The contrast could not be clearer: Republicans are delivering positive COMMON SENSE results that make life better for the American people. Democrats are embracing big government COMMUNISM — electing far left radicals who are hell bent on open borders, higher taxes, and defunding law enforcement. Republicans will ALWAYS fight to preserve American greatness. 🇺🇸

In contrast to some other frontier models, GPT 5.6 has quickly consumed share within the OpenAI model family. 5.6 Sol now sees 50%+ of spend from US businesses on OpenRouter after launching July 9th

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Two contrasting patterns of working with agents are emerging: delegation and collaboration. Delegation makes sense when it's a long-horizon task that you want the agent to tackle asynchronously, your intent and specifications are clear to the agent, and it's easy to verify the output at the end even if you didn't stay in the loop. Effectively delegatable tasks are rarer than the hype would suggest, because it’s limited by what you can cheaply verify, not what the model can do. Collaboration makes sense when the task is hard to fully specify a priori and you want to be in the loop to iteratively figure out what you want, stay in control, recover from mistakes, sharpen your own skills through collaborative task performance, and have fun. The design criteria for automation/delegation agents and collaboration agents are very different. If you're going to delegate a big task, accuracy and reliability are what matter the most. You want a frontier system that will do the best possible job. It doesn’t matter if the task will take minutes or hours. If you're going to collaborate with an agent, the criteria are more multifaceted: latency (even at the expense of accuracy), transparency/controllability, creativity, and more. The agent should allow the user to stay in a “flow state” instead of having to delegate a task and come back later. It should promote the user’s agency and be fun to work with. We're at the very early stages of an emerging bifurcation between these two types of agents. (The conceptual distinction is ancient, but I’m talking about the product design of LLM-based agents + the practice of how people work with them.) I predict that the distinction will sharpen in the coming months. What's less clear is whether the specialization will happen at the level of companies, with some making a bet on automation and others on collaboration / human amplification, or at the level of products, with many companies going after both markets.

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In contrast, China is embarking on a massive centrally planned compute buildout, taking advantage of cheap energy in its depopulated west. However, without market price signals to guide investment, China suffers from significant underutilization and overinvestment.

.@parkeralewis contrasts Saylor hyping Strike preferreds as "the most interesting thing in Bitcoin" while Square was rolling out payments to 4 million merchants. "He's in there... where is that guy?"