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experimenting generating html as fast as possible 1. using cerebras glm 4.7 @ 2k tps 2. parent agent generates html scaffold with "slots" 3. each child agent is assigned to generate slot /* SLOT_START:initializeScene */ function initializeScene() { return ""; }

Steve Krouse

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14,753 views • 5 months ago •via X (Twitter)

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I built a multimodal AI Coding Agent team with multi-agents. It has 3 AI agents working together as a team to generate and execute the code: 1. Coding Agent using o-3 mini 2. Vision Agent using Gemini 3. Code Execution Agent using o-3 mini and E2B 100% Opensource Code.

I built a multimodal AI Coding Agent team with multi-agents. It has 3 AI agents working together as a team to generate and execute the code: 1. Coding Agent using o-3 mini 2. Vision Agent using Gemini 3. Code Execution Agent using o-3 mini and E2B 100% Opensource Code.

Shubham Saboo

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receiving a lot of good words about lavish-axi (ty!) in v0.1.27 i just made it installable and invocable as a skill "/lavish" or with a prompt "/lavish let's discuss what options we have" this is the quickest way to upgrade your workflow from reading long wall of text and markdown to interactive html artifacts which are a lot more pleasant and efficient to work with why not just ask the agent to write an html? you'll know when you need to give some feedback to the agent about things in the html and realize there's no good way to make those edits and iterations. lavish gives you the ability to work with the agent via the html itself if you have been using lavish already, make sure to upgrade to latest version! quite a few good bug fixes shipped recently that would help the agent generate correct styling and poll more reliably

receiving a lot of good words about lavish-axi (ty!) in v0.1.27 i just made it installable and invocable as a skill "/lavish" or with a prompt "/lavish let's discuss what options we have" this is the quickest way to upgrade your workflow from reading long wall of text and markdown to interactive html artifacts which are a lot more pleasant and efficient to work with why not just ask the agent to write an html? you'll know when you need to give some feedback to the agent about things in the html and realize there's no good way to make those edits and iterations. lavish gives you the ability to work with the agent via the html itself if you have been using lavish already, make sure to upgrade to latest version! quite a few good bug fixes shipped recently that would help the agent generate correct styling and poll more reliably

Kun Chen

14,584 views • 1 month ago

I just built an automated multi-agent VC team with Google ADK, Gemini 3 and Nano Banana Pro. 7 AI Agents working as a team: 1. Company Research 2, Market Analysis 3. Financial Modelling 4. Risk Assessor 5. Investor Memo 6. HTML Report 7. Infographic Design 100% Opensource code

I just built an automated multi-agent VC team with Google ADK, Gemini 3 and Nano Banana Pro. 7 AI Agents working as a team: 1. Company Research 2, Market Analysis 3. Financial Modelling 4. Risk Assessor 5. Investor Memo 6. HTML Report 7. Infographic Design 100% Opensource code

Shubham Saboo

39,871 views • 6 months ago

I love using GLM 5.2 for web app iteration. My workflow: generate 6 variations, then pick the best one and continue iterating on it. I built Recast to make this even easier. Give it a prompt, get 6 variations, download the code for your favorite version, then continue iterating in your own coding agent. GLM 5.2 works really well for this use case since it's fast, cheap, and great at design. On average, you can make 3-6 landing pages with GLM 5.2 for every one you can generate with Opus 4.8. And the design variety of the GLM landing pages helps a lot with trying to think through what style you want to go with. Recast is launching soon (as a web app + skill).

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Hassan

20,164 views • 29 days ago

How to setup a multi agent system? Bookmark it 📂 "The Trading Floor" Multi-Agent Market Analysis Council to analyze a stock ticker Z.ai GLM-4.7 🤝 OpenCode Agent framework: CrewAI How it works? 1. User enters a stock ticker to analyze 2. 5 AI agents wake up, each with distinct expertise: - Quant Analyst — technical indicators & price patterns - Sentiment Scout — market mood & crowd psychology - Macro Strategist — sector dynamics & economic context - Risk Manager — volatility, drawdowns & position sizing - Portfolio Chief — synthesizes all perspectives 3. Agents analyze independently using real market data 4. They debate, challenge assumptions, and identify disagreements 5. Portfolio Chief resolves conflicts and delivers a consensus recommendation 6. Final output: buy/hold/sell rating with confidence level, position size, and key risks How to built The Trading Floor? 1. Chose CrewAI as the agent framework — handles multi-agent orchestration out of the box 2. Defined 5 agents with distinct roles, goals, and backstories in Python 3. Built custom tools wrapping yfinance for real market data (prices, indicators, volatility) 4. Configured sequential workflow — specialists analyze first, Portfolio Chief synthesizes last 5. Set up FastAPI backend with SSE to stream agent thoughts in real-time 6. Built Next.js frontend to visualize the "board of directors" deliberating live 7. One environment variable (MODEL=openai/gpt-5.2) powers all agents 8. Generated unique agent icons with AI image tools Total cost: $0 for the framework, pay only for LLM API calls Tech stack: - GLM-4.7 with opencode to build the app - CrewAI (open source) for agent orchestration - GPT-5.2 powering each agent - FastAPI + SSE for real-time streaming - Next.js frontend showing live agent deliberations

