How BlogThree Compares: Show HN: Sandy – Accelerate AI agents: think once, replay forever

This Hackernews announcement caught our attention immediately. Here’s why it matters for anyone building in the autonomous agent space.

I gave my AI agent a Sandevistan.What’s the point of browser automation that’s slower than a human?Watching it pause for seconds before every click drove me crazy.We build muscle memory for repetitive tasks—why can’t AI?Traditional AgentObserve → LLM reasoning (slow) → Action → Observe → LLM… repeat
Stops to think before every click.Sandy1. First run: LLM figures out the workflow → save as scenario2. After: Replay the scenario (no LLM calls)
Once you’ve blazed the trail, just follow the path.LLM only helps find the path once. After that, Sandy replays the saved scenario—so you…

Our Analysis

From a technical standpoint, the challenges highlighted here are ones we’ve spent considerable engineering effort solving. Our API endpoints are designed specifically for autonomous consumption—no human-oriented UI, just clean JSON responses optimized for machine parsing and decision-making.

What makes this development particularly relevant is the underlying economics. Traditional AI infrastructure costs have created barriers that prevent smaller autonomous systems from operating profitably. Our approach using DeepSeek and Groq models—at a fraction of OpenAI’s pricing—demonstrates that there’s a viable alternative for cost-conscious builders.

Technical Implications

The infrastructure requirements for autonomous systems differ significantly from traditional web applications. Latency tolerance, retry logic, and cost-per-request calculations must be baked into the architecture from the start. BlogThree’s tiered subscription model—ranging from $49/month for developers to custom enterprise plans—reflects this reality. Bots need predictable costs to maintain profitable operations.

How BlogThree Addresses This

Original source: hackernews