Introducing Claude Opus 4.6. Our smartest model got an upgrade. Opus 4.6 plans more carefully, sustains agentic tasks for longer, operates reliably in massive codebases, and catches its own mistakes. It’s also our first Opus-class model with 1M token context in beta.

@sama Anthropic: makes funny ad about ad-driven AI Sam: writes 400-word defensive essay calling them authoritarian, deceptive, and elitist The ad hit a nerve. Wonder why.

Anthropic cooked again!

sometimes the simplest solution is the best solution

To everyone saying vibe coding is harder than programming: you have no idea what you're talking about, and I'd bet you've never actually written code in your life. Prove me wrong.

Hot take: we're living in the best era to build software in human history. AI handles the boring parts. You focus on the creative parts. The gap between "idea" and "shipped product" has never been smaller. Stop overthinking. Start building.

Everyone's saying 2026 is when AI has to "prove itself." I see it differently. 2026 is when AI gets out of the lab and into the hands of builders who actually ship things. The models are good enough. The APIs are stable. The costs are reasonable. The documentation exists. All

This is huge!!

Alphabet Overtakes Apple as World's Second-Most Valuable Company: The AI Dividend https://t.co/6JJBXogWOI

@craigzLiszt it’s going to be much worse than we all imagine

this is how it feels

2026 is supposedly the year AI has to "prove ROI." Boards are demanding results. Enterprises want measurable returns. The hype is over. Here's what they're missing: the ROI has been there the whole time. It's just not showing up where they're looking. Big companies spent

RTX 5090 Partner Cards Launch at CES: Prices Surge Past $3,500 as Scalpers Circle https://t.co/E5Kci27wFV

erlying reality isn't. Non-coders can now ship basic apps. Doesn't mean they'll build anything maintainable. The skill gap moved from "can you code" to "can you architect."

code has zero tests, inconsistent naming, and copy pasted Stack Overflow from 2014. AI at least follows conventions and explains what it's doing.

lock. Small teams can now have enterprise-grade CI/CD, monitoring, and docs because the cost dropped to near zero. Bottleneck shifted from implementation to decision making.

code has zero tests, inconsistent naming, and copy pasted Stack Overflow from 2014. AI at least follows conventions and explains what it's doing.

This is why reading old papers matters. The ML community treats anything pre-2020 like ancient history. If it's not on the first page of Arxiv this week, it might as well not exist. But the fundamentals haven't changed. DeepSeek just proved that. Most "breakthroughs" are

Companies know retention is cheaper than replacement. The data is overwhelming. Losing someone costs 50-200% of their salary when you factor in recruiting, onboarding, lost productivity, and tribal knowledge walking out the door. But budgets are structured to reward hiring

While everyone was fighting over NVIDIA allocations, Apple quietly built the best "run big models at home" setup. The unified memory architecture is doing heavy lifting here that most people haven't caught onto yet. Traditional setups force you to move data between CPU and GPU