From 6f9defe29a02ecacd65ed38afcde7b14edad2053 Mon Sep 17 00:00:00 2001 From: "sirin.ph" Date: Thu, 20 Nov 2025 17:04:57 +0700 Subject: [PATCH] initial commit --- .gitignore | 24 + App.tsx | 267 ++++++++ README.md | 20 + STYLE_GUIDE.md | 79 +++ components/ChatWidget.tsx | 110 ++++ components/MatrixViz.tsx | 164 +++++ components/TerminalBlock.tsx | 94 +++ index.html | 66 ++ index.tsx | 15 + metadata.json | 5 + package.json | 23 + pages/BlogPost.tsx | 444 +++++++++++++ pages/HireUs.tsx | 104 ++++ pages/Home.tsx | 699 +++++++++++++++++++++ pages/Playground.tsx | 329 ++++++++++ pages/Research.tsx | 269 ++++++++ pages/Services.tsx | 198 ++++++ pnpm-lock.yaml | 1138 ++++++++++++++++++++++++++++++++++ tsconfig.json | 29 + vite.config.ts | 23 + 20 files changed, 4100 insertions(+) create mode 100644 .gitignore create mode 100644 App.tsx create mode 100644 README.md create mode 100644 STYLE_GUIDE.md create mode 100644 components/ChatWidget.tsx create mode 100644 components/MatrixViz.tsx create mode 100644 components/TerminalBlock.tsx create mode 100644 index.html create mode 100644 index.tsx create mode 100644 metadata.json create mode 100644 package.json create mode 100644 pages/BlogPost.tsx create mode 100644 pages/HireUs.tsx create mode 100644 pages/Home.tsx create mode 100644 pages/Playground.tsx create mode 100644 pages/Research.tsx create mode 100644 pages/Services.tsx create mode 100644 pnpm-lock.yaml create mode 100644 tsconfig.json create mode 100644 vite.config.ts diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..a547bf3 --- /dev/null +++ b/.gitignore @@ -0,0 +1,24 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/App.tsx b/App.tsx new file mode 100644 index 0000000..00e6ab8 --- /dev/null +++ b/App.tsx @@ -0,0 +1,267 @@ +import React, { useContext, useState } from "react"; +import { HashRouter, Routes, Route, Link, useLocation } from "react-router-dom"; +import { Home } from "./pages/Home"; +import { BlogPost } from "./pages/BlogPost"; +import { Research } from "./pages/Research"; +import { Services } from "./pages/Services"; +import { HireUs } from "./pages/HireUs"; +import { Playground } from "./pages/Playground"; +import { ChatWidget } from "./components/ChatWidget"; +import { Cpu, Menu, X, Globe, Terminal } from "lucide-react"; + +// --- Language Context --- +type Language = "en" | "th"; + +interface LanguageContextType { + language: Language; + toggleLanguage: () => void; +} + +export const LanguageContext = React.createContext({ + language: "en", + toggleLanguage: () => {}, +}); + +export const useLanguage = () => useContext(LanguageContext); + +// --- Navigation --- +const Navigation: React.FC = () => { + const [isOpen, setIsOpen] = useState(false); + const location = useLocation(); + const { language, toggleLanguage } = useLanguage(); + + const isActive = (path: string) => { + if (path === "/research" && location.pathname.startsWith("/research")) { + return "text-ink font-semibold"; + } + return location.pathname === path + ? "text-ink font-semibold" + : "text-subtle hover:text-ink transition-colors"; + }; + + return ( + + ); +}; + +const Footer: React.FC = () => { + const { language } = useLanguage(); + return ( +
+
+
+ + Pradit + +

+ {language === "en" + ? "We build production-grade AI systems. No hype, just engineering. Specializing in LLM integration, custom evaluations, and scalable architecture." + : "เราสร้างระบบ AI ระดับใช้งานจริง เน้นวิศวกรรมไม่เน้นโฆษณา เชี่ยวชาญด้านการรวมระบบ LLM, การประเมินผล และสถาปัตยกรรมที่ปรับขนาดได้"} +

+
+
+

+ Offerings +

+
    +
  • + + MVP Development + +
    • +
  • + + System Audits + +
    • +
  • + + Custom Integration + +
    • +
+
+
+

+ Connect +

+ +
+
+
+ ); +}; + +export default function App() { + const [language, setLanguage] = useState("en"); + + const toggleLanguage = () => { + setLanguage((prev) => (prev === "en" ? "th" : "en")); + }; + + return ( + + +
+ +
+ + } /> + } /> + } + /> + } /> + } /> + } /> + +
+
+ +
+ + + ); +} diff --git a/README.md b/README.md new file mode 100644 index 0000000..4450ca2 --- /dev/null +++ b/README.md @@ -0,0 +1,20 @@ +
+GHBanner +
+ +# Run and deploy your AI Studio app + +This contains everything you need to run your app locally. + +View your app in AI Studio: https://ai.studio/apps/drive/1lSdt9DucwKsU3WO83AD5ieLoPx_QLmVF + +## Run Locally + +**Prerequisites:** Node.js + + +1. Install dependencies: + `npm install` +2. Set the `GEMINI_API_KEY` in [.env.local](.env.local) to your Gemini API key +3. Run the app: + `npm run dev` diff --git a/STYLE_GUIDE.md b/STYLE_GUIDE.md new file mode 100644 index 0000000..f210dce --- /dev/null +++ b/STYLE_GUIDE.md @@ -0,0 +1,79 @@ +# Pradit (ประดิษฐ์) Design System + +**Philosophy:** "Engineer First, Sales Second." +The aesthetic of Pradit is minimal, precise, and reminiscent of technical documentation or a high-end code editor. It avoids gradients, heavy drop shadows, and stock photography in favor of clean typography, terminal-inspired visual elements, and high information density. + +--- + +## 1. Color Palette + +We use a strict semantic color system. + +| Token | Hex | Usage | +| :---------- | :-------- | :------------------------------------------------------------------------------- | +| **Paper** | `#fdfdfd` | Main background. Slightly off-white to reduce eye strain. | +| **Ink** | `#111111` | Primary text, strong headers, active states. | +| **Subtle** | `#666666` | Secondary text, metadata, descriptions. | +| **Accent** | `#2563eb` | Primary action color (Blue-600). Used sparingly for links and active indicators. | +| **Code Bg** | `#1e1e1e` | Dark background for terminal blocks and code snippets (VS Code Dark default). | + +--- + +## 2. Typography + +We use a tri-font stack to separate functional UI from reading content. + +### Serif: Merriweather + +_Usage:_ Long-form text, blog posts, titles, quotes. +_Why:_ Establishes authority and academic rigor. + +### Sans-Serif: Inter + +_Usage:_ UI labels, navigation, buttons, short descriptions. +_Why:_ Clean, legible, and modern. + +### Monospace: Fira Code + +_Usage:_ Code snippets, terminal inputs, tags, dates, metadata. +_Why:_ Signals technical precision. + +--- + +## 3. UI Patterns & Components + +### The "Terminal" Aesthetic + +Elements that represent "work" (code, search, inputs) should mimic terminal interfaces. + +- **Inputs:** No rounded corners (or very slight `rounded-sm`). Bottom borders or dark backgrounds. +- **Prompt:** Use `>` or `$` prefixes for input labels. + +### Borders over Shadows + +Use 1px borders (`border-gray-200`) to define hierarchy. Shadows should be subtle and used only for floating elements (modals, sticky headers). + +### Buttons + +- **Primary:** `bg-ink` text-white, `rounded-sm`. Uppercase text, tracking-widest. +- **Secondary:** `border border-gray-200` text-ink. +- **Tags/Pills:** `rounded-full`, `uppercase`, `text-xs`, `font-bold`. + +### Interactive Visualizations + +Do not use static images for technical concepts. Use code blocks (`TerminalBlock`) or interactive React components (`MatrixViz`) that allow the user to manipulate variables. + +--- + +## 4. Layout Grid + +- **Container:** Max-width 7xl (`max-w-7xl`) centered. +- **Spacing:** Generous vertical whitespace (`py-24`). +- **Columns:** 12-column grid for complex layouts (Sidebar takes 3, Content takes 6-9). + +--- + +## 5. Voice & Tone + +- **English:** Professional, concise, jargon-correct. No marketing fluff. +- **Thai:** Formal but accessible. Use English terms for specific technical concepts (e.g., "Fine-tuning", "Latency") where a Thai translation would be ambiguous. diff --git a/components/ChatWidget.tsx b/components/ChatWidget.tsx new file mode 100644 index 0000000..517b83f --- /dev/null +++ b/components/ChatWidget.tsx @@ -0,0 +1,110 @@ +import React, { useState, useRef, useEffect } from 'react'; +import { MessageSquare, X, Send, Terminal } from 'lucide-react'; + +interface Message { + id: string; + text: string; + sender: 'user' | 'bot'; + timestamp: Date; +} + +export const ChatWidget: React.FC = () => { + const [isOpen, setIsOpen] = useState(false); + const [messages, setMessages] = useState([ + { id: '1', text: "System online. I am Pradit's automated assistant. How can I help you engineer your next AI system?", sender: 'bot', timestamp: new Date() } + ]); + const [input, setInput] = useState(""); + const bottomRef = useRef(null); + + useEffect(() => { + if (isOpen) { + bottomRef.current?.scrollIntoView({ behavior: 'smooth' }); + } + }, [messages, isOpen]); + + const handleSend = async (e: React.FormEvent) => { + e.preventDefault(); + if (!input.trim()) return; + + const userMsg: Message = { id: Date.now().toString(), text: input, sender: 'user', timestamp: new Date() }; + setMessages(prev => [...prev, userMsg]); + setInput(""); + + // Mock response + setTimeout(() => { + const botMsg: Message = { + id: (Date.now() + 1).toString(), + text: "ACK. Request received. Our engineers are currently optimizing a training run. Please visit 'Hire Us' to schedule a deep dive consultation.", + sender: 'bot', + timestamp: new Date() + }; + setMessages(prev => [...prev, botMsg]); + }, 800); + }; + + return ( +
+ {/* Chat Window */} + {isOpen && ( +
+ {/* Header */} +
+
+
+ +
+ Pradit/Assistant +
+ +
+ + {/* Messages */} +
+ {messages.map((msg) => ( +
+
+ {msg.text} +
+
+ ))} +
+
+ + {/* Input */} +
+
+ {">"} + setInput(e.target.value)} + placeholder="Type your query..." + className="flex-grow bg-transparent text-sm outline-none text-ink placeholder:text-gray-400 font-sans" + /> +
+ +
+
+ )} + + {/* Toggle Button */} + {!isOpen && ( + + )} +
+ ); +}; \ No newline at end of file diff --git a/components/MatrixViz.tsx b/components/MatrixViz.tsx new file mode 100644 index 0000000..59eff53 --- /dev/null +++ b/components/MatrixViz.tsx @@ -0,0 +1,164 @@ +import React, { useState } from "react"; +import { Sliders } from "lucide-react"; + +export const MatrixViz: React.FC = () => { + const [rank, setRank] = useState(4); + + // Constants for visualization scaling + const d = 16; // Hidden dimension simulation + const k = 16; // Output dimension simulation + + // Calculate imaginary parameters based on user slider + // Assuming a real model like Llama-7B, d_model might be 4096. + // We scale the display numbers to look realistic. + const realD = 4096; + const realK = 4096; + + const paramsFull = realD * realK; + const paramsLoRA = realD * rank + rank * realK; + const reduction = ((1 - paramsLoRA / paramsFull) * 100).toFixed(2); + + // Helper to generate random opacity blue blocks + const renderGrid = ( + rows: number, + cols: number, + colorBase: string, + label: string, + ) => { + return ( +
+ {Array.from({ length: rows * cols }).map((_, i) => ( +
+ ))} + + {/* Matrix Label Overlay */} +
+ {label} +
+
+ ); + }; + + return ( +
+
+
+

