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.

The large AI labs (Anthropic, OpenAI, Grok, Gemini, etc) are constantly releasing new versions and improvements.

People on social media often advocate for one specific model claiming that it performs tasks better than others.

OpenAI just dropped GPT-5.6, their largest model yet, and we're teaming up with them to get it into makers' hands.

Here's the deal:
- Build something with Sol, Terra, or Luna
- Schedule your launch on Product Hunt for July 23

- The top products walk away with $10k in OpenAI credits

OpenAI just dropped GPT-5.6, their largest model yet, and we're teaming up with them to get it into makers' hands.

Here's the deal:
- Build something with Sol, Terra, or Luna
- Schedule your launch on Product Hunt for July 23

- The top products walk away with $10k in OpenAI credits

OpenAI just dropped GPT-5.6, their largest model yet, and we're teaming up with them to get it into makers' hands.

Here's the deal:
- Build something with Sol, Terra, or Luna
- Schedule your launch on Product Hunt for July 23

- The top products walk away with $10k in OpenAI credits

Hi all, there's a distinction I keep thinking about: apps built with AI (Cursor, Claude, Copilot writing the code) vs apps that depend on AI APIs at runtime to function.

I'm in the first camp. I used AI-assisted coding heavily to build Briskly (a cleaning app that scans your room and generates a timed task list), but the app itself makes zero LLM API calls. The scanning runs on Apple's Vision framework, entirely on-device. No tokens, no per-user inference costs, no latency, works offline, and my privacy label is basically empty.

Not another chatbot builder. Not another connect your API to an LLM tool.
I mean the boring, painful infrastructure that you don't want to build every time you ship an AI agent.

Things like:

• Agent orchestration

• RAG

• 900+ integrations

• Evaluations

• Observability & analytics

• LLMOps

• Payments & usage tracking

Basically:

In a discussion forum with @monatruong_murror , we talked about how AI can help us learn things that aren t naturally familiar to us, like programming.

The biggest challenge was/is:
Getting AI to guide you toward a solution, instead of just giving you the answer.

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.

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:

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?

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.

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! 

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.

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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