I built 6 useless (and useful) things with AI in 30 days

I got laid off in March 2026. The day HR handed me the 30-day notice, I had a small panic attack, then opened my laptop and started building things.

Here’s the deal: I had 30 days before severance ran out, and I wanted to see how much I could ship with AI tools before the money (and motivation) ran dry. I gave myself a single rule — every project gets a 7-day deadline, otherwise I kill it. I built 6 things. One has real users. One broke in production. Two I never opened again.

This is what happened, in the order I built them.

1. AI Buddy (Chrome sidebar) — shipped, 15 users

A Chrome extension that puts an AI assistant in a sidebar. Select text on any page, hit a keyboard shortcut, it goes to the AI, reply shows up without you leaving the page. Works with GPT-4, Claude, Gemini, DeepSeek. No login, no credit card.

Time: 11 days (April 1–11).
Status: Live on Chrome Web Store. 15 real users as of June 28, 2026. Rating 4.2.
What I used AI for: 90% of the code (500 lines of JavaScript, written in Cursor). The README, the Chrome Web Store description, the marketing tweets — all AI-drafted, then I rewrote the parts that sounded like AI.
What went wrong: The first version had a Stripe integration. AI wrote 90% of the webhook signature verification. I had to rewrite it from scratch. Also the model-picker UI went through 5 revisions because AI kept proposing what looked right but didn’t work.

→ Chrome Web Store

2. Weekly report generator — personal use only

Every Friday at 4pm, a script grabs my git commits, Slack messages, and Linear ticket changes, throws them at GPT-4, and asks for a “manager-readable” weekly report. I review, tweak, send.

Time: 2 days. ~200 lines of Python.
Status: Running for 11 weeks. Has 1 user. Me. Cost is $0.12/week.
What I used AI for: The prompt. It’s surprisingly tricky to get GPT-4 to write a weekly report that doesn’t sound like a robot. The single most useful line: “if you don’t have data, write ‘no progress this week’ — don’t make things up.” That one line took accuracy from 60% to 95%.
What went wrong: First run, GPT-4 wrote a paragraph about my “fix typo” commit like it was a major deliverable. Second run, it paraphrased a joke someone told in Slack as a “product decision.” Both fixed with filters — len(commit) > 5 and msg.author == me.

→ Not public. Just a script on my machine.

3. Email reply drafter — personal use only

Apple Mail plug-in. Select an email, hit a shortcut, the email goes to Claude API, you get 3 reply drafts in different tones (formal, friendly, short). Pick one. Send.

Time: 1 day. ~150 lines of AppleScript + 80 lines of Python glue.
Status: Daily driver for me. Saves 20–30 min/day. 1 user.
What I used AI for: All of it. AppleScript, prompt, training data. The single most useful change: I told Claude to never write “thank you for your email” and to just say “got it.” Reply time per email dropped from 15s to 3s.
Why this is not a product: AppleScript means it’s Mac-only. To make a Gmail/Outlook version, I’d need to deal with Google’s OAuth review and Microsoft’s enterprise APIs. Not worth the time for a personal tool.

→ Not public.

4. Resume rewriter — 0 users

Upload a PDF resume + a job description, the tool rewrites your resume to better match the JD, gives a match score, and lists “what’s missing.” I built this because I rewrote 30 resumes in a month and the process is genuinely awful.

Time: 5 days. ~400 lines of Python + Streamlit.
Status: Live on Streamlit. 0 users. Used by me 4 times.
What I used AI for: Streamlit UI (100% AI). GPT-4 rewriting with a hard constraint: “do not invent experiences, only rephrase what exists.” Without that constraint, GPT-4 happily writes “improved X by 30%” on a resume that has no metrics.
Why it flopped: Posted to Product Hunt, got 14 upvotes. Posted to r/jobs, got downvoted as “yet another AI wrapper.” The people who need this most (job seekers) won’t pay $9 once for a tool. They will, however, spend 5 hours rewriting their resume by hand because “free” still beats “cheap.”

→ Not public.

