The data isn’t gone. It’s sitting somewhere you can’t really use anymore.

Here are five setups from emails I’ve answered recently, with details changed so nobody’s identifiable.

The export that stops rotting

Many users now send their Hexomatic workflow results straight into their Second Brain database instead of downloading another CSV.

Same data, completely different shelf life. Refreshed on a schedule, it’s still answering questions in November. And sitting next to your contacts it answers better ones than it could alone. Which of these have I already emailed. Which are already paying me. Which did I talk to last year and never follow up.

If you have a Hexomatic account you already have the raw material for this. It’s usually the fastest first import.

Half a million rows nobody could open

One user had a company database of over half a million rows. Size, financials, category, contacts, websites. The spreadsheet took a minute to open and froze on anything real.

He owned that data and couldn’t touch it.

Uploading it to an AI doesn’t solve it either. Half a million rows won’t fit in a context window, so the AI ends up working with pieces of the dataset instead of querying the whole thing.

As a table it’s a different object. Filter by size and revenue in one pass. Pull every company in a category with no website. Then match the whole list against his own contacts, so his team stops cold-emailing people who are already clients.

Thirty gigabytes, no system

Another one wanted nothing to do with connectors. No Notion, no Stripe, no calendar. Just a folder on his laptop, about thirty gigs, years of contracts and proposals piled up. Can I work with this?

Easiest setup there is. Point it at the top folder and it walks everything underneath. PDF, DOCX, TXT, MD, RTF.

Files come in as indexed sections rather than whole documents. Once it’s indexed, the AI can search across thirty gigs without loading thirty gigs into the conversation. Ask about one clause from 2022 and you get the paragraphs back, not the file.

He hadn’t thought about the comparison side. Which agreements still carry a clause you stopped offering. Which proposals promised something you don’t do anymore. By hand that’s a week, so you won’t do it.

What this saves in AI context

When you hand an AI large files directly, the content has to be processed and pushed into the model’s context. Start a new session and much of that work happens again.

Second Brain indexes the material once, at import. After that a query retrieves the six sections that matter instead of two hundred pages. You pay for the reading once instead of every session, and the gap widens as your archive grows.

Five years of research

One setup has nothing to do with customers.

A user is studying a language and had the same problem in a different shape. Vocabulary in a spreadsheet, phrases in a notebook, a few apps that each track part of it. He wanted an honest count of what he actually knew and how it was moving.

We ended up building the Learn section because enough people described that exact thing. The same idea applies to any long project you’ve been accumulating for years. Papers, notes, drafts, interviews. Finding one file was never the hard part. Asking a question across all of them is.

Not betting on one AI vendor

Someone asked whether he could use it with local models instead of depending on one hosted AI company.

That’s the point of keeping the database local and connecting over MCP. Your data sits on your machine and compatible tools query it. You’re not building your knowledge base inside one vendor’s account, which also means it doesn’t depend on us being here.

Start with one pile

Don’t organize everything first. That’s another project you’ll never start.

Nobody above did that. They each picked the thing that had been bothering them and imported that one thing.

The export from March. The spreadsheet that won’t open. The folder of contracts.

Download at brain.hexact.io. Seven days free trial, no card required.

Import one source, then ask it something you couldn’t answer this morning. If you import a customer export, start here: which customers paid me last year and have gone quiet this year.

Want help setting it up?

Book a call and we’ll go through it together on your machine. Your imports, your order, and the questions worth asking once it’s in. Nothing leaves your computer, including during the call.

Here is the part people miss. Google does not just show a page’s meta description. It builds the snippet from the part of the page that matches your query. Google rewrites the author-written description in roughly 60 percent of results. Which means your keyword decides what text comes back.

Search “Nike Air Max 90” and the snippet describes the shoe. Search “Nike Air Max 90 price” and the snippet tends to contain a dollar figure. Same page, different extraction, because you told Google which part of the page you cared about.

That flips the workflow. Instead of scraping a source to get a phone number, you write a keyword that forces the phone number into the snippet, then scrape the search results.

One keyword is a search. Four hundred keywords is a database.

This is the part that makes it worth doing, and it is the part most people skip.

Running one query in Google and copying the answer is not automation. The unlock is that a keyword is a template with variables in it, and every variable has a list behind it. Geography. Job title. Product model. Company name. Zip code. School district. Multiply the lists together and one afternoon of setup produces thousands of rows.

[niche] [city] phone number is not one keyword. With 40 niches and 25 cities it is 1,000 keywords. At 10 results each that is 10,000 rows, and every row has a position, a title, a description and a URL.

In Hexomatic you paste all of them into Data input, one per line, and add the Google Search automation once. The workflow does not care whether the list has 5 rows or 5,000. That is the whole difference between searching and extracting.

Two things make the batch usable instead of messy:

Keep the pattern identical across the batch. Same word order, same field words, same operators, only the variable changes. Google generates snippets per query, so consistent queries return consistent snippet shapes, and consistent shapes are what let you parse 10,000 rows with one rule instead of ten.

The 10 hacks

Each one below is a pattern. The bracketed part is your list.

1. Public sector contacts without building a directory scraper

School districts, municipalities and universities publish staff contacts on hundreds of small pages that all look different. Building one recipe per district is a waste.

Pattern: [district name] school principal phone email, [county] school district superintendent contact phone, site:.k12.fl.us [district] principal email

Multiply by: every district or county in your target states. Florida alone gives you 67 counties. Add three phrasings and you are running 201 keywords for full coverage of one state.

2. Email addresses at a specific company

Pattern: "@[domain]" email, site:[domain] "@[domain]", [domain] contact email sales

Multiply by: your entire target account list. If you have 500 companies, that is 500 keywords and one run.

3. Decision makers by title

LinkedIn public profiles put name, title and company in the page title, and Google keeps that structure.

Pattern: site:linkedin.com/in "[title]" "[industry]" [state]

Multiply by: titles times industries times states. Five titles, four industries, ten states is 200 keywords. You are reading the title column here, not the description. No login, no profile visits.

4. Job vacancies with the salary band

Job boards are protected. Their listing pages are heavily indexed.

Pattern: site:linkedin.com/jobs [role] [city] salary, "[role]" salary posted 2026, site:indeed.com [role] [city] pay

Multiply by: roles times cities. Thirty roles across twenty metros is 600 keywords, and you can rerun the same batch weekly to watch hiring volume move.

5. Real estate prices and specs

Portal snippets are almost always the listing spec line, because that is what the page leads with.

Pattern: [bedrooms] bedroom house for sale [city] price, site:zillow.com condo [neighborhood] price sqft, apartment for rent [zip] 2 bed monthly rent

Multiply by: zip codes. A metro area is 40 to 150 zips. Cross it with property type and bedroom count and one market is a few hundred keywords.

6. Product prices, including resale

Pattern: [model] price, site:stockx.com [model] last sale price, "[model]" price size [n] in stock

Multiply by: your model list. Six hundred sneaker SKUs is 600 keywords and gives you a price sheet across every retailer that ranks, in one run.

7. Software and service pricing

Pattern: [tool] pricing per month, [tool] plans price starting at

Multiply by: your competitor set, run monthly. Small batch, high value, because pricing pages are short and their snippets are unusually clean.

8. Local business phone lists

The Maps scraper is better when you need the full record. When you need a name and a number, search is faster and cheaper.

Pattern: [niche] [city] phone number, [niche] [zip] phone, [niche] [city] contact number

Multiply by: niches times zip codes. This is the pattern that scales the hardest, and the one worth starting with if you want to see the volume for yourself.

9. Company firmographics

Pattern: site:crunchbase.com [industry] companies founded employees, "[company]" founded headquarters employees

Multiply by: your account list or your industry list. Enough to size and rank a target list before you spend anything enriching it.

10. Dates: tenders, deadlines, events, filings

Pattern: [category] application deadline 2026 [state], RFP submission deadline [industry] 2026, site:eventbrite.com [industry] conference 2026 date

Multiply by: categories times states, on a weekly schedule. This one is worth running repeatedly rather than once, because the value is in catching the new posting, not in the archive.

What comes back, and what to do with it

The run gives you four columns per result: position, title, description and URL. The description is where your field lives. The title carries names and roles. The URL tells you the source and lets you dedupe.

Ten thousand rows of that is not something you work by hand, so there are two ways to finish the job.

AI parsing on the description column. Phone numbers, prices, dates and salary ranges are regex-friendly, and if the format varies too much, chain an AI automation and tell it which fields to pull into which columns. One instruction applied across the batch. The one rule that matters: tell it to leave the field empty when the snippet does not contain the value. A dataset that looks complete but is invented in 400 places costs you more than a dataset with gaps.

