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Builders I know who've automated recurring work always hit the same wall: The automation runs. Until it doesn't. Then they're the ones fixing it. The real problem isn't setup, it's that scripts don't improve. They just run until something breaks. Helio Automation is different. You assign the job to... show more

Md Santo

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41,997 просмотров • 2 месяцев назад •via X (Twitter)

Наука и технологии Образование Здоровье и велнес

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FOUR TYPES OF AGENT LOOPS. LOOP ENGINEERING IS PICKING THE RIGHT ONE, NOT THE FANCIEST ONE every loop answers two questions: what starts a run, what ends it hand-run, you answer both yourself, every time 1) turn-based > you prompt, it acts, you review, you prompt again > use while requirements are still forming 2) goal-based > /goal hit Lighthouse 90, stop after 5 tries > an evaluator checks. a no sends it back to work > use when the outcome is measurable but the path isn't 3) time-based > a clock fires, it checks the PR, fixes CI, waits again > /loop runs locally, /schedule survives a closed laptop > use for anything recurring 4) proactive > no human present. it watches a channel, triages, fixes, reviews, closes the task itself > use for standing duties you can't predict in advance the real question isn't which loop is most advanced it's whether your task is exploratory, measurable, recurring, or standing the more you hand off, the less you babysit full breakdown in the article below

FOUR TYPES OF AGENT LOOPS. LOOP ENGINEERING IS PICKING THE RIGHT ONE, NOT THE FANCIEST ONE every loop answers two questions: what starts a run, what ends it hand-run, you answer both yourself, every time 1) turn-based > you prompt, it acts, you review, you prompt again > use while requirements are still forming 2) goal-based > /goal hit Lighthouse 90, stop after 5 tries > an evaluator checks. a no sends it back to work > use when the outcome is measurable but the path isn't 3) time-based > a clock fires, it checks the PR, fixes CI, waits again > /loop runs locally, /schedule survives a closed laptop > use for anything recurring 4) proactive > no human present. it watches a channel, triages, fixes, reviews, closes the task itself > use for standing duties you can't predict in advance the real question isn't which loop is most advanced it's whether your task is exploratory, measurable, recurring, or standing the more you hand off, the less you babysit full breakdown in the article below

Fokki

10,312 просмотров • 2 месяцев назад

Rich Roll on why waiting to "feel like it" is a trap: "You can't think your way into the mood that you seek or the state of mind that you aspire to inhabit. Action is the only thing that can trigger that change." Rich uses running as the perfect illustration of this principle. Imagine you wake up in the morning and you're supposed to do a run because you're training for a race. You don't feel like it. So what do most of us do? "We all resort to that state where we think, 'Well, I don't want to do it right now. I'll just wait until I feel like doing it and then I'll do it then.'" But here's the problem with that logic: "If you're waiting until you feel like doing something, chances are you're probably never going to get to it." The mood you're hoping will arrive on its own? It's not coming. Not without action first. "To take the action despite how you feel about it is the thing that catalyzes the state change." You don't run because you feel motivated. You feel motivated because you ran. He points to what every runner knows from experience: "When they finish the run, they're always glad that they did it. They don't generally regret it. And then they feel better." Notice the sequence. The good feeling comes after the action, not before it. The state change is the reward for showing up, not the prerequisite. And this isn't just about running. As Rich puts it: "That example is applicable to all areas of life." The workout you're avoiding. The conversation you're delaying. The project you're putting off until you're "in the right headspace." You're waiting for a feeling that only exists on the other side of doing the thing.

Rich Roll on why waiting to "feel like it" is a trap: "You can't think your way into the mood that you seek or the state of mind that you aspire to inhabit. Action is the only thing that can trigger that change." Rich uses running as the perfect illustration of this principle. Imagine you wake up in the morning and you're supposed to do a run because you're training for a race. You don't feel like it. So what do most of us do? "We all resort to that state where we think, 'Well, I don't want to do it right now. I'll just wait until I feel like doing it and then I'll do it then.'" But here's the problem with that logic: "If you're waiting until you feel like doing something, chances are you're probably never going to get to it." The mood you're hoping will arrive on its own? It's not coming. Not without action first. "To take the action despite how you feel about it is the thing that catalyzes the state change." You don't run because you feel motivated. You feel motivated because you ran. He points to what every runner knows from experience: "When they finish the run, they're always glad that they did it. They don't generally regret it. And then they feel better." Notice the sequence. The good feeling comes after the action, not before it. The state change is the reward for showing up, not the prerequisite. And this isn't just about running. As Rich puts it: "That example is applicable to all areas of life." The workout you're avoiding. The conversation you're delaying. The project you're putting off until you're "in the right headspace." You're waiting for a feeling that only exists on the other side of doing the thing.

