Renars retweeted
SOUND ON. You’re hearing the first howl of a dire wolf in over 10,000 years. Meet Romulus and Remus—the world’s first de-extinct animals, born on October 1, 2024.
The dire wolf has been extinct for over 10,000 years. These two wolves were brought back from extinction using genetic edits derived from a complete dire wolf genome, meticulously reconstructed by Colossal from ancient DNA found in fossils dating back 11,500 and 72,000 years. This moment marks not only a milestone for us as a company but also a leap forward for science, conservation, and humanity. From the beginning, our goal has been clear: “To revolutionize history and be the first company to use CRISPR technology successfully in the de-extinction of previously lost species.” By achieving this, we continue to push forward our broader mission on—accepting humanity’s duty to restore Earth to a healthier state.
But this isn’t just our moment—it’s one for science, our planet, and humankind. All of which we love and are passionate about. Now, close your eyes and listen to that howl once more. Think about what this means for all of us.
Renars retweeted
I am starting a new company:
Superintelligence is within reach.
Building safe superintelligence (SSI) is the most important technical problem of our time.
We've started the world’s first straight-shot SSI lab, with one goal and one product: a safe superintelligence.
It’s called Safe Superintelligence Inc.
SSI is our mission, our name, and our entire product roadmap, because it is our sole focus. Our team, investors, and business model are all aligned to achieve SSI.
We approach safety and capabilities in tandem, as technical problems to be solved through revolutionary engineering and scientific breakthroughs. We plan to advance capabilities as fast as possible while making sure our safety always remains ahead.
This way, we can scale in peace.
Our singular focus means no distraction by management overhead or product cycles, and our business model means safety, security, and progress are all insulated from short-term commercial pressures.
We are an American company with offices in Palo Alto and Tel Aviv, where we have deep roots and the ability to recruit top technical talent.
We are assembling a lean, cracked team of the world’s best engineers and researchers dedicated to focusing on SSI and nothing else.
If that’s you, we offer an opportunity to do your life’s work and help solve the most important technical challenge of our age.
Now is the time. Join us.
Ilya Sutskever, Daniel Gross, Daniel Levy
June 19, 2024
Renars retweeted
Missed the livestream? Here’s the replay!
⚡ How FAST can you build an AI app with your favorite LLM and zero lines of code? ⚡ Join @MisbahSy, @SonicDMG, and @ogabrielluiz 🎬✨ for the ⚡ tour of Langflow!
youtube.com/watch?v=78eBQNtw…
Renars retweeted
This is surreal.
The leading Presidential candidate is explicitly saying he will make America the leader in bitcoin & crypto, while supporting the right to self-custody.
All the critics were wrong. The government won’t ban it, they’re embracing it.
Renars retweeted
RAG From Scratch: Feedback + self-reflection
Our RAG From Scratch video series walks through impt RAG concepts in short / focused videos w/ code.
This is the final part in our series, focusing on self-reflection + feedback to improve RAG systems.
🔧Problem: RAG systems can suffer from low quality retrieval (e.g., if a user question is out of the domain for the index) and / or hallucinations in generation. A naive retrieve-generate pipeline has no ability to detect or self-correct from these kids of errors.
💡Idea: The concept of "flow engineering" has been recently introduced by @itamar_mar the context of code generation: iteratively build an answer to a code question w/ unit tests to check and self-correct errors.
@HamelHusain has a great blog post that mentions the benefit of unit-testing in inference loop. Several works have applied this RAG, such as Self-RAG (@AkariAsai et al) and Corrective-RAG (@Jiachen_Gu + colleages). In both cases, checks for document relevance, hallucinations, and / or answer quality are performed in the RAG answer flow. We've implemented these ideas using LangGraph to orchestrate the checks and feedback. We've also shown that LangGraph allows both to run reliable w/ smaller OSS models.
📽️ Video:
youtube.com/watch?v=E2shqsYw…
💻 Code:
CRAG:
github.com/langchain-ai/lang…
Self-RAG:
github.com/langchain-ai/lang…
Both with @MistralAI-7b + @ollama:
github.com/langchain-ai/lang…
github.com/langchain-ai/lang…
🧠References:
1/ Self-RAG
arxiv.org/abs/2310.11511.pdf
2/ C-RAG:
arxiv.org/pdf/2401.15884.pdf
3/ Flow-engineering:
xitter.cf/karpathy/status/174804…
4/ Blog on evals, covering unit tests:
hamel.dev/blog/posts/evals/
Renars retweeted
After months of planning and hard work, I am incredibly excited and proud to announce the official launch for @hongcongoworld's genesis exhibition launch entitled 'Welcome to the Mycocene'! 🍄🖼️✨
The series comprises of 33 artworks, which serve as a narrative timeline of events that define the world's story and setting.
The exhibition was created in collaboration with @room33_ and @MuseumofCrypto and is being hosted virtually in the Metaverse through @hyperfy_io
Hit the thread below for more details and hope to see you there :) 👇👀
Renars retweeted
Ever wanted to see what was happening in LLMs like ChatGPT?
This new 3D LLM visualization tool shows all the steps to run a single token inference.
Try here: bbycroft.net/llm
