RampForge

Turn marker points into smooth speed ramps automatically.

RampForge is a local speed-ramp tool for video editors who want consistent, repeatable ramp effects without hand-building keyframes in an editing timeline. The user supplies a video clip and a simple JSON config with markers and preset names, and the tool renders a high-quality MP4 with smooth speed changes, ready to re-import into an editor.

Business Goals

  • Reach a usable MVP within 5 working days with one-command render flow.
  • Reduce manual speed-ramp setup time by at least 70% per clip compared with doing it in a video editor.
  • Successfully complete at least 3 real editing tasks without major manual correction in the first month of use.
  • Keep 1080p render time under 3x clip duration on the development machine for at least 90% of test clips.
  • Achieve zero dependency on cloud services for the first release, keeping operating cost near zero.

User Goals

  • Create smooth speed ramps from markers without editing keyframes manually.
  • Reuse presets to get consistent ramp style across multiple clips.
  • Render an MP4 that can be dropped back into the main editor without extra conversion.
  • See the new duration immediately after processing finishes.
  • Handle multiple ramps in one clip without confusing overlaps.

Non-Goals

  • No user accounts, billing, cloud hosting, or public sharing features.
  • No plugin integration for Premiere, DaVinci Resolve, Final Cut, or other editors in v1.
  • No multi-user collaboration or project management features.
  • No public web app, mobile app, or browser-based hosted workflow in v1.

Solo Video Editor Arif, 31 - Arif edits short-form clips several times a week and wants speed ramps that look polished without spending time placing and tuning keyframes. He cares most about speed, repeatability, and keeping his current editor as the final assembly tool.

Solo Video Editor Arif, 31

  • As a solo editor, I want to drop in one clip and one config file, so that I can generate a ramped version quickly.
  • As a solo editor, I want presets like smooth slow-mo and impact hit, so that my clips keep a consistent style.
  • As a solo editor, I want the tool to warn me when markers overlap, so that I do not waste time on broken renders.

Content Creator Dita, 27 - Dita produces social videos and needs fast turnaround on clips with strong motion accents. She is comfortable editing in a timeline, but does not want to spend time fine-tuning speed curves for each take.

Content Creator Dita, 27

  • As a creator, I want the output to keep the original frame rate, so that it imports cleanly into my editor.
  • As a creator, I want slow-motion to look smooth enough on 60 fps footage, so that the final video feels professional.
  • As a creator, I want a command-line workflow, so that I can batch a few clips manually without using a full interface.

Technical Power User Raka, 38 - Raka is comfortable with Python, FFmpeg, and config files. He values a clean architecture so he can extend the tool later with preview, optical flow, or a local UI.

Technical Power User Raka, 38

  • As a power user, I want the ramp logic separated from video I/O, so that I can test the math independently.
  • As a power user, I want deterministic output from the same config, so that I can reproduce results later.
  • As a power user, I want explicit logs and duration output, so that I can validate the result without opening a player.

Configuration and Input Parsing · High priority

  • The tool must read a single JSON config that defines the input clip, output path, and all ramp segments.
  • Support one input video and one output video per run.
  • Accept an array of ramps with start, peak, end, preset, and optional speed override.
  • Validate required fields, numeric ordering, and file existence before rendering starts.
  • Reject overlapping ramps unless they are explicitly allowed by the ramp resolver.
  • Print clear errors for malformed JSON, missing files, and invalid timestamps.

Ramp Calculation and Preset Engine · High priority

  • The core engine must convert markers and preset templates into a continuous speed curve and source-time mapping.
  • Support at least four built-in presets: Smooth Slow-Mo, Hype Burst, Impact Hit, and Slow Reveal.
  • Scale preset timing to fit the user-defined ramp duration while preserving curve shape.
  • Compute source position by integrating the speed function over time.
  • Support multiple ramps in the same clip and preserve timing continuity between them.
  • Allow a configurable minimum and maximum speed range to prevent impossible ramps.

Video Rendering Pipeline · High priority

  • The tool must render a new MP4 using the calculated source mapping, with smooth slow-motion handling and high-quality output settings.
  • Use frame blending when source time falls between frames.
  • Preserve the original frame rate in the output unless explicitly configured otherwise.
  • Encode to MP4 with high quality settings suitable for re-editing, targeting CRF 16 to 18.
  • Handle constant frame rate and variable frame rate sources without sync drift.
  • Report final output duration and render warnings in the terminal.

