California’s Property Seizure Act - called “The Billionaire Tax” to fool voters is now on the ballot. @HooverInst has a short 4min video breaking it down. You should vote for this if you are comfortable handing over 5%, in cash, of all your tangible and intangible property every year. You should vote no if you don’t want to do that. If it passes, the remaining Billionaires will sue. This will take a decade to meander through the courts and will find its way to the Supreme Court where the odds it survives are low. But in that intervening decade, far fewer business builders will want to bet on California and will focus on building in other states. This will drive a large loss of revenue that will make California’s budget hole even worse. The only solution there is more borrowing and higher taxes on EVERYONE including those that voted NO. This will particularly impact the middle class who are Californias largest revenue source. The only path out of an avoidable budget spiral is a hard landing and structural reset. Sadly, a hard landing will mean a near-miss with California bankruptcy and, more punitively, a reset/retrade of state pensions with California lenders to not come collect. If you aren’t sure or don’t believe me, vote YES and bookmark this post. A decade from now you can tell me I was wrong or give me the opportunity to lord over your stupidity, jealousy and gullibility for voting YES when I am proven right.

🧵 TL;DR: Long-running AI agents rack up token costs fast. Deep Agents turns on prompt caching with zero config, cutting up to 80% of cost on real tasks. Title: Prompt Caching with Deep Agents URL: Key points 💸 Every request reprocesses the full history, system prompt, and tool defs — costs compound ⚡ Prompt caching reuses the compute for static content and only processes the new delta 🧩 Explicit cache breakpoints keep partial hits even when the prompt prefix changes a bit 🤖 Deep Agents auto-applies 3 strategies: explicit breakpoints / provider-side implicit caching / cache-maximizing prompt structure 📊 Measured: Claude Haiku 4.5 -77%, GPT-5.4-mini -80%, Gemini 3.5-Flash -49% 🔭 LangSmith surfaces cache-read tokens per call so you can measure and optimize savings ⏳ The longer the conversation, the bigger the win; short runs see little benefit Abstracting away provider differences with zero config is what makes this land in production. #LangChain# #AIAgents#

Over the past six months, we shipped 30+ models, features, and upgraded tools for the API. Our changelog has been busy. Here’s what you may have missed for the API: New models • GPT-5.5 • GPT-5.4 mini • GPT-5.4 nano • GPT-Realtime-2 • GPT-Realtime-Whisper • GPT-Realtime-Translate • GPT-Image-2 Agent building blocks • Agents SDK harness & sandbox • Skills in the Responses API • Server-side compaction • WebSocket mode • Hosted shell More ways to build • Admin API enhancements • OpenAI CLI • Models on Amazon Bedrock

Can an AI actually mediate a conflict between people? 🤝 A benchmark that tries to measure that, reliably, under realistic conditions. Title: SoCRATES: Towards Reliable Automated Evaluation of Proactive LLM Mediation across Domains and Socio-cognitive Variations URL: 🤝 Overview This work proposes SoCRATES, a comprehensive benchmark for evaluating LLMs as mediators. An agentic pipeline builds realistic conflict scenarios across eight domains from actual public disputes, enabling automated and reliable evaluation of proactive LLM mediation. ❓ Challenges Solved Using LLMs to guide disputing parties toward agreement is gaining attention, but evaluating it is hard. ・Real conflicts shift constantly as disputants' emotions, intentions, and context change mid-mediation ・Existing benchmarks rely on a limited set of expert-authored scenarios ・They also score every turn against every topic, injecting noise that muddies the evaluation signal 💡 Methodology & Proposed Approach SoCRATES integrates three approaches. ・Agentic scenario curation: agents find genuine public disputes, restructure them into mediation scenarios, and filter for cases that truly need intervention ・Socio-cognitive probing: vary each scenario across five independent dimensions (strategic posture, party composition, conversation-history length, emotional reactivity, cultural identity) to pinpoint capability gaps ・Topic-localized evaluation: instead of scoring every topic at every turn, rate only the turns where a topic is actively discussed, reducing noise It spans eight domains: transactional, health, environmental, B2B, policy, international, legal, and intra-organizational. 🌍 Use Cases / Experimental Results The results were sober and revealing. ・The evaluator reached r=0.82 alignment with human experts (trajectory level), more than doubling baseline performance ・Among eight frontier LLMs, even the best, GPT-5.4-mini, closed only about 34.4% of the consensus gap (all-mediator average 25.9%) ・Big domain spread: 41.3% improvement in transactional disputes versus just 16.6% in intra-organizational ones The key takeaway: meaningful progress needs better social adaptation to diverse conditions, not just general capability gains. #LLMEvaluation# #AIMediation#