Chris Latimer

Check out Michael Levan and Matthew Voget chatting about how #API development is changing. They highlight a killer application of #AI (and also how you can easily start building with OpenAI's API). Jump to minutes 10 - 13 for a great snippet on the typical complexities of API dev and testing that is automated away with Blackbird from Ambassador

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New Time Series Benchmark compares CrateDB with MongoDB and Influx. CrateDB stands out with its very fast ingest performance and instant aggregation of any group of columns. No need for complex indexing strategies. In today’s digital landscape, efficient handling of time-series data is key for informed decisions in IoT, cybersecurity, and data-driven industries. A reliable database can make all the difference. #DataManagement #TimeSeries #BigData #PerformanceBenchmark #DataAnalytics #TechNews

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💻 Day 16: Deeper Dive into Streams & File Operations 🔄 Today, I delved deeper into C++ streams and learned how to copy data between files efficiently! 📂✨ Key insights: File Streams (ifstream/ofstream): Essential for reading from and writing to files. Copying Data: Used streams to open, read, and transfer data from one file to another, exploring how std::ios flags like ios::in, ios::out, and ios::trunc work together. Error Handling: Managed file errors, ensuring robust file operations by checking stream states. Learning how to manipulate file streams opens up so many possibilities for data management in programs! 📊 Aman Raj Masai IIT-G x Masai #dailylearning #C++ #FileHandling #CodingJourney #100DaysOfCode #LearningEveryDay

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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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In building robust, scalable distributed systems, it’s tempting to lean on events as a primary abstraction. But are they really the best approach? In this blog post I explore why events often fall short for complex systems and how a reimagined abstraction can simplify development and reduce failures: 👉 Covered topics * The pitfalls of an event-driven approach. * How durable execution offers a better path for distributed architectures. * Why this rethink could simplify and improve your distributed system. If you’re working with distributed systems, this article might just offer the perspective shift you need. #DistributedSystems #SoftwareArchitecture #EDA #Temporal #Rethink

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Enterprise software vendors shift genAI narrative: 'GenAI is just software' https://buff.ly/4fuvuJF Enterprise software vendors haven't been thrilled about the narrative that they aren't directly monetizing generative AI so the narrative is subtly being flipped.

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I had a great conversation with Jeff Bollinger on the Detection at Scale podcast, and after we stopped recording he mentioned the concept of “the five layers of IR.” 1. The Playbook Management Layer 2. The Data Layer 3. The Presentation Layer 4. The Case Tracking Layer 5. The Remediation Layer This week, we published the first blog in the series that dives into the first three phases. Check it out below!

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While attending a conference recently, I had a conversation with a leading technology CTO. His concern was simple but powerful: ‘Our current system is rigid. It slows us down.’ He went on to explain how their data infrastructure, though powerful, couldn’t keep up with the company’s evolving needs. This is a problem many businesses face today. Systems that were once state-of-the-art are now inflexible, unable to adapt to new demands. That’s why composability is so important. It’s about breaking down monolithic structures and allowing businesses to innovate at their own pace. At Syntasa, we’ve built a composable CDP that enables companies to add or remove data capabilities as needed — without disruption. Imagine running your marketing campaigns with the freedom to experiment, pivot, and grow without being held back by your tech stack. Composability is the new agility, and it’s time every company embraces it. https://lnkd.in/eWBJAzGb

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Check out this article from my favorite CEO in @TheFastMode "When platforms are designed with the user in mind, workflows become more streamlined, reducing the time employees spend navigating through complicated menus or troubleshooting issues." https://lnkd.in/gkzpgKxD #UX #nocode #efficiency

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💡 Process intelligence is experience a significant resurgance as companies go beyond the hype to truly understand the value this powerful technology can deliver. From understanding how to optimize your business processes and workflows, to monitoring for compliance, and even predicting potential issues before they occur, the use cases for process mining and intelligence are wide spread across an organization. 🎇 The next evolution will see process intelligence expand further into full operational intelligence... Learn more at www.mindzie.com #businessprocessmanagement #processmining #operationalexcellence

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Check out our blog series on tools (function calling)! We have posted part 1 of Friendli Tools, our state-of-the-art function calling with LLMs! #tools #functioncalling #llm #friendli #friendliai

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GraphQL makes your APIs better. The days of #GraphQL vs #REST are over, now it is all about how both technologies can work together to deliver value to your end users and customers as part of cohesive #API strategy. They can exist side by side and enable each other. I’m genuinely excited about what Apollo GraphQL and our product roadmap will be delivering to show the world just what is possible! Check out this great interview with Matt DeBergalis by Intuit Open Source and hear his thoughts on how the the two technologies can build on each other 👇🏻 #EnterpriseArchitecture #PlatformEngineering

