TechCrunch

JUNE 25, 2022

Just as one company’s success shouldn’t cast a halo on its vertical’s brethren, one company’s layoffs don’t quite mean that its competitors are equally screwed. Instead, I think that changes within a particular startup can be used as benchmark questions for their larger market; in other words, we can use the micro to better understand the macro. With that in mind, I want to talk about MasterClass’ decision to lay off 20% of its staff, around 120 people, across all teams.

Sport 242

Sport Education Windows Sustainability 242

Xebia

JUNE 25, 2022

In this post, I will walk you through the challenges I’ve faced when adopting the object type in my Terraform 0.12 modules and the solutions I came up with to work around the caveats. As an example, I will use the google_storage_bucket resource, as this is part of one of the modules I’ve built. The Terraform Object Type. With the launch of Terraform 0.12 back in May 2019, many new cool features are introduced.

Examples 130

Examples Resources Storage Policies 130

TechCrunch

JUNE 25, 2022

Welcome to The TechCrunch Exchange, a weekly startups-and-markets newsletter. It’s inspired by the daily TechCrunch+ column where it gets its name. Want it in your inbox every Saturday? Sign up here. My home country’s president, Emmanuel Macron, wants to have 100 French unicorns by 2030. Economy minister Bruno Le Maire, 10 homegrown decacorns.

Conference 239

Conference Marketing Analysis 239

Altexsoft

JUNE 25, 2022

In 2019, the travel and hospitality industry accounted for a whopping 10.3 percent of global GDP. While this share decreased to 5.3 percent in 2020 due to pandemic restrictions, in 2021, the industry saw a rise up to 6.1 percent, which is about 5.7 trillion dollars. And everyone knows that fraudsters won’t be far behind from where money is, especially when most transactions happen online.

Travel 52

Travel Software Review Software Airlines 52

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.

Artificial Inteligence