The first step to making reliable IoT devices is understanding that they are inherently unreliable. They will never work 100% of the time. This is partially because we firmware engineers will never write perfect code. Even if we did, our devices need to operate through various networks and gateways, such as cellular modems, mobile phone Bluetooth applications, Wi-Fi routers, cloud backends, and more, and each of these may introduce unreliability.

Banks are putting a fresh set of eyes on how they are using APIs to drive better business outcomes and deliver more value to their customers. This is a relatively new departure toward adopting digital transformation of key operations. Financial organizations are traditionally known for favoring conservative business models that often resist modernizing complex legacy systems or rapid change in product and service offerings. This has been changing rapidly as APIs become more prevalent.

Ready to explore Rollbar without the coding hassle? The Rollbar Error Simulator iOS app is the ultimate solution for carefree error testing, designed for users without coding experience. Seamlessly connecting to your Rollbar account, this user-friendly app lets you simulate errors effortlessly with just a single tap on a button. No technical expertise is needed! Just create a new account, opt for the Error Simulator experience, and you'll be guided.

As a software product company that helps customers bring new products to market, 3Pillar Global relies on tight-knit coordination and communication to move projects forward. When the team was looking for a new collaboration solution that would enable them to communicate in real time, they looked for an open source option.

For the past few months, we’ve been working closely with the LangChain team as they made progress on launching LangServe and LangChain Templates! LangChain Templates is a set of reference architectures to build production-ready generative AI applications. You can read more about the launch here.

Generative AI has already shown its huge potential, but there are many applications that out-of-the-box large language model (LLM) solutions aren’t suitable for. These include enterprise-level applications like summarizing your own internal notes and answering questions about internal data and documents, as well as applications like running queries on your own data to equip the AI with known facts (reducing “hallucinations” and improving outcomes).

Organizations saw a 243% ROI and $1.2 million in savings over three years In today’s complex and distributed IT environments, traditional monitoring falls short. Legacy tools often provide limited visibility across an organization’s tech stack and often at a high cost, resulting in selective monitoring. Many companies are therefore realizing the need for true, affordable end-to-end observability, which eliminates blind spots and improves visibility across their ecosystem.