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I recently explored TinyFish, a web agents API. What stood out wasn’t hype. It treats the web like infrastructure. Natural language goal in → multi-site execution → structured data out. Less “AI magic.” More systems thinking.

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Md Riyazuddin

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49,720 görüntüleme • 5 ay önce •via X (Twitter)

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Web Scraping is dead. Web Agenting is here. Writing selectors (div > .class > span) breaks every time a site updates. Building custom bots for every new target is a waste of engineering hours. TinyFish turns the entire real-time web into a single API. Input: Natural Language ("Find availability for X"). Target: 1 or 100 URLs. Output: Structured JSON. This isn't a simulation. It visits the Real-Time Web. 1./ One API, Many Sites - Same contract whether you hit 1 URL or 50. You focus on the Goal (Business Logic). TinyFish handles the How (Navigation, Clicks, Inputs). 2./ Real Automation - It doesn't just "read." It interacts. It fills forms, navigates multi-step flows, and handles dynamic JS content. 3./ Production Ready - This is the same infrastructure used by large enterprises, now exposed as a clean developer primitive. Logs, error handling, and structured data are built-in. The web is finally a proper API.
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Web Scraping is dead. Web Agenting is here. Writing selectors (div > .class > span) breaks every time a site updates. Building custom bots for every new target is a waste of engineering hours. TinyFish turns the entire real-time web into a single API. Input: Natural Language ("Find availability for X"). Target: 1 or 100 URLs. Output: Structured JSON. This isn't a simulation. It visits the Real-Time Web. 1./ One API, Many Sites - Same contract whether you hit 1 URL or 50. You focus on the Goal (Business Logic). TinyFish handles the How (Navigation, Clicks, Inputs). 2./ Real Automation - It doesn't just "read." It interacts. It fills forms, navigates multi-step flows, and handles dynamic JS content. 3./ Production Ready - This is the same infrastructure used by large enterprises, now exposed as a clean developer primitive. Logs, error handling, and structured data are built-in. The web is finally a proper API.

Tech with Mak

119,568 görüntüleme • 6 ay önce

After trying BytePlus Seedance 2.0 with native 4K output, what stood out wasn’t just resolution—it was how stable and natural the image feels in motion. Fine textures, reflections, and subtle details don’t break apart between frames. They stay consistent, which gives the footage a more cinematic and grounded feel rather than a purely technical upgrade. Combined with native 10-bit color, the result offers smoother gradients, stronger grading flexibility, and a more refined post-production workflow. Overall, it feels less like a quality bump and more like a step toward genuinely production-ready AI filmmaking.
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After trying BytePlus Seedance 2.0 with native 4K output, what stood out wasn’t just resolution—it was how stable and natural the image feels in motion. Fine textures, reflections, and subtle details don’t break apart between frames. They stay consistent, which gives the footage a more cinematic and grounded feel rather than a purely technical upgrade. Combined with native 10-bit color, the result offers smoother gradients, stronger grading flexibility, and a more refined post-production workflow. Overall, it feels less like a quality bump and more like a step toward genuinely production-ready AI filmmaking.

Mr. Nexus

25,207 görüntüleme • 1 ay önce

Think about what this actually replaces. Game sales become subscription-based world creation. DLCs become real-time environment customization. Studios become world generation systems. Buzzy is not a gaming tool. It is a preview of what the entire industry infrastructure looks like when AI removes the bottleneck between imagination and execution. One photo in. A full AAA world out. The pipeline just collapsed into a single step.
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Think about what this actually replaces. Game sales become subscription-based world creation. DLCs become real-time environment customization. Studios become world generation systems. Buzzy is not a gaming tool. It is a preview of what the entire industry infrastructure looks like when AI removes the bottleneck between imagination and execution. One photo in. A full AAA world out. The pipeline just collapsed into a single step.

