Yufeng Guo

Clay doubling down on increasing action costs while a lot of gtm-e world is laser focused on BYOK workflows with Claude Code is an interesting tension. In the last ~week Cargo shipped a CLI, Apollo.io released an MCP connector for Claude & most of my feed is Claude Code. The ecosystem energy feels entirely focused on terminal-first. I’ve loved Clay for orchestration but have increasingly felt that the UI/UX is slowing me down as I ramp up my terminal-first workflows. That said, terminal-first isn’t multiplayer yet and isn’t in it's final form - esp for larger companies where multiple humans benefit from a shared UI. What’s interesting is this is coming from the people who coined “GTM Engineer” in the first place. The early adopters Clay built the category for are the ones feeling the pull away from the orchestration layer Clay monetizes. The dispersion is probably temporary. But if Clay’s pricing move take them further from the early adopters who built the category, that’s a deliberate bet to cross the chasm to enterprise, while some subset of builders build elsewhere. Interesting to watch.

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Lately I’ve been taking small steps into AI and machine learning as part of the Berkeley Certificate in ML and AI. My goal isn’t to master everything at once, but to truly understand the basics before moving deeper. One of the things I’ve worked on recently is experimenting with KMeans, DBSCAN, and PCA, and the difference between reading about a technique versus plotting it and really seeing how it behaves has been eye-opening. For example, I wasn’t fully aware of how useful the elbow chart is for KMeans until I actually created it myself. Seeing how the inertia changes and where it flattens out makes selecting a cluster count feel much more data-informed than guessing. A few simple takeaways from these baby steps: - KMeans works well when you have an idea of how many groups to expect, and the elbow plot gives you a more objective way to decide. - DBSCAN doesn’t need a fixed number of clusters and can pick up outliers naturally, which is interesting for anomaly detection — but parameter tuning really matters. - PCA helps reduce dimensionality and understand where most of the variance lives, but 2D/3D plots don’t always separate classes cleanly when the signal is spread across many features. Still learning one step at a time, but enjoying the small “aha” moments and the growing intuition that comes from practicing rather than just reading. #BerkeleyAI #MachineLearning #ML #AI

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Announcing OpenZL! Today, I'm excited to announce the open source release of a project that my team has been working on for the last five years: It's called OpenZL, a new kind of lossless data compressor that can make your data smaller with less CPU than you might have thought possible. How does it work? It has a toolbox of compression techniques, but each trick can be applied selectively to the formats to which they apply best, and it will entirely skip those that are not well-suited to your data. The end result is a much more efficient compressor which finds more compression opportunities, faster. It really shines for structured data, and it's ready to try at https://lnkd.in/eGtfZ3WT. Learn more from the blog post https://lnkd.in/eWGhNYmj Or go deeper in the whitepaper https://lnkd.in/eEu27QjS to see details on how it works and performance benchmarks

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𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝗹𝗲𝘀𝘀𝗼𝗻 𝗰𝗼𝘀𝘁 𝗼𝘂𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 $𝟲𝟬𝟬 𝗮𝗻𝗱 𝗮 𝗺𝗮𝗿𝗿𝗶𝗮𝗴𝗲 𝗮𝗿𝗴𝘂𝗺𝗲𝗻𝘁. Our VP of Engineering, Xiaofan(James) Luan, was supposed to buy his wife a Dior bag for their anniversary.  Instead, he bought three Claude Code subscriptions and spent the holiday trying to cross-compile 2 million lines of C++. Every fix on one platform broke two others.  $600 later, the only output was "git reset --hard" — and a very cold dinner table.😂 "Make it compile on Windows" is a trap. The real goal was "compile everywhere without hacks" — no AI is going to figure that out for you at 2 am. What worked: constraints before code, review tests not code, bottom-up, one layer at a time. Same task, two days. Then he ran six parallel Claude sessions across three machines with git worktree. The bottleneck stopped being intelligence and started being how fast one person can alt-tab. AI solves exactly the problem you give it. Engineering is in knowing which one to give. His wife is still waiting for that bag. Full story: https://lnkd.in/gtsW_Wvk ——— Follow Milvus, created by Zilliz, for everything related to unstructured data

