Bolt is giving developers 50x more compute. But there’s a catch.

Bolt.new, StackBlitz’s browser-based AI development platform, is testing a new trade with developers: more coding-model usage in exchange for training data.

The company launched Forge on Monday, a research preview for individual Pro subscribers that offers up to 50 times more usage of open weight coding models through October 14. Developers who use Forge must opt in to sharing anonymized versions of their sessions for model training, including prompts, source code and the fix traces it creates as developers work through problems, in addition to their conversations with the coding agent.

The sessions will be used in a project with Arcee AI to help train a trillion-parameter-class open-weight model. The first training run is scheduled to begin in October, with Bolt saying the resulting model weights will eventually be released publicly.

Forge makes that development activity part of the exchange, with developers getting more compute while Bolt and Arcee get data from real coding sessions.

Why Coding Trajectories Matter

Public repositories contain enormous amounts of source code, but they mostly show the end result. A coding session can fill in the gaps left by failed attempts and revisions along the way. The record of what worked (and what did not) is useful as the coding agents take on longer jobs.

Working across a codebase means finding the right files, coordinating changes, and recovering when something breaks. That gets harder when agents inherit code written by other agents, which isn’t always easy for the next one to understand or modify.

SpaceXAI showed one version of this approach last month when it trained Grok 4.6 on agent failure traces — the missteps, retries, and corrections that other labs typically discard. Bolt is making a similar bet but sourcing the data from developer sessions rather than synthetic runs.

Arcee has been working on the same underlying problem. In a blog post about NAC, its open-source agent harness, the company said software engineering tasks can stretch across tens of thousands of tokens as agents read code, edit files, run tests, and debug failures.

How Bolt Gets to 50X More Usage

Coding agents can burn through large numbers of tokens on even a single complex task, so a 50-fold increase in usage is a truly significant offer.

Forge changes the underlying setup by running open weight models on Bolt’s own infrastructure. The agent currently uses GLM 5.3 Flash and GLM 5.3, with Kimi K3 and DeepSeek v4 Pro available as experimental options.

Bolt’s WebContainers technology, built by parent company StackBlitz, gives it another cost advantage by running projects in an isolated environment inside the user’s browser rather than on Bolt’s servers.

Forge applies a similar approach to the models, using open weights on reserved hardware while developer sessions help train future versions, giving Bolt more control over costs and reducing its reliance on proprietary APIs.

That push toward self-hosted models is showing up elsewhere in the industry. Nvidia’s $12.9 billion bid for Hugging Face is arguably the same bet at a very different scale.

Coding agents can burn through large numbers of tokens on even a single complex task, so a 50-fold increase in usage is a truly significant offer.

Forge Scores 91% of Bolt’s Top Model

Forge’s open models scored 92.2 on the company’s internal Bolt Build Index, compared with 101.0 for its top paid model, putting them at about 91% of the top score. That’s only a measure of performance inside Bolt, so the 91% figure doesn’t tell us how those models compare more broadly.

But if Bolt can run more of its coding workloads on its own models instead of paying for proprietary APIs, it has more control over costs and usage, while the Forge sessions help train whatever comes next.

What Developers Are Giving Up

Forge requires an explicit opt-in, with a consent screen appearing each time a developer switches into the workspace. Standard and Max sessions aren’t included, and Teams and Enterprise accounts can’t participate.

Bolt says it anonymizes sessions before they leave its infrastructure, removing secrets, sensitive data, and personal information, and it tests the process against seeded data. Arcee receives the resulting data under a signed processing agreement.

Developers can stop sharing new sessions by leaving Forge, but Bolt says anything already used for training will remain in the models.

The 50× surge ends October 14, though Bolt says Forge itself will stick around as an open-model testing ground once the preview wraps up.

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