Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

🚨 THIS IS SO WILD Microsoft just open sourced a free tool that records you doing a task ONCE — and your AI agent repeats it forever. the GitHub repo already has 3,400+ stars. It lets you: → record a real work session: screen, clicks, windows, pages, even your...

vicky_grok's profile picture

Vikas gupta

10,604 subscribers

128,351 Aufrufe • vor 2 Tagen •via X (Twitter)

Wissenschaft & TechnologieBildungNachrichten & Politik
LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

this is f**king insane. Jack Dorsey just open sourced a free tool for running a one-person business. the GitHub repo already has 14.4K stars. It lets you: → run your own server → keep chat, search, Git, and automation in one place → add AI agents that work like teammates → decide exactly what each agent can do no monthly subscriptions. just you, your AI agents, and full control. this is where one-person businesses are headed. save this for later.
0:43

this is f**king insane. Jack Dorsey just open sourced a free tool for running a one-person business. the GitHub repo already has 14.4K stars. It lets you: → run your own server → keep chat, search, Git, and automation in one place → add AI agents that work like teammates → decide exactly what each agent can do no monthly subscriptions. just you, your AI agents, and full control. this is where one-person businesses are headed. save this for later.

MIKE

706,082 Aufrufe • vor 23 Tagen

this is f**king gold someone just open-sourced a GitHub repo with 2,700+ stars which gave out the entire "loop-engineering" framework for ai agents it lets you: →allows you to manage an entire team of 1000 agents →run auto-research by Andrej Karpathy →Agent-to-agent PR comment loop just you, your AI agents, and full control. this is where one-person businesses are headed. save this for later.
0:45

this is f**king gold someone just open-sourced a GitHub repo with 2,700+ stars which gave out the entire "loop-engineering" framework for ai agents it lets you: →allows you to manage an entire team of 1000 agents →run auto-research by Andrej Karpathy →Agent-to-agent PR comment loop just you, your AI agents, and full control. this is where one-person businesses are headed. save this for later.

Avid

46,150 Aufrufe • vor 22 Tagen

You can spin up an AI workforce with one prompt. Here's why it works: it pulls the context the AI needs to lay the foundation. Give it: - your role - team size - the goals you want to hit. Then say "interview me." After that, build it out in four steps: 1) Start with 1 agent. - What does it feel like to work with one? 2) Make that agent proactive. - Now it's doing things on your behalf before you ask. 3) Expand to 2 agents. - One directs the other, or routes to it. - This is where you learn how context gets passed. 4) Scale the workforce. - A mission control where you watch it all move around. - Now you see what has to run in parallel, which agent never needed that tool, which one runs fine on a smaller model. - Not everything needs Opus. Sub-agents are haiku and sonnet. The catch: the prompt gets you agents with markdown files doing some stuff. Getting to the 90% plus level is iteration, and that part is specific to each person. Nobody can hand it to you. Start with one agent. Spin up one agent this week. The rest is discovery.
3:10

You can spin up an AI workforce with one prompt. Here's why it works: it pulls the context the AI needs to lay the foundation. Give it: - your role - team size - the goals you want to hit. Then say "interview me." After that, build it out in four steps: 1) Start with 1 agent. - What does it feel like to work with one? 2) Make that agent proactive. - Now it's doing things on your behalf before you ask. 3) Expand to 2 agents. - One directs the other, or routes to it. - This is where you learn how context gets passed. 4) Scale the workforce. - A mission control where you watch it all move around. - Now you see what has to run in parallel, which agent never needed that tool, which one runs fine on a smaller model. - Not everything needs Opus. Sub-agents are haiku and sonnet. The catch: the prompt gets you agents with markdown files doing some stuff. Getting to the 90% plus level is iteration, and that part is specific to each person. Nobody can hand it to you. Start with one agent. Spin up one agent this week. The rest is discovery.

The Startup Ideas Podcast (SIP) 🧃

18,006 Aufrufe • vor 15 Tagen

I've said one of the best features I like in the new GitHub Copilot desktop app is /spar It spins up two agents that tear through your task or question and rip you a new one If you read through the demo vid carefully one of them even grilled me on the too generic question I asked it for the spar but hey it was just a demo :-) Are you using it already? what's your fav feat?
0:41

I've said one of the best features I like in the new GitHub Copilot desktop app is /spar It spins up two agents that tear through your task or question and rip you a new one If you read through the demo vid carefully one of them even grilled me on the too generic question I asked it for the spar but hey it was just a demo :-) Are you using it already? what's your fav feat?

