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Again, this is why Ridges AI | SN62 will win.. ๐Ÿ”ต "This is an example of code that we are merging into our codebase, this is legitimate code that the agent on our platform wrote, end to end for us, and is improving our platform, without a human in...

11,207 views โ€ข 7 months ago โ€ขvia X (Twitter)

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How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how ๐Ÿ‘‡

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Why is observability so hard to do well - and so expensive, in general? What is "Observability 2.0" and is Open Telemetry any good? In today's episode of The Pragmatic Engineer Podcast, we answer all of these with Charity Majors , co-author of the O'Reilly book "Observability Engineering," former engineer at Parse/Facebook, and cofounder and CTO at Honeycomb Watch it here: โ€ข YouTube: โ€ข Apple: โ€ข Spotify: โ€ข Summary and transcript: Brought to you by our wonderful sponsors - check out their offerings: โ€ข Sonar โ€” Trust your developers โ€“ verify your AI-generated code. โ€ข Vanta โ€” Automate compliance and simplify security with Vanta ---- Topics we cover in this episode: โ€ข What is observability? Charityโ€™s take โ€ข What is โ€œObservability 2.0?โ€ โ€ข Why Charity is a fan of platform teams โ€ข Why DevOps is an overloaded term: and probably no longer relevant โ€ข What is cardinality? And why does it impact the cost of observability so much? โ€ข How OpenTelemetry solves for vendor lock-in โ€ข Why Honeycomb wrote its own database โ€ข Why having good observability should be a prerequisite to adding AI code or using AI agents โ€ข And more! --- My biggest takeaways: 1. The DevOps movement feels like itโ€™s in its final days, having served its purpose. 2. Lots of people get dashboards wrong! Charity doesnโ€™t think that static dashboards are helpful to engineering teams at all. In fact, misusing dashboards is one of the most common observability practices. 3. Observability will be especially important for AI use cases in these ways: a) o11y for LLMs: to get data on how they behave and to be able to debug behaviors. This is relevant for teams building and operating AI models. b) o11y for code generated by AI: the generated code should have the right amount of observability in place. Once the code is deployed to production, developers need to be able to get a sense of how the code is behaving there!

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Eric Schmidt, former CEO of Google, offers a sobering view: The biggest technological shift in human history is happening, and almost no one is talking about it. Schmidt opens with a startling industry prediction: "We believe as an industry that in the next one year the vast majority of programmers will be replaced by AI programmers. We also believe that within one year you will have graduate level mathematicians that are at the tippy top of graduate math programs." He explains why this matters so much. Programming and math aren't just two fields among many: "Programming plus math are the basis of sort of our whole digital world." And the AI labs are already using AI to build better AI: "The research groups in OpenAI and anthropic and so forthโ€ฆ around 10 or 20% of the code that they're developing in their research programs is being generated by the computer. That's called recursive self-improvement." Eric Schmidt then lays out the timeline most people haven't grasped: "Within 3 to 5 years we'll have what is called general intelligence AGI which can be defined as a system that is as smart as the smartest mathematician physicist artist writer thinker politician." He gives this belief system a name: "I call this by the way the San Francisco consensus because everyone who believes this is in San Francisco it may be the water." But the truly unsettling part comes next. Once AI starts improving itself, humans become optional to the process: "The computers are now doing self-improvementโ€ฆ they don't have to listen to us anymore. We call that super intelligence or ASIโ€ฆ computers that are smarter than the sum of humans. The San Francisco consensus is this occurs within six years." And here's where Schmidt sounds the alarm. The conversation isn't keeping pace with the technology: "This path is not understood in our society. There's no language for what happens with the arrival of this. This is happening faster than our human that our society, our democracy, our laws will address." His closing thought captures why this matters: "That's why it's underhyped. People do not understand what happens when you have intelligence at this level which is largely free."

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