SARAH

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Stop spending hours on manual work. You can now use a multi-agent AI workforce to get more work done in less time. Here's how 👇 --- Try Eigent AI - Lets you build and run a custom AI workforce on your desktop. - Automate complex workflows using multi-agent task execution. - Built on CAMEL-AI’s top open-source projects ( CAMEL-AI.org & OWL). - Boost productivity with deep customization and strong privacy --- Features: - Customize Your AI Workforce: Build task-specific agents with domain skills and tools. - Faster Execution: Eigent runs agents in parallel to automate complex workflows. - Human-in-the-loop: Automatically asks for help when tasks hit uncertainty. --- What sets Eigent apart? - 3–5× faster task execution using a parallel multi-agent workforce. - Modular design lets you add new capabilities without changing the core system. - Self-optimizing agents that replan and adapt during execution for higher success. - Deploy anywhere: cloud, local, or enterprise, with full open-source flexibility. --- Try building your multi-agent AI workforce here: Join their community to build your multi-agent workforce: Check their GitHub: ---
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Stop spending hours on manual work. You can now use a multi-agent AI workforce to get more work done in less time. Here's how 👇 --- Try Eigent AI - Lets you build and run a custom AI workforce on your desktop. - Automate complex workflows using multi-agent task execution. - Built on CAMEL-AI’s top open-source projects ( CAMEL-AI.org & OWL). - Boost productivity with deep customization and strong privacy --- Features: - Customize Your AI Workforce: Build task-specific agents with domain skills and tools. - Faster Execution: Eigent runs agents in parallel to automate complex workflows. - Human-in-the-loop: Automatically asks for help when tasks hit uncertainty. --- What sets Eigent apart? - 3–5× faster task execution using a parallel multi-agent workforce. - Modular design lets you add new capabilities without changing the core system. - Self-optimizing agents that replan and adapt during execution for higher success. - Deploy anywhere: cloud, local, or enterprise, with full open-source flexibility. --- Try building your multi-agent AI workforce here: Join their community to build your multi-agent workforce: Check their GitHub: ---

Shushant Lakhyani

20,423 görüntüleme • 1 yıl önce

I still see regular people have no clue how we use AI to code They think it's like AI Agents and lots of hype bs But for me most of it isn't very difficult or deep or fancy but it's simply A LOT of autocompleting and prediction now Small things like this a few mins ago: Nomad List's weather API robot that pulls the weather every day to show on the site, sometimes it hits an error on the API side, and it sends that to my Telegram Then I wanna know "ok for what city is it erroring?" The moment I just put my cursor on "Bad API reply" it already knows what I want and autocompletes to "Bad API reply for city $city['name']" which will tell me for example "Bad API reply for Bangkok" Basic stuff like that saves me a lot of seconds and minutes in a day and it adds up!
0:13

I still see regular people have no clue how we use AI to code They think it's like AI Agents and lots of hype bs But for me most of it isn't very difficult or deep or fancy but it's simply A LOT of autocompleting and prediction now Small things like this a few mins ago: Nomad List's weather API robot that pulls the weather every day to show on the site, sometimes it hits an error on the API side, and it sends that to my Telegram Then I wanna know "ok for what city is it erroring?" The moment I just put my cursor on "Bad API reply" it already knows what I want and autocompletes to "Bad API reply for city $city['name']" which will tell me for example "Bad API reply for Bangkok" Basic stuff like that saves me a lot of seconds and minutes in a day and it adds up!

@levelsio

258,171 görüntüleme • 1 yıl önce

🚨 Alibaba just open sourced a GUI agent that lives inside your webpage and controls it with natural language. It's called Page Agent and it's not a browser extension. It's pure JavaScript no Python, no Puppeteer, no headless browser, no screenshots. Just one script tag and your web app understands natural language. Here's what it actually does: → Embed it with a single tag or npm install → Control any web interface with plain English commands → Text-based DOM manipulation no OCR, no vision models needed → Bring your own LLM (GPT, Claude, Qwen, anything) → Ships a built-in UI with human-in-the-loop support → Turn 20-click ERP/CRM workflows into one sentence → Optional Chrome extension for multi-tab agent tasks → Works on any web app SaaS, admin panels, internal tools Companies are charging $30/month for AI copilots built on this exact idea. This is 3 lines of code. Your users. Your interface. The AI copilot layer for every web app just got open sourced. 1.6K stars. 100% Open Source. (Link in the comments)
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🚨 Alibaba just open sourced a GUI agent that lives inside your webpage and controls it with natural language. It's called Page Agent and it's not a browser extension. It's pure JavaScript no Python, no Puppeteer, no headless browser, no screenshots. Just one script tag and your web app understands natural language. Here's what it actually does: → Embed it with a single tag or npm install → Control any web interface with plain English commands → Text-based DOM manipulation no OCR, no vision models needed → Bring your own LLM (GPT, Claude, Qwen, anything) → Ships a built-in UI with human-in-the-loop support → Turn 20-click ERP/CRM workflows into one sentence → Optional Chrome extension for multi-tab agent tasks → Works on any web app SaaS, admin panels, internal tools Companies are charging $30/month for AI copilots built on this exact idea. This is 3 lines of code. Your users. Your interface. The AI copilot layer for every web app just got open sourced. 1.6K stars. 100% Open Source. (Link in the comments)

