Data and AI releases

This month1,068Companies169

1,068 this month · 169 companies

Workers, BYOIP, Bots - Workers subrequests now preserve context for BYOIP destinations

UpdateyesterdayUnverifiedAdded Oct 14, 2026

Cloudflare now preserves Workers context when a subrequest targets a Cloudflare-proxied hostname using Bring Your Own IP (BYOIP) . These requests are identified as Cloudflare-to-Cloudflare traffic instead of external Internet traffic. Previously, some BYOIP subrequests could lose Workers context and original client signals.

Topics: Streaming, Security

Cloudflare's release notes

More Cloudflare developer platform releases

Every Cloudflare developer platform release
GAunverified

Cloudflare One Client for Windows (version 2026.8.2110.0)

A new GA release for the Windows Cloudflare One Client is now available on the stable releases downloads page . This hotfix resolves an issue where the client could repeatedly disconnect and reconnect when a split tunnel configuration with hostname-based entries failed to apply.

GAunverified

Cloudflare One Client for macOS (version 2026.8.2110.0)

A new GA release for the macOS Cloudflare One Client is now available on the stable releases downloads page . This hotfix resolves an issue where the client could repeatedly disconnect and reconnect when a split tunnel configuration with hostname-based entries failed to apply.

GAunverified

Cloudflare One Client for Linux (version 2026.8.2110.0)

A new GA release for the Linux Cloudflare One Client is now available on the stable releases downloads page . This hotfix resolves an issue where the client could repeatedly disconnect and reconnect when a split tunnel configuration with hostname-based entries failed to apply.

Also shipped on Oct 13, 2026

Cloudflare in October 2026
Betaunverified

What's included:

- Three training methods: Supervised fine-tuning (SFT) , direct preference optimization (DPO) , and reinforcement learning with group relative policy optimization (GRPO) . Each method trains a low-rank adapter (LoRA) or the full model weights. Custom loss functions : Train with any objective you compute in PyTorch from the model's log-probabilities.