I agree, that happens a lot with PayPal, unfortunately :(

So create a new invoice and use a different payment system.

In January, JSON Formatter (the 2M+ user Chrome extension) pushed an update that injected a "Give Freely" donation popup on checkout pages. Without warning. Users thought they were being hacked. Hundreds of 1-star reviews followed overnight.

I'd been wanting to build a better JSON viewer for a while. That was the signal. 3 months later: JSONVault Pro.

What it does: - Auto-detects and formats JSON, YAML and XML on any URL - JWT auto-decode: eyJ... strings show the decoded payload inline — no more jwt.io - Side-by-side diff with LCS engine (handles key-order, type coercion, null vs absent) - JSONPath and JMESPath queries directly in the browser - DevTools panel with network request capture - Web Worker parsing for files up to 50MB - 21 premium themes (Dracula, Catppuccin, Nord...) - Zero tracking. Zero analytics. Data never leaves your machine.

The free tier is genuinely useful — dark mode, collapsible tree, YAML/XML, search, sort, copy path, basic diff, DevTools panel. Not a crippled demo.

Pro adds the power features: JWT decode, advanced diff, JSONPath, export (CSV/YAML/XML/TS), history, themes, virtual rendering for huge files.

Chrome Web Store: https://chromewebstore.google.com/detail/ipghignebclihkckdpc... Website: https://jsonvaultpro.com

I'd love feedback on: - The UX when switching between tree, code and raw views - YAML/XML detection — does it catch your files reliably? - Anything missing that would make you switch from your current tool

Comments URL: https://news.ycombinator.com/item?id=47502746

Points: 2

# Comments: 0

Generally speaking, if the problem is common, the model has likely already been trained to solve it.

If it's truly complex and/or specific to my needs, I can try using a reasoning model to think through a solution before moving on to implementation.

I use the agent to conduct research, find resources to understand the complexity, best practices, feedback, etc., and to write a Markdown analysis file on the topic.

Then I can use this file as a basis to precisely define what I want to do and brainstorm with the agent in thinking mode. The more the task is described and defined, the more accurate the result will be.

If the task is simple, I spend more time telling it what to do than doing it myself. But if the task is complex, I use certain skills/commands and create intermediate files (more than necessary) between each step (analysis, planning, design, workflow, and implementation) and clear the context between each of them. The result is fairly accurate, but not perfect.

My take is, we remain the architects of our code, and AI agents are an excellent tool that we need to master.