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Most AI agents do not fail because the prompt is weak. They fail because there is no loop around the prompt. A real loop: - Finds work - Executes - Verifies - Saves state - Stops or escalates If an agent can say “done” without tests, a budget cap...

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Fluixo

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16,089 görüntüleme • 4 gün önce •via X (Twitter)

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The guy who kicked off the entire "loop engineering" wave Peter Steinberger: "You shouldn't be prompting coding agents anymore. You should be designing loops that prompt your agents." One post. 6.5M views in a week. In this talk he walks the real stack: the agent loop, a verifier that fails its own work and retries, and a loop that rewrites the agent while he sleeps. Worth more than any $500 vibe-coding course. Watch it, then read the full breakdown of the 4 loops below.
17:30

The guy who kicked off the entire "loop engineering" wave Peter Steinberger: "You shouldn't be prompting coding agents anymore. You should be designing loops that prompt your agents." One post. 6.5M views in a week. In this talk he walks the real stack: the agent loop, a verifier that fails its own work and retries, and a loop that rewrites the agent while he sleeps. Worth more than any $500 vibe-coding course. Watch it, then read the full breakdown of the 4 loops below.

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Boris Cherny runs Claude Code at Anthropic. His line should stop you mid-scroll: "I don't prompt Claude anymore. I have loops running that prompt Claude and figure out what to do. My job is to write loops." The skill moved. From writing prompts to building the loop around them. A loop that works has three things. Drop one and it burns money in circles. 1. A finish line it can prove. Tests pass or they fail. "Looks done" is not a finish line. 2. A checker that did not write the code. A model grading itself always says yes. 3. Brakes set before you start. A step limit. A money ceiling. A stop condition. That is the whole game. The loop does the work. You design the cage it runs in. Full breakdown below.
0:37

Boris Cherny runs Claude Code at Anthropic. His line should stop you mid-scroll: "I don't prompt Claude anymore. I have loops running that prompt Claude and figure out what to do. My job is to write loops." The skill moved. From writing prompts to building the loop around them. A loop that works has three things. Drop one and it burns money in circles. 1. A finish line it can prove. Tests pass or they fail. "Looks done" is not a finish line. 2. A checker that did not write the code. A model grading itself always says yes. 3. Brakes set before you start. A step limit. A money ceiling. A stop condition. That is the whole game. The loop does the work. You design the cage it runs in. Full breakdown below.

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9 out of 10 multi-agent projects never leave demo mode Not because the model is bad. Because the structure is missing. Most people who try to build [ a team of AI agents ] end up with one agent talking to itself in five tabs. The agents don't share context → Don't divide work → Don't know what the others are doing. > stage 1: if your agent doesn't have a real loop, observe, act, iterate, you have a long prompt, not an agent > stage 2: subagents need isolated context, the orchestrator never reads their raw transcript, only the summary > stage 3: the orchestrator plans and delegates, the moment it executes, it drowns in details that belong inside subagents > stage 4: without a shared task list it's not a team, it's five agents duplicating each other's work in parallel > stage 5: a permissions file is what lets you sleep, the model cannot bypass it because the rule lives outside the model a team of AI agents is not more model, it is more structure the most ignored stage in every demo that died: - durability... when a 50-step task crashes at step 47 and starts from zero, that's not a model failure. that's a missing write-to-disk call
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9 out of 10 multi-agent projects never leave demo mode Not because the model is bad. Because the structure is missing. Most people who try to build [ a team of AI agents ] end up with one agent talking to itself in five tabs. The agents don't share context → Don't divide work → Don't know what the others are doing. > stage 1: if your agent doesn't have a real loop, observe, act, iterate, you have a long prompt, not an agent > stage 2: subagents need isolated context, the orchestrator never reads their raw transcript, only the summary > stage 3: the orchestrator plans and delegates, the moment it executes, it drowns in details that belong inside subagents > stage 4: without a shared task list it's not a team, it's five agents duplicating each other's work in parallel > stage 5: a permissions file is what lets you sleep, the model cannot bypass it because the rule lives outside the model a team of AI agents is not more model, it is more structure the most ignored stage in every demo that died: - durability... when a 50-step task crashes at step 47 and starts from zero, that's not a model failure. that's a missing write-to-disk call

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Demo 2: human + agent design loop inside Codex. Not every workflow needs the agent to drive everything. You design in Open Design. Codex reviews the output. It can inspect files, open pages, test interactions, critique the design, and turn feedback into the next prompt. Design becomes a human + agent feedback loop.
1:08

Demo 2: human + agent design loop inside Codex. Not every workflow needs the agent to drive everything. You design in Open Design. Codex reviews the output. It can inspect files, open pages, test interactions, critique the design, and turn feedback into the next prompt. Design becomes a human + agent feedback loop.

