August has not been bad as feared - seasonality suggested $VIX would be on a rocket ship but have been well tamed. Tomorrow will determine how the month finishes $QQQ needs to stay above 700 to record a gain at end of Monday.

Pain releases massive amounts of neurochemical and psychological energy. Untamed it manifests as anxiety, depression or despair. Tamed it conquers the world. Attack the world or attack yourself. Life's biggest winners and losers are driven by the exact same fuel.

US bond yields fell from their highest levels since 2002 as oil prices retreated below $100 a barrel and Treasury Secretary Scott Bessent insisted the government’s debt load can be tamed

When a service fails and everyone upstream retries at once, a small outage can balloon into a stack-wide incident. Here's how Uber tamed that "retry storm" problem. Title: How Uber Protects Against Retry Storms URL: To stop retry storms — where traffic amplifies exponentially as every upstream service retries simultaneously against a degraded downstream — Uber built a system that distinguishes who actually owns an error. Highlights 🔍 Only the error-owning service claims the error A service "claims" an error only when its downstream call succeeded but it still returned an error itself; if the downstream is at fault, it "unclaims" instead. Only the actual owner of the problem retries. 🔁 Retry budgets and propagation cap further amplification Retry budgets cap retries at each hop to a fixed percentage, turning exponential growth into multiplicative growth. When a retry fails, the "unclaimed" status propagates upstream to prevent further amplification higher in the chain. 📊 Stopped 9.5 million requests during a real outage In a November 18, 2025 incident, traffic to a degraded service would have jumped 46–135% without this system. Instead, it blocked 9.5 million spurious requests plus up to 200,000 more from immediate callers, and cut the max retry-storm radius across all user-facing APIs from 25 hops to 3. What stands out is how this runs as automatic, shared infrastructure with no per-service tuning needed — that's exactly what makes it practical at Uber's scale. #ReliabilityEngineering# #DistributedSystems#

The contract negotiations lawyers deal with every day turn out to be more repetitive than they look. Uber's legal team processes thousands of contract negotiations a year, and simply responding to client edits round after round had become a serious bottleneck. But buried inside that high-stakes work was something surprisingly structured: similar clauses, repeatable redlines across clients, and standing legal reasoning patterns. That meant the routine parts could be automated while complex judgment stayed with the lawyers. 🔄 The first attempt used retrieval-augmented generation, but it failed — semantic matching between search keys and content was inaccurate, the tone of generated responses was off, and it generalized poorly to new scenarios. The team evolved it into a feedback loop that carefully logged every lawyer decision (accept, reject, modify) with exponential decay weighting to avoid drifting toward outdated precedent. Next they tamed responses that swung between overly defensive and overly accommodating using expert-defined prompt structure and a reflection technique, and finally reached agentic modifications capable of drafting counter-proposals themselves. Title: Scaling AI in Legal: Building Uber's Redlining Agent URL: Built right into the Microsoft Word lawyers already use, the tool cut contract review time by 20% and reached 91% accuracy on AI-generated decisions. What stands out most is the team's own takeaway: the hardest part wasn't the model — it was successfully weaving an agent into lawyers' daily work. #AIAgents# #LegalTech#