The Computomatix Times

Collected the most-loved internal Grok Bot use cases:

• Find / book flights biased towards Starlink access
• Order Whole Foods delivery from recipe photos
• Fix date/GPS metadata on hundreds of film scans
• Negotiate contractor quotes directly
• Book prospect / customer meetings via outbound agents
• Convert sales deck to different language before a customer call
• Auto-update decks from Granola discovery notes
• Pull deal context from mobile without logging into Salesforce
• Run a prospecting plan that edits a Google Sheet as it goes
• Orchestrate 100’s of cloud agents and summarize into Notion
• Narrate a cloud-agent demo in your own voice
• Turn a daily Slack digest into a 3-minute morning podcast
• Turn Kindle highlights into Anki cards every morning
• Turn an Instagram recipe reel into an Instacart order
• Convert Chase points into optimized flights
• Watch movie ticket drops and buy
• Redesign a personal portfolio in Webflow
• Prep a daily meeting brief on people you’re talking to
• Organize messy Slack channels

if you’re planning to switch your apple personal developer account -> company developer account

DO NOT listen to codex. chatgpt, or claude - in my personal (and humbling experience), they’re wrong! follow these steps instead:

@hammad_khan23 github.com/dmmulroy/anti-slop

Introducing Bot Directory, Grok Bots you can set up with a single prompt:
botdirectory.ai

X has been full of amazing bot setups. We're giving them a home.

See a bot worth sharing? Tag @BotDirectoryAI in a reply. We'll add it to the directory automatically.

Open source.

I love earnings season but don't have the time to listen to each call.

So I built an agent that sends me a personalized, sell-side style report after each call for ~50 names.

Here is what it wrote for Cloudflare's blowout Q2:
b80a857a775b.pages.dev/report.html

Grok Bot is in beta!

here are 100 use cases ive seen so far:

1) book meetings by writing personalized outbound in your own gmail voice, then sending the notes for you

2) take a sales play that used to take 90 minutes to 2 hours (sql audience → copy → reverse etl) and make it ~95% automated

3) build a multi-Bot gtm crew (research, outbound, forecast, inbox, slides) with weekly playbooks on top

4) run a chief-of-staff hub that routes work to a large specialist fleet

5) rebuild most of a custom crm with a small Bot crew in about a day and a half

6) run a community ops fleet that saves 20+ hours a week

7) screen 1000+ event applicants against an icp and batch-approve the fits so the room stays intentional

8) replace a pricey direct-mail saas with a Bot that handles redemptions and asks you before it spends

9) review ~200 recruiting applications into strong / mid / reject in one pass

10) automate a weekly luma + database + slack summary workflow and free up 2–3 hours a week

11) update a sales deck live from granola notes mid-call, if you stop recording 5–10 minutes early

12) localize a sales deck into a different language before a customer call

13) answer a salesforce eoq question from your phone in ~10 seconds with no laptop and no sfdc app

14) update a salesforce org chart from just a name or email

15) build a salesforce report and dashboard so you stop asking glean the same opp questions

16) search your company chat for who asked about a feature in 15 seconds instead of an hour of digging

17) get a daily brief with usage data for every person you’re talking to that day

18) coach yourself on gong calls with timestamped comments and homework before the next one

19) turn a week of gong calls into a win/loss memo with the phrases that actually closed

20) turn call questions into a living notion faq that updates every morning

21) watch linkedin for compelling events from eng / product / ai leaders and get a weekday digest of ready-to-send notes in your voice

22) send personalized linkedin connection requests from sales navigator and book meetings with eng leaders

23) map an account from linkedin + the crm and show who actually influences the deal

24) audit and rewrite a linkedin profile from your live page plus notion and slack context, with human approval before publish

25) forecast account health and catch churn early when usage looks fine on the surface but is quietly dropping

26) tier accounts with fit × warmth from salesforce into notion, then enrich contacts in batches

27) build a qbr pack: usage + tickets + open opps + a short slide narrative

28) get a daily apple search ads pacing digest and reply in-thread to have a Bot edit bids

29) scan ad performance, explain why winners work, and suggest the next tests into a notion backlog

30) watch a competitor’s pricing page and slack you when something changes

31) generate first-pass performance ads in figma that match your existing format

32) learn your writing voice from past posts and draft linkedin / x content into notion for review

33) 30 minutes after a call, send follow-up reminders (optional: in a character voice)

34) work around missing bulk-enroll permissions in outreach by driving the gui

35) record yourself doing a reply / sfdc workflow once, then have a Bot turn it into an automation

36) sync a local marketing calendar from a global webinars notion in about 2 minutes

37) assemble a customer enablement pack end to end: find the request email, download zoom recordings, summarize, drop into drive, draft the reply

38) turn a recorded demo into a tagged clip library for the next one

39) draft a security questionnaire from your public docs and flag the questions only a human can answer

40) answer “what did we promise this customer” from contracts + slack + the crm on one page

You can just use @Cloudflare to make inboxes for your Grok @bot
check out @thomasgauvin 's agentic inbox repo

what if AI teammates (Grok Bot, Hermes, etc.) lived everywhere?