5. Podcast summarizer — killed

Input an RSS feed. Every episode gets downloaded, transcribed with Whisper, summarized with GPT-4, delivered as a 5-minute text version with bullet points.

Time: 2 days. ~250 lines of Python.
Status: Dead.
Why I killed it: Whisper takes 8 minutes to transcribe a 1-hour episode. Cost is $0.50/episode. I had it running for 3 days and realized: I’d rather listen at 1.5x speed than read a summary. The honest reason is that I didn’t actually need this product. I built it because I could, not because I had a problem. That’s the trap with AI in 2026 — the build cost is so low that you ship 100 demos nobody uses.

→ Buried in a private repo. Don’t dig it up.

6. Math error-book — killed

Take a photo of a math problem. GPT-4V recognizes the question, gives a step-by-step solution, recommends 3 similar problems.

Time: 2 days. ~300 lines of Python + GPT-4V API.
Status: Dead.
Why I killed it: GPT-4V’s handwriting recognition is 60-70% accurate. An “error book” is the worst place for 30% error — the entire point is “I got this wrong, help me not get it wrong again.” If the AI misreads the question, the student practices the wrong thing. Also, education products need ops, customer support, parent dashboards — that’s a company, not a side project.

→ Buried in a private repo.

What worked

Three of the six are still running, and I use all three daily. The thing they have in common: each one solves a problem I had personally, repeatedly, before I built it. Chrome sidebar: I was switching tabs 10 times a day. Weekly report: I was spending an hour every Friday. Email drafter: I was rewriting the same 5 templates 5 times a day.

The 7-day deadline saved me. The three I killed, I killed on day 2 or 3 because by then I knew I wasn’t going to use them. Without the deadline, I would have spent 3 weeks polishing the podcast summarizer and gotten the same 0 users at the end.

Shipping a real version is cheap now. All 6 of these are deployed on real servers, with real domains (or in my case, real machine / Chrome Web Store). None of them are “v0 demos.” That used to take months. With AI it takes 1–5 days.

What I got wrong

I built before I validated. I should have done “pretotype” tests on at least 3 of the 6 before writing a line of code. For example: a one-page Google Form asking “would you pay $9 for a tool that rewrites your resume to match job descriptions?” — that would have killed project 4 in 30 minutes, not 5 days.

I didn’t write about it as I went. This blog post should have been 6 short posts, written in April, not one long post in June. The 6 projects have been sitting in my repos doing nothing because I never told anyone about them while the work was fresh.

I didn’t track costs carefully. I’d love to tell you the total API spend, the server cost, the domain cost. I can’t, because I didn’t track it. Lesson: even for personal projects, log every API call.

I over-indexed on “AI can do this.” Projects 5 and 6 were both killed because I started with the AI capability (“GPT-4V can read handwriting!”) instead of a user problem (“students need help with math problems”). Capability-first building is a 2026 trap.

If you want to do this

List 5 things you do every day. 3 of them can probably be rebuilt with AI in a way that saves you 10 minutes.
Pick the one that annoys you the most. Not the biggest problem. The most annoying. Annoyance is the best signal of “I will actually use this.”
Ship a working version in 3 days. Not 7, not 14. If you can’t make it work in 3 days, the problem is that you don’t actually know what you’re building. Stop. Restart.
Use it for 7 days yourself. Day 7, ask: “am I still opening this?” If yes, publish. If no, kill.
Post to ONE channel. Not five. Pick one — Hacker News, Product Hunt, Reddit, X, LinkedIn. Watch the data.
Write a 1000-word postmortem at the end of 30 days. Even if you only ship 1 thing. Even if that 1 thing has 3 users. The post is worth more than the product, for your career.

The biggest lesson: building is cheap in 2026. Finding users is not. AI cut my build cost by 10x. It did not cut my “find someone who actually cares” cost by 1 cent.

Now I’m going back to growing AI Buddy, because I have 15 users and I’d like to have 150 by September. Wish me luck.

Lőng Gē / a person who got laid off and built 6 things

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