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Growth engineer｜I benchmark AI agents & build AI automation, prev @Stanford @tiktok_us @Weixin_WeChat｜Founder @Offloop

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I shared 10 Jev use cases for marketers. Here are 10 more: 11. Ad creative scoring - Feed it 100 ad variations. Jev can score which hooks, headlines, or angles are most worth testing first. 12. Social post filtering - Monitor thousands of posts. Jev can flag the ones worth replying to, reposting, or using as sales signals. 13. ICP detection - Give it a company, profile, or website. Jev can score how closely it matches your ideal customer. 14. Buying signal detection - Someone posts that they're switching tools, hiring, raising money, or struggling with a problem. 15. Comment prioritization - Get hundreds of comments across LinkedIn, X, YouTube, or Product Hunt. Jev can score which ones deserve a reply first. 16. Review analysis - Feed it thousands of customer reviews. Jev can classify sentiment, complaints, feature requests, and purchase intent. 17. Influencer matching - Give it 5,000 creators. Jev can score which ones best match your product, audience, and campaign. 18. Sponsorship qualification - Feed it newsletters, podcasts, or creator media kits. Jev can score audience fit, relevance, and whether they're worth reviewing. 19. UGC selection - Give it dozens of videos, screenshots, and testimonials. Jev can score which ones are strongest for ads or landing pages. 20. Product Hunt monitoring - Scan launches, comments, and makers to find competitors, customers, partners, or interesting products. The more repetitive marketing decisions you have to make at scale, the more interesting Jev becomes.

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this is the best features for marketers and growth teams who need to run more than three AI workstreams from a single Codex/Claude account ->1“Every complex project, I end up re-explaining context.” Offloop lets you fork one campaign brief into dedicated Subchannels for audience research, positioning, and landing-page copy. Each workstream starts with the source context and continues independently. ->2“I periodically start a new Chat when the context window fills up. Each new Chat starts with no memory of previous ones.” Offloop carries the relevant context into separate Subchannels for SEO, creator outreach, and lifecycle marketing, so every Agent can continue without rebuilding campaign history. ->3“The subagent can interpret inherited parent-thread context as still-active instruction.” Offloop gives paid social, SEO, and experiment analysis their own Subchannels and independent Agent runtimes. Each Agent receives a focused assignment without losing the campaign context.

this is the best features for marketers and growth teams who need to run more than three AI workstreams from a single Codex/Claude account ->1“Every complex project, I end up re-explaining context.” Offloop lets you fork one campaign brief into dedicated Subchannels for audience research, positioning, and landing-page copy. Each workstream starts with the source context and continues independently. ->2“I periodically start a new Chat when the context window fills up. Each new Chat starts with no memory of previous ones.” Offloop carries the relevant context into separate Subchannels for SEO, creator outreach, and lifecycle marketing, so every Agent can continue without rebuilding campaign history. ->3“The subagent can interpret inherited parent-thread context as still-active instruction.” Offloop gives paid social, SEO, and experiment analysis their own Subchannels and independent Agent runtimes. Each Agent receives a focused assignment without losing the campaign context.

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10 Jev use cases a marketer can see: 1. Lead scoring - You get 100 new leads overnight. Jev can score which ones are most likely to buy. Sales gets the best ones first. 2. Email prioritization - Drop hundreds of emails into it. Jev can score priority, spam, category, and reply probability. 3. Support routing - A client reports a billing issue, another one has an API problem. Jev can instantly decide where each ticket should go. 4. Customer intent - A user visits pricing three times this week. Jev can score how likely they are to upgrade. 5. Marketplace matching - Someone explains exactly what service they need. Jev can score which provider is the closest match. 6. Lead qualification - A prospect fills out your website form. Jev can decide between sales, self-serve, or nurture. 7. Content clipping- Give it a 60-minute podcast transcript. Jev can score the moments most worth clipping. 8. Browser tasks - Tell an agent to find the right hotel. Jev can help choose the best one in 8 seconds. 9. Human vs AI routing - Jev can decide whether it goes to AI or a human. 10. Workflow decisions - Most workflows eventually hit the same question: What should happen next? That's probably the best place to use Jev.

10 Jev use cases a marketer can see: 1. Lead scoring - You get 100 new leads overnight. Jev can score which ones are most likely to buy. Sales gets the best ones first. 2. Email prioritization - Drop hundreds of emails into it. Jev can score priority, spam, category, and reply probability. 3. Support routing - A client reports a billing issue, another one has an API problem. Jev can instantly decide where each ticket should go. 4. Customer intent - A user visits pricing three times this week. Jev can score how likely they are to upgrade. 5. Marketplace matching - Someone explains exactly what service they need. Jev can score which provider is the closest match. 6. Lead qualification - A prospect fills out your website form. Jev can decide between sales, self-serve, or nurture. 7. Content clipping- Give it a 60-minute podcast transcript. Jev can score the moments most worth clipping. 8. Browser tasks - Tell an agent to find the right hotel. Jev can help choose the best one in 8 seconds. 9. Human vs AI routing - Jev can decide whether it goes to AI or a human. 10. Workflow decisions - Most workflows eventually hit the same question: What should happen next? That's probably the best place to use Jev.

130,108 Aufrufe • vor 1 Monat

THE FDE BOOM WILL CREATE A LOT OF SERVICES COMPANIES PRICED LIKE SOFTWARE. the guy who actually ran palantir's fde rotation program (project frontline) says the quiet part here: fde was never a gtm motion, it was a product strategy to build foundry. the gtm framing came later. palantir’s model works because every ugly deployment compounds into ontology, tooling, and reusable product. copy the customer proximity without the productization loop and you’re just scaling bespoke work with a better title.

THE FDE BOOM WILL CREATE A LOT OF SERVICES COMPANIES PRICED LIKE SOFTWARE. the guy who actually ran palantir's fde rotation program (project frontline) says the quiet part here: fde was never a gtm motion, it was a product strategy to build foundry. the gtm framing came later. palantir’s model works because every ugly deployment compounds into ontology, tooling, and reusable product. copy the customer proximity without the productization loop and you’re just scaling bespoke work with a better title.

26,028 Aufrufe • vor 1 Monat

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