Dispatches from Flux
Practical playbooks, data-driven insights, and real-world stories to help you lead AI-accelerated engineering organizations with clarity and confidence.
Read the latest from Flux
LDX3 New York: every AI conversation came back to proof
What engineering leaders at LDX3 New York asked about AI impact, DORA metrics, review debt, and software factories.
The AI bet is real; can you prove it’s paying off?
Most engineering leaders can’t prove their AI investment is paying off. See the five blind spots standing in the way, and how to close them.
AI broke one of software’s oldest assumptions
AI code generation isn’t deterministic: the same prompt and model can output different code each run. Aaron Beals answers webinar Q&A on what that means.
Your org chart can’t show how your engineering team actually works
An org chart is great at describing reporting lines, but how is the work actually getting done or who’s making it possible to move forward?
Flux release notes: metrics, AI insights, team dynamics, and custom prompts
Flux’s latest release notes: custom prompts, team-level DORA metrics, and AI-powered analysis of lead time findings, direct from the codebase.
How to implement DORA metrics: a practical guide for engineering leaders
A 90-day DORA metrics implementation plan, with benchmarks, common pitfalls, and how AI changes what these metrics mean.
AI solved the code creation problem; now we have a visibility problem
AI solved the code creation problem. Visibility is where engineering leaders win or lose next.
Code-first engineering intelligence is having its moment
Gartner’s latest research reinforces a growing reality: code is the most reliable source of engineering insight.
Why we built Flux, and where we’re taking it
Today we’re announcing $5 million in new funding led by Calibrate Ventures, with participation from True Ventures and Glasswing Ventures.
In the age of LLMs, token usage is a recurring topic
Discover how to filter garbage data from code repositories for LLMs. We share insights from building tools to ensure only useful data is processed for models.
The hidden costs of AI-generated code: what engineering leaders need to know
Learn to manage the hidden costs of AI-generated code by balancing rapid velocity with long-term maintenance and risk.
The signals senior engineers read that juniors don’t (yet)
Ramping in a new codebase is about reading the right signals—where work is happening, what kind it is, and where things keep breaking.