+ + Interactive Decomposition +

+

+ Adjust the Rank (r) to see how LoRA decomposes the weight update + matrix into two smaller dense matrices. +

+
+ +
+ + setRank(parseInt(e.target.value))} + className="w-32 h-2 bg-gray-200 rounded-lg appearance-none cursor-pointer accent-accent" + /> +
+
+ +
+ {/* Matrix B (d x r) */} +
+
+ B +
+ {renderGrid(16, rank, "#93c5fd", `${realD} × ${rank}`)} +
+ +
×
+ + {/* Matrix A (r x k) */} +
+
+ A +
+ {renderGrid(rank, 16, "#2563eb", `${rank} × ${realK}`)} +
+ + {/* Arrow */} +
⟶
+ + {/* Matrix Delta W */} +
+
+ ΔW +
+ {renderGrid(16, 16, "#4ade80", `${realD} × ${realK}`)} +
+
+ + {/* Math Dashboard */} +
+
+
+ Trainable Params (LoRA) +
+
+ {paramsLoRA.toLocaleString()} +
+
+ ({realD}×{rank}) + ({rank}×{realK}) +
+
+ +
+
+ Trainable Params (Full) +
+
+ {paramsFull.toLocaleString()} +
+
+ {realD} × {realK} +
+
+ +
+
+ Memory Reduction +
+
+ {reduction}% +
+
+ More VRAM for batch size +
+
+
+
+ ); +}; diff --git a/components/TerminalBlock.tsx b/components/TerminalBlock.tsx new file mode 100644 index 0000000..654fe1f --- /dev/null +++ b/components/TerminalBlock.tsx @@ -0,0 +1,94 @@ +import React, { useState, useEffect } from "react"; + +interface TerminalBlockProps { + title?: string; + lines: string[]; + className?: string; + typingEffect?: boolean; +} + +export const TerminalBlock: React.FC = ({ + title = "bash", + lines, + className, + typingEffect = false, +}) => { + const [displayedLines, setDisplayedLines] = useState( + typingEffect ? [] : lines, + ); + const [currentLineIndex, setCurrentLineIndex] = useState(0); + const [currentCharIndex, setCurrentCharIndex] = useState(0); + + useEffect(() => { + if (!typingEffect) return; + + if (currentLineIndex < lines.length) { + const currentLineFull = lines[currentLineIndex]; + + if (currentCharIndex < currentLineFull.length) { + const timeout = setTimeout( + () => { + setDisplayedLines((prev) => { + const newLines = [...prev]; + if (newLines[currentLineIndex] === undefined) { + newLines[currentLineIndex] = currentLineFull[currentCharIndex]; + } else { + newLines[currentLineIndex] += currentLineFull[currentCharIndex]; + } + return newLines; + }); + setCurrentCharIndex((prev) => prev + 1); + }, + 15 + Math.random() * 20, + ); // Random variance for realism + + return () => clearTimeout(timeout); + } else { + // Line finished, wait a bit then move to next + const timeout = setTimeout(() => { + setCurrentLineIndex((prev) => prev + 1); + setCurrentCharIndex(0); + }, 300); + return () => clearTimeout(timeout); + } + } + }, [typingEffect, lines, currentLineIndex, currentCharIndex]); + + return ( +
+
+
+
+
+
+
+
{title}
+
+
+ {displayedLines.map((line, idx) => ( +
+ {line.startsWith("$") ? ( + {line} + ) : line.startsWith(">") ? ( + {line} + ) : line.startsWith("#") ? ( + {line} + ) : ( + {line} + )} +
+ ))} + {/* Cursor */} + {typingEffect && currentLineIndex < lines.length && ( +
+ )} + {/* Static Cursor when done */} + {(!typingEffect || currentLineIndex >= lines.length) && ( +
+ )} +
+
+ ); +}; diff --git a/index.html b/index.html new file mode 100644 index 0000000..3c3116e --- /dev/null +++ b/index.html @@ -0,0 +1,66 @@ + + + + + + Pradit | AI Consultancy + + + + + + + + +
+ + + diff --git a/index.tsx b/index.tsx new file mode 100644 index 0000000..6ca5361 --- /dev/null +++ b/index.tsx @@ -0,0 +1,15 @@ +import React from 'react'; +import ReactDOM from 'react-dom/client'; +import App from './App'; + +const rootElement = document.getElementById('root'); +if (!rootElement) { + throw new Error("Could not find root element to mount to"); +} + +const root = ReactDOM.createRoot(rootElement); +root.render( + + + +); \ No newline at end of file diff --git a/metadata.json b/metadata.json new file mode 100644 index 0000000..67ba22f --- /dev/null +++ b/metadata.json @@ -0,0 +1,5 @@ +{ + "name": "Pradit", + "description": "A high-end, academic-style technical consultancy website featuring technical deep dives and clear service offerings.", + "requestFramePermissions": [] +} diff --git a/package.json b/package.json new file mode 100644 index 0000000..0bcb8c8 --- /dev/null +++ b/package.json @@ -0,0 +1,23 @@ +{ + "name": "pradit", + "private": true, + "version": "0.0.0", + "type": "module", + "scripts": { + "dev": "vite", + "build": "vite build", + "preview": "vite preview" + }, + "dependencies": { + "react": "^19.2.0", + "react-dom": "^19.2.0", + "react-router-dom": "^7.9.6", + "lucide-react": "^0.554.0" + }, + "devDependencies": { + "@types/node": "^22.14.0", + "@vitejs/plugin-react": "^5.0.0", + "typescript": "~5.8.2", + "vite": "^6.2.0" + } +} diff --git a/pages/BlogPost.tsx b/pages/BlogPost.tsx new file mode 100644 index 0000000..d678af9 --- /dev/null +++ b/pages/BlogPost.tsx @@ -0,0 +1,444 @@ +import React, { useState, useEffect } from "react"; +import { MatrixViz } from "../components/MatrixViz"; +import { useLanguage } from "../App"; +import { + Play, + Pause, + FileText, + Headphones, + Zap, + Volume2, + X, +} from "lucide-react"; + +export const BlogPost: React.FC = () => { + const { language } = useLanguage(); + const [activeSection, setActiveSection] = useState("intro"); + const [activeTab, setActiveTab] = useState<"read" | "listen">("read"); + const [isPlaying, setIsPlaying] = useState(false); + const [showMiniPlayer, setShowMiniPlayer] = useState(false); + + // Simple scroll spy logic + useEffect(() => { + const handleScroll = () => { + const sections = ["intro", "methods", "results", "discussion"]; + for (const section of sections) { + const element = document.getElementById(section); + if (element) { + const rect = element.getBoundingClientRect(); + if (rect.top >= 0 && rect.top <= 300) { + setActiveSection(section); + break; + } + } + } + }; + window.addEventListener("scroll", handleScroll); + return () => window.removeEventListener("scroll", handleScroll); + }, []); + + const handleMainPlayPause = () => { + const newState = !isPlaying; + setIsPlaying(newState); + if (newState) { + setShowMiniPlayer(true); + } + }; + + const content = { + en: { + title: "LoRA Without Regret", + subtitle: "John Schulman in collaboration with Pradit", + date: "Sep 29, 2025", + tldr_title: "Executive Summary (TL;DR)", + tldr_text: + "Low-Rank Adaptation (LoRA) is a technique to fine-tune large models efficiently. Our research confirms that while LoRA saves 90% of memory, it can match full fine-tuning performance only if the rank (r) is sufficiently high and alpha scaling is tuned correctly. Ideal for post-training but not pre-training.", + tabs: { read: "Read Article", listen: "Listen (TTS)" }, + intro_1: + "Today’s leading language models contain upwards of a trillion parameters, pretrained on tens of trillions of tokens. Base model performance keeps improving with scale, as these trillions are necessary for learning and representing all the patterns in written-down human knowledge.", + intro_2: + "In contrast, post-training involves smaller datasets and generally focuses on narrower domains of knowledge and ranges of behavior. It seems wasteful to use a terabit of weights to represent updates from a gigabit or megabit of training data. This intuition has motivated parameter efficient fine-tuning (PEFT), which adjusts a large network by updating a much smaller set of parameters.", + method_title: "The Method", + method_1: + "The leading PEFT method is low-rank adaptation, or LoRA. LoRA replaces each weight matrix W from the original model with a modified version W' = W + γBA, where B and A are matrices that together have far fewer parameters than W, and γ is a constant scaling factor.", + method_2: + "In effect, LoRA creates a low-dimensional representation of the updates imparted by fine-tuning. LoRA may offer advantages in the cost and speed of post-training, and there are also a few operational reasons to prefer it to full fine-tuning.", + results_title: "Operational Advantages", + results_list: [ + { + title: "Multi-tenant serving", + text: "Since LoRA trains an adapter (i.e., the A and B matrices) while keeping the original weights unchanged, a single inference server can keep many adapters in memory.", + }, + { + title: "Layout size for training", + text: "FullFT usually requires an order of magnitude more accelerators than sampling from the same model does.", + }, + { + title: "Ease of loading", + text: "With fewer weights to store, LoRA adapters are fast and easy to set up or transfer between machines.", + }, + ], + conclusion_title: "Conclusion", + conclusion_text: + "The question remains: can LoRA match the performance of full fine-tuning? Our internal benchmarks suggest yes, provided rank scaling is carefully managed.", + nav: { + intro: "Introduction", + methods: "Methods", + results: "Results", + discussion: "Conclusion", + }, + now_playing: "Now Playing", + paused: "Paused", + transcript_title: "Transcript", + }, + th: { + title: "LoRA โดยไม่เสียใจ", + subtitle: "John Schulman ร่วมกับ Pradit (ประดิษฐ์)", + date: "29 กันยายน 2025", + tldr_title: "บทสรุปผู้บริหาร (TL;DR)", + tldr_text: + "Low-Rank Adaptation (LoRA) เป็นเทคนิคการปรับแต่งโมเดลขนาดใหญ่ที่มีประสิทธิภาพ งานวิจัยของเรายืนยันว่าแม้ LoRA จะประหยัดหน่วยความจำได้ถึง 90% แต่จะให้ผลลัพธ์เทียบเท่าการ Fine-tune แบบเต็มรูปแบบได้ก็ต่อเมื่อค่า Rank (r) สูงเพียงพอและมีการปรับค่า Alpha อย่างถูกต้อง เหมาะสำหรับขั้นตอน Post-training แต่ไม่เหมาะกับ Pre-training", + tabs: { read: "อ่านบทความ", listen: "ฟังเสียงบรรยาย" }, + intro_1: + "โมเดลภาษาชั้นนำในปัจจุบันมีพารามิเตอร์มากกว่าล้านล้านตัว ผ่านการฝึกฝนบนข้อมูลมหาศาล ประสิทธิภาพของโมเดลพื้นฐานดีขึ้นเรื่อยๆ ตามขนาด ซึ่งจำเป็นสำหรับการเรียนรู้รูปแบบความรู้ทั้งหมดของมนุษย์", + intro_2: + "ในทางตรงกันข้าม การฝึกฝนหลังเสร็จสิ้น (Post-training) มักใช้ชุดข้อมูลขนาดเล็กและเน้นขอบเขตความรู้ที่แคบกว่า ดูเหมือนจะสิ้นเปลืองที่จะใช้น้ำหนักระดับเทราบิตเพื่อแทนการอัปเดตจากข้อมูลเพียงกิกะบิต แนวคิดนี้จึงนำไปสู่การปรับแต่งแบบประหยัดพารามิเตอร์ (PEFT)", + method_title: "วิธีการ", + method_1: + "วิธี PEFT ชั้นนำคือ Low-rank adaptation หรือ LoRA ซึ่งแทนที่เมทริกซ์น้ำหนัก W แต่ละตัวด้วยเวอร์ชันแก้ไข W' = W + γBA โดยที่ B และ A เป็นเมทริกซ์ที่มีพารามิเตอร์น้อยกว่า W มาก", + method_2: + "โดยสรุป LoRA สร้างตัวแทนมิติที่ต่ำกว่าของการอัปเดตที่เกิดจากการ Fine-tuning LoRA อาจมีข้อได้เปรียบในด้านต้นทุนและความเร็ว และยังมีเหตุผลด้านการปฏิบัติงานที่ทำให้เป็นที่นิยมมากกว่า Full Fine-tuning", + results_title: "ข้อได้เปรียบด้านการปฏิบัติงาน", + results_list: [ + { + title: "การให้บริการแบบ Multi-tenant", + text: "เนื่องจาก LoRA ฝึกฝน Adapter (เมทริกซ์ A และ B) โดยไม่เปลี่ยนน้ำหนักเดิม เซิร์ฟเวอร์เดียวจึงสามารถเก็บ Adapter หลายตัวในหน่วยความจำได้", + }, + { + title: "ขนาด Layout สำหรับการฝึก", + text: "FullFT มักต้องการตัวเร่งความเร็ว (GPU/TPU) มากกว่าการสุ่มตัวอย่างจากโมเดลเดียวกันถึงหนึ่งระดับ (Order of magnitude)", + }, + { + title: "ความง่ายในการโหลด", + text: "ด้วยน้ำหนักที่ต้องเก็บน้อยกว่า LoRA adapters จึงตั้งค่าและโอนย้ายระหว่างเครื่องได้รวดเร็ว", + }, + ], + conclusion_title: "บทสรุป", + conclusion_text: + "คำถามยังคงอยู่: LoRA สามารถทำงานได้เทียบเท่ากับ Full Fine-tuning หรือไม่? การทดสอบภายในของเราชี้ว่า 'ได้' หากมีการจัดการ Rank scaling อย่างระมัดระวัง", + nav: { + intro: "บทนำ", + methods: "วิธีการ", + results: "ผลลัพธ์", + discussion: "บทสรุป", + }, + now_playing: "กำลังเล่น", + paused: "หยุดชั่วคราว", + transcript_title: "คำบรรยาย", + }, + }; + + const t = content[language]; + + return ( +
+ {/* Article Header */} +
+

