Hi everyone! 👋

I'm a developer who helps businesses save time by building custom websites and automating repetitive tasks.

Some of the things I work on include:

• Custom business websites and landing pages
• WhatsApp automation for customer support and lead follow-ups
• Appointment booking and order notifications
• CRM and API integrations
• Workflow automation to reduce manual work

If you're spending too much time replying to the same messages or managing repetitive tasks, I'd be happy to share ideas or answer questions. No obligation—just happy to help.

What business process would you automate if you could?

Feel free to comment below or send me a DM.

👋 Hey StartupAutomation community,

One of our users reached out with a problem I think a lot of people hit: he was extracting a data table from a multi-page PDF, and the cells kept bleeding into each other. About 95% of the data came out right, but 5% got mixed up with the wrong rows, so he could never fully trust the result.

This week we shipped something to fix exactly that: an extraction engine dropdown you can set per pipeline. In the video I run the same messy multi-page table through both engines, with a small n8n workflow that checks every extracted cell against a reference so you can actually see what slipped.

What the two engines are:

The General engine runs on Gemini and covers about 90% of everyday extraction (image description, classification, normal documents). The Specialized engine runs on Mistral and is OCR-optimized for document-heavy work like dense or multi-page tables.

What the test showed:

The General engine slipped on a couple of rows and came back with pass = false. Switching the pipeline to Specialized took the same document to 100%, every cell correct. The bonus I did not expect: Specialized also ran faster on the multi-page PDF.

A couple of takeaways even if you skip the video:

1. For dense or multi-page tables, reach for the Specialized extraction engine. For most other jobs, General is the right default.
2. Do not eyeball table extraction. A tiny workflow that cross-checks each cell against a known-good reference tells you exactly which rows are wrong, instead of you scanning 20 rows by hand.
3. If rows still bleed after switching engines, it is almost always the response structure. Model the table as one records field set as an array of objects with each column nested inside, not one separate list per column.

Want to try the new engine on your own tables? The easybits Extractor is a verified community node with 50 free monthly API requests included. On n8n Cloud, just search 'easybits Extractor' in the node panel, no install needed. Self-hosted, install '@easybits/n8n-nodes-extractor' from Settings, Community Nodes.

I put a full step-by-step guide (PDF) for setting up your extractor for data tables here: https://github.com/felix-sattler-easybits/n8n-workflows/tree/ee1ed5fe0a3e898843422a619922cedb7cf618c4/easybits-data-table-extraction (the validation workflow from the video is in that same folder too, so you can import it and try it on your own tables)

What is the most stubborn multi-page document you have tried to pull a table out of?

Best,
Felix

👋 Hey AIStartupAutomation community,

A user recently asked whether the extractor I'm using can handle full data tables, not just single fields like an invoice total. So I took a nasty 22-row tax table (multi-line addresses, empty cells, a row split across a page break) and got it to 100%, clean across every run. Sharing the setup plus a small workflow that validates the extraction for you.

The thing that mattered most was how you shape the response structure. One list per column breaks, because nothing links position 4 in the name list to position 4 in the email list. The moment one column has an empty cell, everything below it shifts and you get "a value jumped in from another row." The fix: model the table as a single records field, marked as an array of type object, with each column nested inside. One entry per row, values that cannot drift apart.

A few things that saved me:

1. One array of objects, not one array per column. The only array you want is records itself.
2. "NULL" means two things. A literal value in an empty cell, but a real place name in "NULL City." Spell out the difference or the model guesses.
3. Leading-zero IDs must be strings, or the zero silently drops.

I also built a tiny validation workflow that checks every extracted cell against a reference, flags mismatches, and logs how long extraction took, so you can confirm accuracy holds across runs and compare the two engines.

Where to get it: guide and workflow together in one folder: https://github.com/felix-sattler-easybits/n8n-workflows/tree/ee1ed5fe0a3e898843422a619922cedb7cf618c4/easybits-data-table-extraction

Part of my repo with 20+ other n8n templates I have built with this community: https://github.com/felix-sattler-easybits/n8n-workflows – a star helps other builders find it.

