it for you, starting from $100 with up to 10x leverage.
@coinbureau This is why thinking AI is going to break cryptocurrencies' encryption makes no sense. If AI makes breaking the encryption of today's algorithms easier, then so too does finding new methods to fix these vulnerabilies.