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Your API may be working perfectly and still costing your business money.
A developer integrates with your API and immediately starts asking questions.
1, Why does this endpoint return data, while another returns result?
2, Why does one error return 400, while another returns 200 with "success": false?
3, Why does pagination work differently on every resource?
4, Why did a small backend change break the mobile app?
None of these problems necessarily mean your API is broken.
But they create friction.
Developers spend more time figuring out how your API behaves instead of building the product. Frontend teams write workarounds. Integrations take longer. Support gets harder. Small backend changes become risky.
And that cost grows with every client, partner, and internal team that depends on the API.
The value of a well-designed API isn't simply cleaner code.
It's less guessing, faster integrations, fewer mistakes, and easier maintenance.
That creates real business impact:
💎 Faster development
💎 Lower integration costs
💎 Fewer production issues
💎 Easier onboarding
💎 More reliable products
Your API is part of your product experience.
If developers constantly have to figure out what your API means, DM me.
Let's make your API easier to use, easier to maintain, and easier for your business to scale.
The network for creativity
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I made this website to allow people to be able to play mini games relax and play with friends while being in different places (Caution: Anyone who says that my account is diffrent from here answer yes but this is my second account my main account credits finsihed and i am broke to buy upgrade sory)
I had the pleasure of working with 21st.dev to make small design fixes easy to see.
AI can build a page, but the details don’t always match. Different button sizes. Inconsistent text. Selected states that aren’t clear. The design feels off.
To explain Design Bug Bot, I built a UI component with those problems and animated the fixes one at a time.
I brought each issue into focus, showed what changed, then pulled back to show the result.
Why this approach?
People won’t pause and replay your video to figure out what changed.
That’s why I make each feature obvious, one at a time.
If a feature still needs explaining after you’ve shown it, the video hasn’t done its job.
Project AIR is an open-source toolkit that keeps a tamper-proof record of what AI agents do. Every action an agent takes gets signed, chained to the step before it, and tied to the person who authorized it. The chain is anchored to Sigstore Rekor, a public transparency log, so anyone can check later that nothing was changed or deleted. When something goes wrong, you can show exactly what happened and who approved it, instead of just saying it happened.
It also runs 16 detectors over every record, covering all ten risks on the OWASP Top 10 for Agentic Applications, three from the OWASP list for LLM apps, and three of its own. It can stop an agent mid-action until a person signs off. It plugs into LangChain, LlamaIndex, and the OpenAI, Anthropic and Gemini SDKs with a line or two of code.
I built the Python library and command-line tool, published on PyPI as projectair (now at version 1.3.1) under the Apache 2.0 license, plus the website and dashboard in SvelteKit. We run it on our own infrastructure too: every request to vindicara.io is recorded and anchored to Rekor every minute.
Impressive work! 👏 Excellent approach to AI agent security, tamper-proof audit trails, and human oversight for more trustworthy AI systems.