Chris F.

The newest issue of The Pragmatic Engineer found a surprise uptick in eng hiring in the typically slow summer season - I'm curious to see how the macro environment impacts this uptick as we head into the fall, but I remain hopeful. I asked Gemini to give me the tldr of the article to share with my team (sorry, Gergely Orosz) and found it missed one of the most interesting pieces of the article. Gemini's highlights - Unexpected surge: Tech recruitment has seen an unusual spike in June and July, contrary to typical trends. Increased demand: Startups, especially AI-focused ones, are driving the hiring uptick. Global phenomenon: This trend isn't just limited to the US; Europe is also experiencing increased recruitment activity. Reasons for hiring: Companies are backfilling positions, investing in growth due to improved financials, and responding to increased VC funding. Challenges remain: While the job market is improving, finding qualified candidates and offering competitive compensation remains difficult for many companies. What I found most interesting - The nature of the backfills: the article finds that a lot of new headcount are actually backfills of folks that were "over-slashed" during rounds of layoffs. Not entirely surprising with the "trim the fat" and "rebuild lean" mentality, but interesting nonetheless. Either way, the uptick is encouraging (and don't always rely on Gemini's tldrs). #hiring #startups #VCbacked #softwareengineering

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Quarto v1.6 was just released! 🎉 As someone who works with Quarto a lot, two features in this release are game-changing: 📝 _brand.yml Support Define your organization's branding once, deploy it everywhere - from HTML docs to presentations. Your brand stays consistent across HTML, Typst PDF, RevealJS slides, and dashboards without juggling multiple theme files! 🎯 Contents Shortcode Write your code once and display its output anywhere in your document. Finally, you can structure your narrative exactly how you want without fighting with code chunk placement. The release includes other nice improvements (landscape mode, better RevealJS navigation), but these two features are transformative for my workflows. Interested in learning more? Check out: 📚 Full release notes: https://lnkd.in/eM_6XWDq 🎨 An unofficial implementation of Stanford's identity guidelines: • Live demo: https://lnkd.in/g-x5y6CV • Source code: https://lnkd.in/ghCDWYqg 📖 Get started with brand.yml: https://lnkd.in/eTeT-qnR #quarto #rstats #python

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Listening to Julia Schottenstein who was at dbt Labs before joining LangChain as an early employee to “solve all of the problems that aren’t specifically in product and engineering” talk about AI application design is 45 minutes well spent. Given - “I think we have a million developers now who use LangChain, and our partner ecosystem is really important for making LangChain as special as it is.” - it’s especially interesting to consider these design insights. Top 3 takeaways from Julia’s interview on “The current state of the AI ecosystem” episode of The Analytics Engineering Podcast by Dan Poppy (dbtLabs). 1: Foundation is set, now it's about unleashing next level creativity. Question: “How much do you think the creativity being unlocked in the LangChain ecosystem is still modest relative to what will happen with the next major innovations in the underlying model architecture? Are we seeing the thing or are we just seeing a tiny little precursor to the thing?” Answer: “It has to be the latter. I've been in this space now for a year, and the types of applications people are building today versus last year are very different.” Question: “Because the models have gotten better or because people have gotten better at using it?” Answer: “Skillset mostly. People have figured it out. They've gotten more of the basics down. They've figured out how to push the technology further. Yes, the models are getting better as well and that helps. But as people learn best practices for prompting, for fine tuning, for testing, we can just go a lot further. 2: Accelerating the feedback loop to iterate, faster / better “And the other cool thing that we're, we're starting to experiment with is feedback from production. People liked your responses; can we use that to influence and improve your prompting strategy or improve the way that you actually can design your application.” 3: Jobs to be done, application logic + design “As you start to dissect and think about the different types of user experiences that are now possible, how do we design for them? It's totally new worlds that I took for granted. It is these small new ways to interact with your application and software.” “Your application logic has to reflect how people are going to use your app. And so if they're coming in with certain types of problems that they need to get solved, you're going to design your app in a different way.”

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It’s eye-opening to see how AI model businesses are dealing with tight margins despite big investments. 🤯 Aidan Gomez’s comments about the “zero margin business” highlight the fierce competition and price cuts in the industry. 💸 With giants like OpenAI and Anthropic lowering prices to stay ahead, it’s a tough time for startups. The future of AI might depend on finding new ways to make these technologies profitable. 🚀 #AI #MachineLearning #TechTrends #Startups https://lnkd.in/ejkDu_wT