+ {t.title} +

+
+ {t.subtitle} +
+ + + {/* View Tabs */} +
+
+ + +
+
+
+ + {/* TL;DR Section */} +
+
+
+
+
+ +
+
+

+ {t.tldr_title} +

+

+ {t.tldr_text} +

+
+
+
+
+ + {activeTab === "listen" ? ( + /* Voice Interface */ +
+
+ {/* Left: Sticky Player */} +
+
+
+ {isPlaying && ( + + )} + +
+ +

+ {t.title} +

+

+ Voice: Gemini 2.5 Flash TTS +

+ + {/* Fake Waveform */} +
+ {[...Array(30)].map((_, i) => ( +
+ ))} +
+ +
+ + + +
+
+
+ + {/* Right: Scrollable Transcript */} +
+
+ {t.transcript_title} +
+
+

{t.intro_1}

+

{t.intro_2}

+
+

+ {t.method_title} +

+

{t.method_1}

+

{t.method_2}

+
+

+ {t.results_title} +

+ {t.results_list.map((item, idx) => ( +

+ {item.title}:{" "} + {item.text} +

+ ))} +
+

{t.conclusion_text}

+
+
+
+
+ ) : ( + /* Read Interface */ +
+ {/* Left Sidebar - TOC */} + + + {/* Main Content */} +
+ + +
+

+ {t.intro_1} +

+

{t.intro_2}

+
+ +
+

+ {t.method_title} +

+

{t.method_1}

+

{t.method_2}

+
+ +
+

+ {t.results_title} +

+
    + {t.results_list.map((item, idx) => ( +
  • + {item.title}.{" "} + {item.text} +
    • + ))} +
+
+ +
+

+ {t.conclusion_title} +

+

{t.conclusion_text}

+
+
+ + {/* Right Sidebar - Footnotes */} + +
+ )} + + {/* Mini Player Overlay - Persistent if showMiniPlayer is true */} + {showMiniPlayer && activeTab === "read" && ( +
+
+
+
+ {isPlaying && ( + + )} + +
+
+ + {isPlaying ? t.now_playing : t.paused} + + + {t.title} + +
+
+ +
+ + + + +
+ + +
+
+
+ )} +
+ ); +}; diff --git a/pages/HireUs.tsx b/pages/HireUs.tsx new file mode 100644 index 0000000..8e6c833 --- /dev/null +++ b/pages/HireUs.tsx @@ -0,0 +1,104 @@ + +import React from 'react'; +import { TerminalBlock } from '../components/TerminalBlock'; +import { Mail, MessageSquare, Code } from 'lucide-react'; + +export const HireUs: React.FC = () => { + return ( +
+
+ + {/* Left Column: Context */} +
+

+ Work With Us +

+

+ We are currently accepting new engagements for Q4 2025. We look for partners who have clear technical hurdles in their AI roadmap, rather than open-ended exploration. +

+ +
+
+

Engagement Models

+
    +
  • +

    Strategic Sprint (4-6 Weeks)

    +

    Best for MVPs, Feasibility Studies, and Architecture Audits. We ship a working artifact and a technical roadmap.

    +
    • +
  • +

    Quarterly Retainer

    +

    For teams needing ongoing specialized ML expertise to augment their core engineering staff. Includes weekly syncs and code review.

    +
    • +
+
+ +
+

What we need from you

+
+

+ To ensure we are a good fit, please provide technical details in your inquiry. + Mention your current stack (e.g., AWS, Python, Vercel), specific model constraints, and the primary metric you are trying to improve (latency, accuracy, cost). +

