The world's first Engineering World Model that puts debugging, fixing, and testing your code on autopilot.
We've raised $20M from Foundation Capital, @matei_zaharia (Databricks), @pbailis (Workday), @rauchg (Vercel), @zoink (Figma), @drewhouston (Dropbox), and more
PlayerZero frees up 30% of your engineering bandwidth by:
1. Finding the root cause for bugs & incidents in minutes that engineering teams take days to identify.
2. Predicting in minutes, edge case issues that a 300-person QA team would take weeks to find.
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Here's why this matters:
No one in your org has a complete picture of how your production software actually behaves.
Support sees tickets. SRE sees infra. Dev sees code. Each team builds their own fragmented view - and none of these systems talk to each other. When something breaks, everyone scrambles to stitch the picture together by hand.
PlayerZero connects all of it into a single context graph -
→ The Slack thread where your lead said "we went with X because Y fell apart in prod last time"
→ The PR review where an engineer explained the tradeoff
→ The lifetime history of your CI/CD pipeline, observability stack, incidents, and support tickets
So you can trace any problem to its root cause across every silo.
And it compounds. Every incident diagnosed teaches the model something new. The longer it runs, the deeper it understands - which code paths are high-risk, which configurations are fragile, which changes tend to break which customer flows.
So when you sit down to debug a live issue, you have your entire org's collective reasoning and production memory behind you - instantly.
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Zuora, Georgia-Pacific, and Nylas have reduced resolution time by 90% and caught 95% of breaking changes and freeing an average of $30M in engineering bandwidth.
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Our guarantee:
If we can't increase your engineering bandwidth by at least 20% within one week, we'll donate $10,000 to an open-source project of your choice.
Book a demo - bit.ly/3NlLMeN
We're also giving away a curated collection of 200+ Claude Code Skills our team uses daily — the workflows that made us faster engineers while building PlayerZero.
Repost and comment "100X" to get access.
Dec 31, 2025 • 8 tweets • 3 min read
1/ Since @JayaGup10 and I wrote about context graphs, the response has been huge...but I've also noticed a few misconceptions worth addressing.
The tldr: context graphs aren't a graph database or structured memory. They require a fundamentally different approach to schema and representation.
This matters because I'm seeing teams reach for familiar tools (Neo4j, vector stores, knowledge graphs) and wonder why their agents aren't getting smarter. The primitives are wrong.
A few things I want to clarify:
2/ "Ontology" is an overloaded term. We need to be more precise.
There are prescribed ontologies (rule engines, workflows, governance layers). Palantir built a $50B company on this: a defined layer mapping enterprise data to objects and relationships. You define the schema. You enforce it. It works when you know the structure upfront.
The next $50B company will be built on learned ontologies. Structure that emerges from how work actually happens, not how you designed it to happen. This is important because there’s so much implicit knowledge in decision making that we don’t even realize in the moment, and agents are meant to replicate our judgement!
