Lovable 官方最新动态：$85,000 in tokens later: What I learned from scaling agentic coding at Lovable

来源：Lovable 官方动态 | 发布日期：2026-08-25T09:00:00.000Z

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I joined Lovable in January this year. Before that, I used maybe $600 of tokens per month on my coding tasks. Once I started here, a larger scope, a larger project, and the freedom to use as many toke

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I joined Lovable in January this year. Before that, I used maybe $600 of tokens per month on my coding tasks. Once I started here, a larger scope, a larger project, and the freedom to use as many tokens as a problem warrants brought my individual spend to ~$25K/mo in May and an overall spend of ~$85K since January. Here's what happened when I pushed harder than most, and what I learned along the way. In January 2026, my agentic coding process involved plan mode, permission requests, and classic code review. A productive week meant 20-30 merged PRs. As of June I have a dedicated agent that writes tasks for the other agents, with multiple levels of implementation and review agents. For large changes, the working unit is a 10-PR stack instead of a single PR. Human review happens for the important decisions and rarely for the code. A productive week now means 150+ merged PRs. January: solo dev plus a few agents. June: one human over 6–7 agents, each with its own swarm of subagents. About 75% of my tokens are spent directly on implementation. The other 25% (and growing) is spent on all forms of automation: AI reviews outside of my dev environment, post-merge AI reviews, and all kinds of regular automated tasks. I predict that this share will only increase as more and more work shifts out of the code-creation PR loop. The transition from human-written to AI-written code in many ways is a new level of abstraction, like the transition from assembly to higher-level languages. Back then, people pretty quickly stopped looking directly at the output from the compiler. The same is happening with code review today. Reviewing AI-written code line-by-line isn't practical or a good use of anyone's time. And the usual answer to problems created by the use of AI is to use more AI, so you switch to AI reviews by default. Those are getting notably better over time. What I've seen work at Lovable is reserving human review for the most impactful changes. However, this often doesn't happen in the form of traditional code review on a PR. Instead, it's happening at a higher level with RFCs and ADRs. We often pair debug hard problems or do whiteboard sessions in-person to get aligned on a new initiative. The important thing here is to apply human attention where it gives the most impact. For example, a single discussion of system design or an infrastructure choice may change our system more than 50 implementation PRs would. That's why we try to shift human attention where it matters the most — and it's not code review. The problem is that when code review goes away, we lose a lot of the secondary benefits that are unrelated to approving changes line by line. Code review used to be a learning tool, and a way that knowledge diffused through an organization. It was an automatic discovery and alignment mechanism for style and architecture choices as well. As a discipline, engineering needs to find a way to preserve these second-order effects in a world where code review is largely automated. I don't see any good solutions yet, only space for new experiments.

更多技术细节可访问官方原文：https://lovable.dev/blog/85000-in-tokens-later-scaling-agentic-coding-at-lovable。