Viewing profile — meame2010

which agent did i miss?

thx! contribution is much appreciated!

We built this as a simple open directory: one place to discover, compare, and learn about coding agents. It covers: - coding agents - models - mcp, skills, and protocols - benchmar…

adal supports both terminal and web ui!

thx!

will be out in a week: sign up here: https://sylph.ai/

AdaL is the world’s first local coding agent with web UI. Claude Code has proven that coding agents work best when they are local, bringing developers back to the terminal. Termina…

Accept waiting list now.

links?

Hiring founding full-stack, AI/ML engineers, and growth lead. If you have an itch for startups or you are an ex-founder, and love the promises of agents and model fine-tuning, you …

On-going open source project to reach to product-grad product. Built with AdalFlow library: https://github.com/SylphAI-Inc/AdalFlow Will including dataset creation, evaluation, and…

not few shot, but prompt tuning via text generation via auto-differentiation. https://arxiv.org/abs/2501.16673

Time to move to open-source and smaller reasoning model. Here are the top three learnings from auto-prompt optimizing DeepSeek R1 LLaMA70B for RAG: 1⃣ A trained DeepSeek R1 LLaMA70…

We use gpt4o as the backward model. But I’m excited to try deepseek r1 as it has explicit reasoning available. We are continuously adding more benchmarks to the paper with UTAustin…

Yup. The LLM-AutoDiff is just getting started. But it has proven generation-only without explicitly doing few-shot samples can be even more effective and create shorter final promp…

Author here. Yea, in this fashion. And it can create the feedback using llm as a backward engine

you need a training dataset, and a task pipeline that works. You can refer to this doc: https://adalflow.sylph.ai/use_cases/question_answering.html

Implemented in AdalFlow: https://github.com/SylphAI-Inc/AdalFlow