"Early studies suggest that GLP-1s may be effective in treating opioid, alcohol and nicotine addiction. Researchers are examining whether the drugs can also help people who are addicted to gambling, sex and shopping, among other things."
Meta announced several key updates to enhance building with Llama and strengthen the open-source ecosystem:
1. Llama API Preview: Launched a limited preview of the Llama API, a developer platform simplifying Llama application development with easy API key creation, playgrounds, SDKs, and tools for fine-tuning and evaluation. It emphasizes model portability and privacy.
2. Fast Inference Collaborations: Announced collaborations with Cerebras and Groq to offer developers access to faster Llama model inference speeds via the Llama API.
3. Expanded Llama Stack Integrations: Revealed new and expanded Llama Stack integrations with partners like NVIDIA, IBM, Red Hat, and Dell Technologies to make deploying Llama applications easier for enterprises.
4. New Llama Protection Tools & Program: Released new open-source security tools including Llama Guard 4, LlamaFirewall, and Llama Prompt Guard 2, updated CyberSecEval 4, and announced the Llama Defenders Program for partners to help evaluate system security.
5. Llama Impact Grant Recipients: Announced the 10 international recipients of the second Llama Impact Grants, awarding over $1.5 million USD to support projects using Llama for transformative change.
Overall, the announcements emphasize making Llama more accessible, easier to build with, faster, more secure, and supporting its diverse open-source community.
Just ask the LLM to walk you through each line of code, create an explain the dependency graphs and in a relatively short period of time they’ll know exactly how their code works. Using Claude Code is quite useful for this - I use it on GitHub repositories I’m curious about.
Apparently Pieter Levels:
"
Interior AI now has >99% profit margins
- GPU bill is $200/month for 21,000 designs per month or about 1¢ per render (no character training like Photo AI helps costs)
- Hosted on a shared VPS with my other sites @ $500/mo, but % wise Interior AI is ~$50 of that
+= $250/month in costs
It makes about $45,000 in MRR and so $44,730 is pure profits! It is 100% ran by AI robots, no people
I lead the robots and do product dev but only when necessary"
Yes, it is indeed possible to write LaTeX code that produces different contents when compiled to HTML versus PDF. This is typically done by using conditional commands within the LaTeX document that check for the output format being used. These conditional commands can then include or exclude specific content based on whether the document is being compiled to HTML or PDF.
In LaTeX, the ifpdf package is commonly used to check if the output is being compiled to a PDF. For generating HTML from LaTeX, tools like TeX4ht or LaTeX2HTML are used, and they often define their own specific commands or provide a way to detect the output format.
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It gives simple code that uses:
The \ifpdf ... \else ... \fi command checks if the document is being compiled to PDF. If it is, the content between \ifpdf and \else is included. If not (which would be the case for HTML), the content between \else and \fi is included.
The content outside the \ifpdf ... \fi conditional will appear in both the PDF and HTML versions.
Great looking product. Would you be open to developing this in a more generic way for non-US countries? The rules and compliance parts should be configurable by the company with some guardrails for the countries they operate in.
Does anyone know if something like this exists for non-US markets?
Not a lot of love given to RAG method considering I think for most applications a fine-tuned model in the truest sense won't be the best and most efficient solution to their problem.
Question:
How do you intend to make money from/monetize this?
The reason I ask is because I have several "pet" projects like this I've been meaning to develop but with the time cost and financial cost it has to be something I can at least cover the server and maintenance costs.
First, you’ll learn how GPT-4 works and why human language turns out to play such a critical role in computing. Next, you’ll see how AI-native software is being made.
Taught by Ted Benson, founder of Steamship, MIT Ph.D., & Y Combinator Alum; and Sil Hamilton, researcher of emergent AI behavior at McGill University.