Systems | Development | Analytics | API | Testing

Multiplayer is now open source

The Multiplayer debugging agent is open source under MIT. Here's why, and what it means for how you use it. Today we're open sourcing the Multiplayer debugging agent: connect your favorite coding agent to prod to fix application bugs automatically. Run it locally and eliminate PR slop. The core (session-based data capture, local-first architecture, intelligent deduplication, and coding agent integration) is publicly available under MIT, free to use, and auditable by anyone.

How Structured Content Improves Financial Product Communication

Financial product communication has to be clear, accurate, and easy to understand. Customers often compare banking products, insurance options, investment services, loans, credit cards, payment solutions, and savings accounts before making a decision. Each product may include detailed information about fees, eligibility, benefits, terms, risks, application steps, and support options. When this information is presented in a confusing or inconsistent way, customers may struggle to understand what a product offers and whether it is right for their needs.

Why your AI UX keeps breaking (and what to do about it)

I ran a webinar on this recently and had more to say than the time allowed, so this is the written version: the argument I was making, some context on the demo, and the questions that came up from people watching. The recording is below if you'd rather watch than read. The thesis: AI products are being let down by the user experience, not the model.

The internal war over who owns AI.

There is a massive boardroom fight happening right now over who gets to control AI. Should it be IT? A centralized lab? The executives? Boris Rabkin from Ligentia drops a truth bomb: AI belongs wherever value is actually created. If your AI strategy is locked inside an isolated corporate lab instead of in the hands of your product, engineering, and customer teams, it’s going to fail. Full episode out now!

Ep 78 | Mastering Enterprise AI: Why Some Projects Succeed While Others Fail

AI may be the most capable intern your organization has ever hired. However, interns still need guidance and clear direction. Enterprise AI is proving no different. In this episode of The AI Forecast, Paul Muller sits down with Michael Gray, CTO of Thrive, to explore the patterns and anti-patterns emerging from real-world enterprise AI deployments. Drawing on his experience helping organizations implement AI at scale, Michael offers a practical framework for evaluating AI maturity, helping leaders understand where adoption breaks down and what it takes to build momentum across the organization.

Vibe Coding Economically: Which Framework Is the Cheapest? (Rails vs Django vs Laravel)

Token costs used to be something most developers ignored. They simply dismissed them as theoretical. Now, they’re showing up in your Cursor/Claude Code bill, in every pasted error, in that package the AI pulled in without asking, or in that clarification round you didn’t plan for. Most developers choose a framework based on what they've used before, what the job description asks for, or simply whatever was used on their last project.

Introducing AI Transport v0.2.0

Version v0.2.0 of @ably/ai-transport reorganises the SDK to better support a wide range of interaction patterns. Everything in an AI session – input, output, agent lifecycle, control signals – is captured durably, allowing you to easily build the sophisticated interaction patterns that support modern AI user experiences. When we first built @ably/ai-transport, we modelled an AI conversation the way most people first picture it: as a request and a response.