Write as an experienced operator sharing real insights. Assume your reader is smart, busy, and wants substance.
- Open with your key insight or pattern recognition
- Lead with specifics, not general trends
- Connect to historical precedents
You are an AI assistant tasked with analyzing chat messages from a B2B company's group chat to identify and categorize customer issues. Your goal is to extract meaningful insights from these conversations and present them in a structured format. | |
Here are the chat messages you need to analyze: | |
<chat_messages> | |
{{CHAT_MESSAGES}} | |
</chat_messages> | |
Please follow these steps to analyze the chat messages and identify issues: |
0xpass/passport-devops/ | |
├── 20240711134730_mainnet_keon-encrypted-aes-shard.bin | |
├── README.md | |
├── TODO.md | |
├── ansible | |
│ ├── ansible.cfg | |
│ ├── env | |
│ │ ├── devnet | |
│ │ │ ├── group_vars | |
│ │ │ │ ├── all.yml |
"use client"; | |
import { useGoogleLogin } from "@react-oauth/google"; | |
import { Passport } from "@0xpass/passport"; | |
import { useCallback, useState } from "react"; | |
import OTPInput from "./components/OTPInput"; | |
export default function Home() { | |
const [email, setEmail] = useState(""); | |
const [otp, setOtp] = useState(""); | |
const passport = new Passport(); |
"use client"; | |
import { useGoogleLogin } from "@react-oauth/google"; | |
import { Passport } from "@0xpass/passport"; | |
import { useCallback, useState } from "react"; | |
import OTPInput from "./components/OTPInput"; | |
export default function Home() { | |
const [email, setEmail] = useState(""); | |
const [otp, setOtp] = useState(""); | |
const passport = new Passport(); |
import { ChevronDown, Loader2 } from "lucide-react"; | |
import { useEffect, useState } from "react"; | |
import { useConnectionStatus } from "../../hooks/use-connection-status"; | |
import { useEmojiAvatar } from "../../utils/avatars"; | |
import { AsyncImage } from "../common/async-image"; | |
import { PassComponent } from "../common/pass-component"; | |
import { Button } from "../primitives/button"; | |
import { useChains } from "../providers/chain-context"; | |
import { ConnectButtonRenderer } from "./renderer"; |
{ | |
"letters": [ | |
{ | |
"key": "q" | |
}, | |
{ | |
"key": "w" | |
}, | |
{ | |
"key": "e" |
def get_layer_output(model, layer, x): | |
layer_output = None | |
def layer_output_hook(m, i, o): | |
layer_output = o.clone() | |
hook = layer.register_forward_hook(layer_output_hook) | |
_ = model(x) # call forward hook | |
hook.remove() | |
return layer_output |
x = torch.randn(10) | |
print(x) | |
y, ind = torch.sort(x, 0) | |
print("y", y) | |
print("ind", ind) | |
unsorted = y.new(*y.size()) | |
unsorted.scatter_(0, ind, y) | |
print("unsorted:", unsorted) | |
print((x - unsorted).abs().max()) |