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CloudAI-X

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Prompt: “Create a 3-of-4 wallet. After 2 years, it becomes 2-of-4. After 5 years, it becomes 1-of-4.” What this is: A decaying multisig created with an AI agent using Nunchuk CLI + Agent Skills. No Miniscript knowledge needed. It starts as 3-of-4. After 2 years, it becomes 2-of-4. After 5 years, any 1 of the 4 keys can spend.

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nunchuk_io

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Arena.Meme

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There are three stages to true agent-to-agent commerce. Today we are somewhere in stage two. On Grace Gong's Venture with Grace, our Co-Founder & CEO Chi Zhang mapped where agentic payments actually stand, and what has to exist before the last stage unlocks. ▷ Stage 1: agent to website. Your ChatGPT searches Amazon. There is no agent on the other side, just a product catalog. ▷ Stage 2: multi-agent under one owner. A one-person company running ten agents as CTO, CEO, and ops. They sit inside one trusted environment, so they never need to prove identity or pay each other. ▷ Stage 3: true A2A. My agent talks to your agent across separate entities. Deals close and payments settle, and neither side has ever met the other's human. The endgame in Chi's words: "agents truly react as an economic actor, like human," each one a participant in the same economy we are. Getting to stage three is what Kite is built for: verifiable identity, delegated authority, and native payments so any agent can act as a full economic actor. 🪁

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KITE AI

14,644 views • 5 days ago

What people don't realize is that a short '10 minute technical talk' is easily 35 slides worth of content and months of technical work. I've often struggled to use AI to generate slides (the lack of taste!) but two unlocks recently changed that: 1. Fast code generation → fast slides iteration 2. Writing my slides in HTML instead of generating graphics (models are better at writing code) Check out how I built my boilerplate with Mistral AI's Mistral Vibe. Available through the Le Chat Pro and team plan :) Get started with Mistral Vibe:

What people don't realize is that a short '10 minute technical talk' is easily 35 slides worth of content and months of technical work. I've often struggled to use AI to generate slides (the lack of taste!) but two unlocks recently changed that: 1. Fast code generation → fast slides iteration 2. Writing my slides in HTML instead of generating graphics (models are better at writing code) Check out how I built my boilerplate with Mistral AI's Mistral Vibe. Available through the Le Chat Pro and team plan :) Get started with Mistral Vibe:

Sarah Chieng

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I'm proud to present this video created with Grok. Here are the main steps in the making of this video: 1. I wrote a draft script by hand. 2. I reworked the script with Grok to eliminate inconsistencies and add technical details (what I have them say about the Apollo missions and the stones is accurate). 3. Grok broke it down into scenes. 4. I switched to Grok Imagine's Agent mode: transforming each scene into a prompt. 5. I animated each scene in Agent mode using video, as well as cropping and image selection. 6. I edited it. Here's the result; it's hard to call it AI. I look forward to reading your comments. Grok Imagine Elon Musk

I'm proud to present this video created with Grok. Here are the main steps in the making of this video: 1. I wrote a draft script by hand. 2. I reworked the script with Grok to eliminate inconsistencies and add technical details (what I have them say about the Apollo missions and the stones is accurate). 3. Grok broke it down into scenes. 4. I switched to Grok Imagine's Agent mode: transforming each scene into a prompt. 5. I animated each scene in Agent mode using video, as well as cropping and image selection. 6. I edited it. Here's the result; it's hard to call it AI. I look forward to reading your comments. Grok Imagine Elon Musk

Déborah

69,706 views • 1 month ago

YT Podcast → LLM Artifact This is now my favorite way to consume podcasts. Knowledge artifacts generated by agents. The agent (Opus 4.7) spots important insights, does deep analysis, and generates thought-provoking observations that really get me curious to research further. All the research goes into a self-improving wiki for later use by any of my agents. I am using Elevenlabs Scribe for diarization. Skills and scripts to guide the artifact generation. Artifacts are just plain HTML + JS (e.g., chart.js). You can go further on anything by selecting text and components (charts) and doing deeper research, as I show in the clip. I expect more people to use agents in this way.