Kevin Tanaka

10,256 просмотров • 5 месяцев назад

Right now, I have a few dozen tools to automate my life. Every single thing that I do more than once every week is now a tool: either a Claude Code skill, a scheduled workflow, or an application. • I spend the time doing this once. • I schedule it. • I forget about it (or at least, try to) CREAO is one of the platforms I've used extensively for building some of these automations. They are partnering with me on this post. They are one of the only platforms where you can go from a conversation to a scheduled agent that quickly. This is how it works: 1. You describe the problem you want to solve 2. CREAO's agent builds the logic and executes it 3. You can iterate with the agent to improve the solution 4. When you are done, you turn that solution into a mini app 5. You can schedule that app to run any time you need it Something important: These agents are deterministic. They don't use an LLM to generate answers, so they will always return the same output given the same input. This is critical: when you're automating something that runs every Monday at 9 am, you need it to return the same result every time, not a "creative" answer.

Right now, I have a few dozen tools to automate my life. Every single thing that I do more than once every week is now a tool: either a Claude Code skill, a scheduled workflow, or an application. • I spend the time doing this once. • I schedule it. • I forget about it (or at least, try to) CREAO is one of the platforms I've used extensively for building some of these automations. They are partnering with me on this post. They are one of the only platforms where you can go from a conversation to a scheduled agent that quickly. This is how it works: 1. You describe the problem you want to solve 2. CREAO's agent builds the logic and executes it 3. You can iterate with the agent to improve the solution 4. When you are done, you turn that solution into a mini app 5. You can schedule that app to run any time you need it Something important: These agents are deterministic. They don't use an LLM to generate answers, so they will always return the same output given the same input. This is critical: when you're automating something that runs every Monday at 9 am, you need it to return the same result every time, not a "creative" answer.

Santiago

13,727 просмотров • 6 месяцев назад

Build and operate your ICP apps straight from your favorite AI. That is what the ICP MCP connector unlocks. The first thing it gives you: automation. Hand an agent your repetitive, multi-step work and it runs the whole flow for you. Schedule it, batch it, let it connect the steps into one task. Now in preview. Arshavir Ter-Gabrielyan explains how it works. Full conversation below.

Build and operate your ICP apps straight from your favorite AI. That is what the ICP MCP connector unlocks. The first thing it gives you: automation. Hand an agent your repetitive, multi-step work and it runs the whole flow for you. Schedule it, batch it, let it connect the steps into one task. Now in preview. Arshavir Ter-Gabrielyan explains how it works. Full conversation below.

DFINITY Foundation

24,938 просмотров • 2 месяцев назад

Your AI sucks. And it's not the model. It's you. "Make it better" isn't a prompt, it's a guessing game. I changed one step in my process and the output quality completely flipped. Same ideas, same models, totally different results.

Your AI sucks. And it's not the model. It's you. "Make it better" isn't a prompt, it's a guessing game. I changed one step in my process and the output quality completely flipped. Same ideas, same models, totally different results.

Flick

322,001 просмотров • 2 месяцев назад

“Palestinians do not exist” is not a historical argument. It is the first stage of theft: erase the name, then erase the village, then tell the refugee: you were never here. The problem is not that they do not know history. The problem is that they need to erase it.

“Palestinians do not exist” is not a historical argument. It is the first stage of theft: erase the name, then erase the village, then tell the refugee: you were never here. The problem is not that they do not know history. The problem is that they need to erase it.