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Boris Cherny, the engineer who built Claude Code: "I don't talk to an agent anymore. I talk to a loop" a prompt is one instruction you babysit. a loop is a goal the AI works toward on its own: > it plans > does the work > checks itself > fixes what's weak > and repeats until it's done you step out, the work keeps going that's the shift the best engineers quietly moved to. most people hear a line like that and have no idea what it means in practice this breaks it down without the hype: what a loop actually is, when it's worth building, when it's a money trap, and how to run one yourself today watch his clip, then read the article below
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Boris Cherny, the engineer who built Claude Code: "I don't talk to an agent anymore. I talk to a loop" a prompt is one instruction you babysit. a loop is a goal the AI works toward on its own: > it plans > does the work > checks itself > fixes what's weak > and repeats until it's done you step out, the work keeps going that's the shift the best engineers quietly moved to. most people hear a line like that and have no idea what it means in practice this breaks it down without the hype: what a loop actually is, when it's worth building, when it's a money trap, and how to run one yourself today watch his clip, then read the article below

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It’s much easier to build an AI agent, if all you have to do is prompt it! The backend, code execution and deployment are all handled automatically. Even non-techies can build AI agents

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AI coding agents are great at building frontends, but they usually fail the moment you ask for a database or auth. You end up with a "cool demo" that doesn't actually work. I just tried InsForge 2.0, and it’s the first time I've seen an AI agent ship a fullstack app in seconds. Here is why it’s a game-changer:

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Karpathy compressed the agent problem into 1 sentence: "If you can't evaluate then you can't auto research it, right?" That's the rule I keep coming back to with long-running coding agents Before you launch /goal or /loop, write the verifier: - what counts as done - what evidence proves it - which checks run every pass - which artifact gets saved - which failure sends it back into the loop Then let the agent run The loop can keep going because proof sits outside the agent's own explanation Tests, screenshots, benchmark curves, browser runs, changed files That's how you get autonomy without babysitting a transcript for 6 hours Read the full breakdown on goals, verifiers, loops, artifacts, and session memory in the article below

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There are two types of engineers in 2026 The first opens his laptop in the morning, writes a prompt, waits, reads the diff, fixes it, repeats The second opens his phone, checks what shipped while he was asleep Different answer to one question: who is the loop? The first engineer is the loop, he starts the agent, reviews the output, decides what's next, the moment he steps away it stops The second engineer built the system that is the loop instead of him, PR review, flaky tests, CI triage, all running without him, he just reads the report What stops most people isn't complexity It's that they never noticed they are the bottleneck Full guide to building the system that runs without you below

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Introducing the Legora Agent: the execution engine at the core of the Legora aOS™. Most legal AI responds to prompts. The Agent executes objectives. You give it a goal and it builds a plan, presents it for approval, then carries the work out end-to-end — selecting the right tools as it goes, whether that's research, drafting, or review. It keeps working while you review, instead of waiting for the next prompt — while you're with clients, with family, or even while you sleep. It isn’t a tool you use. It's a super-associate you supervise. The Legora Agent is live today. Read the announcement here:

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1:32

Here is how simple it is to build a UI front-end for an AI agent (assuming you are using . The video here is 90 seconds and will show you everything you need to connect an agent to a web app: 1. Set up CopilotKit🪁 in your app 1. Copy the agent's localhost URL from LangGraph Studio 2. Paste that URL into a CopilotKit🪁 remote endpoint That's all you need to get a front-end to interact with the agent! The secret sauce here is CoAgents, an open-source library. You can use CoAgents to integrate any agent with your web application. Here is what you get: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Watch the attached demo so you see it in action.

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Imagine a computer where you don’t need to learn 10 apps to get work done. You just tell it what you want, and it adapts to how you work. I tested Happycapy with a real use case and created an image and a video. No coding, no complex software. Do this: - Build automations that run on schedule - Deploy agent teams that work for you AI molds how you work, not the other way around. If you can use a computer, you can make anything happen. Start your first Automation and Agent teams today.

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Most people don’t fail because they’re not capable. They fail because fear convinces them not to try and not to commit. What you want doesn't matter. All that matters is what you are willing to work for and commit to. Are you willing to work without a guarantee of success?
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Most people don’t fail because they’re not capable. They fail because fear convinces them not to try and not to commit. What you want doesn't matter. All that matters is what you are willing to work for and commit to. Are you willing to work without a guarantee of success?

