Guangyu Chen

Finding product-market fit isn’t a clean checkpoint — it’s a continuous, often chaotic process that can make or break a startup. At TechCrunch Disrupt 2025, leaders like Rajat Bhageria of Chef Robotics, Ann Bordetsky from NEA, and Murali Joshi of ICONIQ emphasize that it’s not about guessing what customers want, but about smart testing, real-time iteration, and deeply listening without getting overwhelmed by noise. The most striking takeaway is that product-market fit should be viewed as an ongoing journey rather than a milestone to tick off. This perspective shifts how founders approach growth: instead of waiting for a magic moment, they build, learn, adapt, and refine constantly. It also matters for investors who back startups navigating this messy terrain—recognizing signals of traction early while understanding the underlying effort required. Whether companies are prototyping or scaling, success hinges on solving real problems so customers can’t live without the product. How do you keep your teams grounded and focused through the unpredictable, often nonlinear path to product-market fit? What tactics or mindsets help you avoid noise and zero in on meaningful signals? #ProductMarketFit #StartupGrowth #TechCrunchDisrupt #FounderInsights

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Last Tuesday, Deliveroo's rider-switch Rails endpoint spiked to 4s latency and intermittent 503s. The flame graphs told the real story and became our speed playbook. Technical context: Flame graphs visualize time across call stacks using sampling profilers. In Rails, latency often stems from DB calls, serialization, or middleware. This makes root causes visible to newcomers. Recent tech context includes AI agents and RAG patterns (LangGraph, Claude 3.5, GPT-4o, Gemini 2.0) guiding performance analyses. KEY INSIGHTS: 🔍 Flame graphs reveal hot stacks and where wall time sits. ⚡ Latency on the hot path fell from 4s to ~800ms after fixes. ───────────────────────── 🔗 Read the full article: https://lnkd.in/ghcREzpZ 🎯 Practice interview questions: https://lnkd.in/gmy5drNw #performancetesting #cpuprofiling #memoryprofiling #flamegraphs #latency

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Joining Minoa felt obvious after a few conversations with Richard and Max 🧑‍🍳 Before this, I’d seen the same bottlenecks show up again and again in enterprise and value based selling. Sellers are expected to quantify impact, justify ROI, and convince skeptical buyers, while relying on brittle spreadsheets, one off analyses, and tools that were never built for live deal cycles. The gap between how deals are actually won and the software supporting them is massive. Richard and Max didn’t pitch hype. They articulated a clear vision and backed it with real numbers. Meeting the rest of the team sealed it. Small, sharp, ex founders and builders who care about craft and move fast, with a clear bar for quality. What really locked it in was the customers already trusting Minoa. Companies like Vanta, Pendo.io, Snowflake operate in high pressure environments where value selling is not optional. These teams are betting real revenue on Minoa to help sellers prove impact in real customer conversations. That trust creates a serious scaling challenge. Thousands of sellers, customer facing workflows, and AI powered insights running inside active deals means the infra has to be fast, reliable, and correct every time. That is exactly the kind of problem I wanted to work on. Minoa is early, but the shift toward value driven enterprise sales is real and accelerating. Excited to help build the foundation for where this is headed. Let’s cook 🧑‍🍳🚀

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Another great piece quoting Andrew Ng (oh, BTW, a HumanX speaker in SF 2026) that I could NOT agree more with. Coding isn't the bottleneck with our AI assisted tools, it's good product management. But even that understates the need - we need technical product managers who know both good design patterns for products, but have enough development experience to be able to coax the vibe-coding systems to actually write decent, enterprise-readyish systems. In my experience, you really need to be forceful with the tools at a relatively low technical skill level to ensure things like consistency, security and even basic performance are done properly.

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🎉 Thrilled to snag a Top 5 finish at the inaugural Cognee AI-Memory Hackathon yesterday at Pebblebed in SF! 🚀 In the finance world, outdated "beliefs" like an expired vendor discount or a customer's reduced credit limit can lead to wrong decisions and costly errors for the company. Enter Memory Drift Detector Instead of just querying a knowledge graph, it audits the AI's own "belief system" over time, flagging contradictions like outdated discounts before they hit your bottom line. Using Cognee's retriever + Distil Labs SLM, it extracts beliefs from a 14k-node, 56k-edge graph on finance data (vendors, invoices, payments, SKUs) and stores them as vectors in Qdrant. It matches new invoice claims semantically, scores contradictions, flags them on a dashboard, and auto-corrects with one click. Example: A vendor discount belief dropped from 73% to 5% confidence after four conflicting invoices Shout out to cognee, pebblebed, Qdrant , distil labs, DigitalOcean Shout out to Vasilije Markovic , Elizabeth (Lizzie) Siegle, Thierry Damiba, Daniel Scott #AI #Hackathon #Cognee #digitalocean #qdrant #SanFrancisco #MachineLearning

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