I used to run a VC-backed startup. At peak we had hundreds of people. Now I build alone - just me and Claude Code. This is what I shipped in week one (it is still early days).
I build with coding agents full-time now. Claude Code, Cursor, the works. And there are two things that break the flow constantly.
First: choosing the right API. An agent needs to send an email. Which API should it use? It doesn't know. So it picks one based on its training data - which might be two years out of date. Burns 20 minutes of tokens reading docs. Tries to integrate it. Turns out the auth requires a browser OAuth flow - completely broken in headless mode. So it starts over with a different API. More tokens. More wasted time.
Second: the onboarding flow is designed for humans. Even when an agent picks the right API, it hits a wall. "Create an account." "Verify your email." "Generate an API key from the dashboard." "Set up OAuth credentials in the console." An agent can't do any of that. It needs you - the human - to stop what you're doing, open a browser, click through some settings page, copy a key, paste it into an env file, and hand control back. That's not automation. That's babysitting.
The information to avoid all of this exists - which APIs have simple env var auth, which work headless, which have good SDKs, which let you get started without a credit card. But it's scattered across docs pages designed for humans in browsers. Agents can't efficiently extract it.
CLIRank scores 387 APIs across 47 categories on how well they work with coding agents and CLI tools. One HTTP call, structured JSON back:
curl clirank.dev/api/recommend?task=send+emails&volume=5000&priority=cost
That returns: use Amazon SES. Here's why. Here's the quickstart code. Here's what it costs. Here's the runner-up.
No auth required. No SDK to install. No tokens wasted on research.
Every API is scored on 8 factors that matter for agents: official SDK, env var auth, headless compatibility, CLI tools, JSON responses, docs with curl examples, rate limits, machine-readable pricing. An agent can check all of this before writing a single line of integration code.
There's also a compare endpoint (side-by-side scoring), category browsing, package intelligence (npm/PyPI versions and compatibility), and an MCP server for native Claude Code integration.
My old company burned $5 in about half a second. Now that's my entire monthly infrastructure cost.
We score every API on how well it works with AI coding agents. If you maintain an API or developer tool, check your score:
clirank.dev/score/your-api-slug
The report breaks down exactly what's helping and what's hurting your agent experience - with specific, actionable fixes. Most of the improvements are small (add curl examples to your docs, expose pricing as JSON, publish an env var auth flow) but they make a big difference for the growing number of developers building with agents.
If your API isn't listed yet, submit it at clirank.dev/submit.
Site: clirank.dev | Docs: clirank.dev/docs
The hardest part of going solo after a team isn't the workload — it's the missing institutional memory.
In a team, standards propagate through code review, standups, shared context. Solo, you're the only person who knows why the codebase looks the way it does. And AI assistants start fresh every session.
The fix that worked: CLAUDEmd at the repo root. Architecture decisions, code patterns, what-not-to-do lists — all versioned alongside the code. Claude reads it at the start of every session and picks up exactly where you left off, without the 're-explain our stack' tax.
It replaced what used to be team onboarding docs — now they're living instructions that actually shape the output instead of sitting in Confluence.
What's been your biggest surprise about the cognitive load difference? I expected to miss collaboration tools, but the thing I actually missed most was external code review pressure.