Keith Morrison

tldr: I'm hiring! As many of you know, I recently joined Lattice as their SVP of Engineering and I'm excited to announce that I'm hiring for all Engineering levels and more broadly, Lattice has open roles across EPD. If you're wondering, why Lattice? The last few years have seen a step change in how we think about the meaning of work and the employer <> employee relationship - add on the fuel of AI taking our jobs and you could be forgiven for thinking the future of work looks bleak for employees. It doesn't need to be that way - here at Lattice we're building the next generation of talent tools to give employers insights and employees purpose because people matter at work. Among other things, we're using generative AI to securely analyze enormous volumes of data to unlock career insights faster and investing in building equitable feedback, compensation and employer analytics tools - not just for one company but for all companies. If you're interested in making a difference to the future of work then let's chat - all roles are remote eligible and based in the US. We have physical offices in San Francisco and NYC if you prefer the in-office environment.

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Leaders, ask not what your reports can do for you but what you can do for your reports. Maximize your impact by fostering environments where people can do their best work. Great post from John Cutler about shifting a reactive perspective and getting clarity on how to respond when things seem too complex. #Leadership #ProductDevelopment #PeopleFirst #Empathy

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So true! In my opinion, every effort, whether it's new features or services, should include the creation of proper data and analytics to measure its effectiveness and adoption. The necessity of a feedback loop through telemetry and analytics cannot be overstated. It turns data into actionable insights, enabling iteration and refinement of the offerings. Without this, we risk repeating the same errors or just offering the wrong solutions. understanding the customers and having a constant feedback look is a must!

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My #1 management rule that I’ve seen even very senior people get wrong - people’s actions will mostly mirror how you treat them: If you treat devs like children with blinders on they will act like children. If you hide costs from devs they won’t care what things cost. If you don’t have devs on the hook for when things break they won’t care so much about when they break. If you treat office workers as untrustworthy they will act untrustworthy. If you force RTO to keep an eye on people they will waste more time in the office. The tighter you squeeze your fist the more falls through. Thats not to say there aren’t exceptions to this but it’s true in the general case.

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Outsourcing can erode a startup’s institutional knowledge. It’s often an overlooked or underestimated issue, yet I’ve seen firsthand (many times) how it can lead to the downfall (direct or indirect) of startups. This is just me but why start a company if you’re not bringing others along for the ride? It’s about creating something together, where everyone feels invested (contractors aren't) and the team/company grows stronger because of it. Thanks Chris Neumann for this insightful article.

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Does anyone else remember sitting in class thinking, "Am I ever going to actually use this?" 🤦‍♂️ I still ask myself this weekly when reading blog posts, listening to podcasts, catching up on massive Slack threads, or diving into documentation and feature listings. I don't always use the info, but it feels great when a memory from six months ago suddenly becomes relevant. I always think of that original Jurassic Park Unix scene. This often happens when discussing DevOps and Serverless, where boundaries blur already. Trying to keep all of the AWS documentation in my head while discussing the design of new features and optimizations feels like a superhuman task. What are some things you wish you had remembered when solving an issue or problem? For me, we designed a Lambda function to fail purposefully instead of attempting to recover for efficiency. Unfortunately, the Lambda control plane self-throttles a function once a failure threshold is reached... slowing our processing to a crawl and forcing some quick code changes. 😅 #AWS #lambda #serverless #learninginpublic #hugops

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Humanity's approach in pushing forward developing AGI continues to sorta puzzle me. As I see it, there are a few possible outcomes (in the short and long terms), depending on how successful the technology and application are: - We create really sophisticated but ultimately equally unhelpful chat bots, which permeate society to the increased frustration of literally everyone - We automate away manual labor jobs, increasing corporate profits at the expense of the lower class, and shift even more tax burden onto the middle class to compensate - We get productivity multiplying AGI, which is accessible mainly to the wealthy, and magnifies the wealth disparities in society - We get full AGI, which quickly evolves to displace humanity as the dominant life form, in whatever form that takes Now, I get how the people with wealth can think there are some "good" outcomes there, but I kinda still fail to see if/how normal people think this might be "good" for society (if they do at all). Academic points of course (since we as a society can't really stop it), but interesting in a "course of history" sense.

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You don’t need to build complex tests to build performance comparisons. This is by far the most practical tests I ve come across that explains Databricks seemingly cost effective and performant in practical use cases.

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Some takeaways for a Jeff Hinton interview with timeless insights. My POV as an app builder is that models are now being improved for better UX (faster response/multi-modal) and reasoning capability and cost continues to improve. The technology headroom remains strong, and the integration of LLM into all software experience will continue and expansion of the reach (invasion) of software + intelligence will also continue. - Next word prediction is not merely parroting of training data. To do next word prediction well, LLM needs and already exhibits strong reasoning behavior. This would continue to scale / improve with more data / training. - LLM is a compression of human knowledge. The compression mechanism is identifying similar concepts across domains and reduces duplications among them. This can also be a source of "creativity" as LLM "sees" correlations of seemingly unrelated ideas through this compression. LLM can be more creative as it sees similiarity / correlations across all domains beyond (human) convetional wisdom. - LLM can exceed the quality of its training data. Jeff gave an example of an experiment where a model trained on 50% intentionally incorrectly labeled data, can still perform at 90+% accuracy after training. - Multi-modal models can improve the overall world understanding and lead to better overall reasoning. It does not even need to generate multi-modal output. - Not ML specific, but an interesting life insights. Bad intuition comes from believing (or wanting to believe) everything you are told. It diminishes the ability to make a clear choice that would lead to differentiated success (or failure).

