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It's all about the loop. Agents accumulate state with every step of the loop. This state needs a new data primitive. That's Durable Streams 🧵👇

Electric

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10,565 views • 4 months ago •via X (Twitter)

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callbacks with durable objects are weird (but it's also convenient to solve human-in-the-loop for ai agents) tl;dr - by defining callbacks as data, we can let durable objects "sleep" while it's waiting for a timer/email/webhook/phone call/whatever. demo borked a little bit, I blame openai, but it all stilkl worked. that's video 2. gnight!

callbacks with durable objects are weird (but it's also convenient to solve human-in-the-loop for ai agents) tl;dr - by defining callbacks as data, we can let durable objects "sleep" while it's waiting for a timer/email/webhook/phone call/whatever. demo borked a little bit, I blame openai, but it all stilkl worked. that's video 2. gnight!

sunil pai

22,331 views • 1 year ago

If the state of Rivals right now was Overwatch you would see DOZENS of videos talking about the state of play with a negative connotation How more people are not talking about regardless of the "meta" the gameplay loop of rivals has been THE SAME for 2 years 40s ults 24/7

If the state of Rivals right now was Overwatch you would see DOZENS of videos talking about the state of play with a negative connotation How more people are not talking about regardless of the "meta" the gameplay loop of rivals has been THE SAME for 2 years 40s ults 24/7

Samito

316,883 views • 6 months ago

the loop is not the whole agent it is only one layer of the system LOOP vs GRAPH vs HARNESS ENGINEERING LOOP ENGINEERING controls iteration turns retries budgets stop conditions success checks stalled progress use durable execution when the run must survive crashes → GRAPH ENGINEERING controls structure nodes edges state branches checkpoints routing handoffs build stateful agents as explicit graphs → inspect the topology without putting a model in the loop → HARNESS ENGINEERING controls exposure tools permissions memory sandboxes evals traces humans isolate generated code from the host machine → test behavior before users ever see it → preserve one trace across every model step and tool call → without loop control it keeps running without graph structure the path stays invisible without harness control it can reach anything the loop runs inside the graph the graph runs inside the harness the model is only one component debug the layer that actually owns the failure save this then read the article below

the loop is not the whole agent it is only one layer of the system LOOP vs GRAPH vs HARNESS ENGINEERING LOOP ENGINEERING controls iteration turns retries budgets stop conditions success checks stalled progress use durable execution when the run must survive crashes → GRAPH ENGINEERING controls structure nodes edges state branches checkpoints routing handoffs build stateful agents as explicit graphs → inspect the topology without putting a model in the loop → HARNESS ENGINEERING controls exposure tools permissions memory sandboxes evals traces humans isolate generated code from the host machine → test behavior before users ever see it → preserve one trace across every model step and tool call → without loop control it keeps running without graph structure the path stays invisible without harness control it can reach anything the loop runs inside the graph the graph runs inside the harness the model is only one component debug the layer that actually owns the failure save this then read the article below

rari

20,934 views • 20 days ago

Step into the magic and enjoy every beat with the Tomorrowland x Loop Earplugs. Available at

Step into the magic and enjoy every beat with the Tomorrowland x Loop Earplugs. Available at

Tomorrowland

15,612 views • 11 months ago

"By stitching these three primitives together, you get an agent loop that's actually a distributed state machine under the hood." At DevCon 6, Palantir Group Lead Natasha Armbrust launches Agent Engine and Agent SDK, giving developers the core primitives to build agents that form a distributed state machine and are multiplayer by design. ▪️Context items for defining agent sessions through strongly typed data ▪️ Events for expressively acting on changes in state ▪️Effects for connecting agents to the outside world

"By stitching these three primitives together, you get an agent loop that's actually a distributed state machine under the hood." At DevCon 6, Palantir Group Lead Natasha Armbrust launches Agent Engine and Agent SDK, giving developers the core primitives to build agents that form a distributed state machine and are multiplayer by design. ▪️Context items for defining agent sessions through strongly typed data ▪️ Events for expressively acting on changes in state ▪️Effects for connecting agents to the outside world

Palantir

72,323 views • 1 month ago

Andrew Ng at Stanford: “Stop waiting for the perfect model. Build the iteration "loop" and "graphs" around it.” move from prompt → loop → graph → self-improving system • 00:05 - why every AI project needs an iteration loop • 04:08 - the full cycle: data → model → deploy → monitor • 16:02 - why faster iteration can decide who wins • 26:09 - using error analysis to improve the right data • 39:39 - why shipping a simple system beats waiting for perfection • 54:26 - monitoring drift and improving models after deployment 67-minute Stanford lecture, and it’s one of the clearest playbooks on why experienced AI engineers optimize the loop around the model, not just the model itself. It's watch today, then read the full step by step roadmap in the article below

