Lin Li

The ClickHouse abstraction pattern isn't about technical elegance. It's about economics. WITHOUT abstraction: → Analyst headcount drives linear engineering growth → Every new analyst = more query requests = more engineering triage → At 2,500 analysts, this breaks WITH abstraction: → Platform team absorbs complexity once → Org scales without proportional headcount → Sub linear scaling Microsoft's Titan serves 100K daily queries from 2,500 users. With a single platform team. That's why seven companies independently built the same thing. That's why the self-service analytics market is growing 15% annually. Read the full article 👇 #Clickhouse #analytics

You need a data platform built for C2E. Syntasa’s Composable CDP replaces complex data engineering pipelines with a modular Data+AI environment that's built for real-time activation, identity resolution, and predictive decisioning. No more duplicated data. No more hidden compute costs. No more dependency on third-party platforms. Your data, in your cloud, under your control, and built for C2E.

5

We’re proud to partner with Groq to power enterprise AI agents with low-latency inference for performance-critical workloads. Teams on StackAI deploy Groq for latency-sensitive workflows: high-volume document analysis, customer-facing interactions, and internal knowledge retrieval. In each of these situations, fast, reliable responses materially improve user experience and productivity. StackAI and Groq are a prime example of how matching the right inference provider to the right workload helps enterprises build AI agents that perform in real production environments. Want to learn more? Read the full piece at the link in comments.  #StackAI #Groq #EnterpriseAI #AIagents

33

4 Comments

COO told me last month that their AI initiative was "stuck," so I asked what the agent was supposed to do. It was supposed to process standard vendor invoices end-to-end, then match POs, verify delivery, flag exceptions, and approve payments under a set threshold. The model could do all of this, and they'd proven it in testing. So what was stuck? The finance team wouldn't let the system approve payments without a human reviewing each one. Every single invoice...even the ones under $500 that matched perfectly. I asked what their error rate was with human reviewers on those same invoices. Nobody knew. That's the part that gets me. We demand perfection from AI systems while measuring human performance with a shrug. #AI #FractionalCTO #BusinessStrategy

54

33 Comments

Stop hiding behind excuses for not letting LLMs touch your production codebase. “Legacy system.” “Too risky.” “Security concerns.” “Not production ready.” “Compliance.” If that’s still your narrative in 2026, you’re not being cautious — you’re being scared. And fear is not a strategy. The teams that win right now are not the ones debating whether LLMs are safe enough. They’re the ones building guardrails, shipping, learning, and moving. At Clerk.io, we run a big, complex, very real production system. Years of code. Deep domain logic. Edge cases everywhere. And yes — we let LLMs into that codebase. Not recklessly. Not blindly. But unapologetically. We use them to refactor. To explore architecture shifts. To accelerate feature development. To push ideas that would otherwise sit in a backlog for months. The result? We are shipping at a pace that would have been unrealistic just a year ago. And don’t watch what we release next year. Don’t watch what we release next quarter. Watch what we release in the coming weeks. This gap between teams who adopt LLM-native workflows and teams who hesitate is about to become permanent. There will be two types of companies: Those who figured out how to multiply their engineers. Those who protected their comfort zone. If you’re still saying “we can’t because…” — you’ve already started losing. Adapt. Build the guardrails. Train your engineers. Integrate the tools. Or step aside and watch me and other innovators redefine your industry. This isn’t gradual. It’s happening now. 🚀 #AI #LLM #EngineeringLeadership #BuildInPublic #FutureOfSoftware

10

Document-centric tasks sit at the core of many enterprise, business, and government workflows. Search is important, but the real challenge is going beyond retrieval, enabling systems to reason across documents, verify facts, and handle multi-step information tasks. Great to see a new model from Databricks moving in this direction. I expect we’ll see many more models, including open-weight ones, designed specifically for document-driven workflows, an area with huge potential across enterprise and government use cases.

23

1 Comment

The Cost of Risk Decisions You Don’t Quantify I used to think “gut feeling” was enough to prioritize product risks. But feelings don’t pay the SLA. Now we map risk across three dimensions: tech, ops, and market. Then we attach numbers—probability × impact = Expected Risk Value (ERV). Suddenly, risks that felt “minor” are glaring, and we see where to act first. ERV doesn’t remove risk, but it makes decision-making visible and measurable. #RiskManagement #DecisionMaking #ProductStrategy #OpsExcellence

8

4 Comments

It is amazing how much of the world's product strategies exist to cater to one simple human behavior: we don't like dealing with friction. Pricing models assume customers won’t spend hours comparing options across ten marketplaces. Subscriptions assume most users won’t fight their way through intentionally complex cancellation flows. Auto-renewals assume people won’t track, review, and renegotiate every policy they hold. This has worked for the longest period because time, attention and patience are finite for us humans. But that reality is about to change given AI Agents have no concept of time or friction. Humans might not have the time to price compare across platforms, their AI Assistants do. Humans might not have the patience to go through 10 steps to cancel their subscriptions, their AI Assistants do. Humans might not know enough to negotiate better policy rates, their AI Assistants do. The principles that drove product design, growth strategies and experiences are likely to get challenged and this is already being seen with every single SaaS app in the market today. #CXAI #AIAssistants

34

3 Comments