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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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I’m skeptical about measuring open-source package quality with metrics alone.    •   Low commit/update cadence: Doesn’t indicate low quality. It might signal a stable, well-functioning package that needs minimal maintenance.    •   High number of historical CVEs: Not a sign of low quality. It could show that the package has undergone thorough security scrutiny.    •   Many open issues: Not a reliable indicator. Many issues are more like support tickets, and there are often many duplicates. Quality assessment requires more than just metrics!

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"How do you eat a whale? One bite at a time!" If you are in charge of unlocking the AI/ML potential for your enterprise and are looking to learn from how others have done this, hope this article helps. Quang HOANG and Jean-Baptiste Coger have been on a mission to help Engineering Leaders exchange notes and learn from each other. This article is culled from one such effort (from a talk presented in Elevate 2023). https://lnkd.in/gPeeS4qy In this one, I share how we build the AI/ML Platform one step at a time at DoorDash.

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As a leader, managing burnout is one of the most important things you can do for your team to ensure they are able to show up and be successful in all facets of their lives. You have the power to make the workplace more human and a place where people thrive. Come join me in a discussion on managing and mitigating burnout and learn more May 9 at 10 AM PT. RSVP here https://lu.ma/mqlv984g

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“For Graeber, bullshit jobs carry with them a moral imperative: ‘If you’re not busy all the time doing something, anything—doesn’t really matter what it is—you’re a bad person.’ But the flipside of that logic seems to be: if you actually like doing X activity, if it is valuable, meaningful, and carries intrinsic rewards for you, it is wrong for you to expect to be paid (well) for it; you should give it freely, even (especially) if by doing so you are allowing others to profit. In other words, we’ll make a living from you doing what you love (for free), but we’ll keep you in check by making sure you have to make a living doing what you hate.” — Geoff Shullenberger, a writing professor at New York University I couldn't help thinking that here, the professor is describing, how “open source software” thing works. And then, I found: “The free software industry, perversely enough, has become a paradigm in this respect... software engineering work was divided between the interesting and challenging work of developing core technologies, and the tedious labor of ‘applying duct tape’ to allow different core technologies to work together, because the designers had never bothered to think about their compatibility. ... increasingly, open source means that all the really engaging tasks are done for free.” — David Graeber, Bullshit Jobs

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I'm fascinated at how much I don't know. Let me tell you a story about how #TIL my design approach preferences and the way I've learned to work are related to a book called Cheaper by the Dozen, which was later made into movies. We (me and everyone I work with Kaizen I/O) use a human-centric perspective in our design discussions. If you're in the office, you'll hear questions like: - Who can cancel a policy? - Are they the authority for cancelling or is that someone else? - What team is that person on? - And then what do they do next? - Do they need to tell anyone that it's been cancelled? - and so on. With our expertise in architecture and technology, this line of exploration enables us to create software imbued with empathy for humans ;) (this statement is still evolving, so bear (bare?) with me). Anyways, I've been thinking about how to succinctly articulate this approach, which led me to read more about a discipline called Human Factors Engineering. That's a whole other rabbit hole, but let's stay focused on why I'm writing this post. Lillian Moller Gilbreth, an engineer, psychologist and mother of twelve who invented the Pedal Bin, was an early pioneer in applying psychology to time-and-motion studies. "A genius in the art of living". She's noted as pioneering ergonomics on the Engineering Psychology wikipedia article. Her two children wrote Cheaper by the Dozen which tells the story of their family life and describes how time-and-motion studies were applied to the organization and daily actives of their large family. BAM! There is so much to learn. https://lnkd.in/gxgRxGWu

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The subject of support for incremental reads of mutable tables and generating CDC streams in the four table formats I'm covering is far more nuanced than I expected. What was going to be one post is going to have to be a 5 parter to keep it manageable: 1. Introduction to the concepts 2-5. Apache Iceberg, Delta Lake, Apache Hudi, Apache Paimon

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We talk a lot about AI hallucinating but not enough about the other side of that coin: people hallucinating AI. The rise of AI enabled interactions has prompted a lot of folks to chalk up every inauthentic-feeling communication they receive as "AI" when quite a bit of it is good old fassioned humans following the same tired corporate boilerplate they've been using for years (and which that AI was likely trained on). Authenticity is becoming performative out of necessity. If your communications are too polished, too professional (see how I'm using that comma improperly to create a more human sounding sentence?) you'll come off as a robot and alienate your audience in the process.

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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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