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🚨🚨 🚨 Unlocking Innovation: The Power of the Adjacent Possible Innovation programs can stall when the team tries to ideate way beyond their collective perspective and frame of reference. Like a software engineer brainstorming genetic modification of mycelia, or a senior executive ideating process improvements which they haven't been participated in for decades. It's challenging, exhausting, and ultimately self-defeating. Enter the concept of the "Adjacent Possible" - a game-changer for innovation. What is it? The Adjacent Possible, coined by Stuart Kauffman, represents the realm of new ideas within reach based on our current knowledge, resources, and capabilities. Why it matters: ➡️ Realistic Innovation: it focuses on achievable advancements, not far-fetched concepts. ➡️ Incremental Progress: each innovation opens doors to new possibilities. How to leverage it: • Constantly explore the boundaries of your adjacent possible. What's in your line of sight is what you know best. • Combine existing ideas in novel ways. Even small perturbations from status quo can generate large innovation leaps. • Stay curious about developments in adjacent industries. Cross-domain patterns create fresh sources of ideas, while also expanding the adjacent possible. Most of all, it takes the 'genius' pressure off the team. No one knows everything, but each one of us knows a lot about something, and we can all drive plenty of innovation potential by focusing on the spaces we know best. Are you exploring your Adjacent Possible? Share your thoughts below! 👇 #Innovation #innovationsystems #AdjacentPossible

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We just published the much-anticipated part 2 of our experiments, which revolves around our analysis of improving Agentic function calling accuracy across GPT-4-Turbo, Claude, Opus, and Haiku. Surprisingly, Claude is worse than Opus and Haiku, while GPT-4 does an amazing job and maintains a lead over all of them. We write in detail about improving function calling accuracy from 35% to 75%. If you are building and working on similar stuff, do let me know about your experience. https://lnkd.in/gb_mWdkX

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💬 Stuck on what to type next? Meet Inputron’s AI-powered fields - The new co-worker that gets your thoughts before you do! 1️⃣ Predict – Think faster, type smarter. 2️⃣ Enhance – Make your ideas pop effortlessly. 3️⃣ Suggest – AI suggestions to keep you on point. Ready to simplify your writing? Visit Inputron.com and see it in action! #AIPowered #GenerativeAI #WorkSmarter

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Created an AI assistant app to connect patients and doctors using Vercel AI SDK, Assistant-UI.com components, function calling, thread suggestions, check it out (Guillermo Rauch, Simon Farshid, Kajari Ghosh)

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~40% of Frec’s customers are engineers working in tech, many of whom expect frequent and large capital gains. They also prefer to self-manage their money, which makes Frec an ideal platform. Meet Evan: an engineering leader who upgraded from investing in ETFs to direct indexing. Direct indexing appealed to Evan because it offers comparable returns to his ETFs while saving him money on advisory and ETF fees. “ETFs are true and tested, and giving folks a different way of investing in the market while saving more money is really appealing.” He’s used many other robo-advisors and other fintech tools, but “Frec seems to maximize tax loss harvesting opportunities better than any other product that I’ve used.” He also likes Frec's transparent fee structure.  “I feel like with a lot of other companies, that’s muddled in with the cost of investments,” he told us, “and you don’t really know how much you’re paying or how much you’re not saving as a result. I think Frec does a really good job of laying all of that out and giving you a true understanding of the cost of these investment products.” Read more about his Frec journey here ⬇

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Hey Network, happy Monday. I was ruminating today on the ever-increasing complexity of the GenAI tooling decision matrix that many of our clients face when I came across this timely collection of factoids: https://lnkd.in/gDmczhYJ. Something that we contend with in my work, as the major models have become commoditized, is the decision becoming less about benchmark performance and more about the cost/value equation or the nuance of usage patterns favoring different "category leader" capabilities. Or sometimes just platform vs. best-of-breed. With the commercial models all qualifying as "usually quite good," and the open source models closing the gap (something I'm super nerded out about,) I'm curious how you make decisions for yourselves or your clients about the right tool for the job (something, something hammer and nails...)

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Have been brewing up abstractions on Langgraph(from LangChain)! What if there existed a nifty SDK, that allowed you to invoke any agentic design pattern? Why is it that I need to understand "Reflection", why is it that I need to understand "Hierarchical agents"? Possible to improve dev experience, improve the robustness of these agents, and not compromise on configurability? Spun up a quick Loom to braindump this, anyone thinking along these lines?

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Data scientists are not trained to understand debits versus credits, CAPEX versus OPEX, accruals, revenue recognition rules, etc. FP&A professionals are not (initially) trained as data scientists. If they are, as Kurt Shintaffer states, they are torching money and time building it themselves. And what happens when the few individuals at your company with this skill set move on? ‼️🤷‍♀️ Check out what Sandeep Madduri and Naresh Nemali built for finance professionals. Precanto is what this generation of finance professionals need. Monetize the data … Transformation starts now! 🚀 (Disclaimer: This bold post is coming to you from recovery room while on pain meds. All is good and colon repaired. Please eat your fiber, folks, and listen to your body! But in all seriousness, Precanto is the next wave of innovation for CFOs and FP&A teams.)

