Phillip Pearson

Ok, so the Rabbit R1 is a little device running an Android app. Which begs the question: Why not just ship it as an app for phones? From a marketing perspective, it's hard to imagine commensurate hype coming from an app alone. From a customer perspective, it's hard to imagine the experience offered by the dedicated device is worth the burden of lugging it around. At least the Humane pin is always out and not an extra thing to carry around. That being said, I love Teenage Engineering designs. Can I get some likes for a Teenage Engineering Phone?!?! https://lnkd.in/gvMqHj7N #rabbitr1 #teenageengineering #humane

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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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🌶🌶🌶 Hot take… Old problem: if we put more devs on the project, we can deliver it sooner. Same thinking, new manifestation: we can hire more overseas devs for less total money to produce the same result as our US staff. Flaw in that thinking: product development is not an assembly line. Devs are not “interchangeable”. Software is as much an art as it is science. It’s a team sport. The people on your team matters. #tech #staffing #management #hiring #jobs #layoffs #software #startup #teamleadership

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This tweet took off over the weekend (link in the comments). I responded on Twitter but wanted to say a bit more here. I think Ian is right, this is a trend I've been seeing and am part of. The focus is on efficiency and junior folks are inevitably a short-term drag on efficiency. My take is that this is a self-correcting phenomenon. This is a hiring strategy we can pursue right now because the market allows it. As I'm sure you all remember, this was an impossible strategy 5 years ago; you had to hire and train junior folks if you hoped to have any engineers working for you at all! If we go on long enough without training junior folks, eventually we won't be able to find the senior people we need, and the pendulum will swing back. Has happened before in the industry and expect it to happen again. One thing is different this time that gives me pause, which is that AI might end up replacing the work junior programmers used to do, in which case hiring might remain focused on senior folks for longer than expected, and maybe this pattern results in the graceful loss of programming jobs as they get automated away, starting from the bottom up. While from an overall industry perspective this is reassuring, it's cold comfort if you find yourself a recent grad looking for programming work. Here's what I can offer: - First, ask yourself if you really want to be a programmer or did you train in it because you're smart and it's a good way to make money? If the latter, consider this an opportunity to pivot into something you actually have a passion for. - If you have a passion for programming, find a job that will keep you financially afloat, ideally something that gives you plenty of time to write code at home. If you love programming enough, barring AI eliminating the job, you'll weather the storm and develop skills that will serve you well when the market changes. For context, I dealt with similar market conditions when I was coming up. After the dot-com crash the market for programmers was fairly weak, and it took a decade for the boom times to return. I'm sure this contributed to my decision to stay in grad school for a long time, and when I did finally transition to industry, I had a lot of experience from my academic career that allowed me to grow in seniority quickly. I wish the boom times were still here as much as anyone, but we're likely in for several more lean years, especially for folks with less experience, and rather than fight the trend I think it's better to be realistic and find a way to make the best of things. I don't know that there's much I can usefully do to help, but I'm pulling for all the recent grads out there to find their way, and keeping an eye out for ways I can help the folks coming up behind me.

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I don't usually talk politics on here, but this is an exception I am prepared to make as the proposed Fast Track, in its current form, is that outrageous and has to be stopped. I am not opposed to fast tracking certain projects that this country needs, hell no, do it, by all means, BUT the way this government is proposing to go about things, giving just 3 ministers ultimate control and ultimate power of what gets fast tracked allowing them to bypass literally every single protection this country has in place to protect our country and its environment, is simply overreach, dangerous, undemocratic, and quite frankly blatantly open for corruption as has already been shown by the number of recent conflict of interest stories around donations and mining companies linked directly to ministers that are one of the 3 ministers that will have ultimate control. There are companies already on the provisional fast track list that have had various projects denied before for by concerned groups, ministries, ministers, select comittees, and even the courts, yet these projects are now back on the table and entirely within the control of 3 ministers that hate transparency, oversight and due process. So, if you share my concerns with how much control this bill gives with nearly zero oversight and transparency, then come along to this March for Nature and help STOP THE FAST-TRACK bill. This bill is dangerous and needs to be stopped. This government needs to hear this in the strongest way possible.

