Find published dossiers by topic, company, product or technology.

Showing 1–20 of 25 dossiers

Cloudflare makes Python Workers GA — with Hyperdrive and first-class platform bindings

The Hyperdrive integration was the practical database unlock; the larger September 21 change is that Python Workers themselves are now GA. Cloudflare is explicitly positioning Python as a production language on Workers, with native platform bindings and framework support rather than an experimental compatibility layer.

Buttondown cut its database from 2TB to 750GB by deleting three legacy tables

Buttondown’s 'Great Pruning' is a small-SaaS operations story about deleting architecture rather than adding it. The company removed duplicated or over-retained request and email-event data after changing how those workloads were processed.

Neon’s branchable backend reaches Europe as Functions and Object Storage expand to Frankfurt

Neon is extending database branching into a broader backend stack and now into a second geography. The Frankfurt expansion improves latency and data-location choices, but Functions and Object Storage remain beta products with pricing and production boundaries still unsettled.

Railway makes Postgres recovery, HA and pooling machine-operable from its CLI

Agents and operations tooling can inspect HA health, trigger switchovers, restore to a timestamp and change connection pooling from one machine-readable surface. That increases automation power, but recovery actions still create real operational boundaries such as brief failover interruption and forked PITR services.

Amazon Aurora Serverless gets faster burst scaling

Aurora Serverless can now add roughly 12 ACUs in the first second of a scale-up event on platform versions 3 and 4. The change is automatic and is most useful for bursty SaaS, API, batch and agent workloads, but it does not remove the separate resume delay when a database has scaled all the way to zero.

Anthropic’s Model Hardware Standard gives AI agents a shared interface for physical devices

MHS is an attempt to make microscopes, liquid handlers, robotic arms and other programmable hardware look like a consistent tool surface to AI agents. It is still a research preview, but the interoperability layer is already being tested with research institutions and hardware vendors.