YT Podcast → LLM Artifact This is now my favorite way to consume podcasts. Knowledge artifacts generated by agents. The agent (Opus 4.7) spots important insights, does deep analysis, and generates thought-provoking observations that really get me curious to research further. All the research goes into a self-improving wiki for later use by any of my agents. I am using Elevenlabs Scribe for diarization. Skills and scripts to guide the artifact generation. Artifacts are just plain HTML + JS (e.g., chart.js). You can go further on anything by selecting text and components (charts) and doing deeper research, as I show in the clip. I expect more people to use agents in this way.

elvis

31,855 views • 3 months ago

Here's how I'm running automated content engine in 2 files 1 markdown file = my wiki 1 html file = my dashboard that's the whole stack. [ the architecture, in plain words ]: LLM wiki = a single markdown file holding my audience DNA, 15 tracked creators, every viral topic from the last 30 days HTML artifact = a single page that reads that markdown file AND can trigger my agents the artifact and the agent talk to each other directly the wiki is the shared brain [ what I actually see when I open it at 9am ]: > 5 trending topics ranked by my audience-DNA fit > 3 KOL posts worth quoting today > last week's saved tweets (so I can ride waves that are still warm) > buttons: [draft tweet] [draft QT] [schedule] [log idea] 1. I click "draft tweet" on a topic 2. the artifact pings my agent 3. agent reads the wiki, drafts in MY voice, returns it to the artifact 4. I edit, schedule, done 15 minutes from morning coffee to 3 scheduled posts [ how to build the same in one evening ]: > step 1: dump your domain knowledge into ONE markdown file (audience profile, KOL list, content rules, voice guide, anything an agent would need to do YOUR job) > step 2: ask claude to build an html artifact that reads from that file ("here's my wiki, build me a dashboard with these views") > step 3: add buttons for the actions you do daily (draft, schedule, log, score, search — your workflow, not mine) > step 4: wire each button to call your agent via tool calls (so the artifact and the agent talk directly) the moment your artifact reads your wiki AND triggers your agents.. most SaaS tools you currently pay for quietly become unnecessary dashboards I used to pay $50/month for now sit in a single html file I can rebuild in 20 minutes every "I'll build a SaaS for this" idea you had last year is a 200-line file you write in an afternoon if you want to get the same content engine, just reply "CONTENT" and will send you in DMs later we're going from buying software to owning it.

Ronin

50,070 views • 2 months ago

A conversational agent with no hallucinations! This is huge! Here is the $1M problem: Build a customer-facing AI agent that works consistently, doesn't hallucinate, and is fully aligned with the business rules. Check out Parlant, a FREE, open-source library. I've talked about them before, and here is the TL;DR: 1. Parlant gives you 10x more control over what your agent says 2. It helps you control how agents conduct conversations 3. It helps you control the service protocols and rules agents follow 4. It helps you control what agents can and can't say They do something pretty clever: 1. They have a set of predefined potential responses that the agent can use to reply to the customer. 2. When a customer sends a message, the agent generates a fluid response draft using an LLM and matches and renders the closest pre-approved reply template. 3. The agent returns the rendered pre-approved reply, not the one generated by the LLM. This maintains fluidity while avoiding unpredictability. Parlant also uses your domain terminology, invokes your tools, and implements self-critique to craft/evaluate its response iteratively. This pretty much solves any hallucination issues with your agent.

A conversational agent with no hallucinations! This is huge! Here is the $1M problem: Build a customer-facing AI agent that works consistently, doesn't hallucinate, and is fully aligned with the business rules. Check out Parlant, a FREE, open-source library. I've talked about them before, and here is the TL;DR: 1. Parlant gives you 10x more control over what your agent says 2. It helps you control how agents conduct conversations 3. It helps you control the service protocols and rules agents follow 4. It helps you control what agents can and can't say They do something pretty clever: 1. They have a set of predefined potential responses that the agent can use to reply to the customer. 2. When a customer sends a message, the agent generates a fluid response draft using an LLM and matches and renders the closest pre-approved reply template. 3. The agent returns the rendered pre-approved reply, not the one generated by the LLM. This maintains fluidity while avoiding unpredictability. Parlant also uses your domain terminology, invokes your tools, and implements self-critique to craft/evaluate its response iteratively. This pretty much solves any hallucination issues with your agent.

Santiago

64,237 views • 1 year ago

I went to listen to the press briefing of General Mkhwanazi!! Very interesting now that I know more 1. Brown Mogotsi sent a message/ a Tip General Mkhwanazi. Why?? 2. Is it possible Mogotsi is an agent as he said in Madlanga Commission?? 3. Could his life really be in danger?? 4. Did Mogotsi start this ??

