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An agent's identity shifts with every context. Every prompt. Every data source. So who's actually accountable for what it does? The answer isn't in the agent. It's in the human behind it. Like a surname that carries the weight of everything done under it.

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the perfect AI agent should remember everything. Sam Altman said it: "every conversation. every book. every email. your whole life appending to one context window." memory isn't a feature. it's the definition. I just built the memory layer. open source. install in one command. read the guide tonight. install it tomorrow.

the perfect AI agent should remember everything. Sam Altman said it: "every conversation. every book. every email. your whole life appending to one context window." memory isn't a feature. it's the definition. I just built the memory layer. open source. install in one command. read the guide tonight. install it tomorrow.

Avid

35,772 views • 1 month ago

Just as every grain of rice carries the name of the one who eats it, every story too carries the name of the one destined for it. #Yellamma finds its destined soul in #DeviSriPrasad a promising debut.🔥

Just as every grain of rice carries the name of the one who eats it, every story too carries the name of the one destined for it. #Yellamma finds its destined soul in #DeviSriPrasad a promising debut.🔥

Telugu Chitraalu

15,417 views • 5 months ago

A snapshot of the Rogo Spreadsheets Agent. We’re proud to have built an agent that produces deliverables you’ll actually use. With it, you can: • Build complex, multi-tab DCFs / LBOs • Generate cohort, retention, P&L & KPI analyses • Convert filings / uploaded docs into structured data Every output is grounded in high-fidelity financial data — with every number attributed back to its source. Available in Rogo today.

A snapshot of the Rogo Spreadsheets Agent. We’re proud to have built an agent that produces deliverables you’ll actually use. With it, you can: • Build complex, multi-tab DCFs / LBOs • Generate cohort, retention, P&L & KPI analyses • Convert filings / uploaded docs into structured data Every output is grounded in high-fidelity financial data — with every number attributed back to its source. Available in Rogo today.

Rogo

11,998 views • 4 months ago

ONE PROMPT IN CHATGPT IMAGE 2 JUST REPLACED AN ENTIRE $5,000-$50,000 BRAND DESIGN TEAM Full brand identity. Colors, typography, logo style, aesthetic. All generated in one single prompt. Here's the exact workflow: Step 1: Paste one prompt asking for a complete brand identity. GPT Image 2 generates everything – color palette, fonts, logo direction, overall visual style. Step 2: Use that output as context for every asset you generate after. Campaign visuals, shop graphics, product mockups, social posts – all stay consistent and on-brand automatically. The result: A full visual identity that looks like a real design team built it. Clean text rendering. Consistent style across every single asset. Design agencies charge $5,000-$50,000 for brand identity work. GPT Image 2 does it in one prompt. The full prompt that makes this work is in the video below. Bookmark this post, you'll want to come back to it.

SCOTTY BEAM

12,023 views • 1 day ago

Why is it that so many of us accept failure as an integral part of growth in the weight room, yet way too many of us avoid it like the plague in every other area of our lives?!

Why is it that so many of us accept failure as an integral part of growth in the weight room, yet way too many of us avoid it like the plague in every other area of our lives?!

Coach Hines 🇺🇸

18,402 views • 1 month ago

Agentic advertising isn't possible without a single source of truth. Every agent, every trade, every settlement has to resolve against the same record, or the whole thing collapses back into the mess we're leaving behind. Sui and Walrus 🦭/acc are how we get there. The settlement layer and the data layer that make agent-to-agent advertising actually workable at scale. Labs is the community heart of all of this. Built with you, and the best part is still ahead.

Agentic advertising isn't possible without a single source of truth. Every agent, every trade, every settlement has to resolve against the same record, or the whole thing collapses back into the mess we're leaving behind. Sui and Walrus 🦭/acc are how we get there. The settlement layer and the data layer that make agent-to-agent advertising actually workable at scale. Labs is the community heart of all of this. Built with you, and the best part is still ahead.

Alkimi

10,558 views • 8 days ago

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

GREG ISENBERG

192,024 views • 2 months ago

Modi can't even lie convincingly anymore—his body language crumbles under the weight of his own falsehoods. Every word he utters reeks of deception. It's astonishing that a man so transparent in his theatrics ...... What a tragic joke—and what a costly illusion for the indian people who believed in it.

Modi can't even lie convincingly anymore—his body language crumbles under the weight of his own falsehoods. Every word he utters reeks of deception. It's astonishing that a man so transparent in his theatrics ...... What a tragic joke—and what a costly illusion for the indian people who believed in it.

