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Bonk Eco continues to show strength amid $USELESS rally
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Pump.fun to raise $1B token sale, traders speculating on airdrop
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Boop.Fun leading the way with a new launchpad on Solana.

Drew Bredvick
نمو هندسة GTM ▲ @Vercel
متحمس لهذه اللعبة! تعلمنا الكثير خلال هذا المشروع، لذا سعيد بمشاركة ما تعلمناه واستخدام الكود عبر OSS.

Alex Lieberman2 يناير، 07:38
سأقضي الوقت مع @DBredvick العظيم الأسبوع القادم.
(مدير هندسة GTM في @vercel)
قلص وظيفة فيرسيل المكونة من 10 موظفين إلى شخص واحد بالإضافة إلى وكيل.
سأتعلم:
1/ كيف درب الوكيل فعليا على التعامل مع محادثات العملاء الحقيقية
2/ ما وراء الكواليس حول توجيه الرسائل، وجودة الرصاص، ومتى يجب إدخال البشر، وكيف يتدرب النظام ذاتيا + يصبح أكثر ذكاء مع الوقت
3/ إطاره لاتخاذ قرار بشأن ما يجب أتمتته وما يبقى بشريا
نحن نبني مباشرة. انضم إلي 👇

1
رهان مؤكد: سيكون الذكاء الاصطناعي أكثر ذكاء وأرخص في عام 2026.
هناك الكثير من العواقب من الدرجة الثانية والثالثة، لكن إليك قائمة سريعة:
- إذا زاد من العمل البشري، فلا تقلق أبدا بشأن تكاليف نماذج اللغة الكبيرة
- كتابة كود ليستفيد من نماذج أكثر قدرة (ليست صارمة جدا)
- استخدم الذكاء الاصطناعي لعصر كل العصير من المواد الخام
هذا الأخير يستحق مثالا:
الكثير من الشركات لديها قوائم بالعملاء المحتملين. البشر سيردون على الأدلة الجيدة ويرمون الباقي.
بسبب الذكاء الاصطناعي، من المنطقي الاستجابة للجميع لأن الذكاء الاصطناعي يمكنه القيام بالعمل.
اعمل على كل خيط محتمل. لا هدر.

Sam Altman12 ديسمبر 2025
390x cost reduction in a year!
2
مرحبا، أنا المهندس الذي بنى أول وكيل SDR الذكاء الاصطناعي من Vercel المذكور أدناه👋
الاحتكار

Lenny Rachitsky2 ديسمبر 2025
My biggest learnings from Jeanne DeWitt Grosser (ex-Chief Business Officer at @Stripe, now @Vercel COO):
1. What failed seven years ago now works with AI. In 2017, Jeanne tried to build a system at Stripe that would automatically personalize outbound emails based on company data. Despite working with world-class data scientists, it failed due to too many errors. Today, that exact same approach works. This shows how AI has made previously impossible ideas suddenly viable.
2. A single GTM engineer at Vercel reduced a 10-person sales team to 1 (in just 6 weeks). Jeanne’s team at Vercel had an engineer build an AI agent that handles inbound lead qualification, outbound prospecting, and deal loss evaluation. The agent costs $1,000 per year to run versus over $1 million in salaries for the sales team. The nine displaced team members moved to higher-value work rather than being laid off, and the remaining salesperson is 10 times more efficient.
3. Their AI deal-loss bot has become better at understanding what went wrong than humans. When Jeanne analyzed her biggest loss of the quarter, the salesperson blamed pricing. But an AI agent reviewed every email, call transcript, and Slack message and discovered the real reason: they never spoke to the person who controls the budget, and when ROI came up, the customer clearly didn’t believe the value claims. They are now using AI to analyze sales calls in real time and send alerts like “You’re halfway through the sales process and haven’t talked to a budget decision-maker yet.”
4. Wait until $1 million in revenue before hiring your first salesperson. Founders should continue selling themselves until they reach around $1 million in annual revenue with a repeatable process. The key is having a defined ideal customer profile—customers who look alike.
5. Segment customers on what drives their buying decisions, not just company size. OpenAI has roughly 3,000 employees, which would typically put them in the “mid-market” category. But they’re a top-25 website globally by traffic, so Vercel treats them as enterprise customers requiring complex sales. Effective segmentation combines company size with growth rate, web traffic, workload type, and industry—because selling to e-commerce companies requires completely different language than selling to crypto companies.
6. Most customers buy to avoid risk, not to gain opportunity. About 80% of customers purchase to reduce pain or avoid problems, while only 20% buy to increase upside. This means you should focus your sales messaging on what could go wrong without your product—like falling behind competitors or damaging their reputation—rather than just talking about exciting features. This is especially true when selling to larger companies, where individual careers are on the line.
7. Sales teams should be indistinguishable from product managers—for a bit. Jeanne hires salespeople who have such deep product knowledge that if you put one in front of a group of engineers, it should take 10 minutes to realize they’re not a product manager. This credibility allows sales teams to serve as an extension of research and development—a 20-person sales team talks to hundreds of customers weekly and can translate those conversations into product insights at scale.
8. Building your own AI sales tools may beat buying off-the-shelf software. Because AI is so new and every company’s sales process is unique, Jeanne finds that building custom internal agents often delivers more value than buying vendor solutions. A single go-to-market engineer built their deal analysis bot in just two days, perfectly tailored to their specific workflow. These engineers shadow top salespeople to understand their workflows, then build automation that would have taken months or been impossible just a few years ago.
9. Make every sales interaction great, whether customers buy or not. Jeanne replaced boring discovery calls at Stripe with collaborative whiteboarding sessions where customers drew their payment architecture. Many customers had never visualized their own systems before. They left with a useful asset and a feeling of collaboration, regardless of whether they bought. Many returned years later to purchase. Think about your go-to-market process like a product, not just a sales function.
10. Product-led growth has a ceiling—no $100 billion company runs on it alone. While product-led growth (where users can sign up and start using a product without talking to sales) works well for early growth, customers generally won’t spend a million dollars through a self-service flow. Every major technology company eventually builds a sales team for larger deals. The mistake is waiting too long, since building a predictable sales process takes time.
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