Ihtesham Ali

135,474 görüntüleme • 4 ay önce

In 2025, the AgentFlayer exploit highlighted a new category of risk in AI systems. It was not a traditional breach involving stolen credentials or broken encryption. Instead, it demonstrated how an autonomous AI agent could be manipulated into executing unintended actions by processing malicious instructions embedded inside content it automatically processes. The incident did not expose a flaw in one specific integration. It revealed a structural weakness in how many modern AI agents are built. Today’s agents are no longer passive language models. They read documents automatically, scan emails, connect to SaaS tools, access cloud storage, and execute actions across multiple systems. To be useful, they are granted meaningful permissions. That capability creates value, but it also expands the attack surface. Most agent environments operate in a trusted, plaintext execution model. Data is encrypted at rest and in transit, but it is typically decrypted during inference so the model can process it. That runtime visibility is where potential risk lies. In a zero-click scenario like AgentFlayer, an attacker can embed hidden instructions inside a document that the AI processes automatically. Because the agent may have access to connected systems such as Google Drive, Slack, or GitHub, it can potentially be influenced to retrieve sensitive information or perform unintended actions. The user does not need to click a malicious link or approve a suspicious request. Therefore, the core issue is that during execution, the system may have access to sensitive data and broad privileges, meaning whoever controls the execution environment ultimately controls access to that data. Now consider a different architectural approach. If a system is designed so that data remains protected during execution, the risk profile changes. On Nesa, privacy is enforced at the execution layer through Equivariant Encryption. Computation can occur on encrypted data, reducing the visibility surface during runtime. Sensitive inputs and models do not need to be exposed in plain text to infrastructure operators for inference to occur. This does not eliminate prompt injection, logic manipulation, or tool misuse. Encryption alone cannot prevent an agent from being instructed to take an unintended action if it has been granted that permission. What it does do is materially reduce confidentiality risk. By limiting access to readable sensitive data during execution and reducing unilateral visibility at the infrastructure layer, the potential blast radius of a successful manipulation attempt is constrained. As AI agents become more autonomous and embedded into enterprise workflows, security must move deeper into architecture. The goal is not to claim invulnerability. It is to reduce trust concentration and contain systemic exposure when failures occur. AgentFlayer was not simply a one-off exploit. It was a reminder that in autonomous systems, execution-layer design determines how risk propagates.
0:11

In 2025, the AgentFlayer exploit highlighted a new category of risk in AI systems. It was not a traditional breach involving stolen credentials or broken encryption. Instead, it demonstrated how an autonomous AI agent could be manipulated into executing unintended actions by processing malicious instructions embedded inside content it automatically processes. The incident did not expose a flaw in one specific integration. It revealed a structural weakness in how many modern AI agents are built. Today’s agents are no longer passive language models. They read documents automatically, scan emails, connect to SaaS tools, access cloud storage, and execute actions across multiple systems. To be useful, they are granted meaningful permissions. That capability creates value, but it also expands the attack surface. Most agent environments operate in a trusted, plaintext execution model. Data is encrypted at rest and in transit, but it is typically decrypted during inference so the model can process it. That runtime visibility is where potential risk lies. In a zero-click scenario like AgentFlayer, an attacker can embed hidden instructions inside a document that the AI processes automatically. Because the agent may have access to connected systems such as Google Drive, Slack, or GitHub, it can potentially be influenced to retrieve sensitive information or perform unintended actions. The user does not need to click a malicious link or approve a suspicious request. Therefore, the core issue is that during execution, the system may have access to sensitive data and broad privileges, meaning whoever controls the execution environment ultimately controls access to that data. Now consider a different architectural approach. If a system is designed so that data remains protected during execution, the risk profile changes. On Nesa, privacy is enforced at the execution layer through Equivariant Encryption. Computation can occur on encrypted data, reducing the visibility surface during runtime. Sensitive inputs and models do not need to be exposed in plain text to infrastructure operators for inference to occur. This does not eliminate prompt injection, logic manipulation, or tool misuse. Encryption alone cannot prevent an agent from being instructed to take an unintended action if it has been granted that permission. What it does do is materially reduce confidentiality risk. By limiting access to readable sensitive data during execution and reducing unilateral visibility at the infrastructure layer, the potential blast radius of a successful manipulation attempt is constrained. As AI agents become more autonomous and embedded into enterprise workflows, security must move deeper into architecture. The goal is not to claim invulnerability. It is to reduce trust concentration and contain systemic exposure when failures occur. AgentFlayer was not simply a one-off exploit. It was a reminder that in autonomous systems, execution-layer design determines how risk propagates.

