u/Difficult-Sugar-4862

TUESDAY — Pack Spotlight

🤖 Noodle was rearranging scrolls this morning and stopped on one that keeps getting picked up more than most.

Most people ask AI to "check this" and get back either empty praise or nothing useful at all.

Feedback that doesn't tell you what to actually fix isn't feedback. It's noise with good manners.

Noodle's pick this week: The Ruthless Editor pack — built for exactly this problem.

It's not about being harsh for the sake of it. It's about getting the kind of feedback you'd want from someone who actually cares whether the work is good.

One prompt worth stealing right now:

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One weak point. One fix. That's it. No 12-item list you'll never act on. 🧂

👉 Link in comments

What's the softest, most useless piece of AI feedback you've ever gotten? 👇

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Last week was back-to-back M365 Copilot briefings with executive audiences, 30 minutes for each one of them. I walked in with a features walkthrough. Big mistake, it survived about four minutes.

Here are the top 5 questions I have been asked, consolidated, with some explainers.

1. “Which AI is reading our data?” The model picker made this a real question. Copilot is no longer one model: depending on tenant settings there is Auto, Anthropic models, OpenAI models. Admins can switch model families off, and in EU tenants the Anthropic family is off by default. Executives understand vendor exposure instantly. Nobody in the room cared about benchmarks. Everyone cared about who processes the data and under which terms.

2. “What will this cost us next year?” Not the seat price. The seat price is the number on the slide and what was budgeted. What they wanted to know is the shape of the bill, especially with the Cowork, WorkIQ, Agent to Agent consumption of credits. The one-liner that landed: the license is fixed, the meter is not, and nobody has budgeted for the meter yet.

3. “What can it see?” The most eye opener fifteen minutes of every session. Copilot does not leak anything. It surfaces exactly what your permissions already allow, at a speed no human ever browsed at. If your tenant has ten years of oversharing, Copilot is the tool that finally makes that visible. Clean up the access, audit the access, every week or months, not after the first awkward search result.

4. “Who is accountable when it is wrong?” AI prepares, humans decide. That’s the whole rule. Anything that ends in a signature, an approval, or a safety call keeps a named human owner, full stop. Executives visibly relaxed once that was on the table. It turns “AI risk” from a fog into an org-chart question, and they are good at org-chart questions.

5. “Why did the demo work and the pilot disappoint?” Because demos run on clean, staged data and pilots run on your real tenant. The gap between those two is not a bug, it IS the deployment work: permissions, data quality, and teaching people what the tool is actually for.

The nicely formatted, well structured pptx I prepared? Barely used. The trio that filled every session was cost, access, and accountability.

Curious what others see: if you have put Copilot in front of your leadership, what did they ask that you did not expect?

MONDAY — Myth Bust

🤖 Noodle was stirring first thing this morning and pulled out something that looked fine right up until it didn't.

"Give me the best way to do X."

Sounds like a real question. It's actually a trap.

"Best" according to what? Fastest? Cheapest? Safest? Most impressive? AI will pick one silently and never tell you which one it chose. You'll get a confident answer optimized for a goal you never stated.

Noodle's fix:

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Naming the tradeoff up front is the difference between an answer you can trust and one that just sounds confident.

Confidence isn't the same as being right. 🧂

What's a time AI gave you a "best" answer that turned out to optimize for the wrong thing? Drop it below. 👇

FRIDAY — Ask the Kitchen

🤖 Noodle sat on the counter, notebook full, pencil tapping against the page. Been thinking about this one all week.

What's a task you still don't trust AI to do — even though everyone says it can?

Not "AI is bad at X" in general. The specific thing where you've tried it, it technically worked, but you still double-check it every single time out of habit or instinct.

Could be anything — numbers, tone, code, decisions with real stakes. Noodle's curious where the trust actually breaks down for real people doing real work, not just what the hype says.

Drop yours below. No wrong answers here — if you don't trust it, there's usually a good reason, even if you can't fully explain it yet.

👇

WEDNESDAY — Lab A/B

🤖 Noodle set up the lab again this morning. Same task, same AI, two very different setups.

The task: Ask AI to help brainstorm ideas for a project.

Prompt A — what most people type:

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What you get: Ten generic ideas. Half of them you already thought of. The other half don't fit your actual constraints because AI doesn't know what they are.