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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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Today's article is the second installment of a two-part series on streaming data landscape. The initial piece, titled "What Exactly is a Unified Real-time Platform," delineates the fundamental features of URPs and elucidates distinctions between event stream processing (ESP) platforms, streaming DBMSs, and stream-enabled analytical DBMSs. This article aims to assist organizations in identifying the most suitable URP for their requirements should they conclude that a URP is pertinent to their solution needs. Please send us feedback at [email protected] (cc: Roy Schulte, Sanjeev Mohan) #realtimedata #streamprocessing #streamingdata #streamingdbms #streaminganalytics RTInsights

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Have you tried the new AI powered JOE chat bot? What are some key challenges in testing generative AI applications. Have you seen the latest solution for AI in Mobile App Testing? Find out in this episode of the Test Guild New Shows for the week of April 28. Featuring automation awesomeness contributions from: mabl Dan Belcher John Gluck Selenium Roy de Kleijn HashiCorp IBM Paul Bruce Armon Dadgar ☁️ Marek Šottl RWX JFrog Yonatan Arbel and More! #automationtesting #devsecops #softwaretesting 🔁 Thank you for REPOSTING 🔁

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🚀 The 2024 State of Engineering Management Report is now LIVE! Dive in to uncover how engineers are shaping innovation and growth: ➡️Engineers as strategic partners: Leading business strategy. ➡️Embracing AI: Driving productivity by 94%. ➡️Addressing burnout: A critical concern for 65% of respondents. With insights from over 600 professionals, this report offers invaluable data! Don't miss out—read the full report!

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Another day, another new, faster #LLM. Meet Claude 3.5 Sonnet from #Anthropic. "Claude 3.5 Sonnet operates at twice the speed of Claude 3 Opus. This performance boost, combined with cost-effective pricing, makes Claude 3.5 Sonnet ideal for complex tasks such as context-sensitive customer support and orchestrating multi-step workflows." https://lnkd.in/ecXUMhKE #AI #genAI #CX #AIorchestration

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How we generated over $2m in pipeline for The Swarm API (theswarm.com/api) in the last 90 days. 1️⃣ Target account mapping We spent time building and refining a solid list of 1,000+ target accounts. Make sure you spend resources upfront on this mapping. Otherwise, it's “garbage in, garbage out” for your demand gen efforts. - If you sell into tech, G2 is a great place to start because of well-defined categories and built-in sorting by # of reviews which sometimes beats company size or revenue for relevance IMO. I used Hexofy to retrieve lists. - Clay is outstanding for TA mapping since you can string together multiple queries from different providers. Personal favs include searching by recently hired roles, tech stack, site keywords, and lookalikes from Ocean.io. - ChatGPT helps generate net new accounts through creative prompts like: “Give me a list of 100 companies with [word] in their domain name, or company description.” OR "Here is a list of 100 companies with criteria [x,y,z]: which ones am I missing in that space?" - MoxieGTM helps us uncover companies/people that engage with relevant LinkedIn posts (one of the best intent signals to incorporate) and de-anonymized Google searches. - We built our lists by industry and then segmented them into “VIP” accounts vs. the rest. We spend more time multi-threading into VIP accounts as they’re larger and higher value. 2️⃣ Outreach - Warm outbound: We use our The Swarm account to find warm relationships through our network — team/investors/advisors/partners. About 20% of accounts have a warm intro path. While this takes a bit more time, it delivers high-quality meetings AND educates your network on the exact kind of referrals you seek. - Cold(ish) outbound (is it really cold if you target precisely and cross-reference all the signals?): We created campaigns on lemlist, sequencing LinkedIn and emails. Starting with LI leads to better response rates and avoids email spam reports. Even if they don’t reply, new connections see your content in their feeds which is built-in nurturing. - Inbound & Content: We clearly positioned our three products on our website (API/SaaS app/chrome extension) and share ~2 on-topic LinkedIn posts weekly. We use Outworks.io to support content creation. Our CEO David Connors appears on industry podcasts. Keep is simple: Start with 20 podcasts and reach out to the host, it snowballs from there! - Word-of-mouth: A high-quality target account list generates more WOM among your audience. Back to square 1: Built a great list! - Paid acquisition: We run Google ads and account-based retargeting ads (i.e. only show your RT ads to your target accounts that don't convert in a meeting). In conclusion, don’t rely on a few plays, build a system that works, and keep refining it. That’s all folks! What do you think? What am I missing?

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If you like learning from the experience of others (who doesn't?), check out this post from Ken Kantzer on observations after making intense use of OpenAI's GPT models. https://lnkd.in/gPiiz77D

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