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With the @Onyx Goliath Testnet now live, it's time to participate. Get started at goliath.net with testnet $XCN distributed through the faucet at t.me/GoliathFaucetB…

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Updates related to the @Onyx Goliath testnet are posted on the official testnet channel at t.me/goliathupdates ✅
Ready to get started? goliath.net 👈

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Explore Onyx AI v2 and deploy smart contracts or tokens across Ethereum, BNB Chain, Base, and Onyx at ai.onyx.org
Powered exclusively by $XCN ✅

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Get started with the $XCN Ledger by bridging at bridge.onyx.org and adding the network using onyx.org ✅

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When $XCN isn't being used to exclusively power the all new @Onyx AI v2 or Onyx Ledger, it's being used gas-free via the Onyx Wallet ✅
Explore the ecosystem at onyx.org and get ready for goliath.net 🔜

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@karpathy Hi Andrej. A solution to your problem exists:#STLAI
STLAI solved the The 'r' in strawberry problem in one step/prompt. Why? This became possible because STLAI-001 Language Standard performs Reinforcement Learning and System Prompt execution at the same time.
#STLAIDATASETSTRAT
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We're missing (at least one) major paradigm for LLM learning. Not sure what to call it, possibly it has a name - system prompt learning?
Pretraining is for knowledge.
Finetuning (SL/RL) is for habitual behavior.
Both of these involve a change in parameters but a lot of human learning feels more like a change in system prompt. You encounter a problem, figure something out, then "remember" something in fairly explicit terms for the next time. E.g. "It seems when I encounter this and that kind of a problem, I should try this and that kind of an approach/solution". It feels more like taking notes for yourself, i.e. something like the "Memory" feature but not to store per-user random facts, but general/global problem solving knowledge and strategies. LLMs are quite literally like the guy in Memento, except we haven't given them their scratchpad yet. Note that this paradigm is also significantly more powerful and data efficient because a knowledge-guided "review" stage is a significantly higher dimensional feedback channel than a reward scaler.
I was prompted to jot down this shower of thoughts after reading through Claude's system prompt, which currently seems to be around 17,000 words, specifying not just basic behavior style/preferences (e.g. refuse various requests related to song lyrics) but also a large amount of general problem solving strategies, e.g.:
"If Claude is asked to count words, letters, and characters, it thinks step by step before answering the person. It explicitly counts the words, letters, or characters by assigning a number to each. It only answers the person once it has performed this explicit counting step."
This is to help Claude solve 'r' in strawberry etc. Imo this is not the kind of problem solving knowledge that should be baked into weights via Reinforcement Learning, or least not immediately/exclusively. And it certainly shouldn't come from human engineers writing system prompts by hand. It should come from System Prompt learning, which resembles RL in the setup, with the exception of the learning algorithm (edits vs gradient descent). A large section of the LLM system prompt could be written via system prompt learning, it would look a bit like the LLM writing a book for itself on how to solve problems. If this works it would be a new/powerful learning paradigm. With a lot of details left to figure out (how do the edits work? can/should you learn the edit system? how do you gradually move knowledge from the explicit system text to habitual weights, as humans seem to do? etc.).
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@NeelNanda5 I believe what you seek is #STLAI
It wipes the floor with Anthropic's Multi-Agent system. It makes other models and projects redundant before they start.
Let's talk. One Independent to One Independent.
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@Breedlove22 BTC = Car+Parking Space+Garage
(BTC Network = Roads)
(BTC Blockchain = Parking Lot)
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@elonmusk STLAI cuts billions in waste, saves trillions of tokens, aligns Agentic AI, and rewrites the economics of prompting.
If your LLM or AI System still use raw natural language, you're burning money, time, amd energy!
#STLAI #AIeconomy #AgenticAI #LLMFuel #AIFuelEngineers #NoBox
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The power growth is incredible
Jesse Peltan@JessePeltan
Last year, China’s electricity demand grew by more than one Germany …or every data center on Earth combined.
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@elonmusk @grok Everyone’s racing to build faster AI engines.
We built the better engine, and the better fuel system.
STLAI is a paradigm shift: compressed input, clean logic, exponential scale.
Agentic AI runs better on STLAI.
#STLAI #AgenticAI #AIefficiency #FractalIntelligence #DyslexicBrain
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@robertskmiles Have you heard of an LLM and AI Safety System called STLAI-001? It also approaches inner alignment from a different perspective. Thanks for the videos.👍🏾
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@sama @tomieinlove STLAI-001 is the solution. STLAI-001 is a Compact Fractal Intelligence System and Domain-Specific Input Format for Deep Learning Machine-Models and Data Distillation Processes: Data Quality Management, Data Stratification, and Computational Cost-Reduction Systems. PM me for info
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@tomieinlove tens of millions of dollars well spent--you never know
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@rowancheung The language standard also matches the economic model of the systems (I have dyslexia so I find weird patterns). I'd like to share my findings with you and hope that we can connect.
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@rowancheung Hi Rowan. I just found your research paper on 'Principled Instructions'. I am creating a prompt guide for English teachers and educators and have found the language standard required to communicate with A.I.
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Opportunity: We're hiring new positions at The Rundown!
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Editorial Lead
Oversee The Rundown AI newsletter (500k subs) and provide social media channels to distribute our content to a wider audience.
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All positions are completely remote, have opportunities for substantial growth, and you'll work closely with a team extremely excited about the future of AI.
If you think you'd be a good fit, apply with the link in the next tweet.
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