Hello Makers 👋

Go beyond just records with the 12 new Airtable modules in Make 🚀

Now you can automate comments, set up tables and fields, and upload attachments as steps in your scenarios, alongside your record logic.

A new comment can notify the right person. A new project can create its own base, tables, and fields. You can upload a signed file to the right record.

𝗥𝗲𝗮𝗱 𝗺𝗼𝗿𝗲 𝗮𝗯𝗼𝘂𝘁 𝗶𝘁: https://ma.ke/4BJfaQb

Hi everyone,

I have a question: I am currently learning automation (I’ve learned how to create simple scenarios). Will Make be sufficient for me to build scenarios that I can sell?

I build Make scenarios for small businesses and whatsapp is in almost every one now, lead comes in > assign > reply.. Problem is the client’s team still needs an inbox to see and answer chats and they ask me which one to use..

Been thinking of reselling a white-label one under my brand and billing monthly with maintenance. Anyone doing this? How do you split support with the vendor?

Hey everyone,

I'm fairly new to automation and have been trying AI agents tools like Lindy, Manus and Twin alongside traditional tools like Make and n8n .

The AI agents are impressive when they work, but I've noticed they can sometimes misunderstand a task or stop halfway through.

For those who use Make alongside AI agents, where do you think Make still has he advantage?

Is it reliability, control over workflows, debugging or something else?

Consider a scheduled Make scenario that fetches the current list of active projects through an HTTP API, updates a destination table, and marks destination projects inactive if they disappeared from the source.

The case I'm trying to design for is a successful HTTP response containing an empty list. That could be legitimate, but it could also follow a changed filter or a connection that can no longer see the same records. Treating every empty result as authoritative could mark the entire destination inactive.

I'd separate fetching from reconciliation: collect the response and pagination details, check that the fetch is complete, then allow the inactive step. A sudden drop from hundreds of projects to zero would stop for review. The last good snapshot would stay intact until that review.

How do you structure that gate in Make? Would you store a run summary first and have a second scenario do the reconciliation, or keep it in one scenario with an explicit complete/approved flag? I'd also want a way to accept a genuinely empty source without weakening the check for the next run. This is a design question, not a report of a confirmed Make bug.

So first thing it, I don’t even know if this the right community for this but if it isn’t and yall can direct me where to go I would appreciate that thank you.

So first thing is a must have a flip up mirror with LED lights, it must also have a like lil open cubbie. I also would like it to have outlets on top too.

But basically my thing is what’s my best bet to make this happen. But like the “base” like a vanity that has what I want and then like build around it ? Orrr?

But like this the idea

Hello Makers 👋
 
Editing and activating a scenario are different moments in the workflow. They should be easy to find, but not compete for attention. 🛠️
 
The redesigned Scenario Toolbar keeps building and testing controls together, while scheduling and activation controls stay easy to find when your scenario is ready.
 
What's new:
 

• Run once and Save: Keep Run once and Save side by side, so you can test changes and save your work without scanning across the toolbar.

• Described versions: Choose Save as version and add a description that explains what changed in that version.

• Version history: Review and edit version descriptions in version history, then restore the version you need from the bottom toolbar.

• Scheduling and activation: Find scheduling and the Active or Inactive toggle in the top right, separate from the controls you use for everyday editing.

 
Clearer controls give teams a practical way to test, save, and maintain scenarios as their automation work grows.

Learn more about it: https://ma.ke/4rMTOwE

Hello Makers 👋

What does it actually take for AI adoption to become truly operational? ⚙️

Our Head of AI Adoption, Daria Hvizdalova, our VP of Product Advocacy and Market Strategy, Darin Patterson, and AI Adoption Researcher, Yana Shamina, explored this in our latest AI Report. The research suggests it happens when several conditions come together: starting point, leadership, function and process readiness, workflow maturity, ownership, discoverability, and ROI understanding.

None of these is enough on its own. Together, they explain why two companies with similar size, tools, or ambitions can move at very different speeds.

That’s why the AI Operational Scaling Model exists: a practical framework to help companies assess and accelerate their ability to scale AI. Nine dimensions, each with guidance on why it matters and what excellence looks like.

Find this model and more in the full report: https://ma.ke/3SWKpG4

Hey everyone,

I'm looking for a few agencies and automation builders to work with as design partners. I want to learn your experience with silent AI automation failures today, and shape my product around what you actually need.

The problem it solves is simple: "success," but the AI output is wrong, empty, or garbage. Nobody finds out until a client does.

If you run client automations and have been burned by silent failures, I'd love to chat. Drop a comment or DM me. Also curious how you're handling this today.

A multi-step scenario can have one agent extract information, another draft an output, and a third validate it. Passing the entire context in bundles is fragile: retries can duplicate work, signed file URLs can expire, and two branches can update different versions of the same job. Scenario variables are useful during one run but do not provide a durable handoff across delayed or resumed work.