+
+
+
+
+ + {/* Right Column: Form */} +
+
e.preventDefault()}> +
+
+ + +
+
+ + +
+
+ +
+ + +
+ +
+ + +
+ +
+ + +
+ + +
+ + +
+ +
+
+ ); +}; \ No newline at end of file diff --git a/pages/Home.tsx b/pages/Home.tsx new file mode 100644 index 0000000..11b0076 --- /dev/null +++ b/pages/Home.tsx @@ -0,0 +1,699 @@ +import React, { useState } from "react"; +import { TerminalBlock } from "../components/TerminalBlock"; +import { + ArrowRight, + Activity, + Search, + Database, + ChevronRight, + GitCommit, + BarChart, + Server, + Shield, + Box, + Cloud, + Layers, + Cpu, + Lock, + X, + Zap, + Globe, +} from "lucide-react"; +import { Link } from "react-router-dom"; +import { useLanguage } from "../App"; + +// Type definition for Projects +interface ProjectData { + id: string; + category: string; + title: string; + shortDesc: string; + fullDesc: string; + metrics: { label: string; value: string }[]; + stack: string[]; + architecture: string; + challenge: string; +} + +export const Home: React.FC = () => { + const { language } = useLanguage(); + const [selectedProject, setSelectedProject] = useState( + null, + ); + + const content = { + en: { + new_research: "New Research: LoRA Without Regret", + hero_title: "Engineering Intelligence.", + hero_subtitle: + "We bridge the gap between research papers and production environments. Specialized consulting for LLM infrastructure, evaluation pipelines, and custom model adaptation.", + btn_services: "View Services", + btn_research: "Read Research", + latest_thinking: "Latest Thinking", + view_archive: "View Archive", + lora_desc: + "Fine-tuning large models is expensive. Low-rank adaptation (LoRA) promises efficiency, but does it sacrifice performance? Our deep dive into rank scaling and matrix initialization.", + technical_deep_dive: "Technical Deep Dive", + expertise_title: "Our Expertise", + process_title: "The Pipeline", + process_desc: + "We don't just write code; we engineer systems. Our engagement process is designed for transparency and velocity.", + projects_title: "Selected Deployments", + projects_desc: + "We don't sell hours. We sell shipped, production-grade systems. Click on a project to view the architecture.", + trusted_stack: "Built with Production Standards", + modal_stack: "Tech Stack", + modal_impact: "Business Impact", + modal_arch: "Architecture Highlight", + modal_challenge: "The Challenge", + close: "Close", + services: { + mvp: { + title: "MVP & Prototyping", + desc: "From concept to deployed model in weeks. We build robust POCs using the latest foundation models (Gemini, Claude, OpenAI) to validate business value before heavy investment.", + }, + audit: { + title: "System Audit", + desc: "Why is your RAG pipeline hallucinating? We perform deep architectural audits, evaluating retrieval quality, prompt efficacy, and security vulnerabilities.", + }, + integration: { + title: "Custom Integration", + desc: "Fine-tuning and integrating open-weights models into your secure infrastructure. We handle the MLOps so you own your weights and your data.", + }, + }, + learn_more: "Learn more", + process_steps: [ + { + step: "01", + title: "Discovery & Audit", + desc: "We define the metric that matters. Is it latency? Accuracy? Cost?", + }, + { + step: "02", + title: "Prototyping", + desc: "Rapid iteration using state-of-the-art models to validate feasibility.", + }, + { + step: "03", + title: "Evaluation (Evals)", + desc: "We build a custom test suite (Pass@k, RAGAS) before we ship.", + }, + { + step: "04", + title: "Production", + desc: "Containerization, CI/CD pipelines, and observability setup.", + }, + ], + stack: { + infra: "Infrastructure", + orchestration: "Orchestration", + inference: "Inference & Serving", + data: "Data & Vector Ops", + }, + projects: [ + { + id: "compliance", + category: "FinTech / Legal", + title: "ComplianceGuard AI", + shortDesc: + "Automated regulatory compliance checking for a Tier-1 bank. Reduced manual review time by 85% using a secure RAG pipeline.", + fullDesc: + "We architected a secure, on-premise Retrieval Augmented Generation (RAG) system that ingests thousands of changing regulatory documents (PDFs) and cross-references them with loan applications in real-time.", + challenge: + "The client was spending 2000+ man-hours monthly manually checking loan applications against constantly updating Bank of Thailand regulations. Privacy requirements prevented using public APIs.", + architecture: + 'Hybrid-cloud setup. Sensitive data stays on-prem (OpenShift) using a fine-tuned Llama-3-70B for inference. Regulatory documents are indexed in Weaviate. LangChain orchestrates the "Reasoning" step to cite specific regulatory clauses.', + metrics: [ + { label: "Review Time", value: "-85%" }, + { label: "False Positives", value: "< 2%" }, + { label: "Cost Saving", value: "$45k/mo" }, + ], + stack: [ + "Llama 3 70B", + "Weaviate", + "OpenShift", + "LangChain", + "Python", + ], + }, + { + id: "retail", + category: "E-Commerce", + title: "OmniFlow Agentic Router", + shortDesc: + "High-throughput customer support agent handling 50k+ daily tickets. Deflects 92% of queries with sub-2s latency.", + fullDesc: + "A sophisticated multi-agent system that acts as the first line of defense for a major e-commerce platform. It uses tool-calling to actually perform actions (check status, process refund) rather than just