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So, I'm actively looking for a new job (two weeks left before my layoff), and I just found an interesting method for generating cover letters. I've been using ChatGPT for a little while, by simply prompting it with: "Please generate a cover letter for me for the following job posting:" and then pasting the post. That worked pretty well, but I still had to do a decent amount of rewriting. I've tweaked this method a little bit, and am now getting much better results. I started a new chat, and began it with: > For the remaining conversation, I'm going to ask for cover letters for various job postings. Here, I'll give you my resume, and would like you to base the cover letters off the experience listed in this resume. Here is my resume: I pasted a plain text version of my resume after that. It responded with: > Based on your extensive experience, here's a tailored cover letter And then gave me a generic cover letter. But, after that, in the same conversation, I do something like: > Please create a cover letter for me for the following job posting, based on my experience: And I paste in the job posting after that. Now, the cover letters I get include mentions of my previous work. It's not perfect, by any means. I still have to check it for inaccuracies, and weird word usage (it really likes the word "enthusiasms"), but now I can get job-specific cover letters, with my experience included, with very little effort. #JobSearch #OpenToWork #AI

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I've been testing GPT-4o since its recent release. While I haven't really had a use for the multi-modal (images, audio, and text) capabilities yet, the improvement in speed of response is noticeable. Not just noticeable from a spec sheet perspective either—it's noticeable in how the product works in real use cases. I've been thinking about why that is, and here's my theory: there's a breakpoint somewhere based on latency and response speed. Above this breakpoint, interactions feel sluggish and disruptive. Below it, they become seamless and effortless, akin to a natural conversation. Previously, I posted about how Llama and Gemini models seemed noticeably more usable than GPT-4, with my preference being Llama whenever possible. This new experience with GPT-4o further emphasizes the importance of optimizing both latency and throughput in AI systems. This chart from ArtificialAnalysis.ai shows that GPT-4o has a total response time of 1.5 seconds for 100 tokens, compared to GPT-4's 5.7 seconds. This speed difference isn't just a matter of numbers—it's fundamental to achieving a smooth and efficient user experience. When latency drops below a certain threshold, the interaction becomes fluid and engaging, making the AI more practical and user-friendly. It also makes its mistakes more tolerable. This improvement transforms how we use these models in real-time applications, making AI interactions faster and more intuitive. It underscores that achieving low latency and high throughput is no longer a nice-to-have but a critical requirement for practical AI deployment.

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Anthropic released another paper on their ongoing work towards interpretability of large foundational models like their Claude 3 family, built on previous work by many. Their work continues to be enlightening and inspiring. The findings in this work are generally similar to previous findings on smaller models but scaled up to Claude 3 Sonnet: 💡 they find large numbers of "features" corresponding to combinations of neuron activations and these features map to concepts that we recognize/understand: for example, features for transit infrastructure, particular people, concepts (like bias and "objects lacking sentience"). This might sound familiar from classical machine learning, but the interesting thing is that there is no single thing/neuron encoding each of these concepts, it's a particular combination of neuron activations that embeds these concepts in "superposition" in the network. 💡they observe a sort of "distance" of these feature activations that arrange themselves "near" semantically similar features. When they use their sparse autoencoder approach to learn more and more features they observe that the level of abstraction in the detected features becomes more granular, in ways that are interpretable and meaningful. 💡when they artificially fire these activation patterns during inference, it affects the output in ways that relate to the "feature". For example, asking Claude "what's the most interesting science" normally it responds Physics, but if you clamp the feature activations for the feature "Brain Science" to 10x their max values then Claude responds: Neuroscience. Ways to interpret _and_ influence the model. Great readable work [1] along with an interactive explorer of some of the "features" themselves [2] 🎂 There's an easter egg too: if you scroll in the feature explorer tool and try and select one of the "Randomly selected features from 1M" you get a message "if you want to see the rest of the features, we're hiring!" with a link to their recruiting page :) [1] https://lnkd.in/gZbparwp [2] https://lnkd.in/gC9eus53

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I chew the cud on the magic and mechanics of idea diffusion. Beware, I fanboy hard on this book. https://lnkd.in/gaCQKztq