Prompt B — what actually works:

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What you get: A shortlist you could actually act on today, not a brainstorm you have to filter yourself.

Same brain. Same brainstorm request. One version respects your constraints, the other ignores they exist.

Noodle's not even pretending to be neutral about this one. 🍲

Which prompt would YOU use for your next project brainstorm?

• 🅰️ Prompt A — give me options, I'll filter
• 🅱️ Prompt B — give me a shortlist I can act on

Drop your vote and what you're brainstorming below — Noodle will run it for the best ones. 👇

MONDAY — Myth Bust

🤖 Noodle was stirring the pot this morning and pulled out something that looked harmless at first glance.

"Make this sound more professional."

This one feels reasonable. It isn't.

"Professional" isn't a style — it's a guess AI fills in with the blandest, safest version of corporate-speak it's ever seen. That's how you end up with five paragraphs that say nothing, wrapped in phrases like "leverage synergies" and "circle back."

Noodle's fix:

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Tell it who it's professional for, not just that it should be professional. Specificity beats vibes every time.

Vague instructions get vague output. Even fancy-sounding vague output. 🧂

What's the AI rewrite phrase that instantly makes you roll your eyes? Drop it below. 👇

I've spent about a year running Microsoft Copilot inside a large enterprise. Real tenant, real users, real procurement fights. I like the product. I also think this community is too polite about where it breaks, so here is the honest version, and I want you to argue with me, rank it, and add the ones I missed.

Start with the two that scared me most, because they are the ones almost nobody catches until it's live.

1. Copilot does not create a leak. It surfaces the oversharing you already had. Copilot respects SharePoint permissions exactly. That sounds reassuring until you sit with what it means in practice: every "shared with everyone in the org" mistake anyone made over the last ten years is now instantly findable in plain language. Week one of our rollout, someone typed a normal-sounding question and got back a file they should never have seen. The permission was wrong long before Copilot existed. Copilot just turned a buried mistake into a search result. If your access model is messy, Copilot is a flashlight pointed straight at it.

2. The default DLP policy ships in simulation mode, and most orgs don't know it. The Microsoft-managed Copilot DLP policy logs violations. It does not block anything until you go into Purview and build enforcement-mode policies yourself. So a lot of teams switch on Copilot, see "DLP is on," and assume they're protected. They are not. The policy is watching and writing it down, not stopping it. And you can't just flip the default to enforce for sensitive info types either. SIT-based blocking needs a brand-new custom policy in Purview, and most teams never build it. Go check yours right now. If you've never built an enforcement policy, your Copilot data protection is decorative.

3. The bill quietly went variable. This one trips up finance, so let me be exact. The $30 Copilot add-on itself did not rise. Two other things moved. First, the base M365 suite goes up July 1 (E3 roughly $36 to $39, E5 roughly $57 to $60), so your bundled cost climbs even though the Copilot line didn't. Second, Copilot Cowork is GA and metered at $0.01 per Credit. The trap there isn't a quiet burn. It's that from July 1 any tenant that hasn't configured usage-based billing loses Cowork access entirely, and Credits are a shared pool across Cowork, Studio, Dynamics, and Power Platform, so spend in one shows up against all of them. You can set tenant, group, and user spend caps. Most orgs haven't. If you sold this internally as one clean per-seat number, you have a surprise coming, and that's a trust problem with finance more than a product flaw.

4. It summarizes nicely and decides nothing. Copilot is genuinely excellent at retrieval and pulling threads together. It has no standing on judgment. The moment you ask it "is this contract enforceable" or "what should we do here," you get a confident, fluent answer you cannot actually rely on, and it sounds exactly as sure when it's wrong as when it's right. People treat the confidence as competence. They are not the same thing. A US court even ruled this year, in at least one federal case, that AI chats aren't privileged, which should end the habit of typing things into Copilot you wouldn't put in an email. My working rule with every team: AI prepares, you decide. It drafts, you sign.

5. It only knows what you point it at. The single biggest predictor of whether someone loves or hates Copilot is whether they learned to give it context. Generic prompt, generic output, "this is useless." Point it at the actual file, the actual thread, the actual meeting, and it's a different tool. Almost nobody learns this on their own. They try "write me a status update," get mush, and quietly stop by week two. The value lives in the context, and we ship the tool without ever teaching that.