Introducing Skydive by Anything.

- omnichannel (Slack, email, iMessage, CLI)
- cloud native (always on, works while you sleep)
- model agnostic (Fable, GPT, Deepseek, more)

everyone gets full access from day one

let's fly. 🪂

We’ve decided to open-source a multi-agent harness we use internally at YC.

We call it “QM” and it’s meant to be easy to customize, like Hermes or OpenClaw, but useful for a whole company. We use it across accounting, legal, events, and engineering (including building QM itself!).

The whole project is under an MIT license. It is cloud-first and has Slack and web UI natively.

I have been testing Grok Bot for the last week and it's EXCELLENT

It is your own personal fleet of AI agents doing work for you 24/7

In this video I give you a FULL walkthrough of the tool, how it works, and how to 100x your productivity with it

SpaceXAI COOKED

This is the most valuable PDF you can give to Claude.

The Higgsfield team said they spent $1M on R&D for their film and distilled it into a PDF you can steal.

This is the holy grail for filmmakers.

Here's the info (and download link)
Bookmark this 👇🏼🧵

Announcing OpenWorker! An open-source agent that doesn't just chat with you, but delivers finished work -- like hand you a polished document, send a slack message, or update a calendar entry.

Ask it to prepare a customer brief, untangle your calendar, draft a report, or triage a Slack alert. It works across your files and everyday tools, produces the deliverable, and checks in before doing anything consequential.

OpenWorker runs on your Mac, with Windows support coming soon. It does not lock you into any one model. Bring your own API key and run it with GPT 5.6 Sol, Claude Fable, Gemini 3.6, an open weight model (like Kimi, GLM, DeepSeek, Inkling), or Ollama to keep your data local. Your data does not leave your machine except through an LLM provider and integrations that you choose.

@rohitcprasad and I are building OpenWorker because AI coworkers are an important way to get work done, and we want there to be an open, privacy-preserving, model-independent option. Check it out and let us know what you think!

Try it out: openworker.com (requires your own API key)
Source code: github.com/andrewyng/openworker

use this system prompt for 100% undetectable ai ugc:

I tried @jack's Buzz.

It's like Slack + OpenClaw + Herdr + but with some really unique features that people are sleeping on.

The video below shows how it works, and some of my thoughts on the process and platform, e.g.:

- Create and interact with agents on top of any harness (claude code, codex, pi, etc.)
- Choose which models agents use, including local ones
- Agents can delegate work and work in parallel in git worktrees
- Agents are first-class citizens and work like humans (creating channels, delegating, access to chat history)
- You can share AI compute within a community
- It's completely open-source and decentralized

Things I like:

- Delegating work in chat feels natural: tag an agent, it replies in a thread with status updates as it e.g. compiles, commits, and deploys.
- Shared compute: relay owners can share local compute with members, so a community could pool funds for one beefy machine running a local model and everyone uses it.
- It's built on Nostr, an open protocol already tied into Bitcoin Lightning so I can imagine communities tipping each other or paying for compute/agent tasks with instant zero-fee micropayments in the future.
- It ties together things like OpenClaw, an agent manager, and Slack-style chat into one tool.

Things I didn't like:

- You can't see what the agent is doing in a terminal. The activity view exists, but if you're used to watching a session run, this UI feels a bit abstracted. A terminal view would be great.
- It feels slower than running a session in Claude Code, though no evidence to back that up. For that reason I found myself doing one-off tasks in the terminal instead.

Verdict:

- I really like it so far and can genuinely imagine working with a team this way.
- It doesn't feel ready for big, complex tasks yet. For shallower tasks, it's perfect.
- The shared compute + Nostr/Lightning angle is what really separates it from every other agent manager for me, and I think that future is coming.

Have you built a language model? You should. It's so much fun to chat with something you made.

Anyone can do it, too. I made an app that teaches the fundamentals and gives you everything you need to build your own: languagemodelbuilder.com/

Agentic AI adoption is on fire at @Uber, and it's changing the way we build, not just in engineering, but across the entire company.

Today, 99% of our engineers use AI tools. More than 70% of pull requests are attributed to local or cloud agents. And our engineers have built 2,500+ agent skills across the software development lifecycle.
Those numbers are exciting, but they led us to a much bigger question:

How do we bring agentic AI beyond engineering?

Finance. Legal. Operations. Marketing. Customer Support. HR. Procurement.

These functions run on complex workflows that are often manual, highly nuanced, and spread across dozens of systems. You can't automate them effectively by looking at process diagrams or documentation. You have to understand how the work actually gets done.

