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AI is rapidly moving beyond chatbots into real-world interfaces, healthcare, and autonomous workflows. This week’s biggest updates show how AI is becoming faster, more capable, and increasingly embedded in the systems we use every day.
Here’s what matters today.
Solaris gives us a first look at AI-built interfaces
Runway has launched Solaris in early access- a new “Interface World Model” designed to turn websites and apps into live, interactive video. Instead of traditional code powering the interface, Solaris generates every frame in real time as users click, drag, and interact.
Solaris combines Runway’s Gen-4.5 video model with an LLM to interpret user actions and generate interface responses in real time. Demos include virtual try-ons, interactive cooking, and combustion simulations. In internal tests, users preferred Solaris over Claude Opus 5-built interfaces in 71% of behavior comparisons and 61% of instruction-following tests. However, text clarity, long-session consistency, and inaccurate visuals remain challenges as the system enters early access.
Why it matters: Could the web eventually become something AI generates dynamically rather than something developers build with traditional code? Solaris offers an early glimpse of that possibility. More importantly, it reflects a broader trend: AI models are becoming faster, cheaper, and capable enough to make entirely new types of interfaces practical.
In partnership with
AI made PMs faster. Multiplayer mode is still broken.
A PM can summarize research, draft a PRD, and mock up a prototype before lunch. The hard part starts when the team has to decide what actually gets built.
Jira Product Discovery gives product teams one place to capture insights, prioritize ideas with consistent frameworks, and build living roadmaps stakeholders can rally around.
And because it’s connected to Jira, the context behind every decision stays with the work—so developers and their agents know not just what to build, but why.
AI helps PMs move faster. Jira Product Discovery helps the whole team build with confidence.
AI Detects Heart Disease in Seconds
Researchers at Imperial College London have developed an AI model that can analyze a routine ECG in under two seconds and identify signs of heart failure and valve disease that may be missed by doctors. Trained on 10.6 million ECGs and tested on 65,000 patients, the model detected heart failure with 81% accuracy and valve disease with 90% accuracy. A 590-patient trial across six hospitals is now underway, with the team aiming for potential NHS adoption within two years.
Why it matters: Rather than replacing doctors, AI’s immediate healthcare impact may come from improving how existing medical data is interpreted. By adding another layer of analysis to routine tests, these systems could help detect diseases earlier and make diagnosis more efficient.
4 AI Tools & Community Workflows
Agentic Research Workflow: Use AI agents to break a research task into smaller steps- searching sources, comparing findings, summarizing evidence, and producing a structured brief instead of handling each step manually.
AI Coding Workflow: Give coding agents a defined objective rather than individual prompts. Let them inspect the project, make changes, test the output, and iterate while you review the final result. Agentic coding is increasingly being used beyond developers for automation and structured analysis.
AI Knowledge Workspace: Use tools with memory and persistent context to build on previous conversations, documents, and decisions instead of restarting from scratch for every task. Claude’s recent memory updates are an example of this direction.
Human-in-the-Loop Agents: For important workflows, let AI handle research, drafting, analysis, and repetitive execution while keeping human approval at critical checkpoints. This creates a workflow where AI handles the workload without removing human oversight.
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The AI space doesn’t slow down - and neither should your thinking.
See you in the next drop.