The Problem
AI product engineering tidak berhenti di prompt.
Bagian tersulit dari AI app ada di area sekitar LLM: data flow, UI state, auth, validation, streaming, tool execution boundary, logging, deployment, dan cost control.
Pendaftaran Dijeda Sementara
10 Weeks AI Product Engineering with TypeScript
Build the product system around LLMs: UI, backend, data, AI harness, streaming, tools, observability, and deployment in TypeScript.
Program Flyer
Download the AI Product Engineering flyer.
Download PDF ringkasan program ini untuk melihat arah pembelajaran, jalur product engineering, perjalanan kurikulum, profil mentor, harga, dan detail pendaftaran.
Download PDFThe Strategy
This program takes the product engineering path.
AI Engineering punya dua jalur besar. Model engineering fokus pada dataset, training, dan eval model. Program ini fokus pada product engineering: membuat LLM menjadi fitur produk yang bisa dipakai, dijaga, dan dikembangkan.
The System
Calling an LLM API hanya satu layer dari produk.
Student tidak langsung lompat ke inference. Mereka membangun fondasi produk lebih dulu, lalu menambahkan AI layer dengan kontrak, state, permission, observability, dan feedback loop.
Product System
The product system around the LLM.
Product Surface
Reactive UI, streaming states, generative interfaces, and user-facing flows.
Reliable Backend
Hono APIs, Zod contracts, durable objects, permissions, and workflow boundaries.
Data Layers
PostgreSQL, vector embeddings, user-owned data, and application context.
AI Harness
AI SDK, tools, observability, evals, cost control, and deployment infrastructure.
Curriculum
A 10-week journey from web foundation to reliable AI product.
Week 1
Typed Web Foundation
- TypeScript fundamentals, interfaces, generics, and type design
- Vite, PNPM, linting, formatting, and Git workflow
Week 2
Reliable Backend with Hono
- HTTP routing, handlers, middleware, and error boundaries
- Zod validation and typed API contracts
Week 3
Data Layers for AI Products
- PostgreSQL schema design, relations, and migrations
- Drizzle ORM queries, transactions, and maintainable data access
Week 4
Product Surface with React
- React components, forms, routing, and state boundaries
- TanStack Query for caching, mutations, and loading states
Week 5
Product Readiness
- Auth, sessions, permissions, and protected routes
- Deployment workflow, environment variables, and observability
Week 6
AI Harness and SDK Integration
- OpenAI SDK and provider-agnostic AI SDK patterns
- Prompt design, structured output, validation, and fallbacks
Week 7
Streaming UI and Generative Interfaces
- Streaming text from API routes into React UI
- Chat UX, message state, optimistic UI, and generative interfaces
Week 8
Tools, Agents, and Workflow Boundaries
- Tool design, input schemas, approval flow, and safe execution
- Agentic workflows with logs, retries, and user feedback
Week 9
Retrieval, Evaluation, and Reliability
- Document ingestion, embeddings, retrieval, and RAG flow
- Evaluation harness, cost control, rate limits, and abuse prevention
Week 10
AI Product Engineering Final Project
- Build a production-minded AI product in TypeScript
- Present architecture, tradeoffs, and deployment decisions
The order of the curriculum may change, but everything listed in the curriculum will be learned.
Outcomes
What students should be able to do after the program.
Typed Fullstack Skill
Build frontend, backend, API contracts, and database access in one coherent TypeScript workflow.
AI Product Engineering
Build product surfaces around LLMs with streaming UI, structured output, tool calling, and workflow boundaries.
Production Judgment
Understand auth, permissions, deployment, observability, cost control, and failure states for AI features.
Mentor
Indra Zulfi Mushoddaq
Interdisciplinary Software & AI Engineer
- AI Engineering Lead @ AlphaX, Japan
- Prev. Designstripe Canada, Generalist and Software Engineer
- Prev. SSH International Bahrain, Design Team
Scholarship Batch III
Pendaftaran scholarship dijeda sementara
Pendaftaran AI Product Engineering with TypeScript, termasuk scholarship, dijeda sementara karena mentor belum tersedia untuk mendampingi program. Tanggal pembukaan kembali dan jadwal program akan diumumkan melalui website dan kanal resmi Devscale. Form belum menerima pendaftaran baru hingga pengumuman berikutnya.
Lihat detail scholarshipPendaftaran Dijeda Sementara
IDR 1.799.000
Pembayaran bisa dilakukan 2 kali.
Dimulai
Menunggu pengumuman berikutnya
Jadwal
Saturday and Sunday 10:00–12:00 WIB
20 Live Class Sessions
2x 1-on-1 Mentoring
Auto Assignment Reviews
Lifetime Recording Access
Bonus Platform Course
Completion Certificate
Informasi Pendaftaran
Pendaftaran Dijeda Sementara
Pendaftaran AI Product Engineering with TypeScript, termasuk scholarship, dijeda sementara karena mentor belum tersedia untuk mendampingi program.
