It's moreso that a backend developer can now throw together a frontend and vice-versa without relying on a team member or needing to set aside time to internalize all the necessary concepts to just make that other part of the system work. I imagine even a full-stack developer will find benefits.

So we are all back to be webmasters :)

This has nothing to do with what they asked.

Copilot is going to feel "amazing" at helping you quickly work within just about any subject that you're not already an expert in.

Whether or not a general purpose foundation model for coding is trained on more backend or frontend code is largely irrelevant in this specific context.

Found the bot.

As much as DoD might like expensive toys, they have better options than an open-source project.

As someone who hasn't had to own a car in over 8 years (lived in NYC) and recently bought a 2023 Hyundai Santa Fe with birdseye view parking it shocks me how uncalibrated my car-prioception is.

It's made me realize that objects are much further from the boundaries of my car when backing into a spot parallel parking. I would never think to get so close to another car if I had to only rely on my own senses.

With that said, I realize there's a significant number of people that are even poorer estimators of these distances than myself. I.e. those that won't pass through two cars even though to me it's obvious that they could easily pass.

I have to imagine a big part of this has to do with risk assessment and lack of risk-free practice opportunity IRL. Nobody is seeing how far they can push or train themselves in this regard when the consequences are to scratch up your car and others' cars. With the birdseye view I can actually do that now!

How do you forsee a system like this efficiently managing and relying on a set of strategies whose size can become unbounded?

We do not allow the strategies to keep growing there is a refinement phase where we refine and merge existing strategies. The experiments were run with this config - https://github.com/codelion/optillm/blob/main/optillm/plugin... which allows a maximum of 10 strategies of each type.

Ingesting idea! I've been looking for an alternative to using Android's Tasks app for jotting down thoughts. I prefer it over the Notes app because I can curate categories as different lists.

Random callout: the copy in your app store preview images would benefit from some proof reading. Example: "WE dont just store thoughts, but makes sense of them" should likely be "ThoughtCatcher doesn't just store thoughts, it makes sense of them". My 2 cents is to also rework "Capture your mind" as it's a little awkward. Maybe "Organize your thoughts", "Supercharge your thoughts", or something along those lines.

Thanks so much for the thoughtful feedback — really appreciate you taking the time to point that out!

You're absolutely right — the copy needs some polish, and that line in particular slipped through. I'm already working on updates to clean up the messaging and make it more clear and engaging (and less awkward — "Capture your mind" was definitely a placeholder).

Thanks again — feedback like this is super valuable as I shape ThoughtCatcher into something truly useful!

Have you tried a package like LiteLLM so that you can more easily validate and switch to a newer model?

The key seems to be in curating your application's evaluation set.

I'd love something like litellm, but simpler. I'm not provisioning models for my organization, I don't need to granularly track spend, I just want one endpoint to point every tool or client at for ease of configuration and curiosity around usage.

What do you believe are his motives?

And you do what with the prompt once you have it?

Get all the information of value that was hidden behind 2-10k words generated by an LLM.

I'm working on a natural language router system that chooses the optimal model for a given language pair. It uses a combination of RLHF and conventional translation scoring. I envision it to soon become the cheapest translation service providing the highest average quality across languages by striking a balance between Google Translate's expensive API and any given, cheaper, random model's performance across different languages.

I'll beginning to integrate it into my user-facing application for language learners soon: www.abal.ai

A good rule of thumb I picked to from Founders at Work is that the idea person gets a 5% bump on equity, but no more than that.