chatting.", + challenge: + 'During "11.11" sales events, support volume spikes 10x. Traditional chatbots were too rigid, and human agents were overwhelmed. The system needed to be autonomous but safe.', + architecture: + 'We built a Router Agent using GPT-4o-mini for speed. It classifies intent and routes to specialized Sub-Agents (Refund Agent, Logistics Agent). We implemented a "Human-in-the-loop" handover protocol for low-confidence scores.', + metrics: [ + { label: "Deflection Rate", value: "92%" }, + { label: "Avg Response", value: "1.2s" }, + { label: "CSAT Score", value: "4.8/5" }, + ], + stack: ["GPT-4o", "Redis", "FastAPI", "Kubernetes", "Postgres"], + }, + ], + }, + th: { + new_research: "งานวิจัยใหม่: LoRA โดยไม่เสียใจ", + hero_title: "วิศวกรรมแห่งปัญญาประดิษฐ์", + hero_subtitle: + "เราเชื่อมช่องว่างระหว่างงานวิจัยทางวิชาการและการใช้งานจริง เชี่ยวชาญด้านโครงสร้างพื้นฐาน LLM, การประเมินผล และการปรับแต่งโมเดลตามความต้องการ", + btn_services: "ดูบริการของเรา", + btn_research: "อ่านงานวิจัย", + latest_thinking: "แนวคิดล่าสุด", + view_archive: "ดูบทความทั้งหมด", + lora_desc: + "การ Fine-tune โมเดลขนาดใหญ่มีค่าใช้จ่ายสูง LoRA สัญญาว่าจะเพิ่มประสิทธิภาพ แต่จะลดคุณภาพหรือไม่? เจาะลึกเรื่อง Rank scaling และการเริ่มต้น Matrix", + technical_deep_dive: "เจาะลึกทางเทคนิค", + expertise_title: "ความเชี่ยวชาญของเรา", + process_title: "กระบวนการทำงาน", + process_desc: + "เราไม่ได้แค่เขียนโค้ด แต่เราออกแบบระบบ กระบวนการของเราเน้นความโปร่งใสและความรวดเร็ว", + projects_title: "ผลงานที่ผ่านมา", + projects_desc: + "เราไม่ได้ขายชั่วโมงการทำงาน แต่เราส่งมอบระบบที่ใช้งานได้จริง คลิกที่โปรเจกต์เพื่อดูสถาปัตยกรรม", + trusted_stack: "สร้างด้วยมาตรฐานระดับ Production", + modal_stack: "Tech Stack", + modal_impact: "ผลลัพธ์ทางธุรกิจ", + modal_arch: "จุดเด่นของสถาปัตยกรรม", + modal_challenge: "ความท้าทาย", + close: "ปิด", + services: { + mvp: { + title: "MVP และต้นแบบ", + desc: "จากแนวคิดสู่โมเดลที่ใช้งานได้จริงในไม่กี่สัปดาห์ เราสร้าง POC ที่แข็งแกร่งด้วยโมเดลล่าสุด (Gemini, Claude, OpenAI) เพื่อตรวจสอบความคุ้มค่าก่อนการลงทุนจริง", + }, + audit: { + title: "ตรวจสอบระบบ", + desc: "ทำไมระบบ RAG ของคุณถึงให้ข้อมูลผิด? เราตรวจสอบสถาปัตยกรรมอย่างละเอียด ประเมินคุณภาพการสืบค้น และช่องโหว่ด้านความปลอดภัย", + }, + integration: { + title: "การรวมระบบตามสั่ง", + desc: "ปรับแต่ง (Fine-tune) และรวมโมเดล Open-weights เข้ากับโครงสร้างพื้นฐานที่ปลอดภัยของคุณ เราดูแล MLOps เพื่อให้คุณเป็นเจ้าของข้อมูลและโมเดลอย่างแท้จริง", + }, + }, + learn_more: "เรียนรู้เพิ่มเติม", + process_steps: [ + { + step: "01", + title: "ค้นหา & ตรวจสอบ", + desc: "เรากำหนดตัวชี้วัดที่สำคัญที่สุด ไม่ว่าจะเป็น ความหน่วง ความแม่นยำ หรือต้นทุน", + }, + { + step: "02", + title: "สร้างต้นแบบ", + desc: "ทำซ้ำอย่างรวดเร็วโดยใช้โมเดลล่าสุดเพื่อตรวจสอบความเป็นไปได้", + }, + { + step: "03", + title: "การประเมินผล (Evals)", + desc: "เราสร้างชุดทดสอบเฉพาะ (Pass@k, RAGAS) ก่อนส่งมอบงาน", + }, + { + step: "04", + title: "Production", + desc: "การทำ Containerization, ระบบ CI/CD และการติดตั้งระบบสังเกตการณ์", + }, + ], + stack: { + infra: "Infrastructure", + orchestration: "Orchestration", + inference: "Inference & Serving", + data: "Data & Vector Ops", + }, + projects: [ + { + id: "compliance", + category: "FinTech / Legal", + title: "ComplianceGuard AI", + shortDesc: + "ระบบตรวจสอบการปฏิบัติตามกฎระเบียบอัตโนมัติสำหรับธนาคารชั้นนำ ลดเวลาการตรวจสอบด้วยคนลง 85% โดยใช้ RAG pipeline ที่ปลอดภัย", + fullDesc: + "เราออกแบบระบบ Retrieval Augmented Generation (RAG) ภายในองค์กรที่ปลอดภัย ซึ่งนำเข้าเอกสารกฎระเบียบหลายพันฉบับที่เปลี่ยนแปลงตลอดเวลา และตรวจสอบเทียบกับคำขอสินเชื่อแบบเรียลไทม์", + challenge: + "ลูกค้าใช้เวลาคนทำงานกว่า 2,000 ชั่วโมงต่อเดือนในการตรวจสอบคำขอสินเชื่อด้วยตนเองเทียบกับกฎระเบียบธนาคารแห่งประเทศไทย ข้อกำหนดด้านความเป็นส่วนตัวไม่อนุญาตให้ใช้ Public API", + architecture: + 'ระบบ Hybrid-cloud ข้อมูลสำคัญอยู่ภายในองค์กร (OpenShift) โดยใช้ Llama-3-70B ที่ปรับแต่งแล้ว เอกสารกฎระเบียบถูกจัดทำดัชนีใน Weaviate LangChain ควบคุมขั้นตอน "การให้เหตุผล" เพื่ออ้างอิงข้อกฎหมายเฉพาะ', + metrics: [ + { label: "เวลาตรวจสอบ", value: "-85%" }, + { label: "False Positives", value: "< 2%" }, + { label: "ประหยัดต้นทุน", value: "$45k/เดือน" }, + ], + stack: [ + "Llama 3 70B", + "Weaviate", + "OpenShift", + "LangChain", + "Python", + ], + }, + { + id: "retail", + category: "E-Commerce", + title: "OmniFlow Agentic Router", + shortDesc: + "ระบบตอบรับลูกค้าความเร็วสูง รองรับ 50,000+ ตั๋วต่อวัน แก้ไขปัญหาอัตโนมัติได้ 92% ด้วยความเร็วต่ำกว่า 2 วินาที", + fullDesc: + "ระบบ Multi-agent อัจฉริยะที่ทำหน้าที่เป็นด่านหน้าสำหรับแพลตฟอร์มอีคอมเมิร์ซขนาดใหญ่ ใช้การเรียกใช้เครื่องมือ (Tool-calling) เพื่อดำเนินการจริง (ตรวจสอบสถานะ, คืนเงิน) แทนที่จะเป็นแค่การแชท", + challenge: + 'ในช่วงเทศกาล "11.11" ปริมาณการติดต่อเพิ่มขึ้น 10 เท่า Chatbot แบบเดิมแข็งทื่อเกินไป และพนักงานรับมือไม่ไหว ระบบต้องทำงานอัตโนมัติแต่ปลอดภัย', + architecture: + 'เราสร้าง Router Agent โดยใช้ GPT-4o-mini เพื่อความเร็ว โดยจำแนกเจตนาและส่งต่อไปยัง Agent เฉพาะทาง (Refund Agent, Logistics Agent) เราใช้โปรโตคอล "Human-in-the-loop" สำหรับเคสที่มีความมั่นใจต่ำ', + metrics: [ + { label: "อัตราการตอบกลับ", value: "92%" }, + { label: "ความเร็วเฉลี่ย", value: "1.2s" }, + { label: "คะแนน CSAT", value: "4.8/5" }, + ], + stack: ["GPT-4o", "Redis", "FastAPI", "Kubernetes", "Postgres"], + }, + ], + }, + }; + + const t = content[language]; + + return ( +
+ {/* Hero Section */} +
+
+
+
+ + + {t.new_research} + +

