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.

New AI models pop up every week. Some developer tools like @Cursor, @Zed, and @Kilo Code let you choose between different models, while more opinionated products like @Amp and @Tonkotsu default to 1 model.

Curious what the community recommends for coding tasks? Any preferences?

We just shipped multi-provider support in @Mnexium AI so you can change LLMs without resetting conversations, user context or memories.

The problem

When teams switch providers, they usually lose everything:

Claude just launched Claude Opus 4.6 . This is Claude s newest and most capable model so far. It s designed for deep reasoning, long-running agent workflows, and large codebases, with a 1M token context window in beta and stronger planning and code understanding.

Curious to hear from the community.

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.

PH builders: what are key lessons you ve learned whether technical or product or GTM from building agents? This is still such a new discipline that it would be great to share amongst this community of builders.

I ll kick off with an experience that had us scratching our heads for months last year

Hi Founders,

I excited to share that I have built ArchitectGBT - Find Your Perfect AI Model and now its BETA live. ArchitectGBT is an AI-powered tool that takes the guesswork out of LLM selection.

Describe your project in natural language Get instant recommendations ranked by speed, cost, and accuracy for your specific use case.

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?

Hi Everyone!
Solving AI audio end-to-end means tackling both generation and understanding - from text-to-speech to speech-to-text and everything in between. At ElevenLabs, we re working on breakthroughs in AI audio that bridge research and real-world use.
Ask me anything about what we re building, the challenges of scaling AI speech models, and where this space is headed. Also keen to hear what you ve built with ElevenLabs! 

Hi Everyone!
Solving AI audio end-to-end means tackling both generation and understanding - from text-to-speech to speech-to-text and everything in between. At ElevenLabs, we re working on breakthroughs in AI audio that bridge research and real-world use.
Ask me anything about what we re building, the challenges of scaling AI speech models, and where this space is headed. Also keen to hear what you ve built with ElevenLabs! 

Raycast keeps adding new models every week.

I use @Anthropic's Claude 3.7 Sonnet when coding and @OpenAI's o3-mini when speed matters. Curious what works best for the community.
Over to you! What model are you using and why?

Edit 2025-07-30: Raycast just introduced Auto Models. "Let AI choose the best model for the job at hand." Join the discussion here 

Anthropic just released two new models for Claude 4: Opus 4 and Sonnet 4. Let's find out how good they are.

What are you using it for?

What s impressed you? What s broken?

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 :

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.

There's a lot of options for models these days. I've been using Claude 3.7 but I'm curious what's been working well for others. What model are you using and why?

Is the thinking version of @Claude by Anthropic worth the extra credit spend? Does @DeepSeek work well enough to save some credits? Is it worth trying any of the @ChatGPT by OpenAI models?

There's a lot of options for models these days. I've been using Claude 3.7 but I'm curious what's been working well for others. What model are you using and why?

Is the thinking version of @Claude by Anthropic worth the extra credit spend? Does @DeepSeek work well enough to save some credits? Is it worth trying any of the @ChatGPT by OpenAI models?

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!

I am attempting to observe what you use for coding. I have come across many tools on Product Hunt + Web, but I am fairly certain I have missed quite a bit.
I divided them into "traditional" and "specialised".

Traditional AI models:

DeepSeek

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:

Me First

ChatGPT & Cursor