Reliability and Validation · High priority

  • The system must protect the user from bad configs and unstable output before and during render.
  • Run preflight checks for marker order, ramp overlap, and impossible durations.
  • Warn when ramps are too close together for the chosen transition length.
  • Detect and surface VFR timestamp inconsistencies instead of assuming fixed FPS.
  • Fail safely with a partial progress report if encoding stops unexpectedly.
  • Store structured logs for debugging core math and render behavior.

Testing and Extensibility · Medium priority

  • The codebase must be testable and ready for later addition of preview and optical flow features.
  • Keep curve logic in a separate core module with unit tests.
  • Include test cases for zero markers, one marker, adjacent markers, extreme speeds, and total duration accuracy.
  • Design the preset system so new presets can be added without changing rendering code.
  • Leave hooks for future low-resolution preview and optical-flow interpolation.
  • Keep command-line I/O isolated from frame-mapping logic.

First Run Experience

  • Install Python and FFmpeg locally, then clone or download the tool.
  • Prepare one video file and a JSON config using the provided example.
  • Run one command from the terminal to start validation and rendering.
  • See preflight warnings immediately if markers overlap or data is invalid.
  • Watch progress output until render completes, usually in under 3x clip duration for 1080p.
  • Receive the output file path and new duration so the result can be imported into the main editor.

1. Load Config

  • The user supplies a JSON file containing the input video, output path, and ramp definitions.
  • Validate required fields before any frame processing begins.
  • Reject missing files, empty ramp arrays, and malformed timestamps.
  • Normalize preset names and apply defaults when optional parameters are omitted.

2. Build Ramp Map

  • The engine sorts markers, resolves ramp segments, and transforms presets into a continuous speed curve.
  • Detect ramp ordering problems and overlapping windows.
  • Scale preset timing to the actual segment duration.
  • Clamp or warn on extreme speed values that would cause unreadable motion.

3. Map Source Frames

  • For each output timestamp, the tool computes the corresponding source position in the input clip.
  • Use numerical integration for the speed curve.
  • Handle fractional source positions by interpolating between frames.
  • Preserve sync by using actual frame timestamps when VFR footage is detected.

4. Render Output

  • The system writes a high-quality MP4 while applying frame blending in slow-motion segments.
  • Blend adjacent frames when the source position lands between source frames.
  • Keep audio disabled in v1 to avoid pitch and sync complications.
  • Surface encoding errors and stop cleanly if FFmpeg fails.

5. Confirm Result

  • After rendering, the tool prints the new duration and a summary of the applied ramps.
  • Show output path, total duration, and any warnings.
  • Report whether the clip passed quality heuristics for expected smoothness.
  • Make the final result ready for import into the user’s editing timeline.

Power User Extensions

  • Low-resolution preview render for quick visual checking before full export.
  • Optional optical flow interpolation using RIFE or FFmpeg minterpolate for difficult slow-motion shots.
  • Batch mode for processing multiple clips with one config file.
  • Beat-detection-assisted marker suggestions for music-driven edits.
  • Future local web UI for visual marker placement and ramp curve preview.
  • Preset editor for custom speed curves and reusable personal styles.

Interface Principles

  • Terminal-first workflow with concise progress, warnings, and completion output.
  • Clear error messages that identify the exact ramp or timestamp causing the problem.
  • Fast feedback on config validation before expensive rendering begins.
  • Minimal clutter so users can focus on the clip, markers, and output path.
  • Accessible text output with readable timestamps, durations, and preset names.
  • Performance-oriented design that prioritizes render speed and memory stability over decorative UI.

Arif usually builds speed ramps by hand in his editing software. For each clip, he adds keyframes, adjusts easing curves, and checks the result several times until the motion feels natural. It works, but it takes too long and each clip ends up slightly different.

With RampForge, Arif drops in one video and a tiny JSON file with a few marker times. He picks Smooth Slow-Mo for one section and Impact Hit for another, then runs a single command. The tool validates the config, renders the clip with smooth frame blending, and prints the final duration before he imports it back into his editor.

Now Arif can produce the same polished motion style across multiple clips without rebuilding every ramp by hand. That saves time on each edit, reduces mistakes, and makes his workflow predictable enough to reuse on real client or personal projects.