Send it to Second Brain (brain.hexact.io). Export to CSV and you get a file that ages. Push the results into Second Brain and you get a local database you can query, which changes what the data is for.

Instead of filtering a spreadsheet by the columns you defined at export time, you ask questions after the fact. Which zips came back with the fewest results, meaning underserved markets. Which models moved on price since last month. Which districts still have no email on record. Which roles a company started posting that they were not posting in March.

That last one only works because Second Brain keeps every run with a timestamp. A single batch tells you what exists. The same batch run monthly tells you what changed, and what changed is where the money usually is. Nobody sells against a snapshot.

Where this breaks

I am not going to pretend this replaces everything.

Snippets are capped at roughly 160 characters. You get short fields. A phone number, a price, a date, a salary range, a title. You do not get a full product spec table or a complete staff directory.

Snippets are generated per query, so wording changes results. If the field is not coming back, add the field name to the keyword. phone, email, price, salary, deadline, address. Those words are what Google matches against.

Coverage is partial. You get the districts, listings and products that rank. For exhaustive datasets you still need the source.

The rule I use: if the field is short and the page states it plainly, search it in batch. If you need the full structured record, scrape the source. Most people build the scraper first and never test whether the shortcut would have worked. Test the shortcut first. Twenty keywords takes four minutes.

Run it yourself. First test a sample keyword in Google to make sure it shows what you need. Then create a new workflow in Hexomatic (hexomatic.com), paste the list of keywords into Data input, and add the Google Search Scraper.

Or book the Concierge Service and start with working output instead of setup.

Claude answered. The answer sounded perfectly reasonable. It was also wrong.

Claude did not have the information, so it did what AI does in that situation. It filled in the blanks.

Which is the exact problem we built Second Brain to solve.

Months of telling people to put their important information somewhere AI can query it, and we had never done it for our own product.

That is fixed now. The documentation lives inside the Second Brain knowledge base. It installs with the app and stays updated with it.

Connect Claude to Hexact (Second Brain) desktop app and you can ask:

• What file formats can I import?

• How many files can I import at once?

• What happens if a file is unsupported?

• How does OCR work?

• Where is my database stored?

• How do Stripe subscriptions connect to contacts?

• What does Database Cleanup actually do?

Claude does not guess and does not search the web. It reads the documentation for the version of Second Brain you are actually running.

The better questions are not documentation questions.

Say you have four years of contracts, a Stripe export, a Gmail archive, and several folders of project files waiting to go in. Instead of working out the order yourself, ask:

“What should I import first, and is there anything I should organize before I start?”

Claude answers from how the app actually behaves, including the limits you are about to hit.

The same gap shows up in your business

Say you have three Claude chats open. One on pricing. One drafting an email campaign. One going through customer data.

In the pricing chat, you decide to move a promotion to September.

An hour later, the email chat needs the promotion dates. Does it know what you decided?

No.

So you tell it again, or paste from the other chat, or keep a document updated and remember to attach it every time.

That is the same failure as Claude guessing about tagging. The information exists. There is nowhere for Claude to look it up.

A database is somewhere to look.

Tell the pricing chat to write the decision to Second Brain, and the email chat can query it an hour later. Claude gets three tools through MCP: read the schema, run a query, write rows back. The third one is what makes the first two worth anything across sessions.

Need one customer’s history? It queries that customer’s records. Need a clause out of a 200-page contract? It pulls the sections that mention it instead of loading the file. Need to know how OCR works? Now it queries that too.

Claude does not need to know everything. It needs one place to look.

This gets harder the more data you have

Files in a Claude Project work fine, until there are enough of them that you start deciding what deserves to be in there. At that point you are curating context instead of working.

Second Brain holds years of information and Claude takes only what the current question needs.

No reloading the same files. No paying tokens to reread them. No carrying decisions between conversations by hand.

One database, any number of sessions, the same source of truth for all of them.

Try it

Update or download Second Brain at brain.hexact.io. There is a 7-day trial and no credit card required.

Connect Claude and ask:

“What can I import into Second Brain, and what are the import limits?”

The answer now comes from Second Brain itself.

Then ask it the question you actually care about. The one about your imports, your data, your setup.

If the answer does not cover it, book a video call and ask me instead. You get a real answer, and I find out what is missing from the documentation and add it.

We spent months telling people to put their knowledge into Second Brain. Eventually, we remembered to put ours there too.

Import runs OCR now. Scanned files go in as searchable text with everything else.

How it works

The Knowledge files import card checks your machine and shows a label. OCR ready means scanned PDFs get read during import. Drop them in with the rest. No separate step, no converting first.

The recovered text is treated like any other document. Split into ordered sections, stored as rows, tagged, every word indexed. A 60-page scanned lease behaves like a typed one. Ask about the termination clause and Claude pulls the sections that mention it.

Reading them costs you nothing in tokens

Recognition runs through the OCR built into your operating system. Nothing is uploaded to be read. The scan is processed on your machine and the text goes into your local database.

The alternative is handing each page to an AI as an image. A full page runs well over a thousand tokens before a word of the answer is written, and you pay it again every session, because nothing was stored. Two hundred pages of contracts is a real bill for the privilege of turning a folder into text.

Here the reading happens once, at import. What sits in the database afterward is text, and a query pulls the few sections that matched instead of the pages.

What your machine needs

• macOS 10.15 Catalina or later

• Windows 10 or later. Windows may need a language pack. Settings, then Time & language, then Language & region.

• If your system does not qualify you see OCR unavailable. The rest of the app works as before.

Two limits worth knowing before you point this at an archive. Recognition covers the languages your system supports, and a page in a language your OS cannot read comes back as noise. Printed type on a clean scan lands past 99 percent of characters. Handwriting, faded fax paper and photos shot at an angle can produce errors.

International text stops arriving broken

Detection for non UTF-8 documents is better in this build. Windows-1251, Shift-JIS, the ISO-8859 family and others get identified more reliably at import, so the text goes in readable and comes back searchable.

Also in this release

• Higher import limits.

• Tags apply consistently across a batch and stay searchable next to the content.

• An interface pass, plus better help texts.

Try it on your mixed folder

Update at brain.hexact.io. Free download, 7-day trial, no card.

Find the scanned documents you gave up on, the old client contracts, and drop the folder on the Import page. Confirm it says OCR ready, run the import, then ask Claude about a clause.

Migrating an archive, or running several businesses off one database? Book a call and I will tell you what to import first.

Choose a folder, including everything inside it

There is a Choose folder button now. Pick your top-level folder and Second Brain walks every subfolder underneath and imports what it finds. Nothing to flatten first.

The ceiling for one batch is 100,000 files. I do not expect anyone to hit it. It is there so you stop thinking about the limit. Same for a zip: one zip, nested folders inside, same count. Each file up to 2048 MB. Picking files by hand is still capped at 100, since that path is for when you want three specific documents.

Formats: .docx, .pdf, .txt, .md, .rtf. A zip inside a zip or folder gets skipped.

What happens to them

Each file becomes an entry. The text splits into ordered sections, stored as rows and indexed for search. Tags go on at import, all of them searchable.

So a 40,000-file import is not 40,000 files Claude opens. Ask about a clause in a 2022 contract and it pulls the sections that mention it. How much you imported stops mattering, because you never load all of it.

A one-minute tour

The other thing people got stuck on was the first screen after install. Dashboard, Import, Contacts, Settings, Database maintenance, a connection to Claude, and none of it explains itself.

There is now a tour that walks you through the menus and what each does. About a minute, on the real interface, so by the end you have already touched the pages you will use.

Where to start

Free. Download at brain.hexact.io. 7-day trial, no card. Take the tour, then point the folder picker at your messiest documents folder and ask Claude something you could not answer before.

Annual. Keeping it? The annual plan is discounted against monthly and locks the price for the term. Grab now!

Book a call. Several businesses, a shared knowledge base, or an import that looks complicated? Book a call. I will look at your setup and tell you what to import first.

Everything imports into one local SQL database on your machine. Contacts become rows with columns. Stripe and PayPal history becomes transactions tied to those contacts. Calendar events link to participants by email. Word docs and PDFs go in the same database, not as files sitting in a folder. Nothing is a note about a customer. It is the customer, with keys the software understands.

Claude reaches it through MCP with three tools: read the schema, run a query, write rows back. It sees what tables exist, writes SQL, gets the matching rows. When you want the output kept, it writes into the same tables, so the next session starts where the last one finished.

