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1/ Introducing Judge: Gensyn’s verifiable AI evaluation system. Traditional evaluators rely on closed APIs - opaque, silently updated, and impossible to reproduce. Judge executes a pre-agreed, deterministic AI model against real-world inputs & commits to be challenged in public.

130,128 Aufrufe • vor 1 Jahr •via X (Twitter)

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Judge Juan Merchan has ordered Donald Trump to be sentenced for 34 counts on January 10th ahead of inauguration Judge Merchan was never supposed to oversee Donald Trump’s case, he was specifically assigned the case to weaponize our legal system against Trump, here’s the proof “Understand there's absolutely no reason that Judge Merchan should have even had a chance to be assigned to this case? Now, I'm sure the left is just gonna call this a conspiracy theory. ‌ So I'll issue a challenge to them, and maybe they can tell me how he managed to be the judge. Because for these type of cases, the way it's supposed to work is that there is a panel of 24 judges, and they are all put in rotation and randomly assigned these types of cases. Judge Merchan is not on that panel. ‌ That's because he's not a judge. He's an acting judge. So even though they're trying to claim that they didn't pick the judge, that it was randomly assigned, that's not possible because judge Merchan isn't in the pool to be randomly assigned. So the only way he could have caught this case was to be specifically assigned to it. There was no chance of him being randomly selected. ‌ And the wild thing is that according to the left and the department of justice, judge Merchan was not only randomly selected to be the judge in this trial, but he was also randomly selected to be the judge in the Trump Organization case, and he was randomly selected to be the judge in the Steve Bannon case. ‌ So judge Merchan, a judge that is not in the pool of 24 judges that is supposed to catch these cases, a judge that is not an actual judge, but an acting judge caught all 3 Trump related cases randomly. This is a judge who gives heavily to an organization very plainly named Stop Trump, and a judge whose daughter makes tens of millions of dollars every year promoting Democrats. ‌ But, yeah, I'm sure this was just a coincidence. It was a coincidence that one of the most high profile cases ever, we didn't assign a judge, we assigned an acting judge. ‌ And that that judge somehow got selected even though he wasn't in the pool of judges available to be selected, and that that same judge that was selected also caught 2 other Trump related cases in the same year, and then that judge's daughter makes tens of millions of dollars a year promoting Democrats. Yeah. I'm sure that's all a coincidence.”

Wall Street Apes

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New short course: Evaluating AI Agents! Evals are important for driving AI system improvements, and in this course you'll learn to systematically assess and improve an AI agent’s performance. This is built in partnership with Arize AI and taught by John Gilhuly, Head of Developer Relations, and , Director of Product. I've often found evals to be a critical tool in the agent development process - they can be the difference between picking the right thing to work on vs. wasting weeks of effort. Whether you’re building a shopping assistant, coding agent, or research assistant, having a structured evaluation process helps you refine its performance systematically, rather than relying on random trial and error. This course shows you how to structure your evals to assess the performance of each component of an agent and its end-to-end performance. For each component, you select the appropriate evaluators, test examples, and performance metrics. This helps you identify areas for improvement both during development and in production. (If you're familiar with error analysis in supervised learning, think of this as adapting those ideas to agentic workflows.) In this course, you'll build an AI agent, and add observability to visualize and debug its steps. You’ll learn about code-based evals, in which you write code explicitly to test a certain step, as well as LLM-as-a-Judge evals, in which you prompt an LLM to efficiently come up with ways to evaluate more open-ended outputs. In detail, you’ll: - Understand key differences between evaluating LLM-based systems and traditional software testing. - Add observability to an agent by collecting traces of the steps taken by the agent and visualizing them - Choose the appropriate evaluator - code-based, LLM-as-a-Judge, human-annotation based - for each component. - Compute a convergence score to evaluate if your agent can respond to a query in an efficient number of steps. - Run structured experiments to improve the agent’s performance by exploring changes to the prompt, LLM model, or the agent’s logic. - Understand how to deploy these evaluation techniques to monitor the agent’s performance in production. By the end of this course, you’ll know how to trace AI agents, systematically evaluate them, and improve their performance. Please sign up here:

Andrew Ng

126,587 Aufrufe • vor 1 Jahr