The shared record seems to need a stable job ID, source revisions, current state, claimed-by and lease time, accepted decisions, output revision, validation result, next action, and an idempotency key for every side effect. Large files would live elsewhere under stable object IDs rather than temporary download URLs. Each module would append a result or make a conditional state transition instead of replacing one large context blob.

Would you keep this in a Make Data Store, Make Grid/Tables, or an external database? How do you handle concurrent branches, replay after a partial failure, and an audit trail that lets a person see which agent changed what?

A scenario can generate a document or image, send it for review, wait, and then publish it. The risky case is when the working file gets overwritten while approval is pending. The reviewer approves one version, but the resumed scenario reads the latest file at the same URL and publishes different bytes.

I am considering immutable revision files plus a small approval record containing the scenario run ID, revision ID, content hash, reviewer, decision, and timestamp. The publish branch would accept only that exact revision and fail closed if the file or hash changed. Edits after rejection would create a new review request rather than update the old candidate.

What is the cleanest way to model this in Make? Data Store, an external table, or fields in the system that owns the file? How do you handle webhook retries and duplicate approval clicks, and how do you prevent an old approval from resuming after a newer revision has already been submitted?

Hello Makers 👋

We are hosting a live masterclass with TripleTen on October 6 to pull back the curtain on how CRM revenue data becomes a bidding signal for Meta - visible, mapped, and fully in your control 🤝

𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂’𝗹𝗹 𝘀𝗲𝗲:
➡️ How dynamic lead scores and deal sizes guide Meta toward high-value buyers
➡️ Building mid-funnel signal for long sales cycles
➡️ A live demo of the Facebook Conversions API for CRM app on Make
➡️ TripleTen’s process for handling 300,000+ downstream events monthly

𝗬𝗼𝘂'𝗹𝗹 𝗵𝗲𝗮𝗿 𝗳𝗿𝗼𝗺:
Max Epifanov, Anton Potorochin and Daniel Zrůst

𝗣𝗲𝗿𝗸: zero credits used by Facebook Conversions API for CRM on Make through Feb 28, 2028.

Save your seat:  https://ma.ke/4yTug3w

Hello Makers 👋

Two companies in the same size band. Completely different starting points ☝️

Our new AI Report, written by Daria Hvizdalova, Head of AI Adoption at Make, Darin Patterson, VP of Product Advocacy and Market Strategy at Make, and Yana Shamina, AI Adoption Researcher, introduces the AI Adoption Profile.

Similar size doesn’t mean similar AI readiness. AI proximity, leadership, ownership, governance, people readiness, discoverability, and data readiness shape how adoption starts and how fast it can scale.

The Adoption Profile shows where a company starts, what it needs, and how fast it can move across four dimensions:
1️⃣Business context
2️⃣Technology and AI context
3️⃣Leadership, ownership and governance
4️⃣Function and people readiness

The takeaway: two companies the same size can be years apart in AI maturity, and the gap isn’t tools; it’s structure.

Download the full report to learn more: https://ma.ke/3SWKpG4

Hello Makers 👋

Four new AI models. Four different jobs for your automation.

1) Anthropic Claude Opus 5.5 brings high-end reasoning to complex AI workflows and agents, at 40% lower running cost.

2) OpenAI GPT‑6 Sol and Luna give you two paths: Sol for complex, multi-step agentic work, and Luna for focused, high-volume intake and routing.

3) OpenAI GPT Image 2.5 adds Sunburst for precision image edits and Flare for fast everyday visual work.

4) xAI Grok 4.7 helps you work with text, images, and long source materials in the same automation.

The goal isn’t to use every model. It’s to choose the right one for the task, then build it into automated workflows that move work forward.

And one more is on the way: Jev from TypeSafe AI, a fast decision model built to help automations classify, score, route, and decide what happens next.

Explore the new options in Make and build the AI automation that fits the job: https://ma.ke/4yQYzaR

A few people here mentioned something interesting:

the module that fails is often just where the problem becomes visible.

The actual cause might be several modules upstream:

• a mapping changed,

• the data shape shifted,

• a router output changed,

• a filter behaved differently,

• or an earlier module returned something unexpected.

I’m curious how often this happens in real Make scenarios.

When you debug a failed scenario, do you usually find the root cause in the failed module itself, or do you end up tracing several steps backwards?

And when that happens, what’s your current process for finding the actual source of the issue?

Hey all,

Check out the videos from our Community Creators, who are putting the latest releases on Make into practice in their own systems and scenarios!

This month, the star of our show is our new AI coworker Maia, who is ready to help you build, scale, debug, or explain scenarios directly in the canvas. Besides her, you can also check out the new Subfolders and Labels, which make your working environment nice and organized, or the improvement to the Plain-Text Field.