SilencedSirs◼️

30,822 просмотров • 3 месяцев назад

2026 is the year AI automation goes mainstream. But there's one problem. Every automation creates value, yet almost all of that value flows to Big Tech and billion-dollar companies. That's where Actionist.AI changes the game. Powered by Action Model, Actionist is an AI automation platform where the people helping train and grow the AI can share in the value it creates, not just shareholders and billionaires. Automation is inevitable. Who benefits from it isn't. Join our Discord to be among the first to experience Actionist before anyone else.

2026 is the year AI automation goes mainstream. But there's one problem. Every automation creates value, yet almost all of that value flows to Big Tech and billion-dollar companies. That's where Actionist.AI changes the game. Powered by Action Model, Actionist is an AI automation platform where the people helping train and grow the AI can share in the value it creates, not just shareholders and billionaires. Automation is inevitable. Who benefits from it isn't. Join our Discord to be among the first to experience Actionist before anyone else.

Action Model

25,581 просмотров • 3 месяцев назад

Omg 😱😱 Machines don't replace hard work they redefine it. Behind every automated movement is engineering, innovation, and years of human expertise. From boosting efficiency to improving precision, industrial automation is transforming the way the world builds, creates, and grows. 📈 The future isn't coming it's already at work. ⚡🌍

Omg 😱😱 Machines don't replace hard work they redefine it. Behind every automated movement is engineering, innovation, and years of human expertise. From boosting efficiency to improving precision, industrial automation is transforming the way the world builds, creates, and grows. 📈 The future isn't coming it's already at work. ⚡🌍

Kimmiee 🥰

201,614 просмотров • 3 месяцев назад

AI agents are moving past the point where generating an answer is enough. The real question is whether an agent can stay inside a real environment long enough to actually finish the job. That’s what makes Nex-N2.5 interesting to me It is built around long-horizon computer use, with agents that can operate browsers and software, execute programs, see the results through visual feedback, and keep correcting their work instead of stopping at the first output. Nex-N2.5 Pro was put through exactly that kind of workflow: build a SQL interpreter, write tests, run it, identify what was missing, implement the next pieces, and keep iterating. The model progressively handled things like WHERE, JOIN, subqueries, qualified names and table aliases while testing its own implementation along the way. That is the difference I care about with agentic AI. The agent isn't just producing an artifact. It's operating inside a feedback loop: write → run → inspect the result → fix the problem → verify again. Nex-N2.5 is also released as a family of Mini, Pro and Max models, giving developers different options depending on the task and deployment requirements. The Nex-N2.5 Mini and Pro are both available through OpenRouter for free for a limited time: Try it out here: The real shift isn't just better AI outputs. It’s agents that can interact with an environment, respond to what happens, and keep working until the task is actually finished.

AI agents are moving past the point where generating an answer is enough. The real question is whether an agent can stay inside a real environment long enough to actually finish the job. That’s what makes Nex-N2.5 interesting to me It is built around long-horizon computer use, with agents that can operate browsers and software, execute programs, see the results through visual feedback, and keep correcting their work instead of stopping at the first output. Nex-N2.5 Pro was put through exactly that kind of workflow: build a SQL interpreter, write tests, run it, identify what was missing, implement the next pieces, and keep iterating. The model progressively handled things like WHERE, JOIN, subqueries, qualified names and table aliases while testing its own implementation along the way. That is the difference I care about with agentic AI. The agent isn't just producing an artifact. It's operating inside a feedback loop: write → run → inspect the result → fix the problem → verify again. Nex-N2.5 is also released as a family of Mini, Pro and Max models, giving developers different options depending on the task and deployment requirements. The Nex-N2.5 Mini and Pro are both available through OpenRouter for free for a limited time: Try it out here: The real shift isn't just better AI outputs. It’s agents that can interact with an environment, respond to what happens, and keep working until the task is actually finished.

Mide

20,759 просмотров • 25 дней назад

Right now, most developers are still doing the job AI should be doing. Assign a task. Wait. Review the result. Fix it. Repeat. You're not building with AI. You're babysitting it. The shift happening now is much bigger than better coding agents. It's loop engineering. Instead of managing every AI agent yourself, you build a system that manages them for you. One loop assigns the work. Another checks the output. A third retries failures. Everything keeps moving without waiting for your next prompt. Your job doesn't disappear. It changes. You stop acting like a project manager... ...and start designing the system that manages the entire team. That's exactly the transition I break down in the article below.