That means you can ask things nobody built a report for. Which customers paid in 2025 and went quiet in 2026. Who I met with last quarter that never bought anything. What one contact has spent across every payment method I have used.

Documents work the same way

Import splits each file into ordered sections with headings, stores them as rows, tags them, and indexes every word for search. So a 200-page manuscript is not one blob you have to load. It is a few hundred sections Claude can address one by one. It searches for the term, pulls the six sections that contain it, and reads those. Fifty documents behave the same as one, because you are pulling paragraphs instead of files.

What Cowork does

Cowork points Claude at a folder. You describe an outcome and it plans the steps, opens files, runs code, writes results.

Two costs come with that design. Everything it reads passes through the context window, so a question touching thirty files costs thirty files worth of tokens even when the answer lived in one of them. And when it writes, it writes files. It can edit a document or produce a new one, but there is no record underneath to update, so the next session opens everything again instead of picking up where the last one stopped. Second Brain writes rows. Ask it to tag fifty contacts and those tags are in the table, ready for the next query, and no file was rewritten to get there.

Where they split

Selection. A query has a WHERE clause. It returns every matching row and nothing else. An agent reading a folder has to decide which files to open and what to skip once the material outgrows the context window. You never see that decision, and it sets your answer.

Consistency. Same query, same rows, next month too. A pass through a folder depends on phrasing, on what turned up first, on where the window filled. Ask twice, get two answers, both confident.

Tokens. SQL counts, filters, and joins before Claude sees anything. Ask which clients went quiet and it reads 12 rows, not 40,000. The database can be gigabytes while the answer stays a few hundred tokens.

Speed. An index walks straight to matching rows instead of scanning the table. A lookup across 100,000 words lands in milliseconds.

Location. Cowork sessions run on Anthropic’s servers, and local files opened through the desktop app are processed there too. Second Brain keeps the database on your machine. Only the rows a query returned leave it.

I am not going to tell you which one you need. Look at the questions you actually ask in a normal week and judge for yourself which shape they fit. I know my answer for my data. Yours may land somewhere else.

What shipped

Importing files. Better tagging, clearer messages, and faster.

More you can bring in. You can now import emails from a CSV. CSV files from your computer can be up to 32 GB.

Database Cleanup. It now repairs broken links between records and tells you what it fixed.

Faster. Dashboard and Import load noticeably quicker, and Contacts has more ways to filter.

Plus a long list of smaller interface and performance work across settings, contacts, and the import page.

Update at brain.hexact.io.

If you have not imported anything yet, start with one thing. Export your contacts to CSV, or pull the zip from Google Calendar settings, and drop it on the Import page. Then ask Claude something you could not ask before. One query explains this better than I can in writing.

If you want me to walk you through it, book a call. I will show you what to import first for your use case and where the useful queries come from. I run these myself, so bring the hard questions.

Then you ask it a question. “Which of my clients haven’t ordered since January?” Silence. “Which customers are also newsletter subscribers?” Nothing. Your second brain has all the pieces of the answer scattered across pages, and no way to put them together.

That’s not a criticism of Notion or Obsidian. They were never built to answer questions. They were built to store notes. The problem is that “second brain” now describes two completely different things, and most people only know one of them.

What note tools actually are

Notion, Obsidian, Evernote, Logseq. These are filing systems. You write, you link, you tag, you organize. When you need something back, you search for it and read it yourself.

They’re genuinely good at this. If you’re drafting, journaling, building a team wiki, or connecting ideas while you think, a note tool is the right choice. I’m not going to pretend otherwise.

But look at what they store: documents. A note about a customer is a page of prose. The customer’s payment history is a bullet list someone typed three months ago, if anyone typed it at all. There are no records. No fields. No relationships the software understands. A backlink between two notes says “these are related” and nothing more. Related how? Since when? The link doesn’t know.

So when AI plugins arrived for these tools, they inherited the problem. The plugin embeds your notes, finds the chunks that look similar to your question, and summarizes them. Ask “how much did this client pay me last year” and it finds notes that mention the client and money. It doesn’t count anything. It can’t. There’s nothing to count. There’s only text about numbers.

The “just upload it to Claude” path

The other approach people try: skip the note tool, dump your files straight into Claude or ChatGPT. Upload the spreadsheets, upload the documents, ask questions.

For small inputs, this works. A page of text, a few hundred rows. I’ve written about where it breaks in one of my previous articles, so here’s the short version.

Every model has a context window, a hard limit on how much it holds at once. When your files exceed it, the tool doesn’t tell you. It chunks your material, guesses which chunks matter for your question, pulls in the top few, and skips the rest. You never see the selection. When the skipped part was the part that mattered, you get a confident answer built on half your data.

I hit this twice in ways that cost me real time.

First, contacts. Customers in one spreadsheet, leads in another, investors in a third, plus an old CRM export. Different columns, inconsistent formats, duplicates everywhere. Thousands of rows total. I asked AI to merge them. It truncated data, invented entries, and delivered a partial result while acting finished.

Second, my book. 211 pages that needed proofreading. Claude did solid work on the first 20 or 30 pages, then lost track of tone decisions from earlier chapters, hit its output limit, and every “continue” made it worse. Maybe 15% of the book got reviewed, inconsistently.

The AI had the skills for both jobs. The input was in a shape it couldn’t work with. That’s the pattern: uploading files gives AI your data as a pile of text, and a system you can’t see decides which parts to read.

What changes with Second Brain by Hexact

This is the gap we built our product around. It takes a different route: your data lives in a SQL database on your machine. Contacts, payments, subscribers, meetings, notes, scraped pages. Each one a row in a table, each field a column. Claude (or Codex) connects to it through MCP and does something neither a note tool nor a file upload allows: it writes a query and gets back every record that matches.

Ask which customers are two payments behind, and you get every customer who’s two payments behind. Not a summary of the ones the retrieval step decided were worth mentioning. All of them. No hidden ranking, no silent truncation. One approach skims and hopes. The other looks it up.

Both of my failed cases became routine. The contact mess got normalized into one schema, and deduplication became a matching rule on email and phone instead of a guess. The book became 211 rows of clean text, and I could run checks like “list every sentence longer than 35 words” across the whole thing with the same quality on page 211 as on page 1.

The same pattern covers technical documentation, and this one hits engineers and support teams daily. A 400-page equipment manual, an API reference, a compliance handbook, a folder of product spec PDFs. Upload one to a chat and ask "what are the torque specs for this model," and you're trusting the retrieval step to have picked the right chunk out of hundreds of pages. Sometimes it does. Sometimes it answers from the wrong model's section and sounds equally confident. Import the PDFs into Second Brain instead, up to 200+ documents at once through a single ZIP, and every page becomes a searchable record. Now "find every section that mentions this error code," "which procedures require certified personnel," or "compare the safety requirements across these three manuals" run against the full text, not a sample of it. And the answer cites records you can open and verify, instead of a paragraph you have to take on faith.

But exact retrieval is only half of it. The bigger half is joins.

Your data isn’t a pile of separate facts. It’s connected. The person who pays you on Stripe is the same person who reads your Substack newsletter and sat in that Google Calendar meeting last Tuesday. In Notion, those are three unrelated pages, if they exist at all. In a chat upload, they’re three files the model may or may not cross-reference. In a database, they’re linked records, and the questions that cross them are just SQL.

Three examples from my own use:

The newsletter question. For years I couldn’t answer whether people subscribe to my newsletter first and become customers later, or the other way around, and how long the gap is. No single tool has this. Substack knows opens. Stripe knows charges. Once both datasets sat in the same database, I asked the question and had the answer in about five seconds.

The calendar question. Google Calendar knows who I met with. Stripe knows who paid. Neither knows both. With calendar events imported and linked to contacts, I can ask “who did I meet with in the last six months who never became a customer” and get a follow-up list, or “how many meetings did it take before this deal closed” and see the actual count. Your calendar quietly holds your entire sales effort. Alone it’s a schedule. Joined with payments, it’s a conversion report.

The pre-call briefing. Before an important call, I ask Claude “what do I know about this person?” It pulls payment history, subscription status, newsletter engagement, past meetings, and my saved notes into one answer. The CRM alone would have shown me a name and a deal stage. The database shows me that their payment failed twice last month and they unsubscribed from the newsletter. I walk into the call prepared instead of guessing.

No note tool answers these. No file upload answers these reliably. They’re join questions, and joins need a database.