Oren Melamed

11,285 Aufrufe • vor 3 Monaten

this shouldn't be legal Elon's team just dropped an app where you record yourself doing a boring task once and it does that task for you from then on. no prompt. it's called Grok Bot. SpaceX is paying $60,000,000,000 for the people who built it and it costs you $0 if you already pay for SuperGrok Heavy set it up in 3 moves: 1. make a bot, open its computer, hit "teach a task" 2. do the boring job once while it watches, that recording becomes a skill 3. put the skill on a schedule and it runs without you the free week burns in 20 minutes. save this before you waste yours
1:13

this shouldn't be legal Elon's team just dropped an app where you record yourself doing a boring task once and it does that task for you from then on. no prompt. it's called Grok Bot. SpaceX is paying $60,000,000,000 for the people who built it and it costs you $0 if you already pay for SuperGrok Heavy set it up in 3 moves: 1. make a bot, open its computer, hit "teach a task" 2. do the boring job once while it watches, that recording becomes a skill 3. put the skill on a schedule and it runs without you the free week burns in 20 minutes. save this before you waste yours

Argona

103,961 Aufrufe • vor 16 Tagen

Your ai agent is genuinely smart right up until it has to do something in the real world ask it to host a site, send an email, scrape a page, take a payment, and the magic stops. now you're the one doing the boring part: → hunting api keys → paying for subscriptions you'll use once → gluing integrations together → losing an afternoon to config files i gave my agent access to Zero instead and just described the job. it built and hosted a full ai tool marketplace, 120+ tools, live on a real url, and i configured nothing. no keys, no dashboards, no setup zero plugs agents into 14,000+ real services on demand. everything runs on pay-per-use microtransactions, so you pay for the one call you make, not a monthly plan you forget about what's the one task your agent still can't do on its own? that's exactly the gap this closes $5 free credit → #Ad
1:12

Your ai agent is genuinely smart right up until it has to do something in the real world ask it to host a site, send an email, scrape a page, take a payment, and the magic stops. now you're the one doing the boring part: → hunting api keys → paying for subscriptions you'll use once → gluing integrations together → losing an afternoon to config files i gave my agent access to Zero instead and just described the job. it built and hosted a full ai tool marketplace, 120+ tools, live on a real url, and i configured nothing. no keys, no dashboards, no setup zero plugs agents into 14,000+ real services on demand. everything runs on pay-per-use microtransactions, so you pay for the one call you make, not a monthly plan you forget about what's the one task your agent still can't do on its own? that's exactly the gap this closes $5 free credit → #Ad

Freya Lawson

80,316 Aufrufe • vor 1 Monat

Build and operate your ICP apps straight from your favorite AI. That is what the ICP MCP connector unlocks. The first thing it gives you: automation. Hand an agent your repetitive, multi-step work and it runs the whole flow for you. Schedule it, batch it, let it connect the steps into one task. Now in preview. Arshavir Ter-Gabrielyan explains how it works. Full conversation below.
0:49

Build and operate your ICP apps straight from your favorite AI. That is what the ICP MCP connector unlocks. The first thing it gives you: automation. Hand an agent your repetitive, multi-step work and it runs the whole flow for you. Schedule it, batch it, let it connect the steps into one task. Now in preview. Arshavir Ter-Gabrielyan explains how it works. Full conversation below.