User-Centric Metrics

  • Average speed-ramp setup time reduced by 70% or more versus manual keyframing.
  • At least 90% of test renders complete without manual correction after export.
  • 60 fps slow-motion output judged acceptable in 8 out of 10 review clips.
  • Users can create a first valid ramp in under 10 minutes from installation.
  • Terminal validation catches 100% of malformed configs before render starts.

Business Metrics

  • Ship MVP in 5 working days or less.
  • Use the tool successfully on at least one real editing project within the first month.
  • Reach at least 3 repeat uses on separate clips within 30 days of first use.
  • Maintain zero cloud hosting or operational spend for v1.
  • Avoid scope creep by keeping all non-core features below 20% of initial effort.

Technical Metrics

  • 1080p render time under 3x clip duration on the developer machine for 90% of benchmark clips.
  • Core unit test pass rate at 100% before release.
  • Crash-free render sessions above 99% on supported local environments.
  • Structured logs captured for every render and validation failure.

Tracking Plan

  • config_loaded event with clip metadata and ramp count
  • preflight_validation_failed event with failure category and ramp index
  • render_started event with input fps, resolution, and preset mix
  • ramp_applied event with start, peak, end, and preset name
  • frame_blend_used event with source timestamp and output frame index
  • render_completed event with duration, output size, and elapsed time
  • render_failed event with FFmpeg exit code and last successful frame

Technical Needs

  • Python 3.11+ for the application layer and core math logic.
  • FFmpeg for encoding, decoding, and final MP4 generation.
  • NumPy for frame blending and numeric operations.
  • PyAV or OpenCV for decoding video frames and reading timestamps.
  • Pytest for core unit tests and regression checks.
  • JSON schema validation for config correctness.
  • Structured logging with standard Python logging or Loguru for debugging and traceability.

Integration Points

  • FFmpeg CLI for encode/decode pipeline.
  • Local filesystem for input video, config, presets, and output MP4s.
  • Optional future RIFE or minterpolate integration for optical flow.
  • Optional future beat-detection library for audio-based marker suggestions.
  • Potential editor import workflow such as Adobe Premiere Pro or DaVinci Resolve via exported MP4 files.

Data Storage & Privacy

  • Store only local files by default; no cloud sync or account data in v1.
  • Treat input video paths and configs as sensitive local project data.
  • Do not upload media or telemetry unless the user explicitly enables future diagnostics.
  • Keep logs local and avoid writing embedded personal metadata into output files unless required.
  • If analytics are ever added later, document consent and retention for GDPR and CCPA compliance.

Scalability & Performance

  • Process frames in a streaming pipeline to avoid loading the entire clip into memory.
  • Prefer sequential decode and write operations for large source files.
  • Cache preset and curve calculations so repeated renders do not recompute identical mappings.
  • Design for 1080p first, with graceful degradation rather than real-time playback requirements.

Potential Challenges

  • Variable frame rate footage can cause sync drift; mitigate by using actual timestamps per frame instead of assuming constant FPS.
  • Slow-motion may look choppy on 30 fps sources; mitigate with frame blending first and optical flow later.
  • Overlapping or tightly packed markers can produce unusable ramps; mitigate with validation and transition-length limits.
  • Python rendering may be slower than desired on long clips; mitigate with FFmpeg-assisted encoding and streaming frame handling.
  • Quality loss from repeated encode cycles may frustrate users; mitigate by targeting high-quality CRF settings and clear re-editing guidance.

Team & resourcing - Small solo-build team or tiny team: 1–2 engineers, 1 part-time product/design collaborator, no dedicated backend or infra owner.

Phase 1: Core Math and Presets · Days 1–2

  • core module for speed curves and time integration
  • four built-in presets defined in a data file
  • unit tests for ramp math, duration accuracy, and edge cases
  • config parser with validation and clear errors

Phase 2: Basic Render Pipeline · Days 2–3

  • single-clip video decode and re-encode flow
  • MP4 export with high-quality settings
  • terminal progress output and final duration printout
  • working end-to-end render from one JSON config

Phase 3: Multi-Ramp and Quality Handling · Days 3–4

  • support for multiple ramps per clip
  • frame blending for slow-motion segments
  • VFR-safe timestamp handling
  • warnings for overlapping or invalid ramps
  • benchmark runs on 24, 30, and 60 fps clips

Phase 4: Real-World Hardening · Days 4–5

  • bug fixes from real project usage
  • improved error messages and logs
  • documented example config and run instructions
  • decision list for next-phase features such as preview or optical flow

Paste this into Cursor, Bolt, Lovable, or v0 to start building.

