You’ve probably already heard all about OpenClaw (formerly Clawdbot/Moltbot). The viral sensation is an open-source AI assistant that runs on your own device, connects with messaging apps you already use, and goes beyond chat to actually execute tasks like managing your email, calendars, files, workflows, and more. Now meet the man behind it. YC’s Raphael Schaad sat down with Peter Steinberger, the creator of OpenClaw, to discuss the “aha” moment behind the viral personal AI agent, why local-first agents could replace many of today’s apps, and how personal agents will reshape the future of software.

SaaS based stocks are down more than 60% and traditional software companies are facing AI disruption happening as the first wave. How will the AI industry and the broader economy value software companies as the unit economics change with how pricing works with agents and agentic applications that interact with applications? Will this affect on-prem, IaaS, and PaaS also?

Your Kubernetes CPU requests might not mean what you think they mean. For a long time, the conversion from cpu.shares (cgroup v1) to cpu.weight (cgroup v2) used a linear formula that unintentionally penalized smaller CPU requests. In practice, services requesting 100m-250m CPU could receive disproportionately low weight under contention, leading to unfair throttling and unpredictable latency especially in microservice-heavy clusters. A new conversion formula corrects that mismatch. CPU weights now align much more closely with the original intent behind Kubernetes resource requests when running on cgroup v2. Under load, workloads compete more fairly, small services are no longer excessively disadvantaged, and behavior becomes more predictable in multi-tenant environments. This doesn't make your cluster magically faster. It makes it more honest under pressure. And in production, that's often what really matters. https://kubernetes.io/blog/2026/01/30/new-cgroup-v1-to-v2-cpu-conversion-formula/

Price Gouging: Corporate Greed Is Out of Control Artificial intelligence is transforming pricing across industries by enabling personalized, dynamic price adjustments based on individual consumer data. While dynamic pricing originated in the U.S. airline industry in the 1970s, advances in AI have dramatically expanded its reach. Companies now analyze browsing behavior, location, device type, and purchasing patterns to estimate how much each customer is willing to pay. Studies show revenue gains for airlines, hotels, retailers, and ride-hailing platforms like Uber, often without proportional benefits for workers. Critics warn that AI-driven pricing deepens economic inequality by segmenting consumers and charging higher prices based on perceived wealth rather than product value.

In a blog post on Thursday, OpenAI disclosed how it is using the open-source PostgreSQL database. OpenAI runs ChatGPT and its API platform for 800 million users on a single-primary PostgreSQL instance — not a distributed database, not a sharded cluster. One Azure PostgreSQL Flexible Server handles all writes. Nearly 50 read replicas spread across multiple regions handle reads. The system processes millions of queries per second while maintaining low double-digit millisecond p99 latency and five-nines availability. The setup challenges conventional scaling wisdom and offers enterprise architects insight into what actually works at massive scale. https://venturebeat.com/data/how-openai-is-scaling-the-postgresql-database-to-800-million-users https://openai.com/index/scaling-postgresql/