DFINITY Foundation

24,162 Aufrufe • vor 1 Monat

your agent has thirty tools. it calls two of them. the other twenty eight are not sitting idle somewhere. they are in the request, every request, and they are doing damage in two places at once. first the obvious one. tool schemas go into the prompt, and a schema is not a name. it is a description, a parameter list, types, required fields, an example. thirty of those is a few thousand tokens that ship with every single call, including the ones where the agent just says thanks and stops. you are paying rent on twenty eight tools that have never fired. second, and this is the one that costs more. when the request says cancel the order, the model picks by matching against everything available. four of your tools are plausible: cancel_order, refund_order, update_order, void_order. it is choosing among them based on the descriptions you wrote, one afternoon, months ago. every tool you add is another candidate in that shortlist. the twenty eight you never call are not neutral. they are noise in the one decision that determines whether the run works. > why it grows without anyone deciding to nobody adds thirty tools on purpose. you add one for a task, it works, it stays. six months later the registry is a catalogue and no one has ever removed anything, because removing a tool feels risky and adding one feels free. and there is no feedback telling you otherwise. the unused ones never error. they never appear in a failing trace. they are invisible in exactly the way that lets them accumulate. > what to actually do count calls per tool over the last thousand runs. this is one group-by and it usually shocks people. the ones at zero are pure cost. ship the tools the task needs, not the whole registry. a research phase does not need deploy. a writing phase does not need the database. swap the set between phases instead of loading everything up front. same agent, different tools, depending on where the run is. and when two tools could both plausibly answer the same request, that is not redundancy you can ignore. it is a coin flip you built into the system. the twenty eight tools are not unused. they are used every time, by the part of the run you cannot see.
0:52

your agent has thirty tools. it calls two of them. the other twenty eight are not sitting idle somewhere. they are in the request, every request, and they are doing damage in two places at once. first the obvious one. tool schemas go into the prompt, and a schema is not a name. it is a description, a parameter list, types, required fields, an example. thirty of those is a few thousand tokens that ship with every single call, including the ones where the agent just says thanks and stops. you are paying rent on twenty eight tools that have never fired. second, and this is the one that costs more. when the request says cancel the order, the model picks by matching against everything available. four of your tools are plausible: cancel_order, refund_order, update_order, void_order. it is choosing among them based on the descriptions you wrote, one afternoon, months ago. every tool you add is another candidate in that shortlist. the twenty eight you never call are not neutral. they are noise in the one decision that determines whether the run works. > why it grows without anyone deciding to nobody adds thirty tools on purpose. you add one for a task, it works, it stays. six months later the registry is a catalogue and no one has ever removed anything, because removing a tool feels risky and adding one feels free. and there is no feedback telling you otherwise. the unused ones never error. they never appear in a failing trace. they are invisible in exactly the way that lets them accumulate. > what to actually do count calls per tool over the last thousand runs. this is one group-by and it usually shocks people. the ones at zero are pure cost. ship the tools the task needs, not the whole registry. a research phase does not need deploy. a writing phase does not need the database. swap the set between phases instead of loading everything up front. same agent, different tools, depending on where the run is. and when two tools could both plausibly answer the same request, that is not redundancy you can ignore. it is a coin flip you built into the system. the twenty eight tools are not unused. they are used every time, by the part of the run you cannot see.

Hanako

24,656 Aufrufe • vor 20 Tagen

🚨 stop rebuilding your agent harness. langchain just open-sourced the finished one. free sub-agent spawning, todo-list planning, a virtual filesystem, durable runs on LangGraph. batteries included. it's called deepagents. the exact harness you keep re-inventing - already done. 26,000+ stars. MIT. one install. you were about to build this again. don't. it's already done, and it's free. save it before your next agent build.
0:21

🚨 stop rebuilding your agent harness. langchain just open-sourced the finished one. free sub-agent spawning, todo-list planning, a virtual filesystem, durable runs on LangGraph. batteries included. it's called deepagents. the exact harness you keep re-inventing - already done. 26,000+ stars. MIT. one install. you were about to build this again. don't. it's already done, and it's free. save it before your next agent build.

Granite

181,937 Aufrufe • vor 1 Monat

What the f*ck is this GitHub repo, 33k+ stars in less than a month. It's called OpenHuman, and the newest feature is why it's exploding, Super Context. > The moment you open a new chat, it runs a context pass first > Gathers what's relevant about you, your screen, your work > Then answers turn one like it's turn ten, already grounded It lives at the OS level, speaks in your voice across your whole system, connects to 118 apps you already use. Runs locally, never leaves your machine, and keeps learning you the longer you use it. It was #31 trending on GitHub for ten days straight and now it's the #1 repo on GitHub. > Repo:
0:21