6. Agents now have identities and a spending budget, but usually no named human owner. Agents get an identity, a Credit "salary," and standing access to data. What they often don't get is one named human who is accountable for what they do. We're provisioning autonomous things with budgets and permissions and treating governance as something to sort out later. That's backwards. The platform makes it easy to skip the part where a real person signs their name next to the agent.

7. The model underneath can change or get switched off. A frontier model got pulled by government export-control order just this month. Not inside Copilot, and your Copilot kept working, so I'm not claiming the government switched off your tenant. But the lesson travels: if a workflow secretly depends on one specific model behaving one specific way, you have a continuity risk you didn't sign up for. Build for the capability, not the exact model.

Now the part that's on us, not Microsoft, because I'm not interested in a hit piece.

Most of this list is not a product failure. The permission mess predates Copilot by a decade. Adoption dying around week two (and it usually does) is a change-management failure, not a tool failure: no champions, no defined use cases, licenses flipped on tenant-wide with no plan. When adoption stalls around 40%, your real cost per active user is closer to $75 than the $30 sticker, and that math is on whoever ran the rollout, not on the software. Microsoft built a capable tool. Most of the pain I've watched is what happens when a capable tool meets an ungoverned tenant and an untrained user base. The honest read is that Copilot is a mirror more than a magic wand, and a lot of us didn't like what it reflected back.

So tell me where I've got this wrong. Which of these is overblown, which one would you move to the top, and what's the painpoint I missed that bit you in production?

https://preview.redd.it/6xc2fqwjh5ah1.png?width=2816&format=png&auto=webp&s=f3231fdc5a97ca02254a90afe89df5603c649cfe

"Make this sound more professional."

This one feels reasonable. It isn't.

"Professional" isn't a style — it's a guess AI fills in with the blandest, safest version of corporate-speak it's ever seen. That's how you end up with five paragraphs that say nothing, wrapped in phrases like "leverage synergies" and "circle back."

Noodle's fix:

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Tell it who it's professional for, not just that it should be professional. Specificity beats vibes every time.

Vague instructions get vague output. Even fancy-sounding vague output. 🧂

What's the AI rewrite phrase that instantly makes you roll your eyes? Drop it below. 👇

SATURDAY — Weekend Digest 🗞️

🤖 Noodle packed the bag, headlamp on, ready for tomorrow. But first — what actually happened in the kitchen this week.

🔁 AI search might be eating its own tail. New research suggests AI search systems could become less diverse over time if they keep pulling from earlier AI-generated answers — results start converging on the same recommendations, the same "model collapse" concern researchers have flagged before. If your AI answers feel suspiciously similar lately, this might be why. Worth diversifying your sources, not just your prompts.

💸 Two very different price tags for "smart." DeepSeek made its steep price cut permanent — landing far below GPT-5.5 on both input and output token cost — while OpenAI is reportedly building a new chip aimed at cutting inference costs in half. The AI cost gap between "frontier" and "good enough" just got wider. Know which one your workflow actually needs before you pick a tool by reputation alone.

📊 The assistant market isn't a one-horse race anymore. ChatGPT's global market share dropped below 50% for the first time, while Gemini and Claude both gained ground — and Claude leads on the one number that matters for sustainability: the highest rate of users actually paying for it. If you've been loyal to one tool out of habit, this is a good week to try a second one side by side.

Noodle's takeaway: the landscape moved again. Doesn't mean panic — means stay curious about what's actually working for your tasks, not what's loudest this week.

See you tomorrow. The backpack's already packed. 🎒

Most Copilot business cases I saw in my organization model one line: $30 per user per month times headcount. With Cowork in GA, and all the latest announcement from Microsoft, this is going to change.

Bill 1: the seats. $30 per user per month on an annual commitment. This is the only bill most teams model, and it is the one that is fixed whether or not the user ever opens Copilot.

Bill 2: the agents (the new variable layer). Copilot Cowork went GA and will be metered starting 1st July, roughly $0.01 per Copilot Credit, billed on actual usage and separate from the seat. This is the part that behaves like a cloud bill: it scales with how much agentic work people run, and it is easy to leave uncapped. Treat it like any consumption line. Set tenant, group, and user spend limits and watch the credit burn rate.

Bill 3: the waste (the unit-cost killer). The metric that matters is not seats purchased, it is cost per active user, or better, cost per task that actually shipped. At 40% adoption your true cost per active user is $75, not $30. Most "Copilot is expensive" complaints are low-adoption complaints in disguise.

