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Built SignalForge, an AI-powered sales research and outreach agent. It takes a prospect's name, company, and role, researches relevant information and signals, analyzes the findings, and generates personalized email and LinkedIn outreach. I built the system end-to-end, including the agentic research workflow, LLM processing, web research, and product interface.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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I've been sending a lot of outreach messages lately as part of my job search, and pretty quickly ran into the same wall every time.
Writing a good, personalized message was never the problem. The problem was everything before it: finding the right contact, pulling context on the role or company, checking if I'd already reached out, then logging it somewhere so I wouldn't lose track.
There was a lot of repetitive work standing between me and writing the message itself.
So I started building a small dashboard (React, Vite, deployed on Vercel) to handle the deterministic stuff. It looks up contacts, pulls the context I need, and tracks everything in Notion automatically. I pretty much just do the writing.
Still early and still just for me, but it's already saved a chunk of time I used to burn on busywork instead of actually reaching out to people.
#ProductDesign #BuildInPublic #JobSearch
Building your own dashboard to kill the repetitive lookup work instead of just grinding through it manually says a lot. Did you set out planning to build something, or did the busywork hit a point where vibe-coding a fix felt faster than doing the research by hand one more time?
I've built Signalwatch with one purpose... to identify the gaps and deliver solutions.
Love the clarity of the mission; turning raw public signals into actionable decisions feels genuinely useful.
Just published: Strategy and Copy Agent.
Most AI copy tools optimize for generation. I wanted to solve the harder problem:
How do you make AI-generated strategy evidence-backed, reviewable, versioned, and safely approved?
This case study breaks down the system I built around:
Evidence → Strategy → Copy → Review → Approval → Handoff
Including role controls, immutable revisions, deterministic checks, and exact-version approval.
If you’re building production AI systems where trust and control matter as much as output quality, this one is for you.
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