What the f*ck is this GitHub repo, 33k+ stars in less than a month. It's called OpenHuman, and the newest feature is why it's exploding, Super Context. > The moment you open a new chat, it runs a context pass first > Gathers what's relevant about you, your screen, your work > Then answers turn one like it's turn ten, already grounded It lives at the OS level, speaks in your voice across your whole system, connects to 118 apps you already use. Runs locally, never leaves your machine, and keeps learning you the longer you use it. It was #31 trending on GitHub for ten days straight and now it's the #1 repo on GitHub. > Repo:

Defileo🔮

206,429 Aufrufe • vor 2 Monaten

Most AI tools today are just command-takers. You type, it responds. You click, it executes. There's truly no learning, no memory, no adaptation. You're still doing most of the work. Which is why Miles by Avo on Solana would make so much sense if built well. From what I understand so far, miles learns about you over time, adapts to how you operate, and figures out the tools you struggle to navigate on your own. Less clicking, more conversation, the more you use it, the better it gets at working with you, not just for you. That shift from command-follower to learning companion is bigger than it sounds. It's the difference between a tool and an agent that actually knows you. The second one is Agent Grid and this one is for builders and power users. But as I mentioned in this video, I'm still studying the Agent Grid. I'd come talk about it once I learn properly about it. Souren just keeps shipping!
1:28

Most AI tools today are just command-takers. You type, it responds. You click, it executes. There's truly no learning, no memory, no adaptation. You're still doing most of the work. Which is why Miles by Avo on Solana would make so much sense if built well. From what I understand so far, miles learns about you over time, adapts to how you operate, and figures out the tools you struggle to navigate on your own. Less clicking, more conversation, the more you use it, the better it gets at working with you, not just for you. That shift from command-follower to learning companion is bigger than it sounds. It's the difference between a tool and an agent that actually knows you. The second one is Agent Grid and this one is for builders and power users. But as I mentioned in this video, I'm still studying the Agent Grid. I'd come talk about it once I learn properly about it. Souren just keeps shipping!

Sir Khaycee

20,419 Aufrufe • vor 5 Monaten

NVIDIA JUST DROPPED A FREE AI MODEL THAT READS PDFS, WATCHES VIDEOS, LISTENS TO AUDIO, AND UNDERSTANDS YOUR SCREEN SIMULTANEOUSLY. Not one at a time. ALL AT ONCE. In a single pass. It is called Nemotron 3 Nano Omni and it runs 9 times faster than every other multimodal model currently available. Think about what that actually means for how you work. Right now you are switching between tools constantly. One tool for transcribing your call recordings. A different tool for analyzing your client PDFs. Another tool for processing your training videos. A separate workflow for understanding what is happening on your screen. Four tools. Four contexts. Four different outputs you have to manually synthesize into one decision. Nemotron 3 Nano Omni does all of it in one model. One pass. One output. The use cases that just got dramatically simpler: Meeting recordings where you need the transcript, the visual context, and the document references all analyzed together. Training videos where the audio, the slides, and the on-screen demonstrations all feed into one coherent summary. Client PDFs where you need the document content cross-referenced against your screen data and your call notes simultaneously. Sales call transcripts analyzed alongside the proposals and the CRM data in one unified pass. This is not a marginal improvement on existing multimodal models. It is a 9x speed increase on a capability that was already changing how people work. Free. From NVIDIA. Available right now. Bookmark this before everyone catches on. Follow CyrilXBT for every AI capability shift the moment it drops.
8:03

NVIDIA JUST DROPPED A FREE AI MODEL THAT READS PDFS, WATCHES VIDEOS, LISTENS TO AUDIO, AND UNDERSTANDS YOUR SCREEN SIMULTANEOUSLY. Not one at a time. ALL AT ONCE. In a single pass. It is called Nemotron 3 Nano Omni and it runs 9 times faster than every other multimodal model currently available. Think about what that actually means for how you work. Right now you are switching between tools constantly. One tool for transcribing your call recordings. A different tool for analyzing your client PDFs. Another tool for processing your training videos. A separate workflow for understanding what is happening on your screen. Four tools. Four contexts. Four different outputs you have to manually synthesize into one decision. Nemotron 3 Nano Omni does all of it in one model. One pass. One output. The use cases that just got dramatically simpler: Meeting recordings where you need the transcript, the visual context, and the document references all analyzed together. Training videos where the audio, the slides, and the on-screen demonstrations all feed into one coherent summary. Client PDFs where you need the document content cross-referenced against your screen data and your call notes simultaneously. Sales call transcripts analyzed alongside the proposals and the CRM data in one unified pass. This is not a marginal improvement on existing multimodal models. It is a 9x speed increase on a capability that was already changing how people work. Free. From NVIDIA. Available right now. Bookmark this before everyone catches on. Follow CyrilXBT for every AI capability shift the moment it drops.

