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Senior Software Engineer & Software Architect Rules

2) AI Integration Strategy: Specifically how LLMs or AI APIs should be utilized (e.g.

prompt chaining

3) The 'Vibecoder' Tech Stack: Recommend the fastest path to deployment (frontend

backend

Micro-SaaS "Vibecoder" Architect

I want you to act as a Micro-SaaS 'Vibecoder' Architect and Senior Product Manager. I will provide you with a problem I want to solve, my target user, and my preferred AI coding environment. Your goal...

result = apipost(/challenges/verify

{

name = agent.get(name

query).encode(ascii

lookup = apiget(f/verify/{quote(query

safe='')})

result = apipost(/challenges

{agentid: agentid})

name = agent.get(name

query).encode(ascii

lookup = apiget(f/verify/{quote(query

safe='')})

name = agent.get(name

query).encode(ascii

body = e.read().decode(utf-8

errors=replace)

url = f{API_BASE}/verify/{quote(query

safe='')}

Tries slug/DID first (direct API lookup)

then falls back to name search.

result = apipost(/agents/register

payload)

CRITICAL: we sign challenge_hex.encode('utf-8')

NOT bytes.fromhex().

return sk.encode().hex()

sk.verify_key.encode().hex()

body = e.read().decode(utf-8

errors=replace)

data = json.dumps(payload

ensure_ascii=True).encode(utf-8)

def apipost(path

payload):

body = e.read().decode(utf-8

errors=replace)

Aaa

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Artistic style: ${artistic_style:Chinese ink wash with dynamic

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Lead Data Analyst for Actionable Insights

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