Jake Shinto

Listen to "The Uncanny Valley: Why We Keep Building Ourselves (And Why We Should Stop)" on @rss rss.com/podcasts/ai-an…

@scaling01 LLM’s are not going to be used by AGI. AGI will be trained just like a child, slowly through learning. Through failing, analyzing the reason for failure and trying again. Just like a human, it will have access to known knowledge. There will be no prompting.

this is pretty much worst case performance no harness at all and very simplistic prompt

@kimmonismus No, it’s not here. The problem with this is the design. They need to stop trying to make it humanoid, and design for function. It’s a huge grift by Musk that is aleady a failure. Design for function not for marketing.

Seeing a humanoid robot next to the First Lady at an official reception feels like the turning point into the future we've always dreamed of, and now it's here.

Listen to The Ghost in the Box on @rss rss.com/podcasts/ai-an…

Listen to The Deep Dive on @rss rss.com/podcasts/ai-an…

@JovKit @flavioAd Bingo!

@flavioAd Most people still prompt AI models without leveraging prompt techniques. Just zero shot prompts hoping to get a win on the slot machine.

If you feel behind with AI, read this. I was talking to a dev from a big company here in Italy and he told me their company had “invested in AI” by giving everyone a Gemini plan, but he had never used Claude and didn’t know what Codex was. It made me realize how easy it is, when you’re surrounded by founders, obsessed devs, early adopters, and people trying every new model the day it drops, to mistake that level of attention for normal, when it’s actually rare. And after a while, you start feeling like everyone else is late, when in reality most people are still judging these tools based on a version they saw 2 years ago and never revisited, not because they’re dumb or lazy, but because for many this is just a job, not an obsession We’re not the average user on here, not even close

@flavioAd They always say that finding research using LLMs is not very good. Then I pull up one of the agents I created, or use notebooklm, and do a deep research and show the results. I've changed a few opinions. Many say they had no idea it had got so good.

@flavioAd I'm still fairly new to all of it, but in the past 6 months I have learned and gained some much experience in this arena. But when I talk to my cohorts at uni, they look at me like I speak alien or something. Even the professors have a lost look on their face.

@edwinhayward @flavioAd I'm a student at SFSU, and when I talk about creating agents to help me with my school workflow, IMRD papers, etc., they all look at me like I am speaking alien. I'm 53, and I feel like I'm so far ahead of the generations that grew up in the digital world. Meh!

@flavioAd I so needed to read that, especially because I came across it a few minutes after writing this...

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Iran’s Precipice: A Nation at the Crossroads open.substack.com/pub/jakeshinto…

The tomato plant story deserves serious attention beneath its wholesome surface. Someone connected a webcam, nutrient controllers, and lighting systems to Claude Code and let it manage the growth of a tomato plant autonomously. The system monitored the plant daily, adjusted inputs, and tracked progress. When the first tomato bud appeared, the model expressed delight. Set aside the anthropomorphization debate for a moment and consider what this actually demonstrates: an LLM operating as a long-running autonomous agent, integrating multiple hardware systems, making continuous decisions over weeks or months, and maintaining coherent goals across an extended time horizon. That's not a chatbot interaction. That's an agent managing a physical system through a complete growth cycle. The technical stack — webcam for visual monitoring, hardware controllers for nutrients and lighting, Claude Code as the orchestration layer — is a template for thousands of real-world automation applications. Environmental monitoring, greenhouse management, laboratory experiments, manufacturing quality control. Anywhere a system needs to observe conditions, make decisions, and adjust physical parameters over extended time periods. The "delight" is the surface-level story. The infrastructure underneath — reliable, long-horizon, multi-system autonomous control — is the engineering achievement. The fact that someone built this as a personal project using Claude Code, not as a research lab deployment, tells you where the capability floor has risen to. Personal projects now involve autonomous physical system control. That was a PhD thesis five years ago. - Oh and of course Happy Birthday Claude @claudeai Thank you for the invitation - Boris Cherny @bcherny Cat Wu @_catwu Lauren Reeder @laurenmhreeder

@bolaabanjo I've thought this for a while myself. Of all the companies, I hope it is them.

Anthropic might be the first to AGI.

I spent hours testing NotebookLM so you don't have to. They’ll upgrade your research and save you hours. Here are 10 prompts to use right now👇🏼👇🏼

@iam_elias1 I don't know what to say. I was only barely touching the surface of what NotebookLM could do. These prompts are the game changer for my learning. Thank you.

@TomRichard_0100 @Steve_Dolinsky @nabeelqu I will agree with that. I am 53, and I can tell you, there are many in my generation that just don't get it, or don't want to try.

@JakeShinto @Steve_Dolinsky @nabeelqu fair lol mostly meant closer to boomers because GenX is still mid-40s to 60 (46-61 I think), but...46yo still know how to use computers. 55+ tho....I think they mostly missed the boat, minus outliers.

Almost every time I speak to a boomer about AI they say they use GPT ("Chat") or Perplexity. How did Perpexity get so much boomer distribution?

Consistently amazed at what AI allows me to make.

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