@wanerfu: What's it like to apply to 500 jobs? I had ChatGPT do it, and got 12 interviews within 24 hours. How exactly? Read on

What’s it like to apply for 500 jobs?

I had ChatGPT do it, and landed about 12 interviews within 24 hours

How exactly did I do it? Keep scrolling down 👇

1/ Find your 20 job titles

First, upload your resume to ChatGPT, then run this prompt.

Paste this into ChatGPT: “Act as a senior recruiter. Based on my resume, list the 20 job titles I’m best suited for, along with the exact keywords the ATS system scans for in each position.”

Now you know what you’re suited for and what the recruiting bots are looking for.

2/ Build a master resume

Stay in the same chat thread. This becomes the foundation for instant customization.

Paste this into ChatGPT: “Rewrite my resume as a master template for quick customization. Use the Google XYZ formula and remove every red flag a hiring manager would spot within 10 seconds.”

3/ Turn on agent mode

This is the trick that makes everything else work.

Click: the “+” icon next to the message box → enable agent mode.

Now ChatGPT can open boss直聘 and Indeed itself, not just talk about them.

4/ Build a job spreadsheet

It goes out and finds all the matches, then organizes them for you.

Paste this into ChatGPT: “Go into LinkedIn and Indeed, find all matching jobs posted in the last 7 days, and build me a spreadsheet with links, match scores, and customized resume versions.”

5/ Let it apply

The craziest part.

Paste this into ChatGPT: “Apply to the top 500 best-matching positions. Customize each application based on the job description.”

ChatGPT handles them on its own — you can go to sleep.

Now run Kimi K3 on a single CPU — a 2.78-trillion-parameter model, requiring just 8.24GB of memory. It’s a 100% open-source project called kimi-k3-in-c.

Most inference frameworks assume you need a GPU cluster and 5+TB of memory to run a frontier MoE model.

The entire engine is just 176KB of portable C99 code. No BLAS, PyTorch, CUDA, any framework, GPU, or AVX-512 required.

The 1.56TB checkpoint is stored on NVMe. Only 16 of 896 experts are activated per token; the dormant 93% streams in on demand and never occupies RAM.

The dense backbone adapts to any memory budget: 8GB, 32GB, 128GB, or 224GB all use the same weights and produce byte-identical output.

Key features: → Run Kimi K3 on an 8GB-RAM laptop → Read MXFP4 weights directly, no conversion to float32 → Stream from disk with O_DIRECT, faster than page cache → KDA attention state stays fixed, independent of context length → Scalar, OpenMP, and AVX2 paths output identical bits → Every kernel is verified against the PyTorch reference implementation

The code is just a 45MB source tree: 6 C files, 1 Python packaging script.