Dongxu Wang

Im really looking forward to presenting at this session. I love how the tools and patterns for Governance and Security with IaC have the capability to retain and even improve developer speed and productivity.

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In the era of LLMs, "sexy" might not be the term, but bag-of-words solutions are still kicking! When the position of a word matters, consider using Positional Vectorizer over TFIDF. Check it out: https://lnkd.in/dDRYFrAX

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Large Language Models (#LLMs) are evolving rapidly, and the demand for running them at scale is skyrocketing. #Ollama is stepping up to meet this need with its official container image on Docker Hub, excelling in local #deployments. However, there’s a catch for containerized environments: downloading #models requires the ollama serve command to be running within the #container. This extra step complicates automated deployments via #CI #CD #pipelines, making seamless model access challenging. To take your #model to production, it needs to be #container-ready—a feature Ollama has yet to fully support. The gap in #AIOps for smooth deployments gets blurred with the inference services available as an easy way to take it to production. Goal is to overcome obstacles and deliver solutions 😉 so here is to get you started on having your (O)#llama #production ready in a private #deployment from your #local to #cloud all in #oneshot . Enjoy Medium: https://lnkd.in/g9jqBQ8d dev.to: https://lnkd.in/gi_gGwam #AI #LLM #Ollama #llama3 #phi3 #mistral #S6overlay #AIOps #CI #CD #oneshot #container #docker #podmanEnjoy

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Amazon-to-Google-to-Amazon #a2g2a thought of the day: the way Amazon thinks about its Principal Engineers is unique - and I love it. “I always thought of you (and my other Principal Engineers) as Amazon employees that were on loan to me.” Yesterday I was catching up with my good friend and ex-manager Dan Sommerfield. Dan promoted me to Principal Engineer back in 2014 and was my manager for a number of years. As we were laughing about some of our shared past, Dan said that and it stuck with me. A few weeks ago I posted in my a2g2a series about Swimming Lanes. I’ve also posted in the past about the Amazon Principal Engineering community (see links to both of those in my comments). Today’s reflections combine those. Amazon Principal Engineers tend to have a healthy disregard for organizational and role boundaries. And Amazon’s DNA gives them the freedom to do so. At these big faang companies, for better or for worse, most orgs tend to optimize for themselves. It makes sense in terms of business needs: they need to move fast and expand their business. But this is often how we end up with duplicative work siloed usually at Director level. I’ve seen, time and time again: Principal Engineers look across organizations and connect dots that others don’t. The Amazon Principal Engineering community is a giant organic mesh. It worked incredibly well as a big mesh when I became a PE in 2014 and we were about 250 PEs in the entire company, but I’m continuously amazed that it still works in 2024 at today’s scale. This is a testament to the culture that some of our founding figures set decades ago- and our Distinguished Engineers continue to carry the torch today. It’s not that this doesn’t happen in other companies; it did happen at Google and Microsoft too. But it’s so intrinsically embedded in the DNA of an Amazon Principal Engineer that it’s a lot more natural and expected here than anywhere else where I’ve worked or considered working.

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I reached 17 years at Google two weeks ago. Google has changed quite a bit over the years, though I won't get into that now. Sometimes, though, it's not the big things you've landed, but the smaller things that unexpectedly turned into bigger things. I frequently reflect on: - Android lint testing for Google apps - Fake "Save" button in Google Docs - The algorithm for Google Calendar's event layout - A couple of random test frameworks I created along the way, e.g. one that repeatedly prevented new crashes in Android OS itself - Some novel algorithms for detecting production issues in Android - Helping land XSS-free autoescaping for one of our most common HTML templating languages Many of these were things I started on a whim because I thought they might be valuable. Some of them I landed myself, but some were just started and took on lives of their own. Of course, I tried a lot of other things that didn't work, but it's important to try and experiment even when not everything works. What small things bring _you_ disproportionate pride?

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Miss the conversation between Mark Zuckerberg and Jensen Huang at SIGGRAPH? Watch the whole conversation as they dive into the latest advancements in generative AI and open-source software, and explore how these technologies are empowering developers and creators to build new and innovative experiences. #GAI #TheNextComputingPlatforms #opensource #opencompute

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Exciting news! I'm thrilled to announce that I'll be speaking at PG Day Chicago (https://lnkd.in/g_-vytmF) on the topic of "Probing PostgreSQL and Linux kernel interactions." My talk covers some upcoming aspects of PG17 but mainly caters to beginners dealing with PostgreSQL and Linux Kernel internals. Don't miss out on this opportunity to expand your knowledge! Check out the abstract for my talk here: https://lnkd.in/gJVcGb4u #PGDayChicago #LinuxKernel, #PostgreSQL, #PGInternals, #pgengine.

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Are LLMs capable of Artificial General Intelligence (AGI)? I came across this article in the NY Times: https://lnkd.in/geXXbi8B (for non-subscribers here's the paper it talks about https://lnkd.in/gP-m7sxA) The upshot is that researchers are finding strong evidence that language is a very distinct part of the human brain whose purpose is to communicate and that it is not intrinsically involved in reasoning. This gets at a very fundamental question about the use of LLMs as the basis for Artificial General Intelligence. Can a model based on language be used as the basis for AGI? Time will tell I'm sure, but when I've asked this question to researchers I know, they generally say yes. However, I'm not sure what the basis is for this belief. CAVEAT: I'm not in the AI field, nor have I delved deeply into the scholarly articles. I have a fairly rudimentary understanding of LLMs, but would be happy to learn from those who know more. One of the fundamental articles shaping my personal views on LLM based AI is Ted Chiang's excellent piece "ChatGPT is a blurry JPEG of the web" (https://lnkd.in/gmkRBe6A). For me, it captured the idea that LLMs are just summarizing what we know. To be sure, they are capable of really cool things, but are they really doing any thinking? More important are they even capable of thinking? Again, time will tell, but I'm increasingly skeptical. The kicker for me is that they have no common sense. One of the reasons we find LLMs so amusing is they crazy ways they go off the rails (aka hallucinations). I think that these are largely because there's no common sense 'boundary'. When a human's thoughts start to get carried away, we have ways of reigning them in by asking ourselves "is this actually a reasonable thought?" Indeed, it can be quite alarming to encounter people who communicate about ideas that we find to be outside the bounds of common reason. It feels like this 'framework of rationality' is very important but I don't think it's something you can just use as guard rails. It seems like you need some kind of other AI to be able to generate that framework. What do you think? Are LLMs capable of AGI? Or are they like our own language processing center, just a small piece of a much larger 'intelligence framework'? [Edited to use AGI instead of GenAI so as not to be confused with Generative AI] [Edit: Thanks to Tareq Alkhatib here is a direct link to the paper in question: https://lnkd.in/g9b7Q66c]

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