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𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝗹𝗲𝘀𝘀𝗼𝗻 𝗰𝗼𝘀𝘁 𝗼𝘂𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 $𝟲𝟬𝟬 𝗮𝗻𝗱 𝗮 𝗺𝗮𝗿𝗿𝗶𝗮𝗴𝗲 𝗮𝗿𝗴𝘂𝗺𝗲𝗻𝘁. Our VP of Engineering, Xiaofan(James) Luan, was supposed to buy his wife a Dior bag for their anniversary.  Instead, he bought three Claude Code subscriptions and spent the holiday trying to cross-compile 2 million lines of C++. Every fix on one platform broke two others.  $600 later, the only output was "git reset --hard" — and a very cold dinner table.😂 "Make it compile on Windows" is a trap. The real goal was "compile everywhere without hacks" — no AI is going to figure that out for you at 2 am. What worked: constraints before code, review tests not code, bottom-up, one layer at a time. Same task, two days. Then he ran six parallel Claude sessions across three machines with git worktree. The bottleneck stopped being intelligence and started being how fast one person can alt-tab. AI solves exactly the problem you give it. Engineering is in knowing which one to give. His wife is still waiting for that bag. Full story: https://lnkd.in/gtsW_Wvk ——— Follow Milvus, created by Zilliz, for everything related to unstructured data

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What I'm Reading This Week 📚 ⚡ Diffusion Coming to LLMs https://lnkd.in/gMrhRsvj Lots of announcements at Google IO this week. One that caught my eye is Gemini's new Diffusion model—applying image diffusion algorithms to text generation for faster speeds. This could be a game changer for latency-sensitive applications like IDE auto-completion and real-time code editing. 🔄 Real-Time Collaboration Architecture https://lnkd.in/ghwrBAxT Matthew Weidner delivers a brilliant deep dive into synchronizing state between central servers and multiple clients. If you've ever wondered how Google Docs manages concurrent edits without chaos, this is your answer. His exploration of operational transformation and server reconciliation patterns gives me new appreciation for the complexity behind seamless collaboration. 📈 Economic Reality Check https://lnkd.in/gfTf2TG9 Thomas Klitgaard from the NY Fed provides a down-to-earth explanation of why the US consistently runs trade deficits. While the current administration's trade war rhetoric continues, one thing is clear: rebalancing trade won't happen without painful adjustments for American consumers. The macroeconomic forces at play are more complex than political soundbites suggest. 🎯 Algebraic Effects Explained https://lnkd.in/g8zeFzWX A fascinating introduction to algebraic effects—essentially resumable exceptions that enable elegant control flow. While this cutting-edge programming language feature might be hard to wrap your head around, the Ante team does an excellent job explaining when and why you'd need such abstractions. 🤖 Google's Coding Agent https://jules.google/ Google launches Jules, their take on cloud-hosted agentic coding loops. Between AI-powered IDEs, local agents like Claude Code, and these cloud solutions, we're clearly still figuring out the right form factor for agentic programming. The overloaded "Codex" branding from OpenAI isn't helping clarity either, but the underlying trend toward autonomous code generation is undeniable. What's your take on the current state of AI coding assistants? Are you finding them more helpful or distracting in your development workflow? #TechArchitecture #AICodeGeneration #DistributedSystems #EconomicPolicy #FutureOfProgramming #AgenticDevelopment #DeveloperTools

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There is a lot of talk about AI replacing developers (with many thousands of layoffs from large companies seemingly lending credence), but what if there's a twist? These two stories suggest a potential case of incoming dramatic irony: the very power consumption plus business model that makes AI coding tools so useful and intriguing might also make them unsustainable at scale: 1️⃣ There will be throttling and pricing tiers, possibly leaving business in the lurch as they hit those limits: 🔗 https://lnkd.in/gEAQfapb 2️⃣ Meanwhile, the infrastructure costs behind AI tools are real and rising. This isn't a free lunch; it's a business product, again suggesting the inevitability of pricing tiers and throttling: 🔗 https://lnkd.in/gzaJHbYy What if the eventual "plateau of productivity" for AI coding includes the realization that the best ROI on development still means a team of skilled, intelligent, reliable, engaged humans?

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Meet Gitnar. A modern SaaS platform built to make software collaboration more structured, more efficient and easier to scale. Instead of jumping between multiple tools and losing context, teams can bring structure, visibility and control into a single platform. https://lnkd.in/eFjkXgsb Linked to this post is a first look at a bulk operation for importing SonarQube and SonarCloud issues into a GitHub environment. More to come soon! #Gitnar #SoftwareDevelopment #DevOps #SaaS #Engineering #Github #SonarQube #SonarCloud

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How to disrupt an industry with AI? On the latest Hard Calls, Trisha Price sits down with Vantaca CEO Ben Currin to explore how they’re reshaping community management with AI-powered workflows.

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The Claude ecosystem is exploding faster than I expected. My newest favorite plugins: - Get Shit Done (orchestration layer that plans and builds apps phase by phase) - Ralph Wiggum Plugin ( iterative, self-referential AI development loops) - n8n-MCP (direct Claude integration with 1,000+ automation nodes) What's interesting is how quickly we've moved from simple AI coding assistants to full orchestration platforms. The shift feels similar to what happened with CI/CD tools a few years ago. First you had basic scripts, then comprehensive pipelines, now full DevOps orchestration. We're seeing the same evolution with AI coding tools, just compressed into months instead of years.  #AI #Claude #SoftwareDevelopment #AIAgents #DevTools #Automation

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Comparing models.. Finding that a small 1B #llm hallucinates less than a 4B for semantic to structural matching. Small llms for targeted tasks for the win. The new Meta llama3.2 1B is better than expected and cheap on token cost.