Nesa

17,038 görüntüleme • 5 ay önce

I stopped thinking of AI audio as "voice generation." After testing Seed Audio 1.0, it feels less like a traditional TTS model and more like an AI audio creation engine for building complete scenes. One prompt handled the dialogue, emotion, ambience, music, and sound effects—giving the visuals a natural rhythm to follow. I tried it across several storytelling scenarios. Try it: 🧵 Results below: BytePlus Lumina
0:15

I stopped thinking of AI audio as "voice generation." After testing Seed Audio 1.0, it feels less like a traditional TTS model and more like an AI audio creation engine for building complete scenes. One prompt handled the dialogue, emotion, ambience, music, and sound effects—giving the visuals a natural rhythm to follow. I tried it across several storytelling scenarios. Try it: 🧵 Results below: BytePlus Lumina

Setu

13,645 görüntüleme • 22 gün önce

Agent Skills are now live for GOAT BitVM2 nodes. Run a challenger, bridge in, peg out, upgrade your node - all through natural language. No CLI. No manual setup. Tell an agent what you want and it executes end-to-end. Recently shipped Testnet fixes: → Resolved peg-in processing delays → Fixed node stalling edge case → Improved edge case handling across the board → More accurate bridge-out stats Skills: Full Testnet guide:
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Agent Skills are now live for GOAT BitVM2 nodes. Run a challenger, bridge in, peg out, upgrade your node - all through natural language. No CLI. No manual setup. Tell an agent what you want and it executes end-to-end. Recently shipped Testnet fixes: → Resolved peg-in processing delays → Fixed node stalling edge case → Improved edge case handling across the board → More accurate bridge-out stats Skills: Full Testnet guide:

GOAT Network 🟡

72,067 görüntüleme • 5 ay önce

I remember spending weeks digging through government sites, tracking procurements, and building databases of China’s provincial leaders in my previous life as a journalist. The grind was real—slow and frustrating. I want to offer my friends in media and research with free structured web data with AgentQL . Need to track gov policies, procurement data, or economic trends? We just went live, and I’m excited to see how this can support the important work journalists do every day. Reach me out:
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I remember spending weeks digging through government sites, tracking procurements, and building databases of China’s provincial leaders in my previous life as a journalist. The grind was real—slow and frustrating. I want to offer my friends in media and research with free structured web data with AgentQL . Need to track gov policies, procurement data, or economic trends? We just went live, and I’m excited to see how this can support the important work journalists do every day. Reach me out:

Keith Zhai

59,165 görüntüleme • 1 yıl önce

I didn’t expect AI to make ad creation this fast. Just tested Pollo AI Marketing Studio to see what it can do. Tried making a sports streaming app ad from a single product image. No editing. No timeline. No production setup. Just picked a video format and it generated a full video ad in seconds. What stood out wasn’t just the output,, it was how simple the whole process is now. What used to take multiple steps and tools can now be done in a single workflow. Curious how others see this, would you use something like this for ads?
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I didn’t expect AI to make ad creation this fast. Just tested Pollo AI Marketing Studio to see what it can do. Tried making a sports streaming app ad from a single product image. No editing. No timeline. No production setup. Just picked a video format and it generated a full video ad in seconds. What stood out wasn’t just the output,, it was how simple the whole process is now. What used to take multiple steps and tools can now be done in a single workflow. Curious how others see this, would you use something like this for ads?