+ {t.hero_title} +

+

+ {t.hero_subtitle} +

+
+ + {t.btn_services} + + + {t.btn_research} + +
+
+ + {/* Technical Trust Signal with Typing Effect */} +
+ Running 164 test cases...", + "> Pass@1: 92.4% [OK]", + "> Latency (p99): 450ms [WARN]", + "# Optimization suggestion:", + "# Switch to batch inference or semantic caching.", + "$ _", + ]} + className="transform lg:rotate-1 hover:rotate-0 transition-transform duration-500" + /> +
+
+
+
+ + {/* Trusted Stack / Logos (Revamped) */} +
+
+
+ {/* Infrastructure */} +
+
+ +
+

+ {t.stack.infra} +

+

+ AWS, GCP, Azure, Terraform +

+
+ + {/* Orchestration */} +
+
+ +
+

+ {t.stack.orchestration} +

+

+ Kubernetes, Docker, Ray, Airflow +

+
+ + {/* Inference */} +
+
+ +
+

+ {t.stack.inference} +

+

+ vLLM, TorchServe, ONNX Runtime +

+
+ + {/* Data */} +
+
+ +
+

+ {t.stack.data} +

+

+ Pinecone, Weaviate, Postgres, Redis +

+
+
+
+
+ + {/* Featured Research Snippet */} +
+
+
+

+ {t.latest_thinking} +

+ + {t.view_archive}{" "} + + +
+ + +
+
+

+ LoRA Without Regret +

+

+ {t.lora_desc} +

+ + {t.technical_deep_dive} + +
+
+ {/* Mini visualization for the teaser */} +
+ {[...Array(48)].map((_, i) => ( +
+ ))} +
+
+
+ +
+
+ + {/* Services Preview */} +
+
+

+ {t.expertise_title} +

+ +
+ {/* Service 1 */} +
+
+ +
+

+ {t.services.mvp.title} +

+

+ {t.services.mvp.desc} +

+ + {t.learn_more} + +
+ + {/* Service 2 */} +
+
+ +
+

+ {t.services.audit.title} +

+

+ {t.services.audit.desc} +

+ + {t.learn_more} + +
+ + {/* Service 3 */} +
+
+ +
+

+ {t.services.integration.title} +

+

+ {t.services.integration.desc} +

+ + {t.learn_more} + +
+
+
+
+ + {/* Process Pipeline Section */} +
+
+
+

+ {t.process_title} +

+

+ {t.process_desc} +

+
+ +
+ {t.process_steps.map((step, idx) => ( +
+
+ {step.step} +
+

{step.title}

+

+ {step.desc} +

+ {/* Connecting Line */} + {idx < 3 && ( +
+ )} +
+ ))} +
+
+
+ + {/* Featured Projects (Improved Enterprise) */} +
+
+
+

+ {t.projects_title} +

+

{t.projects_desc}

+
+ +
+ {t.projects.map((project) => ( +
setSelectedProject(project)} + className="border border-gray-200 rounded-sm hover:shadow-xl transition-all duration-300 p-8 bg-white flex flex-col cursor-pointer group hover:-translate-y-1" + > +
+
+
+ {project.id === "compliance" ? ( + + ) : ( + + )} +
+ + {project.category} + +
+
+ +
+
+ +

+ {project.title} +

+

+ {project.shortDesc} +

+ +
+ {project.metrics.map((metric, idx) => ( +
+
+ {metric.value} +
+
+ {metric.label} +
+
+ ))} +
+
+ ))} +
+
+
+ + {/* Project Modal */} + {selectedProject && ( +
+
setSelectedProject(null)} + >
+
+ {/* Modal Left: Overview */} +
+

+ {selectedProject.title} +

+ + {selectedProject.category} + + +
+
+

+ {t.modal_challenge} +

+

+ {selectedProject.challenge} +

+
+
+

+ {t.modal_arch} +

+

+ {selectedProject.architecture} +

+
+
+

+ {t.modal_impact} +

+
+ {selectedProject.metrics.map((metric, idx) => ( +
+
+ {metric.value} +
+
+ {metric.label} +
+
+ ))} +
+
+
+
+ + {/* Modal Right: Stack */} +
+ + +

+ {t.modal_stack} +

+
+ {selectedProject.stack.map((tech, i) => ( +
+
+ {tech} +
+ ))} +
+ +
+ +

+ "Pradit delivered this system 2 weeks ahead of schedule. The + documentation was better than our internal standard." +

+
+ - CTO, Client Partner +
+
+
+
+
+ )} +
+ ); +}; diff --git a/pages/Playground.tsx b/pages/Playground.tsx new file mode 100644 index 0000000..c57d744 --- /dev/null +++ b/pages/Playground.tsx @@ -0,0 +1,329 @@ +import React, { useState, useEffect } from "react"; +import { + UploadCloud, + FileText, + Database, + Search, + CheckCircle2, + ArrowRight, + Loader2, + Lock, + Cpu, + File, + Terminal, +} from "lucide-react"; +import { useLanguage } from "../App"; +import { TerminalBlock } from "../components/TerminalBlock"; + +export const Playground: React.FC = () => { + const { language } = useLanguage(); + const [step, setStep] = useState<"upload" | "processing" | "ready">("upload"); + const [processingStage, setProcessingStage] = useState(0); // 0: Parsing, 1: Chunking, 2: Embedding, 3: Indexing + const [query, setQuery] = useState(""); + const [showResult, setShowResult] = useState(false); + + const content = { + en: { + title: "Enterprise Knowledge Engine", + subtitle: + "Experience our secure RAG pipeline architecture. Turn unstructured documents into queryable intelligence.", + upload_title: "Data Ingestion", + upload_desc: + "Drag and drop your internal PDFs, DOCX, or Wiki dumps here. We simulate the ingestion process secure enterprises use.", + drop_label: "Drop corporate assets here", + processing_title: "Processing Pipeline", + stages: [ + "Optical Character Recognition (OCR)", + "Semantic Chunking", + "Vector Embedding (text-embedding-3-large)", + "Pinecone Indexing", + ], + query_placeholder: "Ask a question about the uploaded documents...", + query_btn: "Retrieve Insight", + result_title: "Synthesized Answer", + source_title: "Source Attribution", + value_prop: + "Why this matters: Most enterprise knowledge is trapped in PDFs. We build pipelines that unlock this data while respecting RBAC (Role-Based Access Control).", + }, + th: { + title: "เครื่องยนต์ความรู้องค์กร", + subtitle: + "สัมผัสสถาปัตยกรรม RAG ที่ปลอดภัยของเรา เปลี่ยนเอกสารที่ไม่มีโครงสร้างให้เป็นข้อมูลอัจฉริยะที่ค้นหาได้", + upload_title: "การนำเข้าข้อมูล", + upload_desc: + "ลากและวาง PDF, DOCX หรือ Wiki ภายในองค์กรที่นี่ เราจำลองกระบวนการนำเข้าข้อมูลที่องค์กรระดับสูงใช้", + drop_label: "วางไฟล์ข้อมูลองค์กรที่นี่", + processing_title: "ท่อประมวลผลข้อมูล", + stages: [ + "การแปลงภาพเป็นข้อความ (OCR)", + "การแบ่งส่วนความหมาย (Semantic Chunking)", + "การสร้าง Vector (text-embedding-3-large)", + "การจัดทำดัชนี Pinecone", + ], + query_placeholder: "ถามคำถามเกี่ยวกับเอกสารที่อัปโหลด...", + query_btn: "ค้นหาข้อมูลเชิงลึก", + result_title: "คำตอบที่สังเคราะห์แล้ว", + source_title: "การอ้างอิงแหล่งที่มา", + value_prop: + "ทำไมสิ่งนี้ถึงสำคัญ: ความรู้องค์กรส่วนใหญ่ติดอยู่ใน PDF เราสร้างระบบที่ปลดล็อกข้อมูลนี้โดยยังคงรักษาความปลอดภัยตามสิทธิ์การเข้าถึง (RBAC)", + }, + }; + + const t = content[language]; + + const handleSimulateUpload = () => { + setStep("processing"); + setProcessingStage(0); + + // Simulate pipeline stages + const interval = setInterval(() => { + setProcessingStage((prev) => { + if (prev >= 3) { + clearInterval(interval); + setTimeout(() => setStep("ready"), 800); + return 3; + } + return prev + 1; + }); + }, 1500); + }; + + const handleSearch = (e: React.FormEvent) => { + e.preventDefault(); + if (!query) return; + setShowResult(true); + }; + + return ( +
+
+
+

