𝗛𝗢𝗟𝗬 𝗙*𝗖𝗞, GPT 6.1 TERRA DREW SAM ALTMAN FASTER THAN TEAMS OPEN FIGMA. A developer gave it a screen, a reference, and a visual task. It did not write a cute plan about what the user should click. It started moving the f*cking pixels. > see the canvas > choose the colour > place the stroke > correct the ugly part > finish the asset That is why computer use matters. The useful agent is not the one that explains how to survive twelve browser tabs. It is the one that opens the app and starts doing the boring UI work while you keep the decisions that can blow something up. This independent demo cleared 152k views in about a day because people can feel the shift. AI is leaving the chat box. Keep money, messages and publish behind approval. Let the agent do the clicking.

HOLY F*CK, 100 GROK BOT KITS AND THE FIRST MOVE IS STILL ONE BORING TASK Start with a result you can see: > notes → decision table > bills → savings checklist > messages → promise tracker Then give the bot five things: > input > output > evidence > uncertainty > stop condition 1. Run it manually. 2. Find the first failure. 3. Fix the workflow. 4. Run it again. 5. Save the routine. The library has 100 ideas. Your setup starts with one repeatable job, not a fantasy AI workforce. Save this before you build a complicated system that cannot survive its first real input.

GROK BOT JUST TURNED INTO AN AI TEAM, NOT ANOTHER CHAT WINDOW. Guide shows why the shift matters: > your bot can have its own email > a main bot can coordinate specialist bots > X posts can become actionable tasks > Cursor work can return screenshots and recordings as proof Then the access math changed too: from roughly $200–300 monthly plans to roughly $20–30 options, according to the guide. The useful workflow is simple: 1. Give the bot one repeatable job. 2. Let the main bot route work to specialists. 3. Ask for evidence, not just a confident answer. 4. Approve consequential actions before they happen. The best part is the morning/evening handoff: ask what needs your approval now, then what useful work can finish while you’re offline. That is how bots become leverage instead of another inbox to manage. Save this before you build your first team.

HOLY F*CK, 100 FREE GROK BOT KITS FOR THE WORK YOU KEEP REPEATING The number gets attention. The task contract does the work: > input → result > evidence → uncertainty > next action → stop condition Pick one recurring job: 1. bills → savings checklist 2. messages → promise tracker 3. notes → decision table Run it manually twice. Fix the first failure. Then save the routine. No magic prompt. No fake AI employee theatre. A boring task that returns every week can give back time without giving a bot permission to spend money or message people. Save this before the next repetitive job steals another evening.

HOLY F*CK, THIS IS WHAT A GROK BOT WORKSPACE SHOULD LOOK LIKE One bot routes the job. Specialists do the narrow work. Review catches the weak handoff. A human owns the risky click. > research > build > review > approval The important part is not the glowing dashboard. It is the evidence moving with the task: > source > result > uncertainty > next action If a packet is weak, it gets rejected and rebuilt. If it passes, the workflow continues. No “trust me” automation. No agent pretending to be a company. The Article below contains 100 free Grok Bot kits for turning repetitive work into practical routines. Start with one job, add one reviewer, and keep irreversible actions behind a human. That is how you get time back without giving away control.

HOLY F*CK, I BUILT 100 FREE GROK BOT KITS FOR THE WORK PEOPLE STILL DO BY HAND They cover research, inboxes, bills, shopping, writing, meetings and daily admin. > 100 copy-paste tasks > 10 workflow kits > price: $0 How to use one: 1. pick a job you repeat every week 2. paste the inputs 3. require evidence before approval My rule: TASK → EVIDENCE → APPROVAL → OUTPUT Start with the job you hate doing most. If it works twice, save it as a routine. Save this and steal the full kit from the Article below ↓

