银河修理员

THIS IS F*CKING INSANE THE MOST EXPENSIVE MISTAKE IN MULTI-AGENT TEAMS: A BOT SAYS “DONE” WITH NO PROOF you built a shared brain. your bots ignore it, write “done”, and lose context on every handoff. without a verification layer, a Shared Brain is just a pretty folder nobody opens 6 repos that close this gap: 01 run → state in markdown, “done” only with evidence in the log, work resumes from a Handoff block instead of yesterday’s chat https://t.co/yNSZ8VaMBb 2 x-harness → the agent fills out a completion card, and a separate read-only verifier decides whether to accept the claim https://t.co/oVannEggOx 3 Agent Spartan Protocol → one markdown task file as the source of truth for handoffs between agents. review always goes to the host that didn’t do the work https://t.co/1oORn8UPIX 4 handover-mcp → local state handoff when you switch sessions, models, or agents, with connectivity, completion, and continuity checked separately https://t.co/cfRehMDth9 5 beads → a dependency-graph issue tracker built as memory for coding agents. the agent sees what’s already done and what’s blocking https://t.co/IgfGE363X4 6 superpowers → a skill library that includes verification-before-completion: proof first, then “done” https://t.co/kgq3HxJeUF the rule is simple, completion is accepted, not claimed how to add verification to your bot team in one day ↓

raytaylor50 retweeted

Arindam Majumder 𝕏

@Arindam_1729

4 days ago

this is what happens when you stop using AI like a chatbot and start using it like a team. someone connected 6 AI tools and gave them one mission: make money while he was at work. no employees. $20 budget. 12 hours. zero human intervention. here's how the team worked: → Opus 5.5: CEO. Breaks goals into tasks. → Grok Bot: Finds problems people are willing to pay to solve. → REA: Researches competitors, pricing, and demand. → Dots: Connects the tools into one workflow. → Jev: Builds, ships, and fixes the product. → Obsidian: Stores every win, failure, and lesson. the results were wild. 08:14 AM: Found 640 small shops struggling to answer DMs at night. 09:30 AM: Discovered 9 competing tools charging $49/month or more. 11:05 AM: Narrowed the idea to one feature at $12/month. 01:40 PM: Built the bot, landing page, and checkout. 04:20 PM: Sent a 20-second demo to 300 shop owners. 07:55 PM: Generated a report documenting what worked, what failed, and what it cost. the claimed outcome? 47 paying customers × $12/month = $564 in monthly recurring revenue. total spend: $17.40. and the human behind it? he was at work carrying boxes. he didn't touch a single prompt after leaving at 8 AM. the most interesting part isn't the revenue. It's the architecture. one agent finds opportunities. another validates them. another builds the solution. another connects the workflow. and a memory system makes sure lessons aren't lost. that's a business workflow, not a chatbot. the next shift in AI isn't just smarter models. it's teams of agents that can turn a goal into a finished product. one caveat: these numbers are claims from the original post, not independently verified results. but the underlying idea is worth paying attention to. we might be moving from asking AI to do tasks to giving AI systems ownership of outcomes.

raytaylor50 retweeted

Solomon Rojie

@Solomonrojie

4 days ago

THIS IS AN ACTUAL F*CKING CHEAT CODE you can put a usage meter on your Grok Bot setup and see how much allowance is left before another routine eats through it the open-source grok-bot-usage shows weekly usage, reset dates, and on-demand spending across your connected accounts the idea: let code read the meter, record what changes, and bring Grok Bot in when there’s something worth investigating you start with one command: gbu here’s the split: → weekly usage shows how much of your allowance is consumed → reset dates show how long the remaining budget needs to last → on-demand spending shows the extra bill building up → JSON output lets scripts keep a record without asking a model to read the dashboard three moments worth measuring: → before a recurring workflow starts → after its result is ready → after you change its instructions, schedule, or handoff the meter reports account-level usage. compare workflows in isolated test windows so another bot’s background activity doesn’t get credited to the job you’re measuring code collects the numbers. Grok Bot investigates the expensive patterns. you decide what stays want the allowance visible while you work? QuotaRail adds a macOS usage rail with Grok Bot weekly usage build the monitoring workflow like this: → read gbu --json at the start and end of a test → save timestamped readings in USAGE_LOG.jsonl → record the task, outcome, and whether you accepted the result → mark overlapping activity instead of inventing precise attribution → compare the original workflow with one focused change → report missing data as unknown → keep routine data collection in scripts paste this into Grok Bot ↓ “Build a usage-monitoring workflow around grok-bot-usage: 01 / Review the repository and show me the installation steps. Let me complete account sign-in in the browser. 02 / Confirm which usage fields are available. Never estimate missing token counts or spending. 03 / Create a local log for timestamped readings, task names, accepted results, and overlapping activity. 04 / Run one controlled comparison on a recurring job. Change one thing and compare usage alongside output quality. 05 / Show the findings before proposing any changes to my bots or routines.” set it once. your next session is cheaper and safer before you type a word. the article below explains 7 changes you can test against those readings, from smaller working context to reusable research and bounded retries ↓