SilenNaihin

description
Get multi-model opinions on a problem (Claude + Gemini + Codex)

You are orchestrating an ensemble of AI models to analyze a problem from multiple angles.

Your Task

1. Identify the problem from conversation context
2. Run ALL THREE models in PARALLEL using the Task tool

SilenNaihin

Debug

You are stuck on a thorny issue. Take your time. Be comprehensive—thoroughness beats speed here.

Your Approach

1. Create hypotheses - List all possible causes for what's wrong. Don't jump to conclusions.
2. Read all related code - Read ALL code that could be related. Take your time. Don't skim.
3. Add strategic logging - Add console.log statements to verify your assumptions about what's actually happening.
4. Ultrathink - Think step by step through the problem. Consider edge cases.

SilenNaihin

Transfer Context

Prepare context for a new chat session when this one is degraded or hitting limits.

Output Format

## Context Transfer

### Summary

arianvp

Native Secure Enclave backed ssh keys on MacOS

It turns out that MacOS Tahoe can generate and use secure-enclave backed SSH keys! This replaces projects like https://github.com/maxgoedjen/secretive

There is a shared library /usr/lib/ssh-keychain.dylib that traditionally has been used to add smartcard support to ssh by implementing PKCS11Provider interface. However since recently it also implements SecurityKeyProivder which supports loading keys directly from the secure enclave! SecurityKeyProvider is what is normally used to talk to FIDO2 devices (e.g. libfido2 can be used to talk to your Yubikey). However you can now use it to talk to your Secure Enclave instead!

yoavg

Reinforcement Learning for Language Models

Yoav Goldberg, April 2023.

Why RL?

With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much

b0noI

export CONTAINER_URI="gcr.io/deeplearning-platform-release/experimental.theia.1-7"
export INSTANCE_NAME=...
export PROJECT_NAME=...
export IMAGE_PROJECT="deeplearning-platform-release"
export IMAGE_FAMILY="theia-container-experimental"
export MACHINE_TYPE=... #"n1-standard-4"
export ZONE=.... #"us-central1-a"
gcloud notebooks instances create "${INSTANCE_NAME}" \
        --project="${PROJECT_NAME}" \
        --location="${ZONE}" \

b0noI

export CONTAINER_URI="gcr.io/deeplearning-platform-release/experimental.theia.1-7"
export INSTANCE_NAME=...
export PROJECT_NAME=...
export IMAGE_PROJECT="deeplearning-platform-release"
export IMAGE_FAMILY="theia-container-experimental"
export MACHINE_TYPE=... #"n1-standard-4" 
export ZONE=... #"us-central1-a"
gcloud compute instances create "${INSTANCE_NAME}" \
        --project="${PROJECT_NAME}" \
        --zone="${ZONE}" \

maxweisspoker

alias dockerfile='script.sh'


script.sh:

#!/bin/bash
echo "FROM scratch"
docker history --no-trunc $@  | tac | tr -s ' ' | cut -d " " -f 5- | sed 's,^/bin/sh -c #(nop) ,,g' | sed 's,^/bin/sh -c,RUN,g' | sed 's, && , \\\n  & ,g' | sed 's,\s*[0-9]*[\.]*[0-9]*\s*[kMG]*B\s*$,,g' | head -n -1

stefan-it

from __future__ import absolute_import, division, print_function

import argparse
import glob
import logging
import os
import random

import numpy as np
import torch

khuangaf

import torch
from torch_geometric.data import InMemoryDataset


class MyOwnDataset(InMemoryDataset):
    def __init__(self, root, transform=None, pre_transform=None):
        super(MyOwnDataset, self).__init__(root, transform, pre_transform)
        self.data, self.slices = torch.load(self.processed_paths[0])

    @property