Delong Fu

Having lived in SF bay area for so long, I came to know the big coffee culture scene here. I was totally consumed by it a while ago and spent years trying to attain the “perfect" latte. I published a fun coffee story and my entire home setup, which took weeks of research and multiple iterations, in my blog link in bio. Meanwhile, to all fellow coffee loving tech peeps, here are some top tips that no one told me. Put it in practice at home or in microkitchens. - A big factor in preparing milk suitable for latte art is pressure of the wand. If this is important to you, you need to get a machine that has a strong enough pressure. - When pulling shots, put in 18g of beans and extract 30g of espresso. - The darker the roast, the coarser the setting should be on the grinder, the lighter the roast, the finer the setting should be. - When adjusting the grind setting, do so while the grinder is working if there are beans in the hopper. Otherwise, a bean can get stuck between the burrrs. - Pulling a shot should take 20-30 sec in time, or based on observation. It should look like caramel going out, not a waterfall. - Eventually work your way to using a bottomless portafilter. It punishes you for all the small imperfections when preparing your coffee puck, like any tilt, and uneven distribution. It does so by spraying the espresso all over the place. The more you can get it to flow uniformly, the better your preparation of espresso, objectively. - Seal your beans in a vacuum, so they don’t go stale. The more stale the beans, the more minor adjustment you need to make to grind finer. - When steaming milk in your pitcher, you can tell if it’s done by holding the pitcher and as soon as the heat becomes unbearable i.e. stings, it’s done. - Taking a latte art course at a local coffee shop was the best boost for me to start nailing latte art.

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Want to know more about How Figma's Databases Team Lived to Tell the Scale? Still skeptical of why we built horizontal sharding on RDS instead of <insert-cooler-database-technology>? Wondering if you should copy what we did? Check out out my new podcast with Software Engineering Daily as we dive deep on spicy stories that didn't make the blog post from surviving my first Figma Config conference to debating which database technology comes out on top. https://lnkd.in/gFfh7C-A

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Meta Introduces Chameleon: A State-of-the-Art Multimodal Model 🦎 Meta has unveiled Chameleon, a groundbreaking multimodal model that pushes the boundaries of AI capabilities. Chameleon seamlessly integrates vision and language, enabling it to understand and generate images and text with remarkable accuracy. Key Takeaways: 🎨 Chameleon excels in both image captioning and text-to-image generation, showcasing its versatility across multiple tasks. 🧠 The model leverages a novel architecture called Mixture-of-Attention (MoA), which efficiently processes and combines visual and textual information. 🌐 Chameleon outperforms existing state-of-the-art models in zero-shot image captioning and text-to-image generation on various benchmarks. 🔍 The model's ability to handle complex, compositional prompts sets it apart from other multimodal models. 🔒 Meta has open-sourced Chameleon's code and trained weights, fostering transparency and enabling further research in the field. Chameleon represents a significant milestone in the development of multimodal AI systems. Its ability to understand and generate both images and text opens up exciting possibilities for applications in fields such as creative content generation, assistive technologies, and more. Read the full article to learn more about Chameleon and its implications for the future of AI: https://lnkd.in/gk_ZTZdR #chameleon #multimodalai #artificialintelligence #ai #machinelearning #ml