The part nobody else has: data that updates itself

Here’s the limitation shared by every option above, including a static database. The data is only as fresh as your last manual effort. Your Notion notes describe last quarter. Your uploaded CSV was accurate the day you exported it. A lead list is dead the day you download it.

This is where Hexomatic changes the equation. So Hexact (Second Brain) connects to Hexomatic directly, and scraping workflows feed the database automatically while the app runs. No exports, no file management, no pasting into chat.

Set the Google Maps Scraper on a monthly schedule per territory, and every run lands timestamped in your database. “Who’s new in this zip code since last month” becomes a query. So does “who crossed 100 reviews this quarter.” Chain the Page Content Extractor and Emails Scraper off the filtered list, and the unstructured parts, what businesses say about themselves on their own sites, become queryable too.

Same with competitor pricing. A scheduled workflow scrapes competitor pricing pages, the results flow in, and “which competitors raised prices this quarter” gets answered from actual timestamped pages, not from the model’s training data.

One scrape is a snapshot. Months of scheduled scrapes are a dataset that shows movement. That’s the compound effect: your internal records and live market data in one place, and Claude reasoning across both. Which customers in a region haven’t been contacted since a competitor changed pricing there. That question touches your CRM and the outside world at once, and it only works when both live in the same database.

No note tool does this. No chat upload does this. This is the combination.

Honest boundaries

Where the other tools win, they win clearly.

Use Obsidian or Notion when the work is writing and thinking. Drafts, wikis, freeform idea capture. Second Brain is not a writing environment and doesn’t try to be.

Upload files directly to Claude when the job is small and one-off. A single document to summarize, a few hundred rows to clean. Setting up a database for that is overkill.

Use Hexact (Second Brain) when your questions sound like database queries. Counting, filtering, comparing across time, joining sources. The moment your question has a “which,” a “how many,” or a “since when” in it, text storage stops being enough.

The second signal is repetition. Customers, leads, a knowledge base, a market you track. That data grows and changes every week. Uploading it to a chat means starting from zero each time. In a database, nothing resets. Every question runs against everything you accumulated before it.

And it works in both directions. Claude doesn’t just read your database, it writes to it. After a call, say “save this note about the client” and it lands on the contact’s record. Paste a meeting summary, it’s saved. Tell it “this person is now CEO of Acme Corp” and the record updates. No forms, no dropdowns. And writes happen only when you ask. Nothing changes on its own.

You don’t have to abandon your notes to get there. Second Brain imports from Notion, Obsidian, Evernote, Logseq, and OneNote, alongside Stripe, PayPal, Google Contacts, Google Calendar, Substack, email archives, and CSV exports from any CRM. 21 sources total. Your note tool becomes one input among many.

Where to start

Self-serve. Download Hexact Second Brain at brain.hexact.io, 7-day free trial, no card. Import one thing you already paste into AI by hand, a contacts export, your Stripe transactions, your Notion workspace. Connect Claude and ask a join question you’ve never been able to answer. That’s usually the moment the difference stops being theory.

Concierge. Already scraping with Hexomatic, or want to? Send us your brief and our team wires the full pipeline, workflows to database to first queries, through the Concierge Service.

Enterprise. Multiple businesses, multiple territories, a shared knowledge base your whole team queries? Book a call and we’ll architect it end to end.

Here’s what actually happens next. The CSV lands in a spreadsheet. Someone works the top 30 rows. The rest sits there. Three months later the file is stale, half the effort is wasted, and you scrape again from zero.

That’s not a scraping problem. Collection is a commodity now. The Maps Scraper pulls up to 700 listings per keyword, and your competitor can run the same workflow this afternoon. The edge has moved to two things almost nobody does: qualification the spreadsheet can’t do, and timing the spreadsheet can’t see.

The collection pass, in one paragraph

For completeness, because the basics still decide data quality: cover the territory with keyword variations (zip codes, neighborhoods, language variants where the market has them), dedupe by business name if you want unique brands or by address if you want every branch, and cut the list hard before enriching. Review count and website presence are the fastest filters. When we scraped 19,044 local listings for a study, businesses with websites averaged 4.70 stars and 226 reviews against 4.48 and 18 for those without. A website is a proxy for a business developed enough to buy from you, unless no website is exactly the problem you solve. I’ve covered the full collection setup in previous articles, so this piece starts where those ended: with data in hand.

A spreadsheet only answers yesterday’s questions

Here’s the limitation nobody names. A spreadsheet answers the questions you defined at export time. Columns for rating, reviews, website. Sort, filter, done.

But real qualification questions come up while you’re selling, and they don’t map to columns:

• “Which of these have 50+ reviews but no website?” Proven demand, weak digital presence. If you sell websites, marketing, or booking software, that’s not a list, that’s a queue.

• “Who’s busy but slipping? Rank by review count, flag everything under 4 stars.” Visible, high-volume, underperforming. They feel the problem before you call.

• “Which ones mention emergency or same-day service on their sites?” That answer isn’t in any Maps field. It’s in the page text.

• “Which describe themselves as family-owned?” Changes the first line of your email.

• “What service does nobody in this zip code offer?” That’s a market gap, and it’s sitting in the same dataset.

The first two you could brute-force with filters. The rest live in unstructured text: what businesses say about themselves on their own sites. So the enrichment pass matters, and it’s short: chain Page Content Extractor (https://hexomatic.com/automation/get-page-content), Emails Scraper, and Social Links Extractor off your filtered list. The listing told you who exists. The website tells you whether they’re worth your time.

Then send it all into Second Brain instead of a spreadsheet. Connected to Hexomatic, the data flows into your local database as it’s collected, timestamped, tied to the workflow. And now Claude can answer every question above, including the ones you think of mid-campaign, because the database feeds it the right slice instead of you pasting 200 pages into a chat window and watching it silently drop half of them. I’ve written about why that happens; the short version is that models have a hard limit on what they hold at once, and a local database is how you work around it instead of pretending it isn’t there.

Two rules keep the dataset trustworthy. First, Claude structures only what’s in the data: no email on the page means an empty field, not an invented one. A list that looks complete but is wrong in ten places costs you mid-call. Second, write your qualification criteria into a Skill once, and every future batch gets judged by the same standard without re-explaining.

Timing is the part nobody automates

Every sales manager knows the difference between a cold call and a well-timed one. What almost nobody does with local data is systematize the timing.

Local markets move constantly. New businesses open. Ratings slip. A shop crosses 100 reviews. Somebody finally launches a website. Each of these is a buying moment for someone:

• A new business in your territory needs everything: signage, POS, insurance, cleaning, marketing. Whoever calls first wins.

• A rating dropping below 4 means an owner who just started caring about reputation.

• A review count doubling means an operation growing faster than its systems.

None of this is visible in a one-time scrape. It’s visible in the difference between scrapes. Put the collection workflow on a monthly schedule per territory, and Second Brain keeps every run with timestamps. “Who’s new in 33139 since last month” becomes a query. So does “who crossed 100 reviews this quarter” and “who launched a website since our last pass.”

That’s the real upgrade this article is about. A list tells you who exists. A pipeline tells you who to call this week, and why. Same data source, entirely different sales motion.

What this looks like in practice

One monthly Hexomatic workflow per territory: Google Maps Scraper, then Page Content Extractor and Emails Scraper on the survivors of your filters. Second Brain holding the history. A Skill holding your definition of a good lead. And a Monday routine that starts with one question to Claude: “What changed in my territories since last month, and who should I contact first?”

That’s the whole system. No dashboard to build, no CRM migration, no analyst to hire. Which is exactly why it fits a small or medium business: the parts you’d normally pay a team for, the watching, the comparing, the qualifying, run on their own.

Self-serve. Build it in Hexomatic, connect it to Second Brain (brain.hexact.io), and start with one territory and one niche.

Concierge. Or send us the brief: your niche, your geography, your definition of a good lead. Our team builds the workflows and wires the pipeline through the Concierge Service, and you start with working output instead of setup.

Enterprise. Running many territories on a schedule, feeding your CRM and a shared knowledge base your whole team queries? Book a call and we’ll architect it end to end.

It wasn’t being lazy. The truth is more annoying than that. It can’t actually hold everything you give it at once, so behind the scenes it picks. It decides which chunks of your files look relevant to this question, summarizes some of them, ranks what it thinks matters, and leaves the rest out of the answer. All of that happens without you. You don’t get to say “use this part, ignore that one.” The system makes that call for you, every time, and sometimes it calls it wrong.

