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Guide Tutorials : P1 - The Orchestration Workflow 6 terminals. 1 orchestrator. Parallel execution. From Shorthand: - Embedded skills in prompts - Subagents - Orchestration + Planning - tmux From Longform: - Parallelization - Groundwork - Verification loops (hidden easter egg)

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56,319 görüntüleme • 8 ay önce •via X (Twitter)

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THIS IS THE MOST COMPLETE CLAUDE CODE SETUP I'VE EVER SEEN someone open-sourced a full agent harness for Claude Code 38 agents. 156 skills. 72 commands. all pre-configured the kind of setup you'd spend weeks building yourself what's inside: → planner - feature implementation planning → architect - system design decisions → tdd-guide - test-driven development workflow → code-reviewer - quality and security review → security-reviewer - vulnerability analysis → build-error-resolver - fixes broken builds → refactor-cleaner - dead code cleanup → doc-updater - keeps docs in sync → chief-of-staff - communication triage → + 29 more agents 156 skills covering token optimization, memory persistence, continuous learning, verification loops, parallelization, subagent orchestration it ships with AgentShield baked in a security auditor that runs vulnerability checks on every agent action before execution works on Claude Code, Cursor, OpenCode, and Codex CLI one repo, one install, configured everywhere one warning: loading 38 agents and 156 skills into your context at once will burn your usage limits in minutes pick the 3-4 you actually need and load only those free. open-source. MIT licensed like + bookmark - you'll need this
1:15

THIS IS THE MOST COMPLETE CLAUDE CODE SETUP I'VE EVER SEEN someone open-sourced a full agent harness for Claude Code 38 agents. 156 skills. 72 commands. all pre-configured the kind of setup you'd spend weeks building yourself what's inside: → planner - feature implementation planning → architect - system design decisions → tdd-guide - test-driven development workflow → code-reviewer - quality and security review → security-reviewer - vulnerability analysis → build-error-resolver - fixes broken builds → refactor-cleaner - dead code cleanup → doc-updater - keeps docs in sync → chief-of-staff - communication triage → + 29 more agents 156 skills covering token optimization, memory persistence, continuous learning, verification loops, parallelization, subagent orchestration it ships with AgentShield baked in a security auditor that runs vulnerability checks on every agent action before execution works on Claude Code, Cursor, OpenCode, and Codex CLI one repo, one install, configured everywhere one warning: loading 38 agents and 156 skills into your context at once will burn your usage limits in minutes pick the 3-4 you actually need and load only those free. open-source. MIT licensed like + bookmark - you'll need this

self.dll

19,908 görüntüleme • 5 ay önce

Opus 5.5 built this CRM from one prompt (delegating to 6 subagents). how: - the main agent wrote the brief: product, fictional data, design system - 1 subagent built the foundation, 5 built the rest in two parallel waves - Mobbin MCP: 57 searches for real app patterns - 11 design skills (frontend-design, emil-design-eng, impeccable, apple-design…) - screenshot review loops on every page, dark and light - one review from me; the agents fixed what I flagged - ~4 hours in Claude Code find links below 👇
0:36

Opus 5.5 built this CRM from one prompt (delegating to 6 subagents). how: - the main agent wrote the brief: product, fictional data, design system - 1 subagent built the foundation, 5 built the rest in two parallel waves - Mobbin MCP: 57 searches for real app patterns - 11 design skills (frontend-design, emil-design-eng, impeccable, apple-design…) - screenshot review loops on every page, dark and light - one review from me; the agents fixed what I flagged - ~4 hours in Claude Code find links below 👇

Adonias

70,439 görüntüleme • 10 gün önce

this is f**king dangerous. someone just open sourced an entire library of ready-made JEV skills for building insane AI agent workflows. instead of building common agent patterns from scratch, you can grab skills for things like: → jev-route for routing tasks → jev-guardrail for adding checks and verification → jev-fanout for running multiple tasks in parallel → reusable skills you can plug into your own workflows the interesting part is that these solve problems you'll run into once your agents start doing more than simple tasks. you don't have to reinvent routing, verification, or parallel execution every time you build a new workflow. save this now. you'll probably need one of these skills the next time you're building an agent and don't want to start from scratch. repo below:
0:33

this is f**king dangerous. someone just open sourced an entire library of ready-made JEV skills for building insane AI agent workflows. instead of building common agent patterns from scratch, you can grab skills for things like: → jev-route for routing tasks → jev-guardrail for adding checks and verification → jev-fanout for running multiple tasks in parallel → reusable skills you can plug into your own workflows the interesting part is that these solve problems you'll run into once your agents start doing more than simple tasks. you don't have to reinvent routing, verification, or parallel execution every time you build a new workflow. save this now. you'll probably need one of these skills the next time you're building an agent and don't want to start from scratch. repo below:

MIKE

125,483 görüntüleme • 7 gün önce

Anthropic engineer: "You're not supposed to watch Claude Code work You're supposed to wake up and review what it shipped" In 22 minutes, she builds the entire asynchronous workflow live, step by step from an empty terminal Chat Prompts → Assistant Mode → Continuous Loops → Graphs → Autonomous Production Most people close their laptop and everything stops This setup keeps running in a persistent verification loop executing tests, patching edge cases, and getting sharper the longer it runs If this were a $500 course, people would call it the best agentic engineering breakdown of the year You probably think you don't have 22 minutes right now Don't let this vanish from your feed Watch the video first Then save the exact setup guide below
21:31

Anthropic engineer: "You're not supposed to watch Claude Code work You're supposed to wake up and review what it shipped" In 22 minutes, she builds the entire asynchronous workflow live, step by step from an empty terminal Chat Prompts → Assistant Mode → Continuous Loops → Graphs → Autonomous Production Most people close their laptop and everything stops This setup keeps running in a persistent verification loop executing tests, patching edge cases, and getting sharper the longer it runs If this were a $500 course, people would call it the best agentic engineering breakdown of the year You probably think you don't have 22 minutes right now Don't let this vanish from your feed Watch the video first Then save the exact setup guide below

Ryan Carter

312,314 görüntüleme • 19 gün önce

The next big thing is Asynchronous Coding Agents. Such Agents change our role as engineers from Conductor (directing one agent at a time) to Orchestrator (defining tasks for a "fleet" of agents working in parallel), for very well-defined tasks with human review. GitHub's Copilot Agent, as covered at #githubuniverse, is one such example I've been trying and liking. There are a number of options also worth considering, including Jules (also great!), Codex web by OpenAI Developers, and Anthropic's Claude Code for web. Editors like Cursor are also exploring this with some very interesting UX ideas. For the more advanced, orchestrators like Claude Squad, Magnet and Conductor by (nice balance on the engineering/product spectrum) are certainly worth considering. Some caveats: Orchestration has been working well for me in small-medium sized projects: for fixing UI bugs, adding small features etc. This can be good for tackling migrations, upgrading dependencies etc. Enterprise teams are very much still figuring out their "Conductor" patterns but eventually, I do see more orchestration becoming a standard part of the engineering workflow.
10:45

