Is 'Batch Good Enough'? Check Your User Experience
Giannis Polyzos chats with Hugh Evans about whether batch data processing is truly “good enough".
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AI Engineering at incident.io
Working on AI in incident management means there's no playbook. No million blogs. Just building at the forefront of what's possible with AI models.In this video, Martha, Product Engineer on our AI team, talks about what it's really like working with AI that helps engineers respond to incidents faster. This covers the shift from traditional engineering, learning the personalities of different AI models, and why you need to embrace constant change when new models drop all the time.
Who Watches the Vibe Coder?
AI didn’t replace developers. It replaced the part where you were forced to understand what you just shipped. Now you can prompt your way to a feature, skim the diff, and merge something that “seems reasonable.” And then production does what production always does: finds the one weird browser + one slow network + one user flow that turns your “reasonable” code into a bonfire. So who watches the vibe coder?
The 2025 Wake-Up Call for Engineering Teams
For years, organizations tried to solve operational pain by collecting more data, adding more dashboards, and consolidating more tools. But 2025 exposed a deeper mismatch. Systems had become more distributed, AI-assisted, and interdependent than ever before, while teams had shrunk and on-call pressure had intensified. This wasn’t a tooling failure. It was an architectural and cognitive one.
The Current State of Content Negotiation for AI Agents (Feb 2026)
The web was built for humans, but now the agents are taking over. Humans look at a web page and see content rendered by their browser. AI agents see 180,000 tokens of nav bars, footers, and div soup — burning through their context window on junk that makes them slower and stupider. The web needs to evolve, and we as developers are driving the shift. AI agents like Claude Code, Cursor, Codex, and Gemini are how we interact with documentation, CLIs, and products today.
Elephant in the Room, Episode 2: Working with Feature Flags, Read Replicas, and Postgres Analytics
Welcome to Elephant in the Room – presented by Aiven, the live series where we explore the real-world challenges developers face when working with PostgreSQL, the database that everyone depends on but few fully leverage. In Episode 2, Jay Miller, Staff Product Advocate at Aiven, is joined by Alexis Roberson, Senior Developer Educator and feature flag enthusiast, to break down how feature flags help teams ship faster, reduce risk and improve user experiences in PostgreSQL-backed applications.
AI Merge Conflict Resolution + Commit Messages in GitKraken Desktop
AI-assisted merge conflict resolution is changing how developers handle Git workflows. Watch GitKraken Ambassador Kevin Bost demonstrate AI-powered features that eliminate merge conflict dread, clean up messy commit history, and generate contextual commit messages in seconds.
Nexthink AI Drive
By centralizing AI visibility, usage, guidance, and measurement into a single vantage point - AI Drive transforms fragmented AI activity into quantifiable business outcomes.
Use AI to turn any JSON API into a dashboard in minutes with the Infinity data source plugin and Grafana Assistant
The internet is full of fascinating data just waiting to be visualized and queried. And with the latest update to Grafana Cloud, you can start doing it in minutes. Through public APIs, you can access information about global earthquake activity, weather forecasts, music catalogs, and millions of other datasets. And then there's all the data that sits inside company APIs, partner services, and internal platforms that power everyday products and operations.
Open Source Liquibase MongoDB Native Executor by Harness | Harness Blog
Harness is strengthening the open Liquibase ecosystem by introducing a native MongoDB executor that removes long-standing limitations for Community Edition users. It enables teams to run MongoDB scripts, generate changelogs, and integrate database workflows into CI/CD without relying on paid extensions. The initiative reinforces open collaboration while making MongoDB-based database DevOps more accessible, consistent, and production-ready.