You’ve felt this even if you never thought about why. You correct the AI on one thing, it fixes that, and in the same breath it drops something it had right a minute ago. You add more context to help, and now it’s juggling so much that it starts missing the parts you actually cared about. It takes one, it misses another. Projects, custom instructions, uploaded files, none of it removes the fact that a system you can’t see is deciding what to pay attention to.

For casual stuff, fine. When the part it skipped is the part that mattered, a customer’s payment status, the one instruction you were clear about, the detail your whole question hinged on, it stops being cute. You’re making decisions on an answer that quietly left things out.

This is the problem Second Brain (brain.hexact.io) was built around, and the fix is boring in the best way.

Second Brain (brain.hexact.io) doesn’t turn your world into a pile of text that the AI then skims and summarizes. It keeps your data as real records in a real database on your machine. Your customers, payments, subscribers, meetings, notes, each as actual rows. When you ask a question, the AI writes a query and pulls the exact records that match it. If you ask which customers are two payments behind, it gets every customer who’s two payments behind. Not a summary of the ones it decided were worth mentioning. All of them. There’s no hidden step ranking your own data and throwing half of it out before you see the answer.

That’s the difference that’s hard to feel until you have it. With a project full of files, the AI is guessing which parts of your stuff to look at. With a database, it’s reading the parts your question actually points to. One skims and hopes. The other looks it up.

And because it’s a real store that you own, two more things fall out of it. It’s complete and it stays. Nothing you put in gets quietly compressed into a sentence and lost. Week one it knows a little. A few months in it’s the most useful thing you’ve got, because everything is still there, still exact, still yours to query. You decide what goes in, you can see all of it, and when the AI answers you know it read the real records, not a sketch it wrote about them.

Here’s when it clicks for people on my calls. It’s the moment they stop trying to write the perfect project description to make the AI behave, and realize the problem was never their wording. The AI was always going to decide what to use. Once the data lives somewhere it has to look things up instead of guess, that whole fight disappears.

Grab Second Brain at brain.hexact.io, 7-day free trial, no card. Import one thing you already have, a contacts export, your Stripe transactions, bank statements, last year’s calendar. Ask it a specific question with a specific answer, like which clients haven’t ordered since January, the kind of thing a project full of files always gets almost right. Watch it return the actual list instead of a confident maybe. That’s the moment the difference stops being theory.

Why this happens, in plain terms. A model can only look at so much text at one time. That limit is called the context window. When your projects, files, and saved memory add up to more than fits, the tool works around it by breaking your material into chunks, guessing which chunks matter for your question, pulling in the top few, and shortening or skipping the rest. Often the guess is good. The catch is that you never see it, so when something relevant gets left out, you have no way to know. A database works differently. Through MCP, the AI runs an exact query against your records and gets back every row that matches, with no guess about what's relevant. It also works both ways. The AI can read from your records and write back to them, so a summary it produces or a note you dictate after a call gets saved into the database instead of vanishing when you close the chat. Your data stays whole and you stay in control of it. That's what Second Brain runs on.

Your competitors publish more about themselves than they realize. Prices, product changes, hiring, positioning, the language they use to sell, the documents they leave on their own servers. It’s all sitting on the public web. The companies that win the game aren’t the ones with secret information. They’re the ones who collect the public information systematically while everyone else checks a few tabs by hand and calls it research.

Web scraping is how you do that at scale. Done well, it turns scattered public data into a structured view you can actually act on. Done badly, it turns into a pile of broken templates you spend more time fixing than using.

This is the full picture: the use cases worth your time, and the two ways to deliver them depending on the job. We run both, and most of this article is about helping you pick the right one before you build anything.

The research use cases that matter most

Scraping earns its place when the manual version is too slow, too shallow, or too easy to skip. These are the jobs where it consistently pays off.

Competitor monitoring. Track pricing, product availability, new features, and messaging across the competitors you care about. The point isn’t a one-time snapshot. It’s noticing the day a rival changes a price, drops a product, or rewrites how they pitch themselves, while it still matters.

Lead generation and prospecting. Build targeted lists of businesses that fit your customer, then enrich them with contacts. For local leads, the Google Maps Scraper is the fastest route: one pass returns business name, address, phone, website, and reviews, so the list is half enriched before you start. For wider, non-local prospecting, the Google Search Scraper pulls relevant companies from across the web, then the Emails Scraper and Phone Number Scraper attach contact details from their sites. Smaller, sharper lists beat giant generic ones every time.

Market and niche research. Map a space you’re entering or expanding into. Who the players are, how they position, where the gaps sit. The Google Search Scraper is built for this wider view: it pulls relevant results from across the web, not just one local area, and the Get Page Content reads the pages it surfaces. Together they give you the lay of the land without a week of clicking.

Person and company research. Due diligence before a deal, partner vetting, sales research before a call. Pull what’s public about a person or a company from across the web so you walk in informed instead of guessing. I’ve written separately about why the first step here is never to ask AI to recall facts, it’s to collect the real data first, then let AI work on it.

Tender, RFP, and bid discovery. Bids and contract opportunities are scattered across portals, agency sites, and procurement pages, each formatted differently. Scraping the right keywords across the right sources turns a daily manual hunt into a feed.

Content and SEO research. See what competitors publish, which topics they cover, and where they don’t. The Page Content Extractor pulls the actual text so you can analyze structure, messaging, and keyword focus across dozens of pages at once.

Public files hiding in plain sight. The Files and Documents Finder uncovers PDFs, decks, brochures, and spec sheets hosted on a company’s own site. Sales material, pricing documents, and product detail that never made it onto a normal page.

Local business data. Beyond single leads, the Google Maps Scraper gives you a full local dataset: names, addresses, phones, websites, and reviews across an area. Useful for market sizing and seeing competitive density in a given location, not just building a contact list.

Every one of these comes down to the same two jobs underneath: collect the data, then structure it into something you can use. The mistake most people make is trying to do both at the moment of collection. That’s what makes scraping feel hard. Separate the two and the right approach becomes obvious.

The two questions that decide your approach

Before you build anything, answer these:

How many sources? A handful of fixed sites, or dozens to hundreds?

How stable and how often? A few predictable sites you’ll re-scrape on a schedule, or a wide, changing list you’ll touch once or a few times?

Few sources, repeated often, stable structure points to one approach. Many sources, broad and mixed points to the other. We’re good at both, and they’re genuinely different jobs.

Approach one: a few stable sources on a schedule. Build the recipe.

You track three or four competitor product pages and a couple of supplier catalogs, and you want fresh prices and stock every morning. The pages are already structured, and you’re going to hit the same URLs again and again.

This is where a custom scraping recipe is exactly right.

When the structure is stable and you’re returning daily or weekly, precise setup pays for itself fast. A recipe in Hexomatic targets the exact fields you want, handles pagination, walks every product, and delivers clean structured rows on a schedule. Price in its column, stock in its column, no cleanup, no babysitting. Build it once and it runs.

Two ways to get there. Build it yourself with the scraping recipe tool: point and click the fields, set the schedule, done. Or hand the brief to our Concierge Service and our team builds the recipe for you, including the awkward sites that fight back. The output is precise and repeatable, which is the whole point when the same numbers feed a decision every single day.

The honest trade: a recipe is tied to that site’s layout, so a redesign means a fix. For a handful of sources that makes sense. Maintaining few recipes (templates) is trivial. It stops being trivial the moment the list grows, which is exactly when the second approach takes over.

Approach two: many mixed sources at scale. Grab the raw page, structure it in Second Brain.

Now you want forty competitors in a niche, a couple hundred local sites, or a long list where every site is built differently and some are product pages too. A custom recipe for each is a non-starter. You’d spend more time engineering and re-fixing templates than using anything they produce.

Here you don’t want a scraper that understands each site. You want one that reads any of them.

Get Page Content, the Page Content Extractor automation, pulls the readable text off any URL. No selectors, no per-site setup. Give it a link, get the text. It works on a pricing page, a service page, a directory listing, an article, because you’re not asking it to understand the layout, you’re asking it to grab the words. Google Search Scraper does the same one level up: a query in, the top results out, ready to feed the extractor. For a large site, the Website Crawler finds the pages and the extractor pulls each one.

One simple step replaces dozens of fragile ones. But raw text isn’t a spreadsheet yet, and that’s the part people get stuck on. The structuring still has to happen. It just happens later, by AI, inside Second Brain.

Where the raw path gets its structure

Connect Get Page Content to Second Brain and the extracted text lands in your local database as scraped pages, tied to the workflow that pulled it. Your data sits on your machine, not on someone’s cloud. Then you ask Claude to turn it into what you need: company name, positioning, pricing signals, key claims, contact details, whatever the job calls for, written into clean columns.