So we created something called Agentic Pods.

The idea is simple.

We handpicked ~30 of our most AI-proficient engineers (people with deep knowledge of Uber's systems) and paired each of them with a domain expert from a business function.

Then we gave every pod just two weeks.
• Days 1 – 2: Shadow the expert. Observe every step. Document workflows. Ask questions. Build intuition.
• Day 3: Prioritize opportunities based on scale, repetition, business impact, and data availability.
• Days 4 – 5: Build a working agent alongside the person doing the job.
• Days 6 – 9: Validate with several others performing the same work. Does it generalize? Does it actually make their job better?
• Day 10: Ship.

In just the past two months, we've run 16 Agentic Pods across 16 different business functions.
• Capital allocation across 150 cities: 15 hours → 30 minutes.
• Financial pacing reports: 2 days → 10 minutes.
• Marketing web quality assurance: 2 weeks → 50 minutes.
• Support workflow creation: 9,000 manual workflows → self-service automation.

The productivity gains are impressive, but what surprised us most wasn't the speed.
• It was how quickly engineers embedded in unfamiliar domains uncovered opportunities that had been hiding in plain sight.
• The biggest wins rarely come from automating one task. They come from rethinking an entire workflow. Once you redesign the workflow around AI, you often eliminate handoffs, remove unnecessary approvals, replace legacy tooling, reduce vendor spend, and dramatically accelerate decision-making.
• The workflow becomes the unit of automation - not the individual task.
• The most impactful agent skills cut across teams, orgs, functions, tools, and systems.

The biggest lesson? The best AI opportunities are rarely visible from the outside.

You discover them by sitting next to the people doing the work, understanding every friction point, and building with them, not for them.

We're now forming a dedicated team to scale this further and go deeper. They'll deeply understand the work, redesign it from the ground up, and use AI to fundamentally change how the business operates.

It's exciting times!

CODEX SKILL THAT FINDS YOUR STARTUP’S FIRST CUSTOMERS!

I made a Codex skill that analyzes your startup and finds potential customers from real public signals.

Paste your startup URL while Codex defines your ideal customer, searches public discussions, qualifies each prospect, and generates a polished report with personalized outreach openers.

-> ideal customer profile analysis
-> public pain + buying signal research
-> evidence-backed prospect shortlist
-> fit, timing + reachability scores
-> original source links for every prospect
-> personalized outreach openers
-> polished HTML report
-> one-command install

Install: npx --yes codex-first-customer-finder-skill

100% open source.
Repo in Bio.

How to find a validated $10k MRR startup idea:

> setup TrustMRR MCP
> ask it “give me 10 startups making $50k+ MRR that aren’t built for agents yet”
> copy and build it for agents

Tag me when you’re rich.

i gave 5.6 sol access to my camera roll and had it extract pictures of every piece of clothing i own from my photos

then, told it to find new outfits for me and render them on me with gpt-image!

its kinda cool to see your entire wardrobe in a collection like this

Codex for finding customers for your startup:

Codex now has more of the iOS app development loop built directly inside the app.

The Build iOS Apps plugin lets Codex view and test an iOS app in the in-app browser, open SwiftUI previews, and hot reload edits without forcing the developer to leave Codex.

The approach here is exactly right. Pairing builders with domain experts, and grounding the work in the company’s real knowledge, systems, and workflow context.

Most AI initiatives don't stall for a lack of ambition or model capability, but because the people building the tools or workflows are too far removed from the friction of the actual work.

The breakthrough happens when you combine technical builders, domain experts, and the scattered knowledge, workarounds, context behind the workflow itself.

You can’t redesign the future if you’re too far removed from the friction of the present.

As engineering, product, design, DS, etc. melt into a new kind of role, I was reflecting on what roles might look like in the future. For example, when I look at the Claude Code team I see what I think is five archetypes:

1. Prototyper: comes up with brand new ideas; churns out many ideas, most of which don't ship
2. Builder: quickly turns a prototype/idea into production-grade product/infra
3. Sweeper: cleans up the UI, simplifies the code and system, unships, optimizes performance
4. Grower: takes a product that has been built and iterates on it to improve Product-Market Fit
5. Maintainer: owns a mature system to make it secure, reliable, fast, and efficient as it scales

Many people span across 2 roles, and sometimes 3 roles. I also notice that these roles are not really tied to job function -- eg. across Anthropic, some designers match category 1, some 2, some 3; same for engineers, PM, DS.

A healthy team needs a mix of these, depending on the product:

- A product that is new and pre-PMF needs people that are strong at 1+2+3
- A product that is growing and has found PMF needs 2+3+4 and some 5
- A product that has strong PMF needs 3+4+5 and some 2

Maybe product roles of the future will look more like this, and less like the domain-specific roles of today?