Joe Shockman

Mac Chaffee just wrote an interesting new piece called "The Next Platform" - https://lnkd.in/gEgnspFh - check it out. I think he did a good job of looking at some of the existing infra choices/patterns and more importantly listing some of the hard requirements: ✓ run on premise ✓ use custom hw (gpu, nvme) ✓ legacy app support but without the bloated/insecure garbage ✓ affordability at scale ✓ fully open source (apache2) w/ability to deploy yourself What do you think?

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This is a cool project, and another example of the emerging "replace stateful storage with S3" trend. I do wonder at what point someone will package this sort of thing up into a FUSE filesystem to make it generally usable across existing services. Or maybe every service has unique enough needs that it's better to handle it custom each time. https://lnkd.in/etcVBfVf

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ICYMI: Here's Richard Artoul from WarpStream on the Orb podcast talking about how WarpStream is both *simpler* and *cheaper* than Kafka for a ton of use cases, while still being a drop-in replacement for Kafka. With an object-storage based architecture, Warpstream is the sort of company that puts cloud economics on *offense* as opposed to an afterthought.

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"If you're relying on AI not becoming 100X better, you're about to get steamrolled." -- Jason Warner, ex-CTO of Github and CEO of poolside How we code and build software is being rapidly transformed with AI. Sitting down with Jason the future looks clearer. Over the next 5 years: 🌏 All 7 billion people can code 🏗️ Search bars will spit out software 🔋 AI will generate its way out of data scarcity 🌩️ Foundation models to become cloud companies I unpack the 5 year frontier of software development in Future of Code: AI-led Software Development https://lnkd.in/gze2QSbu #AI #Software #Code

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Ever been in a meeting where the CTO starts talking about "complex database work" and you just know—deep down—they're stalling because they really don’t want to do it? Yeah, we’ve all seen it. Instead of tackling a simple feature request, they spin it into some mystical tech problem, drop a line about "focusing on growth, not features," and suddenly your whole idea is on life support. Brilliant deflection. And if you’re using an outsourced tech platform? Welcome to the development queue of death—where good ideas go to die, or get resurrected with a price tag that makes you wonder if you’re paying for an actual resurrection. Even worse, that “innovative feature” you paid through the nose for ends up farmed out to your competitors. Feels good, right? You end up feeling stuck—constrained from growth, forced into clunky workarounds, battling limited resources, and hoping the CTO stops having bad days. But here’s the thing—DIY tools like OpenAI, Claude, Anthropic, and a growing list of others are rewriting the script. The old song and dance of "we can’t because it’s too complex" is getting real tired—if you know where to look. Still hesitating? Keep in mind, while you're sitting there debating, others have already grabbed the tools and moved on. Can you relate? How often have you seen a platform bottleneck your business? In Leadgen, it's practically a rite of passage. Let’s hear your stories—how did you handle it?

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Compose SDK minor version 1.15.0 released [1.15.0] - 2024-07-15 *Added* - Extend component MemberFilterTile to support excluded members *Changed* - Make improvements to dashboard rendering

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There's now no shortage of options for open source model hosting providers. i.e., take your pick for Llama 3 (Groq, Together AI, Telnyx, etc.). Am I wrong in thinking the only real difference now between them is speed, cost and readability? If so, this space just became commoditized quickly and reminds me of the telco provider wars where its a race to the bottom on price. Which means the cost of running AI applications is approaching something close to zero... 🤯 #ai #cx

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Got an email "GCP's Apache Kafka service is now live" at noon. After dinner, spent half an hour confirming it works well with Timeplus. I don't think you can make the bootstrap server publicly accessible. Has to setup a bastion host or run your app within the same project/VPC. Setting up the SASL username/pwd was not easy. Follow the guide carefully https://lnkd.in/gcKEVh3g and you may need to disable the iam.disableServiceAccountKeyCreation Anyway, if your workload are in GCP already, having such Kafka service in GCP is nice way to burn your cloud credits. Yet another Kafka vendor, great.

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I really like this PMF Framework from Sequoia. https://lnkd.in/gT2nWttx

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If you are working with Docker... I highly recommend looking at the Slim toolkit. https://lnkd.in/gX_Gb9Kf Why? Slim allows developers to inspect, optimize, and debug their containers using its xray, lint, build, debug, run, images, merge, registry, vulnerability (and other) commands. It simplifies and improves your developer experience building, customizing and using containers. It makes your containers better, smaller and more secure while providing advanced visibility and improved usability working with the original and minified containers. Don't change anything in your container image; minify it by up to 30x to make it secure, too! Optimizing images isn't the only thing it can do, though. It can also help you understand and author better container images. Keep doing what you are doing. No need to change anything. Use the base image you want. Use the package manager you want. Don't worry about hand-optimizing your Dockerfile. You shouldn't have to throw away your tools and your workflow to have small container images. Node.js application images: from ubuntu:14.04 - 432MB => 14MB (minified by 30.85X) from debian:jessie - 406MB => 25.1MB (minified by 16.21X) from node:alpine - 66.7MB => 34.7MB (minified by 1.92X) from node:distroless - 72.7MB => 39.7MB (minified by 1.83X) Go application images: from golang:latest - 700MB => 1.56MB (minified by 448.76X) from ubuntu:14.04 - 531MB => 1.87MB (minified by 284.10X) from golang:alpine - 258MB => 1.56MB (minified by 165.61X) from centos:7 - 615MB => 1.87MB (minified by 329.14X) Rust application images: from rust:1.31 - 2GB => 14MB (minified by 147.16X) Java application images: from ubuntu:14.04 - 743.6 MB => 100.3 MB

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Earlier this week, I mentioned that we shipped over 30 new endpoints for the Vanta API at the end of June. Another exciting set of improvements centered on controls: adding and removing custom controls, updating control metadata, assigning control owners, adding and removing test or document (evidence) mappings to controls, etc. These building blocks allow you to bring your program into Vanta – keep the work you've already done, gain a high-level view of your security and compliance program, and benefit from continuous control monitoring. Thanks to Bo Y. and Eric Yuan for building these new endpoints!