The structure is applied after collection, by AI, to text it can actually read. You get recipe-quality output without writing a recipe for every site. And because text extraction doesn’t care about layout, a redesign on any of those forty sites breaks nothing. The words are still the words. Rerun the same two steps and your sheet updates. This is also how you keep a research dataset current: schedule the collection, and Second Brain stays fresh without you reimporting by hand.

This is the real connection between Hexomatic and Second Brain. Hexomatic collects across many sources. Second Brain holds it locally and structures it at scale. AI sits in the middle doing the one thing it’s reliably good at.

Why not skip Second Brain and paste it all into Claude directly?

Because that’s where AI quietly falls apart, and it’s worth being exact about why. I covered this in one of my previous articles, but the short version: AI is excellent at structuring a small, focused chunk of text and unreliable the moment the input gets big. Every model has a context window, a hard limit on how much it holds at once. Dump fifty pages of raw scraped text into one prompt and it starts dropping records and blending one source into another. The capability is there. The capacity isn’t.

That gap is the entire reason Second Brain exists. The data lives in a local database, and Claude is fed the right slice at the right time instead of everything at once. You get the structuring quality of a small input applied across a large dataset, because the database does the holding and the feeding.

One discipline that protects every dataset

When you ask Claude to structure scraped text, tell it explicitly to use only what’s in the data. If a phone number isn’t on the page, the field stays empty. It does not invent a plausible one. Structure what’s there, leave blanks as blanks. A dataset that looks complete but is quietly wrong in ten places is worse than one with honest gaps, especially when you’re about to make a competitive decision on it.

Mapping use cases to approach

Most price monitoring and product tracking on a fixed set of competitors fits the scheduled recipe: stable sources, repeated often, structured fields you want clean and on time.

Most market mapping, lead research, person and company research, tender discovery, content analysis, and document hunting fits the raw path: many sources, mixed and changing, where the value is breadth and where AI structuring in Second Brain beats building and maintaining a template per site.

Plenty of real projects use both. A scheduled recipe watching your core competitors, and a raw-plus-Second-Brain sweep whenever you research a new market or batch of prospects. The skill isn’t loyalty to one method. It’s reading the job correctly before you start.

Get the right setup for your job

Self-serve. Start in Hexomatic. For a wide research sweep, run Google Search Scraper and Get Page Content across your URLs, pipe the results into Second Brain, and ask Claude to structure them. For a few fixed competitors, build a scraping recipe with the visual scraper and put it on a schedule.

Concierge. Don’t want to build the recipe or wire up the pipeline yourself? Send us the brief through our Concierge Service. We’ll set up either path for your exact use case, the scheduled recipe or the raw-to-Second-Brain flow, and hand you working output.

Enterprise. Running this across many sources on a schedule, feeding internal systems and a structured local knowledge base your team queries with AI? Book a call and we’ll architect the full pipeline around your data, from collection to a research database your whole team can ask questions of.

Your data. The customers, payments, contracts, and notes that no public model was ever trained on. That is the one thing the next founder with the same subscription doesn’t have. Second Brain exists to put it to work.

AI guesses when it should know

Ask a general AI how many customers you have and it makes up a number. Ask what you decided about pricing last quarter and it gives you a confident answer built on nothing. It doesn’t have your records, so it fills the gap with something that reads well and falls apart the moment money depends on it.

Memory features don’t fix this. They store a preference, not a record. There is a real difference between an AI that remembers “I run a SaaS company” and one that can tell you which customers signed up last month, haven’t logged in since, and are still paying. The first is a sticky note. The second is a decision you can act on. And the second only happens when your actual data is sitting somewhere the AI can query directly.

That is the whole idea. AI is excellent at reasoning over your data once it can reach it. It was never a replacement for the data itself.

Your data, your machine, your control

Second Brain is a database that lives on your computer, not in someone else’s cloud. You own the file. You back it up by copying it. Nothing leaves unless you decide it does.

Claude connects to it directly and becomes the interface. You ask a question in plain language and get an answer from your real records. You tell it to log a meeting or update a contact and it writes straight to your database. The key choice: it only touches your data when you’re in the conversation. Nothing runs on its own in the background, nothing acts while you sleep. You stay the operator, not the passenger. For real business data, that is the tradeoff that matters.

Getting data in is the part most tools get wrong, so this one reads the exports you already have. Stripe, PayPal, Notion, Evernote, Asana, Jira, your email archives, folders of contracts brought in hundreds at a time, more than twenty sources in total. You bring the file, it lands as structured, searchable records.

What it feels like once it’s running

The value shows up in the questions you can suddenly answer without building a report.

Instead of “what’s a good pricing strategy,” you get “based on your last three pricing changes and how churn responded each time, here’s what the data suggests.” One is a blog post. The other is built from your numbers and nobody else’s.

The strongest questions cross two layers at once, your own records and outside signals in the same place. Which customers in a region haven’t been contacted since a competitor changed their pricing there. Which clients are due for follow-up based on their last service date. Those only work when internal data and live market data sit together, which is what the built-in scraping keeps current for you while the app runs.

If you sell across channels, the scattered-customer problem disappears. A buyer who ordered on Amazon, then Shopify, then Faire normally lives as three records in three dashboards. Second Brain matches them into one person with full history and lifetime revenue, then answers in plain language: which products sold most last quarter, who hasn’t reordered in 90 days, revenue by channel.

And it shapes itself to you. If you’re not in ecommerce, those tables never appear. The database shows only what holds your data. The product fits your business instead of forcing your business into a template. Your database even travels: it stays on your computer, but you can reach it from your phone mid-meeting while the machine stays home (details here). Only the question and the answer move.

The best part is what happens over time. The data you add today makes the answers better next month, and better still the month after. This is not a tool you use once. It is an edge that compounds every time you feed it.

Where it’s headed

The current version makes data easy to get in and easy to query. Next comes structure: contacts that link to meetings, meetings to decisions, decisions to tasks, so when you ask about a customer the AI connects their payments, the meeting where you discussed them, and the email you sent. More import sources are coming across accounting, email marketing, and ecommerce. An optional assistant layer is in the works, opt-in only, with local-only staying the default because that is the right default and it is not changing.

You don’t need any of that to start. You need one import.

Try it

Take whatever you already paste into AI by hand, get it into the database, connect Claude, and ask a question you couldn’t answer before. That is usually enough to see what changes.

The early-adopter price is locked for life if you join before it ends. After that it goes up and stays up.

Lock in your plan at brain.hexact.io, or book a free walkthrough and we’ll set up your first import with you.

Multiple tags per import

You can now assign multiple tags when importing knowledge base entries, separated by commas. Previously you were limited to one tag per import, which meant either under-tagging your data. Now you can label an entry as “research, competitors, Q3” in one go.

This is useful when a document genuinely belongs to more than one category. A vendor contract might be tagged “legal, vendors, 2024.” A research PDF could be “market research, product, competitors.” Tagging at import time means your data is organized from the moment it lands, not after the fact.

Batch imports: up to 100 files, or 200 in a zip

You can now import up to 100 files in a single batch, or package up to 200 files into a zip and import them all at once.

Here’s where this becomes practical:

• Contracts and legal documents. Drop in a folder of vendor agreements, NDAs, or client contracts and query across all of them. Ask Claude to surface renewal dates, liability clauses, or payment terms without opening each file individually.

• Research and reports. Import a set of industry PDFs, analyst reports, or academic papers and treat them as a searchable knowledge base. Ask questions, get summaries, compare findings across documents.

• Internal documentation. Product specs, SOPs, meeting notes, onboarding materials. Import the whole folder, tag it by department or project, and anyone on your team can query it in plain language.

• Email exports or CRM data. Export a batch of customer conversations or deal notes, bring them into Second Brain, and ask Claude to identify patterns, summarize sentiment, or pull out specific details.

The zip option is particularly useful when you’re migrating an archive or setting up Second Brain for the first time with an existing document library. Package everything, set your tags, import once.

Query your knowledge base from anywhere

Second Brain runs locally on your desktop, but you’re not limited to your desk. By connecting Second Brain to Codex, you can access and query your entire knowledge base from the ChatGPT mobile app. Pull up contract details, search your notes, or ask questions across your documents from your phone, with your data staying local the entire time.

General bug fixes and UI improvements

We’ve also pushed a round of bug fixes and interface refinements based on user feedback. Nothing dramatic, just things that were slightly off and are now right.