CyrilXBT

37,847 Aufrufe • vor 3 Monaten

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
0:24

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

58,186 Aufrufe • vor 2 Monaten

Right now, I have a few dozen tools to automate my life. Every single thing that I do more than once every week is now a tool: either a Claude Code skill, a scheduled workflow, or an application. • I spend the time doing this once. • I schedule it. • I forget about it (or at least, try to) CREAO is one of the platforms I've used extensively for building some of these automations. They are partnering with me on this post. They are one of the only platforms where you can go from a conversation to a scheduled agent that quickly. This is how it works: 1. You describe the problem you want to solve 2. CREAO's agent builds the logic and executes it 3. You can iterate with the agent to improve the solution 4. When you are done, you turn that solution into a mini app 5. You can schedule that app to run any time you need it Something important: These agents are deterministic. They don't use an LLM to generate answers, so they will always return the same output given the same input. This is critical: when you're automating something that runs every Monday at 9 am, you need it to return the same result every time, not a "creative" answer.
5:00

Right now, I have a few dozen tools to automate my life. Every single thing that I do more than once every week is now a tool: either a Claude Code skill, a scheduled workflow, or an application. • I spend the time doing this once. • I schedule it. • I forget about it (or at least, try to) CREAO is one of the platforms I've used extensively for building some of these automations. They are partnering with me on this post. They are one of the only platforms where you can go from a conversation to a scheduled agent that quickly. This is how it works: 1. You describe the problem you want to solve 2. CREAO's agent builds the logic and executes it 3. You can iterate with the agent to improve the solution 4. When you are done, you turn that solution into a mini app 5. You can schedule that app to run any time you need it Something important: These agents are deterministic. They don't use an LLM to generate answers, so they will always return the same output given the same input. This is critical: when you're automating something that runs every Monday at 9 am, you need it to return the same result every time, not a "creative" answer.

Santiago

13,712 Aufrufe • vor 5 Monaten

This is the future of web design. Gamma 3.0 has just been released, and I used it to create a complete website from a URL in seconds. No prompts. No code. No input. Their new AI agent will design, review, fix, and iterate on your content. The best part: you can watch it in real-time as it builds your website! This is pretty amazing! There are many AI design agents out there, but this one is one of the most hands-off tools I've seen. This is incredible.
1:11

This is the future of web design. Gamma 3.0 has just been released, and I used it to create a complete website from a URL in seconds. No prompts. No code. No input. Their new AI agent will design, review, fix, and iterate on your content. The best part: you can watch it in real-time as it builds your website! This is pretty amazing! There are many AI design agents out there, but this one is one of the most hands-off tools I've seen. This is incredible.

Santiago

39,140 Aufrufe • vor 11 Monaten

Wow! Someone open sourced 100+ full AI apps you can just clone and ship.. and it's sitting at 124,000 stars on github it's one repo, awesome-llm-apps. and the important part.. these aren't tutorials teaching you to build an app. they're the finished apps themselves. RAG systems, AI agents, multi-agent teams.. tested end to end, apache licensed, yours to copy. a few that are actually in there: - a fraud investigation agent that cross-references public records to flag things that don't add up - a VC due-diligence agent team that writes startup investment analysis - a financial coach that builds you a budget, debt and savings plan - "chat with anything".. your github, gmail, PDFs, arxiv papers, youtube videos - a deep research agent that runs full web research on its own here's how to actually use it, not just star it: clone the repo, open the one app closest to what you need it's a working template.. swap in your data, your API key, your use case ship it as your own thing, or bolt three of them together into one bigger product the license literally lets you take these, customize them, and sell them. that's not a loophole, that's the point of apache-2.0. repo:
0:56