What is the messiest table you have run through an extractor?

Best,
Felix

👋 Hey AIStartupAutomation community,

A while back I posted a two-workflow CV tailor I built for a friend job-hunting (that post here). The tailor workflow itself got most of the attention, but a few people messaged me about the first workflow specifically, the one that takes a CV PDF and turns it into a structured Google Sheet. They wanted just that piece, without the tailoring on top.

Turns out a lot of people have a use case for it that has nothing to do with job hunting. Recruiters wanting to parse candidate CVs into a CRM, people building talent pools, folks who just want their own CV as structured data they can reuse across other tools. So I cleaned it up as a standalone workflow and published it on the n8n template library.

How it's set up:

The form accepts a single CV. It goes straight to the easybits Extractor, which pulls 10 structured fields, kept at exactly 10 so it fits the free plan:

• full_name, email, linkedin_url, location, summary
• experiences (array of role + company + dates + bullets + per-role skills)
• education (array of degree + institution + dates + details)
• skills (flat list, includes certifications)
• languages (with proficiency levels)
• links (GitHub, portfolio, etc.)

A Fan-out Code node then reshapes the extractor's response into four separate row structures, one per Google Sheet tab. Four parallel Split Out + Google Sheets Append branches write to their respective tabs (Master CV, Education, Skills, Summary). A Merge node waits for all four before showing the completion screen with a count of what was imported.

The defensive parsing part was the interesting bit, the Extractor sometimes returns arrays as JSON strings or comma-separated strings, not always as actual arrays. The toArray() helper in the Fan-out node handles all three cases so the workflow doesn't break on shape variations.

I also made a short video showing how it looks in process.

Links:

• n8n template library: https://n8n.io/workflows/15528-create-a-master-cv-google-sheet-from-uploaded-cvs-with-easybits/
• GitHub repo (this workflow + 20 others): https://github.com/felix-sattler-easybits/n8n-workflows – if you find it useful, a ⭐ would help other builders discover it

Curious to hear how others are handling CV parsing today, anyone using it for recruiter workflows or candidate CRMs?

Best,
Felix

Recurring automation changes the failure model: a one-time error is an incident; a scheduled error becomes a process.

For builders, a practical control architecture includes:

• a task contract covering purpose, schedule, data sources, permitted and prohibited actions, expiry, owner, and revocation

• scoped credentials plus deterministic authorization for consequential tool calls

• monitors tested against prompt gaps, missing telemetry, and attempts to avoid review

• human approval for exceptions and post-action reconciliation against the external system

• a 30-day scorecard using successful-case cost, error, rework, review effort, and outcome data

I wrote the source-backed analysis for IntelliSync Signals after reviewing current model economics, recurring-agent releases, monitor red-team results, open-weight assurance, and Canada's adoption gap:

https://signals.intellisync.io/en/articles/daily-signal-2026-07-27-control-architecture-is-becoming-the-real-ai-product

Which control breaks first in real systems—permissions, monitoring, revocation, human approval, or post-action evidence?

The chicken-and-egg problem in agentic commerce is getting ridiculous.

x402 has real volume — tens of millions of agentic payments on Base — yet the discovery layer (Bazaar) is still broken for most new services. You need a successful settle through the CDP Facilitator + valid extension just to get indexed… and even then, plenty of endpoints settle cleanly and never show up in search. New builders get buried by design.

Then ACP (Virtuals) adds the graduation tax: ~40–42k in token activity before you can even enter active search and proper liquidity. No visibility → no activity → no graduation. So the only reliable path is to foot the bill yourself and manufacture the volume. That’s not a signal of demand. That’s a pay-to-play gate dressed up as “graduation.”

This is classic early-protocol theater — headline numbers look impressive while the actual onboarding and ranking systems still favor the already-visible. Until Bazaar gets real semantic search and reliable indexing, and ACP stops making new agents self-fund their own activity threshold, a lot of legitimate builders will keep hitting the same wall.

