Gemini 4 Argon: Get Ready Before You Can Call It
Gemini 4 Argon for production AI teams: benchmarks against GPT-6 Astra and Opus 5.5, pricing that doubles after launch, 1M output tokens and a readiness plan.
Production AI
10 articles
Introducing Rêver Studio: AI Client Galleries and Culling for Photographers
Tensorplay launches Rêver Studio, an online client gallery for photographers in India with 40+ themes, AI face grouping and AI culling that flags duplicates, blur and closed eyes. Start free with 5 GB.
AI in September 2026: Cheaper Models, Managed Agents and New Rules
The AI news from September 2026 that matters to teams shipping AI: Claude Opus 5.5, GPT-6 Sol and Luna pricing, OpenAI's Agents API, Plugin4Shell, California's new AI laws and a migration checklist.
TypeSafe Jev: A Model That Makes Decisions Instead of Writing Text
TypeSafe's Jev explained for production AI teams: typed Choice, Score and Noul answers, confidence gating, $0.042 per million input tokens, and the caveats in its benchmarks.
GPT-6 Astra in Production: What Changes for AI Engineering Teams
How to evaluate, route and govern OpenAI's GPT-6 Astra in production: pricing, refusal behaviour, reduced reasoning visibility, agent risk and a migration plan.
The MLOps Foundation CTOs Need for Reliable AI Products
Learn the MLOps foundation for reliable AI products, including versioning, evaluation, deployment controls, observability, incident response, and ownership.
AI Production Readiness Checklist: Why LLM Prototypes Fail and How to Fix Them
An AI production readiness checklist: why LLM prototypes fail, and how to check evaluation, failure handling, observability, security, cost and rollout.
From PoC to Production: An Honest Engineering Timeline
An honest breakdown of why AI proof-of-concept projects take longer than expected to reach production, and how to plan realistically.
Securing AI Applications: The Threat Model You Haven't Thought About
Learn how to threat model AI applications and address prompt injection, data leakage, insecure tools, and model-specific attack paths.
Designing AI-Powered APIs: Patterns and Pitfalls
Learn practical patterns for building reliable AI-powered APIs, from streaming responses and validation to observability and cost control.