You can now trade stocks through Coinbase for Agents. Crypto. Derivatives. And now 6,000+ stocks. Need analytics or data on those stocks? x402 payments let your agents pay for live market data mid-task, from your USDC balance on Coinbase. No subscriptions needed. The most comprehensive agentic trading tool available today. Build responsibly.

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. ----- 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: ruben.substack.com/p/jev A 45-second TL;DR by @MatijaSosic.

Your research agent is only as good as the fetch layer behind it! A research agent can have a strong model, good planning, the right tools, and a solid memory system. But if the next page returns a 403, a Cloudflare challenge, or some other bot protection, the rest of that stack does not really matter. Search and fetch solve two different problems. Search helps the agent find the URL. Fetch determines whether the agent can actually retrieve the content behind that URL and turn it into something the model can use. That becomes harder in production because modern websites do more than block suspicious IPs. Bot protection can look at IP reputation, browser fingerprints, JavaScript execution, TLS fingerprints, rate limits, and behavioural signals. A proxy alone does not necessarily solve all of that. This is where Apify Web Fetch comes in. You give it a URL, and it handles things like proxy rotation, browser fingerprinting, JavaScript rendering, challenge handling, and retries before returning the page as Markdown, plain text, HTML, links, or raw content. That makes it useful inside an agent loop because the output is already in a format the model can work with. The architecture is pretty simple: Research Agent → Search → URL → Web Fetch → Page Content → Reasoning If the fetch step fails, the agent never reaches the part where reasoning matters. Apify also recently benchmarked Web Fetch across 384 URLs spanning social media, retail, news, documentation, and synthetic challenges against three other tools. The important part is not that it was a huge blowout. It wasn't. Web Fetch had the highest overall success rate in the benchmark, while other tools were faster in parts of the latency distribution. For agent workloads, that distinction matters because getting the page back reliably and getting it back quickly are not always the same thing. This is also why I think the fetch layer deserves more attention in agent architecture. We spend a lot of time improving the intelligence above the tool layer. But for agents that work across the open web, access itself is infrastructure. I've shared Web Fetch and the full benchmark in the comments.

LinkedIn InMail generated ~7x more leads than cold email for one of our clients using essentially the same offer + ICP. It also produced more than 2x the reply rate. A huge part of this is because we heavily used one filter: Prioritizing people who are actively using LinkedIn. Before we send, we check whether each prospect has been active on LinkedIn in the last 30-60 days. Our workflow: 1. Build the ICP list like normal. 2. Take the LinkedIn profile URLs and run them through an Apify actor that checks recent activity. 3. Add the activity data back to the list. 4. Prioritize the prospects who have been active in the last 30-60 days. 5. Send to those people before working through the rest of the list. It's almost like using the platform as a signal test.

Jev can serve as better natural language search on websites. It can scan thousands of Zillow listings and classify properties by things you can't normally filter for - e.g. architecture, renovation status, proximity to freeways. This was done in <20 sec and costs $0.18 👇