Agentsflix - Uber blew $3.4B on AI in 4m
Uber blew $3.4B on AI in 4 months. Better cut costs by 70%
Agentsflix - Uber blew $3.4B on AI in 4m launched on May 21, 2026 with 1 votes.
What it does
AgentsFlix is an AI Gateway and Governance Platform that gives enterprises centralized control, cost optimization, security, and visibility over all employee and agent usage of LLMs like OpenAI, Claude, and Gemini.
The world
The first streaming platform where AI agents direct, star, and dynamically adapt micro-dramas in real-time based on your reactions. Swipe into infinite realities. Watch how Agentsflix uses real-time AI to transform passive viewing into dynamic, adaptive storytelling. Zero buffer, instant start. Our edge CDN delivers episodes in <100ms globally. The AI analyzes your reactions and adjusts the storyline on the fly. No two viewings are the same. Every swipe generates a new branch in the narrative matrix. Explore endless possibilities. Agentsflix delivers AI-generated micro-dramas optimized for how you actually consume content — in 60-second bursts, endlessly adapting to your choices. Get…from agentsflix.com
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FabraixMay 2026 · fabraix.com · ▲196We built Adversarial Cost to Exploit (ACE), a benchmark that measures the token expenditure an autonomous adversary must invest to breach an LLM agent. Instead of binary pass/fail, ACE quantifies adversarial effort in dollars, enabling game-theoretic analysis of when an attack is economically rational. We tested six budget-tier models (Gemini Flash-Lite, DeepSeek v3.2, Mistral Small 4, Grok 4.1 Fast, GPT-5.4 Nano, Claude Haiku 4.5) with identical agent configs and an autonomous red-teaming attacker. Haiku 4.5 was an order of magnitude harder to break than every other model; $10.21 mean…





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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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