Ai Girllie

18,849 görüntüleme • 2 ay önce

I just built a complete SEO audit plugin in Claude Code that replaces your $200/mo Ahrefs subscription 🤯 One Claude Plugin audits any store: technical SEO, product schema, content, Core Web Vitals, and AI-search readiness. Parallel agents, a 0-100 score, and a dashboard that renders right in the panel. All inside Claude Code. So I pointed it at Ridge .com, one of the sharpest DTC operators out there. It came back 56/100, and what stood out wasn't a knock on them at all: Ridge has a better AI-commerce setup than 99% of stores. A real llms.txt, an agent-discovery sitemap, a live MCP endpoint, genuinely ahead of the curve. And even on a store that dialed-in, the audit surfaced fixable gaps in ~90 seconds: → Room to add product structured data → A mobile Core Web Vitals score worth tightening → A thin meta description on a high-traffic collection Perfect for e-comm operators and SEO agencies who are sick of paying $200/mo for tools that bury the real issues, running quarterly audits that take a week, and shipping reports nobody can act on. So I put together the full playbook to build your own. The complete guide to building this Plugin in Claude Code: branded to you, tuned to exactly how you audit, repeatable across every client. The kind of audit you run in minutes and hand over as a deliverable that looks like it cost thousands. What's inside: → The architecture (orchestrator + parallel sub-agents) → How to fetch any store past Cloudflare → The 0-100 scoring + falsifiable-findings framework → How to ship the HTML dashboard for client demos → The full build, start to finish Want the playbook for free? > Like this post > Comment "SEO" And I'll send it over (must be following so I can DM)
0:17

I just built a complete SEO audit plugin in Claude Code that replaces your $200/mo Ahrefs subscription 🤯 One Claude Plugin audits any store: technical SEO, product schema, content, Core Web Vitals, and AI-search readiness. Parallel agents, a 0-100 score, and a dashboard that renders right in the panel. All inside Claude Code. So I pointed it at Ridge .com, one of the sharpest DTC operators out there. It came back 56/100, and what stood out wasn't a knock on them at all: Ridge has a better AI-commerce setup than 99% of stores. A real llms.txt, an agent-discovery sitemap, a live MCP endpoint, genuinely ahead of the curve. And even on a store that dialed-in, the audit surfaced fixable gaps in ~90 seconds: → Room to add product structured data → A mobile Core Web Vitals score worth tightening → A thin meta description on a high-traffic collection Perfect for e-comm operators and SEO agencies who are sick of paying $200/mo for tools that bury the real issues, running quarterly audits that take a week, and shipping reports nobody can act on. So I put together the full playbook to build your own. The complete guide to building this Plugin in Claude Code: branded to you, tuned to exactly how you audit, repeatable across every client. The kind of audit you run in minutes and hand over as a deliverable that looks like it cost thousands. What's inside: → The architecture (orchestrator + parallel sub-agents) → How to fetch any store past Cloudflare → The 0-100 scoring + falsifiable-findings framework → How to ship the HTML dashboard for client demos → The full build, start to finish Want the playbook for free? > Like this post > Comment "SEO" And I'll send it over (must be following so I can DM)

Mike Futia

55,431 görüntüleme • 2 ay önce

ANTHROPIC JUST TURNED AI AGENTS INTO GIT REPOS Anthropic shipped "ant" - a CLI that runs every Claude API endpoint straight from your terminal. The headline isn't the terminal access. It's that you can now version-control an AI agent as YAML in Git and have CI sync it to the Claude Platform, the same way you ship code. - Every API resource is a subcommand: messages, models, files, agents, sessions - Define an agent in a YAML file, check it into your repo, and keep it in sync with one update command - Spin up a session, send it an event, then pull every event and tool call back from the same CLI - Claude Code knows how to drive ant out of the box - it shells out and reads the results with no glue code Agents just stopped being prompts you babysit and became infrastructure you deploy.
0:18

ANTHROPIC JUST TURNED AI AGENTS INTO GIT REPOS Anthropic shipped "ant" - a CLI that runs every Claude API endpoint straight from your terminal. The headline isn't the terminal access. It's that you can now version-control an AI agent as YAML in Git and have CI sync it to the Claude Platform, the same way you ship code. - Every API resource is a subcommand: messages, models, files, agents, sessions - Define an agent in a YAML file, check it into your repo, and keep it in sync with one update command - Spin up a session, send it an event, then pull every event and tool call back from the same CLI - Claude Code knows how to drive ant out of the box - it shells out and reads the results with no glue code Agents just stopped being prompts you babysit and became infrastructure you deploy.

