Patrick Ames

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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🚨 I took down a major piece of infrastructure as a tech lead at Yahoo! and didn’t get fired…. I think this is the nightmare scenario for engineers - to not only break your own product, but to do broader carnage to systems at the company. I think we all fear what the repercussions are if we do this. However, when intent is good, recovery is swift and learnings documented, these things are actually quite formative. When it happened, I was able to remain calm and walk through a rollback in 2 minutes, which allowed the downstream infrastructure to recover in about 10 minutes. This was also the start to my reputation as a level-headed technical leader. Good relationships with my peers in the infrastructure team allowed us to quickly diagnose that my code push was the cause of the incident and take action. I was leading a team building a very powerful piece of software. We had a client library that was installed on every server and allowed us to push out front-end code to every web property simultaneously. Code changes would propagate in about 2 minutes from push. It allowed us to provide a consistent branding, search and navigation system.  Because we understood the power of the system, we also had plans in place if something went wrong and had practiced rollback and forwards. Typically, when things went wrong, they were minor UX bugs in the interface. Medium problems cost us some dollars in search revenues. However, this time, my change had a massive impact downstream. I had added a new polling feature that worked against the caching strategy of the platform I was calling. Within 2 minutes of diagnosis I executed the rollback strategy. The bad news was the product manager had to wait a few more days for the new feature to roll again respecting the caching strategy of the platform. The good news is that we recovered quickly. I learned a few lessons from this outage: 🚨 Outages happen all the time and you will likely not lose your job if you cause one. 📝 Your team should have plans should an outage happen how you will approach debugging and recovery. ⏰ Practice for urgent situations before they occur. 🏋‍♀️ Building and maintaining strong connections with leads of systems where you have dependency is key. 🧞‍♀️ I have a special ability to appear completely calm while feeling absolute panic inside, which is a good capability to have. 

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Project update! My application tracking extension is now doing fuzzier matching and injecting new information into LinkedIn (and other sites) to keep me from doubling up on applications. Next up: local storage and bulk import/export operations...

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A data-driven approach to driving wound healing outcomes Marcella Gomez, University of California, Santa Cruz https://lnkd.in/gAkWZYJT Meet: https://lnkd.in/g4AAWkcK YouTube Stream: https://lnkd.in/gwtqaTmf Talk details: https://lnkd.in/gWn3B3cU Subscribe for talk announcements: https://lnkd.in/g5ciuNuX Abstract: Precision medicine requires an ability to predict the response of an individual to a prescribed treatment regimen a priori. Thus, advancement in the field is challenged by a lack of predictive models and, arguably, a lack of time-series information for a highly dynamic system. Here, we discuss work in wound healing, for accelerating wound closure. We argue that timing of treatments is as critical to consider as the choice of drug or therapy. Due to system size and complexity, data-driven methods need to be explored to develop multi-dimensional quantifiable indicators tracking systemic changes. In this work I discuss how bioelectronic devices enhanced with deep learning can help facilitate real-time sensing and actuation for automated decisions in treatment for wound healing and preliminary work in transcriptomic based classification of wound states. Bio: Marcella M. Gomez is an associate professor at UC Santa Cruz in the department of Applied Mathematics and Associate Dean for Diversity, Equity, and Inclusion for Baskin Engineering. She received her PhD from Caltech in 2015 and a B.S. from UC Berkeley in 2009; both degrees in Mechanical Engineering. Her research interests are in the broad field of bio-control leveraging methods in machine learning and control theory. Applications range from controlling single-cell response to driving complex systems such as wound healing. #modeling #simulation #medicine #healing #trauma #predictivemodeling #datascience #data #woundhealing #biology #bioengineering #systemanalysis #sensing #omics

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100% agree with this: If tech workers are doing measurable work, they're doing easy work. They're doing repetitious work. This work isn't the most valuable work, and you should minimize it when possible. If tech workers are adding huge value, such as inventing new things their company (or the world) hasn't seen, then their work is hard/impossible to measure. They can swag (guess), but not accurately predict their work.

