Collin Hachwi

IT Director @GreatMindsED

[Basis @trybasis]

https://t.co/MvL8lsNinx

[claire vo 🖤 @clairevo]

good news: /goal in codex has solved all my problems
bad news: i'm sitting here twiddling my thumbs while the agents do all my bidding

it's no secret i'm a little bit obsessed with codex, and /goal is a big part of that.

i put together a 30m ep on /goal including:
- what it is
- how to write a good goal
- how to drive bug zero with @vercel @sentry and codex
- two non-technical use cases for goal, including cleaning up 4,000 emails over 4 hours

A HUGE thank you to our wonderful sponsor, @mercury - radically different banking loved by over 300K entrepreneurs: https://t.co/eQRKWOCxKh

[claire vo 🖤 @clairevo]

a small (elite) team and I have been working on a little something here

exec + eng focused
led by technical operators
actually know wtf we're talking about
$$$$ but worth it

a sneak peek ahead of launch 👇

[Aaron Levie @levie]

Starting to hire and retrain for new agent engineering roles for *internal* functions to help get more powerful agents working well on critical business processes. I expect this type of role to be a very big deal over time at Box and other companies.

It looks something like an internal FDE, whose job it is to wire up internal systems and get agents working with them effectively. The person will be extremely technical and capable of building secure, governed agents for internal workflows that connect to business systems (like Box, Salesforce, Workday, etc.), and codify workflows in skills.

In some cases this person may understand the business process well enough to do it fully, but in most cases I expect them to work with the business directly in an embedded fashion. Ironically, that may introduce another new role on the business side that is more akin to agent product management for internal processes. The key is that you need technical + process people that can span multiple teams or functions in an organization. It’s not about brining automation to a job, but bringing automation to a process.

This is going to be a very big trend in most companies going forward. Fun to watch the early innings of what this will look like.

[Collin Hachwi @chachwi]

Already adjusting our AI rollout plan based on this.

The part that resonates most: don’t wait for perfect training -embed AI into the workflows people were already doing.

Applies well beyond tech. Any team with repetitive, knowledge-based workflows can use this playbook.

[Geoff Charles @geoffintech]

https://t.co/CaKyQS1Fl4

[claire vo 🖤 @clairevo]

Tomorrow you'll see a very special How I AI, where I turn @lennysan's deeply research product into 🚧✨🧱LENNY'S PRODUCT ZONEEEEEE The #1 cyber-newsletter for PRODUCT PEOPLE on the Information Superhighway! ⭐️💕💣

SMASH that subscribe button

[claire vo 🖤 @clairevo]

What I love about this course is that it is the exact opposite of do as I say not as I do.

I tell you to use AI; we build a whole bunch of AI to replace a bunch of our course processes.

I tell you that agents are going to be on your team; we add an openclaw to our slack.

I tell you that AI might break production; we DDOS ourselves as soon as all the students, the notetaker agent and a bunch of live streams joints our aggressively yolo’d app.

I tell you your old job is dead; we are no longer instructors, we are devops.

I tell you it will be ok; everyone loves it.

[Dan Shipper 📧 @danshipper]

“An agent is just folder” is an insanely good mental model

Kieran has changed the way I think about AI systems. Worth your time 👇

[Peter Steinberger 🦞 @steipete]

@dee_bosa This is a good primer! Make sure to understand it's *your* agent. Don't put it in a group chat. https://t.co/Y6PYY4zg5W

[claire vo 🖤 @clairevo]

Just to be clear: this was 3+ hrs for a basic “AI for families” setup: new Mac user, Homebrew, Node, OpenClaw CLI, Claude API + CC, config the gateway, Google Cloud project + OAuth creds, GCal/Gmail/Drive APIs and oauth across gmail + workspace, Telegram BotFather wiring, multi-user 1Password vaults, per-agent workspaces + soul/memory files, browser + tool permissions, and a forest of kids’ sports/work/personal calendars.

It's not done.

Setting up @openclaw is onboarding a junior engineer + training a chief of staff, not installing an app.

[Dan Shipper 📧 @danshipper]

we just wrote the ultimate beginner's guide to OpenClaw

almost everyone @every has one now, and they have completely changed the way we work and live. we're using our claws to:
- build product
- answer customer service queries
- book hard-to-get restaurant reservations
- track our reading notes

and much more

this is the guide we wish we'd had at the start:
https://t.co/66n3Wz6MT0

[Peter Yang @petergyang]

My top 5 takeaways from @nateliason on how he set up his OpenClaw bot to build a business that makes $4,000 / week:

1. Set up a 3-layer memory system first

The 3 layers are a knowledge graph (facts about your projects and life), daily notes (what's actively happening), and tacit knowledge (your preferences, patterns, and security rules).

2. Give your bot its own accounts — never your personal ones.

Felix has his own X/Twitter, Stripe, GitHub, and email accounts. If something breaks, the blast radius stays contained.

3. Every time your bot asks you for help, ask it: “How can I remove this bottleneck?”

Each time Felix needed Nat to do something manually, Nat asked it this question. “The more I asked, the more capable he has become.”