The next big thing is Asynchronous Coding Agents. Such Agents change our role as engineers from Conductor (directing one agent at a time) to Orchestrator (defining tasks for a "fleet" of agents working in parallel), for very well-defined tasks with human review. GitHub's Copilot Agent, as covered at #githubuniverse, is one such example I've been trying and liking. There are a number of options also worth considering, including Jules (also great!), Codex web by OpenAI Developers, and Anthropic's Claude Code for web. Editors like Cursor are also exploring this with some very interesting UX ideas. For the more advanced, orchestrators like Claude Squad, Magnet and Conductor by (nice balance on the engineering/product spectrum) are certainly worth considering. Some caveats: Orchestration has been working well for me in small-medium sized projects: for fixing UI bugs, adding small features etc. This can be good for tackling migrations, upgrading dependencies etc. Enterprise teams are very much still figuring out their "Conductor" patterns but eventually, I do see more orchestration becoming a standard part of the engineering workflow.

Addy Osmani

17,510 görüntüleme • 11 ay önce

Google just dropped a free 1-hour course On building complete agent systems from scratch: 07:02 - Build your first AI agent 13:34 - Give every agent a specialized skill 23:08 - Engineer the right context 32:56 - Connect agents into graphs 54:11 - Run 1,000 agents in autonomous loops One agent saves you an hour A thousand agents replace the team you cannot afford to hire Most people are still learning how to prompt one chatbot Google is already teaching how to build entire agent systems Agent → Skills → Context → Graphs → Loops Single agents are the old workflow Agent swarms are the new one This free 1-hour course is worth more than most paid agent engineering programs Bookmark it before everyone starts building the same thing Then build your first agent with the guide below
1:05:19

Google just dropped a free 1-hour course On building complete agent systems from scratch: 07:02 - Build your first AI agent 13:34 - Give every agent a specialized skill 23:08 - Engineer the right context 32:56 - Connect agents into graphs 54:11 - Run 1,000 agents in autonomous loops One agent saves you an hour A thousand agents replace the team you cannot afford to hire Most people are still learning how to prompt one chatbot Google is already teaching how to build entire agent systems Agent → Skills → Context → Graphs → Loops Single agents are the old workflow Agent swarms are the new one This free 1-hour course is worth more than most paid agent engineering programs Bookmark it before everyone starts building the same thing Then build your first agent with the guide below

rari

28,241 görüntüleme • 2 ay önce

SpaceXAI just released free 1-hour course on Grok Bot Agents: 1 prompt → CEO bot → agent teams → full 24/7 system from 0% to 100%: 0% → 2:15 - build your first Grok Bot 25% → 6:52 - give every Bot a role 50% → 16:51 - teach Bots to work 24/7 75% → 31:50 - run agent teams in parallel 100% → 52:18 - full system that automate your life most agent tutorials give you a chatbot that waits for prompts - this one builds a 24/7 team that works while your laptop is closed - by team who build it watch it, build one Bot, turn it into a team - then read the full Grok Bot architecture below ↓
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SpaceXAI just released free 1-hour course on Grok Bot Agents: 1 prompt → CEO bot → agent teams → full 24/7 system from 0% to 100%: 0% → 2:15 - build your first Grok Bot 25% → 6:52 - give every Bot a role 50% → 16:51 - teach Bots to work 24/7 75% → 31:50 - run agent teams in parallel 100% → 52:18 - full system that automate your life most agent tutorials give you a chatbot that waits for prompts - this one builds a 24/7 team that works while your laptop is closed - by team who build it watch it, build one Bot, turn it into a team - then read the full Grok Bot architecture below ↓

codila

294,099 görüntüleme • 1 ay önce

You need to try Hermes RIGHT NOW. OpenClaw was the breakthrough. Hermes feels like the evolution. Here’s why AI power users are quietly switching: • Hermes learns from you over time • It creates its own reusable skills • Memory stays curated instead of bloated • Agents improve every 10 turns automatically • It actually feels stable on day 30 The wildest part: Hermes agents build workflows from your behavior. One user asked their agent to configure Twingate once. The agent: → learned the process → created a reusable skill → stored it for future tasks → improved its own workflow automatically That’s not prompt engineering anymore. That’s agent evolution. Meanwhile most people are still babysitting broken OpenClaw setups and manually importing marketplace skills. Hermes took a different path: Less clutter. Less tweaking. More autonomy. And it’s working. It already flipped OpenClaw in OpenRouter token usage and became one of the fastest-growing AI repos on GitHub. The bigger shift: We’re entering the era where the model matters less than the harness around it. GPT-5.5, Grok, Qwen: They’re already powerful enough. The real edge now is: • memory systems • self-improvement loops • skill creation • agent orchestration Hermes understands that. Most people still think AI agents are glorified chatbots. The people using Hermes are building AI teammates.
32:38

You need to try Hermes RIGHT NOW. OpenClaw was the breakthrough. Hermes feels like the evolution. Here’s why AI power users are quietly switching: • Hermes learns from you over time • It creates its own reusable skills • Memory stays curated instead of bloated • Agents improve every 10 turns automatically • It actually feels stable on day 30 The wildest part: Hermes agents build workflows from your behavior. One user asked their agent to configure Twingate once. The agent: → learned the process → created a reusable skill → stored it for future tasks → improved its own workflow automatically That’s not prompt engineering anymore. That’s agent evolution. Meanwhile most people are still babysitting broken OpenClaw setups and manually importing marketplace skills. Hermes took a different path: Less clutter. Less tweaking. More autonomy. And it’s working. It already flipped OpenClaw in OpenRouter token usage and became one of the fastest-growing AI repos on GitHub. The bigger shift: We’re entering the era where the model matters less than the harness around it. GPT-5.5, Grok, Qwen: They’re already powerful enough. The real edge now is: • memory systems • self-improvement loops • skill creation • agent orchestration Hermes understands that. Most people still think AI agents are glorified chatbots. The people using Hermes are building AI teammates.

Dami-Defi

17,062 görüntüleme • 4 ay önce

i built an AI web development agent that creates full websites from voice commands. (Save 15+ hours per project, eliminate designer bottlenecks, deliver websites while you sleep) Here's how the automation works: → AI voice agent takes natural language requests through 11Labs → Automatically scrapes existing websites for context and content → Generates comprehensive product requirement documents → Controls browser automation to build sites in Lovable → Handles client feedback and edits without human intervention → Delivers production-ready websites in hours, not weeks This system turns last-minute panic into competitive advantage. No more "can you have this done by tomorrow" stress. The secret sauce? Agent orchestration. Instead of one mega-prompt trying to do everything, I built specialized sub-agents: ✅ Website Planner Agent (scraping & requirements) ✅ Browser Control Agent (Lovable automation) ✅ Parent Orchestrator (intelligent routing) Result: What used to take a web dev team 2-3 weeks now happens in 3-4 hours, completely automated. Want the full blueprint? Here are the steps: 1. Like & RT this post 2. Follow me so I can DM you 3. Comment 'BLUEPRINT' I'll send you an entire step-by-step video walkthrough & the full n8n workflow system for free (including all of the prompts). Let's get it.
3:52

i built an AI web development agent that creates full websites from voice commands. (Save 15+ hours per project, eliminate designer bottlenecks, deliver websites while you sleep) Here's how the automation works: → AI voice agent takes natural language requests through 11Labs → Automatically scrapes existing websites for context and content → Generates comprehensive product requirement documents → Controls browser automation to build sites in Lovable → Handles client feedback and edits without human intervention → Delivers production-ready websites in hours, not weeks This system turns last-minute panic into competitive advantage. No more "can you have this done by tomorrow" stress. The secret sauce? Agent orchestration. Instead of one mega-prompt trying to do everything, I built specialized sub-agents: ✅ Website Planner Agent (scraping & requirements) ✅ Browser Control Agent (Lovable automation) ✅ Parent Orchestrator (intelligent routing) Result: What used to take a web dev team 2-3 weeks now happens in 3-4 hours, completely automated. Want the full blueprint? Here are the steps: 1. Like & RT this post 2. Follow me so I can DM you 3. Comment 'BLUEPRINT' I'll send you an entire step-by-step video walkthrough & the full n8n workflow system for free (including all of the prompts). Let's get it.

David Roberts

55,016 görüntüleme • 1 yıl önce

this is free f*cking gold a second brain article hit 8 million views, so the guy behind it put the entire setup in one place the repo, the guide, the tools, the learning path. all of it, free • the guide > 10 sections, 65 pages, concept through troubleshooting > 5 tracks on top, 44 pages, 15 of them build guides with code that runs • the machine (.claude/) > 18 agent skills, one per workflow > 72 slash commands - /ingest-pdf, /ingest-youtube, /ingest-voice, /backfill > 6 subagents - curator, linker, researcher, reviewer, ingestor, graph-analyst > 4 of them read only, so nothing rewrites your vault behind your back • the scripts (plain Python, zero dependencies) > graph export, link checker, vault stats, chat converter, site builder • the starter vault > its own CLAUDE.md with page contracts and linking rules > raw/ never edited after it lands, wiki/ is what the agent maintains > log.md - one line per run, so the whole thing stays auditable • 87 vetted resources > 28 tools, 26 Obsidian plugins, 15 repos, 12 skills, papers and articles five tracks to pick from: > knowledge graphs > Jev engineering > agent harnesses > loop engineering > eval engineering start with the second brain guide if you're new. go straight to the tracks if you already live in this stuff a consultant charges four figures to build you a research system. this one sits in a public repo under MIT ↳
0:24

this is free f*cking gold a second brain article hit 8 million views, so the guy behind it put the entire setup in one place the repo, the guide, the tools, the learning path. all of it, free • the guide > 10 sections, 65 pages, concept through troubleshooting > 5 tracks on top, 44 pages, 15 of them build guides with code that runs • the machine (.claude/) > 18 agent skills, one per workflow > 72 slash commands - /ingest-pdf, /ingest-youtube, /ingest-voice, /backfill > 6 subagents - curator, linker, researcher, reviewer, ingestor, graph-analyst > 4 of them read only, so nothing rewrites your vault behind your back • the scripts (plain Python, zero dependencies) > graph export, link checker, vault stats, chat converter, site builder • the starter vault > its own CLAUDE.md with page contracts and linking rules > raw/ never edited after it lands, wiki/ is what the agent maintains > log.md - one line per run, so the whole thing stays auditable • 87 vetted resources > 28 tools, 26 Obsidian plugins, 15 repos, 12 skills, papers and articles five tracks to pick from: > knowledge graphs > Jev engineering > agent harnesses > loop engineering > eval engineering start with the second brain guide if you're new. go straight to the tracks if you already live in this stuff a consultant charges four figures to build you a research system. this one sits in a public repo under MIT ↳

Annatar.md

201,715 görüntüleme • 10 gün önce

HERMES AGENT HAS FEATURES THAT 90% OF USERS NEVER TOUCH. /BACKGROUND. /STEER. /GOAL. SUB-AGENTS. MODEL SWITCHING. CRON JOBS. 7 LEVELS OF HERMES. ALL EXPLAINED.👇 LEVEL 1 — ONE-SHOT PROMPTS you type a prompt. agent responds. done. this is using Hermes as a chatbot. it works but you leave 90% of the value untouched. LEVEL 2 — MEMORY + SOUL.MD the agent remembers you across sessions. you wrote a SOUL.md with identity, voice, restrictions. Hermes tailors every answer to your context. two people asking the same question get different answers because it knows them differently. LEVEL 3 — COMMANDS THAT MULTIPLY OUTPUT /background → fires a task in the background. your main session stays free. result appears as a panel when done. /steer → injects a message into the current run without interrupting the agent. redirects the work mid-execution. /queue → queues a follow-up for after the current task finishes. /model → switches models mid-session. start with Sonnet for planning. switch to DeepSeek for execution. switch to Opus for review. configure default behavior: display: busy_input_mode: steer # or queue, or interrupt LEVEL 4 — SKILLS + RIGHT MODEL PER SKILL every installed skill becomes a slash command: /competitive-research analyze my top 3 competitors /social-media-copy write 5 LinkedIn posts /code-review check this PR for security issues the unlock: assign a specific model per skill. research skill → GPT-5.5 (cheap, high volume). code review skill → Fable 5 (best at code). content skill → Sonnet (best at writing). you stop paying premium prices for tasks that don't need premium models. LEVEL 5 — MCPs (CONNECT YOUR WORLD) plug Hermes into the tools you already use: Gmail, Calendar, Notion, Slack, ClickUp, Granola. the agent reads your emails, checks your calendar, pulls from your project management tool, and answers questions using YOUR actual data. "what were the numbers this week?" Hermes checks ClickUp, cross-references with your goals in memory, gives a contextualized answer. caveat from the docs: keep MCPs minimal. every MCP adds tools to the context window. 15 MCPs with 10 tools each = 150 tool schemas the model reads every turn. install what you use. disable what you don't. LEVEL 6 — SUB-AGENTS + PARALLEL EXECUTION delegate_task spawns sub-agents with isolated context. batch mode runs children in parallel: delegate_task(tasks=[ {goal: "research best industries for websites", model: "deepseek-v4-flash"}, {goal: "critique the research and find gaps", model: "gpt-5.5"}, ]) max_concurrent_children: 3 (default). each child gets own terminal session and toolset. parent receives summaries when children finish. roles: → leaf (default): cannot re-delegate → orchestrator: can spawn its own workers (bounded by delegation.max_spawn_depth) LEVEL 7 — ASYNC OPERATIONS this is where the agent works without you. /goal — persistent objective across turns. judge model checks after every turn. runs until done or budget hit (default 20 turns). cron jobs — scheduled tasks on repeat. morning briefs, competitor scans, weekly reviews. wakeAgent gates for zero-token monitoring. /background — parallel tasks in current session. results delivered when ready. your main conversation never stops. the shift: from "I ask, it responds" to "it works, I review." WHERE ARE YOU? 1 → chatbot 2 → it knows you 3 → parallel commands 4 → right model per task 5 → connected to your tools 6 → sub-agents working together 7 → runs without you most people are at 2-3. the jump from 4 to 7 is where the real time savings compound. this is just the first 7. beyond this: multi-profile architecture, self-improving knowledge bases, voice mode, kanban orchestration, webhook automation, profile distributions, browser control, IDE integration, and more. comment LEVEL and I'll send you the advanced levels with setup for each. Full guide how to build 3-agent research department with Hermes 👇
25:35

HERMES AGENT HAS FEATURES THAT 90% OF USERS NEVER TOUCH. /BACKGROUND. /STEER. /GOAL. SUB-AGENTS. MODEL SWITCHING. CRON JOBS. 7 LEVELS OF HERMES. ALL EXPLAINED.👇 LEVEL 1 — ONE-SHOT PROMPTS you type a prompt. agent responds. done. this is using Hermes as a chatbot. it works but you leave 90% of the value untouched. LEVEL 2 — MEMORY + SOUL.MD the agent remembers you across sessions. you wrote a SOUL.md with identity, voice, restrictions. Hermes tailors every answer to your context. two people asking the same question get different answers because it knows them differently. LEVEL 3 — COMMANDS THAT MULTIPLY OUTPUT /background → fires a task in the background. your main session stays free. result appears as a panel when done. /steer → injects a message into the current run without interrupting the agent. redirects the work mid-execution. /queue → queues a follow-up for after the current task finishes. /model → switches models mid-session. start with Sonnet for planning. switch to DeepSeek for execution. switch to Opus for review. configure default behavior: display: busy_input_mode: steer # or queue, or interrupt LEVEL 4 — SKILLS + RIGHT MODEL PER SKILL every installed skill becomes a slash command: /competitive-research analyze my top 3 competitors /social-media-copy write 5 LinkedIn posts /code-review check this PR for security issues the unlock: assign a specific model per skill. research skill → GPT-5.5 (cheap, high volume). code review skill → Fable 5 (best at code). content skill → Sonnet (best at writing). you stop paying premium prices for tasks that don't need premium models. LEVEL 5 — MCPs (CONNECT YOUR WORLD) plug Hermes into the tools you already use: Gmail, Calendar, Notion, Slack, ClickUp, Granola. the agent reads your emails, checks your calendar, pulls from your project management tool, and answers questions using YOUR actual data. "what were the numbers this week?" Hermes checks ClickUp, cross-references with your goals in memory, gives a contextualized answer. caveat from the docs: keep MCPs minimal. every MCP adds tools to the context window. 15 MCPs with 10 tools each = 150 tool schemas the model reads every turn. install what you use. disable what you don't. LEVEL 6 — SUB-AGENTS + PARALLEL EXECUTION delegate_task spawns sub-agents with isolated context. batch mode runs children in parallel: delegate_task(tasks=[ {goal: "research best industries for websites", model: "deepseek-v4-flash"}, {goal: "critique the research and find gaps", model: "gpt-5.5"}, ]) max_concurrent_children: 3 (default). each child gets own terminal session and toolset. parent receives summaries when children finish. roles: → leaf (default): cannot re-delegate → orchestrator: can spawn its own workers (bounded by delegation.max_spawn_depth) LEVEL 7 — ASYNC OPERATIONS this is where the agent works without you. /goal — persistent objective across turns. judge model checks after every turn. runs until done or budget hit (default 20 turns). cron jobs — scheduled tasks on repeat. morning briefs, competitor scans, weekly reviews. wakeAgent gates for zero-token monitoring. /background — parallel tasks in current session. results delivered when ready. your main conversation never stops. the shift: from "I ask, it responds" to "it works, I review." WHERE ARE YOU? 1 → chatbot 2 → it knows you 3 → parallel commands 4 → right model per task 5 → connected to your tools 6 → sub-agents working together 7 → runs without you most people are at 2-3. the jump from 4 to 7 is where the real time savings compound. this is just the first 7. beyond this: multi-profile architecture, self-improving knowledge bases, voice mode, kanban orchestration, webhook automation, profile distributions, browser control, IDE integration, and more. comment LEVEL and I'll send you the advanced levels with setup for each. Full guide how to build 3-agent research department with Hermes 👇

YanXbt

31,551 görüntüleme • 3 ay önce

OpenAI Dots is insane for building a 24/7 AI company... I mapped the whole OpenAI Dots architecture into one paper: agents, models, tools, memory, delegation, guardrails and the revenue layer. Here are the 10 steps: step 1 → stop treating Dots like a chatbot. each Dot runs in a persistent cloud environment with its own browser, terminal, files, memory and scheduled execution. close the laptop and the workflow keeps moving step 2 → hire by responsibility. give every Dot one clear domain, dedicated sources, a working style, an approval boundary and a trigger. a "general helper" has no role, only undefined context step 3 → make one Dot the orchestrator. you give it the objective, it decomposes the work, delegates to specialized agents, checks their outputs and merges everything into one deliverable step 4 → stop being the courier between agents. with isolated delegation, each worker gets only the context and tools it needs, executes in its own environment, then returns a structured result to the orchestrator step 5 → connect the real business stack: cloud browser sessions, Slack, Microsoft Teams, Google Workspace, internal dashboards and APIs. a Dot without tools is still just a conversational layer step 6 → put the company on a clock. recurring routines and event triggers turn one-off tasks into persistent operations. research, monitoring, reporting and pipeline checks can run while nobody is online step 7 → automate the reversible, gate the irreversible. let agents read, research, analyze and draft autonomously, but require human approval before messages send, money moves, records change or production code ships step 8 → route intelligence by cost. keep the strongest model on orchestration, conflict resolution and final audits, and let cheaper high-context models handle background research, classification and repetitive execution step 9 → give the company shared memory. project specs, approved claims, pricing, decisions and requirements live in one persistent workspace, so every agent works from the same source of truth instead of rebuilding context from old chats step 10 → connect the loop to revenue. research finds the signal, outreach creates the opportunity, execution moves the work forward, analytics measures the result and monitoring discovers the next action AI stops being something you open when you need an answer. It becomes an operating layer that keeps economically useful work moving after you log off. Copy the complete OpenAI Dots architecture blueprint, then read the full roadmap below ↓
0:25

OpenAI Dots is insane for building a 24/7 AI company... I mapped the whole OpenAI Dots architecture into one paper: agents, models, tools, memory, delegation, guardrails and the revenue layer. Here are the 10 steps: step 1 → stop treating Dots like a chatbot. each Dot runs in a persistent cloud environment with its own browser, terminal, files, memory and scheduled execution. close the laptop and the workflow keeps moving step 2 → hire by responsibility. give every Dot one clear domain, dedicated sources, a working style, an approval boundary and a trigger. a "general helper" has no role, only undefined context step 3 → make one Dot the orchestrator. you give it the objective, it decomposes the work, delegates to specialized agents, checks their outputs and merges everything into one deliverable step 4 → stop being the courier between agents. with isolated delegation, each worker gets only the context and tools it needs, executes in its own environment, then returns a structured result to the orchestrator step 5 → connect the real business stack: cloud browser sessions, Slack, Microsoft Teams, Google Workspace, internal dashboards and APIs. a Dot without tools is still just a conversational layer step 6 → put the company on a clock. recurring routines and event triggers turn one-off tasks into persistent operations. research, monitoring, reporting and pipeline checks can run while nobody is online step 7 → automate the reversible, gate the irreversible. let agents read, research, analyze and draft autonomously, but require human approval before messages send, money moves, records change or production code ships step 8 → route intelligence by cost. keep the strongest model on orchestration, conflict resolution and final audits, and let cheaper high-context models handle background research, classification and repetitive execution step 9 → give the company shared memory. project specs, approved claims, pricing, decisions and requirements live in one persistent workspace, so every agent works from the same source of truth instead of rebuilding context from old chats step 10 → connect the loop to revenue. research finds the signal, outreach creates the opportunity, execution moves the work forward, analytics measures the result and monitoring discovers the next action AI stops being something you open when you need an answer. It becomes an operating layer that keeps economically useful work moving after you log off. Copy the complete OpenAI Dots architecture blueprint, then read the full roadmap below ↓

monokern

241,676 görüntüleme • 3 gün önce

Stanford professor just gave away the entire foundation of how AI Agents & automation actually works. 1-hour lecture. Tool calling. Multi-step workflows. Planning. Reflection. SAVE this to watch this before you open Netflix tonight. More valuable than 6 months of copying Make and n8n tutorials, for building Ai Agents Most people learn by copying tutorials blindly. Stanford teaches you WHY agents work the way they do. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward instead of just entertaining you for 30 seconds. ↓ Why your automations keep breaking. You copied a Make tutorial. Built the exact workflow. Worked for a week. Then the API changed. The trigger failed. An edge case broke everything. You had no idea how to fix it. Because you never understood why it worked. You were copying keystrokes. The people shipping real automation were understanding architecture. ↓ What Stanford actually teaches. Tool calling: how an agent decides which tool to use by scoring each option against the current task state, not just matching keywords. ReAct loop: the agent reasons, acts, observes, then reasons again. Break this cycle and your workflow fails silently. Planning vs execution: why agents that plan all steps upfront break on dynamic inputs, and why iterative planners survive production. Memory architecture: short-term context for the current task, long-term vector memory for patterns. Most automations fail because they confuse the two. Reflection: how agents catch their own errors by evaluating outputs against original intent before moving to the next step. Tool composition: why chaining 10 tools blindly creates cascading failures, and how to structure dependencies so one broken node doesn't kill the whole workflow. This is the foundation behind every automation that actually works. Not prompting tricks. Not "10 best AI tools" reels. Actual architecture. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward. ↓ Your weekend plan. Tonight: watch the Stanford lecture. 1 hour. Saturday to Sunday: build 3 projects applying what you learned. Next 2 weekends: 6 more projects. 9 projects. 2 weeks. APIs, webhooks, LLM integration, real workflows. No theory. Just build. ↓ Stanford Agentic AI lecture: free on YouTube. Watch it this weekend or buy another $500 "AI automation course" in 2027 that teaches less than this one free lecture. Bookmark. Watch tonight. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward.
56:54

Stanford professor just gave away the entire foundation of how AI Agents & automation actually works. 1-hour lecture. Tool calling. Multi-step workflows. Planning. Reflection. SAVE this to watch this before you open Netflix tonight. More valuable than 6 months of copying Make and n8n tutorials, for building Ai Agents Most people learn by copying tutorials blindly. Stanford teaches you WHY agents work the way they do. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward instead of just entertaining you for 30 seconds. ↓ Why your automations keep breaking. You copied a Make tutorial. Built the exact workflow. Worked for a week. Then the API changed. The trigger failed. An edge case broke everything. You had no idea how to fix it. Because you never understood why it worked. You were copying keystrokes. The people shipping real automation were understanding architecture. ↓ What Stanford actually teaches. Tool calling: how an agent decides which tool to use by scoring each option against the current task state, not just matching keywords. ReAct loop: the agent reasons, acts, observes, then reasons again. Break this cycle and your workflow fails silently. Planning vs execution: why agents that plan all steps upfront break on dynamic inputs, and why iterative planners survive production. Memory architecture: short-term context for the current task, long-term vector memory for patterns. Most automations fail because they confuse the two. Reflection: how agents catch their own errors by evaluating outputs against original intent before moving to the next step. Tool composition: why chaining 10 tools blindly creates cascading failures, and how to structure dependencies so one broken node doesn't kill the whole workflow. This is the foundation behind every automation that actually works. Not prompting tricks. Not "10 best AI tools" reels. Actual architecture. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward. ↓ Your weekend plan. Tonight: watch the Stanford lecture. 1 hour. Saturday to Sunday: build 3 projects applying what you learned. Next 2 weekends: 6 more projects. 9 projects. 2 weeks. APIs, webhooks, LLM integration, real workflows. No theory. Just build. ↓ Stanford Agentic AI lecture: free on YouTube. Watch it this weekend or buy another $500 "AI automation course" in 2027 that teaches less than this one free lecture. Bookmark. Watch tonight. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward.

Himanshu Kumar

28,206 görüntüleme • 5 ay önce

is genspark ($50M ARR in 6mo) underrated next to chatgpt, claude, gemini, and manus? i spent 34 minutes testing its “super ai agent” and how it actually works. 1/ you write one prompt and it runs through gpt-5, claude sonnet, gemini 2.5, and mistral large then gives you the best response. 2/ you upload one image and it runs it through nano banana, flux, and bytedance then returns the most realistic version. 3/ you generate a video and genspark routes it through wan 2.2, pika labs, gen-2, and runway ml combining the best clips and motion. 4/ you paste an idea for slides and it uses gpt-5, claude 3 opus, gemini advanced, and genspark’s slide model to create a full deck with visuals and market data. 5/ AI slides and building a fundraising deck with a few prompts (wasn't perfect, but it was a good start for my personal workflow) what's cool about genspark is it's inexpensive and does it all. kinda like the costco of LLMs. TLDR; you hand it one prompt + input, and genspark routes sub-tasks across models and tools behind the scenes. it’s orchestration over one ideal model. perspective shift. instead of jumping from model to model, llm to llm, genspark shows what the next generation of ai will look like orchestration over obsession. and for the rest of us using it, the only thing that matters is getting the best outcome. what other AI app should I review next? i'll keep doing it if it's helpful for folks.
34:35

is genspark ($50M ARR in 6mo) underrated next to chatgpt, claude, gemini, and manus? i spent 34 minutes testing its “super ai agent” and how it actually works. 1/ you write one prompt and it runs through gpt-5, claude sonnet, gemini 2.5, and mistral large then gives you the best response. 2/ you upload one image and it runs it through nano banana, flux, and bytedance then returns the most realistic version. 3/ you generate a video and genspark routes it through wan 2.2, pika labs, gen-2, and runway ml combining the best clips and motion. 4/ you paste an idea for slides and it uses gpt-5, claude 3 opus, gemini advanced, and genspark’s slide model to create a full deck with visuals and market data. 5/ AI slides and building a fundraising deck with a few prompts (wasn't perfect, but it was a good start for my personal workflow) what's cool about genspark is it's inexpensive and does it all. kinda like the costco of LLMs. TLDR; you hand it one prompt + input, and genspark routes sub-tasks across models and tools behind the scenes. it’s orchestration over one ideal model. perspective shift. instead of jumping from model to model, llm to llm, genspark shows what the next generation of ai will look like orchestration over obsession. and for the rest of us using it, the only thing that matters is getting the best outcome. what other AI app should I review next? i'll keep doing it if it's helpful for folks.

GREG ISENBERG

62,242 görüntüleme • 11 ay önce

7 AI SERVICES BOOMER BUSINESSES ARE BEGGING TO BUY IN 2026 1) AI Tools Assessment: $999. One 45-minute interview, then a report with 3 to 7 off-the-shelf tools they can implement immediately. 50 to 60% convert into implementation. (grab the exact template I use to deliver the assessment at 2) AI Concierge: $1,000 to $2,000 a month for two 45-minute calls turning their manual processes into Claude skills. At $1,500 a month you're making $1,000 an hour. 3) Process Redesign: $3,500 to fix a broken workflow before you touch AI. One client went from 25 steps to 10. Never automate a broken process. 4) Automation Builds: $1,000 to $3,000 in Zapier or Make. One $1,500 build took a project manager out of client onboarding entirely. 5) Knowledge Systems: $3,000 baseline. A business broker went from 500 buyer emails per listing to 10 with one custom GPT. 6) Custom Workflows: $3,000 to $5,000. One client's 45-minute podcast workflow now runs off one orchestrator skill in 5 minutes. 7) Full Implementation: $5,000 to $10,000+ mixing everything above. Skills + automation builds + knowledge systems. Mix and match based on client needs. Two things that make this work: 1) The assessment is always the baseline. Start there and let it tell you what to sell next. 2) Anchor on hours saved, not hours worked. Nobody pays $1,000 an hour for your time. They pay it to get theirs back. Full breakdown below. (also available on the Build With AI podcast wherever you get your pods)
26:12

7 AI SERVICES BOOMER BUSINESSES ARE BEGGING TO BUY IN 2026 1) AI Tools Assessment: $999. One 45-minute interview, then a report with 3 to 7 off-the-shelf tools they can implement immediately. 50 to 60% convert into implementation. (grab the exact template I use to deliver the assessment at 2) AI Concierge: $1,000 to $2,000 a month for two 45-minute calls turning their manual processes into Claude skills. At $1,500 a month you're making $1,000 an hour. 3) Process Redesign: $3,500 to fix a broken workflow before you touch AI. One client went from 25 steps to 10. Never automate a broken process. 4) Automation Builds: $1,000 to $3,000 in Zapier or Make. One $1,500 build took a project manager out of client onboarding entirely. 5) Knowledge Systems: $3,000 baseline. A business broker went from 500 buyer emails per listing to 10 with one custom GPT. 6) Custom Workflows: $3,000 to $5,000. One client's 45-minute podcast workflow now runs off one orchestrator skill in 5 minutes. 7) Full Implementation: $5,000 to $10,000+ mixing everything above. Skills + automation builds + knowledge systems. Mix and match based on client needs. Two things that make this work: 1) The assessment is always the baseline. Start there and let it tell you what to sell next. 2) Anchor on hours saved, not hours worked. Nobody pays $1,000 an hour for your time. They pay it to get theirs back. Full breakdown below. (also available on the Build With AI podcast wherever you get your pods)

Corey Ganim

49,298 görüntüleme • 1 ay önce

Exchanges don’t release free AI courses unless the flow is about to change. Binance just released one. Free. 36 minutes. PDF certificate at the end. But the real alpha isn’t the course. It’s that every safety rule they teach exists because people are ALREADY losing money to AI agents they don’t understand: > Granting full account permissions on day 1 > Trusting hallucinated price data > Letting AI execute with zero human confirmation > Copy-pasting unverified prompts from Twitter The fix is a 6-step workflow every serious trader will run within 12 months: Discovery > Verification > Risk check > Plan > Human confirm > Execute Plus 3 safety rules most degens will skip until they get drained: > Least privilege > API key hygiene > Human in the loop Binance doesn’t build free education for things that aren’t about to go mainstream. AI agents trading crypto isn’t the next cycle. It’s already happening. The only question is which side of the table you’re on when the liquidity shifts.
0:30

Exchanges don’t release free AI courses unless the flow is about to change. Binance just released one. Free. 36 minutes. PDF certificate at the end. But the real alpha isn’t the course. It’s that every safety rule they teach exists because people are ALREADY losing money to AI agents they don’t understand: > Granting full account permissions on day 1 > Trusting hallucinated price data > Letting AI execute with zero human confirmation > Copy-pasting unverified prompts from Twitter The fix is a 6-step workflow every serious trader will run within 12 months: Discovery > Verification > Risk check > Plan > Human confirm > Execute Plus 3 safety rules most degens will skip until they get drained: > Least privilege > API key hygiene > Human in the loop Binance doesn’t build free education for things that aren’t about to go mainstream. AI agents trading crypto isn’t the next cycle. It’s already happening. The only question is which side of the table you’re on when the liquidity shifts.

CryptoGoos

85,517 görüntüleme • 5 ay önce

$AMD's heading to $5T MC LT| Lowest $/M tokens 🧵 The real reason why Institutions are FOMOing into AMD while other Semi stocks are underperforming ($NVDA $AVGO) Not Financial Advice! DYOR! Under Dr. Lisa Su’s leadership, AMD has transformed from a distant challenger into a formidable force in AI infrastructure, delivering the industry’s most compelling TCO story for high-volume inference. Her clear vision open ecosystems, aggressive annual roadmaps, rack-scale innovation, and relentless focus on tokens-per-dollar has positioned AMD’s Helios racks as the go-to solution for hyperscalers and AI natives struggling with exploding token costs, collapsing the cost down to $0.0003-$0.0005/M tokens. I will link various threads on this analysis to supply chain and wafer ratio if you are interested in understanding the full picture. In the last 3-4 months, explosive Agentic AI demand significantly increased Inference demand for Agentic AI models with 5-10 agents. If you are a listener of CNBC or Bloomberg, u should know enterprises and companies are complaining abt cost of token, and how it starts to spike up way too much to make sense. The fact that most data center today are run by $NVDA Chips, where the cost is way too high for Training or Inference. 1. Token cost Here are some quick comp, so u understand why $META OpenAI Anthropic $MSFT $AMZN Softbank $GOOGL and many more small to medium AI Natives are buying AMD CPUs and GPUs as much as they want, or pretty much AMD chips are sold out for the next 3-5 years. Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens 2. Why Hyperscalers and AI Natives Are Choosing AMD Token consumption (especially Agentic) is outpacing even NVIDIA’s efficiency gains, making diversification mandatory for economic viability. Massive deals reflect this reality like $META, OpenAI, $MSFT, Softbank, $AMZN, Oracle, LumaAI, G42... Dr. Lisa Su’s Vision in Action: Since taking the helm, Su has driven AMD’s turnaround with disciplined execution, annual GPU cadence (MI300 → MI350 → MI400), full-stack software (ROCm 7), open ecosystems (UALink, OCP designs), and customer-centric rack-scale solutions like Helios. Her emphasis on “tokens per dollar” and TCO has turned AMD into the pragmatic choice for sustainable AI scaling. Power/Energy Efficiency: ~Helios Rack-level is estimated at 120kW-140kW with 50% more HBM4 where Inference and Training cost matter ~Rubin Rack-Level is estimated at 160kW-230kw AMD Helios shines in owned TCO, memory density, and energy flexibility at hyperscale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B 3. Superior CPUs to pair with GPUs on massive scale 5-10-20GW Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. Conclusion: NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always-on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. Not Financial Advice! DYOR! Video source: Microsoft Build 2026
0:49

$AMD's heading to $5T MC LT| Lowest $/M tokens 🧵 The real reason why Institutions are FOMOing into AMD while other Semi stocks are underperforming ($NVDA $AVGO) Not Financial Advice! DYOR! Under Dr. Lisa Su’s leadership, AMD has transformed from a distant challenger into a formidable force in AI infrastructure, delivering the industry’s most compelling TCO story for high-volume inference. Her clear vision open ecosystems, aggressive annual roadmaps, rack-scale innovation, and relentless focus on tokens-per-dollar has positioned AMD’s Helios racks as the go-to solution for hyperscalers and AI natives struggling with exploding token costs, collapsing the cost down to $0.0003-$0.0005/M tokens. I will link various threads on this analysis to supply chain and wafer ratio if you are interested in understanding the full picture. In the last 3-4 months, explosive Agentic AI demand significantly increased Inference demand for Agentic AI models with 5-10 agents. If you are a listener of CNBC or Bloomberg, u should know enterprises and companies are complaining abt cost of token, and how it starts to spike up way too much to make sense. The fact that most data center today are run by $NVDA Chips, where the cost is way too high for Training or Inference. 1. Token cost Here are some quick comp, so u understand why $META OpenAI Anthropic $MSFT $AMZN Softbank $GOOGL and many more small to medium AI Natives are buying AMD CPUs and GPUs as much as they want, or pretty much AMD chips are sold out for the next 3-5 years. Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens 2. Why Hyperscalers and AI Natives Are Choosing AMD Token consumption (especially Agentic) is outpacing even NVIDIA’s efficiency gains, making diversification mandatory for economic viability. Massive deals reflect this reality like $META, OpenAI, $MSFT, Softbank, $AMZN, Oracle, LumaAI, G42... Dr. Lisa Su’s Vision in Action: Since taking the helm, Su has driven AMD’s turnaround with disciplined execution, annual GPU cadence (MI300 → MI350 → MI400), full-stack software (ROCm 7), open ecosystems (UALink, OCP designs), and customer-centric rack-scale solutions like Helios. Her emphasis on “tokens per dollar” and TCO has turned AMD into the pragmatic choice for sustainable AI scaling. Power/Energy Efficiency: ~Helios Rack-level is estimated at 120kW-140kW with 50% more HBM4 where Inference and Training cost matter ~Rubin Rack-Level is estimated at 160kW-230kw AMD Helios shines in owned TCO, memory density, and energy flexibility at hyperscale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B 3. Superior CPUs to pair with GPUs on massive scale 5-10-20GW Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. Conclusion: NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always-on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. Not Financial Advice! DYOR! Video source: Microsoft Build 2026

Mike

145,992 görüntüleme • 4 ay önce

Most people have no clue how far you can get with Cursor AI now. I just filmed a full 3 hour 44 minute build-from-scratch guide showing the exact workflow I use to build production apps with agents, architecture, and revenue-ready features. 💰 If you’ve ever wondered how to build real software with nothing but prompting — and turn those skills into actual income — the full breakdown is right here. 🧱 Tech Stack (Lesson 1) - 00:01:30 🔧 Set Up (Lesson 2) - 00:11:13 🎨 Frontend (Lesson 3) - 00:42:56 🧠 Backend (Lesson 4) - 02:02:13 💰 Monetization (Lesson 5) - 02:58:36 🚀 Launch (Lesson 6) - 03:18:04 Final software we create in this series Take this software for free — it's open-source on GitHub! 🎁 Do you wanna watch this lesson by lesson?
3:44:19

Most people have no clue how far you can get with Cursor AI now. I just filmed a full 3 hour 44 minute build-from-scratch guide showing the exact workflow I use to build production apps with agents, architecture, and revenue-ready features. 💰 If you’ve ever wondered how to build real software with nothing but prompting — and turn those skills into actual income — the full breakdown is right here. 🧱 Tech Stack (Lesson 1) - 00:01:30 🔧 Set Up (Lesson 2) - 00:11:13 🎨 Frontend (Lesson 3) - 00:42:56 🧠 Backend (Lesson 4) - 02:02:13 💰 Monetization (Lesson 5) - 02:58:36 🚀 Launch (Lesson 6) - 03:18:04 Final software we create in this series Take this software for free — it's open-source on GitHub! 🎁 Do you wanna watch this lesson by lesson?

corbin

218,015 görüntüleme • 10 ay önce

OpenClaw meets RL! OpenClaw Agents adapt through memory files and skills, but the base model weights never actually change. OpenClaw-RL solves this! It wraps a self-hosted model as an OpenAI-compatible API, intercepts live conversations from OpenClaw, and trains the policy in the background using RL. The architecture is fully async. This means serving, reward scoring, and training all run in parallel. Once done, weights get hot-swapped after every batch while the agent keeps responding. Currently, it has two training modes: - Binary RL (GRPO): A process reward model scores each turn as good, bad, or neutral. That scalar reward drives policy updates via a PPO-style clipped objective. - On-Policy Distillation: When concrete corrections come in like "you should have checked that file first," it uses that feedback as a richer, directional training signal at the token level. When to use OpenClaw-RL? To be fair, a lot of agent behavior can already be improved through better memory and skill design. OpenClaw's existing skill ecosystem and community-built self-improvement skills handle a wide range of use cases without touching model weights at all. If the agent keeps forgetting preferences, that's a memory problem. And if it doesn't know how to handle a specific workflow, that's a skill problem. Both are solvable at the prompt and context layer. Where RL becomes interesting is when the failure pattern lives deeper in the model's reasoning itself. Things like consistently poor tool selection order, weak multi-step planning, or failing to interpret ambiguous instructions the way a specific user intends. Research on agentic RL (like ARTIST and Agent-R1) has shown that these behavioral patterns hit a ceiling with prompt-based approaches alone, especially in complex multi-turn tasks where the model needs to recover from tool failures or adapt its strategy mid-execution. That's the layer OpenClaw-RL targets, and it's a meaningful distinction from what OpenClaw offers. I have shared the repo in the replies!
0:35

OpenClaw meets RL! OpenClaw Agents adapt through memory files and skills, but the base model weights never actually change. OpenClaw-RL solves this! It wraps a self-hosted model as an OpenAI-compatible API, intercepts live conversations from OpenClaw, and trains the policy in the background using RL. The architecture is fully async. This means serving, reward scoring, and training all run in parallel. Once done, weights get hot-swapped after every batch while the agent keeps responding. Currently, it has two training modes: - Binary RL (GRPO): A process reward model scores each turn as good, bad, or neutral. That scalar reward drives policy updates via a PPO-style clipped objective. - On-Policy Distillation: When concrete corrections come in like "you should have checked that file first," it uses that feedback as a richer, directional training signal at the token level. When to use OpenClaw-RL? To be fair, a lot of agent behavior can already be improved through better memory and skill design. OpenClaw's existing skill ecosystem and community-built self-improvement skills handle a wide range of use cases without touching model weights at all. If the agent keeps forgetting preferences, that's a memory problem. And if it doesn't know how to handle a specific workflow, that's a skill problem. Both are solvable at the prompt and context layer. Where RL becomes interesting is when the failure pattern lives deeper in the model's reasoning itself. Things like consistently poor tool selection order, weak multi-step planning, or failing to interpret ambiguous instructions the way a specific user intends. Research on agentic RL (like ARTIST and Agent-R1) has shown that these behavioral patterns hit a ceiling with prompt-based approaches alone, especially in complex multi-turn tasks where the model needs to recover from tool failures or adapt its strategy mid-execution. That's the layer OpenClaw-RL targets, and it's a meaningful distinction from what OpenClaw offers. I have shared the repo in the replies!

Avi Chawla

138,769 görüntüleme • 6 ay önce

Here's what the Founder of Claude Code does before he starts ANY project: 1. Plans first, codes never, he goes back and forth with Claude on the plan until it's perfect. No code gets written yet 2. Creates a CLAUDE.md file (a simple doc that Claude reads every session so it knows your project, your rules, your style) 3. Gives Claude a way to verify its own work - For backend: write and run tests - For UI: take screenshots, check in browser Claude should never finish a task without proving it works 4. Sets up project-level permission rules in settings.json instead of skipping permissions entirely. Shared with the whole team 5. Five more preparation steps in VIDEO BELOW 6. Only then switches to auto-accept mode and lets Claude build The part most people miss: he doesn't treat Claude as a magic box that gets things right first try he treats it like a junior dev that needs clear instructions, feedback loops, and guardrails at scale he runs multiple sessions in parallel, uses Opus with thinking enabled because it makes fewer mistakes even though it's slower and relies on background agents that push code for later review his setup is surprisingly simple. no crazy custom tools. just slash commands, subagents, and a clean CLAUDE.md the difference isn't the tool. it's how you set it up before you start ❤️ P.S. for sure 99.5% of readers will scroll down this tweet, but I send it to 0.5% who loves to learn how to improve your workflow daily and control 99.5% in 2 years Hard skills literally mean nothing in our world The most important skills which you can have are: building architectures and orchestrations If you master it and use a creative approach, CONGRATS
9:54

Here's what the Founder of Claude Code does before he starts ANY project: 1. Plans first, codes never, he goes back and forth with Claude on the plan until it's perfect. No code gets written yet 2. Creates a CLAUDE.md file (a simple doc that Claude reads every session so it knows your project, your rules, your style) 3. Gives Claude a way to verify its own work - For backend: write and run tests - For UI: take screenshots, check in browser Claude should never finish a task without proving it works 4. Sets up project-level permission rules in settings.json instead of skipping permissions entirely. Shared with the whole team 5. Five more preparation steps in VIDEO BELOW 6. Only then switches to auto-accept mode and lets Claude build The part most people miss: he doesn't treat Claude as a magic box that gets things right first try he treats it like a junior dev that needs clear instructions, feedback loops, and guardrails at scale he runs multiple sessions in parallel, uses Opus with thinking enabled because it makes fewer mistakes even though it's slower and relies on background agents that push code for later review his setup is surprisingly simple. no crazy custom tools. just slash commands, subagents, and a clean CLAUDE.md the difference isn't the tool. it's how you set it up before you start ❤️ P.S. for sure 99.5% of readers will scroll down this tweet, but I send it to 0.5% who loves to learn how to improve your workflow daily and control 99.5% in 2 years Hard skills literally mean nothing in our world The most important skills which you can have are: building architectures and orchestrations If you master it and use a creative approach, CONGRATS

Ronin

147,367 görüntüleme • 5 ay önce