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Thrilled to enable 🔎 Gemini 'Grounding' support 👉 https://lnkd.in/gUKu3bv4 (+4 more updates 👇) 🛠️ Azure default api version fix 💪 'additional_drop_param' support for Embedding+Img Gen - https://lnkd.in/gZ8ZwccW 🗣️ Speech to text - 'srt' support - https://lnkd.in/gE69asjN 🎉 'IP Address' filtering - https://lnkd.in/gTu9q-iB

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Large language models are wonderful at ingesting large amounts of content & summarizing. Uploading an academic paper and I can pester it with an infinite list of questions & it will respond with equally infinite patience. In comparing the two most recent Microsoft earnings calls, Claude highlighted: - faster than expected Azure growth (29% vs 27%) - AI contributing 8% of Azure revenue up from 7% - higher CapEx spending & greater capacity constraints for data centers - much better commercial bookings growth : 17% vs 13% Excellent analysis. Benn Stancil described LLMs as great averagers of information. But what if I don’t want the average? If I seek the data point two standard deviations out? If I’m channeling my inner Anthony Bourdain & I’m seeking the fermented shark or cobra heart within my query? At Google, we ranked web pages & ads with many signals. An engineer taught me that of them found rarity of a word across a set of documents. For example, if there’s a document containing 10 instances of the word ghotiacross a collection of documents, where the average document has 0 ghotis, that document is likely the best to answer a search about fish. I haven’t found a way to goad an LLM to produce the rare result. Why did one user decide to use a particular piece of software when 10 others did not? Maybe I haven’t learned how to prompt an LLM well. Ideogram launched a feature called Magic Prompt that expands a basic prompt into richer instructions that elicit better results, marrying the language of a user & a computer. A user might write : A boy with a dog in a park. Magic Prompt replies : A heartwarming scene of a young boy playing with his happy, wagging dog in a lush, green park. The boy has a playful smile on his face while he tosses a bright yellow ball for his furry friend to fetch. The park is filled with beautiful flowers and trees, creating a serene atmosphere for this bonding moment between boy and dog. Magic Prompt surprised me in three ways. First, unlike Google search where terse queries tend to work very well, LLMs relish verbosity. Less Hemingway & more Faulkner. Second, maybe there’s a prompt for rarity. If you know what it is, please share with the rarity shibboleth. Third, I wonder what other prompt techniques are out there that have yet to be discovered to wrest the best from an LLM. This technique is called TF-IDF. It means Term Frequency Inverse Document Frequency.

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Thanks Joanne Chen and Jaya Gupta from Foundation Capital for the nod! Great to be in the cohort of top notch AI companies. Observability data is untapped goldmine of information, but when the time comes (in the middle of incident), it still requires "expert" skills to find the needle in the haystack. We at Deductive AI are democratizing the expertise to everyone in the organization.

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Despite thoughts and sentiments from yesteryear... Today we introduce optional O_DIRECT to the nanos unikernel to help modern databases such as TigerBeetle and others ensure durability versus merely relying on the page cache. In depth article coming soon.

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Keep Learning New Things... Indeed, the path to efficiency requires discipline, consistency, and sacrifice. I can't believe that it is already the 3rd week of the Headstarter Fellowship! To be honest, I felt quite lazy throughout this week, with low dopamine levels. However, I'm getting back on track and am ready for the next week of the fellowship. During the previous week, I had the opportunity to work on some exciting projects: - The Pantry App Project: I focused on the Firebase backend, learning how to integrate features such as authentication, analytics, and database CRUD operations. Live: https://lnkd.in/dCGFCiH5 Github: https://lnkd.in/dWpHgc4y Pathway to Tech Event 2: Here's My Key Takeaways: Dennis Crowley: Founder of Foursquare ($150M Revenue) When asked about the ideal tech stack, He emphasized the importance of being "willing to fill in gaps." It's not just about knowing specific technologies but being able to and ready to learn. His experience on failures "People quit on themselves or quit on their startup.", reminding me to stay resilient and never quit Tariq Afaq: First Engineer at AppLovin ($30Bn IPO) Tariq's talk was packed with valuable information. when he mentioned that this field is more than just coding, like being an engineer we can solve problems under pressure and get things back on track He also mentioned that you don't need to be super intelligent to succeed. Instead, focus on creating in-depth products, being quick and responsive, and working hard. Julian Alvarez: Ex-Meta, CEO of Wisdolia ($20K+ MRR) The most energetic speaker at this event! , His thoughts on how startup founders stick to their ideas rather than believing in their team's capabilities become the reason for their failure something exceptional and new for me to learn. His startup Wisdolia— is truly something. It generates flashcards for you from any YouTube video, blog, article, or PDF. I can already see it becoming a vital part of my learning routine! Hamza Ali - CEO of Olostep I was deeply inspired by Hamza Ali's vision for Olostep, which is enabling APIs to interact with the web as humans do. It's truly a brilliant idea that showcases his commitment to shaping the future of tech. 🔗 Here's the link to the recording: https://lnkd.in/dzX6Saaz

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Who are notable companies who run on-prem? I know some of the recent, popular companies, but I'm looking for companies (or divisions of companies) that never went to the cloud too. I'm going to be PRing references for Chick-fil-a (edge), Target, and GitHub to this repo to track some of them. Looking for more references. https://lnkd.in/d7QkaVp8

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