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WIESNER ON HUMANS VS AI: MIT Media Lab co-founder and science advisor to JFK, Jerome B. Wiesner was a unique figure in the history of MIT at the crossroads of science, engineering, and the arts with a lifelong pursuit of world peace. There's a terrific talk he gave at the founding of the MIT "E15" building designed by I.M. Pei — often nicknamed "the bathroom building" — that's worth a watch, especially during these extraordinary times https://lnkd.in/g2zEJRrk and a piece of the talk he gave is below. Information on the talk I'm giving with Jerome B. Wiesner Professor Hiroshi Ishii is here https://lnkd.in/gJpKkeY8 on November 8th (Fri) from 11 am EST, and you can register online for free here https://lnkd.in/gADUyCs7 --- Jerome B. Wiesner at the founding moment of the MIT Media Lab's E15 building https://lnkd.in/g2zEJRrk > ... Now some people will be offended by what I'm going to say, but I think man-made systems use better components. Electrical signals travel a million times faster than signals on nerve fibers. And semiconductor devices also act billions of times faster than neurons, which are the biological information processing elements. Also, manmade elements appear to be more reliable. But nature has a very large edge, maybe a decisive edge. In design complexity involving a number of individual elements, it can use it in a system effectively. And these are advantages man may never match. Nature may always retain this edge, who knows? > But for the moment, it really doesn't matter. The manmade systems that we are involved with enhance human creative capacities, that's what they can do, but so far they cannot displace them. Cognitive and computer scientists are having an exciting time trying to understand and imitate human reasoning with computing systems. And though they can do a few things that would be regarded as thinking if done by a human, they differ violently among themselves on whether or not machines with human thinking and creative abilities are possible. Even the so-called artificial intelligence field, which prompted Walter Rosenblith to say he preferred natural stupidity, his greatest achievement in this field is capturing knowledge and skills of humans -- of human experts. > For now, it’s clear that thinking machines and true creativity don’t exist. We should focus on how tech can expand human creative capacity.

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Ralph Otto, our Director of Product, is helping us build best practices with AI. We see it as a force multiplier for the humans at COLAB, and we're testing ways our clients can benefit from it as well. Check out our latest article to learn how we leverage AI to do our work faster and better than ever. #AI #AITools #Marketing #DigitalMarketing #WebDevelopment

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This is a good article on using LLMs in the real world. It covers many aspects that we learned at Nurish over the last few months and more. Using LLMs is an inexact science, but has its own needs in processes and tools. While most of the attention has been on the LLMS, their capabilities, and RAG, there are mundane issues that need to be taken care of, issues where the solutions for pre-LLM development are not that well-suited. These include version management, integration, testing, data management, quality assessment, and more. This article covers many of those practical aspects. Eugene Yan, Bryan Bischof, Charles Frye & more https://applied-llms.org/

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AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

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AI, like us, must balance speed and quality. Modern LLMs show that taking time—sometimes 10+ minutes—to plan, iterate, and refine solutions can lead to better outcomes. Chatbot-like UX may not be the best fit for those slow cases.

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Llama3 is out. It's the new open weights model from Meta, and you can try it out for free at https://www.meta.ai/. Ollama had it ready to go within hours (https://lnkd.in/eJks-Vg2). If you grabbed a copy from Ollama within the first ~5 hours, pull again; they fixed an incorrect end token issue. In my last post, I suggested that stiff competition between models would lead to competing on price. Meta.ai is now free, even without a Facebook login. Initial claims and benchmarks put Llama3 on par with private models in many areas – a significant development. The proprietary vs. open model gap is closing, thanks to Meta's commitment to top-tier open weight models. I can't imagine where the open LLM community would be without Meta's financial commitment. I tried Llama-3-8B and a low-quant Llama-3-40B briefly last night. While I haven't formed a solid opinion yet, the benchmarks claim Llama-3-8B outperforms Llama-2-40B. If that holds true, it's a significant win, as it's not just about throwing more compute at the problem – making models that can run on consumer hardware better is a big deal. While a 40B model is currently out of reach for most consumers, it's easy to imagine that it will become increasingly accessible as new video cards with higher memory become the norm. I managed to run Llama-3-40B on a 2quant on a 3090 without running out of RAM. I don't have high hopes for great results, but after some tuning, models based on Llama-3-40B could make for a very reasonable private assistant on modest hardware. If you've tried Llama-3 let me know your experience so far. Also if you have any tips for the best options for a 3090 let me know.

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