Yesterday, Meta announced that they have released a new collection of AI models, Llama 4, in its Llama family.

(It consists of Llama 4 Scout, Llama 4 Maverick, and Llama 4 Behemoth.)

Historically, Open AI with its ChatGPT has been on the market for the longest period.

Hi Founders,

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I keep seeing advice like use this model for the easy stuff and that one for complex problems. But it makes me wonder what really counts as a complex problem for an LLM?

For us, complex usually means lots of steps, deep reasoning, or tricky knowledge. But for AI, the definition might be different. Some things that feel easy for us can be surprisingly hard for models, while things that seem tough for us (like scanning huge datasets quickly) might be trivial for them.

Lately, I have been experimenting with how to feed context into GPT models more effectively.

For example, when fine-tuning or working with larger context windows, I have noticed that the dilemma is in organizing the surrounding information, rather than the prompt itself. Last week, I came to know that it's called Context Engineering.

I've seen ChatGPT and other LLMs recommend tools, products, and websites based on user queries.

As a solo maker, I'm curious,

how do these models decide what to recommend?

When integrating internal knowledge into AI applications, three main approaches stand out:

1. Prompt Context  Load all relevant information into the context window and leverage prompt caching.
2. Retrieval-Augmented Generation (RAG)  Use text embeddings to fetch only the most relevant information for each query.
3. Fine-Tuning  Train a foundation model to better align with specific needs.

Each approach has its own strengths and trade-offs:

Hi everyone,

I'm currently exploring a project idea : create an ultra-simple tool for launching open source LLM models locally, without the hassle, and I'd like to get your feedback.

The current problem:

I'm not a dev or into IT or anything, but I've become fascinated by the subject of local LLMs , but running an LLM model on your own PC can be a real pain in the ass :

As a marketer trying to be AI first and making sure my team does the same, I do have some issues such as:

1. Juggling many LLMs to find a base I can work on (image generation, content creation, strategy builder, content optimizer, social media content and hashtag generation,...)

2. Having to test many prompts to get what i need. Sometimes I even need to repeat the same prompt in different LLMs to get what I need

3. Need to copy paste the result on a different tool for final edits and tweaks.

4. Ending up using the same tools I used before, such as Canva, google Sheets, Google Docs, ...

I do think that AI is super helpful for content optimization, reducing time to do research, cutting labor cost and many more stuff, but I'm afraid we're still using too many tools for doing the same tasks and it's not that cheap at all!

On their livestream today, OpenAI just released a bunch of new tools for reliably building and using AI agents. From what I can tell, this is what's new-

New APIs:

• Responses API - a new multi-modal API that builds on chat completions to allow for the next-generation of tool calling, starting with the new tools announced today.

Title says it all, but in general i find that llm's are pretty bad with v3 components.
both prop names and the general composition of components seem to have sufficient variance from v2 that the llm's often use the "wrong" paradigm.
i like chakra, how can i use my tools more effectively so that it works well with gen ai?
do you have context you set, or do you constantly mentioned you are using v3?
appreciate any tips :)