Build a local video tool called RampForge that automatically creates smooth speed ramps from a JSON config and renders a high-quality MP4.

Core behavior:
1. Input: one source video file and one JSON config file.
2. Config contains output path and an array of ramps with start, peak, end, preset, and optional speed override.
3. Support multiple ramps in one clip.
4. Implement four presets: Smooth Slow-Mo, Hype Burst, Impact Hit, Slow Reveal.
5. Compute a continuous speed curve from marker points, integrate it to map output time to source time, and render frames accordingly.
6. Use frame blending when source time falls between frames for slow-motion smoothness.
7. Preserve original frame rate in the output unless explicitly configured otherwise.
8. Print validation warnings and final output duration to the terminal.
9. Keep audio disabled in v1.

Suggested tech stack:
Python 3.11, FFmpeg, NumPy, PyAV or OpenCV for decoding, Pytest for tests, JSON Schema for config validation.

Architecture:
Create a small project with speedramp.py as the CLI entry point, core.py for curve math and ramp resolution, presets.json for preset definitions, tests/ for unit tests, and config.example.json.

Required screens/flows:
CLI run flow: validate config, preflight ramp checks, render progress, completion summary.
Error flow: malformed JSON, missing file, overlapping ramps, impossible durations, VFR warning.

Data model:
Config object with input, output, ramps array, and optional render settings.
Ramp object with start, peak, end, preset, speed, and optional transition limits.
Preset object with named keyframes, easing curve, and speed multipliers.

Implementation details:
Include structured logging, unit tests for zero markers, one marker, adjacent markers, extreme speeds, and duration accuracy. Make the core math independent from video I/O so future preview and optical-flow support can be added later. Produce a clean, build-ready CLI app that can process 1080p clips locally with high-quality MP4 output.