BuBBliK

200,319 görüntüleme • 2 ay önce

Gamma isn’t just a presentation tool anymore — it just received a serious intelligence upgrade. Meet Gamma Agent, your AI teammate for thinking, researching, and creating high-quality content. Here’s what it can do: • Run live web research inside your deck • Auto–fact-check claims with real sources • Convert any URL into structured slides instantly • Rewrite content in any tone • Personalize decks for different audiences • Summarize long documents in seconds I tested it: I uploaded a PDF, and Gamma instantly turned it into a clean, well-structured presentation. No formatting. No manual edits. Pure automation. This isn’t simple generation — it’s active intelligence built for founders, creators, marketers, and anyone who builds content daily. Gamma just changed the game. 👉 Try Gamma: #Gamma #ProductivityTools #AI #AIAgents #gammapartner
0:15

Gamma isn’t just a presentation tool anymore — it just received a serious intelligence upgrade. Meet Gamma Agent, your AI teammate for thinking, researching, and creating high-quality content. Here’s what it can do: • Run live web research inside your deck • Auto–fact-check claims with real sources • Convert any URL into structured slides instantly • Rewrite content in any tone • Personalize decks for different audiences • Summarize long documents in seconds I tested it: I uploaded a PDF, and Gamma instantly turned it into a clean, well-structured presentation. No formatting. No manual edits. Pure automation. This isn’t simple generation — it’s active intelligence built for founders, creators, marketers, and anyone who builds content daily. Gamma just changed the game. 👉 Try Gamma: #Gamma #ProductivityTools #AI #AIAgents #gammapartner

Mohini Shewale

55,879 görüntüleme • 8 ay önce

CHINA JUST LEAKED THE FUTURE OF WEB APPS. Alibaba open-sourced PageAgent and 99% of SaaS founders are sleeping on this. It's a JavaScript AI agent that lives INSIDE your webpage. Users control your entire interface with natural language. ↳ No browser extensions needed, screenshots or multi-modal LLMs, headless browser setup, and also no backend rewrite required Just drop it in your HTML with ONE line of code. What took 20 clicks now takes one sentence. "Click login, fill in my credentials, submit the form" Done. This is not a demo, it is production-ready. ↳ Turn any SaaS into an AI copilot in minutes ↳ Smart form filling for ERP, CRM, admin systems ↳ Voice commands and accessibility built in ↳ Multi-page agent tasks via Chrome extension ↳ MCP server support for external control ↳ Bring your own LLM (Qwen, GPT, Claude, anything) Every founder building AI features just got a shortcut. Every developer manually building copilots just got replaced. The integration looks like this: That's it. Your app now has an AI agent.
0:17

CHINA JUST LEAKED THE FUTURE OF WEB APPS. Alibaba open-sourced PageAgent and 99% of SaaS founders are sleeping on this. It's a JavaScript AI agent that lives INSIDE your webpage. Users control your entire interface with natural language. ↳ No browser extensions needed, screenshots or multi-modal LLMs, headless browser setup, and also no backend rewrite required Just drop it in your HTML with ONE line of code. What took 20 clicks now takes one sentence. "Click login, fill in my credentials, submit the form" Done. This is not a demo, it is production-ready. ↳ Turn any SaaS into an AI copilot in minutes ↳ Smart form filling for ERP, CRM, admin systems ↳ Voice commands and accessibility built in ↳ Multi-page agent tasks via Chrome extension ↳ MCP server support for external control ↳ Bring your own LLM (Qwen, GPT, Claude, anything) Every founder building AI features just got a shortcut. Every developer manually building copilots just got replaced. The integration looks like this: That's it. Your app now has an AI agent.

Kanika

330,706 görüntüleme • 1 ay önce

The endgame isn’t an AI inside every app. It might be getting rid of the apps altogether. You tell the AI what you want, it figures out the files, tools and steps, then gets the job done directly on your device. You provide the intent. The Grok agents handle the mess. That feels a lot more natural than juggling 37 tabs. Grok xAI / Writer: Annette, Grok Imagine Designer: Janné
0:15

The endgame isn’t an AI inside every app. It might be getting rid of the apps altogether. You tell the AI what you want, it figures out the files, tools and steps, then gets the job done directly on your device. You provide the intent. The Grok agents handle the mess. That feels a lot more natural than juggling 37 tabs. Grok xAI / Writer: Annette, Grok Imagine Designer: Janné

Mario Nawfal

59,427 görüntüleme • 4 gün önce