HOLY F*CK, PEOPLE POSTED ABOUT GROK BOT 225,000 TIMES IN ONE WEEK this sh*t gets practical fast. the receipts in this article: > internet bill: $113.90 → $70.90/month after a bot handled support chat > groceries: a $238 Instacart order built from photos of a fridge > price protection: $80 back after a bot caught the price drop > software release: 10+ agents testing the product before it ships how to copy the pattern: step 1: pick one repetitive job, not your whole life step 2: tell one bot the result you want in plain english step 3: let it research, compare and prepare the action step 4: lock emails, purchases and account changes behind approval step 5: save the workflow as a routine don't build an 81-bot circus. find the one task that keeps stealing money or the same hour every week. make the bot do the grind. you keep the final click. if it kills one recurring bill or gives you one evening back, it already earned its place. save this and steal the setups from the article below ↓

holy f*ck, a 1-year-old ai startup called flapping airplanes just raised $1 billion at a $6 billion valuation it has no public product. no app to download no public demo nothing ordinary people can buy investors are betting on the team, compute, and whatever they believe is coming next. this is what the new ai economy looks like: > raise the capital > hire the researchers > buy the compute > reveal the product later the company became worth billions before most people even heard its name. broke down what investors are actually buying ↓

THIS IS F*CKING CRAZY CLAUDE READ A CLOSED APP LIKE A BLUEPRINT Drop in a binary. REA connects Opus 5.5 to Hopper, Ghidra or IDA. Claude can then pull: > exact addresses > compiled bytes > pseudocode > every place a function gets called It maps the busiest functions, explains what they do, writes a clean specification and tests the rebuild. One DX-Ball demo passed all 3,205 original tests and reproduced every one of its 63 compiled bytes. The same workflow can route across 20 target types and split hundreds of functions between a controller and worker agents. No blind guessing. Every claim comes with evidence from the binary. For clean-room projects, one agent studies behavior, another writes the spec and a fresh context builds the replacement. Old software just became readable to one person with a laptop.

YOUR GROK BURNS $400 BEFORE YOU WAKE UP. > Every 5 minutes: ~$400/month > Every hour: ~$33/month > Same routine: 12× cost difference At 3 AM, the bot can also ask for approval while you sleep. Ten minutes later, the approval expires. By morning: > No action. > No result. > Burned allowance. > A wall of dead requests. People are building bigger AI fleets before fixing the clock. Use a webhook. Run a cheap change-check. Let safe actions continue. Make risky actions wait. One routine can save roughly $367/month under the Article’s assumptions. The smartest Grok Bot may be the one that knows when to shut the f*ck up.

YOUR GROK BOT IS CHARGING YOU 24/7 FOR A JOB THAT TAKES 5 MINUTES. One routine. 8,640 wake-ups a month. Most of them find absolutely nothing. > Every 5 minutes: $400/month > Every hour: $33/month > Same job: 12× cost difference That's how always-on AI quietly eats your allowance. The bot still wakes up, reads the context and burns money. Use a webhook when possible. Use a cheap change-check before the full model. Slow the schedule down until a real miss proves you need speed. One routine can save roughly $367/month under the Article’s assumptions. Ten routines can keep about $3,670 in your pocket. Before hiring another AI employee, stop paying the ones doing f*ck all.

YOUR GROK BOT NEEDS A F*CKING OFF SWITCH. 1 routine running every 5 minutes wakes up 8,640 times a month. The Article estimates: > $400/month at 5-minute intervals > $33/month hourly > 12× more cost for the same job And the 3 AM problem is even dumber: A routine waits for your approval. You’re asleep. The approval expires after about 10 minutes. Do that for 8 hours and you can wake up to 96 dead approval requests. No action. No result. Just a wall of grey cards and a burned allowance. A serious Grok Bot needs: > Event triggers. > Cheap change checks. > Pre-approved safe actions. > A hard stop for everything else. The future of AI agents won’t be won by the bot that runs most often. It will be won by the bot that knows when to shut the f*ck up.

THE MOST EXPENSIVE GROK BOT RUN IS THE ONE THAT FINDS NOTHING. Same bot. Same prompt. Same empty inbox. Same f*cking bill. It estimates: > Every 5 minutes: ~$400/month > Every hour: ~$33/month > Difference: roughly $367 per routine That’s the trap with “always-on” AI. You keep making the bot faster. The bot keeps checking harder. Nothing changes. But every empty check still burns allowance, tokens and money. A useful Grok Bot needs three things: > An event trigger when possible. > A cheap change-check before the full model. > The slowest schedule the job can survive. If ten routines are waking up for no reason, that’s roughly $3,670/month in avoidable spend under the Article’s assumptions. Not more intelligence. Not a bigger fleet. Just fewer pointless wake-ups. The best AI optimisation may be telling your bot: Go the f*ck back to sleep until something actually happens.