Wow! Someone open sourced 100+ full AI apps you can just clone and ship.. and it's sitting at 124,000 stars on github it's one repo, awesome-llm-apps. and the important part.. these aren't tutorials teaching you to build an app. they're the finished apps themselves. RAG systems, AI agents, multi-agent teams.. tested end to end, apache licensed, yours to copy. a few that are actually in there: - a fraud investigation agent that cross-references public records to flag things that don't add up - a VC due-diligence agent team that writes startup investment analysis - a financial coach that builds you a budget, debt and savings plan - "chat with anything".. your github, gmail, PDFs, arxiv papers, youtube videos - a deep research agent that runs full web research on its own here's how to actually use it, not just star it: clone the repo, open the one app closest to what you need it's a working template.. swap in your data, your API key, your use case ship it as your own thing, or bolt three of them together into one bigger product the license literally lets you take these, customize them, and sell them. that's not a loophole, that's the point of apache-2.0. repo:

Axel Bitblaze 🪓

46,487 Aufrufe • vor 1 Monat

🚨 THIS IS ABSOLUTELY INSANE a developer just released a free tool that builds a vertical video with a talking presenter. out of one script and one photo. it's called lanshu-create-ai-presenter-video. it landed on GitHub two days ago. 267 stars. MIT. here's how it works: 1. You give it a script and a presenter photo you have the rights to. 2. The AI writes and voices the narration, generates the presenter, and locks the lips to the audio. 3. The skill edits, burns in subtitles and a cover, checks the sync, and hands back a master with a QA report. 9:16, 1080x1920, 30fps, 45-75 seconds. all of it happens in one run, on your own machine. save this before you sit down to cut your next video. i left the link in the next comment 👇
0:28

🚨 THIS IS ABSOLUTELY INSANE a developer just released a free tool that builds a vertical video with a talking presenter. out of one script and one photo. it's called lanshu-create-ai-presenter-video. it landed on GitHub two days ago. 267 stars. MIT. here's how it works: 1. You give it a script and a presenter photo you have the rights to. 2. The AI writes and voices the narration, generates the presenter, and locks the lips to the audio. 3. The skill edits, burns in subtitles and a cover, checks the sync, and hands back a master with a QA report. 9:16, 1080x1920, 30fps, 45-75 seconds. all of it happens in one run, on your own machine. save this before you sit down to cut your next video. i left the link in the next comment 👇

Granite

157,370 Aufrufe • vor 6 Tagen

IF YOU HAVE BEEN HOLDING OFF ON HERMES AGENT BECAUSE OF API COSTS, READ THIS FIRST. Four real ways to run it for free. Local, through Ollama, complete privacy and no rate limits. Cloud, through free tiers from Groq, OpenRouter, or NVIDIA NIM, no card required. A subscription you already have, GitHub Copilot connects natively. A fallback configuration so a rate limit on one provider does not stop your whole session. Hermes Agent is free and open source. Now there is no excuse on the model side either. Bookmark this. Follow CyrilXBT
10:45

IF YOU HAVE BEEN HOLDING OFF ON HERMES AGENT BECAUSE OF API COSTS, READ THIS FIRST. Four real ways to run it for free. Local, through Ollama, complete privacy and no rate limits. Cloud, through free tiers from Groq, OpenRouter, or NVIDIA NIM, no card required. A subscription you already have, GitHub Copilot connects natively. A fallback configuration so a rate limit on one provider does not stop your whole session. Hermes Agent is free and open source. Now there is no excuse on the model side either. Bookmark this. Follow CyrilXBT

