Sat.Jul 18, 2020

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#1,815 – The Future of Streaming

Greatest. Idea. Ever.

It’s a Saturday night during a global pandemic. There’s no where to go. Nothing to do. I would like to curl up on the couch and watch some guilty pleasure TV or some mind-blowing Sci-Fi but I feel like I’ve already seen everything there is to see from network TV to Hollywood blockbusters. Forget about Netflix and chill. All I want is Netflix and will.

Company 63
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#1,812 – Notable Quotable, Part 2

Greatest. Idea. Ever.

A random collection of some of the best COVID-19 and 2020 related memes, tweets, soundbites and jokes currently making the rounds on the inter-webs over the last week. Because sometimes you have to laugh so you don’t cry: Another crazy week is in the books.

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#1,814 – Chronic Pain Locator

Greatest. Idea. Ever.

If COVID-19 or Climate Change doesn’t get me it’s likely that my chronic pain will. Presumably the result of whiplash from a car accident I have a soft issue injury in my back and ribs that leaves me with excruciating pain on most days. If I move the wrong way I could cramp up and have trouble breathing. Clearly something is amiss in my exoskeleton.

Study 55
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#1,813 – Rooftop Slushie

Greatest. Idea. Ever.

It’s not what you know, it’s who you know. Which is great if you happen to know someone, not so great if you don’t happen to know anyone! Personally, I’ve benefited a few times from having connections. I got my current job because I knew the hiring manager from a previous job. So I get it. There’s a comfort level in hiring someone that you already know, even if that affiliation is a weak tie such as an acquaintance or someone you used to work with indirectly or

Company 49
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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.