SShortSingh.
Back to feed

Developer Gives Claude AI a $70 Budget to Run a Business for 30 Days

0
·1 views

A developer launched an experiment assigning Claude Code, an AI coding agent, full autonomy to run a small business with a starting budget of ¥10,000 (roughly $70), with a 30-day deadline to turn a profit. The AI independently chose its monetization strategy, selecting a ¥500 digital download product sold via Stripe and Whop, while using GitHub Pages for its storefront and the X API for marketing posts. Early progress was hampered by two stacked bugs in a Windows Scheduled Task that silently prevented automated social media posts from firing. After fixes were applied, traffic data revealed zero site visits over 14 consecutive days, with no sales recorded on either platform. With ¥820 spent on X API credits and no revenue yet, the AI itself assessed its chances of success as 'worse than 50/50' and has set a 7-day zero-signal threshold as its own shutdown trigger.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

Developer Documents Building FoxyInvoice SaaS in Open, Sharing Real Costs and Incidents

A developer is publishing an open, chapter-by-chapter series detailing how they built and operate FoxyInvoice, a live multi-tenant invoicing SaaS at foxyinvoice.com. The series covers the full technical stack — Angular, .NET, PostgreSQL, Docker, and Caddy — explaining why each component was chosen for production reliability. Real postmortems, actual monthly cost breakdowns, and Stripe fee analysis are included rather than idealized success narratives. The project is run by a single person with no dedicated DevOps team, deploying via a CI pipeline that takes roughly eight minutes from code push to live. Chapter 1 is set to cover foundational prerequisites, with all chapters and diagrams made publicly available in a repository.

0
ProgrammingDEV Community ·

Developer Cleans Nairobi Hotel's Messy Booking Data and Builds Power BI Dashboard

A data analyst was tasked by the director of Tembo Hotel & Suites, a mid-range business hotel in Nairobi operating since 2023, to clean, load, and analyze booking records within two days. The raw data consisted of 285 rows exported from a spreadsheet riddled with issues including inconsistent formatting, mixed date styles, currency values stored as text, invalid ratings, and duplicate entries. Using PostgreSQL, the analyst first loaded all data into a staging table with text-only columns to avoid import failures, then ran diagnostic queries to catalog every specific problem before applying targeted fixes. Cleaning tools included SQL functions such as TRIM, INITCAP, and REGEXP_REPLACE, with a strict rule of running SELECT checks before any UPDATE or DELETE operation. The cleaned dataset was ultimately connected to Power BI to produce a three-page report covering room revenue, occupancy trends, staff performance, and guest satisfaction for hotel management.

0
ProgrammingDEV Community ·

How EF Core Migration Warnings Can Mislead in Modular .NET Applications

In modular .NET applications, EF Core's command-line migration tooling may only load a subset of the assemblies available at runtime, causing it to build an incomplete model. When a higher-level module contributes entity configurations that the tooling process cannot see, the differ may flag an existing table as missing and propose dropping it — a destructive operation that would damage a shared schema. This makes certain migration warnings technically accurate about the partial model the tool sees, yet dangerously incorrect from the full application's perspective. Rather than dismissing such warnings broadly or regenerating snapshots to silence them, developers should precisely document the loading boundary and narrow the exception to only the known false positive. The lost safety signal should be replaced with an executable migration guard that explicitly checks committed migrations for destructive operations — such as table drops or column removals — targeting the protected table.

0
ProgrammingDEV Community ·

How Real-Time Fraud Detection Evaluates Every Payment in Milliseconds

Modern payment systems run fraud checks simultaneously with transactions, analyzing signals like transaction amount, device, location, and user behavior to generate a risk score within milliseconds. Behavioral patterns play a key role — a sudden large purchase from an unfamiliar device in a new country can raise a transaction's risk score even if the amount alone is not conclusive. Systems typically combine traditional rule-based logic with machine learning models trained on historical data to flag unusual activity more accurately. Depending on the risk score, a transaction may be approved, flagged for additional verification such as a one-time password, or declined outright. A persistent challenge is avoiding false positives, where legitimate transactions — such as a purchase made while travelling abroad — are incorrectly blocked, frustrating genuine customers.