+ {t.title} +

+

+ {t.subtitle} +

+
+ +
+ {/* Left: Demo Interface */} +
+ {/* Step 1: Upload */} +
+
+
+ +
+

+ {t.upload_title} +

+ {step === "upload" && ( + <> +

+ {t.upload_desc} +

+ +
+ + policy_v2.pdf + + + technical_specs.docx + +
+ + )} +
+
+ + {/* Step 2: Processing Pipeline */} + {(step === "processing" || step === "ready") && ( +
+

+ {t.processing_title} +

+
+ {t.stages.map((stage, index) => ( +
+
index + ? "bg-green-500 border-green-500" + : processingStage === index + ? "bg-white border-accent animate-pulse" + : "bg-white border-gray-200" + }`} + >
+ +
= index ? "opacity-100" : "opacity-30"}`} + > +

+ {stage} +

+ {processingStage === index && ( +

+ Processing... +

+ )} +
+
+ ))} +
+
+ )} + + {/* Step 3: Query Interface */} + {step === "ready" && ( +
+
+
+ Secure Context +
+
+ +
+ setQuery(e.target.value)} + className="w-full bg-gray-50 border border-gray-200 p-4 pr-12 rounded-sm text-ink focus:outline-none focus:border-ink focus:ring-1 focus:ring-ink font-serif placeholder:text-gray-400" + placeholder={t.query_placeholder} + /> + +
+ + {showResult && ( +
+
+

+ {t.result_title} +

+

+ Based on the{" "} + + technical_specs.docx + + , the maximum concurrent connection limit for the + websocket gateway is{" "} + 10,000 users per + shard. To increase this, you must scale the Redis + cluster horizontally. +

+
+ +
+

+ {t.source_title} +

+
+ "...gateway.max_connections = 10000 # Hard limit per + shard. See Redis scaling section..." +
+ Score: 0.89 +
+
+
+
+ )} +
+ )} +
+ + {/* Right: Architecture Context */} +
+
+
+
+ + pipeline_logs.txt +
+
+
+ $ init_ingestion_job --secure +
+ {step === "processing" && ( + <> +
+ [INFO] File uploaded: technical_specs.docx (2.4MB) +
+
+ [INFO] Parsing PDF structure... +
+ {processingStage >= 1 && ( +
+ [PROC] Chunking strategy: RecursiveCharacter (512tk) +
+ )} + {processingStage >= 2 && ( +
+ [EMBD] Batch embedding 142 chunks... +
+ )} + {processingStage >= 3 && ( +
+ [INDX] Upserting to namespace 'corp-knowledge'... +
+ )} + + )} + {step === "ready" && ( + <> +
+ [DONE] Indexing complete. Latency: 4.2s +
+
+ ---------------------------------------- +
+
+ $ waiting_for_query... +
+ {showResult && ( + <> +
$ query_received
+
+ [RETR] Semantic search k=3 +
+
+ [GEN] LLM Context window filled (1200 tokens) +
+ + )} + + )} +
+
+ +
+

+ Business Value +

+

+ {t.value_prop} +

+
+
+
+
+
+
+ ); +}; diff --git a/pages/Research.tsx b/pages/Research.tsx new file mode 100644 index 0000000..6c5d434 --- /dev/null +++ b/pages/Research.tsx @@ -0,0 +1,269 @@ +import React, { useState, useMemo } from "react"; +import { Link } from "react-router-dom"; +import { ArrowRight, Search as SearchIcon, Filter, X } from "lucide-react"; +import { useLanguage } from "../App"; + +export const Research: React.FC = () => { + const { language } = useLanguage(); + const [searchQuery, setSearchQuery] = useState(""); + const [selectedCategory, setSelectedCategory] = useState("All"); + + const content = { + en: { + title: "Research Archive", + subtitle: + "Our collection of white papers, technical deep dives, and engineering retrospectives. We believe in open-sourcing our learnings from the frontier of applied AI.", + read_paper: "Read Paper", + search_placeholder: "grep search-query...", + no_results: "No research found matching criteria.", + categories: [ + "All", + "Fine-tuning", + "Systems", + "Architecture", + "Small Models", + ], + articles: [ + { + id: "lora-without-regret", + title: "LoRA Without Regret", + date: "Sep 29, 2025", + authors: "John Schulman, Pradit", + abstract: + "Fine-tuning large models is expensive. We investigate whether Low-Rank Adaptation (LoRA) can truly match full fine-tuning performance across various downstream tasks, analyzing rank scaling and matrix initialization.", + tags: ["Fine-tuning", "Optimization", "Deep Learning"], + category: "Fine-tuning", + }, + { + id: "rag-latency-optimization", + title: "Optimizing RAG Latency: A Systems Approach", + date: "Aug 14, 2025", + authors: "Pradit Engineering", + abstract: + "Retrieval-Augmented Generation often suffers from p99 latency spikes. We break down the contribution of embedding generation, vector search, and reranking, proposing a caching layer that reduces latency by 40%.", + tags: ["RAG", "Systems", "Latency"], + category: "Architecture", + }, + { + id: "small-models-reasoning", + title: "The Unreasonable Effectiveness of Small Models", + date: "July 02, 2025", + authors: "Pradit Research", + abstract: + "Can a 7B parameter model reason as well as a 70B model? With specific chain-of-thought fine-tuning on high-quality synthetic data, we demonstrate parity on GSM8K benchmarks.", + tags: ["Small Models", "Reasoning", "Data Efficiency"], + category: "Small Models", + }, + ], + }, + th: { + title: "คลังงานวิจัย", + subtitle: + "คลังเอกสารทางเทคนิคและการถอดบทเรียนทางวิศวกรรม เราเชื่อในการเปิดเผยสิ่งที่เราเรียนรู้จากพรมแดนแห่ง AI ประยุกต์สู่สาธารณะ", + read_paper: "อ่านบทความ", + search_placeholder: "ค้นหาบทความ...", + no_results: "ไม่พบงานวิจัยที่ตรงกับเงื่อนไข", + categories: [ + "ทั้งหมด", + "Fine-tuning", + "Systems", + "Architecture", + "Small Models", + ], + articles: [ + { + id: "lora-without-regret", + title: "LoRA โดยไม่เสียใจ", + date: "29 ก.ย. 2025", + authors: "John Schulman, Pradit (ประดิษฐ์)", + abstract: + "การ Fine-tune โมเดลขนาดใหญ่มีค่าใช้จ่ายสูง เราตรวจสอบว่า Low-Rank Adaptation (LoRA) สามารถให้ประสิทธิภาพเทียบเท่าการ Fine-tune เต็มรูปแบบได้หรือไม่ โดยวิเคราะห์การปรับขนาด Rank และการเริ่มต้น Matrix", + tags: ["Fine-tuning", "Optimization", "Deep Learning"], + category: "Fine-tuning", + }, + { + id: "rag-latency-optimization", + title: "การปรับปรุงความหน่วง RAG: แนวทางเชิงระบบ", + date: "14 ส.ค. 2025", + authors: "Pradit Engineering", + abstract: + "ระบบ RAG มักประสบปัญหาค่าความหน่วง p99 สูง เราจำแนกส่วนประกอบของการสร้าง Embedding, Vector Search และ Reranking พร้อมเสนอชั้น Caching ที่ลดความหน่วงได้ถึง 40%", + tags: ["RAG", "Systems", "Latency"], + category: "Architecture", + }, + { + id: "small-models-reasoning", + title: "ประสิทธิภาพที่น่าทึ่งของโมเดลขนาดเล็ก", + date: "02 ก.ค. 2025", + authors: "Pradit Research", + abstract: + "โมเดล 7B สามารถให้เหตุผลได้ดีเท่ากับ 70B หรือไม่? ด้วยการ Fine-tune แบบ Chain-of-thought บนข้อมูลสังเคราะห์คุณภาพสูง เราแสดงให้เห็นถึงความสามารถที่เทียบเท่ากันบน GSM8K", + tags: ["Small Models", "Reasoning", "Data Efficiency"], + category: "Small Models", + }, + ], + }, + }; + + const t = content[language]; + + // Filtering Logic + const filteredArticles = useMemo(() => { + return t.articles.filter((article) => { + const matchesSearch = + article.title.toLowerCase().includes(searchQuery.toLowerCase()) || + article.abstract.toLowerCase().includes(searchQuery.toLowerCase()); + + // Map 'All' or 'ทั้งหมด' to show everything + const isAll = + selectedCategory === "All" || selectedCategory === "ทั้งหมด"; + const matchesCategory = + isAll || + article.category === selectedCategory || + article.tags.includes(selectedCategory); + + return matchesSearch && matchesCategory; + }); + }, [searchQuery, selectedCategory, t.articles]); + + return ( +
+
+ {/* Header */} +
+

