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This post is an adaptation of a talk I recently gave at the Amazon Web Services (AWS) community day event in Dublin about the technical strategies I’ve experienced that don’t work and the ones that have helped us to grow and scale at Intercom. Building for scale. The top ten technical strategies to avoid. Multi-cloud architectures.
Data Teams are Becoming Software Teams : DevOps created a movement within softwaredevelopment that empowers developers to run the software they wrote. Most sophisticated data teams run like softwareengineering teams with product requirement documents, ticketing systems, & sprints.
When I say “execute”, I don’t simply mean the engineering challenges of building something. I’m referring to the full spectrum of business execution, from product management to design to engineering to marketing to sales to support and all the other functions needed to scale a business. They are called product engineers.
The quality of collaboration in softwaredevelopment is measured by a direct line of sight into the customer experience. DevOps is a given in today’s softwareengineering world. Cultural alignment within an engineering organization is necessary, but not sufficient. Read more about this in my prior post.
In my conversations with softwaredevelopers and technical founders over the years, I’ve heard how complicated these tech stack choices are to make. We know that conversion rates for SaaS and software companies will vary by 30% or more just based on the checkout experience. Will it scale with you? Is it the interface?
After a decade of expanding complexity in the modern data stack, companies are dramatically simplifying their architectures - and getting better results Second, we’re seeing a renaissance of scale-up computing. Content: I use my MacBook Pro to run 70 billion parameter models, which are equivalent to GPT 3.5 I did this in 15 minutes.
Why can’t we escape hands-on cloud operations work to unlock softwaredevelopment nirvana (aka frictionless, faster development and deployment processes)? An environment where there’s virtually no hands-on operations work can deliver a faster, more frictionless development and deployment experience. The ideal result?
If you want features in your lakehouse (on top of open source Iceberg) for ingestion, CDC, streaming (file loading, Kafka connect, etc), schema evolution, compaction, optimization, time travel, snapshots, auto-scaling, maintenance (no more writing spark jobs to delete snapshots!),
[Melbourne, Australia] - Audacix, a leading global provider of automated testing, application security and DevSecOps solutions for software companies, today announced a strategic partnership with Meteonic Innovation Pvt Ltd, a leading company in technology oriented software consulting.
Since then, I have been leading Uberall product team, which we’ve scaled from 3 people to 12 people over the past 5 years. There are many posts out there explaining how startup co-founders should “land the first sales, then hire the first 2 sales and when to hire a VP sales”.
Back in 2012 when I founded Initial State, there were not many people writing about SaaS, and a widely accepted “playbook” for building and scaling different types of SaaS companies did not exist. All too often we were winging it and learning the same marketing/sales/product lessons in parallel to other SaaS companies.
Codium is one of the fastest-growing startups in the AI coding assistant space, having scaled its go-to-market team from 3 to 75 in just under a year. Recently valued at over $1 billion , Codium is proving that AI-driven softwaredevelopment is not just the futureits the present. Their strategy? The real impact?
Occupation Employment (in millions) AI Technology SoftwareDevelopers & IT 2.71 Computer adaptive instruction & testing Engineers 1.73 Ad creative production, AI design, customer lifecycle at scale Management Analysts 0.88 Sales Managers 0.4 Softwareengineers were the first to benefit with Copilot.
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