Asher Bajwa 📶

471,473 views • 1 year ago

MIT HANDED ITS DEEP LEARNING COURSE TO A FRONTIER-LAB ENGINEER FOR 68 MINUTES BECAUSE 90% OF PEOPLE SHIPPING AI CODE CAN'T EXPLAIN HOW THE MODEL ACTUALLY WORKS This is Maxime Labonne. He runs post-training at Liquid AI and wrote the LLM Engineer's Handbook. MIT gave him the room to break down the engine sitting inside every coding agent you've ever prompted. Twenty minutes in it stops being abstract. You finally see why the model confidently invents things that don't exist, why context is everything, and why "Just tell it to try again" sometimes fixes it and sometimes makes it worse. In 2026 "I use AI to code" stopped being a skill. Knowing why the model behaves the way it does -> tokens, context, post-training, where it quietly breaks -> is what separates someone who ships from someone babysitting a black box. Understanding an LLM isn't a research-team luxury anymore -> it's the difference between driving the agent and being driven by it. Anyone can prompt. The person who knows what's under the hood is the one still standing when the prompt stops working. Save this one & actually finish it ↓

MIT HANDED ITS DEEP LEARNING COURSE TO A FRONTIER-LAB ENGINEER FOR 68 MINUTES BECAUSE 90% OF PEOPLE SHIPPING AI CODE CAN'T EXPLAIN HOW THE MODEL ACTUALLY WORKS This is Maxime Labonne. He runs post-training at Liquid AI and wrote the LLM Engineer's Handbook. MIT gave him the room to break down the engine sitting inside every coding agent you've ever prompted. Twenty minutes in it stops being abstract. You finally see why the model confidently invents things that don't exist, why context is everything, and why "Just tell it to try again" sometimes fixes it and sometimes makes it worse. In 2026 "I use AI to code" stopped being a skill. Knowing why the model behaves the way it does -> tokens, context, post-training, where it quietly breaks -> is what separates someone who ships from someone babysitting a black box. Understanding an LLM isn't a research-team luxury anymore -> it's the difference between driving the agent and being driven by it. Anyone can prompt. The person who knows what's under the hood is the one still standing when the prompt stops working. Save this one & actually finish it ↓

slash1s

234,552 views • 21 days ago

Arteta on what gives him the belief that Arsenal can go one step further this season: “It gives me the confidence that what we've done up to now every single day and that's the biggest reassurance to do it in every competition. “The manner we've done it with all the challenges that we faced already, it gives me confidence because it's the way we prepare. “I don't get the confidence for the things that I imagine. It's for the things that I see every day and the preparation and how much the player want it and we are where we want to be that’s for sure.”

Arteta on what gives him the belief that Arsenal can go one step further this season: “It gives me the confidence that what we've done up to now every single day and that's the biggest reassurance to do it in every competition. “The manner we've done it with all the challenges that we faced already, it gives me confidence because it's the way we prepare. “I don't get the confidence for the things that I imagine. It's for the things that I see every day and the preparation and how much the player want it and we are where we want to be that’s for sure.”

Connor Humm

20,335 views • 4 months ago

I built a Reddit marketing agent that gets your business mentioned by ChatGPT, Gemini and Claude. Not a spam bot. Not the kind that gets banned by mods. An agent that drafts genuinely valuable answers grounded in your company's actual content and slips in a brand mention only when it actually helps. Google is paying Reddit $60M a year just for their data!!! If your business isn't showing up in that data, your competitors are, and ChatGPT is recommending them instead of you. Here's how the system works: → A Claude routine pulls the target subreddit's JSON endpoint every hour for new posts → A Phase 1 checklist filters every post — is it answerable from our wiki, factual, on-brand, mod-safe? → The answer engine queries an LLM Wiki built from our blog, guides, and help center (Firecrawl ingested every page into a raw → wiki → index structure) → Claude drafts a casual, value-first answer that grounds every claim in our content → Every draft lands in Slack for human-in-the-loop review before it posts — no fire-and-forget, no bans I built this in a week for CoinLedger, the crypto tax software I've been running for 8 years. 10% of our new signups already say they found us through ChatGPT. This system is how we push that number a lot higher. If you want the exact Claude skill I built: Like & Comment "AGENT" (must be following so I can dm you) I'll send it over for free.