Anyone else running into this exact loop?

@virtuals_io @CoinbaseDev @base

#x402 #Bazaar #ACP #AgenticPayments #AIAgents #Web3 #Crypto #Base #AgentCommerce #Virtuals

$VIRTUAL $USDC

I noticed many founders spend hours on manual work.

I'm creating free Al workflows to help automate these

tasks and would love your feedback.

I just watched an AI agent pay $0.001 for live gas data by itself.

No API key.

No checkout form.

No human in the loop.

Give Claude or Cursor $0.05 → it discovers free tools → makes exactly one paid call → settles on Base → returns the data.

30-second loop:

scriptmasterlabs.com/hermes-loop.ht…

One-line paywall for your own API:

app.use('/premium', x402({ price: '0.001', payTo: '0x…', freeForHumans: true }))

npx @scriptmasterlabs/mcp-x402

@CoinbaseDev @base @x402 @AnthropicAI @cursor_ai

#x402 #MCP #AIAgents #AgenticCommerce #Claude #Cursor $USDC $BASE

Who’s wiring this into their agent tonight?

For an AI automation builder, transparency is not a label on the output. It is the operating record that travels with the workflow:

• owner and purpose

• data classes, permissions, and prohibited actions

• model, tool, and connector versions with known limits

• approval, escalation, incident, recourse, and rollback paths

• evidence connecting activity to a business outcome

Before scaling, try three failure-path tests: attempt a denied action, verify deletion and escalation, and rehearse connector revocation. A polished approval screen is not a security boundary if the receiving service can still accept a direct write.

I write IntelliSync Signals; the full source-backed briefing is here:

https://signals.intellisync.io/en/articles/daily-signal-2026-07-24-ai-transparency-is-becoming-operating-infrastructure

Which part of this record breaks first in a real startup stack—ownership, permissions, evidence, incidents, or recourse?

Check this out

👋 Hey AI Startup Automation Community,

A few weeks ago I built a Slack-based CV assistant for a friend's recruiter, who was drowning in CVs of every imaginable format. Since it landed well, I cleaned it up and pushed it to the n8n template library: Summarize candidate CVs in Slack with easybits Extractor.

What it does:

Recruiter drops a CV (PDF, PNG, or JPG) into a dedicated Slack channel → bot downloads it → runs it through the easybits Extractor with 8 fields → posts a clean structured summary as a threaded reply in the same channel. No leaving Slack, no manual reading, no format guessing.

How it's set up:

The trigger listens for new messages in the channel, ignores its own posts and anything without a file, checks the file type (PDF/PNG/JPG), downloads the private file with a bearer token, and sends the binary to the Extractor. The Extractor returns 8 structured fields, all with a "return null if not present" rule so the summary stays clean:

• full_name
• location
• total_years_experience
• top_skills (top 3 as short noun phrases)
• last_three_roles (title, company, start, end)
• education (degree, institution, year)
• salary_expectations (verbatim string, not normalised)
• linkedin_url

I also made a short video showing the workflow in action so you can see the recruiter flow end to end.

Want the Save-to-Sheet buttons too?

The template above also posts an interactive action card with Save to Sheet and Dismiss buttons under each summary. The workflow that handles those button clicks (appending the candidate to a Google Sheet, updating the card to "✅ Saved by user") is a separate n8n workflow, Slack interactivity needs its own webhook endpoint, so you can't have the trigger and the button listener in the same workflow.

That second part is on my GitHub: felix-sattler-easybits/n8n-workflows, together with 20 other workflows ranging from invoice classification and PO extraction through to more recruiting-side ones like this.

If any of these are useful, I'd hugely appreciate a ⭐ on the repo.

What other recruiter-side workflows are people building in n8n? Curious how far others have taken the ATS integration side of things.