Right now, most developers are still doing the job AI should be doing. Assign a task. Wait. Review the result. Fix it. Repeat. You're not building with AI. You're babysitting it. The shift happening now is much bigger than better coding agents. It's loop engineering. Instead of managing every AI agent yourself, you build a system that manages them for you. One loop assigns the work. Another checks the output. A third retries failures. Everything keeps moving without waiting for your next prompt. Your job doesn't disappear. It changes. You stop acting like a project manager... ...and start designing the system that manages the entire team. That's exactly the transition I break down in the article below.

Pure

11,344 просмотров • 2 месяцев назад

Introducing Scheduled Tasks 2.0 — a major upgrade to recurring work in Manus. Scheduling is no longer just about running something at the right time. It is about running it in the right place, with the right context. Now scheduled work can continue in the same task, power background actions in Manus-built web apps, and give you clearer visibility into upcoming and past runs. Available now to all users.

Introducing Scheduled Tasks 2.0 — a major upgrade to recurring work in Manus. Scheduling is no longer just about running something at the right time. It is about running it in the right place, with the right context. Now scheduled work can continue in the same task, power background actions in Manus-built web apps, and give you clearer visibility into upcoming and past runs. Available now to all users.

Manus

96,276 просмотров • 4 месяцев назад

Cognition CEO Scott Wu says he couldn’t sleep after he saw Devin work for the first time: “ The first time it did a real task, I could not sleep that night.” “ The first task that Devin did was it set up MongoDB for us.” “It’s a standard thing, issues that people run into all the time. You have this whole flow where you find an error. Google the error. Find some message on Stack Overflow. Ask ChatGPT. It tells you, ‘Here’s what you should try.’ You try it. Run into a different error, paste that in…” “ At some point we're just like, ‘Okay, Devin, just try to fix it. Go run commands. Do whatever you need to.’” “And it worked.” “ It was just seeing the exponential curve ahead because this was a very specific case. It was the one success that we had.” “ There was this feeling of—why shouldn't all software and all products be built this way now where you can just tell it what you want it to do, and have it go do it.”

Cognition CEO Scott Wu says he couldn’t sleep after he saw Devin work for the first time: “ The first time it did a real task, I could not sleep that night.” “ The first task that Devin did was it set up MongoDB for us.” “It’s a standard thing, issues that people run into all the time. You have this whole flow where you find an error. Google the error. Find some message on Stack Overflow. Ask ChatGPT. It tells you, ‘Here’s what you should try.’ You try it. Run into a different error, paste that in…” “ At some point we're just like, ‘Okay, Devin, just try to fix it. Go run commands. Do whatever you need to.’” “And it worked.” “ It was just seeing the exponential curve ahead because this was a very specific case. It was the one success that we had.” “ There was this feeling of—why shouldn't all software and all products be built this way now where you can just tell it what you want it to do, and have it go do it.”

David Senra

72,493 просмотров • 3 месяцев назад

Just some of the things you have been lied to about: "Democracy doesn't exist." "Fluoride isn't for your teeth—it's for your brain." "Injections don't save lives—they ruin them." "Education doesn't make people smarter—it makes them indoctrinated." "Doctors don't know about health—they only know about drugs." "And the Sun isn't the enemy—sunscreen is." "Everything that we have been taught is complete garbage."

Just some of the things you have been lied to about: "Democracy doesn't exist." "Fluoride isn't for your teeth—it's for your brain." "Injections don't save lives—they ruin them." "Education doesn't make people smarter—it makes them indoctrinated." "Doctors don't know about health—they only know about drugs." "And the Sun isn't the enemy—sunscreen is." "Everything that we have been taught is complete garbage."

