LLMs

Mem0: The Memory Layer for Personalized AI

Mem0 provides a smart, self-improving memory layer for Large Language Models, enabling personalized AI experiences across applications.

Note: The Mem0 repository now also includes the Embedchain project. We continue to maintain and support Embedchain ❤️. You can find the Embedchain codebase in the embedchai

GitHub - mem0ai/mem0: The memory layer for Personalized AI

One thing that is still confusing to me, is that we've been building products with machine learning pretty heavily for a decade now and somehow abandoned all that we have learned about the process now that we're building "AI".

The biggest thing any ML practitioner realizes when they step out of a research setting is that for most tasks accuracy has... See more

Ask HN: What are some actual use cases of AI Agents right now? | Hacker News

You are assuming that the probability of failure is independent, which couldn't be further from the truth. If a digit recogniser can recognise one of your "hard" handwritten digits, such as a 4 or a 9, it will likely be able to recognise all of them.

The same happens with AI agents. They are not good at some tasks, but really really food at others.

The quality of dataset is 95% of everything. The rest 5% is not to ruin it with bad parameters.

After 500+ LoRAs made, here is the secret

Notion – The all-in-one workspace for your notes, tasks, wikis, and databases.

Disruptive innovation comes in two flavors: (1) New-market disruption, where the company creates and claims a new segment in an existing market by catering to an underserved customer base, or (2) Low-end disruption, in which a company uses a low-cost business model to enter at the bottom of an existing market and claim a segment.

Copilots don’t... See more

Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]

The new seed parameter enables reproducible outputs by making the model return consistent completions most of the time. This beta feature is useful for use cases such as replaying requests for debugging, writing more comprehensive unit tests, and generally having a higher degree of control over the model behavior. We at OpenAI have been using this... See more

New models and developer products announced at DevDay

𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦: it will improve your LLM performance on given use cases (e.g., coding, extracting text, etc.). Mainly, the LLM will specialize in a given task (a specialist will always beat a generalist in its domain)

𝘤𝘰𝘯𝘵𝘳𝘰𝘭: you can refine how your model should behave on specific inputs and outputs, resulting in a more robust product

𝘮𝘰𝘥𝘶𝘭𝘢𝘳𝘪𝘻𝘢𝘵𝘪𝘰𝘯:... See more

Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]

Motivation for finetuning

We went to OpenAI's office in San Francisco yesterday to ask them all the questions we had on Quivr (YC W24), here is what we learned:

1. Their office is super nice & you can eat damn good croissant in SF!

2. We can expect GPT 3.5 & 4 price to keep going down

3. A lot of people are using the Assistants API to build their use cases

4. It costs 2M$ to... See more

Paul Venuto • feed updates

So right now, LLMs (Large Language Models) are all the rage. But in the future, it’s possible that the way we get things done is composing things with a combination of LLMs, SMMs (Small, Mighty Models), agents and tools.

It’s what I call Cognitive Composition (because it sounds cool and I have a longtime love affair with alliteration).

This is how we... See more