If you haven’t tried Second Brain yet, it’s free to download. You connect it to Claude, point it at your data, and start querying it in plain language. No SQL, no dashboards, no setup overhead.

• Download Second Brain: https://brain.hexact.io

• Already using it or have a larger use case or need a custom setup? Book a call with us

Performance and UI improvements

We’ve shipped a round of performance and optimization improvements to Second Brain, along with a refreshed Learn feature dashboard and UI. Queries run faster, data loads more reliably, the overall experience feels tighter, and the Learn section is now easier to navigate and read at a glance.

Unified commerce order history

If you sell across multiple platforms, your customer data has probably always been scattered. Someone buys from you on Amazon, again on Shopify, and they exist as three separate records across three separate dashboards. Second Brain now solves that.

You can import full order history from any sales channel, including Amazon, Shopify, Faire, Walmart, eBay, and more, with support for multiple stores on the same platform. Orders and individual line items land in one place, tied to your contacts. Buyers are automatically matched across platforms by email, phone, or customer ID, so a customer who purchased from you on three different channels shows up as one person with their complete history.

Every platform exports data differently, so we built a universal database structure that can receive any of them. You bring your export file and describe it to Claude or Codex, and it maps your data into the right structure automatically. Once it’s in, you can ask for any report in plain language: which products sold most last quarter, which customers haven’t reordered in 90 days, lifetime revenue by channel. No dashboards to configure, no exports to run.

Contact details in the desktop app now show commerce orders alongside Stripe and PayPal activity, with products, quantities, and prices in one view, and commerce spend included in each contact’s lifetime revenue totals. Stripe and PayPal records now also capture gross, fees, net, refunds, and currency-converted amounts. Multi-currency is supported throughout.

If you’re not in ecommerce, none of this appears in your Second Brain. No commerce tables, no order fields. This is a core principle we’re building around: you only see what’s relevant to how you actually use the product. The Database tab works the same way. It shows only tables that contain your data, and the moment you import something new, that table becomes available to navigate, manage, and search. There’s more to do here, but the direction is set.

Your Second Brain, accessible from anywhere

Second Brain stores your data locally. Your SQLite database lives on your computer, not on a cloud server somewhere, which means you stay in control of it. That’s intentional. But it also raised an obvious question: what if I’m not at my desk?

We’ve added instructions for connecting Second Brain to Codex on the desktop. Once you set that up, you can access your knowledge base from two places: Claude Desktop on your computer, and the ChatGPT app’s Codex tab on your phone. As long as your computer is on, Codex on mobile can reach your local database through that desktop connection.

What that means in practice: you’re in a meeting, you remember you wanted to add a contact or update a note. You pull out your phone, open the Codex tab, and do it. Your computer stays home. Your data stays local. You just gained a remote interface into it.

What you can do from mobile

Anything you can do in Second Brain from the desktop, you can now do from your phone. Query your knowledge base in plain language, add new records, edit existing entries, look something up mid-conversation. The database never leaves your machine. What travels between your phone and your computer is only the query and the response. The data stays where you put it.

How to set it up

The connection instructions are inside your dashboard. Follow the Codex setup guide once, and mobile access works automatically from that point on.

Already using Second Brain? Download the latest version to get all of the above, including the unified commerce history, the refreshed Learn dashboard, and the Codex setup instructions.

New to Second Brain? Start a 7-day free trial and see how it fits into your workflow before committing to anything.

Want a walkthrough first? Book a free demo call and we’ll show you how the whole thing works, including the mobile setup.

I’m learning Spanish. It will be my fifth language. Every time I’ve learned a new one, I’ve started the same way: words. Not grammar rules, not conversation practice, not apps that gamify everything into streaks and badges. Just words, a few minutes a day, as consistently as possible.

Vocabulary is the foundation, and it’s the only part of language learning that’s genuinely measurable. You either know a word or you don’t. You can set a goal like “500 words this year” and actually track it, which is more than you can say for “get better at speaking.”

The problem is I’ve been counting manually. A spreadsheet here, a notebook there, trying different apps, none of them doing exactly what I needed. Most tools are built around flashcard decks or spaced repetition algorithms. Useful, but not the point. I just wanted a simple, honest count: words I know, words I’m working on, progress over time.

Then I started hearing the same thing from Second Brain customers. They were already using AI to learn languages, having lessons with Claude, building vocabulary through conversation. But the tracking was still a mess, manual counts, scattered notes, no real picture of progress. Same problem, same frustration. So we decided to just build it properly.

Introducing Learn

Second Brain now has a Learn section, built specifically for language study. It’s on by default when you open the app.

Here’s what it does:

Language profiles. Add one or more languages you’re studying. Each gets its own word list, phrase list, and progress tracking. You can archive a language you’re not actively working on and bring it back anytime.

Word and phrase tracking. Every word or phrase you add gets a status: new, learning, known, or ignored. When you mark something known, the date is recorded. That’s how your progress becomes measurable over time, not just a feeling.

A real dashboard. You get a progress summary showing total tracked items, known count and percentage, and weekly charts of words marked known over the last 8 weeks. You can filter by the last 7 days, last 30 days, or all time. It’s a simple, honest picture of whether you’re actually making progress.

Claude does the logging. This is the part that changes the workflow entirely. During a lesson with Claude, it can add words you miss, mark ones you already know, log phrases, and update statuses, all through your existing MCP connection. Everything stays on your machine. No cloud sync, same local-first model as the rest of Second Brain.

What This Looks Like in Practice

You open a lesson with Claude and tell it you want to practice Spanish vocabulary. During the conversation, Claude logs new words automatically. After the session you open Learn, check your dashboard, and see exactly where you stand.

You can also ask Claude directly: “How many Spanish words have I marked known this month?” and get a real answer pulled from your data.

A few starter prompts worth trying:

• “Add the top 100 most common Spanish words to my Learn list.”

• “Populate about 50 useful Spanish phrases, greetings, travel, and small talk.”

• “During our lesson, log new words I miss and mark ones I already know.”

• Already have a word list somewhere? Upload your spreadsheet or CSV and ask Claude to insert it into your Learn list. No manual re-entry.

Getting Started

Open Learn from the main menu (it sits after Settings). Go to Profiles and add your first language. Then update your Claude skill from the in-app skill setup page, that’s what gives Claude access to read and write your Learn data. After that, study with Claude or browse and edit your lists directly in the app.

If you don’t need Learn, you can hide it entirely from Settings without losing any data.

Is This for Everyone?

Not everyone is learning a language, and not everyone who does learns through vocabulary lists. That’s fine. If Learn isn’t relevant to you, you can hide it from Settings in one click and nothing changes about how you use Second Brain.

But if you are learning a language and you’ve been tracking progress manually or not tracking it at all, this is worth trying. The AI side was already covered. What was missing was the structure around it.

If you try it and have ideas for what else would be useful, I’d genuinely like to hear them. There’s more to build here.

Download the new version at brain.hexact.io. A 7-day free trial is included, no credit card required.

Email archive import was already in Second Brain. This update expands it significantly. We added support for more file types and removed the size limits that were blocking larger archives. If you tried importing before and hit a wall, try again.

Supported formats now include .eml, .mbox, .zip containing .eml files, and .pst for Outlook. The dry run option lets you preview the import before it runs, which is useful when you are dealing with a large archive and want to know what you are working with first.

Custom datasets

You can now create named datasets and import any CSV file into Second Brain. Price lists, product catalogs, inventory, whatever structured data you work with regularly. You can have multiple datasets and manage them independently.

Deduplication is handled by ID. Re-import an updated file and existing records get updated, not duplicated. Update your catalog, re-import, done.

Claude can query any dataset directly in conversation. Ask about your prices, look up a product, cross-reference a price list. No manual copying, no context window gymnastics.

Second Brain as a source of truth

The core idea behind Second Brain is that Claude should always have accurate, current context about your business without you having to paste it in every time. You build the knowledge base once, keep it current, and Claude works from it.

It works both ways. Claude reads from your knowledge base to answer questions and help you make decisions. You can also ask Claude to update records, retag entries, reorganize datasets. The app and Claude are two ways to manage the same data. As your business evolves, your knowledge base evolves with it.

Bug fixes and performance improvements

This release also includes:

• Bug fixes across import pipelines and dashboard rendering

• Better handling of large file imports without timeouts

• Stability improvements for MCP connectivity

• General performance improvements across the app

If you are on an older version, the update is worth it.

Download the latest version — available on macOS and Windows.

7-day free trial, no credit card required.

Want a walkthrough? Book a free demo call.

