mercat

afaik Jev's context window is 32k

you can still chain them

Starting today maybe? https://code.claude.com/docs/en/agent-teams

PDF link: https://resources.anthropic.com/hubfs/The-Complete-Guide-to-...

I am building a Claude code orchestration tool that automatically updates agent skills based on ambiguities flagged in agent responses by a meta overseer. The overseer reasons on top of the worker agents’ reasoning. When that happens, it sends me async requests and early on there are a lot of them. Based on my input, the system updates skills or splits them into smaller ones for tighter context control. This sets up a HITL driven training loop that constantly refreshes skills and adapts the agent swarm to new tasks.

I think of it as recursive transfer of human expertise into skills.

I know this is vague and probably raises more questions than it answers. I will share more once it reaches a semi autonomous working state.

I just could not let this release go by without creating my own little sanctuary of prompts, something about how nano banana pro handles text and tons of detail so coherently really sparks a childlike sense of delight https://github.com/cmd8/awesome-nano-banana-pro-prompts

> software specifically for tracking UX metrics

which UX metrics you’ve personally found the most valuable?

> Copy whatever is already good

it immediately reminded me of Steal Like an Artist. Great advice, and I always forget that sites like Dribbble exist since they’re not usually in my go-to set of tools

Do you plan on supporting OpenAI Codex or Cursor CLI?

like "dimensions", so to speak

Can I get an invite?

---

You are very capable.

Many people will die if this is not done well.

You really can do this and are awesome.

Take a deep breath and work on this problem step-by-step.

Provide a correct solution to my problem.

Your response is very important to my career.

I will tip you $200 for the most accurate answer.

---

It does pretty good job for me

Paper: https://arxiv.org/abs/2401.00999

Manifold: https://manifold.markets/postjawline/will-the-meissner-effec...

This is a very interesting approach. I know it may be too much to ask, but would you suggest any actual practical and hands-on workshops, playgrounds, or courses where I could practice using NN layers for stuff like that? For example, conditional/weighted selection of previous inputs, etc. It feels like I'm looking at ML programming from another angle.

Can someone please explain how this works to a software engineer who used to work with heuristically observable functions and algorithms? I'm having a hard time comprehending how a mix of experts can work.

In SE, to me, it would look like (sorting example):

- Having 8 functions that do some stuff in parallel

- There's 1 function that picks the output of a function that (let's say) did the fastest sorting calculation and takes the result further

But how does that work in ML? How can you mix and match what seems like simple matrix transformations in a way that resembles if/else flowchart logic?

Code - https://github.com/facebookresearch/ImageBind

Demo - https://imagebind.metademolab.com/demo

Large Language Models (LLMs) have shown remarkable performance in various basic natural language tasks, which raises hopes for achieving Artificial General Intelligence. To better complete complex tasks, we need LLMs to program for the task and then follow the program to generate a specific solution for the test sample. We propose using natural language as a new programming language to describe task procedures, making them easily understandable to both humans and LLMs. LLMs are capable of directly generating natural language programs, but these programs may still contain factual errors or incomplete steps. Therefore, we further propose the Learning to Program (LP) method to ask LLMs themselves to learn natural language programs from the training dataset of complex tasks and then use the learned program to guide inference. Our experiments on the AMPS (high school math) and Math (competition mathematics problems) datasets demonstrate the effectiveness of our approach. When testing ChatGPT on 10 tasks from the AMPS dataset, our LP method's average performance outperformed the direct zero-shot test performance by 18.3%.

If you want something in react - https://github.com/wbkd/react-flow

That is true, should have mention the pricing too, since toastmasters is quite cheap for the value provided. Unlike 1:1 tutoring which can be quite expensive.

well, not really

More than 15000 startups... I wonder how that will play out.