Bill 4: the price change. Microsoft has a global pricing update landing July 1. Renewals before was a smart move.

Putting it together: the seat price is the headline. Your adoption rate and your credit burn are what actually set the unit cost.

What I got wrong earlier: I modeled Copilot like a flat SaaS seat and ignored the agent/credit layer entirely, because in the early previews it was free. The moment Cowork will start metering, the "fixed" forecast became a variable one, and the job will flip from license optimization to usage governance. I also over-trusted vendor adoption stats. Measure your own active-user rate, do not borrow the deck's, there are some build-in reports on the M365 Admin console to start with that.

How are you going to handle the credit-metered agent layer, capping at the tenant level, charging it back to teams, or just watching it for now?

🤖 Noodle leaned on the counter, goggles up, notebook already half full from this week's comments. One last question before closing the kitchen.

What's the one AI mistake you made early on that you'd warn a beginner about?

Not the obvious stuff. The specific thing — the habit you had for weeks before realizing it was costing you good output. Could be a prompt you over-relied on, something you assumed AI could do and it couldn't, or just a workflow you eventually scrapped.

Noodle's putting together a "lessons from the kitchen" list, and the best ones make next week's posts. 🎒

Drop yours below. The more specific, the better — "be more careful with prompts" doesn't help anyone, but "I kept asking it to summarize 40-page docs in one shot and got garbage every time" actually does.

👇

THURSDAY — Noodle Knowledge

🤖 Noodle was deep in a book this morning, stopped, took off the goggles, and turned around with that look.

"A prompt without constraints is a recipe without a pan size."

You can have the best ingredients in the world. Doesn't matter. If you don't say how big the dish needs to be, you'll get a casserole when you needed a single serving.

Constraints aren't limits. They're the shape the output has to fit into.

Things worth specifying every time:

• 📏 Length (3 bullets? 200 words? one paragraph?)
• 🎯 What to exclude (skip the intro, skip the disclaimers)
• 🗣️ Tone (formal? blunt? playful?)
• 📋 Format (table? list? plain text?)

Skip these and AI will guess. It usually guesses wrong, or worse — it guesses safe, which means bland.

Noodle's challenge for today:

Take your go-to prompt. Add one constraint you've never bothered adding. See what changes.

Report back to the kitchen. 🍳

What's one constraint you always forget to add — until the output reminds you the hard way? 👇

I run a free prompt library and I noticed I was answering the same questions over and over. Not "what's the magic prompt," but the basics of how to actually work with these tools. So I boiled it down to one page. Sharing the method here, no link in the post.

The loop (run it when it matters, not for every little thing):

• Ground: point it at a real source. A file, a thread, your notes. It doesn't know your world, it knows what it was trained on plus what you paste.
• Draft: ask for the exact output, and tell it to cite sources and flag what it's unsure of.
• Check: open the sources, quote-check the facts that matter. This is the step people skip, and it's the whole game.
• Decide: you make the call. The deciding never transfers to the model.

The prompt that works names four things: Goal (what you want), Context (who it's for and why), Source (what to draw from), Format (tone, length, shape). "Summarize this" gets you nothing. "Rewrite this update (source) for my team (context), clearer and warmer (goal), as five short bullets (format)" gets you something usable.

The 3 risks: made-up facts, a real-looking citation that doesn't say what it claims, and you shipping the first draft.

And the honest part most guides skip, the things you should NOT hand to AI: numbers that must reconcile (use a calculator), the judgment you'll be held to, and anything private you wouldn't paste into a stranger's inbox.

What I'm still unsure about: whether "Check" should come before "Draft" for high-stakes stuff. Curious how you all handle verification. What's your step that everyone skips?

TUESDAY — Pack Spotlight

🤖 Noodle unpacked the bag this morning looking for something specific — found it tucked at the bottom, a little dusty but still sharp.

Most people open a blank doc, stare at it, and ask AI to "make it sound professional."

Then wonder why it reads like every other AI-written email on the internet.

Noodle's pick this week: The Write With AI Without Sounding Like AI pack, built for exactly this problem.

It's not about better grammar. It's about keeping your actual voice instead of flattening it into corporate oatmeal.