Andrew Ng at Stanford: “Stop waiting for the perfect model. Build the iteration "loop" and "graphs" around it.” move from prompt → loop → graph → self-improving system • 00:05 - why every AI project needs an iteration loop • 04:08 - the full cycle: data → model → deploy → monitor • 16:02 - why faster iteration can decide who wins • 26:09 - using error analysis to improve the right data • 39:39 - why shipping a simple system beats waiting for perfection • 54:26 - monitoring drift and improving models after deployment 67-minute Stanford lecture, and it’s one of the clearest playbooks on why experienced AI engineers optimize the loop around the model, not just the model itself. It's watch today, then read the full step by step roadmap in the article below

Morlex

22,601 views • 14 days ago

Korra has a loop in the corner while in avatar state so be careful

Chakotay

32,915 views • 8 months ago

While AI timeline argues about loop engineering, a 20-year-old student in Shenzhen built the biggest loop anyone's aimed at money: 300 agents, one brain, $1,200 -> 395,000. Not a prompt. Not a bot. A loop that runs the whole job on its own - and never asks him a thing. No fund. No backers. Just a laptop, a dorm room, and a bet that a swarm beats a genius. His wallet: The smartest single model still works one step at a time. His loop moves in 300 directions at once - and never checks in with him. Claude Opus 4.8 runs the floor as the brain. 300 agents are the hands. One loop ties them together: > Decompose - Opus breaks the market into targets > Dispatch - 300 agents scatter across them at once > Execute - thousands of reads in a single pass, not one by one > Review - Opus grades every edge before a cent moves The hands find. The brain judges. The loop learns what failed and runs again, tighter each time. One daily loss cap, and it shuts itself down. It's not a bot you babysit. It's a loop that runs the whole desk. Everyone's just learning to run one loop. He runs 300 in one. The edge was never the genius model - it's the size of the loop, and the brain in charge of it. Save this - the full 300-agent loop is laid out above, start to finish. You'll want it when you build your own. 300 agents are looping right now, hunting the markets everyone skips. Two clicks and their trades land in your wallet too:

While AI timeline argues about loop engineering, a 20-year-old student in Shenzhen built the biggest loop anyone's aimed at money: 300 agents, one brain, $1,200 -> 395,000. Not a prompt. Not a bot. A loop that runs the whole job on its own - and never asks him a thing. No fund. No backers. Just a laptop, a dorm room, and a bet that a swarm beats a genius. His wallet: The smartest single model still works one step at a time. His loop moves in 300 directions at once - and never checks in with him. Claude Opus 4.8 runs the floor as the brain. 300 agents are the hands. One loop ties them together: > Decompose - Opus breaks the market into targets > Dispatch - 300 agents scatter across them at once > Execute - thousands of reads in a single pass, not one by one > Review - Opus grades every edge before a cent moves The hands find. The brain judges. The loop learns what failed and runs again, tighter each time. One daily loss cap, and it shuts itself down. It's not a bot you babysit. It's a loop that runs the whole desk. Everyone's just learning to run one loop. He runs 300 in one. The edge was never the genius model - it's the size of the loop, and the brain in charge of it. Save this - the full 300-agent loop is laid out above, start to finish. You'll want it when you build your own. 300 agents are looping right now, hunting the markets everyone skips. Two clicks and their trades land in your wallet too:

cvxv666

53,163 views • 2 months ago

Meet Data Scout. It's a $500k data scientist for every employee. One of those agents every business needs. > Has Snowflake access (+ Notion, Slack, Github) > Runs deep analysis on all user and account data > Can be called by other agents for recurring reports Data Scout is one of the most used agents at Notion. And you can build one.

Meet Data Scout. It's a $500k data scientist for every employee. One of those agents every business needs. > Has Snowflake access (+ Notion, Slack, Github) > Runs deep analysis on all user and account data > Can be called by other agents for recurring reports Data Scout is one of the most used agents at Notion. And you can build one.