4. Use Telegram group chats to work on multiple projects at once

Create a separate group chat for each project and add your bot to it. This way, context stays clean in each chat.

5. Cron jobs are super useful for OpenClaw

Felix has 6-8 cron jobs for X/Twitter alone. Without cron jobs, your bot just waits for instructions. With them, it becomes proactive.

📌 Watch my full episode now on how Nat set up his OpenClaw bot to build its own business:
https://t.co/Y01F5eC7yW

[Stripe @stripe]

Over 1,300 Stripe pull requests merged each week are completely minion-produced, human-reviewed, but contain no human-written code (up from 1,000 last week).

How we built minions: https://t.co/GazfpFU6L4.

[claire vo 🖤 @clairevo]

https://t.co/uNCLciJ6Rd

[Mike Murchison @mimurchison]

My relationship with my computer has changed more in the last month than in the previous 10 years.

Here's a walkthrough of how I as a CEO am using Claude Code as my AI Chief of Staff to roughly double my productivity.

I show how show how with near perfect context on our company (Ada) and by personal life, my chief of staff helps me:

- Unify 6+ inboxes across Slack, Email, Whatsapp, etc. and speed through them
- Manage a multiplayer todo list that it works on for me overnight
- Increase the number of deep relationships I can manage by automatically enriching contact records from all Granola transcripts
- Push back on core decisions I'm making and ensure my time is aligned to my key goals
- more...

I've put first version on Github below. If you're a CEO or an exec, give it a try and let me know how it works for you.

[SOLELINKS @SOLELINKS]

Ad: NEW adidas Equipment EVO SL 'Cloud White/Core Black' dropped via adidas US => https://t.co/O08veeibIa

[Nityesh @nityeshaga]

How We Built an AI Project Manager Using Claude Code

Claude Code for non-technical work is going to sweep the world by storm in 2026. This is how we built Claudie, our internal project manager for the consulting business. This process provides a great peek into my role as an applied AI engineer.

My Role

I'm an applied AI engineer at @every. My job is to take everything we learn about AI — from client work, from the industry, from internal experiments — and turn it into systems that scale. Curriculum, automations, frameworks. I turn the insights clients give us on discovery calls to curriculum that designers can polish into final client-ready materials. When there's a repetitive task across sales, planning, or delivery, I build the automation, document it, and train the internal team to use.

The highest-value internal automation I've built so far is the one I'm about to tell you about.

What We Needed to Automate

Every Consulting runs on Google Sheets. Every client gets a detailed dashboard — up to 12 tables per sheet — tracking people, teams, sessions, deliverables, feedback, and open items. Keeping these sheets accurate and up-to-date is genuinely a full person's job.

@NataliaZarina, our consulting lead, was doing that job on top of 20 other things. She's managing client relationships, running sales, making final decisions on scope and delivery — and also manually updating dashboards, cross-referencing emails and calendar events, and keeping everything current. It was the work of two people, and she was doing both.

So I automated the second person.

Step 1: Write a Job Description

The first thing I did was ask Natalia to write a job description. Not for an AI agent — for a human. I asked her to imagine she's hiring a project manager: what would she want this person to do, what qualities would they have, what would be an indicator of them succeeding in their role, and everything else you'd put in a real job description.

See screenshot 1.

Once I had this job description, I started thinking about how to turn it into an agent flow. That framing — treating it like hiring a real person — ended up guiding every architectural decision we made. More on that later.

Step 0: Build the Tools

Before any of the agent work could happen, we needed Claude Code to be able to access our Google Workspace. That's where the consulting business lives — Gmail, Calendar, Drive, Sheets.

Google does not have an official MCP server for their Workspace tools. But here's something most people don't know: MCP is simply a wrapper on top of an API. If you have an API for something, you basically have an MCP for it. I used Claude Code's MCP Builder skill — I gave it the Google Workspace API and asked it to build me an MCP server, and it did.

Once it was confirmed that Claude Code could work with Google Sheets, that was the biggest unknown resolved, and we knew it would be able to do the work we needed.

Version 1: Slash Commands

Now it was time for context engineering. The first thing we tried was to create a bunch of slash commands — simple instructions that tell Claude what to do for each piece of work.

This treated slash commands as text expanders, which is what they are, but it didn't work. It failed for one critical reason: using MCP tools to read our data sources and populate our sheets was very expensive in terms of context. By the time the agent was able to read our data sources and understand what was needed, it would be out of context window. We all know what that does to quality — it just drops drastically.

So that didn't work.

Version 2: Orchestrator and Sub-Agents

This is also exactly when Anthropic released the new Tasks feature. We decided the new architecture would work by having our main Claude be the orchestrator of sub-agents, creating tasks that each get worked on by one sub-agent.

But this ran into another unexpected problem. The main Claude would have its context window overwhelmed when it started 10 or more sub-agents in parallel. Each sub-agent would return a detailed report of what they did, and having so many reports sent to the orchestrator at the same time would overwhelm its context window.