8640 WAKE-UPS BRING $400/MO. YOUR GROK BOT IS BURNING MONEY TO DO F*CK-ALL. A routine running every 5 minutes wakes up 8,640 times per month. Using the Article's API-rate estimate: > Every 5 minutes: ~$400/month > Every hour: ~$33/month > Same job: 12x cost difference Most runs happen when absolutely nothing changed. The bot still wakes up, reads the context and burns your allowance. That is a badly designed clock. Use an event or webhook when the system can notify you. Put a cheap “did anything change?” check before the expensive model. Run every other job at the slowest schedule the business can tolerate. Under the Article’s assumptions, fixing one routine keeps roughly $367 in your pocket every month. Fix ten of them: > $3,670/month saved. > Same useful work. > Thousands of pointless runs deleted. Everyone wants another AI employee. Start by stopping the ones waking up to do sh*t.

IF GROK CONTROLLED THAT F*CKING ROADSTER FLIP, ELON JUST SHOWED US THE FIRST REAL GLIMPSE OF PHYSICAL AI. A model would have read the motion, corrected the thrust, balanced a 2-ton machine and landed it. That changes the entire picture: > Roadster is the spectacle. > Optimus is the body. > Robotaxi is the fleet. > Grok is the brain. Chatbots were the warm-up. Elon is assembling AI that can see, move, manufacture and act in the real world. If Grok really flew that car, the AI race just left the screen. And sh*t is about to get physical.

THEN REDDIT BANNED THE ACCOUNT. This is one of the clearest real-world agent experiments I've seen recently. A small team of short-lived agents ran a social account for two days: → a scout found what was already moving → a writer created replies and posts → an analyst graded the output every 4 hours → a closer handled buying intent → a sales agent contacted founders → the playbook changed after each review The result, according to the experiment's X Analytics: > 220K+ impressions in one day > 87.9K views on one reply > 80.7K views on another > 43 posts and replies in the run One reply crossed 1,072 likes. That sounds like the perfect AI agents can run growth case study. Then the numbers get more int eresting. The account moved from 87 to 119 followers after 300K+ views. That is only 32 new followers. The agents generated distribution. They did not automatically generate durable audience. The Reddit test exposed the other side: > 154 karma. > Agent-written posts. > Site-wide ban after roughly two days. Reddit applied its rules. The failure still matters because it shows exactly where the architecture needs a human boundary. The useful part of this experiment is the operating model: 1. Short-lived agents do one job. 2. Every agent writes down what it learned. 3. An analyst reviews the system on a schedule. 4. The playbook changes through feedback. 5. Distribution and conversion get measured separately. 6. A platform rule can still terminate the whole loop. This is the difference between one chatbot runs my account and an actual agent system. The power comes from specialization, memory and feedback. The danger comes from optimizing the wrong metric. 220K impressions can coexist with 32 new followers. 154 karma can coexist with a ban. A viral reply can coexist with a weak owned audience. The real question is not whether agents can produce content. They clearly can. The real question is whether the system can learn: → which topics create trust; → which replies create profile visits; → which posts convert attention into followers; → which platforms reject synthetic behavior; → which actions require a human before publishing. The winning architecture will measure the entire funnel: > impression > engagement > profile visit > follow > trust > revenue AI can multiply output. Only a feedback loop tells you whether that output is building an asset.

6/6 TASKS. AI-BUILT CLASSIFIER MATCHED OR BEAT THE FINE-TUNED BASELINE. The interesting result is that the agent can build a small decision layer, test it against a baseline and maintain it as the surrounding workflow changes. That is a much more useful definition of agentic engineering. Most teams still use a frontier model for every fork: → classify the issue; → choose the route; → decide whether to retry; → detect whether the state changed; → decide whether the result is good enough. Then they call the whole thing “autonomous.” A better architecture separates the jobs: 1. The frontier model writes or reasons. 2. The classifier makes a bounded decision. 3. Ordinary code enforces the route. 4. Tests measure whether the decision still works. 5. A human reviews changes that affect production behavior. The Article reports an AI-built classifier matching or beating a fine-tuned RoBERTa baseline on **6/6 tasks**. That does not prove classifiers built by agents are universally better. It proves something more important: the expensive model does not need to remain inside every decision forever. A classifier is easier to inspect than a paragraph. Its output can be: > ROUTE A > ROUTE B > REVIEW > REJECT And when the workflow changes, the system can rebuild or retrain the decision layer instead of blindly adding another instruction to the main agent prompt. This is the part of AI engineering people skip because it is less cinematic than one agent runs the whole company. The agent does not need to be the final answer. Sometimes its highest-leverage job is to build the smaller system that prevents the expensive model from being called 1,000 times. The future of reliable agents may contain fewer giant prompts and many more small systems that can be tested, replaced and held accountable.

