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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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38,129 просмотров • 4 месяцев назад •via X (Twitter)

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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 просмотров • 2 месяцев назад

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

15,478 просмотров • 6 месяцев назад

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

12,083 просмотров • 5 месяцев назад

Senator Cotton called for an investigation with the DOJ into why every data center or power project in the U.S. faces so much misinformation, negative propaganda and lawsuits to slow down and sometimes stop the development all together. How is that happening? Why is it happening and who's behind it?

Senator Cotton called for an investigation with the DOJ into why every data center or power project in the U.S. faces so much misinformation, negative propaganda and lawsuits to slow down and sometimes stop the development all together. How is that happening? Why is it happening and who's behind it?

Kevin O'Leary aka Mr. Wonderful

21,357 просмотров • 15 дней назад

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 🇺🇸

18,412 просмотров • 2 месяцев назад

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 просмотров • 29 дней назад

Journalist Alex Newman exposes the fundamental deception of the climate narrative—the claim that carbon dioxide is harmful pollution. Since humans emit only a minuscule portion of atmospheric carbon dioxide, and plants actually benefit from higher levels, demonising it as "pollution" serves a darker agenda. It turns ordinary human activity—every breath, every light switch, every car ride—into a source of guilt, providing totalitarians with the perfect pretext to control every aspect of daily life. This isn't environmentalism—it's control disguised as "saving the planet".

Journalist Alex Newman exposes the fundamental deception of the climate narrative—the claim that carbon dioxide is harmful pollution. Since humans emit only a minuscule portion of atmospheric carbon dioxide, and plants actually benefit from higher levels, demonising it as "pollution" serves a darker agenda. It turns ordinary human activity—every breath, every light switch, every car ride—into a source of guilt, providing totalitarians with the perfect pretext to control every aspect of daily life. This isn't environmentalism—it's control disguised as "saving the planet".

Wide Awake Media

16,753 просмотров • 21 дней назад

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

10,825 просмотров • 1 месяц назад

A screen recording is the best context you can give an AI agent. It's also the worst. A few seconds of video is thousands of images. Feed that to an agent and you instantly blow up its memory. So I built a tool that fixes it. How it works: Hit record and just do your thing. Click around, type, and talk out loud about what you're doing. While you record, it quietly captures the stuff that actually matters: your voice transcription, every click and the exact thing you clicked, what you typed, and the shortcuts you press. When you stop, it doesn't hand the agent the whole video. It writes a short, readable summary: a timeline of what you said and did. The agent reads that tiny summary first. Then, only when it needs to actually see a moment, it pulls that one exact frame from the video, like flipping to a single page instead of reading the whole book. The result: the agent understands everything you did! Full context. Less tokens.

A screen recording is the best context you can give an AI agent. It's also the worst. A few seconds of video is thousands of images. Feed that to an agent and you instantly blow up its memory. So I built a tool that fixes it. How it works: Hit record and just do your thing. Click around, type, and talk out loud about what you're doing. While you record, it quietly captures the stuff that actually matters: your voice transcription, every click and the exact thing you clicked, what you typed, and the shortcuts you press. When you stop, it doesn't hand the agent the whole video. It writes a short, readable summary: a timeline of what you said and did. The agent reads that tiny summary first. Then, only when it needs to actually see a moment, it pulls that one exact frame from the video, like flipping to a single page instead of reading the whole book. The result: the agent understands everything you did! Full context. Less tokens.

Milind S

12,334 просмотров • 17 дней назад

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

192,483 просмотров • 3 месяцев назад

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 📶

471,489 просмотров • 1 год назад

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.

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.

Open Design

20,103 просмотров • 1 месяц назад

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

236,758 просмотров • 1 месяц назад

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 просмотров • 5 месяцев назад

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

36,047 просмотров • 2 месяцев назад

The right score changes everything. It builds the tension, shapes the emotion, and gives every moment its weight. Get a glimpse of the music behind #MW4 with Stephen Barton Jason Graves.

The right score changes everything. It builds the tension, shapes the emotion, and gives every moment its weight. Get a glimpse of the music behind #MW4 with Stephen Barton Jason Graves.

Infinity Ward

624,091 просмотров • 16 дней назад

🚨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 просмотров • 1 год назад

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

57,975 просмотров • 1 месяц назад

SOMEONE BUILT AN OPEN SOURCE GAME ENGINE MADE SPECIFICALLY FOR BUILDING 2.5D GAMES WITH AI instead of using a complex general purpose engine, this one is built from the ground up for the ai workflow it keeps everything the ai needs to control in one small, simple place, so the agent can actually understand the whole engine and build with it instead of getting lost in a giant codebase > generate all your assets with ai, the maps, characters, props, audio and ui, then the agent drops them into a real playable game > a built in server sdk handles the boring stuff, player accounts, cloud saves, even multiplayer, in a line or two > it ships with claude and codex agent setups baked in, so it works with whatever youre already using > free and open source most people vibe code games inside engines that were never meant for it, so he built one where the ai workflow is the whole point the whole idea is dead simple, keep it small enough that an agent can understand all of it at once, then let it go wild this is what tooling built for the agent era actually looks like

SOMEONE BUILT AN OPEN SOURCE GAME ENGINE MADE SPECIFICALLY FOR BUILDING 2.5D GAMES WITH AI instead of using a complex general purpose engine, this one is built from the ground up for the ai workflow it keeps everything the ai needs to control in one small, simple place, so the agent can actually understand the whole engine and build with it instead of getting lost in a giant codebase > generate all your assets with ai, the maps, characters, props, audio and ui, then the agent drops them into a real playable game > a built in server sdk handles the boring stuff, player accounts, cloud saves, even multiplayer, in a line or two > it ships with claude and codex agent setups baked in, so it works with whatever youre already using > free and open source most people vibe code games inside engines that were never meant for it, so he built one where the ai workflow is the whole point the whole idea is dead simple, keep it small enough that an agent can understand all of it at once, then let it go wild this is what tooling built for the agent era actually looks like

Om Patel

42,173 просмотров • 5 дней назад