Wide Awake Media

56,302 просмотров • 9 месяцев назад

i watched gemma 4 12b build something genuinely impressive today, and then loop itself to death right in front of me. the full run is in the video, sped up but completely uncut, watch it to the end and you will catch the exact moment it stops building and starts looping right in the middle of the work. the task was clean, build a single file gravity simulator, n-body physics, orbits, collisions, running locally on one 3090 through an agent. and for ten minutes it was a joy to watch. it reached for a symplectic integrator on its own, the correct one, the kind that keeps orbits stable instead of spiralling out. real gravity with softening, proper orbital velocities, momentum conserved on collision. the physics was right. the thing actually worked. then on the very last step, writing a few tests to prove its own code, it fell into a loop. not a crash, a loop. it started repeating itself and would not stop. ten more minutes, thirty four thousand tokens into a single answer, the same fragments over and over, until i killed it myself. so it's not that gemma can't code. it did the hard part beautifully. it cannot finish. it cannot hold a long task together without unravelling, and finishing is the entire job in agentic work. here's the part that stings. i run this exact task, same harness, same card, on the chinese open models, qwen especially, and i never see this. they build it, they test it, they stop. every single time. google has the raw capability, you can see it sitting right there in the code, and then the model loops itself to death on a task a 27b from alibaba finishes clean. open weights, apache 2.0, so much to love on paper. i just need it to know when to stop talking.

i watched gemma 4 12b build something genuinely impressive today, and then loop itself to death right in front of me. the full run is in the video, sped up but completely uncut, watch it to the end and you will catch the exact moment it stops building and starts looping right in the middle of the work. the task was clean, build a single file gravity simulator, n-body physics, orbits, collisions, running locally on one 3090 through an agent. and for ten minutes it was a joy to watch. it reached for a symplectic integrator on its own, the correct one, the kind that keeps orbits stable instead of spiralling out. real gravity with softening, proper orbital velocities, momentum conserved on collision. the physics was right. the thing actually worked. then on the very last step, writing a few tests to prove its own code, it fell into a loop. not a crash, a loop. it started repeating itself and would not stop. ten more minutes, thirty four thousand tokens into a single answer, the same fragments over and over, until i killed it myself. so it's not that gemma can't code. it did the hard part beautifully. it cannot finish. it cannot hold a long task together without unravelling, and finishing is the entire job in agentic work. here's the part that stings. i run this exact task, same harness, same card, on the chinese open models, qwen especially, and i never see this. they build it, they test it, they stop. every single time. google has the raw capability, you can see it sitting right there in the code, and then the model loops itself to death on a task a 27b from alibaba finishes clean. open weights, apache 2.0, so much to love on paper. i just need it to know when to stop talking.

Sudo su

39,764 просмотров • 4 месяцев назад

ELON: I ALWAYS ASK THE SAME QUESTIONS WHEN HIRING SOMEONE “When I interview somebody, my interview question is always the same - tell me the story of your life and the decisions that you made along the way and why you made them. And then also tell me about some of the most difficult problems you worked on and how you solved them. That question, I think, is very important, because the people that really solved the problem, they know exactly how they solved it - they know the little details. And the people that pretended to solve the problem, they can maybe go one level and then they get stuck.” Source: Elon Musk

ELON: I ALWAYS ASK THE SAME QUESTIONS WHEN HIRING SOMEONE “When I interview somebody, my interview question is always the same - tell me the story of your life and the decisions that you made along the way and why you made them. And then also tell me about some of the most difficult problems you worked on and how you solved them. That question, I think, is very important, because the people that really solved the problem, they know exactly how they solved it - they know the little details. And the people that pretended to solve the problem, they can maybe go one level and then they get stuck.” Source: Elon Musk

Mario Nawfal

105,434 просмотров • 1 год назад

I gave Qoder a task instead of a prompt. And it actually did the work. It read the context, made a plan, used the tools it needed, executed the task, and verified the result before finishing. That’s the big difference with Qoder. It feels less like chatting with AI and more like handing a task to an AI agent on your computer. You describe the outcome you want. Qoder can then: • Read your workspace and context • Plan how to complete the task • Execute commands and make file changes • Use the browser or desktop when needed • Let you pause, adjust, or take over • Verify the result when it’s done And right now, Qwen3.8-Flash runs at 0.0x Credits through September 30. New users can also get 600 Credits in total. Try Qoder here: If you already use AI for coding or everyday computer work, this is worth experimenting with.