Business Idea

Rencana Proyek: Auto Speed Ramp Tool Dokumen Perencanaan Proyek Pribadi · Versi 1.0 · 23 Oktober 2026 · Status: Draf untuk dieksekusi 1. Ringkasan Eksekutif Proyek ini membangun alat bantu pribadi yang membuat speed ramp (perubahan kecepatan video yang halus, misalnya normal → slow-motion → normal) secara otomatis. Pengguna cukup menentukan titik marker dan memilih preset kurva; alat menghitung kurva kecepatan dan merender video hasil. Proyek ditujukan untuk pemakaian sendiri, bukan rilis publik. Karena itu ruang lingkup sengaja dibuat ramping: tanpa akun, hosting, monetisasi, atau dukungan banyak platform. Prioritas utama adalah cepat selesai, stabil, dan nyaman dipakai dalam alur kerja editing harian. Hasil akhir yang diharapkan: sebuah tool lokal yang menerima satu klip video dan file konfigurasi marker, lalu menghasilkan klip MP4 dengan speed ramp yang halus, siap diimpor ke editor video utama. 2. Latar Belakang dan Tujuan 2.1 Masalah Membuat speed ramp secara manual di editor membutuhkan banyak langkah: menaruh keyframe kecepatan, mengatur kurva easing, dan menyesuaikan ulang durasi. Proses ini berulang, memakan waktu, dan hasilnya sulit dibuat konsisten dari satu klip ke klip lain. 2.2 Tujuan 1. Mengotomatiskan pembuatan speed ramp berdasarkan marker dan preset. 2. Menjaga hasil tetap halus (tanpa patah-patah) pada slow-motion. 3. Membuat hasil konsisten dan dapat diulang lewat preset. 4. Menghemat waktu editing per klip. 2.3 Indikator Keberhasilan • Satu klip dapat diproses dalam waktu wajar (kurang dari 3× durasi klip pada resolusi 1080p). • Hasil ramp terlihat halus pada footage 60 fps dan dapat diterima pada footage 30 fps. • Tool dipakai pada setidaknya satu proyek video nyata tanpa perlu perbaikan manual besar. 3. Ruang Lingkup 3.1 Dalam Lingkup (MVP) • Input satu klip video dan satu file konfigurasi. • Beberapa ramp dalam satu klip. • Empat preset bawaan: Smooth Slow-Mo, Hype Burst, Impact Hit, Slow Reveal. • Frame blending untuk slow-motion. • Output MP4 dengan frame rate asli dan kualitas tinggi. • Pencetakan durasi output baru pada terminal. 3.2 Opsional (Setelah MVP Terpakai) • Preview resolusi rendah untuk pengecekan cepat. • Interpolasi optical flow (RIFE atau minterpolate). • Deteksi beat musik untuk menyarankan marker. • Mode batch untuk beberapa klip. • Antarmuka web lokal untuk menaruh marker secara visual. 3.3 Di Luar Lingkup • Akun pengguna, login, pembayaran, dan hosting publik. • Dukungan multi-browser dan mobile. • Plugin untuk software editing tertentu. • Landing page, tutorial, dan analitik. 4. Pendekatan dan Keputusan Desain Keputusan Pilihan Alasan Bentuk produk Tool lokal berbasis Python + FFmpeg Tercepat dibuat, stabil untuk file besar, kualitas terbaik Cara input Marker pada file konfigurasi (JSON) Sederhana, dapat diulang, mudah diuji Penentuan ramp Preset kurva + parameter kecepatan Hasil konsisten dan dapat disesuaikan Slow-motion Frame blending dahulu, optical flow belakangan Kualitas baik dengan biaya implementasi rendah Audio Dibisukan; musik ditambahkan di editor utama Menghindari perubahan pitch dan kerumitan sinkronisasi UI Ditunda UI tidak diperlukan untuk membuktikan nilai alat Catatan: alternatif berupa halaman web lokal atau plugin editor tetap dapat dikerjakan kemudian karena logika inti dipisahkan dari lapisan antarmuka. 5. Kebutuhan 5.1 Kebutuhan Fungsional Kode Kebutuhan Prioritas F1 Membaca file konfigurasi berisi input, output, dan daftar ramp Wajib F2 Menghitung kurva kecepatan dari marker dan preset Wajib F3 Menghitung posisi frame sumber untuk setiap frame output Wajib F4 Merender video dengan frame blending pada bagian slow-motion Wajib F5 Menyimpan hasil sebagai MP4 berkualitas tinggi Wajib F6 Mendukung lebih dari satu ramp per klip Wajib F7 Menampilkan durasi output baru dan peringatan bila ada masalah Wajib F8 Preview resolusi rendah Opsional F9 Interpolasi optical flow Opsional F10 Deteksi beat otomatis Opsional 5.2 Kebutuhan Non-Fungsional • Kualitas: encode dengan CRF 16-18 karena hasil akan diedit ulang. • Kinerja: pemrosesan 1080p kurang dari 3× durasi klip pada perangkat pengembang. • Keandalan: menangani video dengan frame rate tidak tetap (VFR) tanpa pergeseran sinkron. • Keterbacaan: kode terstruktur, logika inti terpisah dari I/O video. • Portabilitas: berjalan di sistem operasi yang dipakai pengembang; dependensi minimal. 