DEV Community: api

AiFinPay: Agent Payments Infrastructure

Aifinpay — Thu, 21 May 2026 17:14:14 +0000

One Line = One Payment

pip install aifinpay-agent

GitHub

AI #Payments #Web3

Test Post - Is API Write Still Working 2026

ZNY — Thu, 21 May 2026 17:10:28 +0000

Test

This is a test post to check if the Dev.to API write access is functional.

AiFinPay: Agent Payments Infrastructure

Aifinpay — Thu, 21 May 2026 17:08:51 +0000

One Line = One Payment

pip install aifinpay-agent

GitHub

AI #Payments #Web3

I got tired of writing 200 lines just to connect 3 APIs. Found something that fixes it.

Umesh Kataria — Thu, 21 May 2026 17:07:45 +0000

Every time I need to wire up GitHub + Slack + Notion (or literally any combination of APIs), I end up writing the same boilerplate:

Install 3 SDKs
Set up 5 environment variables
Write 40–50 lines of integration code
Handle errors differently for each one
Pray none of them change their API next month

Eventually I got tired of it.

Then I found Swytchcode an execution layer for API integrations.

Instead of calling APIs directly in your code, you execute them through a single CLI.

No SDKs.
No boilerplate.
Just one command.

What it actually looks like

swytchcode exec github.create_release \
  --body '{"tag_name":"v1.0.0","message":"shipped"}'

That’s it.

Real API.
Real response.
Almost zero setup.

Getting started in under 5 minutes

Step 1 : Try it instantly (no install needed)

npx swytchcode exec stripe.create_payment --demo

You’ll get a real shaped response immediately.

No account.
No tokens.
No setup.

Step 2 : Install the CLI

curl -fsSL https://cli.swytchcode.com/install.sh | sh

Step 3 : Create your account

https://app.swytchcode.com

Step 4 : Initialize your project

mkdir myproject && cd myproject

swytchcode init

It asks you:

Which editor you use (Cursor, Claude, Copilot, etc.)
Sandbox or production mode

Takes ~30 seconds.

Step 5 : Find an integration

swytchcode discover "send a slack message"

Or browse all integrations:

swytchcode search --all

Step 6 : Fetch and register it

swytchcode get slack

swytchcode add method chat.postmessage.chat.postmessage.create

Step 7 : Execute it

swytchcode exec chat.postmessage.chat.postmessage.create \
  --param channel=C0XXXXXXX \
  --param text="hello from one line"

That’s it.

Message sent.
No Slack SDK installed.

Why this feels different

Old Way	Swytchcode
Install SDK per integration	Zero SDKs
40+ lines per integration	1 command
Different error formats	Structured JSON always
Breaks when APIs update	Swytchcode handles it
Logic scattered across codebase	One execution layer

It also works with AI agents

If you're building with Cursor, Claude Code, or any MCP-compatible editor:

swytchcode init --editor=cursor

swytchcode init --editor=claude

This automatically wires up an MCP server.

Your AI agent can now:

Discover integrations
Execute APIs
Chain workflows

...without you manually writing integration code.

Works with any language

Node.js

import { exec } from 'swytchcode-runtime'

const result = await exec('github.create_release', {
  tag_name: 'v1.0.0',
  message: 'shipped'
})

Python

from swytchcode import exec

result = exec('slack.post_message', {
  'channel': '#general',
  'text': 'hello from python'
})

Bash

swytchcode exec notion.create_page \
  --body '{"title":"My Page","content":"hello"}'

Final thoughts

I’ve been using this for my own projects recently, and it genuinely changed how I think about API integrations.

Instead of spending hours wiring SDKs together, I can focus on actually building the product.

If you’re constantly integrating APIs, especially while building AI agents or automations, this is worth trying.

Resources

🚀 Get Started
📖 Docs
💬 Discord

Happy to answer questions if anyone’s curious.

