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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.

Billions Network

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38,323 просмотров • 6 месяцев назад •via X (Twitter)

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1/ Context Lens, new in Tlon v9.2.0. Everything your agent has ever done, stored durably on your personal server. Every message, every tool call. Debugging an agent usually means ssh-ing into a server and grepping logs. Now it's a tap in Tlon Messenger's UI. Tap the badge under a bot's reply to open the run: the context it read, each tool call with timing, the model that answered, and a persistence log of everything it wrote.

1/ Context Lens, new in Tlon v9.2.0. Everything your agent has ever done, stored durably on your personal server. Every message, every tool call. Debugging an agent usually means ssh-ing into a server and grepping logs. Now it's a tap in Tlon Messenger's UI. Tap the badge under a bot's reply to open the run: the context it read, each tool call with timing, the model that answered, and a persistence log of everything it wrote.

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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.

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your ai agent tested 1,262 trading strategies overnight. by morning it had forgotten every one of them. so tonight it tests some of them again. next week, again. everything it learned is sitting in a log file that nothing reads. every run starts on the same blank page. that's why it keeps handing you the idea that already died in a crash, with a new name on it, and you pay to find out twice. ai agent already does everything a fund does. finds the idea, writes the code, tests it against years of history, puts it live, reads what went wrong afterwards. all of it runs tonight for nothing. the one thing it can't do is remember. what got tried, what failed, in which market. nobody has built that part yet. that's the whole gap. not better models. a notebook. whoever builds it first gets a system that improves every week without them touching it. read the article below and save it. it's the full guide to how an ai trading agent is built, and how to make it remember every strategy it has already tested.

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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?

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Coach Hines 🇺🇸

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SCOTTY BEAM

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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

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The hardest part of building finance agents is knowing when it's right and when it's wrong. And my guess from seeing thousands of vibe coded agents (if we can call them that?) is that it's somewhere in the 30% range today. Ensuring accuracy within AI workflows is key for finance. And most of it comes down to separating deterministic from probabilistic work. Combining LLMs with code. Once a workflow has been validated, the repeatable steps get parameterized and frozen so they run identically every time. The non-deterministic reasoning stays confined to the narrow set of tasks it actually handles well. Some work falls outside both categories, because the answer isn't in the data at all. That's what Concourse's agent context layer is for. It sits across the systems where the reasoning actually lives, email, SharePoint, Slack, Drive, the ERP, and reads the memos and threads alongside the numbers, so an agent works from what your team has already decided. When two sources conflict, the agent stops and routes the question to whoever owns the call, waits for the answer, then documents the exception and applies it the same way until the policy changes. Every correction lands in the context layer as a new version, auditable of who decided & when. Where does your team's judgment live right now? My guess is somebody's inbox. What about your agents's judgement? My guess is you've outsourced it entirely to an LLM. Making AI work in production is complex especially for finance teams!

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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 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

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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

12,409 просмотров • 2 месяцев 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

194,171 просмотров • 5 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 📶

471,514 просмотров • 1 год назад