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🍜Anybody up for some ramen? I am excited to share that I am back in the garage, building again (it’s actually a house in the Berkeley Hills). We are taking the wraps off of Supercog AI, a new startup focused on solving Application Integration using LLM powered agents. My co-founder in this new venture is a great friend of mine (and a former co-founder partner), Alex Osborne. Alex and I have worked around the application integration space for a long time, and we are unrealistically excited about applying the GenAI stack to this problem. Not many people relish the task to get some bits to move from system A to system B. But it’s a critical job to be done, and that’s led to a lot of brittle scripts, a myriad of inflexible “no code” tools, and a lot of hand crafted SQL. But the power of the Large Language Model, trained on a huge corpus of information, offers a remarkable tool to solve this problem. The LLM you use today already knows the APIs of hundreds of popular systems. It knows the SQL dialects of every major database. It understands the semantics and data schemas of many popular SaaS systems. It understands much of the specific domain in which your business operates. I like to call the AI revolution “the last platform shift”. That may turn out to be hyperbole, but I definitely believe we are in the very early stages of seeing what this new stack can enable. I know we are hardly the first folks to claim that “amazing stuff is coming!”. But this is why Alex and I have decided to focus on a real and hard problem. If we can prove that LLMs can power a new way to solve this problem, it will be the first successful *new* approach in 15 years.. We will have much more to share about this new platform soon. In the meantime, if you’re interested in getting a peek - or even better if you have application or data integration tasks that you would like some help with - please reach out because we want to help. #genai #startups #backinthegarage

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We released a new update of our Visual Studio Code Java extension. Go try it! Bunch of new improvements and already supporting JDK 23 Early Access builds as the extension is directly leveraging javac. https://lnkd.in/g8BKsNjS #java #IDE #OpenJDK #VSCode

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Interesting article from Bloomberg about life at Google's X in a time of layoffs and cost reduction at https://lnkd.in/gmw3GG7C. X is a component of Alphabet (Google's parent company) that incubates new ideas. They have been responsible for some great successes - getting Waymo and a lot of Google's AI work started - but they've also had projects that didn't survive - like Google's attempt to provide Internet access via weather balloons. When you work at a company that has hundreds of billions of dollars in revenues, it's hard to get a lot of interest in an entirely new business idea that brings in hundreds of millions. If you have a project that improves ads revenue by that amount, or decreases capital expenditures, you can do it, but if you want to, say, build wearable tech, it becomes hard to attract the attention of marketing or product. So, new ideas often die on the vine. For the most part, this is good and proper. Companies may have the financial wherewithal to branch out, but more often than not, they consider it a distraction. And they are probably right - it's hard to argue that Meta was right to divert such a huge chunk of their resources to chase after the metaverse. They've (rightfully) pulled back, and are more focused on their core business again. On the flip side, if you are in the tech sector, and you don't innovate, you are likely to get left behind at some point. So, you need to make and manage these investments somehow. When I joined Google, the CEO had a 70-20-10 rule - 70% of effort went to core businesses, 20% to broader efforts to improve Google, and 10% to stuff that's completely blue-sky. This is probably not completely right, but it is also probably not completely wrong. X is the division of Alphabet that is *specifically* designed to bring blue-sky products to life. Unsurprisingly, they've had to pivot and rethink their strategy many, many times. The article interviews a number of people who have worked at X over the years; it's well worth a perusal, for folks who are interested in the story.

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Today I'm sharing this 🔥 video from our recent Engineering onsite at Demandbase HQ in SF! https://lnkd.in/eFAFR6QG We were so fortunate to host Zhengxuan Wu and Aryaman Arora, two upcoming AI researchers from Stanford University Department of Computer Science. Their approach to fine tuning, called ReFT, targets the representations emitted by neural net layers rather than the weights of the layers themselves. This makes it very flexible when using the same base model for swapping multiple fine tunes. The memory overhead is also very low for their intervention weights. Another highlight is how powerful it can be. One POC they showed forced the LLM to speak in only emojis exhibiting ReFT's power to control output. Control is crucial when, for example, you want to prevent your bot from offering customers unauthorized refunds!! If you're already familiar with LoRA, it should be pretty simple to use with only a few extra hyperparameters -- details in the video. Here's a link to the repo. Give them a star! https://lnkd.in/eJScNbzK Thanks again for letting us host your first external talk Aryaman and Zen. It's very exciting tech!!

