Location-based marketing has been around for a while. Marketers have used ZIP codes to send personalized marketing messages to target audiences. However, these tactics are limited. For example, you can’t trigger ads in real-time or at the moment of highest intent. But what if you could target the right customers at the right place and time? Enter geofencing.

Generative AI projects like ChatGPT have motivated enterprises to rethink their AI strategy and make it a priority. In a report published by PwC, 72% of respondents said they were confident in the ROI of artificial intelligence. More than half of respondents also state that their AI projects are compliant with applicable regulations (57%) and protect systems from cyber attacks, threats or manipulations (55%). Production-grade AI initiatives are not an easy task.

Earlier this month, we released the first version of our new natural language querying interface, Query Assistant. People are using it in all kinds of interesting ways! We’ll have a post that really dives into that soon. However, I want to talk about something else first. There’s a lot of hype around AI, and in particular, Large Language Models (LLMs).

If you’re anything like me, you’re burnt out by all the hullabaloo surrounding AI lately. It just happens to be one of those trending tech topics everyone and their mother wants to talk about these days (case in point: ChatGPT). Truth be told, a lot of this fuss is justified, especially when you consider the incredible developments we’ve seen in the field of AI, to the extent where a lot of things that were once considered impossible have become a reality.

As a mobile game developer, there are many components of your game that you need to monitor. Everything from the servers that are hosting your game, to your best players, and your best-converting actions. That’s a lot of data, and it’s hard to know how to get the most out of that data. This article will look at the KPIs (Key Performance Indicators) you need to monitor, the best tools for monitoring these metrics, and how to handle this data in the most effective way.

DevAlert 2.0, a major upgrade to Percepio’s edge observability platform, provides much improved diagnostic capabilities, including core dumps for Arm Cortex-M devices. This allows for remote inspection of crashes, errors or security anomalies in full detail, including the function call stack, parameters and variables and with source code display.

Tracealyzer version 4.8 will be released in the first week of June, with major optimizations and improvements for Zephyr RTOS, and support for 64-bit target processors (FreeRTOS, Zephyr and SafeRTOS only). In addition, the ESP32 support is upgraded to use the latest TraceRecorder library, supporting all recent versions of ESP-IDF up to v5.2 dev. Snapshot tracing is now primarily supported by the implementation for streaming mode, using the RingBuffer stream port.