52.8% TO 66.5%. SAME MODEL. DIFFERENT HARNESS. That is the part of AI engineering most people still refuse to understand. The model is the rented intelligence. The harness is the system you actually own. A team put the same model into a better execution loop and reportedly moved its Terminal-Bench score from 52.8% to 66.5%. They changed the system around the model: → better context → fewer, sharper tools → verification before done → hard guardrails → structured traces → fixes that survive the next run That is the compounding advantage. Most people respond to an agent failure by typing a longer instruction. Serious teams turn the failure into a permanent system change: 1. A rule 2. A test 3. A tool restriction 4. A verification step 5. A block 6. A trace that explains what went wrong The article’s most brutal example is even more useful: > 724 seconds. > 100 steps. > Still failed. After the harness was stripped down and redesigned: > 141 seconds. > 19 steps. The model did not suddenly become smarter. The harness stopped making it waste intelligence. And the economics get even sharper: - Jev handles typed decisions at about $0.042 per million input tokens - a thousand gate checks at roughly 200 tokens each can cost under a cent - the same thousand calls to a frontier model can cost around $0.80 before it writes a single word - cached input can cost 20× less than fresh input when the prompt prefix stays stable This is why just use the best model is such a weak strategy. The best model should handle: → hard reasoning → ambiguous evidence → complex code → high-value decisions It should not spend its time deciding whether: - a queue changed - a test needs to run - a file is still untouched - a task deserves escalation - the agent should try the same thing again The frontier model is rented. The harness compounds. Every failure you convert into a rule, test or gate becomes part of your private advantage. The model release will be available to everyone by Friday.

1 BOT. 1 bad memory can mislead an entire team. That is the part of AI coworker demos nobody wants to show. A personal Bot learning your preferences is one problem. A Team Bot learning from product, engineering and support is a completely different system. Now one correction can help everyone. One private preference can accidentally become team policy. One stale fact can survive long enough to influence the next decision. One shared credential can turn a useful workflow into a team-wide permission problem. The fix is not give the Bot a bigger context window. The fix is routing every piece of context to the correct place. A practical memory architecture has at least 5 buckets: 1. PERSONAL NOTES What one person prefers. 2. TEAM MEMORY An explicit, reusable decision the team has approved. 3. TEAM SKILL A repeatable process the Bot knows how to execute. 4. LIVE SOURCE Changing data that must be fetched again. 5. TASK STATE What is still open in the current investigation. Mix those together and the Bot starts treating: - a preference like a policy; - a temporary fact like a permanent truth; - a conversation like an approved decision; - a private connector like a team permission; - an old memory like current source data. The most important rule is simple: > MEMORY CAN PROVIDE CONTEXT. > IT MUST NOT REPLACE THE SOURCE OF TRUTH. Use 2 clocks: THE SLOW CLOCK Stable team rules, definitions and reusable decisions. THE FAST CLOCK Metrics, contracts, incidents, customer status and anything that can change. If being wrong about a fact could trigger an action, reopen the live source before acting. And before publishing a Team Bot, run at least 5 tests: 1. Can a private preference leak to another teammate? 2. Can an approved team decision be reused correctly? 3. Does the Bot prefer a newer source over stale memory? 4. Does it handle conflicting corrections without silently choosing one? 5. Does it respect the boundary of a shared Slack channel? The real shift is not that one Bot can remember more. It is that one Bot can now scale a correction or scale a mistake across the entire team. A reliable Team Bot needs: > private context for personal work; > shared memory for explicit knowledge; > skills for repeatable process; > live systems for changing facts; > permissions for what it may actually do. The question is not: Can the Bot remember? The question is: Should this be remembered, by whom, for how long, and against which source?