Tanggal pembukaan kembali dan jadwal program akan diumumkan melalui website dan kanal resmi Devscale. Form belum menerima pendaftaran baru hingga pengumuman berikutnya.
Program Structure
Everything you need to know about the program structure.
Schedule
Saturday and Sunday 10:00–12:00 WIB
Menunggu pengumuman berikutnya
Duration
10 Weeks
2 live sessions per week
Prerequisites
Basic HTML, CSS, and JavaScript
TypeScript will be taught from the foundation
Format
100% Online Live Interactive Classes
Live class, assignment, review, and mentoring
Final Output
AI product engineering project
A deployable fullstack product for portfolio and review
Stack
React, Hono, PostgreSQL, TypeScript
With AI SDK, Zod, Drizzle ORM, and deployment workflow
Student Support
Support system to keep students moving.
Discord Community
Learn alongside Devscale students and alumni in a focused engineering community.
Assignment Feedback
Submit work, receive feedback, and improve through practical iteration instead of passive watching.
Mentor Guidance
Use 1-on-1 sessions to discuss technical blockers, project direction, and career context.
Devscale Direction
Why this direction fits Devscale.
One Language Across the Product
TypeScript lets students share types, schemas, and product assumptions across frontend, backend, data, and AI harness code.
Built for AI Product Engineering
The curriculum focuses on the hard parts around the model: UI state, streaming, tools, data access, auth, and deployment.
Modern Web Stack
Students work with React, Hono, PostgreSQL, Drizzle ORM, Zod, TanStack Query, and AI SDK patterns used in current product teams.
Mentor-Led Feedback
Live classes, assignments, and 1-on-1 sessions give students direct feedback on architecture, code quality, and product decisions.
Related Reading
The thinking behind this program direction.
Career direction
Two Paths for AI Engineers
Kenapa program ini fokus ke LLM product harness, bukan model training dari nol.
Read the article
Program direction
Menutup Program AI-Enabled Python Web Development
Alasan Devscale mengalihkan fokus program publik dari Python ke full-stack TypeScript.
Read the article
FAQ
Common questions before joining the program.
Mengapa pendaftaran dijeda dan kapan dibuka kembali? ⌃
Pendaftaran AI Product Engineering with TypeScript, termasuk scholarship, dijeda sementara karena mentor belum tersedia untuk mendampingi program. Tanggal pembukaan kembali dan jadwal program akan diumumkan melalui website dan kanal resmi Devscale. Form belum menerima pendaftaran baru hingga pengumuman berikutnya.
Apakah program ini menggantikan AI-Enabled Python? ⌃
Untuk program publik Devscale, iya. AI-Enabled Python kami sunset agar fokus kurikulum berikutnya lebih tajam ke AI Product Engineering dengan TypeScript. Python tetap bagus dan tetap relevan, terutama untuk data, automation, machine learning, dan eksperimen model. Namun untuk membangun produk web modern dengan UI, backend, database, auth, streaming, dan AI feature dalam satu workflow, kami memilih TypeScript sebagai fokus utama.
Apakah saya harus sudah menguasai TypeScript? ⌃
Tidak harus. Kamu perlu nyaman dengan dasar HTML, CSS, dan JavaScript terlebih dahulu. TypeScript akan dipelajari dari fondasi, mulai dari type dasar, interface, narrowing, generics, sampai cara memakai type untuk menjaga API contract, schema, dan data flow di aplikasi fullstack.
Apakah program ini cocok untuk pemula? ⌃
Cocok untuk beginner yang sudah punya dasar web development. Program ini bukan mulai dari nol absolut, jadi kamu sebaiknya sudah memahami struktur HTML, styling CSS, JavaScript dasar, function, array, object, dan async dasar. Jika belum, kamu tetap bisa mendaftar, tapi kami sarankan menyelesaikan materi dasar web terlebih dahulu sebelum batch dimulai.
Apakah pembayaran bisa 2x? ⌃
Ya. Pembayaran bisa dilakukan 2 kali. Jika memilih pembayaran 2x, konfirmasi pilihan pembayaran ke admin setelah mengirim bukti transfer pertama.
Apakah akan belajar AI model training? ⌃
Tidak. Fokus program ini bukan melatih model dari nol. Fokusnya adalah membangun produk yang mengintegrasikan AI secara utuh: memakai LLM API, AI SDK, structured output, streaming UI, tool calling, workflow automation, retrieval, evaluation sederhana, dan production safety. Jadi arahnya adalah AI Product Engineering, bukan machine learning research.
Apa final project yang akan dibuat? ⌃
Kamu membangun satu aplikasi fullstack TypeScript dengan fitur AI yang benar-benar dipakai, bukan demo prompt — misalnya document assistant dengan RAG atau internal copilot yang terhubung ke database kamu sendiri. Yang dinilai adalah produknya secara utuh: arsitektur, database, UX, auth, dan deployment-nya.