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Sam Altman said on camera at Stripe Sessions that we are in an insane area, stuck with old devices and old operating systems. The OS and the browser are built for humans clicking. Both get rewritten around agents that do not click. Two years deep building agents. Sharpest framing I have heard a frontier lab CEO put on the next interface. ↓ Read it before the gap locks in The part nobody is sitting with: >the chat window is the old interface >the new one is an agent in a loop with your machine >the gap between people prompting and people running agents is forming right now >in six months it will be permanent Open Claude, type a question, copy the answer, paste it into VS Code. The average dev runs this loop every day. They think they are using AI. That is 5% of what these tools can do. The people pulling ahead do not prompt. They build harnesses. They run agents in loops. Same model. Different output. Full 2026 agent playbook in the article below here:
0:35

Sam Altman said on camera at Stripe Sessions that we are in an insane area, stuck with old devices and old operating systems. The OS and the browser are built for humans clicking. Both get rewritten around agents that do not click. Two years deep building agents. Sharpest framing I have heard a frontier lab CEO put on the next interface. ↓ Read it before the gap locks in The part nobody is sitting with: >the chat window is the old interface >the new one is an agent in a loop with your machine >the gap between people prompting and people running agents is forming right now >in six months it will be permanent Open Claude, type a question, copy the answer, paste it into VS Code. The average dev runs this loop every day. They think they are using AI. That is 5% of what these tools can do. The people pulling ahead do not prompt. They build harnesses. They run agents in loops. Same model. Different output. Full 2026 agent playbook in the article below here:

Rohit

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The fastest Hermes agent is the one that reads less. I didn’t make Hermes 10x faster by changing the model. I made it faster by removing the tax I was charging it on every task: bad structure. Hermes was powerful already. The problem was that I had built my workspace for a human brain, then expected an agent to navigate it like it had memory, taste, and intuition. It doesn’t. It searches. And when your files are arranged by how you think, not how the agent moves, Hermes burns tokens opening the wrong docs before the real work even starts. That was my mistake. I had folders like: >Articles >Research >Assets >Strategy >Old drafts >Clean for me. Terrible for an agent. A launch plan might need brand strategy, previous launches, voice rules, current articles, and promotion notes. For me, that context is obvious. For Hermes, it was scattered. So I stopped optimizing the model and started optimizing the terrain. The fix was stupidly small: > One folder per concern >Numbered folders for reading order >One INDEX.md at the root of every major folder >Archived files separated from active files >A clear “Where To Go” section so Hermes knows where to start My INDEX.md became the map. Not a giant table of everything. Not documentation theater. Just enough scaffolding to tell Hermes: - what exists - what matters - what is current - where to start - what to ignore unless asked Before that, Hermes opened 7 files to find one current brief. After that, it opened the index, followed the pointer, and got to work. That is the real 10x. Not “better prompting.” Less wandering. This is where loop engineering actually matters. The video said it best: “You were the loop.” That line hit because it explains why most agent workflows still feel manual. You are still checking. You are still redirecting. You are still telling the agent which file is current, which folder matters, and what done means. Hermes gets powerful when you stop being the loop and start designing the loop. The loop I’d build looks like this: State: Hermes reads the folder index and current task state Action: it opens only the canonical files Feedback: tests, screenshots, diffs, or human notes tell it what happened Verification: a gate decides whether the work is actually done Termination: the task stops only when the done condition is met For deterministic work, the gate is simple: tests pass build succeeds deployment checks clear For non-deterministic work, I’d use an adversarial loop: one model builds another model reviews Hermes updates the skill when the reviewer finds a pattern That is where Hermes becomes different. It is not just running prompts. It is carrying memory through files, using skills, checking its own work, and improving the process around the task. The agent was never the slow part. The missing map was. A powerful agent inside a messy workspace becomes a very expensive intern. A powerful agent inside a mapped system becomes leverage. My new rule: Before I ask Hermes to do more, I ask: “Does it know where to look?” Because most agents do not fail from lack of intelligence. They fail from lack of scaffolding. Build the scaffolding. Let Hermes do the work.
13:20