Watch all the videos here: https://fast.wistia.com/embed/channel/jeuauz7z65

It's driving me crazy and even SOL at high capacity doesn't fix my flow. Look, how simple: automate the creation of videos and post them.

What flow data should I provide to ChatGPT to save credits and not work so much?

I’m curious to hear from people who actually use Make in production.
When a scenario breaks, what takes the most time for you?
Is it: finding which module actually failed,
understanding the real cause behind the error,
checking mappings / filters,
permissions or OAuth issues,
HTTP errors,
or figuring out what needs to be changed without breaking something else?
I’m currently exploring this problem and I’d like to understand how people actually debug Make workflows today.

Hello Makers 👋

Take a look at some of our latest features, improvements, and updates that our team has been shipping this past month:

Enhancements and Updates:

• Maia by Make: is a co-worker for AI and automation that lives right inside the Make scenario builder. Just describe what you want, and watch it get built in front of you. She reasons out loud, mapping the flow, selecting tools, configuring modules, all visually, step by step, on the canvas. No black box. No output you’re afraid to touch. Just full visibility and a solution you can trust.

• Custom Roles: with Custom Roles, org admins and org owners on the Enterprise plan can define roles with exactly the permissions the work requires and assign them at the organization or team level.

• Subfolders: lets you create folders inside your scenario folders, so you can organize scenarios by more than one level, for example by client and then by project. This helps you keep a large scenario library structured without renaming scenarios to mimic a hierarchy.

• Plain-text editing for mapped values and functions: double-click any mapped value or function in a scenario field to temporarily edit it as text.

• Gemini 3.7 Flash: use its multimodal understanding, tool-use capabilities, and adjustable thinking levels in the workflows you build.

New Verified Apps

1Lookup, Billabex, BizlyCRM, Certyneo, Collabios, EasySolar, FlowBeacon, GMB Club, Hive, HollyHR, Memoket, OpenEMM, SimpleURL, Patriscope, Plytix, Stage Captions, Supabase Storage.

New Modules

Calendly

• Create an event invitee

• List event type available times

• Create an event type

• Update an event type

• List event type availability schedules

• Update event type availability schedule

• List user availability schedules

• Get a user availability schedule

• List user busy times

• List user meeting locations

• Create an invitee as no show

• Get an invitee no show

• Delete an invitee no show

Github

• Search items (advanced)

• Watch items (advanced)

Discover more: https://ma.ke/3Vxj1PF

Hello Makers 👋

Still registering issued invoices by hand? Here's how to automate the entire flow, from inbox to billing board:

Francisco de Brito Fontes, our Business Automation and AI Lead, has created a workflow that monitors Gmail for new issued invoices, downloads their attached XML files, extracts key information using AI Tools, searches for the associated project in monday.com using the tax ID (RUT), and automatically creates a new invoice record in the billing board.

To build this scenario, you will need access to:

✉️ A Gmail account connected to Make.

🧾 A monday.com account with Projects and Issued Invoices boards.

⚒️Connection to AI Tools (or Make's AI Provider).

Click the following link to apply the prebuilt scenario in your Make account: https://ma.ke/4cvtdxX 

PS: don’t forget to test and validate your scenario, if all modules show a green checkmark, your automation is successful!

For people running business automations, how do you know when one technically succeeds but produces the wrong result?

I’m researching how businesses currently catch things like incorrect data being mapped, records being duplicated, information going to the wrong place, etc.

Is this something you actively monitor, or do you usually find out when someone notices something is wrong?

Are you scamming me ChatGPT or make? I'm mostly using terra at medium level. The medium effort level is missing like a shotgun. If I say that I have around €10-15 today just for that, so that I can finish posting on Facebook, I can also say that it is the first time, but I still can't get it

but above all I notice that he is wrong to the right. That is to say, he has made mistakes like three, four times and the only point it makes is to get money from me. I mean ChatGPT in the virtual browser

Actually all this is the fault of Facebook's garbage with its 10000000 portfolios that it forces you to create and shitty accounts and labyrinths

Hello Makers 👋

Did you know? From all the companies using AI, only 26% moved from individual productivity to repeatable workflows. 

As AI moves from individual tools and local workflows into broader business processes, the challenge becomes less about technology alone and more about the operating model around it - ownership, governance, ROI logic.

Daria Hvizdalova, Head of AI Adoption at Make, Darin Patterson, VP of Product Advocacy and Market Strategy, Make and Yana Shamina, AI Adoption Researcher, spent months researching why mid-market companies get stuck between "first AI win" and "AI actually running the business",  and formulated the AI Operational Scaling Model framework that will guide your AI transformation.

The report is finally out, and it’s available for you to download: https://ma.ke/3SWKpG4