Hao Tang

I’ve been testing a few AI coding assistants across team workflows. The most capable one so far looks like Claude Code. OAI Codex CLI can sometimes crack tougher problems, but it’s missing a lot of practical features that Claude Code’s CLI has. Despite a lot of noise about them degrading the tool, I think it’s very capable. Better use of tools, making regular commits, opening PRs with good descriptions, and actually sticking to the todo-based plan without drifting off into “on the one hand, on the other hand” territory. Good bot! Anthropic also added analytics last week, following Cursor, which has had it for about a month. But for team use you need a solution that works in IDE and terminal. Cursor does this, with analytics and a web-based agent too. With those pieces in place, Cursor feels like a more complete package you could roll out across a team, something most alternatives don’t offer yet. This matters because analytics gives visibility into how these tools are actually being used. Coinbase shared they adopted Cursor wholesale and have 40% ai code, and now they’re aiming for 60%. That kind of feedback loop is key if you want to make the case for rolling out across a full team. (https://lnkd.in/eHGvkAxC) That said, Cursor also rolled out Bugbot and auto-enabled it on one of my repos, flagged an issue in a PR, then pushed an unverified commit straight to main, which caused a merge conflict in the PR. All terrible practice. So while Cursor seems to have the most team-focused tool at the moment, I’d definitely avoid Bugbot for now. Curious how others are navigating all these options. Do you think teams benefit from one joined-up tool across everyone, or is it better to stay agnostic and let devs pick what works for them?

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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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🚨 Software Engineers: Know Your Worth. Negotiate with Confidence. 🚨 Even in a tough market, great roles still exist—for those who are prepared. Yes, layoffs are real. But so is demand for high-impact, self-aware engineers who know how to communicate value. Here’s how to stay competitive and visible: ✅ Polish your LinkedIn – Make your headline value-driven, not just a job title. Highlight impact, scale, and tech scope. ✅ Refresh your resume – Lead with outcomes, not responsibilities. Data > adjectives. ✅ Ask for strong recommendations – Past peers, EMs, and PMs can validate your execution and collaboration chops. ✅ Show up in communities – Engage in topics you care about (AI infra, DevX, system design). Visibility builds trust. ✅ Mentor someone – Pay it forward. You grow by lifting others. ✅ Track your wins – Metrics, promotions, launches—keep receipts. You will need them when it’s time to negotiate. 💡 Bonus: Practice telling your story. What problems do you love solving? What teams thrive with you in the room? 🔥 There are still $350K+ roles out there. But the market rewards clarity, not just code. Let’s help each other move with purpose. The right opportunity isn’t luck—it’s alignment + preparation. #SoftwareEngineering #TechLayoffs #NegotiationTips #CareerGrowth #LinkedInTips #EngineeringLeadership #JobSearchStrategy #MentorshipMatters #DevCommunity #KnowYourWorth #FAANG #EngineeringCareers #Amazon #Meta #AmazonLayoffs

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🚀 LangChain’s leap to unicorn status? According to TechCrunch, AI infrastructure startup LangChain is raising a new round at an impressive ~$1 billion valuation, led by IVP.  What’s driving this surge? ° 111K GitHub stars and robust open‑source traction ° Innovative tools like LangSmith fueling $12–16M ARR A strong footing in the fast‑evolving LLM ops space 💡 If you’re building or investing in AI-powered applications LangChain’s trajectory is one to watch. #AI #Startups #UnicornAlert https://lnkd.in/ddcnHXRd

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For understanding the virtual thread state transitions... Specifically, thread pinning on Java 21 vs. Java 24 and when the thread waits on IO how the platform thread views the state compared against the virtual thread enabling interruption

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Over the past year, my team and I built Pinterest's Model Context Protocol (MCP) ecosystem from the ground up: a central registry, a growing fleet of domain-specific MCP servers, and production integrations across our IDEs, internal chat surfaces, and AI agents. MCP is an open standard that gives large language models a unified way to talk to tools and data sources. At Pinterest, we used it as the substrate for AI agents that can safely automate real engineering tasks, from querying Presto data on demand to diagnosing Spark job failures and surfacing institutional knowledge. A few things I'm particularly proud of: * We designed for security from day one: two-layer auth (end-user JWTs + mesh identities), business-group-based access gating, and mandatory human-in-the-loop for sensitive actions. * We made it easy for any team to ship a new MCP server by creating a unified deployment pipeline that handles all the infrastructure, so domain experts just write business logic. * The ecosystem has scaled to 66,000+ invocations per month across 844 monthly active users, saving an estimated 7,000 engineering hours per month. This was a cross-functional effort. Thank you to everyone on the Agent Foundations, Security Engineering, and Traffic Engineering teams who made this possible, and to our engineering sponsors for their support and guidance. Full write-up on the Pinterest Engineering Blog: https://lnkd.in/e-mH26Bw

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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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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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DeepSeek V4 drops in upcoming days. February 17th. Lunar New Year. I've been tracking the leaks. The technical specs. The benchmark rumors. Here's what has Silicon Valley engineers quietly stressed: Engram conditional memory architecture. Built for coding tasks. Early benchmarks suggest it outperforms Claude and GPT in specific workflows. Cost to build? $5.6 million. OpenAI spent 100X that on GPT-4. Here's what matters: This isn't about model size anymore. It's about memory architecture. V4 reportedly remembers context across sessions without burning tokens. Every coding conversation builds on the last. For developers, that changes everything. No re explaining your codebase every session. No context window limits killing momentum. No starting from scratch daily. DeepSeek already overtook ChatGPT as the #1 app on US App Store last month. V4 could be the moment coding assistants split into "before" and "after." Prediction? If these benchmarks hold, February 17 becomes the day the AI coding wars got real. Google, OpenAI, Anthropic they're all watching. Sources: TechCrunch, The Verge, LinkedIn Tech Reports (Jan 2026) #DeepSeek #AI #SoftwareEngineering #CodingAI #TechNews

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