The fastest Hermes agent is the one that reads less. I didn’t make Hermes 10x faster by changing the model. I made it faster by removing the tax I was charging it on every task: bad structure. Hermes was powerful already. The problem was that I had built my workspace for a human brain, then expected an agent to navigate it like it had memory, taste, and intuition. It doesn’t. It searches. And when your files are arranged by how you think, not how the agent moves, Hermes burns tokens opening the wrong docs before the real work even starts. That was my mistake. I had folders like: >Articles >Research >Assets >Strategy >Old drafts >Clean for me. Terrible for an agent. A launch plan might need brand strategy, previous launches, voice rules, current articles, and promotion notes. For me, that context is obvious. For Hermes, it was scattered. So I stopped optimizing the model and started optimizing the terrain. The fix was stupidly small: > One folder per concern >Numbered folders for reading order >One INDEX.md at the root of every major folder >Archived files separated from active files >A clear “Where To Go” section so Hermes knows where to start My INDEX.md became the map. Not a giant table of everything. Not documentation theater. Just enough scaffolding to tell Hermes: - what exists - what matters - what is current - where to start - what to ignore unless asked Before that, Hermes opened 7 files to find one current brief. After that, it opened the index, followed the pointer, and got to work. That is the real 10x. Not “better prompting.” Less wandering. This is where loop engineering actually matters. The video said it best: “You were the loop.” That line hit because it explains why most agent workflows still feel manual. You are still checking. You are still redirecting. You are still telling the agent which file is current, which folder matters, and what done means. Hermes gets powerful when you stop being the loop and start designing the loop. The loop I’d build looks like this: State: Hermes reads the folder index and current task state Action: it opens only the canonical files Feedback: tests, screenshots, diffs, or human notes tell it what happened Verification: a gate decides whether the work is actually done Termination: the task stops only when the done condition is met For deterministic work, the gate is simple: tests pass build succeeds deployment checks clear For non-deterministic work, I’d use an adversarial loop: one model builds another model reviews Hermes updates the skill when the reviewer finds a pattern That is where Hermes becomes different. It is not just running prompts. It is carrying memory through files, using skills, checking its own work, and improving the process around the task. The agent was never the slow part. The missing map was. A powerful agent inside a messy workspace becomes a very expensive intern. A powerful agent inside a mapped system becomes leverage. My new rule: Before I ask Hermes to do more, I ask: “Does it know where to look?” Because most agents do not fail from lack of intelligence. They fail from lack of scaffolding. Build the scaffolding. Let Hermes do the work.

Rohit

37,093 görüntüleme • 16 gün önce

putting the Claude Agent SDK to the test with an Ad Agent - finds competitors, learns from their ads - prompts and generates on brand creatives - packages it in a shareable report I can run this autonomously or with a prompt. no node based workflows needed
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putting the Claude Agent SDK to the test with an Ad Agent - finds competitors, learns from their ads - prompts and generates on brand creatives - packages it in a shareable report I can run this autonomously or with a prompt. no node based workflows needed

The Boring Marketer

77,498 görüntüleme • 6 ay önce

Simplest way to create an AI agent today: You don't need to write any code. You don't need to build a workflow. You can literally build agents that complete useful tasks for you using a single prompt. Check the quick video I recorded!
2:54

Simplest way to create an AI agent today: You don't need to write any code. You don't need to build a workflow. You can literally build agents that complete useful tasks for you using a single prompt. Check the quick video I recorded!

Santiago

62,639 görüntüleme • 8 ay önce

ANTHROPIC LEAD ENGINEER MAKING $2.3M/YEAR JUST LEAKED A 12-PAGE DOCUMENT - AND GOT FIRED 15 MINUTES AFTER PUBLISHING most developers build loops - but almost every loop breaks in one of five ways, and each failure has a symptom visible from the outside without reading a single line of code > Blind loop - the agent waits for a human to hand it work every morning - automating execution but not discovery > Tangled loop - parallel agents share one directory and overwrite each other parallelism that destroys instead of multiplies > Nodding loop - the agent grades its own work and approves it every time a loop that has never said no to itself is broken > Amnesiac loop - results live only in the context window every morning the system wakes up with no memory of yesterday > Manual loop - no trigger a human presses the button to start it that is not a loop that is a script waiting for a person a real loop finds its own work - remembers what it did has something that can say no never lets two agents touch the same file and fires itself on a timer remove any one of the five - and the loop either breaks or never starts 12 pages that changed how I build agentic systems today
0:50

ANTHROPIC LEAD ENGINEER MAKING $2.3M/YEAR JUST LEAKED A 12-PAGE DOCUMENT - AND GOT FIRED 15 MINUTES AFTER PUBLISHING most developers build loops - but almost every loop breaks in one of five ways, and each failure has a symptom visible from the outside without reading a single line of code > Blind loop - the agent waits for a human to hand it work every morning - automating execution but not discovery > Tangled loop - parallel agents share one directory and overwrite each other parallelism that destroys instead of multiplies > Nodding loop - the agent grades its own work and approves it every time a loop that has never said no to itself is broken > Amnesiac loop - results live only in the context window every morning the system wakes up with no memory of yesterday > Manual loop - no trigger a human presses the button to start it that is not a loop that is a script waiting for a person a real loop finds its own work - remembers what it did has something that can say no never lets two agents touch the same file and fires itself on a timer remove any one of the five - and the loop either breaks or never starts 12 pages that changed how I build agentic systems today

Sprytix

152,649 görüntüleme • 11 gün önce