For example, our very first tasks launch data investigation agents which look at our raw data sources and create a detailed report about what has happened with a client over a specific period of time, based on a particular source like Gmail or Calendar. The output of these sub-agents needs to be read by all the sub-agents down the line — up to 35 of them. There would definitely be a loss in signal if it was the job of the main orchestrator to pass all required information between sub-agents.

The Fix: A Shared Folder

So we made one little change. We made every sub-agent output their final report into a temp folder and tell the orchestrator where to find it. Now the main Claude reads reports as it sees fit, and every downstream sub-agent can read the reports from earlier phases directly.

This totally solved the problem. And it also improved communication between sub-agents, because they could read each other's full output without the orchestrator having to summarize or relay anything.

See screenshot 2.

Version 3: From Skills to a Handbook

With the orchestration working, I initially created separate skills for each specific piece of work — gather-gmail, gather-calendar, check-accuracy, check-formatting, and so on. Eleven skills in total. Each sub-agent would read the skill it needed and get all the context for its task.

This worked, but it was ugly. These were very specific, narrow skills, and it created all sorts of fragility in the system. Not to mention that it was difficult for even the humans to read and maintain.

That's when the job description framing came back around. We started by treating this like hiring a real person. We wrote them a job description. So what do you do once you've actually hired someone? You give them an onboarding handbook — a document that covers how you approach things on your team and tells them to use it to get the job done, all aspects of their job.

So that's what we built. One single project management skill that contains our entire handbook, organized into chapters:

• Foundation — who we are, the team, our tools and data sources, when to escalate, data accuracy standards
• Daily Operations — how to gather data from all our sources
• Client Dashboards — how the dashboards are structured, what the master dashboard tracks, how to run quality checks
• New Clients — how to onboard a new client and set up their dashboard from scratch

Now when a sub-agent spins up, it reads the foundation chapters first (just like a new hire would), then reads the chapters relevant to its specific task. The handbook replaced eleven fragmented skills with one coherent source of truth.

Here's what the final architecture looks like: See screenshot 4.

What This Felt Like

This was the most exhilarating work I've done in two weeks, and it was all of the things at once.

Working with @NataliaZarina was the most important part. We were on calls for hours, running Claude Code sessions on each of our computers and trading inputs. She has the taste — she knows what the dashboards should look like, what the data should contain, what quality means for our clients. I have the AI engineering. Working together on this was genuinely exciting.

Then there's the speed. We went through three major architectural generations in a span of two weeks. Everything was changing so fast. And what was actually the most exciting was how hard we were driving Claude Code. I've been using Claude Code for programming for months, but I was not driving it this hard before. This last couple weeks, I was consistently running out of my usage limits. In fact, both Natalia and I were running out of our combined usage limits on the ultimate max plans on multiple days. When you're consuming that much AI inference, you can imagine how fast things are moving. And that was just exciting as fuck.

This was also a completely novel problem. Applied AI engineering as a discipline is still new, and this was the first real big shift in how I think about it.

Why Now, and Why 2026

Here's why I opened with the claim that Claude Code for non-technical work will sweep the world in 2026.

We realized that if you give Claude Code access to the tools you use as a non-technical person and do the work to build a workflow that covers how you actually use those tools, that is all you need. That's how non-technical work works.

The reason this hasn't been done until now is that we were running Claude Code at its limits. This would not have been possible with a previous version of the AI or a previous version of Claude Code. We're literally using the latest features and the latest model. It requires reasoning through and understanding of the underlying tools and how to operate them, along with planning capabilities and context management capabilities that did not exist even six months ago.

But now they do. And we're only in January.

Every piece of the stack that made this possible is brand new:

• MCP Builder skill — I built our own Google Workspace MCP server by asking Claude Code to use the Google Workspace API. That was not possible before Anthropic released MCP Builder on Oct 16, 2025

• Opus 4.5 — Its reasoning and planning capabilities made the entire orchestration possible. The agent needs to understand complex sheet structures, figure out what data goes where, and coordinate across dozens of sub-agents. Released Nov 24, 2025.

• The Tasks feature — Sub-agent orchestration through Tasks made Version 2 and 3 possible at all. This was released Jan 23, 2026.

That's why I'm saying Claude Code for non-technical work will sweep 2026. The building blocks just arrived.

[Aaron Levie @levie]

This is the question every software company is asking themselves right now. What happens to our roadmap if an engineer can produce 2X or 5X more output.

The general direction will be roadmap expansion. Companies that just use this leverage to cut costs will be outcompeted by those that decide to do more.

As a result, this will mean we will see more competitive battles between companies, but also the expansion of many more categories since software can touch more surface area.

The limiter then becomes how rapidly your customers can actually adopt new software, how good you make that software (vs. it becomes slop because it’s so much easier), and whether you can get paid for more software or if customers’ expectations just go up over time for what they get from each vendor.

As an aside, building up a brand, ecosystem, and distribution moat ends up being critical. If software development cost per unit go down, then the new game is how you can get customers to adopt and remain sticky. GTM becomes a critical factor in all this.

[HackerRank @hackerrank]

"Claude usage limit reached. Your limit will reset at 7 AM"

[Collin Hachwi @chachwi]

@bhaines0 Seeing the same thing !