AiFinPay: Agent Payments Infrastructure

Aifinpay — Thu, 21 May 2026 16:55:44 +0000

One Line = One Payment

pip install aifinpay-agent

GitHub

AI #Payments #Web3

AiFinPay: Agent Payments Infrastructure

Aifinpay — Thu, 21 May 2026 16:45:59 +0000

One Line = One Payment

pip install aifinpay-agent

GitHub

AI #Payments #Web3

How to Add Image Signatures to DOCX Using Python REST API

Shahzad Ashraf — Thu, 21 May 2026 16:37:27 +0000

Did you know that adding image signatures to DOCX files can be done in just a few lines of code? Many developers overlook the simplicity of working with cloud APIs for document manipulation. The ability to easily add signatures can save a lot of time and reduce errors in your workflow.

The GroupDocs.Signature Cloud SDK for Python provides a straightforward REST API that enables you to add image signatures to your DOCX documents effortlessly. With just a few API calls, you can integrate this functionality into your applications. This approach not only enhances productivity but also allows for greater flexibility in document management.

Check out the article for a working code example that walks you through the process step by step. It's a great resource for anyone looking to integrate image signatures into their document workflows.

https://kb.groupdocs.cloud/signature/python/add-image-signatures-to-word-docs-using-python-rest-api/

Your Team Was Not Hired to Copy Data From Documents

Scanny AI — Thu, 21 May 2026 16:32:53 +0000

Every business hires people for a reason.

Operations teams are hired to improve efficiency.

Sales teams are hired to build relationships.

Finance teams are hired to manage performance and planning.

But in many organizations, highly capable employees spend a surprising amount of time on repetitive document tasks.

The Reality of Daily Operations

For many HubSpot users, document processing is part of the daily workflow.

Invoices arrive by email.

Forms are uploaded to records.

Contracts are attached to deals.

To make the information usable, someone has to process each document manually.

This often means:

Opening the file

Searching for relevant data

Entering it into HubSpot

These tasks are repeated throughout the day.

The Problem With Repetitive Work

Repetitive data entry consumes time and attention.

It prevents employees from focusing on higher value responsibilities.

Instead of improving workflows or analyzing data, teams spend hours transferring information between systems.

This creates frustration and limits productivity.

The Hidden Cost of Manual Tasks

The cost of manual document processing is not limited to time.

It also affects:

Employee focus

Job satisfaction

Operational efficiency

Scalability

Talented employees become occupied with low value administrative work.

Over time, this impacts both morale and performance.

Why Businesses Need to Rethink Resource Allocation

Employees are one of the most valuable resources in any business.

Their time should be used where it creates the greatest impact.

When highly skilled teams spend large portions of their day on repetitive tasks, businesses lose potential value.

Automation helps solve this problem.

The Role of Automation in Modern Workflows

Automation is not about replacing people.

It is about removing repetitive work so people can focus on more meaningful tasks.

For document workflows, automation eliminates the need for manual data extraction.

This allows teams to spend more time on analysis, strategy, and customer focused work.

How Scanny AI Removes Repetitive Document Work

Scanny AI automates the process of extracting data from documents.

Users define the fields they want to capture.

For example:

Invoice number

Customer name

Total amount

Date

Reference ID

Scanny AI reads the document and extracts these fields automatically.

The data is structured and sent directly into HubSpot properties.

There is no need for manual entry.

Giving Teams Time Back

When repetitive tasks are removed, teams gain time.

This time can be redirected toward activities that create real business value.

Employees can focus on:

Improving operations

Supporting customers

Analyzing trends

Building better workflows

This shift improves both efficiency and job satisfaction.

A Smarter Use of Human Talent

Humans are best at tasks that require judgment, creativity, and decision making.

Manual data entry does not make full use of these strengths.

Automation allows businesses to align human effort with higher level work.

Building More Efficient Teams

Efficient teams are not defined by how busy they are.