I gave Qoder a task instead of a prompt. And it actually did the work. It read the context, made a plan, used the tools it needed, executed the task, and verified the result before finishing. That’s the big difference with Qoder. It feels less like chatting with AI and more like handing a task to an AI agent on your computer. You describe the outcome you want. Qoder can then: • Read your workspace and context • Plan how to complete the task • Execute commands and make file changes • Use the browser or desktop when needed • Let you pause, adjust, or take over • Verify the result when it’s done And right now, Qwen3.8-Flash runs at 0.0x Credits through September 30. New users can also get 600 Credits in total. Try Qoder here: If you already use AI for coding or everyday computer work, this is worth experimenting with.

Shushant Lakhyani

143,425 просмотров • 9 дней назад

EVERYONE HAS BEEN TALKING ABOUT AI AGENTS. BUT THE REAL UNLOCK ISN'T BETTER PROMPTS: IT'S BETTER LOOPS. Everyone is chasing the perfect prompt. The top AI engineers are chasing something else: loops. Instead of chatting back and forth with AI, they give it one goal and let it continuously improve its own work until the objective is met. Here's the framework: • Define the goal once • AI discovers the work required • Specialized agents tackle tasks in parallel • A verification agent checks the results • If the goal isn't met: loop again. If it is: deliver. The key ingredient is memory. Each cycle remembers what was completed, what failed, and what still needs work, so every iteration gets smarter instead of starting from scratch. There are two approaches: • Open loops: Maximum autonomy, but higher token costs as AI explores freely. • Closed loops: Clear boundaries, defined success metrics, lower costs, and far more practical for everyday workflows. Don't ask AI to complete a task. Build a system where AI can plan, execute, verify, remember, and repeat until the task is actually finished. Prompt engineering got us here. Loop engineering is what takes AI from assistant to autonomous teammate.

EVERYONE HAS BEEN TALKING ABOUT AI AGENTS. BUT THE REAL UNLOCK ISN'T BETTER PROMPTS: IT'S BETTER LOOPS. Everyone is chasing the perfect prompt. The top AI engineers are chasing something else: loops. Instead of chatting back and forth with AI, they give it one goal and let it continuously improve its own work until the objective is met. Here's the framework: • Define the goal once • AI discovers the work required • Specialized agents tackle tasks in parallel • A verification agent checks the results • If the goal isn't met: loop again. If it is: deliver. The key ingredient is memory. Each cycle remembers what was completed, what failed, and what still needs work, so every iteration gets smarter instead of starting from scratch. There are two approaches: • Open loops: Maximum autonomy, but higher token costs as AI explores freely. • Closed loops: Clear boundaries, defined success metrics, lower costs, and far more practical for everyday workflows. Don't ask AI to complete a task. Build a system where AI can plan, execute, verify, remember, and repeat until the task is actually finished. Prompt engineering got us here. Loop engineering is what takes AI from assistant to autonomous teammate.

Dami-Defi

13,363 просмотров • 2 месяцев назад

Figma CEO Dylan Field just identified the only competitive advantage that AI cannot commoditize. It isn’t your technical skill. It isn’t your speed. It isn’t your tools. Field: “If an agent can do it for you, an agent can do it for someone else.” That’s the fatal flaw in the entire AI productivity argument nobody wants to say out loud. When execution becomes free, execution becomes worthless. The moment anyone can build anything by typing a prompt, the output stops being the differentiator. What remains is taste. The one thing the agent cannot generate for you. Field: “What is different about your setup than others?” If you are typing generic prompts and accepting the first output the agent hands you, you aren’t building a product. You are retrieving a commodity. The same commodity available to every competitor on earth. Field: “You at least have to have something different there in order to not think that you’re just gonna get the same out.” But taste alone isn’t enough. The other half is exploration. Field: “The more you can sample the possibility space, it gives you something to react to.” The blank page is gone. The new constraint isn’t creation. It’s selection. The agent generates hundreds of possibilities in seconds. Your job is to go wide enough to find the best one hiding inside all of them. And then be honest enough with yourself to know when none of them are good enough. Field: “If you find areas where you’re going, ‘Hey, I don’t feel like I am liking this enough,’ then you got to keep pushing.” The creators who win this era won’t be the fastest builders. They’ll be the harshest critics. The ones who can generate the widest possibility space and identify the single best solution inside it. The ones whose taste is specific enough, developed enough, and honest enough to reject everything the agent produces until it produces something worth keeping. The AI can build anything you can describe. It cannot want anything. It cannot feel when something is wrong. It cannot tell the difference between good and extraordinary. That gap is the only moat left.

