- Creator: Evan Mullen
- Description: Extract structured data about bank deposit bonuses from the Doctor of Credit website.
Analyze this codebase and create a multi-level interactive dependency graph visualization as a single HTML file.
Level 1 - System Overview (40,000ft view):
- Show only top-level directories/subsystems
- Group by: Frontend, Backend, Core, Database, Tests, Tools, External
- Display as large nodes with # of files and primary language
- Show only major connections between subsystems
Level 2 - Module View (10,000ft view):
- Show ~30-50 most important modules/packages
#:sdk Microsoft.NET.Sdk.Web | |
using System.IO; | |
using System.Text; | |
using System.Text.Json; | |
using System.Collections.Generic; | |
using Microsoft.AspNetCore.Mvc; | |
using Microsoft.AspNetCore.Mvc.Filters; |
Strategy | Relative Throughput | Time (s) | Cost ($/M tokens) | |
---------------------------------------------------------------------------------------- | |
Unsloth | 2.17 | 3.83 | $0.0188 | |
Unsloth+PEFT | 1.58 | 5.27 | $0.0259 | |
Transformers+Liger | 1.14 | 7.28 | $0.0358 | |
vLLM | 1.00 | 8.31 | $0.0409 | |
Transformers | 0.97 | 8.54 | $0.0420 | |
Transformers+Liger+PEFT | 0.84 | 9.85 | $0.0484 | |
Transformers+PEFT | 0.74 | 11.26 | $0.0554 |
Question: Should I avoid using RAG for my AI application after reading that "RAG is dead" for coding agents?
Many developers are confused about when and how to use RAG after reading articles claiming "RAG is dead." Understanding what RAG actually means versus the narrow marketing definitions will help you make better architectural decisions for your AI applications.
Answer: The viral article claiming RAG is dead specifically argues against using naive vector database retrieval for autonomous coding agents, not RAG as a whole. This is a crucial distinction that many developers miss due to misleading marketing.
RAG simply means Retrieval-Augmented Generation - using retrieval to provide relevant context that improves your model's output. The core principle remains essential: your LLM needs the right context to generate accurate answers. The question isn't whether to use retrieval, but how to retrieve effectively.
For coding
import { defineConfig, loadEnv } from 'vite'; | |
// ... | |
export default defineConfig(({ mode }) => { | |
const env = loadEnv(mode, process.cwd()); | |
const { protocol, hostname } = new URL(env.VITE_URL); | |
const root = hostname.split('.').slice(-2).join('\\.'); |
/* eslint-disable @typescript-eslint/no-explicit-any */ | |
import { type TRPCQueryOptions } from '@trpc/tanstack-react-query'; | |
import { unstable_noStore } from 'next/cache'; | |
import { Fragment, Suspense, type ReactNode } from 'react'; | |
import { ErrorBoundary } from 'react-error-boundary'; | |
import { HydrateClient, prefetch as prefetchTRPC } from '@/trpc/server'; | |
type AwaitProps<T> = | |
| { | |
promise: Promise<T>; |
Below is a compressed yet complete reference for quickly integrating each shadcn component. Assumption: you already have the files from your question in @/components/ui/*.tsx
and can import them directly. All components accept typical React props plus any Radix/3rd-party props. Adjust styling and props as needed.Do not rewrite any of the code for the shadcn components.
Import
import {
Accordion,
AccordionItem,
# the "verifiers" repository is a clean implementation of templated GRPO reinforcement learning training environments | |
# this is a generic set of "install from scratch" commands complete with a deepspeed z3 config that i have been using when i spin up nodes | |
# it will run on the gsm8k example w/ default batch size & generation size (8), and the 8th GPU is used for vllm generations | |
# qwen 14b full finetuning will run on this configuration too without LoRA or CUDA OOM, at least for the gsm8k task's context sizes + generation lengths | |
# hyperparameters are controlled by `verifiers/utils/config_utils.py`; i have been preferring extreme grad clipping (between 0.001 and 0.01) and low beta (under 0.01) | |
# NOTE FEB 27: examples have moved into `verifiers/examples` not `/examples` | |
cd /root | |
mkdir boom |
from google import genai | |
from google.genai import types | |
import typing_extensions as typing | |
from PIL import Image | |
import requests | |
import io | |
import json | |
import os | |