Post Rick Onboarding Call

[0x4007]

This is a ChatGPT generated summary of the meeting.

Will need to break down into separate issues.

Priority Shipping Based on the Conversation

1. Refocus on Core Value Proposition (High Priority)
   • Shift focus from pricing/marketplace features to project management and efficiency tracking.
   • Rebrand positioning to appeal to broader tech organizations beyond Web3.
   • Develop a clear Unique Selling Proposition (USP) that differentiates from existing AI-powered project management tools.

2. Task Completion & Time Tracking (High Priority)
   • Implement start and stop timestamps for task tracking:
   • When an issue is assigned, record a timestamp.
   • When a PR closes the issue, calculate total work time.
   • Compare time estimates vs. actual completion time.
   • Track developer work habits and efficiency over time.
   • Generate reports for managers to see how accurately time estimates align with execution.

3. AI-Driven Task Recommendation System (High Priority)
   • Improve vector embeddings to automatically assign relevant tasks based on historical work.
   • Display task recommendations on GitHub issues with >80% relevance.
   • Make task assignments more dynamic, allowing managers to see which developer is best suited for a task.

4. Automate Prioritization & Kanban Sorting (Medium Priority)
   • Automatically sort tasks in GitHub project boards based on priority labels.
   • Ensure high-priority issues don’t get buried in multiple pages.
   • Provide dashboard insights showing what’s most urgent and who is assigned to what.

5. Launch Customer Discovery & Validation (High Priority)
   • Conduct 10+ customer interviews in the next 2 weeks with tech orgs (outside Web3) to refine feature set.
   • Ask: Would they pay for automated efficiency tracking?
   • Identify common pain points in software project management that overlap across companies.

6. Create a New Landing Page & Demo (High Priority)
   • Remove all crypto/Web3 references.
   • Focus messaging on:
   • How it helps project managers track efficiency.
   • How it improves team prioritization and accountability.
   • How it offers AI-driven insights into engineering workflows.
   • Add a waitlist button and simple form:
   • “Company Name”
   • “Role”
   • “Biggest challenge in managing development teams”
   • Share the landing page with GitHub Universe contacts, OpenQ, and broader tech communities.

7. Remove Marketplace-Dependent Features & Test Core Plugins (Medium Priority)
   • Set up mock repositories that exclude pricing/marketplace features.
   • Test standalone plugins for efficiency tracking, prioritization, and task recommendations.
   • Measure whether these features still deliver strong value.

8. Ensure Recommendations Work With Limited Data (Medium Priority)
   • Improve logic so recommendations work even with a small number of completed issues.
   • Allow recommendations based on job titles, GitHub contributions, and past assignments.

Next Steps
1. Set a 2-week goal to interview 10+ tech orgs and refine core features based on common feedback.
2. Launch the new landing page and waitlist ASAP.
3. Ship the task tracking & efficiency reports ASAP, as Riccardo expressed willingness to pay for this today.
4. Test AI task recommendations and priority automation, improving GitHub integration.
5. Remove crypto dependencies in key plugins and validate their standalone value.
6. Schedule follow-up call with OpenQ in a few weeks to show progress on the recommendations and efficiency tracking.

TL;DR – What to Prioritize?
• Efficiency tracking (time estimates vs. actuals).
• AI task recommendations (based on past work).
• Automatic prioritization & sorting (so high-priority tasks surface first).
• Customer interviews (to validate non-crypto use cases).
• Landing page & demo (to gather real-world interest).

This aligns directly with what Riccardo was most excited about and has the highest potential for market traction.

[sergfeldman]

TL;DR – What to Prioritize?
• Efficiency tracking (time estimates vs. actuals).
• AI task recommendations (based on past work).

@0x4007 These priorities are in line with what we discussed some time ago. One of the pieces about effiency https://github.com/orgs/ubiquity/discussions/15

Develop a clear Unique Selling Proposition (USP) that differentiates from existing AI-powered project management tools.

To determine the USP, it makes sense to analyze key competitors.
According to my research early 2024:

• The most popular Project Management with AI on GitHub was Zenhub
  https://www.zenhub.com/
  https://github.com/marketplace/zenhub

• One of the most advanced Project Management with AI was ClickUp
  https://clickup.com/ai
  https://github.com/marketplace/clickup
  Their recent release
  https://clickup.com/blog/clickup-brain/

[0x4007]

Rick mentioned several times how it's a headache accounting wise to do micro cash bonuses for incentives.

I think it would be novel to allow for the configuration to convert the concept of rewards into "XP" purely. Then the "rewards" can be just a virtual number, XP, that we keep track of internally.

Maybe we can have a special config for where we currently have "rewardErc20Address" or whatever in the text-conversation-rewards config, perhaps we can set that to a special value like "xp" or maybe even default it to XP only. I'm not sure what the simplest way to accommodate this would be. @gentlementlegen rfc

XP is a great performance metric that can be reviewed at the end of any time interval. I.e. "contributor X earned 2500 XP this month" which can easily be compared to contributor Y earning 1000 XP.

We can reasonably deduce that contributor X provided a lot more value to the organization than contributor Y that month.

[0x4007]

• One of the most advanced Project Management with AI was ClickUp
  https://clickup.com/ai
  https://github.com/marketplace/clickup
  Their recent release
  https://clickup.com/blog/clickup-brain/

"ClickUp Brain

One AI to
replace them all

The world's first neural network connecting tasks, docs, people, and all of your company’s knowledge with AI."

This is a really interesting find. I like the way they presented the product and I think it's an interesting positioning for some currently implemented aspects of UbiquityOS (text-vector-embeddings)

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Series C $400M raise at a $4B valuation source.