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What started as a great Cassandra meetup just went GINORMUS. If you are in the Bay Area on October 11th, you must get to the Netflix campus for some of the best free content you'll get all year. Cutting-edge data and AI won't get any more legit. That sounds like a big claim, so what do I have to back it up? To start, we have speakers from some of the most hardcore Cassandra users lined up with "What's next" talks on how Cassandra is slowly taking over the data center in all sorts of ways. Apple, Netflix, Uber, and DataStax. Combined, we are probably running over a half million nodes that power your daily life. Cassandra 5 just dropped, and we'll talk about how it lets you do more with less and is ready for the thing you are building tomorrow. Operators who know, know. Scale is a constant battle between cost, schedule and quality. Check out these talks: How Netflix Handles C* Tombstones at Scale & How Netflix Does SLO-based Data Retrieval and Back Pressure - Vidhya Arvind, Netflix Incremental Repair Scheduling on C*, The Future of C* Repair - Chris Lohfink, Netflix, Jaydeepkumar Chovatia, Uber AI AI AI. Yep, that is THE topic here in the valley (and everywhere else). I'll tell you about a topic DataStax has been championing: Vector Search at scale! None of this one laptop business, and was just named by Forrester Research as the leader in Vector Search engines. Generative AI with Vector Search in Cassandra 5 - Patrick McFadin, DataStax I'm sure you've heard that there is no AI without data? How do you think data is processed at the scale and speed needed for today's largest models? Terabyte scale is entry level now. How about Petabyte? You'll want to see this talk: Securely Processing petabytes of data with Apache Cassandra 5.0 without passwords! - Dinesh Joshi, Apple Go to the link below and find out why Cassandra will be in your toolbox as we enter this next speed run on data infrastructure. https://lnkd.in/gVZb_npM

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I was reading one of the 2 newsletters I follow - The Grappler's Diary on Substack - and the author (Chris Wojcik) pointed out one of those "well, yeah, of course" kind of ideas that made me stop and think for a bit. Wojcik pointed out that it takes more and more effort to reach higher levels of skill in jiu jitsu. This is of course true of any skill. I've certainly seen that with programming. The easiest and biggest leaps are at the beginning. Going from zero to your first working program is pretty easy. Becoming a true professional developer takes years. Continuous improvement takes introspection, feedback, seeking out training and education, and lots and lots of work. And the noticeable improvements these days are small: cleaner code, improved readability, and better organization. Little things that matter. And the more time I spend as a "programmer" the less I write code and the more time I spend on related things: technical design, code review, stakeholder relations, project management, and more. Just as you get more skilled in martial arts, the more time you spend teaching, coaching, and advising, and the less time you spend getting beat up by kids.

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/*********Mentorship**************/ I was watching today Andy Jassy's video (https://lnkd.in/gRV2mGPr) on career advice. He shared one of the very important thing on Mentor's. Mentor's overall objective is too help you grow. They let you do things which you even think you can not do and then give support if needed. They give honest feedback on areas of growth.

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🚀 Big News for Mac Users! 🚀 🔍 LM Studio(https://lmstudio.ai/) now ships an MLX(https://lnkd.in/gUeed3TP) backend! If you have an M1, M2, or M3 silicon chip MacBook's, you can run any large language model (LLM) from the Hugging Face Hub on your Mac with blazing speed. 🏎️💨 Say goodbye to lag—optimize your AI workflows and bring the power of LLMs right to your desktop. Whether you're coding, building apps, or diving into data science, this update makes it easier than ever to tap into cutting-edge machine learning. 🎉 💡 Get ready to supercharge your Mac’s performance! #LLMs #HuggingFace #MacOS #AppleSilicon #mlx

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Take it from FAANG: crystal clear writing makes an enormous difference on the bottom-line

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So many things to unpack in the wake of another company squeezing their open source users towards licensing. Thinking of the current OSS climate and the lack of participation of companies generally as well as not being able to endorse using a solution fearing that a company will pull the rug out from under you once you're fully integrated with a solution. https://lnkd.in/gVpME4Uz

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Now we have two first class data catalogs integrated with MS Fabric now available in Azure. Of course DataBricks is the heavy heavy favorite in the enterprise lakehouses as well as AI (as of the conference that just finished 🤓)

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This is a fun blog post about first steps with Rust -- and what looks like a legitimately useful tool!

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In my latest blog post, I dive in on a topic everyone can relate to — teamwork. Read more on our website where I discuss the benefits of design and engineering teams working in tandem

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https://lnkd.in/gcJsEKrG Q. What happens when #neural #networks are applied to fields like #Physics? A. In short, a Nobel Prize. More broadly, neural networks, built on the laureates' foundational work, have wide-ranging applications across science, technology, and various industries. They excel at tasks like pattern recognition, classification, and data generation. #Physics #AI #HVSays

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It's been very cool watching this go from idea to reality. I'm glad to see this new tool making its way to customers! Check out the blog to see how you can use it to experiment with #RAG applications using #Elasticsearch

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