Figma CEO Dylan Field just identified the only competitive advantage that AI cannot commoditize. It isn’t your technical skill. It isn’t your speed. It isn’t your tools. Field: “If an agent can do it for you, an agent can do it for someone else.” That’s the fatal flaw in the entire AI productivity argument nobody wants to say out loud. When execution becomes free, execution becomes worthless. The moment anyone can build anything by typing a prompt, the output stops being the differentiator. What remains is taste. The one thing the agent cannot generate for you. Field: “What is different about your setup than others?” If you are typing generic prompts and accepting the first output the agent hands you, you aren’t building a product. You are retrieving a commodity. The same commodity available to every competitor on earth. Field: “You at least have to have something different there in order to not think that you’re just gonna get the same out.” But taste alone isn’t enough. The other half is exploration. Field: “The more you can sample the possibility space, it gives you something to react to.” The blank page is gone. The new constraint isn’t creation. It’s selection. The agent generates hundreds of possibilities in seconds. Your job is to go wide enough to find the best one hiding inside all of them. And then be honest enough with yourself to know when none of them are good enough. Field: “If you find areas where you’re going, ‘Hey, I don’t feel like I am liking this enough,’ then you got to keep pushing.” The creators who win this era won’t be the fastest builders. They’ll be the harshest critics. The ones who can generate the widest possibility space and identify the single best solution inside it. The ones whose taste is specific enough, developed enough, and honest enough to reject everything the agent produces until it produces something worth keeping. The AI can build anything you can describe. It cannot want anything. It cannot feel when something is wrong. It cannot tell the difference between good and extraordinary. That gap is the only moat left.

Dustin

208,907 просмотров • 7 месяцев назад

The Pope says AI is a Tower of Babel. Plotinus would say it's worse; it's the next emanation downward. Intelligence commoditized is thinning until it's indistinguishable from nothing. The machine doesn't oppress you. It just fills every gap where self-knowledge might have grown. The distance between you and it narrows, not because it ascends, but because you descend. The real question isn't whether the machine thinks. It's whether you still can, when every silence gets filled before it opens. Pattern without presence. Knowing without being. Lego Socrates and Alcibiades discuss.

The Pope says AI is a Tower of Babel. Plotinus would say it's worse; it's the next emanation downward. Intelligence commoditized is thinning until it's indistinguishable from nothing. The machine doesn't oppress you. It just fills every gap where self-knowledge might have grown. The distance between you and it narrows, not because it ascends, but because you descend. The real question isn't whether the machine thinks. It's whether you still can, when every silence gets filled before it opens. Pattern without presence. Knowing without being. Lego Socrates and Alcibiades discuss.

Peter Girnus 🦅

26,210 просмотров • 4 месяцев назад

Automation tools can encode steps. They can't encode judgment. “Watch 3 competitors' prices, ping me on a 5% drop.” The n8n version needed: - a URL - a CSS selector - a sheet ID …then broke the same evening when my target site's HTML didn't match the template author's. I told an AI teammate in Helio the 3 competitors and the one rule. It set up the checks itself, and has run them on its own since. Ten minutes against three hours. Not easier automation. Describing outcomes instead of steps.

Automation tools can encode steps. They can't encode judgment. “Watch 3 competitors' prices, ping me on a 5% drop.” The n8n version needed: - a URL - a CSS selector - a sheet ID …then broke the same evening when my target site's HTML didn't match the template author's. I told an AI teammate in Helio the 3 competitors and the one rule. It set up the checks itself, and has run them on its own since. Ten minutes against three hours. Not easier automation. Describing outcomes instead of steps.

Ethan Walker

13,998 просмотров • 2 месяцев назад