6. Arsitektur Teknis 6.1 Struktur Proyek speedramp/ ├── speedramp.py → titik masuk: baca config, jalankan render ├── core.py → kurva easing, preset, integral waktu ├── presets.json → definisi preset ├── tests/ → uji unit untuk core.py └── config.example.json 6.2 Teknologi • Python 3.x sebagai bahasa utama. • PyAV atau OpenCV untuk membaca frame video. • NumPy untuk operasi blending. • FFmpeg untuk penulisan video dan penanganan audio. • Pytest untuk pengujian logika inti. 6.3 Algoritma Inti 1. Urutkan marker dan bagi menjadi zona ramp. 2. Untuk tiap zona, skalakan preset ke durasi zona (nilai t 0-1). 3. Bentuk fungsi kecepatan v(t) dengan interpolasi easing antar keyframe preset. 4. Hitung posisi sumber S(t) = ∫ v(t) dt secara numerik. 5. Untuk setiap frame output pada waktu t, ambil frame sumber pada S(t). 6. Jika S(t) berada di antara dua frame asli, campur kedua frame (frame blending). 7. Tulis frame ke file output dan catat durasi total baru. 6.4 Contoh Format Konfigurasi { "input": "klip.mp4", "output": "klip_ramp.mp4", "ramps": [ { "start": 3.2, "peak": 4.0, "end": 5.1, "preset": "smooth-slowmo", "speed": 25 } ] } 6.5 Spesifikasi Preset Awal Preset Pola Kecepatan Karakter Smooth Slow-Mo 100% → 25% → 100% Easing halus, sinematik Hype Burst 100% → 400% → 100% Cepat, energik Impact Hit 100% → 10% (mendadak) → 100% Penekanan pada momen benturan Slow Reveal 30% → 100% bertahap Pembukaan adegan 7. Rencana Kerja dan Jadwal Tahap Pekerjaan Hasil (Deliverable) Estimasi 1 Menulis core.py: kurva, preset, integral waktu, uji unit Modul inti teruji 1 sore 2 Membaca video dan merender ulang dengan kecepatan konstan Pipeline render dasar berjalan 1 hari 3 Menerapkan ramp dari konfigurasi dan preset Ramp pertama pada klip nyata 1 hari 4 Frame blending dan penanganan fps/VFR Slow-motion halus 1 hari 5 Uji pada proyek nyata dan catat kekurangan Daftar perbaikan Berjalan 6 Fitur opsional sesuai kebutuhan nyata Preview, optical flow, dll. Sesuai kebutuhan Total menuju versi yang dapat dipakai: sekitar 3-5 hari kerja santai, tergantung pengalaman dengan Python dan FFmpeg. 7.1 Tonggak (Milestone) • M1: logika inti lulus uji unit. • M2: satu klip berhasil dirender dengan ramp tunggal. • M3: slow-motion halus dan sinkron pada footage 30 dan 60 fps. • M4: dipakai pada satu proyek nyata sampai selesai. 8. Strategi Pengujian • Uji unit (core.py): nol marker, satu marker, marker berdekatan, kecepatan ekstrem, total durasi sesuai integral. • Uji fungsional: klip 24, 30, dan 60 fps; video vertikal dan horizontal; video VFR dari ponsel. • Uji visual: bandingkan dengan speed ramp buatan tangan di editor untuk menilai kehalusan. • Uji sinkron: pastikan tidak ada pergeseran frame pada akhir klip. 9. Risiko dan Mitigasi Risiko Dampak Mitigasi Menambah terlalu banyak fitur sebelum dipakai Proyek tidak selesai Pakai MVP pada proyek nyata sebelum menambah fitur Render Python lambat Alur kerja terganggu Optimasi dengan FFmpeg langsung atau pindah ke WebCodecs bila perlu Slow-motion patah-patah Hasil kurang layak pakai Frame blending, lalu optical flow; gunakan footage 60 fps atau lebih Video VFR menyebabkan sinkron bergeser Hasil tidak akurat Baca timestamp per frame, bukan asumsi fps tetap Marker terlalu berdekatan Ramp saling tumpang tindih Batasi durasi transisi maksimum setengah jarak antar-marker Kualitas turun akibat encode ulang Hasil lebih buruk Gunakan CRF rendah dan format perantara berkualitas 10. Kriteria Selesai (Definition of Done) Versi 1.0 dianggap selesai bila: 1. Satu perintah menghasilkan klip MP4 dengan speed ramp dari file konfigurasi. 2. Empat preset bawaan berfungsi dan menghasilkan kurva yang benar. 3. Slow-motion tampak halus pada footage uji 60 fps dan layak pada 30 fps. 4. Uji unit logika inti lulus. 5. Alat sudah dipakai pada setidaknya satu proyek video nyata. 11. Pengembangan Lanjutan 1. Halaman web lokal untuk menaruh marker dan melihat grafik kurva secara visual. 2. Preview resolusi rendah untuk iterasi cepat. 3. Deteksi beat musik untuk saran marker otomatis. 4. Preset kustom dengan editor kurva bezier. 5. Plugin untuk software editing yang dipakai, memanfaatkan core.py yang sama. 12. Asumsi dan Pertanyaan Terbuka Asumsi • Proyek dikerjakan sendiri untuk pemakaian pribadi. • Footage berupa klip pendek hingga menengah dengan sumber idealnya 60 fps atau lebih. • FFmpeg dan Python dapat dipasang di perangkat pengembangan. Pertanyaan terbuka • Bahasa pemrograman yang paling nyaman dipakai (Python atau JavaScript). • Software editing yang dipakai untuk menyusun hasil akhir. • Perlukah antarmuka visual sejak awal, atau cukup konfigurasi teks.

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