Agentic LUM

LUM Agentic MVPs

MVP for Web-Hunters focus:

Simple, verifiable hiring network that connects skilled professionals (starting with veterans) to job opportunities, both IRL and remote, using AI-assisted credential verification.

Core Features for MVP
Decentralized Talent Verification

Users can zkTLS (zero-knowledge proof) verify their skills, credentials, and past experience without exposing sensitive data.
Example: A veteran can prove they were a mechanic in the military for X years without sharing personal documents.

Basic Job Matching & Listings

A simple job board-style interface where employers or individuals can list tasks, remote gigs, or in-person jobs.
AI-assisted skill matching to recommend the best candidates.
Trust Layer for Hiring

Ratings and verified experience badges (e.g., “US Army Mechanic – 8 Years”) provide trust signals for employers.
AI ensures scammers/bots are filtered out while improving recommendations over time.

Continued 👇

Combat and Degen Veteran -
/clankpad Academy Class #1
Channels: /dv /socks /parksrec
ICEBREAKER: https://app.icebreaker.xyz/profiles/bJLRVoekOGpE10heahpwW?key=

Great concept! Ensuring privacy with zkTLS while providing verifiable credentials is crucial. How do you plan to handle the scalability of AI-assisted matching as the network grows?

Scalability question ❓ 

Preferably whatever is the current best practices if rolled out. However, as of now...

We'll start with the Rule-Based Matching (Scenario 1) for the MVP.


As usage grows, we’ll gradually roll out the Hybrid ML System (Scenario 2).

Long-term, we aim to make Web-Hunters the first Fully Decentralized, Federated Talent Protocol (Scenario 3) powered by zkTLS and AI.

Unless @naaate or @janicka or anyone else that may be a part of this team if it continues has a better plan.

collaboration /ottp +AI /luminous +creativity /arrow -> 1 story /luminary  Writing: https://paragraph.xyz/@nate collective venturing @venturetothex

music ~ travel ~ movies |
/theoffice & /dv & /parksrec mod | /sonata’s biggest fan

Scalability question ❓ 

Preferably whatever is the current best practices if rolled out. However, as of now...

We'll start with the Rule-Based Matching (Scenario 1) for the MVP.


As usage grows, we’ll gradually roll out the Hybrid ML System (Scenario 2).

Long-term, we aim to make Web-Hunters the first Fully Decentralized, Federated Talent Protocol (Scenario 3) powered by zkTLS and AI.

Unless @naaate or @janicka or anyone else that may be a part of this team if it continues has a better plan.

https://warpcast.com/kafenvinshi/0xd68b7f70

I think it's not usually worth the effort to worry about scalability when at the MVP phase. They key is getting to something people want. It's more important to start with a structure you can learn from and iterate on, even if HIGHLY unscalable. 

So yeah, this makes sense to me. Playing around with the rules yourself and talking to a lot of people trying to use the service (or not using it, and why they don't want to use it) gives you a lot of learning you can then try to apply to the ML system.

Thanks for the feedback!

I did not think it was too important at this phase either however I wanted to answer the question (kind of).

Ultimately I left the pending best practices as things change so often with the rapidly advancing technology.