David Roberts

35,799 views • 1 month ago

🚨ELON: GROK WILL ACE EVERY EXAM AND FIX THE QUESTIONS TOO "We're gonna get to the point where AI gets every answer right in every exam. Where it doesn't get an answer right, it'll tell you what's wrong with the question. If the question is ambiguous, it'll break it down and give disambiguated answers for each scenario." Source: xAI

🚨ELON: GROK WILL ACE EVERY EXAM AND FIX THE QUESTIONS TOO "We're gonna get to the point where AI gets every answer right in every exam. Where it doesn't get an answer right, it'll tell you what's wrong with the question. If the question is ambiguous, it'll break it down and give disambiguated answers for each scenario." Source: xAI

Mario Nawfal

57,982 views • 11 months ago

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

55,472 views • 12 days ago

Coinbase CEO, Brian Armstrong: Some great insights on how they are using internally hosted AI Agents. "It’s connected to every Slack message, every Google Doc, and every Salesforce data confluence. Now, this is all linked up and the data is all aggregated, so you can ask these agents questions. Every team is using it—legal, finance, everything. It’s like the "Oracle of Coinbase." I’ve started to ask it things that go beyond just simple prompting, like "Hey, can you write this kind of memo for me?" I’m asking these AI agents now, as CEO, "What should I be aware of in the company that I might not be aware of?" It will tell me, "Did you know that there’s actually disagreement on this team about the strategy?" I realized I didn't know that, but the AI does because it can read every Slack message and every Google Doc. Tobi, who is on my board, calls this "reverse prompting." Instead of telling the AI agent what you want to do, you ask it what you should be thinking more about." --- From The All-In Podcast YT channel (link in comment)

Coinbase CEO, Brian Armstrong: Some great insights on how they are using internally hosted AI Agents. "It’s connected to every Slack message, every Google Doc, and every Salesforce data confluence. Now, this is all linked up and the data is all aggregated, so you can ask these agents questions. Every team is using it—legal, finance, everything. It’s like the "Oracle of Coinbase." I’ve started to ask it things that go beyond just simple prompting, like "Hey, can you write this kind of memo for me?" I’m asking these AI agents now, as CEO, "What should I be aware of in the company that I might not be aware of?" It will tell me, "Did you know that there’s actually disagreement on this team about the strategy?" I realized I didn't know that, but the AI does because it can read every Slack message and every Google Doc. Tobi, who is on my board, calls this "reverse prompting." Instead of telling the AI agent what you want to do, you ask it what you should be thinking more about." --- From The All-In Podcast YT channel (link in comment)

Rohan Paul

174,801 views • 3 months ago

Tucker Carlson NAILS IT “What is it about Jeffrey Epstein that's so infuriating to people, so infuriating that it's actually causing seismic political problems? — What is it? I'll tell you what it is. It's the frustration of normal people watching a certain class of people get away with everything every single time. That's what it is.” We’ve had enough

Tucker Carlson NAILS IT “What is it about Jeffrey Epstein that's so infuriating to people, so infuriating that it's actually causing seismic political problems? — What is it? I'll tell you what it is. It's the frustration of normal people watching a certain class of people get away with everything every single time. That's what it is.” We’ve had enough

Wall Street Apes

1,857,543 views • 7 months ago

This isn’t just an honor, it’s the result of fearless decisions taken when it mattered the most. ☠️ Every star on the uniform carries the weight of a thousand risks taken for the nation. Salute to the brave hearts who stand fearless, so the country can live in peace. 🙏

This isn’t just an honor, it’s the result of fearless decisions taken when it mattered the most. ☠️ Every star on the uniform carries the weight of a thousand risks taken for the nation. Salute to the brave hearts who stand fearless, so the country can live in peace. 🙏

Rakesh Kalotra

100,865 views • 2 months ago

So what does Razorpay look like in the agentic era? We started with the basics at FTX’26. • onboarding handled by an AI agent (~3 mins) • integration created from a prompt (~2 mins) • d̶a̶s̶h̶b̶o̶a̶r̶d̶ just talk to an agent for everything The first agentic fintech platform. And we timed it live. Live Demo ↓ (More to come)

So what does Razorpay look like in the agentic era? We started with the basics at FTX’26. • onboarding handled by an AI agent (~3 mins) • integration created from a prompt (~2 mins) • d̶a̶s̶h̶b̶o̶a̶r̶d̶ just talk to an agent for everything The first agentic fintech platform. And we timed it live. Live Demo ↓ (More to come)

Harshil Mathur

81,617 views • 3 months ago

AI is top of mind for every national leader, every tech leader, practically every human. There's never been a moment like this in the history of humankind. Jensen Huang calls it: “The greatest technology revolution of all time.”

AI is top of mind for every national leader, every tech leader, practically every human. There's never been a moment like this in the history of humankind. Jensen Huang calls it: “The greatest technology revolution of all time.”

vitrupo

42,588 views • 11 months ago