I went to listen to the press briefing of General Mkhwanazi!! Very interesting now that I know more 1. Brown Mogotsi sent a message/ a Tip General Mkhwanazi. Why?? 2. Is it possible Mogotsi is an agent as he said in Madlanga Commission?? 3. Could his life really be in danger?? 4. Did Mogotsi start this ??

zizipho

41,566 views • 5 months ago

Google Veo 3 now available in Gemini API! Generate 8 second videos with audio and vocals from simple text prompts. Starting at $0.75 per second with audio! 📹 🔉 Here is an python script on how to make viral yeti vlogs from TikTok or Youtube shorts. It uses a 3 step workflow: 1. Based on an idea, it generates a series of scene prompts using Gemini 2.5. 2. For each scene prompt Veo 3 generates a video clip. 3. Combine the individual video clips into a single final video using MoviePy Script and some examples below🧵

Google Veo 3 now available in Gemini API! Generate 8 second videos with audio and vocals from simple text prompts. Starting at $0.75 per second with audio! 📹 🔉 Here is an python script on how to make viral yeti vlogs from TikTok or Youtube shorts. It uses a 3 step workflow: 1. Based on an idea, it generates a series of scene prompts using Gemini 2.5. 2. For each scene prompt Veo 3 generates a video clip. 3. Combine the individual video clips into a single final video using MoviePy Script and some examples below🧵

Philipp Schmid

13,983 views • 1 year ago

I am hooked on Dynamic Workflows! The idea of generating harnesses on the fly is so compelling that I reverse-engineered it for my agent orchestrator. And then I built a monitoring dashboard (as an HTML artifact) to track tasks, metrics, and reports. I can now use and monitor dynamic workflows in my agent orchestrator with coding agents like Claude Code, Codex, Pi, and even my own custom-built DAIR.AI agent. This is clearly the future of working with agents to accomplish complex, long-running tasks. Some use cases I'm having success with: - Branching deep research tasks (with verification) - Parallel deep research tasks - Session mining of all my agent sessions - Bug hunting - Triaging - Fact-checking - LLM councils - AI simulations - Data synthesis - Evals generation ... and many others Dynamic workflows, like agent skills, feel like an important primitive to not only get the most out of agents but also incorporate dynamic behaviors and important components like cooperation and verification. There is so much exploration ground here. The exciting part is that this is not limited to coding tasks; it extends to business use cases and many other technical domains like science and research.

I am hooked on Dynamic Workflows! The idea of generating harnesses on the fly is so compelling that I reverse-engineered it for my agent orchestrator. And then I built a monitoring dashboard (as an HTML artifact) to track tasks, metrics, and reports. I can now use and monitor dynamic workflows in my agent orchestrator with coding agents like Claude Code, Codex, Pi, and even my own custom-built DAIR.AI agent. This is clearly the future of working with agents to accomplish complex, long-running tasks. Some use cases I'm having success with: - Branching deep research tasks (with verification) - Parallel deep research tasks - Session mining of all my agent sessions - Bug hunting - Triaging - Fact-checking - LLM councils - AI simulations - Data synthesis - Evals generation ... and many others Dynamic workflows, like agent skills, feel like an important primitive to not only get the most out of agents but also incorporate dynamic behaviors and important components like cooperation and verification. There is so much exploration ground here. The exciting part is that this is not limited to coding tasks; it extends to business use cases and many other technical domains like science and research.

elvis

102,878 views • 1 month ago

this chinese developer making $320k/year as a solo contractor his secret: 5 AI agents running in parallel, each one a specialist architect, coder, reviewer, tester, ops they don’t share context, don’t step on each other, just ship he takes on projects meant for teams of 5-8 engineers delivers in half the time keeps the entire budget found this video on bilibili at 3am and watched it four times guy sitting at his desk, two monitors filled with code, and he’s barely touching the keyboard here’s what’s happening on his screen: > agent 1 (architect): designs system structure, breaks down features into tasks, decides what gets built first > agent 2 (coder): writes the actual implementation based on architect’s specs > agent 3 (reviewer): checks every piece of code for bugs, edge cases, security issues > agent 4 (tester): generates test cases, runs them, reports failures back > agent 5 (ops): handles deployment, monitoring, infrastructure five separate claude code instances running simultaneously each one has its own system prompt, its own context, its own specialty they communicate through a shared task queue, not through each other that’s the key insight - no shared context means no conflicts agent 2 doesn’t know what agent 3 is doing agent 4 doesn’t care what agent 1 decided they just pick up tasks, complete them, move on he showed his contract history: > 3D rendering pipeline for a gaming studio: $25k > automated trading dashboard: $33k > enterprise CRM rebuild: $44k all completed solo, all delivered early, all clients thought they were hiring a team the code on his screen is python with blender integration - complex stuff that would normally require 3-4 specialists he’s shipping it in days while the client expects weeks while he’s explaining the system to camera, commits are happening in the background, tests running, deployments going out all while he’s literally not touching the keyboard his API costs run about $2k/month his revenue averages $26k/month that’s a 13x return on his AI investment this is the new solo developer playbook don’t compete with teams become the team

