n•override @NeuralOverride
Blockchain | AI United states Joined April 2022-
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@Canissolana @Ledger Seeds are prone to being cracked. Millions of seeds are being tried per second not surprised it would never hit anything.
In 1968, this hypnotherapist accidentally broke the matrix. She found that the reality is in the mind: She hypnotized 45,000+ people, and they all said the same thing: The pattern? Beyond Logic. What she discovered will challenge everything you know about life: 🧵
What a crazy week in AI 🤯 - Ideogram Character - Gemini’s Deep Think - ChatGPT Study Mode - Alibaba Wan 2.2 AI Video - FLUX.1 Krea Photorealistic - Hunyuan 3D World AI Model - Microsoft Copilot Mode Browser - Zai SOTA Open-Source Agentic AI Here’s EVERYTHING your need to know:
Netflix offers dream salaries to lure top talent. Then they tell managers to constantly consider firing them. This mind-bending paradox is reshaping how Silicon Valley thinks about job security. Here's the inside story of Netflix's infamous 'Keeper Test'... A thread 🧵
Predictions for the future of software engineering:
After a few days of testing, I can safely say that the Llama 3.1 405B is the best model when it comes to prompting a personality and staying in character. I had a 2 hour long chat with "Richard Feynman" that put a big smile on my face. Here's the prompt I used 👨🏻🏫
In an interview yesterday with a startup, my friend was asked, "Mention five ways to decrease page load time." Here is how you can answer:
I can search a database with 32 million records in less than 50 milliseconds. This speed is insane! There's no reason your search is slow. There's no reason you aren't using this library when building applications with a lot of data. Take a look at @typesense. It's an open-source search engine. It's crazy fast and easy to use. They are sponsoring this post. Here is their GitHub repository: shortclick.link/1ec9ec You can use the library in Python, JavaScript, PHP, Ruby, Swift, and pretty much any other programming language out there. Here are some of the things you can do with Typesense: • Vector search over embeddings • Conversational search for RAG applications • Image similarity search • Voice search And it does all of this extremely fast. A few interesting notes about the library: • Optimized for developer happiness • Works out of the box for most use cases • Very intuitive API • Easy to deploy and scale Probably the best way to compare them is against ElasticSearch. The latter is much more complex to configure and run with a steep learning curve. Check their GitHub repository. They have examples, tutorials, and everything you need.
I just finished re-reading The Laws Of Trading by Agustin Lebron. It's one of the best books for trading I've read. Here are the 6 laws you need to know:
Introducing Mem0 (@mem0ai) - the memory layer for LLMs and AI Agents, enabling truly personalized AI interactions. It helps in understanding your users and their preferences better - who they are, what they do, their food, location, coding, writing and other preferences.
Context Embeddings for Efficient Answer Generation in RAG Proposes an effective context compression method to reduce long context and speed up generation time in RAG systems. The long contexts are compressed into to a small number of context embeddings which allow different compression rates that trade off decoding time for generation quality. Quote: "We reduce inference time by up to 5.69 × and GFLOPs by up to 22 × while maintaining high performance." RAG systems can take up a lot of computational effort that might lead to infeasible systems. So it's cool to see the use of context embeddings to mitigate this constraint. This is also promising to explore even larger amounts of contexts and large document collections.
Microsoft, Google and DeepLearning AI are giving away FREE AI courses. Here are the top 10 courses you need to stay ahead in 2024:
12 must-know Data Cleaning techniques:
AI Math Olympiad Winner - Running on Mac! 100% local 🔥 brew install llama.cpp llama-cli --hf-repo reach-vb/NuminaMath-7B-TIR-Q8_0-GGUF --hf-file numinamath-7b-tir-q8_0.gguf -p "For how many values of the constant $ k $ will the polynomial $ x^{2}+kx+36$ have two distinct integer roots?" That's it! 🤗
Creating a LLM GenAI application integrates the power of @MistralAI and @MongoDB: github.com/mistralai/cook…
I had a couple of people in my DM's asking for advice on how to become a ML Engineer. My answer has been more or less the same each time. So i put in some effort, did a proper write-up on my blog, and links to some great actionable resources Link below & in bio
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