Di Li

Most startups die from moving too fast. But plenty die from moving too slow. So how do you actually balance speed vs. technical debt? I sat down with Shuzhi Huang, former Engineering Leader at Google, someone who's seen this play out at massive scale, and got a remarkably honest answer. The insight that hit hardest: technical debt isn't the enemy. Unacknowledged technical debt is. If you're a founder scaling your engineering team right now, or a hiring manager trying to decide when to prioritize velocity over quality — this one's for you. What's your take? The full episode is live. Link in the comments 👇 #Engineering #TechnicalDebt #StartupFounders #EngineeringLeadership #Hiring #BotifySessions #BotifyTech #TechLeadership #Founders

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Terminal Use (YC W26): Vercel for background agents. Terminal Use is hosting infrastructure for background agents that makes harness engineering easy. https://lnkd.in/gv99wN2t

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The hottest role in AI startups right now isn’t Forward Deployed Engineers. It isn't GTM Engineers. It’s Deployment Strategists. Decagon calls it an “Agent Product Manager.” Harvey calls it a “Solutions Architect.” Palantir Technologies has had versions of this role for years. And the salaries are climbing fast: - Decagon: $200k–$285k - Palantir Technologies: $120k–$200k - Figma: $150k–$260k - Ramp: $100k–$180k - Harvey: $190k–$260k So who are these people? They are usually pseudo-technical -- CS or engineering majors, or folks with technical work experience. Many come from 2 years in consulting, IB, or PE, then jump into startups to get their hands dirty. They are young, hungry, polished, and comfortable being in front of customers. What do they actually do? They make sure enterprise AI deployments succeed. A $100k+ deal does not survive on a nice pitch or a self-serve onboarding flow. It survives if the customer sees value in the pilot. That means: - Embedding directly with the customer - Designing prompt logic for specific workflows - Working with engineering to align integrations and data flow - Helping exec teams define their AI roadmap - Running feedback loops into product and GTM Why does this role matter so much? Because enterprise AI is messy. Integrations, data transfer, and adoption make or break a deal. Most buyers are using AI for the first time, and each has unique workflows. Deployment Strategists bridge that gap. They own the outcome. They are accountable for making pilots successful, which often means millions in revenue down the line. At Conversion, Sam Bochner has been leading this work for us. We are now thinking about scaling it into a full team. Because a few successful pilots can fund an entire department, and the cost of failed deployments is too high to ignore. Is this just a rebrand of customer success? Not really. Success is about answering tickets and renewals. Deployment Strategy is about going deep with a few enterprise accounts, extracting maximum value, and ensuring the pilot closes into a multi-year contract. Call it Agent PM, Solutions Architect, or Deployment Strategist. Whatever the title, this is becoming one of the most important roles in AI SaaS.

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When nearly anything can be built with AI, the question shifts from what is possible to who is behind it. The advantage lies with the talent steering the systems. AI is rapidly making many parts of life more efficient, but companies still need top talent, and the hiring process has not followed the same trajectory. The market has grown more saturated, making it difficult to cut through the noise and break through. Paraform is building an AI-powered recruiting marketplace designed to ensure companies are able to hire the right candidates for specialized roles with ease. This addresses one of the most persistent and unsolved challenges for building strong teams today. Paraform fits into a broader shift we’re seeing, away from AI as a tool, and toward AI as a service that delivers outcomes. It’s been incredible to watch the business take off and to see how strongly this resonates with customers. Congrats to John Kim , Jeffrey Li, and the entire Paraform team on your Series B! cc: Felicis James Detweiler

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after working with hundreds of candidates at VibeScaling trying to place them at the top AI-native startups in SF/NYC, here are 5 unconventional tactics that we've seen work to stand out 👇🏻: 1️⃣ customer interviews tactic: when referrals fail, go directly to their customers - outbound 30-50 asking about pain points, turn 3-5 responses into a short analysis, and send it to the head of sales. Shows you can create value before you're hired while most people just send resumes and pray 2️⃣ the "not now" drip campaign tactic: when you get rejected for a specific reason, don't walk away - fix the objection and stay in touch. If they say you're "not technical enough," learn their space, use their product, and send updates on your progress. They often come back when timing changes, and you'll be top of mind as someone who actually takes feedback 3️⃣ the brand book tactic: hiring managers have tunnel vision and only see your recent experience. Create a portfolio with career highlights, peer quotes, outbound examples, and deal case studies to tell your full story. Helps you control the narrative and show skills beyond what's on your resume 4️⃣ proactive references tactic: instead of waiting for companies to backchannel you, have a strong reference reach out to hiring managers before your final rounds. Lets someone handle objections for you while most candidates just wait around. Being proactive with references gives you a huge edge 5️⃣the gratitude loop tactic: send thank you notes to everyone who helped - interviewers, referrers, advisors - regardless of outcome. People remember genuine appreciation, and it's a small tech world where today's "no" often becomes tomorrow's opportunity invest in relationships and stand out by doing what others won't wrote the full breakdown with templates in GTMBA 👇🏻

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I've been discussing this recently with Frances Buontempo in the context of coding assistants. We hear a lot from developers saying that LLM assistants in their fave IDEs are great for generating a lot of the (boring) boilerplate code needed to stand up a non-trivial system. However, most popular IDEs have been able to do exactly this for decades--whether with "wizards", code-block and project templates, or other various forms of meta-programming. The difference is that the "old" way is much less hungry of resources, while simultaneously being much more predictable and reliable at automatically generating the "boring" stuff. Coding assistants are also good at other things, of course, but many of the popular (in my conversations with developers) tasks are those I would much prefer to do myself, such as writing tests.

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Hot take: Startups want Big Tech candidates... but only AFTER another startup has "de-risked" them first 🙃 I'm seeing this pattern more and more and believe it's short sided. Start-ups LOVE when a candidate starts their career at Google/Meta/Amazon/Netflix - it's a strong signal of excellence. But they don't want to be the ones to bet on them making the jump to startup land. Instead, they want someone who did FAANG → startup for 2-3 years → NOW they're interested. Translation: "We love the credibility, but we want another start-up to test them first." Meanwhile, the best startups with strong hiring processes? They're scooping up these FAANG candidates directly, like some of our clients 🙂. Recruiters/founders - are you seeing this too? Has anyone been on the receiving end of this?

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