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:

If you use AI dev tools daily, you ve probably felt this:

You start a new session and immediately have to re-explain:

• what the project is

• what you already tried

• why certain decisions exist

• what not to repeat

Not because the AI is bad.
Because the workflow forgets.

Every AI coding tool today suffers from the same flaw amnesia.
It forgets what you ve done, what you re building, and how you think.

One of the biggest pain points in AI chatbots has been their forgetfulness having to repeat the same context over and over again. AI memory aims to solve this by allowing models like ChatGPT and the newly launched Gemini to retain past interactions.

But how well do these memory features work? Which AI ChatGPT or Gemini handles memory better? And more importantly, does AI memory provide more value in personal use or enterprise settings?

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

People are talking about MCP so much and it feels like a secret hack to unlock AI/LLM capabilities and make them do more with other tools/softwares.
Can anyone help me explain MCPs to my mom in as little words as possible? Preferably avoiding saying "Model Context Protocol"

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.

I ve been exploring MCP, an open standard from @Anthropic that aims to simplify AI integrations.

In theory, this should make it easier to connect AI with databases, task managers, or even development tools. But I m curious to know how well it actually works in practice.

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.

I'm fascinated by the ability to extend what Cursor can do with MCP features but there are so many out there, with some of questionable pedigree, that I'm having a hard time finding the gems.

I've tried a few but so far I've only gotten good usage from the Think tool which allows Cursor to basically jot down notes on it's process which it can then refer to later. Theoretically allowing more context than just the context window https://github.com/DannyMac180/m... Since I've installed it Cursor seems to use it a lot but it's hard to gauge how much it helps in practice. I'm glad the AI likes it though :)

After rebuilding the same project three times because AI forgot my architecture, I got fed up and built @CodeRide (Beta) with my team.

The problem: AI code assistants lose track of your project between sessions. Every time I start coding with Cursor, Claude, or any AI assistant, I waste time re-explaining my codebase structure, architectural decisions, and coding patterns.

What we built: The project management tool for coding agents using MCP. Upload your project documentation or PRD, and CodeRide breaks it into optimized, fully contextual tasks ready for your AI agent.

When we launched Flamme back in 2022, the vision was simple:

Help couples stay and grow in love every day.

Hey everyone, I ve been playing around with different ways to keep my ideas, research, and drafts in check, but it still feels like I m drowning in research. :P

I ve tried traditional note-taking apps, but they re not flexible. And mind maps? They start out fine but turn into a mess as it gets complex with more data.

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:

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 really enjoyed this breakdown by @raphaelschaad and @aaron_epstein (YC partner):