From the original post
If you are confused about why 𝗝𝗲𝘃 is being called the "Internet" moment for the AI industry. This is 100% worth your time. In fact, you should watch it:
It tells LLMs what to do next, in milliseconds & at almost zero cost. If you set it up correctly, you will have the AI engineer’s setup for 2028.
How to set up & use Jev (to actually get the 100x):
1. Join the waitlist; it's fairly quick: typesafe .ai.
2. Then go to Claude Code or Codex.
3. Choose Opus 5-Low or Sol-Low.
4. Copy and paste this prompt:
"[claude or codex] plugin marketplace add typesafe-ai/skills
[claude or codex] plugin install typesafe@typesafe-ai"
5. When you type /typesafe, the skill shows up.
6. Paste your API key once and click "Allow"
7. Start with $5 in free credit. It's hard to spend more.
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Now, here are the 3 ways to actually use Jev:
1. Jev for Linkedin
I have 38,000 connections & invitations on LinkedIn.
I have a new company to launch. I need to find a couple of hundred people to message. to do while saving time:
> Export your LinkedIn connections and invitations.
> Connect Claude to GitHub, Vercel & Apify.
> Create an Apify API key to enrich your data.
> Go to LinkedIn Settings → Data privacy.
> Get a copy. LinkedIn will email you a ZIP file.
> Open the file & find the Connections CSVs.
> Upload the files to Jev.
> Use Jev to classify your contacts.
> Review the shortlist.
2. Jev for Gmail
To go through all of my Gmail contacts and email the right people.
> Go to Google Contacts. Open Other contacts.
> Select all contacts. Export them.
> Upload the file to Claude Code or Codex.
> Use Jev to sort them into:
Keep, Review, Remove and Review everything before removing anything.
3. You got lost in Claude Code, GitHub, Vercel, Apify, Jev, Typesafe.
I feel you.
It is overwhelming.
That’s why I included the entire copy-and-paste prompt for each use case in the newsletter:
How Jev fits in the loop
- Agent collects runtime context or input state from external sources.
- Bounded decision options are formatted into Jev System One questions.
- Jev scores each candidate option and returns a probability for it.
- Deterministic code executes the selected action or routes to specialized models.
How to reproduce
- Read the author's original post on X at https://x.com/rubenhassid/status/2101989831425642605.
- Inspect the described workflow architecture and bounded decision points.
- Test Jev System One decision queries against the provided task context.
Why this build matters
Demonstrates real-world Jev Skills experimentation by @rubenhassid with verified community reach of 162.7k views.
Limitations
- Implementation details are based on the author's public post on X.
- Performance figures and benchmarks are author-reported community claims.
- Production deployments may require custom calibration and policy thresholds.