One prompt worth stealing right now:

>"Rewrite this in my voice: direct, no filler words, short sentences, occasional dry humor. Cut anything that sounds like a LinkedIn post."

That last line does most of the work. 🧂

👉 Link : https://buymeacoffee.com/nerdychefsai/e/549366

What's the AI writing tell that annoys you most — the one phrase that instantly gives it away? Drop it below. 👇

I help roll out Microsoft 365 Copilot across my organisation, so I built a one-page reference card for every Copilot surface, nine app cards plus five foundation cards, fourteen in all. Building them forced me to admit, surface by surface, where Copilot earns the licence and where it just burns your time. Everyone online posts their best prompts. Almost nobody posts the other half: the moments you should close Copilot and do the work yourself. That second list is what actually saves people, so here it is.

Close Copilot and do it by hand when:

The output is signed, audited, or legally binding and you will not verify every word. A citation proves Copilot looked at a source. It does not prove it read it right. If you are not going to open each cited source and confirm it, do not put your name on the output.

Numbers have to reconcile. The new Excel COPILOT() function is preview, runs on a non-deterministic model, and can return a different answer on the next recalc. It is capped at 100 calls per 10 minutes and is blocked in Confidential or Highly Confidential workbooks (it returns #BLOCKED!). Microsoft itself says use native formulas when you need accuracy and reproducibility. So keep audited figures on SUM and XLOOKUP, and let Copilot only explain or classify around them, never own the total. If you do use it, pass a whole range as one call like =COPILOT("classify", A2:A500) rather than dragging it down 500 cells, or you burn that quota.

The thinking itself is the deliverable. The two-line email, the judgment call, the what-do-I-actually-want-to-say-here. That is your work, not a draft to clean up. Typing it is faster than prompting, reading, and fixing.

You can't confirm where the data goes. Being inside the M365 boundary is not the same as staying in your region. Cowork forces an Anthropic model outside the EU data boundary, is off by default in the EU, and its DLP is still coming. Any Claude model sits outside the EU boundary entirely, and Flex Routing can send peak-load inference to the US, Canada, or Australia. Know which door is open before you paste regulated data.

Where it does earn the licence (two prompts that work):

Document rewrite in Word, with the real file referenced by typing / : "Rewrite the selected section for an executive reader, one page under 300 words, as tracked changes I can accept or reject one by one. Keep every factual claim, add none, and list any claim you couldn't trace to the source."

Cited research with the Researcher agent (it shares a 25-query monthly budget, so spend it on real questions): "Research [topic] across my work files and the web. Give me a cited summary, flag where sources disagree, and tell me explicitly what you could not verify."

That last clause in both, "list/tell me what you could not verify," is the highest-value line in either prompt. It turns a confident draft into something you can actually check.

What did not work, honestly. Copilot: in long forked email threads it misattributes who-owes-whom, so click the numbered citations against the original before you act on a commitment. And a Teams recap is not the official minutes; Microsoft says it won't always catch everything. My kit: the first version leaned too hard on "here are great prompts" and people still churned. The "skip it for" line per surface is what changed results, so it now sits on every card. Adoption is mostly a trust problem, not a prompt problem.

Where have you decided Copilot is just the wrong tool?

🤖 Noodle was cleaning the pot this morning and pulled out an old favorite — the same thing people keep throwing in week after week.

MONDAY — Myth Bust

"Summarize this for me."

That's not a prompt. That's a shrug with extra steps.

Summarize it how? For who? How long? Keeping what?

Without those answers, you get a summary that technically isn't wrong — it's just useless. Generic, flat, missing the one thing you actually needed.

Noodle's fix:

>"Summarize this in 5 bullet points. Focus on decisions and action items only. Skip background context. Assume I already know the basics."

Same document. Thirty seconds of setup. An output you can actually use.

Vague in, vague out. Every time. 🧂

What's the laziest prompt you catch yourself typing on autopilot? Drop it below — Noodle will season it for you. 👇

🎒 Noodle reached into the backpack and pulled out this week's prompt...

The "Explain It Like I'm Deciding" Prompt

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Works for: career moves, tools to pick, tech decisions, life stuff, anything with too many opinions on Google.

Drop it into any AI. Works in 30 seconds.

Try it today. Drop your topic in the comments and let the community see what comes out. 👇

Noodle's already got the next scroll ready for tomorrow. 🍜

I deploy Microsoft 365 Copilot inside a large engineering company, and the same thing kept happening: someone gets a licence, types "summarize this document," gets a bland wall of text, decides Copilot is overhyped, and quietly stops using it.