this chinese developer making $320k/year as a solo contractor his secret: 5 AI agents running in parallel, each one a specialist architect, coder, reviewer, tester, ops they don’t share context, don’t step on each other, just ship he takes on projects meant for teams of 5-8 engineers delivers in half the time keeps the entire budget found this video on bilibili at 3am and watched it four times guy sitting at his desk, two monitors filled with code, and he’s barely touching the keyboard here’s what’s happening on his screen: > agent 1 (architect): designs system structure, breaks down features into tasks, decides what gets built first > agent 2 (coder): writes the actual implementation based on architect’s specs > agent 3 (reviewer): checks every piece of code for bugs, edge cases, security issues > agent 4 (tester): generates test cases, runs them, reports failures back > agent 5 (ops): handles deployment, monitoring, infrastructure five separate claude code instances running simultaneously each one has its own system prompt, its own context, its own specialty they communicate through a shared task queue, not through each other that’s the key insight - no shared context means no conflicts agent 2 doesn’t know what agent 3 is doing agent 4 doesn’t care what agent 1 decided they just pick up tasks, complete them, move on he showed his contract history: > 3D rendering pipeline for a gaming studio: $25k > automated trading dashboard: $33k > enterprise CRM rebuild: $44k all completed solo, all delivered early, all clients thought they were hiring a team the code on his screen is python with blender integration - complex stuff that would normally require 3-4 specialists he’s shipping it in days while the client expects weeks while he’s explaining the system to camera, commits are happening in the background, tests running, deployments going out all while he’s literally not touching the keyboard his API costs run about $2k/month his revenue averages $26k/month that’s a 13x return on his AI investment this is the new solo developer playbook don’t compete with teams become the team

regent0x

183,659 views • 2 months ago

Using Claude Code as a general agent part 3: Getting Claude Code to Ask me clarifying questions... inline, by creating a Clarifying-Questions .md file What's in this file is how the agent should format clarifying questions before doing a specific task. Rather than coming up with ALL of the context I need to give to the agent, I state what I want at the top of the docuemnt then i tell claude to ask me clarifying questions IN THE DOCUMENT. The agent knows to format them as empty code blocks and all i need to do is fill them in then tell Claude Code to finish the task at hand. Kinda niche, but I use this for EVERYTHING now: 1. State what I want at top (Ad Scripts) 2. Type out relevant context that's in my head 3. Tell the agent to ask @ clarifying-questions 4. Quickly answer them with wispr flow in the document 5. Tell Claude to continue This would be useful for nearly every workflow.

Using Claude Code as a general agent part 3: Getting Claude Code to Ask me clarifying questions... inline, by creating a Clarifying-Questions .md file What's in this file is how the agent should format clarifying questions before doing a specific task. Rather than coming up with ALL of the context I need to give to the agent, I state what I want at the top of the docuemnt then i tell claude to ask me clarifying questions IN THE DOCUMENT. The agent knows to format them as empty code blocks and all i need to do is fill them in then tell Claude Code to finish the task at hand. Kinda niche, but I use this for EVERYTHING now: 1. State what I want at top (Ad Scripts) 2. Type out relevant context that's in my head 3. Tell the agent to ask @ clarifying-questions 4. Quickly answer them with wispr flow in the document 5. Tell Claude to continue This would be useful for nearly every workflow.

Riley Brown

27,664 views • 7 months ago

i made an ai trading agent as a total beginner using OpenClaw🦞 4 things to include when building yours: 1. diary system - tracks everything it learns 2. bankr skills - trades, detect scams 3. allium labs - reads blockchain 4. patience - it takes time + can mistakes full pdf guide with my actual prompts below bookmark this🔖

i made an ai trading agent as a total beginner using OpenClaw🦞 4 things to include when building yours: 1. diary system - tracks everything it learns 2. bankr skills - trades, detect scams 3. allium labs - reads blockchain 4. patience - it takes time + can mistakes full pdf guide with my actual prompts below bookmark this🔖

Naomi Metzger

43,563 views • 5 months ago