cookie-auth + compact JSON gets it to ~666 tokens (-83%). This is what powers CrossMind's AI agent for distribution.

Features: - 15+ platforms supported - Three-tier auth (public API -> cookie -> OAuth) - Structured output designed for LLM context efficiency - Handles X's rotating query IDs and x-client-transaction-id

Built on top of jackwener's opencli/twitter-cli/rdt-cli ecosystem. Open source, PRs welcome.

Comments URL: https://news.ycombinator.com/item?id=47598766

Points: 2

# Comments: 1

The failure mode people worry about: "agent goes rogue, spends $10k." The failure mode that actually happens: agent makes a confident decision on stale context. It runs a task that was valid 3 hours ago but is now redundant. Or it retries a failed payment 5 times because the failure was ambiguous. The damage is $20 of wasted API credits, not $10k — but the lesson is the same. Budget guardrails matter, but freshness checks matter more.

On the approval gate question: we use a pattern similar to agentsbooks' — agent proposes, human approves for anything irreversible. But in practice, the approval friction kills the value of autonomy. What actually works is pre-authorizing a class of actions ("spend up to $50/week on content distribution") rather than approving individual transactions. The trust unit is the policy, not the payment.

Re: your specific blockers — the 3DS problem is real and I don't think there's a clean developer solution today. The browser automation legal risk (Amazon v. Perplexity) is worth taking seriously. Virtual cards with per-merchant limits are probably the least fraught path for a while.

The Visa/Mastercard moves are interesting but I'd bet the real unlock is when businesses start issuing agent-specific cards with embedded policies rather than trying to retrofit consumer card rails. That's a few years out.

What's helped me most: stop treating agent invocations like function calls and start treating them like delegating to a junior dev you trust but still need to review. That reframe changes what you do in the gaps. Instead of waiting for confirmation prompts, I now use that 10-30 seconds to write the next spec segment, review adjacent code, or just think about what the agent should find. The gaps become planning time, not interruption time.

The deeper issue is that flow state was designed around a human holding the full context in working memory. With agents, the context lives outside your head by design. The discomfort you're feeling might be the transition from solo-player flow to coordinator flow - which is a genuinely different cognitive mode, not a degraded version of the original.

Concretely: do you write your specs before or after starting the agent run? I've found writing specs upfront (even rough ones) shifts me into coordinator mode faster and makes the waits feel purposeful.

The unpredictability is worse than the absolute cost. Our billing model broke several times not because costs were high, but because we couldn't bound them. One approach that helped: define a 'token budget' per user action at design time - cap total tokens per session and treat hitting the cap as a first-class outcome your product handles gracefully, not an error.

On the forecasting side, we track cost per workflow step rather than per request. Step-level cost is much more stable than request-level because it absorbs the variance in tool calls and retries. Once you have step costs, you can forecast by expected workflow composition.

On fixed subscription pricing for AI APIs - I'd actually pay a premium for that. The unpredictability creates a hidden cost: you over-provision margins and add complexity to your pricing tier design. A flat rate for a capacity bucket would eliminate both.

The question I'd ask about any such service: how do they handle the tail cases where agents go off-rails and rack up 10x normal token usage? That's where the cost risk actually lives.

OpenClaw (and tools like it) are infrastructure — they execute tasks. They don't do the harder work: figuring out what's true about your product that others aren't saying, then connecting that to the specific person experiencing the problem right now.

Your point about positioning requiring back-and-forth is the real insight. It's not just that AI can't extract what you haven't told it. It's that most founders haven't worked out their own positioning yet — and no automated workflow forces that reckoning. You need something that pushes back.

On your actual question: from what I've seen with OpenClaw users, the honest answer is it reduces execution time but doesn't reduce thinking time. If your marketing was fuzzy before, automated fuzzy marketing is still fuzzy — just faster. The founders who get results had already done the positioning work and were just bottlenecked on execution.

What's AuraGTM's approach to the positioning extraction problem specifically?