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Senior engineers lose money and responsibility at Meta partly because of an unwritten rule. Candidates are expected to not follow their interviewer's instructions to SUCCEED. Yes, you read that right. That blew my mind. There are other common reasons for downleveling as well. Some of these are expected but others are surprising. Kevin Landucci + Kashish Singhal, loved the simple practical takeaway for candidates. And the full episode rocked! Subscribe to ApplyPass to get more tips in your feed 🛎

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Just like the big boys, bookend.ai now supports Llama3, alongside our menagerie of open source LLMs. While Llama3 is great, it's also just the tip of the iceberg. What will truly separate the herd from the herd-wranglers is going to take a lot more work than house training a llama. Real value lies in automating the real-life, workhorse apps that power the enterprise and that requires maximizing both safety and simplicity without compromising accuracy and flexibility.

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We’re excited to announce that we’re #hiring another engineer to join our founding team in New York! I want to share a little about why I started Niva with Abhinav Rai: 1. It's a chance to use modern AI to solve a real-world problem. I’ve felt the pain of manually verifying businesses throughout my fintech career. At Gusto, we built an internal tool called ZenSA to make this process more efficient, but the technology wasn’t quite there to fully automate it. 2. The problem is huge and a major friction point for scaling global commerce. In just the first few months, we've partnered with several fast-growing international fintechs and fully automated their business onboarding. It's been a challenge to keep up with the demand 🚀🚀 Onboarding is just the start - our mission is to scale global commerce by building trust between businesses worldwide! DM me for details.

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Feeling like I'm searching for the square root of -1 in the job market lately. #AndroidDev Anyone else out there feeling like their dream job is an imaginary number? Sending virtual hugs to all the job seekers braving the application process. But hey, there's a reason mathematicians invented imaginary numbers, right? #NeverGiveUp It might not exist in the real world, but that doesn't mean it's not valuable. Just like the imaginary unit unlocks new possibilities in math, finding the right job can unlock a whole new level of career satisfaction. So, to all my fellow job seekers, let's keep grinding, keep learning, and keep that positive attitude. P.S. Who else wants to commiserate and share some job search tips in the comments? We're all in this together! #AndroidDevelopment  #JobSearch  #WeGotThis

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Hey there, Domo users quick update coming your way!  We're super excited to announce this: you can now harness external AI models in Magic ETL. Our new AI Inference tile lets you deploy any of your eligible externally hosted models, like Databricks and Amazon Bedrock, within Magic ETL using Domo’s ML connectors.  (Note that the models will need CSV input and output formats. We plan to expand in the near future to include JSON-based models in the near future.) I'm stoked to share this update, being able to supercharge your ETL with your own model will give you the ability to elevate your data game like never before!  Curious about what this means for you? Dive into all the details right here: https://lnkd.in/gcguUqs6 #domoupdates #domoproduct 

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A new development environment is ostensibly a behavior change. We've thought long and hard about what an AI-native workflow should be, and the dev environment to house it. GitHub Copilot Workspace is our take, where AI is a thought partner supporting developers from the beginning of a task -- where developers tell us they struggle most -- all the way to submitting a pull request. Task -> spec -> plan -> code, is a good candidate for a human-AI collaboration flow because it mirrors how developers organically work on complex tasks, and where they embrace more AI support that is steerable. Now, I can't wait to learn from how developers use Copilot Workspace, and to understand the impact they experience.

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I started writing regularly about AI. If you're an innovators or product developer eager to apply the latest in AI, whether you're in a startup or a big company, consider checking https://lnkd.in/gGdW8PYe I won't teach you ML, but I share real-world insights from launching - and sometimes failing at - numerous ML features on a large scale.

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A successful systems design and delivery strategy begins by assuming that change is likely the only constant. Be adaptable, or fail.

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We've been making some cool examples of things that can be built with substrate.run lately. Here's a simple chatbot that can help you cook: https://lnkd.in/eEZm-nRJ If you're working on building a chatbot, take a look and see what it might look like to use Substrate 🧑‍🍳

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