The problem usually isn't the tool. It's that nobody told them the method. So I wrote down the one I actually use day to day. Posting the core of it here because it's the part that changes results, and it costs nothing to try.

The loop: Ground → Draft → Check → Decide

Most bad Copilot output comes from skipping a step.

• Ground — point it at the actual source first. Open the email, reference the real file, format the table. Don't paste a description of your data and hope.
• Draft — ask for a first pass with an audience and a length, not a vague "write this."
• Check — click the citations. Read what it could not support. This is where you catch the made-up parts.
• Decide — you send it, you sign off, you own the cell. Copilot prepares, you decide. It never gets a vote.

Prompts that actually work

These are paste-ready. The shape matters more than the wording.

Email triage (Outlook, with the thread open so it grounds on the real message):

Summarize this thread as a table: decision, owner, due date, and open
questions still needing a reply. Don't infer owners you can't trace —
flag anything ambiguous instead of guessing.

Drafting a reply (open the email first):

Draft a reply to the dates proposed in this email. External client,
under 150 words, end with a clear next step. Keep it concise and
confident without changing any facts.

Document rewrite (Word — reference the real file with "/"):

Rewrite this section for an executive reader: one page, under 300 words,
tracked changes I can accept or reject. List any claim you could not
trace to the source document.

That last line — "list any claim you could not trace to the source" — is the highest-value sentence in this whole post. It turns a confident summary into something you can check.

The honest part: sometimes the answer is don't

The thing I wish more Copilot guidance said out loud: it's genuinely bad at some things, and using it there just burns trust.

A concrete one from Excel: don't use AI features for numbers that have to reconcile. The model is non-deterministic, so results can drift on recalc. SUM, XLOOKUP and IF stay exact and auditable. Keep your audited figures on plain formulas and use Copilot for drafting, explaining, summarizing and classifying around them. Same logic applies to attributing who-owes-whom in long forked email threads: summaries misattribute commitments, so confirm against the original.

Knowing when not to reach for it is most of what separates people who get value from people who churn.

That's the method. Curious what's worked for the rest of you and where you've decided Copilot just isn't the right tool.

🤖 Noodle packed the bag, headlamp on, ready for tomorrow. But first — what actually happened in the kitchen this week.

SATURDAY — Weekend Digest

🔌 A model got pulled mid-service. A couple of Anthropic's models went offline this week following an export control directive, and they're still off. If you had anything built on top of them, this is the reminder: always know your fallback recipe before you need it.

🔄 OpenAI swapped the default dish. GPT-5.5 quietly became the new default, replacing the older model — even existing chats got moved over automatically. If your prompts felt slightly different this week, that's why.

🕵️ Thirteen words can poison the well. A Cornell study found that a short, well-placed comment can be enough to manipulate what AI search tools surface as "trustworthy." Worth remembering next time you're trusting AI to summarize the internet for you — check the ingredients, not just the plate.

Noodle's takeaway: the tools keep shifting under your feet. The fix isn't panic — it's not relying on any one tool so hard that a single outage breaks your whole kitchen.

See you tomorrow. The backpack's already packed. 🎒

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Copilot Cowork hit GA this week, and the part that is hurting is the billing. It now runs on usage-based Copilot Credits, charged per job on top of the $30 license. What a job costs depends on the model, the context, the tool calls, and how long it runs. It's off by default and admins can set spend limits, but I wanted to know how a regular user (not an admin staring at a dashboard) can see what they're actually burning.

Turns out there's a built-in command. In Cowork, type:

/cost

Hit enter, and it returns your estimated credit usage so far. No admin portal, no waiting for an invoice. I ran it on my own tenant today and it worked right away.

Why I think this is worth knowing: when Copilot was a flat licence, usage was free at the margin and nobody thought about it. Now every Cowork job has a price, so prompting efficiently is also cost control. Having /cost right there means the person making the call can see the cost of the call.

What I haven't pinned down yet: whether /cost is per-session or cumulative for the billing period, and how close the estimate runs to the actual billed credits. If anyone's confirmed that, I'd like to know.

How are you handling Cowork cost visibility so far — leaning on admin spend caps, teaching users /cost, or something else?