360SOFTY

Insights

Engineering Insights

Practical writing on software architecture, SaaS products, AI automation, legacy modernisation, and the business of building reliable systems.

RSS

Curated links from external sources — not 360Softy original articles.

ExternalDatabase
PlanetScale Blog

Stripe Projects partnership: Provision PlanetScale Postgres and MySQL databases from the Stripe CLI

We're excited to announce that PlanetScale is participating as a co-design and launch partner for Stripe Projects, a new developer preview from Stripe that centralizes dev tool provisioning and billing in one place. What is Stripe Projects? Stripe Projects is a new way for developers and coding agents to discover, provision, and pay for developer tools all from the Stripe CLI. Instead of jumping between dashboards, entering payment info, and copying credentials across services, everything lives

PlanetScale BlogRead original
ExternalCloud
Cloudflare Changelog

Access - Code mode for MCP server portals

MCP server portals support code mode, a technique that reduces context window usage by replacing individual tool definitions with a single code execution tool. Code mode is turned on by default on all portals. To turn it off, edit the portal in Access controls > AI controls and turn off Code mode under Basic information. When code mode is active, the portal exposes a single code tool instead of listing every tool from every upstream MCP server. The connected AI agent writes JavaScript that calls

Access
Cloudflare ChangelogRead original
ExternalCloud
Cloudflare Changelog

Access - Context optimization for MCP server portals

MCP server portals support two context optimization options that reduce how many tokens tool definitions consume in the model's context window. Both options are activated by appending the optimize_context query parameter to the portal URL. minimize_tools Strips tool descriptions and input schemas from all upstream tools, leaving only their names. The portal exposes a special query tool that agents use to retrieve full definitions on demand. This provides up to 5x savings in token usage. https:

Access
Cloudflare ChangelogRead original
ExternalCloud
Cloudflare Changelog

Containers - Easily connect Containers and Sandboxes to Workers

Containers and Sandboxes now support connecting directly to Workers over HTTP. This allows you to call Workers functions and bindings, like KV or R2, from within the container at specific hostnames. Run Worker code Define an outbound handler to capture any HTTP request or use outboundByHost to capture requests to individual hostnames and IPs. export class MyApp extends Sandbox {} MyApp.outbound = async (request, env, ctx) => { // you can run arbitrary functions defined in your Worker on

Containers
Cloudflare ChangelogRead original
ExternalCloud
Cloudflare Changelog

Data Loss Prevention - Streaming ZIP file scanning removes per-file size limits

DLP now processes ZIP files using a streaming handler that scans archive contents element-by-element as data arrives. This removes previous file size limitations and improves memory efficiency when scanning large archives. Microsoft Office documents (DOCX, XLSX, PPTX) also benefit from this improvement, as they use ZIP as a container format. This improvement is automatic — no configuration changes are required.

Data Loss Prevention
Cloudflare ChangelogRead original
ExternalCloud
Cloudflare Changelog

Radar - URL Scanner improvements on Cloudflare Radar

Radar ships several improvements to the URL Scanner that make scan reports more informative and easier to share: Live screenshots — the summary card now includes an option to capture a live screenshot of the scanned URL on demand using the Browser Rendering API. Save as PDF — a new button generates a print-optimized document aggregating all tab contents (Summary, Security, Network, Behavior, and Indicators) into a single file. Download as JSON — raw scan data is available as a JSON download for

Radar
Cloudflare ChangelogRead original
ExternalAI
NVIDIA Technical Blog

Maximize AI Infrastructure Throughput by Consolidating Underutilized GPU Workloads

In production Kubernetes environments, the difference between model requirements and GPU size creates inefficiencies. Lightweight automatic speech recognition... In production Kubernetes environments, the difference between model requirements and GPU size creates inefficiencies. Lightweight automatic speech recognition (ASR) or text-to-speech (TTS) models may require only 10 GB of VRAM, yet occupy an entire GPU in standard Kubernetes deployments. Because the scheduler maps a model to one or more

NVIDIA Technical BlogRead original

Work with 360Softy

Building a SaaS product, AI system, or business platform?

Book a free consultation and we will tell you honestly whether we can help.

Book a consultationView our services →