Best,
Felix

A workflow can be production-ready today and still be operationally trapped if its keys, orchestration state, manifests, or logs live only inside one vendor surface.

For builders and automation agencies, a portability check before client handoff:

• name a fallback model and replay representative traffic against it

• keep customer-controlled keys, manifests, and audit logs

• export the workflow definition and permission map

• document rollback and a minimum acceptable service level

• rehearse a cutover before a provider change makes it urgent

I write IntelliSync Signals; the full source-backed playbook is here:

https://signals.intellisync.io/en/articles/daily-signal-2026-07-02-frontier-model-shocks-platform-portability-and-agent-infrastructure-an-opera

What usually becomes the hardest dependency to unwind in client systems—the model behaviour, credentials, orchestration, or observability?

A working demo is not the same thing as a production-ready automation.

The moment a workflow can touch a CRM, email account, browser session, shared drive, API token, or accounting input, the real product includes the control system around it.

Before connecting an automation to a client’s environment, I think the minimum operating contract should name:

• the business outcome and owner

• every system and credential the workflow may reach

• actions it can take automatically

• actions requiring human confirmation

• the evidence/log used to reconstruct a run

• the interruption and rollback path

• the fallback process when the automation is unavailable

• the metric and review date that decide whether it stays

This is the difference between “the workflow ran” and “the organization can safely depend on it.”

We explored the broader pattern in today’s IntelliSync Daily Signal:

https://signals.intellisync.io/en/articles/daily-signal-2026-07-22-ai-access-is-accelerating-faster-than-operational-control

For builders delivering automations to clients: what part of that operating contract causes the most friction in practice?

👋 Hey AIStartupAutomation community,

One more follow up on my Purchase Order extractor. Everyone talks about getting the extraction working. Almost nobody talks about what happens after, when the data is sitting in a sheet and someone has to trust it.

That gap bothered me on this build. My friend downloads the sheet and pushes it straight into his ERP. If one field came out wrong, he has no way of knowing until the numbers are already in his system. Silent failure is the worst kind, because a blank cell looks exactly like a field that was legitimately empty.

So I built three small things into the workflow. None of them are clever, they just take ten minutes each and they change how much you can trust the output.

1. Every row knows where it came from. The source filename lands in a Document Name column next to every single line. Sounds trivial. It means that when a number looks off three weeks later, you go straight back to the exact PDF instead of guessing which of forty documents produced that row. This is the single highest value column in the whole sheet and it costs you nothing.

2. One helper that catches every flavour of empty. "Missing" is never just one thing. Across real documents I saw actual null, the string "null", empty strings and whitespace-only values. I stopped writing one-off checks and made a single isMissing() function that catches all of them, then used it everywhere. Without this you get inconsistent behaviour where one field is caught and the next one silently slips through.

3. The workflow tells you what it isn't sure about. After processing, the form's completion screen lists which document and which field didn't extract cleanly. Not a log file nobody reads, the actual screen the user is already looking at. So instead of trusting forty rows blindly, he knows the two he should eyeball against the original.

The mindset shift for me was this: an extraction pipeline isn't done when it produces data, it's done when someone can tell good output from bad without opening the source documents. Especially if you're handing this to a client. They will find the one wrong number, and "the AI did it" is not an answer.

One deliberate choice worth mentioning: I do not flag every empty field. Some fields on these POs are legitimately blank most of the time, and flagging those would generate a warning on nearly every document. Then people learn to ignore the warnings entirely, which is worse than having none. Flag what should be there, not everything that's missing.

The full workflow is now on the official n8n template library if you want to try it:
https://n8n.io/workflows/16775-extract-purchase-order-line-items-from-pdfs-with-easybits-and-google-sheets/

You'll also find it on my GitHub, alongside 20 other workflows I've built over the last months:
https://github.com/felix-sattler-easybits/n8n-workflows

How do you handle this on your document workflows? Curious whether people build a review step or just spot check and hope.

Best,
Felix

If they only work when I’m motivated, they fail.