The end of predictable storage economics and what that means for infrastructure planning
The enterprise storage market is currently experiencing unprecedented SSD price volatility driven by massive AI demand and multi-year capacity commitments from hyperscalers. Between Q2 2025 and Q1 2026, for instance, 30TB TLC SSD pricing increased by 257% (from $3,062 to $10,950), while HDD pricing remained relatively stable, increasing by 35%. The situation is challenging some fundamental, long-term assumptions about storage architecture strategy, particularly the collective experience that flash pricing declines over time. Until recently, it was a trend fully supported by the facts, and even factoring in cyclical variation, long-term cost curves have generally supported predictable cost-per-GB reductions. This generally solid predictability has underpinned everything from multi-year infr
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7 CVEs in 48 Hours: How PraisonAI Got Completely Owned — And What Every Agent Framework Should Learn
PraisonAI is a popular multi-agent Python framework supporting 100+ LLMs. On April 3, 2026, seven CVEs dropped simultaneously. Together they enable complete system compromise from zero authentication to arbitrary code execution. I spent the day analyzing each vulnerability. Here is what I found, why it matters, and the patterns every agent framework developer should audit for immediately. The Sandbox Bypass (CVE-2026-34938, CVSS 10.0) This is the most technically interesting attack I have seen this year. PraisonAI's execute_code() function runs a sandbox with three protection layers. The innermost wrapper, _safe_getattr , calls startswith() on incoming arguments to check for dangerous imports like os , subprocess , and sys . The attack: create a Python class that inherits from str and over
Anthropic says Claude subscriptions will no longer support OpenClaw because it puts an 'outsized strain' on systems
Why It Matters The decision by Anthropic to stop supporting OpenClaw for Claude subscriptions is significant because it highlights the challenges of integrating third-party tools with AI systems. According to a report from Business Insider, Anthropic cited the "outsized strain" that tools like OpenClaw put on their systems as the reason for this move. This strain is likely due to the additional computational resources required to support these tools, which can impact the overall performance and reliability of the AI system. The impact of this decision will be felt by users who rely on OpenClaw to enhance their experience with Claude subscriptions. OpenClaw's founder has already expressed disappointment, stating that cutting support would be "a loss." This reaction is understandable, given
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Ryanair CEO says book summer trips before fares soar, predicting French air traffic controllers more likely to cause flight chaos than fuel shortages
"At Ryanair, we have lots of flights on a daily basis. We will re-accommodate you, get you back, get you out, whatever it's going to be."
Two-Pass LLM Processing: When Single-Pass Classification Isn't Enough
Here's a pattern I keep running into: you have a batch of items (messages, tickets, documents, transactions) and you need to classify each one. The obvious approach is one LLM call per item. It works fine until it doesn't. The failure mode is subtle. Each item gets classified correctly in isolation. But the relationships between items -- escalation patterns, contradictions, duplicate reports of the same issue -- are invisible to a single-pass classifier because it never sees the full picture. The problem Say you're triaging a CEO's morning messages. Three Slack messages from the same person: 9:15 AM : "API migration 60% done, no blockers" 10:30 AM : "Found an issue with payment endpoints, investigating" 11:45 AM : "3% of live payments failing, need rollback/hotfix decision within an hour"
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