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In this week's issue of "Large Scale Web Apps", we share a soundbite from Jem Young (Engineering Manager at Netflix) on how him and his team at Netflix have cultivated a successful testing environment. A few months ago, we asked friends and folks in the industry to share their perspectives on building large-scale web apps with React. Jem Young (Engineering Manager - Web Platform & UI Data Platform at Netflix) was kind enough to share some valuable insights into how testing is integrated into his team’s development culture. Some key takeaways that we love from Jem’s soundbite: 👩‍💻 Testing is not just about the code; it’s about the culture as well. Establishing a culture where testing is viewed as an essential, integrated practice within engineering not only prepares teams to preemptively catch errors but also reinforces a commitment to quality and reliability. This cultural shift ensures that testing becomes a habit rather than an afterthought. 🧪 You need to have a cohesive testing toolset (and strategy). In the modern JavaScript ecosystem, tools change often, which sometimes makes it challenging to keep up with the latest and most effective methods. Despite this, the benefits of standardizing and sticking with a cohesive testing toolset are substantial. A unified set of tools across a development team reduces fragmentation and streamlines both the learning process and the application maintenance. Read the new issue here — https://lnkd.in/gm6VW-pt #programming #javascript #reactjs #testing #developers

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Something I've been musing about recently (and semi work related): I think we're probably in the tail-end period of usability and value of the public internet. With the proliferation of content generated by LLM's which are trained on the internet data, but also trained to generate content which "sounds believable" our of the aggregate data (without any actual knowledge or ability to check validity), the reliability and usefulness of the public internet data will decline over time. As the bot-garbage propagates, it will likely overwhelm "real" information, even without people leveraging LLM's for disinformation (as is already happening, with successful effect, because people are relatively easy to influence). I wonder if, at some point, we will be telling stories to our grand children about the period of time when the information which was free on the internet was also reasonably good and reliable, and not a wasteland of bot-garbage and propaganda spewed into the world, to be consumed by perpetually ill-informed people, and/or those conditioned to be easily entertained by zero-value content (not unlike what most of cable television has become now).

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Isn’t it funny that 60% of engineering managers see a shortage of qualified candidates while 56% plan to increase headcount? The math is not mathin’. 🧮 Maybe it’s time to rethink what makes a “good engineer”. Skills are evolving with AI and machine learning, and our criteria needs to evolve too. The “passion engineer” coding all night is rare now, and that’s okay — maybe even better — because we can introduce more diverse skill sets, experiences, and problem-solving. Let’s evaluate our talent definitions, invest in training, and embrace diverse paths of excellence. ✨ For more insights like these, check out Jellyfish's 2024 State of Engineering Management report: https://lnkd.in/eKAJ5jZy

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Technology is a never-ending journey of skill acquisition, akin to surfing. In surfing, you prepare for the next wave and make adjustments while riding it to stay balanced. Similarly, every 10-20 years, there's a significant shift in technology, and you must continually upskill to catch these waves and stay ahead. Keeping up with skill trends is essential for maintaining employment, as the learning process never truly ends. Specializations can be costly and often become obsolete very fast. Over time, new tools simplify and streamline previously complex and error-prone workflows. My strategy involves cultivating a broad skill set and maintaining a passion for learning that surpasses anyone I know. I dedicate every waking hour after work to learning something new.

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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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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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🚨 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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For years, I've led and mentored diverse teams of software engineers, and one gripe consistently surfaces, irrespective of team makeup or project nuances: "Why do I have to resolve conflicts when cherry-picking commits from the master branch?" 🤔 This complaint hits home. Engineers crave independence to innovate and deliver swiftly, yet software development thrives on collaboration, demanding robust code management and teamwork. 🚀💻 Cherry-picking commits can be an efficient way to incorporate specific changes or bug fixes into a hotfix branch or release candidate, but it often triggers conflicts as changes collide with ongoing developments. This conflict resolution process can be time-consuming, error-prone, and frustrating for engineers, detracting from their productivity and morale. 😫⚔️ The absence of a structured cherry-picking process exacerbates the problem. Here's a simple fix to avoid conflicts: 1️⃣ Track the last merged PR number into the base branch when cutting release/hotfix branches. 2️⃣ When receiving a cherry-pick PR, confirm its base branch PR number. 3️⃣ Ensure all PRs between the last merged and new ones are cherry-picked before merging, preventing conflicts. 4️⃣ Once merged, update the base PR number of the last merged cherry-pick for the next cherry-pick PR review can be verified. Yes, adopting this process entails a bit of change, but the dividends in smoother workflows and happier engineers are well worth it. Let's make life easier for our fellow coders! 💪🛠️ #SoftwareEngineering #CodeReview #Collaboration #ConflictResolution #Teamwork #DeveloperLife #CherryPicking #CodeManagement #ProductivityTips #EngineeringCulture #TechTips #Mentorship #WorkflowOptimization #CodingCommunity

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