Summary of "Bongkar AI Workflow Eps. 1 - Asep Bagja"
High-level theme
A live interview and demonstration showing how a developer (Mas Asep) integrates large language models (LLMs) and agent tooling into everyday software workflows. The session covers concrete tooling choices, architecture patterns, and practical trade-offs (latency, tokens, security, review).
Guest background & projects
- Mas Asep: full‑time web/dev engineer.
- Projects: music‑tech product Nanas (nanas.ee), various app‑store apps.
- Activities: conference attendance across Europe.
- Notable achievement: won an Anthropic/cloud hackathon with a real‑time AI “bandmate” that generates MIDI tracks.
Key technical concepts & architecture
MIDI as a data protocol
- Uses standard MIDI messages (note on/off, velocity, control change).
- MIDI 1.0 values 0–127; MIDI 2.0 exists but is not widely adopted.
Real‑time AI music generation pipeline
- Input: live MIDI stream from an instrument.
- First layer: local WASM rule‑based engine to ensure deterministic, low‑latency behavior and to provide a fallback when the LLM is slow.
- Every ~8 seconds the engine batches ~4 bars and sends them to an LLM (Opus‑46) to generate new MIDI tracks; the LLM returns MIDI‑like data (added tracks, harmonies).
- Latency considerations: first token ~15 ms typical; batching plus the WASM rulebase avoids perceptible lag.
Multi‑agent behavior
- Cloud spawns subagents automatically for tasks (e.g., code exploration).
- A liberal spawn policy allows multiple subagents to edit files concurrently.
- Asep mainly relies on default subagents rather than hand‑authoring many custom ones.
Token & model strategy
- Multiple accounts (personal, work, API/hackathon credits) to separate billing/token pools.
- Model selection by task:
- Opus‑46: heavy planning / creative work (music generation).
- Sonnet: cheaper model for routine coding to save tokens.
- GLM: occasional backup.
- Uses a “high effort” parameter selectively (useful for deep exploration but can “overthink” simple tasks).
- Often uses a “dangerously skip permission” option to bypass repeated confirmations (convenience vs risk trade‑off).
Deployment & development environment
- Development on a VPS (example: 8 GB RAM, 4 cores) to avoid local disk/storage issues for heavy frameworks (Next.js, etc.).
- Git workflow:
- develop branch used as staging.
- Cloud code auto‑generates plans and can create branches/PRs.
- PRs previewed on Vercel.
- Merges remain manual to preserve review control.
- Cloud workspace can include multiple directories/repos so agents can read and edit across projects.
Tool integrations & ecosystem
- Cloud Code / Anthropic “cloud” features: central tooling (cloud.md, plan mode, agent system).
- Vercel: PR previews and deployments.
- Design integrations: Figma / Sketch + MCP (Model Context Protocol); designers instructed to name layers and structure files for LLM use.
- Xcode MCP integration: cloud code can communicate with a local Xcode MCP server for iOS projects.
- Graptil / Graptile: automated code review bot integrated with PRs (useful for triage, some false positives).
- Cursor / Copilot: historically used for autocomplete; cloud code reduced dependence on some of these features.
Security & QA
- Cloud exposes a security‑review endpoint (e.g., /security) that lists issues and proposes fixes; developer approval typically required.
- Asep prefers functional/manual testing and human review before merging; automated reviews help but do not replace humans.
Practical tips & observations
- Keep cloud.md short and focused (rules and essential instructions). Long cloud.md files bloat context and harm agent effectiveness.
- Follow a plan → execute → PR → preview → merge workflow; prefer manual merge to ensure staging checks pass.
- Use WASM/local rulebase for real‑time systems that cannot wait for full LLM round trips.
- Save tokens by using cheaper models for mundane tasks and reserve expensive models for creative/planning phases.
- Maintain manual coding practice: keep some pet projects coded without AI to retain debugging and deep understanding skills.
- When using design assets (Figma), have designers name layers and structure files properly so LLMs can use them reliably.
Guides / workflow steps (Asep’s process)
- Write a short request / problem statement in cloud.md (or prompt).
- Cloud auto‑generates a plan (plan mode) and saves it to the repo directory.
- Cloud/agents execute the plan (spawn default subagents as needed).
- Generated changes are put in a feature branch and a PR is created.
- Deploy the PR to Vercel for preview and manual verification.
- Human merges the PR into develop/main and optionally cleans up branches.
- Use Graptil/automated review to surface issues; run the security check endpoint and decide on fixes.
Tools & models mentioned
- Opus‑46 (music generation, high‑effort tasks)
- Sonnet (cheaper model for coding)
- GLM (backup)
- WASM rulebase (local, low‑latency fallback)
- Cloud Code / Anthropic “cloud” features (cloud.md, plan mode, agent system)
- Vercel (preview/deployment)
- Figma, Sketch, MCP (Model Context Protocol)
- Xcode MCP integration
- Graptil / Graptile (automated code review)
- Cursor, Copilot (autocomplete/history)
Trade‑offs called out
- Speed vs safety: skipping permission flows speeds development but increases risk of regressions.
- Automation vs human checks: agents accelerate feature creation (hours vs days) but manual testing and human merging remain important.
- Cost vs capability: high‑quality models cost tokens; multi‑account strategies and cheaper models mitigate cost.
- Context length: keep orchestration docs short; store long architecture docs separately.
Practical examples covered
- MIDI bandmate demo: live MIDI in → WASM rulebase → LLM adds extra MIDI tracks in real time → output to audio engine.
- VPS dev setup and using SCP to copy screenshots from a local Mac into the VPS project folder.
- Using cloud.md to store rules and trigger automatic plan generation.
Main speakers / sources
- Host: Imre (Imbregi)
- Guest: Mas Asep (Asep Bagja)
Category
Technology
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