Zhuzeng Lu

Great piece by Emilia David on the LangChain open source integration ecosystem, the power of LangGraph, and our newly released LangGraph Platform Helps put everything you see us working in perspective Link: https://lnkd.in/ghajS7zx

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Building a production-ready streaming user embedding pipeline in ~5 minutes with Zipline + Claude code: https://lnkd.in/gnWsjrbn Why is Zipline + Claude Code such an effective combo? 1) Chronon offers a simple API that unifies batch + streaming computation, online serving and offline backfilling, and allows for flexible aggregations, transformations and embeddings. It automates the complex infrastructure involved in building these pipelines, allowing the AI to focus on getting the semantics right (which is what it’s good at). 2) The Zipline skill gives the AI a few critical tools: the ability to read metadata about various data sources (giving it starting schemas to build pipelines), plus the ability to call `eval` on the Zipline entities that it creates, which allows it to verify that everything that it creates is indeed correct quickly without needing to any data processing. The result is that you build powerful pipelines easier than ever before. Something that would have taken a team of engineers weeks to do can now be done with a single prompt in 5 minutes. Check it out in this brief demo.

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🚀 AI Minute Mondays: Data Uptime & Availability for AI with Mandar Naik, Global VP at Redis. This week, I had the chance to sit down with Mandar Naik, Global VP of Sales and Partnerships at Redis to unpack one of the most underestimated pillars of AI systems: data availability. As AI workloads scale, the conversation can’t just be about models and GPUs. If your data isn’t available, consistent, and resilient, your AI simply won’t perform. Mandar and I dug into what “availability” really means in modern AI architectures—and why it’s far more than a checkbox.   We explored: 🔹 Redundancy as a design principle, not an afterthought 🔹 How latency and throughput shape real‑time AI performance 🔹 The role of failover and resilience in keeping systems running 🔹 Why consistency models matter more than most teams realize 🔹 The growing importance of monitoring & observability for AI data pipelines   We also talked about the common pitfalls teams run into when building for availability: ⚠️ Not planning for scale early enough ⚠️ Treating availability as a “nice to have” ⚠️ Relying on a single data source ⚠️ Failing to design for failure (because failure will happen)   Mandar brought clarity to a topic that every AI‑driven organization needs to take seriously. If you’re building AI systems that need to be fast, reliable, and production‑ready, this conversation is worth your time. Stay tuned for the full episode—and let me know what topics you’d like us to explore next on AI Minute Mondays. #AIMinuteMondays #AIMinuteswithSuchi #DataUptime #DataAvailabilityforAI #AILearning

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If you use Elasticsearch, OpenSearch, or Solr - you're using the Lucene search engine at their core. Here's a great blog highlighting what happened in 2025 👇 #Search #OpenSource #ApacheLucene

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To all the engineers in my network who might or might not know what test-driven development is: The California government’s approach to innovation is like test-driven development. In some cases it actually *IS* test-driven development. I just found out that California’s failed SB 1047 would have “required developers to perform risk assessments of models prior to release, with guidance from the Government Operations Agency.” (wikipedia) There’s one place where the analogy to test-driven development falls short, though. Whereas in TDD you know what you’re testing for, SB 1047 doesn’t even know what it’s trying to regulate, as its goal is to “mitigate the risk of catastrophic harms from AI models so advanced that they are not yet known to exist.” Unfortunately, the state senator who introduced SB 1047 is back at it and still doesn’t realize he is trying to put the cart before the horse. Source: Politico

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I’m excited to share a new benchmark, MIPfeas, which shifts the focus in Mixed-Integer Programming from "final optimality" to "early feasibility." Using the Primal Integral metric, we evaluated how solvers like HiGHS, SCIP, and NVIDIA cuOpt deliver high-quality feasible solutions within a short time window. While direct comparison with commercial MIP codes remains difficult, we have grouped the results of three major commercial solvers—COPT, CPLEX, and Xpress—into three synthetic baselines: the Virtual Best, Mean, and Worst Commercial Solvers in this benchmark. A huge thank you to Hans Mittelmann, the NVIDIA team (Burcin Bozkaya, Akif Çördük, and Chris Maes), and ZIB Zuse Institute Berlin for the technical collaboration. Special thanks also to Timo Berthold, Gerald Gamrath, and Ferenc Katai for their expert insights. Check out the full results here: https://lnkd.in/dSfNc58v #Optimization #MIP #GAMS

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We are excited to announce major updates to RayData. Ray data now has native join support, improved aggregation APIs, and a brand-new shuffle engine. These updates large-scale workloads faster and more efficient while also unlocking lot more capabilities. https://lnkd.in/gRcZDDcp

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Lambdas are a low friction but expensive way to consume Kinesis. Not only is Lambda compute > 8x more expensive than equivalent EC2 under continuous use, but you pay per invocation. To consume a 1 million records / s stream with a Lambda, you're looking at 2000 invocations per second, each doing a batch of 500 records per invocation and taking (say) 100ms. This costs ~ $44k per month. There's huge savings just by consuming with regular EC2 compute. However, a lot of Kinesis use is as an intermediary channel, with data ultimately going to S3. Storing streams directly in S3 can be both cheaper and operationally simpler. That's what we do here, while keeping the same API for producers and consumers: https://lnkd.in/gA3raSJa

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Northflank is partnering again with Cerebral Valley, PyTorch, CoreWeave (and more) for the OpenEnv Hackathon in San Francisco, March 7–8. Participants will be building RL environments and post-training base models across 5 themes, solving the most pressing problems in RL and agentic orchestration. We'll be supporting teams in deploying their models through the Northflank platform with compute from our wonderful partners, CoreWeave (Matthew Lu, Jacob Feldman). $100K+ prize pool and and a stacked lineup of judges and mentors from Meta, Hugging Face, University of California, Berkeley, Cursor, Scale AI, and more. You can submit an application at the link in comments.

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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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