Dev News: Apollo Drama, Monster API and Mobile App Discontent
Condolences to Apollo, the popular Reddit app, which will be shutting down June 30. The gritty details were posted on Reddit, of course, but essentially app developer Christian Selig blamed Reddit’s API price increase. Selling said it was a 20x price increase, amounting to approximately $2.50 per month per user. At that price with the apps current usage, it would cost almost $2 million per month or over $20 million per year, Selig claimed.
Apparently, things got ugly between Reddit and Selig, who offers a link to an audio of a call with Reddit and links to a screenshot of a Mastodon poster accusing him of attempting to blackmail Reddit. It seems Reddit also accused the site of scrapping, leading to Selig to post the app’s backend code on Github. The price increase also led to other turmoil for Reddit, including news of a subreddit strike planned for Monday.
Monster API Platform to Simplify AI
A new company called Monster API launched its platform this week. It’s designed to give developers access to graphics processing units (GPUs) infrastructure and pre-trained artificial intelligence models at a lower cost than other cloud-based options, according to the press statement.
Monster API uses decentralized computing to allow developers to create generative AI applications. The new platform allows developers to access AI models such as Stable Diffusion, Whisper AI and StableLM “out-of-the-box.”
Monster API’s full stack includes an optimization layer, a compute orchestrator, a massive GPU infrastructure, and ready-to-use inference APIs. It also supports fine tuning large language models such as LLaMA and StableLM.
“We eliminate the need to worry about GPU infrastructure, containerization, setting up a Kubernetes cluster, and managing scalable API deployments as well as offering the benefits of lower costs,” said Sarah Vin, CEO and co-founder of the company. “One early customer has saved over $300,000 by shifting their ML workloads from AWS to Monster API’s distributed GPU infrastructure.”
Monster API is the collaboration of two brothers, Saurabh Vij and Gaurav Vij. Gaurav, who faced a significant challenge for his startup when his AWS bill skyrocketed, according to the company statement. In parallel, Saurabh, formerly a particle physicist at CERN (European Council for Nuclear Research), recognized the potential of distributed computing in projects. Inspired by these experiences, the brothers sought to harness the computing power of consumer devices like PlayStations, gaming PCs, and crypto mining rigs for training ML models.
After multiple iterations, they successfully optimized GPUs for ML workloads, leading to a 90% reduction in Gaurav’s monthly bills.
The company promises a predictable API bill versus the current pay by GPU time. Its APIs also scale automatically to handle increased demand, from one to 100 GPUs. The company also announced $1.1 million in pre-seed funding this week.
The Mobile Release of Our Discontent
A majority of companies “are not happy” with how often they release new versions of their mobile apps, according to a survey of 1,600 companies conducted by Bitrise, a mobile DevOps platform. Sixty-two percent of teams said that their release frequency is “unsatisfactory.”
The survey found React Native is the most popular cross-platform framework, used by 48.33% of teams, followed by Flutter at 37.5%. When it comes to testing, only 10.4% of teams said they test as many devices as possible with a device farm, and 31% reported they test the most commonly used devices in their user base.
It also found that 25.7% of teams don’t have the features and functionality of their iOS and Android apps in sync.
Bitrise is also proposing a benchmark for the mobile app market similar to Google’s DORA metrics, which it calls MODAS: the Mobile DevOps Assessment. MODAS uses five key performance metrics for apps:
- Creation
- Testing
- Deployment
- Monitoring
- Collaboration
The study also links to a number of online case studies about mobile speed, noting that when it comes to “mobile app iterations for example, speed is everything: there is a strong correlation between the frequency of updates and the ranking in the app stores.”