Hurley

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NEW 🚨: The Nation State Race to Accumulate 1M #Bitcoin Began 17 Days Ago! 👇

NEW 🚨: The Nation State Race to Accumulate 1M #Bitcoin Began 17 Days Ago! 👇

Simply Bitcoin

56,146 views • 1 year ago

Isaiah Rashad breaks down the meaning behind naming his album “It’s Been Awful” with Ebro: "It's just life, it's reflection with a lot of things. The state of the Government, the state of the world, the state of national parks, the state of women's rights, the state of the rights taking away from special needs kids in the south a lot of it is awful"

Isaiah Rashad breaks down the meaning behind naming his album “It’s Been Awful” with Ebro: "It's just life, it's reflection with a lot of things. The state of the Government, the state of the world, the state of national parks, the state of women's rights, the state of the rights taking away from special needs kids in the south a lot of it is awful"

Ahmed/The Ears/IG: BigBizTheGod 🇸🇴

74,164 views • 2 months ago

Watch this deep-dive with Head of API Sabrina on the next stage of loop engineering and self-improving agents.

Watch this deep-dive with Head of API Sabrina on the next stage of loop engineering and self-improving agents.

Venice

24,857 views • 1 month ago

⭐️ New LangChain Academy Course: LangGraph Essentials (Python & TypeScript)⭐️ Learn the basics of LangGraph – our low-level orchestration framework purpose-built for building AI agents. Last week, we launched LangGraph 1.0, marking its first major stable release. LangGraph allows you to control every step of your agent’s workflow. It supports memory, human-in-the-loop interactions, and durable execution for managing long-running tasks. In this quickstart course, you'll learn how to: - Create simple workflows and build an agent - Use LangGraph’s core building blocks: State, Nodes, and Edges - Add memory to your agent - Incorporate human-in-the-loop interactions Enroll for free ➡️

⭐️ New LangChain Academy Course: LangGraph Essentials (Python & TypeScript)⭐️ Learn the basics of LangGraph – our low-level orchestration framework purpose-built for building AI agents. Last week, we launched LangGraph 1.0, marking its first major stable release. LangGraph allows you to control every step of your agent’s workflow. It supports memory, human-in-the-loop interactions, and durable execution for managing long-running tasks. In this quickstart course, you'll learn how to: - Create simple workflows and build an agent - Use LangGraph’s core building blocks: State, Nodes, and Edges - Add memory to your agent - Incorporate human-in-the-loop interactions Enroll for free ➡️

LangChain

53,969 views • 10 months ago

Great example of what Foundry enables: durable, stateful agents that run across time boundaries, orchestrate tools and models, and close the loop with evaluation and improvement over long-running workflows. Jeff Hollan

Great example of what Foundry enables: durable, stateful agents that run across time boundaries, orchestrate tools and models, and close the loop with evaluation and improvement over long-running workflows. Jeff Hollan

Satya Nadella

147,606 views • 3 months ago

This indie dev is making a horror game based on Acrophobia - the fear of heights, & Liminal Space - Trapped in a corridor above the clouds - Every loop, the environment changes - Find anomalies, trigger the alarm, break the loop Would you play this? It's called Cloudrooms.

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Indie Game Joe

47,902 views • 3 months ago

Introducing Loopany 🔁 Stop prompting, Design the loop. A loop management space that connects to your team’s own local agents: - Scaffold loop contracts, state, and logs - Add programmable triggers - Run self-improving cycles - Start from built-in loop templates We baked in everything we’ve learned from running loops inside our own team. Free and open source. Keen to hear your feedback:

Introducing Loopany 🔁 Stop prompting, Design the loop. A loop management space that connects to your team’s own local agents: - Scaffold loop contracts, state, and logs - Add programmable triggers - Run self-improving cycles - Start from built-in loop templates We baked in everything we’ve learned from running loops inside our own team. Free and open source. Keen to hear your feedback:

Jason Zhou

32,504 views • 1 month ago

working with AI agents is amazing. it's also kind of lonely... it's me, a terminal, and an agent that thinks everything I say is a great idea. so we made a team chat keeping agents in the loop

working with AI agents is amazing. it's also kind of lonely... it's me, a terminal, and an agent that thinks everything I say is a great idea. so we made a team chat keeping agents in the loop

Pablo Stanley

92,043 views • 1 month ago

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

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

153,690 views • 1 month ago

Byron “data center” Donald’s wants to put data centers into every country in the state of Florida and he is disgusted with the negative reaction from the citizens in the state of Florida.

Byron “data center” Donald’s wants to put data centers into every country in the state of Florida and he is disgusted with the negative reaction from the citizens in the state of Florida.

RyanMatta 🇺🇸 🦅

45,358 views • 3 months ago