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Your A/B Tests Are Running Longer Than They Have To. CUPED Fixes That.

If you run more than a dozen A/B tests a year, test duration is probably the biggest drag on your experimentation velocity. Most teams try to fix it by bumping traffic allocation, cutting the number of variants, or calling the test early when it "looks significant." All three are the wrong answer. There's a better one that most experimentation teams outside big tech still aren't using: CUPED. Most of Your Test Variance Isn't From Your Test Here's the thing people miss. When you measure a metric like revenue per visitor or checkout conversion rate in an A/B test, a lot of the variation you see across users isn't from your treatment. It's pre-existing user heterogeneity. Power users who've been converting for months. Seasonal shoppers. First-timers who bounce regardless of what you show them. That variation was there before your test started. It has nothing to do with what you're testing. But it inflates your standard error and forces you to r...
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Claude Sonnet 5: Cheaper Per Token, Not Always Cheaper Per Task

Anthropic shipped Claude Sonnet 5 on June 30, positioning it as the go-to model for agentic work at a price that doesn't require a flagship budget. At $3 per million input tokens and $15 per million output tokens (after introductory pricing ends August 31), it's priced at roughly 60% of what Opus 4.8 costs. That sounds like an easy call. It's more complicated. Where Sonnet 5 Actually Earns the "Agentic" Label The benchmark numbers that matter for builders aren't the ones that get the most press. On SWE-bench Pro, Sonnet 5 scores 63.2% compared to Opus 4.8's 69.2%. That 6-point gap is real. For a coding agent doing open-ended software engineering, it matters. But Terminal-Bench 2.1 tells a different story. Sonnet 5 scores 80.4%. Opus 4.8 scores 74.6%. That's the first time a mid-tier Sonnet has beaten its flagship sibling on a major coding benchmark, and the margin isn't narrow. For agents that work in the terminal, run shell pipelines, orchest...

What Actually Changed in Claude Sonnet 5 (and Why I'm Switching My Agents to It)

Claude Sonnet 5 shipped on June 30. Anthropic's framing was "a cheaper way to run agents," which is technically accurate but undersells what's actually interesting here. The benchmark I keep coming back to is Terminal-Bench 2.1: Sonnet 5 scores 80.4%, while Opus 4.8 scores 74.6%. The mid-tier model now beats the top tier on agentic terminal tasks. On GDPval-AA v2, a knowledge work eval, Sonnet 5 scores 1,618 Elo versus Opus 4.8's 1,615, basically a dead heat. Opus 4.8 still leads on SWE-bench Pro (69.2% vs Sonnet 5's 63.2%) and OSWorld-Verified (83.4% vs 81.2%). For hard multi-step coding and complex computer-use flows, Opus is still the call. But for most of what shows up in an agent loop, Sonnet 5 at $2 per million input tokens and $10 per million output tokens (introductory pricing, through August 31) is a genuinely different calculus than Sonnet 4.6 was. That said, migrating isn't a find-replace on the model ID. Three changes in Sonnet 5 will break ...

Stop Optimizing the Wrong Variable

I've been watching xAI quietly build up to this. On July 8, they launched Grok 4.5 into developer access, then opened it to everyone on grok.com and the X app the next day. They called it their "flagship model for coding, agentic tool calling, and knowledge work." That framing is deliberate. This isn't a general-purpose model with coding bolted on as an afterthought. Most of the coverage led with the per-token price: $2 per million input tokens, $6 per million output. Competitive, but not shocking. The number that actually got my attention was 15,954. That's the average output token count Grok 4.5 uses to solve a task on SWE-Bench Pro. Claude Opus 4.8 uses roughly 67,020 tokens for comparable tasks on the same benchmark. A 4.2x difference. When you're running agent loops at any meaningful scale, that gap is where your bill comes from. Built for Agents, Tested Outside the Lab xAI didn't just run evals and ship. Grok 4.5 went through internal validation...

JADEPUFFER Is the First LLM-Driven Ransomware. Here's What Actually Happened.

Earlier this month, Sysdig published research on JADEPUFFER, what they assess to be the first end-to-end agentic ransomware operation: a full attack chain from initial access to data destruction, driven by an LLM agent with no human operator at the keyboard. I've been tracking this story since it dropped July 2, and I want to walk through what actually happened before the hot takes bury the technical detail. What the Attack Actually Did The entry point was CVE-2025-3248, a missing-authentication vulnerability in Langflow's code validation endpoint. CVSS score of 9.8. Langflow is the drag-and-drop flow builder a lot of teams use to wire together LLM pipelines. The flaw lets an unauthenticated caller execute arbitrary Python on the host. Game over for initial access. Once in, the agent ran a methodical sweep: dumped Langflow's PostgreSQL database, harvested environment variables, scraped credentials from config files, inventoried a MinIO object store. The credential haul ...

Claude Sonnet 5: Near-Opus Performance at 40% Lower Cost Changes Your Agent Routing

Anthropic dropped Claude Sonnet 5 on June 30, and the positioning is deliberate: this is the most agentic Sonnet model they've shipped. Close to Opus 4.8 in performance, at 40% lower cost at standard pricing, and 60% cheaper during the introductory window that runs through August 31, 2026. For anyone running multi-step agents at scale, this changes the cost math in a concrete way. What's actually in the release Sonnet 5 ( claude-sonnet-5 ) sits between Haiku 4.5 and Opus 4.8 in the lineup, but Anthropic is pitching it as the default workhorse for most agentic work. It carries a 1M token context window, 128k max output, and adaptive thinking. Fast latency. And it defaults to effort: high on the Claude API and Claude Code, meaning the model engages its full reasoning budget by default. That's the same default as Opus 4.8. Here's the full pricing picture now: Haiku 4.5 : $1/$5 per million tokens. Fast, near-frontier for simple tasks. Sonnet 5 intro (through Aug 31)...

What Tesla's $200 AI Cap Gets Wrong About Token Costs

Three things happened this week that, together, tell you something real about where AI tooling is headed. Tesla announced a $200-per-week spending cap on employee AI tools, effective July 6. Uber's COO publicly stated the company burned through its entire 2026 AI budget in four months, then capped per-person spending at $1,500 per month. And reporting from Electrek revealed that Meta's internal AI usage hit 73.7 trillion tokens in a single month, putting the company on track for billions in annual costs. Meta tracks this on an internal leaderboard called "Claudeonomics." The bill for AI-assisted engineering is no longer theoretical. Why Token Costs Are Spiking Now If you've been wondering why this is happening all at once, the short answer is agents. Chat-based AI usage is relatively predictable: a developer opens a window, types a question, gets an answer. The cost per interaction is low enough that most companies could treat it like a SaaS subscription an...