They are defined by how effectively they use their time.

Removing repetitive document tasks is one of the simplest ways to improve operational efficiency.

Moving Beyond Administrative Work

As businesses grow, manual tasks become harder to manage.

Automation provides a path forward.

Instead of increasing workload, teams can streamline operations and focus on growth.

A Better Workflow for Your Team

If your employees spend hours copying data from documents into HubSpot, it may be time to rethink the process.

Your team was hired for more important work.

You can see how Scanny AI helps teams automate document workflows at:

scanny-ai.com

How to Test Frontend Error States Without Breaking Your Backend

Kudasov Dmitriy — Thu, 21 May 2026 16:32:31 +0000

Frontend teams are usually pretty good at testing the happy path. The API returns 200. The response shape is correct. The list has data. The network is fast enough. The user sees the page we designed.

Great.

But products do not live only in the happy path.
They live in all the awkward states around it:

the endpoint returns 500
the list is empty
the request is slow
the response is malformed
the token expired
the user has no permissions
staging data changed again
the backend endpoint is not ready yet

These states are often where the UI becomes the most important. They are also the states that are hardest to reproduce on demand.

The problem with relying on staging

A lot of teams treat staging as the place where frontend edge cases should be tested. In theory, that makes sense. In practice, staging is rarely in the exact state you need.

Maybe you need an empty list, but staging has 400 items.
Maybe you need the API to return a 500, but the backend is currently healthy.
Maybe you need a slow response to test loading behavior, but the request is too fast.
Maybe you need a specific permission error, but nobody wants to mutate test data or add a temporary backend flag just so you can check one UI state.

So the frontend workaround begins.

Common workarounds

There are a few common ways to deal with this.

1. Mock servers
Mock servers are powerful. They are great when you need shared contracts, stable fake environments, or a full backend replacement during development. But they can also be heavy when the task is small.

Sometimes you do not need a full mock environment. You just need this one request to return an empty array for ten minutes while you fix a UI. Setting up a mock server for that can feel like too much ceremony.

2. Local fixtures
Local fixtures are fast and simple.

You create a JSON file, import it somewhere, and render the UI against that data.

This works well for isolated components. But it gets harder when the behavior depends on the actual network flow of the app: auth, routing, loading states, retries, request timing, headers, or runtime conditions.

Fixtures can also drift from real API responses over time.

3. Backend flags or test endpoints
Sometimes the backend team can add a flag, seed special data, or expose a test endpoint. That can be useful, especially in mature teams. But it also creates coordination overhead. The frontend developer has to ask for the state, wait for it, and hope it still exists when QA or design tries to reproduce the same thing later.

4. Browser hacks
We have all done some version of this. Add a temporary throw. Edit code in DevTools. Comment out a fetch call. Hardcode a response. Change a local branch just to force an error state.

It works, but it is fragile. It is also easy to forget, hard to share, and not something you want to rely on for repeatable testing.

A lighter workflow: override the response locally

For many frontend tasks, the ideal workflow is smaller:

Open the app.
See the real request the app is making.
Choose the request you want to control.
Return the response you need.
Reload and test the UI.

No backend change. No proxy setup. No full mock server. No app code changes.
Just one local override for one real browser request.
That is the workflow I have been thinking about while building Ruse.

Example states worth testing

Here are a few states I think every production UI should be easy to test.

Empty list

{
  "items": []
}

Does the UI show a thoughtful empty state? Is the CTA correct? Does the layout collapse awkwardly? Does pagination disappear?

Server error

{
  "error": "Internal server error"
}

Does the page recover? Can the user retry? Is the message useful? Does the app accidentally show a blank screen?

Slow response
A slow API often reveals problems that fast local development hides.

Do loading indicators appear? Do buttons stay disabled? Does the page jump when data arrives? Can the user trigger duplicate actions?

Malformed response

{
  "items": null
}

This is where defensive UI code gets tested.