+ {t.title} +

+

+ {t.subtitle} +

+
+ + {/* Controls: Search & Filter */} +
+ {/* Categories */} +
+ {t.categories.map((cat) => ( + + ))} +
+ + {/* Search Bar */} +
+
+ +
+ setSearchQuery(e.target.value)} + className="block w-full pl-9 pr-3 py-2 border-b-2 border-gray-200 bg-transparent text-sm font-mono text-ink focus:outline-none focus:border-ink transition-colors placeholder:text-gray-400" + placeholder={t.search_placeholder} + /> + {searchQuery && ( + + )} +
+
+ + {/* Results List */} +
+ {filteredArticles.length > 0 ? ( + filteredArticles.map((article) => ( +
+ {/* Metadata Column */} +
+ + {article.date} + + + {article.category} + +
+ {article.tags.slice(0, 2).map((tag) => ( + + #{tag} + + ))} +
+
+ + {/* Content Column */} +
+

+ + {article.title} + +

+ +
+ {article.authors} +
+ +

+ {article.abstract} +

+ + + {t.read_paper} + +
+
+ )) + ) : ( +
+ +

{t.no_results}

+ +
+ )} +
+
+
+ ); +}; diff --git a/pages/Services.tsx b/pages/Services.tsx new file mode 100644 index 0000000..dab29ee --- /dev/null +++ b/pages/Services.tsx @@ -0,0 +1,198 @@ + +import React from 'react'; +import { TerminalBlock } from '../components/TerminalBlock'; +import { Check, ArrowRight } from 'lucide-react'; +import { Link } from 'react-router-dom'; + +export const Services: React.FC = () => { + return ( +
+
+ +
+

+ Services +

+

+ We operate at the intersection of software engineering and machine learning research. Our engagements are typically project-based or retainer-style for high-growth technical teams who need to ship, not just explore. +

+
+ +
+ {/* Service Block 1: MVP */} +
+
+
+ 01 — Prototyping +
+
+ +

Accelerated MVP

+

+ We help founders and technical leads validate AI hypotheses quickly. Instead of spending months hiring a full ML team, we deliver a production-ready prototype in 4-6 weeks. +

+ +
    + {[ + "Full stack implementation (React + Python/Node)", + "Model selection (Gemini, Claude, OpenAI) & prompt engineering", + "Basic evaluation pipeline setup", + "Deployment to your cloud (AWS/GCP)" + ].map((item, i) => ( +
  • + + {item} +
    • + ))} +
+
+ +
+
+ +
+
+ + {/* Service Block 2: Audit */} +
+
+
+ 02 — Architecture +
+
+ +

RAG & System Audit

+

+ Your retrieval system is likely the bottleneck. We perform a rigorous audit of your embedding strategies, chunking logic, and reranking stages to improve context relevance and reduce hallucinations. +

+ +
    + {[ + "Vector Database Indexing Strategy Review", + "Embedding Latency & Cost Analysis", + "Context Window Optimization", + "Security & Prompt Injection Vulnerability Scan" + ].map((item, i) => ( +
  • + + {item} +
    • + ))} +
+
+ +
+
+ +
+
+ + {/* Service Block 3: Integration */} +
+
+
+ 03 — Fine-tuning +
+
+

Custom Model Integration

+

+ When prompt engineering hits a wall, we fine-tune open weights (Llama 3, Mistral, Gemma) on your proprietary data. We handle the data cleaning, training runs (LoRA/QLoRA), and deployment optimization. +

+
    + {[ + "Dataset curation and cleaning pipelines", + "Parameter-Efficient Fine-Tuning (LoRA)", + "Evaluation against base model benchmarks", + "Private cloud deployment (AWS/GCP/Azure)" + ].map((item, i) => ( +
  • + + {item} +
    • + ))} +
+
+ +
+
+ +
+
+ +
+
+

Ready to build?

+

+ We take on a limited number of clients per quarter to ensure deep technical focus on every project. +

+ + Schedule Consultation + +
+ {/* Background decoration */} +
+
+ {[...Array(12)].map((_, i) => ( +
+ ))} +
+
+
+
+
+
+ ); +}; \ No newline at end of file diff --git a/pnpm-lock.yaml b/pnpm-lock.yaml new file mode 100644 index 0000000..ee1d838 --- /dev/null +++ b/pnpm-lock.yaml @@ -0,0 +1,1138 @@ +lockfileVersion: '9.0' + +settings: + autoInstallPeers: true + excludeLinksFromLockfile: false + +importers: + + .: + dependencies: + lucide-react: + specifier: ^0.554.0 + version: 0.554.0(react@19.2.0) + react: + specifier: ^19.2.0 + version: 19.2.0 + react-dom: + specifier: ^19.2.0 + version: 19.2.0(react@19.2.0) + react-router-dom: + specifier: ^7.9.6 + version: 7.9.6(react-dom@19.2.0(react@19.2.0))(react@19.2.0) + devDependencies: + '@types/node': + specifier: ^22.14.0 + version: 22.19.1 + '@vitejs/plugin-react': + specifier: ^5.0.0 + version: 5.1.1(vite@6.4.1(@types/node@22.19.1)) + typescript: + specifier: ~5.8.2 + version: 5.8.3 + vite: + specifier: ^6.2.0 + version: 6.4.1(@types/node@22.19.1) + +packages: + + '@babel/code-frame@7.27.1': + resolution: {integrity: 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typescript@5.8.3: {} + + undici-types@6.21.0: {} + + update-browserslist-db@1.1.4(browserslist@4.28.0): + dependencies: + browserslist: 4.28.0 + escalade: 3.2.0 + picocolors: 1.1.1 + + vite@6.4.1(@types/node@22.19.1): + dependencies: + esbuild: 0.25.12 + fdir: 6.5.0(picomatch@4.0.3) + picomatch: 4.0.3 + postcss: 8.5.6 + rollup: 4.53.3 + tinyglobby: 0.2.15 + optionalDependencies: + '@types/node': 22.19.1 + fsevents: 2.3.3 + + yallist@3.1.1: {} diff --git a/tsconfig.json b/tsconfig.json new file mode 100644 index 0000000..2c6eed5 --- /dev/null +++ b/tsconfig.json @@ -0,0 +1,29 @@ +{ + "compilerOptions": { + "target": "ES2022", + "experimentalDecorators": true, + "useDefineForClassFields": false, + "module": "ESNext", + "lib": [ + "ES2022", + "DOM", + "DOM.Iterable" + ], + "skipLibCheck": true, + "types": [ + "node" + ], + "moduleResolution": "bundler", + "isolatedModules": true, + "moduleDetection": "force", + "allowJs": true, + "jsx": "react-jsx", + "paths": { + "@/*": [ + "./*" + ] + }, + "allowImportingTsExtensions": true, + "noEmit": true + } +} \ No newline at end of file diff --git a/vite.config.ts b/vite.config.ts new file mode 100644 index 0000000..ee5fb8d --- /dev/null +++ b/vite.config.ts @@ -0,0 +1,23 @@ +import path from 'path'; +import { defineConfig, loadEnv } from 'vite'; +import react from '@vitejs/plugin-react'; + +export default defineConfig(({ mode }) => { + const env = loadEnv(mode, '.', ''); + return { + server: { + port: 3000, + host: '0.0.0.0', + }, + plugins: [react()], + define: { + 'process.env.API_KEY': JSON.stringify(env.GEMINI_API_KEY), + 'process.env.GEMINI_API_KEY': JSON.stringify(env.GEMINI_API_KEY) + }, + resolve: { + alias: { + '@': path.resolve(__dirname, '.'), + } + } + }; +});