SaaS Metrics
Product Positioning & Context
Measure how you and your team actually work with AI coding agents. Pheebs is an open-source AI telemetry tool created by Eversynced. It sits quietly inside Claude Code, Cursor, and Codex via hooks, capturing lightweight interaction signals: the shape of the session, not its contents.
Related Ecosystem & Alternatives
Discover adjacent products, open-source repositories, and developer tools sharing similar technical architecture.
Deep-Dive FAQs
What is Pheebs?
Pheebs is a digital product or tool described as: Measure how engineers and teams actually work with AI
Where did Pheebs originate?
Data for Pheebs was aggregated directly from the Product Hunt community ecosystem, representing raw developer and early-adopter sentiment.
When was Pheebs publicly launched?
The initial public indexing or launch date for Pheebs within our tracked developer communities was recorded on October 6, 2026.
How popular is Pheebs?
Pheebs has achieved measurable traction, logging over 78 traction score and facilitating 2 recorded discussions or engagements.
Which technical categories define Pheebs?
Based on metadata extraction, Pheebs is categorized under topics such as: Open Source, Artificial Intelligence, GitHub.
What are some commercial alternatives to Pheebs?
Our semantic intelligence engine identifies potential commercial alternatives in the SaaS space, such as PHBench, which offers overlapping value propositions.
Are there open-source alternatives related to Pheebs?
Yes, the GitHub ecosystem contains correlated projects. For example, a repository named lightseekorg/tokenspeed shares highly similar architectural descriptions and topics.
How does the creator describe Pheebs?
The original author or development team describes the product as follows: "Measure how you and your team actually work with AI coding agents. Pheebs is an open-source AI telemetry tool created by Eversynced. It sits quietly inside Claude Code, Cursor, and Codex via hooks,..."
Community Voice & Feedback
Congrats Davi! that's awesome
I've spoken to several engineering leaders who want to better understand their team's AI usage, but don't know where to start. I've also spoken to several engineers who are stuck and don't know how to level up. There's a lot of FOMO building up and being vulnerable about your AI skills is not easy.
Adoption metrics like DAU/WAU and token counts tell you who has access and who uses AI tools. While an ok proxy for AI proficiency and productivity, they say very little about what happens inside a session and how engineers and teams actually use AI.
Which models get used and were they the right fit for the work? Are we spending consciously? Do tests run before a commit? Do outputs get questioned or accepted as is? Do outputs need a lot of repair or do they just get refined? Are repos missing out on scaffolding opportunities (skills, context files)? How's my team doing in AI competencies like context management, evals, and orchestration?
Pheebs captures that and more. It records the shape of sessions and gives engineers a private view of their own work, separate from the team reports. To make sense of the data, Pheebs proposes an AI Proficiency Model, which looks at two things:
1- Repertoire: which harness capabilities show up in an engineer's work across six competencies: models, artifacts, MCP, evals, context management, and orchestration.
2- Judgement (inspired by the Discernment competency from Anthropic's AI Fluency Index): what happens to AI output before a PR is created and merged. Does it get verified, challenged, refined? And was the model used appropriate for the task? Most teams run the largest model for everything. Pheebs shows how much they could save by running smaller models where they fit.
Pheebs is open source so give it a try and send us some feedback!
Adoption metrics like DAU/WAU and token counts tell you who has access and who uses AI tools. While an ok proxy for AI proficiency and productivity, they say very little about what happens inside a session and how engineers and teams actually use AI.
Which models get used and were they the right fit for the work? Are we spending consciously? Do tests run before a commit? Do outputs get questioned or accepted as is? Do outputs need a lot of repair or do they just get refined? Are repos missing out on scaffolding opportunities (skills, context files)? How's my team doing in AI competencies like context management, evals, and orchestration?
Pheebs captures that and more. It records the shape of sessions and gives engineers a private view of their own work, separate from the team reports. To make sense of the data, Pheebs proposes an AI Proficiency Model, which looks at two things:
1- Repertoire: which harness capabilities show up in an engineer's work across six competencies: models, artifacts, MCP, evals, context management, and orchestration.
2- Judgement (inspired by the Discernment competency from Anthropic's AI Fluency Index): what happens to AI output before a PR is created and merged. Does it get verified, challenged, refined? And was the model used appropriate for the task? Most teams run the largest model for everything. Pheebs shows how much they could save by running smaller models where they fit.
Pheebs is open source so give it a try and send us some feedback!
Discovery Source
Product Hunt Aggregated via automated community intelligence tracking.
Tech Stack Dependencies
No direct open-source NPM package mentions detected in the product documentation.
Media Tractions & Mentions
No mainstream media stories specifically mentioning this product name have been intercepted yet.
Deep Research & Science
No direct peer-reviewed scientific literature matched with this product's architecture.