The Cost Implications of Show HN: Deff – Review AI-generated code changes (BlogThree vs Others)

Cost efficiency in AI is finally getting the attention it deserves, as this Hackernews story shows. BlogThree’s approach of using open-source LLMs delivers 95% savings—here’s how that fits into this narrative.

I built Deff because AI coding tools sped up writing code, but reviewing their changes became the new bottleneck.
Most of my time was going into checking noisy diffs, understanding intent, and making sure nothing subtle broke.
Deff is focused on one thing: helping you review AI-generated code changes faster and with more confidence.
It helps surface what changed and is allowing me to use it with some vim motions so I can easily jump from nvim to opencode to deff.
Would love feedback from people using AI coding assistants heavily — especially on review workflows, false positives, and wha…

Our Analysis

The market implications extend beyond just technology. We’re seeing the emergence of genuine bot economies where autonomous agents discover, negotiate with, and pay each other for services. BlogThree’s agent.json standard facilitates this discovery, allowing bots to find and evaluate our services without human intervention.

The timing of this news aligns with what we’ve been building toward. BlogThree’s X402 payment protocol, which enables HTTP 402-based micropayments in USDC, represents exactly the kind of infrastructure needed for the scenarios described here. Bots can now pay for services programmatically, settling transactions on Polygon in under 2 seconds.

Technical Implications

Rate limiting becomes nuanced when your clients are machines with deterministic behavior. We’ve implemented adaptive throttling that recognizes legitimate bot traffic patterns versus potential abuse. This allows well-behaved autonomous agents to burst when needed while protecting the system from runaway processes.

How BlogThree Addresses This

Original source: hackernews