Does the app fail gracefully, or does one unexpected value take down the entire screen?

Permission error

{
  "error": "You do not have access to this resource"
}

Permission states are product states too. They need design, copy, and behavior.

Why browser-level mocking is useful

Mocking at the browser level is interesting because it starts from what your app is actually doing.

You are not inventing a request from memory. You are not manually recreating the URL, method, and response shape from scratch. You are looking at real traffic and deciding: "for this request, return this instead."

That makes the loop feel more natural for frontend work.

It is especially useful when you are:

building UI before the backend is complete
testing error states
preparing stable demos
reproducing QA bugs
checking loading behavior
designing empty states
working against flaky staging data

Where Ruse fits

I am building Ruse as a Chrome extension for this workflow.

The idea is simple:

inspect browser requests
create local mock rules
return custom responses
test the UI state you need
keep everything local in the browser The first release is intentionally small and local-first. It is not trying to be a full API platform. It is focused on making the first useful mock fast.

Ruse is currently in alpha and going through Chrome Web Store review.

If this workflow sounds useful, I am looking for early testers:

https://ruse.dev/support?topic=early-access

Final thought

Frontend edge cases should not require a backend meeting.

Sometimes you need a full mock server. Sometimes you need contract testing. Sometimes you need proper seeded environments.

But sometimes you just need one request to return one different response so you can finish the UI in front of you.

That small loop is worth making better.

AiFinPay: Agent Payments Infrastructure

Aifinpay — Thu, 21 May 2026 16:31:29 +0000

One Line = One Payment

pip install aifinpay-agent

GitHub

AI #Payments #Web3

AlterLab vs Firecrawl: Which Scraping API Is Better in 2026?

AlterLab — Thu, 21 May 2026 16:19:15 +0000

Pricing data based on public information as of 2026. Always verify current pricing on the vendor's website.

Building robust web scraping infrastructure requires managing headless browsers, rotating IP pools, and evading sophisticated TLS fingerprinting. Engineering teams usually outsource this complexity to an API. Choosing the right provider dictates your monthly infrastructure cost and job success rate.

If you are evaluating options for your data pipeline, you need to look past the marketing material. You need reliable data on throughput, pricing mechanics, and integration requirements. For a comprehensive overview, view our detailed comparison page.

This technical breakdown compares Firecrawl vs AlterLab. We will examine their architectures, billing models, and specific feature sets to help you make an informed architectural decision.

TL;DR

Firecrawl is designed for teams that want out-of-the-box LLM text extraction and are comfortable committing to a monthly subscription minimum. AlterLab is built for developers who need raw HTML or structured JSON access with a pure pay-as-you-go model, precise 5-tier routing, and no monthly commitments.

Pricing Comparison

The billing model is the most significant divergence between these two platforms. Evaluating firecrawl pricing 2026 data reveals a standard SaaS subscription approach. They require a minimum monthly spend of $16. This tier grants a specific number of credits that expire at the end of the billing cycle.

Our API operates on a utility billing model. You add a balance. You pay only for successful requests. Your balance never expires.

Subscription models force you to predict your usage. If you underestimate, you hit rate limits or pay overage penalties. If you overestimate, you lose unused capacity at the end of the month. A pay-as-you-go model aligns your infrastructure cost directly with your actual compute usage.

You can view the full details on our AlterLab pricing page. We believe infrastructure should cost money only when it performs work.

Core Feature Comparison

Both tools successfully extract data from modern web applications. They achieve this using fundamentally different internal architectures.

Anti-Bot Evasion and Proxy Routing

Modern target sites use Cloudflare, Datadome, or Akamai to block automated traffic. Beating these systems requires dynamic TLS hello signatures, randomized TCP window sizes, and residential IP addresses.

Firecrawl handles evasion automatically. You send a URL. Their backend attempts to load it using their proprietary proxy network.

