From 4c0982a903cea246e4679c5620fa94e691885486 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Sun, 22 Dec 2024 19:14:28 -0600 Subject: [PATCH] improve website layout --- website/src/app/page.tsx | 419 ++++++++++++++++++---------- website/src/app/playground/page.tsx | 4 +- 2 files changed, 281 insertions(+), 142 deletions(-) diff --git a/website/src/app/page.tsx b/website/src/app/page.tsx index ac594cb6..16509172 100644 --- a/website/src/app/page.tsx +++ b/website/src/app/page.tsx @@ -1,18 +1,52 @@ "use client"; -import React, { useState } from "react"; +import React, { useState, useEffect } from "react"; import Link from "next/link"; import Image from "next/image"; -import { Scroll, ChevronDown, ChevronUp } from "lucide-react"; +import { + Scroll, + ChevronDown, + ChevronUp, + Play, + FileCode, + Github, + BookOpen, + FileText, + MessageCircle, + Gamepad2, + Menu, +} from "lucide-react"; import PresidentialDebateDemo from "@/components/PresidentialDebateDemo"; import { Button } from "@/components/ui/button"; import { sendGAEvent } from "@next/third-parties/google"; import { Card, CardContent } from "@/components/ui/card"; +import { + DropdownMenu, + DropdownMenuContent, + DropdownMenuItem, + DropdownMenuTrigger, +} from "@/components/ui/dropdown-menu"; export default function Home() { - const [showDemo, setShowDemo] = useState(true); + const [showDemo, setShowDemo] = useState(false); const [showVision, setShowVision] = useState(false); + useEffect(() => { + const handleResize = () => { + if (window.innerWidth >= 640) { + setShowDemo(true); + } else { + setShowDemo(false); + } + }; + + handleResize(); + + window.addEventListener("resize", handleResize); + + return () => window.removeEventListener("resize", handleResize); + }, []); + const toggleDemo = () => { setShowDemo(!showDemo); if (!showDemo) { @@ -44,108 +78,207 @@ export default function Home() { Powering complex document processing pipelines

-
-

- New IDE Released!{" "} - - Dec 2, 2024 - - ! Try out our new web-based IDE. -

-

- New blog post!{" "} - - September 24, 2024 - -

+
+
+

+ Launched our IDE!{" "} + + Dec 2024 + +

+

+ New paper on agentic query optimization!{" "} + + Oct 2024 + +

+

+ New blog post!{" "} + + Sep 2024 + +

+
-
- +
+ {/* Mobile Dropdowns */} +
+ + + + + + + + Demo + + + + + Playground + + + + + + GitHub + + + + - + + + + + + + + Research Projects + + + + + Discord + + + + + + Docs + + + + + + Paper + + + + +
+ + {/* Desktop Buttons - Same as before but hidden on mobile */} +
+
+ + + + + +
- {/* */} - - - - +
+ + + + +
+
@@ -154,79 +287,83 @@ export default function Home() {

- Reimagining Data Systems for Semantic Operations + Building the Future of AI-Powered Data Systems 🚀

- While traditional database systems excel at structured data - processing, semantic operations powered by LLMs bring - unprecedented expressiveness and flexibility. However, these - operations introduce new challenges: they can be incorrect, - are computationally intensive, and typically rely on remote - API calls. We're reimagining data systems throughout the - stack to address these unique challenges. Here are some - projects we are working on: + Traditional databases are great for structured data, but they + weren't built for the Gen AI era. We're rethinking + how data systems should work with LLMs - making them more + reliable, cost-effective, and actually usable in production. + Here's what we're working on:

- Query Optimizer + Agentic Query Optimizer

- Current LLM-powered systems focus mainly on cost - reduction. But for complex tasks, even well-crafted - operations can produce inaccurate results. The DocETL - optimizer uses LLM agents to automatically rewrite - pipelines, by breaking operations down into smaller, - well-scoped tasks to improve accuracy.{" "} + Most LLM systems just try to cut costs, but accuracy is + the real challenge. Our optimizer uses LLM agents to + automatically break down complex operations into smaller, + more focused tasks; kind of like having a smart teaching + assistant that helps structure your work. Early results + show this approach can significantly improve reliability. + We are also working on finding plans that are both cheap + and accurate.{" "} - Read our paper → + Check out our paper →

- Execution Engine + High-Performance Execution Engine

- Our users consistently highlight map operations as the - most valuable feature, but these require at least one LLM - call per document—making them prohibitively expensive at - scale. We're exploring novel techniques to - dramatically reduce costs for open-ended map operations - without sacrificing accuracy. + Users consistently highlight map operations as the most + powerful operationts, but they can get expensive fast - + imagine paying for an LLM call on every single document if + you have tens of thousands of documents. We're + working on techniques to dramatically reduce these costs + without compromising on quality. Approximate query + processing will have its comeback!

- Interactive Interface + Interactive Playground

- Semantic operations are highly expressive, but this power - comes with a challenge—they can be fuzzy and ambiguous in - practice. Consequently, users often need many iterations - to get semantic operations right. Through the DocETL IDE, - we're designing interfaces that help users explore - data, refine their intents, and quickly iterate on prompts - and operations. + Prompts are the primary interface between humans and + LLM-powered data systems, but crafting them is more art + than science. Our IDE explores new ways to make prompt + engineering systematic and intuitive, with interactive + tools that help users express their intent clearly and + debug unexpected behaviors.{" "} + + Try it yourself → + {" "} + + (Paper coming January 2025) +

- There are many domain-specific unstructured data processing - needs that can benefit from systems like DocETL. We work with - partners at universities, governments, and institutions to - explore how AI can improve data workflows, especially for - domain experts and those who may not have data or ML - expertise. If you'd like to learn more (e.g., bring - DocETL to your team or join our case studies), please reach - out to{" "} + We're working with universities, governments, and + organizations to solve real-world data challenges - especially + for teams without ML expertise. Want to use DocETL for your + project or be part of our case studies? Drop a line at{" "} (