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Building Large Language Models for the Public Sector on AWS Niki Sotiria Kokkalas Eliuth Triana Laura Verghote Wenhan Tan Excited to share our latest deep-dive on developing custom LLMs tailored for government and public sector organizations! 🏛️🤖 Off-the-shelf models often fall short of the regulatory, cultural, and operational requirements that define public sector missions. This comprehensive guide walks through the complete development lifecycle: ✅ Use case & requirements definition ✅ Evaluation framework establishment ✅ Model selection & training approach ✅ Data collection & preparation ✅ Infrastructure provisioning ✅ Production deployment & testing Key highlights: 🌍 National LLMs that preserve linguistic heritage & cultural context 🔒 Data sovereignty & compliance frameworks 💰 Cost analysis: API vs. self-hosting TCO 🛠️ Tools: NeMo Curator, SageMaker HyperPod, LM Harness Whether you're building language models for underrepresented languages or creating domain-specific solutions for healthcare, education, or legal services—this guide provides the scientific methodology and AWS infrastructure blueprint you need. Read the full article: https://lnkd.in/eKZ8FaKN Would love to hear your thoughts and experiences with custom LLM development! 💬

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Today I spoke with two frinds from Google AI and learned that the KPI for engineers at Google and many large tech companies has fundamentally changed. A key metric now is how many tokens you are able to use. The more tokens you use, the greater your potential contribution—one person can now accomplish the work that once required ten or even a hundred people. #HerPowerAI #Google #WorkModel #KPI

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Indeed. A small number of big tech companies have grossly overinvested in LLMs and overextended themselves -- even laying off tens of thousands of critically needed human employees and enshittifying their primary products and services to reallocate investment into it -- shooting Nvidia's profits and shares to the moon -- all in an insane attempt to "be first" for something that has no forseeable compelling ROI. It's a collective delusion and hype bubble. This is markedly different from the strategic "operate in the red to build marketshare" strategy of the few survivors of the dot-com bust, because at least those survivors were able to articulate (and realize) a logical ROI after reaching a tipping point of critical mass. This is more like the 98% of dot-com startups that burned bright fueled by VC right until they burned themselves out because they never actually had any believable plan for monetizing anything they were building. The lack of ROI will come rudely crashing down on these companies over the next several quarters as they run out of tricks to cook the books and hide the losses. Shareholders will get spooked and back out, share prices will crash, and big tech companies that have bet the farm on LLMs will suddenly find themselves without the necessary employees, capital, quality products/services, or reputational sway needed to save themselves. And when they collapse, Nvidia will collapse with them. And then maybe, finally, we can get back to the business of making things of actual value to humans.

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I made an explorable esoteric research environment with a Custodian named MERIDIAN that can launch its own agent swarms to solve problems. Does isopsephic arithmetic and lookup for any Neoplatonists :-) https://lnkd.in/gYzqVn7m

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With AI, according to all of the marketing materials from all of the companies, including Meta, SWE not required anymore and you can just scale your engineering up by using LLM models alone... Yet: - Meta cannot keep Desktop app for their own Facebook Messenger alive - https://lnkd.in/ekKTbEfG keeps growing and Google cannot continue to develop already released products - Windsurf cannot keep up and to have their IJ plugin for Remote Development in sync with their non-RD plugin https://lnkd.in/eeMjrZ_6 - Claude IJ plugin is also far from perfect What has happened to "You need to practice what you preach"?

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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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As enterprise workflows move toward a wide range of autonomous AI agents, how do we maintain control and data sovereignty? Based on my experience, the key lies in an age-old paradigm: Hunters vs. Farmers. In my latest article, I explore why a strict Separation of Duties (SoD) is essential for the long-term safety of Agentic AI. We look at: 🏹 Hunter AI (The Edge): Proactive executors deployed in end-user environments to fetch privileged data and execute tasks. 🚜 Farmer AI (The Core): Strategic orchestrators that distill information, manage bias, and interface with humans. I also break down the "Maturity Curve"—how the role of Human-in-the-Loop evolves from approving every small adjustment to managing only high-risk, high-cost actions. Read the full article here: 🔗 Medium: https://lnkd.in/gfC_5pbh 🔗 Personal Blog: https://lnkd.in/gQdA2FvG #AI #AgenticAI #EnterpriseArchitecture #AISafety #MachineLearning #TechLeadership

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