malcolmgreaves

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

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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.

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Transfer Context

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

Output Format

## Context Transfer

### Summary

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Common Options

-#, --progress-bar Make curl display a simple progress bar instead of the more informational standard meter.

-b, --cookie <name=data> Supply cookie with request. If no =, then specifies the cookie file to use (see -c).

-c, --cookie-jar <file name> File to save response cookies to.

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from threading import Thread, Lock
from Queue import Queue
import itertools, traceback, multiprocessing

class FixedThreadPoolExecutor(object):
    """
    Executes tasks in a fixed thread pool.
    
    Makes sure to gather all returned results and thrown exceptions in one place, in order of task
    submission.

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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

malcolmgreaves

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

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If you are seeing Mongo soft rlimits warnings in your logs, or a WARNING: soft rlimits too low. Number of files is 256, should be at least 1000 when you login to mongo shell via mongo from the commandline, or any mysterious/unexplained mongo connection errors... follow this how-to exactly and it will resolve the issue for you.

(Source of this how to found at basho/basho_docs#1402)

First file: sudo vi /Library/LaunchDaemons/limit.maxfiles.plist

...containing:

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Get all gists from the user santisbon.
user:santisbon

Find all gists with a .yml extension.
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Find all gists with HTML files.
language:html

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How to mount a GCP compute instance filesystem locally using sshfs

This guide assumes that:

• you already have an instance set up on GCP that you want to mount locally
• the GCP CLI (gcloud) is installed on your local machine
• you have authenticated locally to your google account gcloud auth login

1. make sure your gcloud config is correct for the instance you're trying to access: