20 GROKBOT TIPS from @poteto Founder Session If you missed Lauren Tan's founder session on Grok Bot Galaxy here are my top 20: 1. start with a mission, not roles. "compare 3 competitors' pricing by friday" beats "make a research bot". 2. first hire: a chief of staff bot. it hands out tasks and keeps context between bots. 3. starter team: coordinator, engineering owner, independent reviewer. more only once those 3 work. 4. day 1: teach it your top 3 priorities. ban sending, paying, publishing. 5. day 2: hand off one repetitive task. day 3: make it a skill. week 1: add 2-3 specialists. 6. show a flow once and it becomes a skill. no perfect prompt needed. 7. trust in stages: bots draft, you approve. full autonomy comes later. 8. approvals per action. external emails and calendar changes wait for your "yes". 9. "done" isn't proof. ask for screenshots, videos, data checks. 10. verify the actual behavior. bug was duplicates → proof is no duplicates, not "tests passed". 11. the builder never approves its own work. a fresh reviewer bot checks the final version. 12. overnight jobs need: a checkable finish condition, an isolated worktree, a decision log. push, never auto-merge. 13. write briefs like tickets: problem, repro steps, owner, acceptance criteria. 14. no guessing. bots run the app, take traces, use simulators. 15. give bots a feature map: tabs, selectors, shortcuts. fewer wasted tokens. 16. evals = unit tests. coordinator writes the rubric, sub-agents run blind in separate folders. 17. put your taste in CI, not prompts. Lauren bans useEffect and code comments there. 18. package methods as skills. her pstack plugin: /add-plugin pstack → /setup-pstack 19. verify locally before scaling in the cloud. 50 agents without checks = 50 expensive wrong answers. 20. count accepted work, not PRs: fixes accepted, review time, rework, escaped bugs, cost per change. save this. give your chief of staff its first mission tonight.

20 GROKBOT TIPS from @poteto Founder Session If you missed Lauren Tan's founder session on Grok Bot Galaxy here are my top 20: 1. start with a mission, not roles. "compare 3 competitors' pricing by friday" beats "make a research bot". 2. first hire: a chief of staff bot. it hands out tasks and keeps context between bots. 3. starter team: coordinator, engineering owner, independent reviewer. more only once those 3 work. 4. day 1: teach it your top 3 priorities. ban sending, paying, publishing. 5. day 2: hand off one repetitive task. day 3: make it a skill. week 1: add 2-3 specialists. 6. show a flow once and it becomes a skill. no perfect prompt needed. 7. trust in stages: bots draft, you approve. full autonomy comes later. 8. approvals per action. external emails and calendar changes wait for your "yes". 9. "done" isn't proof. ask for screenshots, videos, data checks. 10. verify the actual behavior. bug was duplicates → proof is no duplicates, not "tests passed". 11. the builder never approves its own work. a fresh reviewer bot checks the final version. 12. overnight jobs need: a checkable finish condition, an isolated worktree, a decision log. push, never auto-merge. 13. write briefs like tickets: problem, repro steps, owner, acceptance criteria. 14. no guessing. bots run the app, take traces, use simulators. 15. give bots a feature map: tabs, selectors, shortcuts. fewer wasted tokens. 16. evals = unit tests. coordinator writes the rubric, sub-agents run blind in separate folders. 17. put your taste in CI, not prompts. Lauren bans useEffect and code comments there. 18. package methods as skills. her pstack plugin: /add-plugin pstack → /setup-pstack 19. verify locally before scaling in the cloud. 50 agents without checks = 50 expensive wrong answers. 20. count accepted work, not PRs: fixes accepted, review time, rework, escaped bugs, cost per change. save this. give your chief of staff its first mission tonight.

Please build a Higgsfield competitor. We’ll even power it. Higgsfield API is live. Cheapest on the market. Seedance 2.5 in the US, with face inputs. No other provider offers that. 👀 We also open sourced everything we’ve built over the last 18 months. The code behind a $5.4B startup is on GitHub. I’ll personally give $50k to whoever builds something people actually fcking use. QT this with your demo. You have 7 days.

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i don't think people realize how big this is... this is the Claude Code moment for video editing yes, vibe coding just came for video. you plug in the Claude you already use, describe the video in plain english, and.. an agent cuts your footage, pulls B-roll, adds voice, music and captions, all locally on your Mac. no uploads. no editor. no $4k agency quote. 4 billion people watch video every day and almost nobody makes it. @videoclawapp wants to fix that. more below:

GPT-6 Astra is the most powerful engine for building multi-agent systems. I wrote a 12-page paper on how to turn it into a full quantitative trading desk. 12 prompts. 300 agents. Goal → Context → Instruction priority → Autonomy → Tools → Delegation → Output → Verification → Stop Here is the full architecture: • Chief of Staff agent: routes requests, returns one answer. Can't surface a trade risk hasn't cleared. 300 monitoring agents: equities, perps, options flow, SEC filings, X, macro. Read-only, never ask questions. • Filter agent: thousands of signals → 10-30 candidates/day. Won't lower the bar to fill a quota. • Hypothesis agent: must name the inefficiency. Statistical arbitrage, volatility mispricing, flow imbalance. • Backtest agent: 5 years, Sharpe > 1.5, drawdown < 15%. STOP block forbids tuning after a failed gate. • Validation agent: t-stat > 2.0, out-of-sample Sharpe > 60% of in-sample, deflated Sharpe > 0. Test 200 strategies and 10 look good by chance. This catches them. • Risk agent: deterministic code, not a prompt. 5% drawdown, everything closes. An LLM gets talked into exceptions. Code doesn't. this 12-page PDF includes all prompts for GPT-6 Astra to build such a system from scratch. read it today, then explore how to build a multi-agent trading desk with Astra in the article below.

GPT-6 Astra is the most powerful engine for building multi-agent systems. I wrote a 12-page paper on how to turn it into a full quantitative trading desk. 12 prompts. 300 agents. Goal → Context → Instruction priority → Autonomy → Tools → Delegation → Output → Verification → Stop Here is the full architecture: • Chief of Staff agent: routes requests, returns one answer. Can't surface a trade risk hasn't cleared. 300 monitoring agents: equities, perps, options flow, SEC filings, X, macro. Read-only, never ask questions. • Filter agent: thousands of signals → 10-30 candidates/day. Won't lower the bar to fill a quota. • Hypothesis agent: must name the inefficiency. Statistical arbitrage, volatility mispricing, flow imbalance. • Backtest agent: 5 years, Sharpe > 1.5, drawdown < 15%. STOP block forbids tuning after a failed gate. • Validation agent: t-stat > 2.0, out-of-sample Sharpe > 60% of in-sample, deflated Sharpe > 0. Test 200 strategies and 10 look good by chance. This catches them. • Risk agent: deterministic code, not a prompt. 5% drawdown, everything closes. An LLM gets talked into exceptions. Code doesn't. this 12-page PDF includes all prompts for GPT-6 Astra to build such a system from scratch. read it today, then explore how to build a multi-agent trading desk with Astra in the article below.

this is f**king insane GPT-6 Astra is the best model I've tested for building multi-agent trading systems. I just built five .md files you can drop into any LLM to turn it into a multi-agent trading desk with an Astra brain. Here's the full architecture: / architecture.md - 300 parallel agents across 7 sources: equity books, perps, Polymarket, options flow, SEC Form 4, X sentiment, macro calendar / reasoning-pipeline.md - hypothesis → backtest → validation, with 3 programmatic gates. Sharpe > 1.5, drawdown < 15%, or the candidate gets killed / agent_loops.md - the 4 self-improving loops: signal quality, hypothesis refinement, code repair, live performance / risk.md - the independent Risk Bot. 5% drawdown closes everything. 2% NAV position cap. Deterministic code, no LLM, cannot be overridden / build_path.md -the 3-phase system path from paper trading to live these five .md turn your setup from 1 solo agent into a 300-agent research and trading graph. read all five .md today, then read the full article on building a multi-agent trading desk with Astra below ↓

this is f**king insane GPT-6 Astra is the best model I've tested for building multi-agent trading systems. I just built five .md files you can drop into any LLM to turn it into a multi-agent trading desk with an Astra brain. Here's the full architecture: / architecture.md - 300 parallel agents across 7 sources: equity books, perps, Polymarket, options flow, SEC Form 4, X sentiment, macro calendar / reasoning-pipeline.md - hypothesis → backtest → validation, with 3 programmatic gates. Sharpe > 1.5, drawdown < 15%, or the candidate gets killed / agent_loops.md - the 4 self-improving loops: signal quality, hypothesis refinement, code repair, live performance / risk.md - the independent Risk Bot. 5% drawdown closes everything. 2% NAV position cap. Deterministic code, no LLM, cannot be overridden / build_path.md -the 3-phase system path from paper trading to live these five .md turn your setup from 1 solo agent into a 300-agent research and trading graph. read all five .md today, then read the full article on building a multi-agent trading desk with Astra below ↓

GPT-6 Astra is the most powerful engine for building multi-agent systems. i wrote a 12-page research paper on how to build a one-person hedge fund that runs 24/7 using Astra. Here is the full architecture: 300 parallel agents monitoring equities, perps, options flow, SEC filings, X accounts, and macro calendar at once • Monitoring swarm: filters thousands of signals down to 10-30 candidates per day. Everything else gets thrown out • Hypothesis agent: generates a structured thesis for each candidate: instrument, edge, catalyst, invalidation • Backtest agent: writes Python, runs 5 years of data. Gate: Sharpe > 1.5, drawdown < 15% • Validation agent: checks statistical significance, out-of-sample on last 12 months, regime dependence, transaction costs. Bull-market-only strategies get killed • Deployment agent: sends to broker, telegram pings your phone: confidence, Sharpe, position size • Risk agent: runs independently. 5% drawdown from peak = all positions close. No override This 12-page PDF changed how I think about systematic trading. Read it now, then explore the article below to learn how to build a multi-agent trading desk with Astra using real cases.

GPT-6 Astra is the most powerful engine for building multi-agent systems. i wrote a 12-page research paper on how to build a one-person hedge fund that runs 24/7 using Astra. Here is the full architecture: 300 parallel agents monitoring equities, perps, options flow, SEC filings, X accounts, and macro calendar at once • Monitoring swarm: filters thousands of signals down to 10-30 candidates per day. Everything else gets thrown out • Hypothesis agent: generates a structured thesis for each candidate: instrument, edge, catalyst, invalidation • Backtest agent: writes Python, runs 5 years of data. Gate: Sharpe > 1.5, drawdown < 15% • Validation agent: checks statistical significance, out-of-sample on last 12 months, regime dependence, transaction costs. Bull-market-only strategies get killed • Deployment agent: sends to broker, telegram pings your phone: confidence, Sharpe, position size • Risk agent: runs independently. 5% drawdown from peak = all positions close. No override This 12-page PDF changed how I think about systematic trading. Read it now, then explore the article below to learn how to build a multi-agent trading desk with Astra using real cases.

Two-Sigma pays $750K/year for engineers who know how to build multi-agent systems for quant trading. their Senior AI Engineer just explained in 20-minutes how they build agents for Quant trading internally: “at Two Sigma, 60% of research & trading tasks are already handled by AI agents; by the end of 2027, it will be 80%.” this one watch will replace your 3-month engineering internship at a Wall Street quant desk. watch today, no matter what. Then learn how to build a multi-agent trading desk with GPT-6 Astra in article below.

SpaceXAI engineers (ex-Cursor): "99% of people use GrokBot like Google and only 1% are building teams of self-learning GrokBot agents i'm running a team of 10+ agents. The better instructions I give my GrokBot agents, the more efficient the team" in 30-minute workshop, SpaceXAI engineers revealed how to build a team of coding agents from scratch this will replace a $500 agentic engineering course skip Netflix today and watchworkshop, then learn how to build a team of GrokBot agents below

SpaceXAI engineers (ex-Cursor): "99% of people use GrokBot like Google and only 1% are building teams of self-learning GrokBot agents i'm running a team of 10+ agents. The better instructions I give my GrokBot agents, the more efficient the team" in 30-minute workshop, SpaceXAI engineers revealed how to build a team of coding agents from scratch this will replace a $500 agentic engineering course skip Netflix today and watchworkshop, then learn how to build a team of GrokBot agents below

SpaceXAI engineer just released a 1-hour course on how to master GrokBot from 0 to running autonomous teams of agents: • 00:00 - introduction to GrokBot agents • 12:31 - building first team of GrokBot agents • 29:18 - 4 layers of GrokBot agent stack • 44:41 - GrokBot templates building & using • 52:08 - using "phone mode" for GrokBot this 1-hour watch will replace 10 paid agentic engineering courses skip Netflix today, watch this course and ship a fully autonomous team of GrokBot workers by Monday

SpaceXAI engineer just released a 1-hour course on how to master GrokBot from 0 to running autonomous teams of agents: • 00:00 - introduction to GrokBot agents • 12:31 - building first team of GrokBot agents • 29:18 - 4 layers of GrokBot agent stack • 44:41 - GrokBot templates building & using • 52:08 - using "phone mode" for GrokBot this 1-hour watch will replace 10 paid agentic engineering courses skip Netflix today, watch this course and ship a fully autonomous team of GrokBot workers by Monday

SpaceXAI engineer, Lauren Tan: "GrokBot is the most powerful agentic tool we have ever built, but only 1% of users use it correctly right now I'm running a team of 20+ GrokBot agents. I have a Chief of Staff agent, 3 managers and 16 workers - that's how the team looks like" In a 1-hour workshop, a SpaceXAI engineer reveals how to use Grok agents at 100% of their potential worth more than a $500 agent engineering course on the internet Skip Netflix and watch today, it will change the way you use GrokBot forever, then read the article below

SpaceXAI engineer (ex-Cursor): "99% of people are building cloud Agents wrong - we built GrokBot to solve it at SpaceXAI, 85% of our engineers running 10+ GrokBot agents team. Now you can launch a senior AI engineer in 1-click" in 1-hour podcast, SpaceXAI engineers revealed how to build self-learning cloud agentic systems from scratch after this podcast I asked for a refund on my $500 agentic AI course watch today, then read the article below on how to build a team of GrokBot agents from scratch

Google Brain founder, Andrew Ng: "100% of my tasks are already done by AI agents. in 2-3 years 40% of human jobs will automated by agents. at Google, 85% of engineers are running agentic loops and graphs, that's how the engineering setup will look" in a 40-minute podcast, Andrew Ng reveals how the future of AI will look and how to be prepared skip Netflix today and watch this one podcast, it will change the way you use AI watch today, then read article below on how to build self-learning agentic systems with graphs