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š š¼šš ššš®šæššš½ ššŖš¦ šÆš¶š¹š¹š š®šæš²š»'š šµš¶š“šµ šÆš²š°š®ššš² š¼š³ ššæš®š³š³š¶š°.
š§šµš²š'šæš² šµš¶š“šµ šÆš²š°š®ššš² š¼š³ ššµš¶š»š“š š»š¼šÆš¼š±š šæš²šŗš²šŗšÆš²šæš šššæš»š¶š»š“ š¼š».
If your bill creeps up every month, here's where I look first:
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š”šš§ šš®šš²šš®š š±š®šš® š½šæš¼š°š²ššš¶š»š“
Every GB is billed on top of the hourly rate. Chatty private subnets rack this up silently. VPC endpoints for S3 and DynamoDB skip it entirely.
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šš±š¹š² š®š»š± š¼šš²šæšš¶šš²š± š¶š»ššš®š»š°š²š
Provisioned for a spike that never came, still running months later. Right-sizing is often the single biggest win on the whole bill.
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Instances get terminated. Their storage doesn't always go with them. You keep paying for disks nobody uses.
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ššæš¼šš-šš š±š®šš® ššæš®š»šš³š²šæ
Traffic between availability zones is billed. Architectures that ignore it leak money in small, constant amounts.
ā
šš¹š¼šš±šŖš®šš°šµ š¹š¼š“š šš¶ššµ š»š¼ šæš²šš²š»šš¶š¼š» š½š¼š¹š¶š°š
Logs default to "keep forever." Set a retention period and the bill stops climbing on its own.
None of these are exotic. They're just easy to miss when the team is heads-down shipping.
The pattern underneath all of them:
ššŖš¦ š°š¼šš š¶šš»'š š® š½šæš¶š°š¶š»š“ š½šæš¼šÆš¹š²šŗ. šš'š š® šš¶šš¶šÆš¶š¹š¶šš š½šæš¼šÆš¹š²šŗ. You can't fix what you never see.
A tagging strategy plus Cost Explorer, reviewed monthly, catches most of this before it compounds into a number that hurts.
Start with visibility, before you start turning things off.
#AWS #CloudCost #FinOps #DevOps #Startups #CloudEngineering
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I designed and built Cloudex Technologies as an AI-native company. It's staffed by 18 "Digital FTEs": AI agents with written roles, real tools and a reporting line. They sit in four departments: the Office of the CEO, Delivery, Inbound & Demand, and Outbound & Sales. They run on Paperclip with Claude Code as the main lane and Codex as an adversarial reviewer. The whole company is defined in a git repo (constitution, governance, per-agent instructions), so the runtime is a deploy, not a pet.
The rule it's built on: author ā verifier ā releaser. No agent approves its own work, every build passes five quality gates, and a human principal releases every outbound batch, which can be done by replying SEND or HOLD to an email digest. The sales department runs its own AutoGTM engine. It sources leads from the free Companies House register and researches each prospect's website, keeping only facts quoted word for word. It then drafts short, human emails and sends them within warm-up limits, with auto opt-outs and bounce stops. It runs privately on a VPS over a Tailscale mesh, with budget caps and a full audit trail.
[add your number] contracts signed without a single manual follow-up.
Every one tracked and reminded automatically until it was done.
ā Want this for your process? Let's talk.
Automating the reminders means you never chase a signature again, and the tracking stays clean.
Snapbox: a booking flow that never wastes a slot.
A booking app for a fictional self-photo studio in Bandung, built with Lovable + Supabase.
Problem: in short-session businesses, every empty slot is lost revenue. Studios like this often deal with DM bookings, double bookings, and no-shows nobody has time to refill.
What I built:
Interactive backdrop previewer: tap Y2K, Vintage or Minimal and watch the booth change before booking
Real-time slots with database-level double-booking protection
30% deposit step to reduce no-shows
Self-service reschedule/cancel up to 2 hours before the session
Automatic waitlist: when someone cancels, the next person gets a 10-minute claim link, so the slot becomes revenue again
Owner dashboard: schedule, revenue, no-show rate, busiest hours, slots recovered
Security: Row Level Security and server-side functions keep customer data private; passed Lovable's quick and deep scans.
Honest notes: fictional business for a portfolio demo. Payments and WhatsApp are simulated, and dashboard data is seeded. Voiceover is AI-generated.
Code and demo login: https://github.com/DropzyID/snapbox-studio-bdg