CyrilXBT

28,802 Aufrufe • vor 2 Monaten

Hermes agent just left the terminal. 𝗛𝗲𝗿𝗺𝗲𝘀 𝗗𝗲𝘀𝗸𝘁𝗼𝗽 dropped yesterday. native app for macOS, Windows, and Linux. for months Hermes was the agent that learned your projects, wrote its own skills, and built a model of who you are. all of it buried in terminal logs. now it has a window. the important part is that it's not a wrapper. it runs the same agent core, the same sessions, memory, and skills as the CLI. you can start a task in the terminal and finish it in the app without anything resetting. the state is shared across every interface, not copied between them. what the GUI actually adds: → streaming chat that shows live tool calls and inline reasoning instead of a spinner → a preview rail that renders pages, code, and images right beside the conversation → an artifacts panel that collects every file the agent has ever produced → remote gateway mode, so you can point the app at a VPS and run the heavy work elsewhere → skills, cron, profiles, and gateways managed point-and-click instead of through YAML → voice mode, drag-drop files, and inline image generation remote gateway mode is the one worth slowing down on. the agent runs 24/7 on a $5 server while you control it from your laptop like a local app. other agent UIs are chatboxes with a logo. this one shows the autonomy instead of hiding it, so you watch the skills load, the tools fire, and the artifacts pile up as it works. it was teased in Jensen's GTC keynote. MIT licensed, local-first, no telemetry. if you already run Hermes, download it and everything is already there. your chats, memory, and skills carry straight over. i wrote a full masterclass on Hermes Agent that walks through the SOUL. md identity layer, the three-tier memory system, the self-evolving skills loop, and how to run three specialized agents 24/7. desktop is the interface that finally does all of it justice. the article is quoted below.
1:18

Hermes agent just left the terminal. 𝗛𝗲𝗿𝗺𝗲𝘀 𝗗𝗲𝘀𝗸𝘁𝗼𝗽 dropped yesterday. native app for macOS, Windows, and Linux. for months Hermes was the agent that learned your projects, wrote its own skills, and built a model of who you are. all of it buried in terminal logs. now it has a window. the important part is that it's not a wrapper. it runs the same agent core, the same sessions, memory, and skills as the CLI. you can start a task in the terminal and finish it in the app without anything resetting. the state is shared across every interface, not copied between them. what the GUI actually adds: → streaming chat that shows live tool calls and inline reasoning instead of a spinner → a preview rail that renders pages, code, and images right beside the conversation → an artifacts panel that collects every file the agent has ever produced → remote gateway mode, so you can point the app at a VPS and run the heavy work elsewhere → skills, cron, profiles, and gateways managed point-and-click instead of through YAML → voice mode, drag-drop files, and inline image generation remote gateway mode is the one worth slowing down on. the agent runs 24/7 on a $5 server while you control it from your laptop like a local app. other agent UIs are chatboxes with a logo. this one shows the autonomy instead of hiding it, so you watch the skills load, the tools fire, and the artifacts pile up as it works. it was teased in Jensen's GTC keynote. MIT licensed, local-first, no telemetry. if you already run Hermes, download it and everything is already there. your chats, memory, and skills carry straight over. i wrote a full masterclass on Hermes Agent that walks through the SOUL. md identity layer, the three-tier memory system, the self-evolving skills loop, and how to run three specialized agents 24/7. desktop is the interface that finally does all of it justice. the article is quoted below.

Akshay 🚀

51,540 Aufrufe • vor 2 Monaten

There's an AI that runs 19 models at the same time on a Mac Mini. It works while you sleep. Not one brain. Nineteen. Each one a specialist. Here's what the Perplexity Personal Computer actually does: → You say "build me a website." → One model researches your competitors. → Another designs the layout. → Another writes the copy. → Another makes the images. → Then it publishes the whole thing. By itself. No clicking. No dragging. No switching between apps. It runs 24/7 on your Mac Mini. Always on. Never stops. You go to bed. You wake up. Your AI already finished the work. It also has access to your real files, folders, and apps. Not stuck in a chat window anymore. And unlike OpenClaw, it has a kill switch, audit logs, and confirmation steps before anything sensitive happens. We went from AI as a tool to AI as a worker. Save this. In two years, everyone will have one of these.
8:08

There's an AI that runs 19 models at the same time on a Mac Mini. It works while you sleep. Not one brain. Nineteen. Each one a specialist. Here's what the Perplexity Personal Computer actually does: → You say "build me a website." → One model researches your competitors. → Another designs the layout. → Another writes the copy. → Another makes the images. → Then it publishes the whole thing. By itself. No clicking. No dragging. No switching between apps. It runs 24/7 on your Mac Mini. Always on. Never stops. You go to bed. You wake up. Your AI already finished the work. It also has access to your real files, folders, and apps. Not stuck in a chat window anymore. And unlike OpenClaw, it has a kill switch, audit logs, and confirmation steps before anything sensitive happens. We went from AI as a tool to AI as a worker. Save this. In two years, everyone will have one of these.

Julian Goldie SEO

25,880 Aufrufe • vor 5 Monaten