The Week Solana Brought Google to x402
Pay.sh shows x402 moving from unauthorized API wrappers toward native integrations, with Google Cloud anchoring Solana's gateway to agentic payments.
KDNugget·
Deployment is not just about calling an API or hosting a model. It involves decisions around architecture, cost, latency, safety, and monitoring.
Read full articlePay.sh shows x402 moving from unauthorized API wrappers toward native integrations, with Google Cloud anchoring Solana's gateway to agentic payments.
OpenAI adds new voice intelligence tools to its API, including real time speech, live translation, and live transcription for apps.
The new features could be handy for customer service systems, but OpenAI says they have applications that work across a variety of other fields, including education and creator platforms.
Moonshot's annualized recurring revenue topped $200 million in April, driven by rapid growth in paid subscriptions and API usage.
Explore new realtime voice models in the OpenAI API that can reason, translate, and transcribe speech, enabling more natural and intelligent voice experiences.
Writing code has always been the most time- and resource-intensive task in software development. AI is changing that, and faster than most engineering organizations are prepared for. Tools like Claude Code and Cursor are already handling significant parts of code construction, freeing developers to spend more time on requirements, architecture, and design. But that shift creates a new challenge nobody is talking about enough. As AI takes on the heavy lifting, the skills that matter most are moving upstream: how to provide the right context for a prompt, how to evaluate what the model produces, and how to understand a problem deeply enough that you can’t be fooled by a confident but wrong answer. This piece explores those three skills and why developers who master them will have a significant edge over those who don’t. Beyond coding: Mastering the art of the prompt Software translation tools such as compilers and assemblers map a high-level description of code to a lower-level represent
A step-by-step road map for building the enterprise architecture required to deploy AI safely, quickly, and at scale.
Why reasoning models dramatically increase token usage, latency, and infrastructure costs in production systems The post Inference Scaling (Test-Time Compute): Why Reasoning Models Raise Your Compute Bill appeared first on Towards Data Science.