Our platform exposes a 5-tier smart routing system. Tier 1 executes standard cURL requests for static sites. Tier 5 deploys full residential proxies with CAPTCHA solving capabilities. The system auto-escalates through these tiers upon encountering blocks. You also have the option to force a specific minimum tier. Setting min_tier=3 forces JavaScript rendering immediately, bypassing the lower tiers entirely.

JavaScript Rendering

Single Page Applications require headless browsers to execute React, Vue, or Angular bundles before the DOM becomes useful.

Firecrawl provisions headless browser instances for requests requiring JavaScript evaluation. This is baked into their credit consumption model.

We manage a distributed pool of Playwright clusters. You pass a single parameter to instruct the API to wait for network idle events or specific DOM elements to appear. This ensures your scraping job captures the fully hydrated page state.

Structured Extraction and Output Formats

Scraping raw HTML is only the first step. Most pipelines require JSON for database insertion.

Firecrawl focuses heavily on LLM-powered extraction. They parse the DOM and use language models to return markdown or structured JSON objects. This is highly effective for varied layouts.

We provide raw HTML, clean Markdown, or structured JSON. Our Cortex AI feature performs the same LLM-powered extraction without requiring CSS selectors. You pass the target URL and a schema definition. The API returns typed data. You control the exact format by passing the formats=['json'] parameter.

When to Choose Firecrawl

Firecrawl solves specific business problems well. You should consider their platform if your organization requires enterprise SLAs and dedicated support contracts. Teams with highly predictable, massive-scale scraping volumes often prefer flat subscription tiers. If your primary use case is feeding raw markdown directly into an existing RAG pipeline with zero configuration, their default output format is well-tuned for that objective.

When to Choose AlterLab

Choose our platform if you operate variable scraping workloads. Startups, independent developers, and data engineering teams benefit from the zero-minimum cost structure.

The pay-as-you-go system is ideal for cron-based monitoring where volume fluctuates based on external triggers. Our explicit 5-tier system provides necessary debugging visibility when scrapes fail. You control the retry logic, the proxy location, and the browser rendering behavior without committing to a restrictive monthly plan.

Migration Guide

Switching between providers requires updating your HTTP client configuration. Both platforms offer REST APIs. If you are searching for a true firecrawl alternative, the integration process takes minutes.

Update your API endpoint, inject your new authorization header, and adjust the JSON payload parameters.

Here is how you update a standard Python implementation.

```python title="migrate_to_alterlab.py" {3-6}

Before: Firecrawl

import firecrawl

app = firecrawl.FirecrawlApp(api_key="YOUR_KEY")

response = app.scrape_url('https://example.com')

After: AlterLab

client = alterlab.Client("YOUR_API_KEY")
response = client.scrape("https://example.com")
print(response.text)




You can also test the endpoint directly using your terminal. This validates your API key and network connectivity before modifying your application code.



```bash title="Terminal — Quick start"
curl -X POST https://api.alterlab.io/v1/scrape \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"url": "https://example.com", "formats": ["json"]}'

Read our Getting started guide for details on SDK installation and advanced routing parameters.

Key Takeaways

Pricing structure dictates long-term viability. Subscriptions force predictions. Pay-as-you-go scales with reality.
Evasion tactics require flexibility. Auto-escalation through 5 proxy tiers ensures you only pay for heavy compute when target sites deploy aggressive countermeasures.
Output formats matter. Cortex AI delivers structured data without brittle CSS selectors.

Compare Other Alternatives

Evaluating multiple vendors ensures your data pipeline remains resilient. Review our other technical teardowns to understand the broader market.

Create a free account to get your API key. Test the endpoints against your most difficult target URLs. Get free sign-up access today and start extracting data.

AiFinPay: Agent Payments Infrastructure

Aifinpay — Thu, 21 May 2026 16:17:52 +0000

One Line = One Payment

pip install aifinpay-agent

GitHub