I’ve written a lot about why we built Second Brain and what problems it solves. But I keep getting the same question: “What exactly does it do?” Not the philosophy. Not the vision. The actual features, right now, in the current version.

So here’s the full breakdown. Everything that’s in the app as of today.

Your local database

Second Brain runs on a local database on your machine. No cloud. No subscription server. Your data sits in a file on your computer that you fully own and control.

The dashboard shows you what’s in your database: how many records, how much space it takes, and shortcuts to the main sections. You can browse every table in the database directly from the app, search across all your data from one search bar, and edit or delete individual records without touching any code.

When you first open the app, a short onboarding walks you through the basics. The app checks for updates automatically and tells you when a new version is available.

Contacts and CRM

This is where most of your data comes together. You get a full contact management system with search and filtering across names, emails, phone numbers, and organizations.

Each contact has a detail page with everything linked to them: identity info, email addresses, phone numbers, social profiles, physical addresses, company affiliations, newsletter subscriptions, billing records, calendar events, notes, and a timeline of all activity. You can see lifetime revenue per contact when payment data is imported (stripe, paypal).

Need a list? The CSV export lets you pick which columns to include, applies whatever filters you’ve set. That’s your email campaign list, or your outreach segment, built from real data in seconds.

21 import sources

We built parsers for the tools people actually use, so you can bring existing data into the database without reformatting anything.

Payments and billing: Stripe subscriptions, Stripe transactions, PayPal subscriptions, PayPal transactions, and bank statements (CSV). Each one scoped per company. Bank statements use a column mapping step so you can import from any bank’s CSV export format.

Email: Email suppression lists (bounces, unsubscribes, spam reports, blocks) and full email archives.

Contacts: A universal CSV importer with column mapping that works with any CRM or spreadsheet export, plus dedicated parsers for Google Contacts and survey responses.

Calendar: Google Calendar event imports.

Publishing: Substack archive imports with author attribution for articles.

Notes and knowledge: Notion (Markdown and CSV), ChatGPT conversation exports, Evernote (.enex files), OneNote (multiple formats), Obsidian vaults, Logseq graphs, and general knowledge files (PDFs, Word docs, text files, ZIP archives with 200+ documents at once).

Project and task tools: Asana tasks, Jira issues (all fields), Trello boards, and Todoist backups.

Every import that supports it has a dry run mode. You can preview exactly what will be created or changed before anything gets written to your database. Background imports show progress in real time with status updates and logs, and you can cancel mid-import if something looks wrong.

Database management

You can browse every table in your database. Edit any row directly. Delete individual records or bulk delete. Deletion shows you exactly what will be affected before you confirm.

The maintenance tools handle the cleanup work that databases need over time. There’s a cleanup tool that removes junk contacts (disposable email domains, spam-pattern names), tags suppressed emails, and refreshes database indexes. You can customize the blocklist of disposable domains and define your own junk name detection rules.

Backup and restore is built in. Export your entire database as a backup file.

Live automations

Three integrations that keep your database current while the app is running.

Hexomatic. Connect your API key, pick a scraping workflow, and map it to a company and a target field in your database. Hexomatic runs on its schedule, and the results flow into your local database automatically. No manual downloads. You can pause and resume sync, view activity logs, and manage multiple workflow mappings.

Stripe. Enter your secret key per company, and Second Brain syncs your payment data automatically. Pause, resume, or trigger a manual sync anytime. When Stripe automation is active, the manual Stripe CSV import on the Import page is disabled to avoid conflicts.

PayPal. Same setup. Client ID and secret, live or sandbox mode, pause and resume, manual sync.

Claude Desktop connection

This is what turns a database into a working AI tool.

Second Brain generates the MCP configuration snippet that connects your local database to Claude Desktop (watch this video to learn how to connect to Claude Desktop). One setup step, and Claude can read from and write to your local knowledge base directly inside a conversation.

The app also generates a starter Skill file, a markdown document that teaches Claude how to work with your data safely. Permission-first rules: Claude can read anything, but writes only happen when you explicitly ask. The Skill explains how it works, and you can customize to your specific needs. See more skills here.

Claude Desktop’s free plan works with this connection. You don’t need a paid Claude subscription to start using your database as context.

Settings and profile

Your owner profile holds your email addresses, phone numbers, companies and roles, social profiles, and other details. This is the context Claude uses when it needs to know who you are.

You can manage multiple owner companies, which is how imports, automations, and contact scoping all stay organized when you run more than one business.

CRM settings let you define your own relationship types for contact imports and customize field labels for column mapping, so your import workflow matches your business terminology.

What’s new in this version

We ship updates regularly. The latest version includes stability improvements across import pipelines, better error handling on large datasets, and refinements to the database management tools. If you’re on an older version, download the latest build.

Try it

Download Second Brain at brain.hexact.io. 7-day free trial, no credit card required.

Start with one import. Whatever data you already paste into AI conversations manually. Get it into the database, connect Claude, and ask a question you couldn’t answer before.

That’s usually enough to see whether this changes how you work.

Want a walkthrough first? Book a free 15-minute demo.

It won’t.

AI is great at processing text and data. But every model has a context window, a limit on how much it can hold in memory at once. When your input exceeds that limit, the model either truncates it silently, loses track of earlier content, or gives you a partial result and stops. The capability is there. The capacity isn’t.

That’s the problem the Second Brain solves. It sits between your raw data and Claude, breaking large inputs into structured, queryable records in a database. Instead of one massive file that overwhelms the context window, Claude sees clean rows it can process, search, and cross-reference without hitting any limits.

Here are two real use cases from my own experience that show exactly how this works.

Use Case 1: Merging Contacts from Multiple Sources into One Clean Database

If you run more than one business, or even one business with more than one data source, you know this situation. Customers in one spreadsheet, leads in another, investors in a third, maybe a fourth export from some CRM or platform you used two years ago. Every file has different columns. Some split first name and last name. Some have a single “Name” field. One has phone numbers, another doesn’t. Formats are inconsistent. Duplicates are everywhere.

At some point you need a unified view. Who are these people? Where do they overlap? Who is a customer in one business and an investor in another?

This looks like a straightforward AI task. Upload the spreadsheets, ask Claude to merge them. Done.

Except it doesn’t work at scale. If your dataset is a few hundred rows, AI can handle the merge. But when you’re dealing with thousands of contacts across multiple exports, the model truncates data, hallucinates entries, or gives you a partial result and acts like it’s done. Large CSVs exceed what any chat-based AI can process in one shot.

How the Second Brain makes this work

The Second Brain treats contacts as a proper database, not a chat attachment. Each contact is a record in a SQLite table. Each field (name, email, phone, company, source, payment history) is a column. When you import from multiple spreadsheets, the data gets normalized into one unified schema.

Now Claude can do the job it couldn’t do before.

Query across sources: “Show me everyone who is both a paying customer and an investor.” That’s a SQL join. Claude runs it in seconds.

Deduplicate properly: Match on email, name + company, or phone number. You define the rules. The AI doesn’t guess.

Enrich on clean data: “Which contacts are missing an email address?” “Which customers from 2023 never logged in again?” These questions are easy when the data is structured. They’re impossible when you’re staring at five different Google Sheets.

Sometimes the source data is even messier than mismatched columns. I’ve dealt with CSV exports where everything was stuffed into raw JSON payloads. Name, phone, address, service history, review status, all buried inside a single field. The Second Brain extracts and normalizes this automatically. You go from unreadable blobs to clean, queryable records, and suddenly you can ask things like “show me all clients in a specific city who completed more than 3 jobs but never left a review” and get an answer in seconds.

The AI didn’t get smarter. The data got structured. That was the only difference.

Use Case 2: Proofreading a 200+ Page Book

I wrote a book called “Bones: 21 Clichés Running and Ruining Your Business.” After months of writing and rewriting, I needed a serious proofread. Not just grammar. English is not my native language, and when you produce 200+ pages, small issues compound. A slightly unnatural preposition, an odd word choice, a sentence structure that’s technically correct but sounds off to a native ear. Over that many pages, these pile up.

Proofreading a book. That’s exactly what AI should be able to do. Upload the document, ask Claude to review it. Simple.

If you haven’t tried this with a long document, let me save you the frustration. It does not work.

Why large documents break AI

Large language models have context windows. Even the biggest ones cannot hold a 200+ page document in working memory at once. Here’s what actually happens.

The model reads the first chunk and does solid work on it. By the page 20-30 of the book, it’s lost track of tone and style decisions from the first chapters. By the end, it’s proofreading in isolation, with no awareness of the whole.

The response hits its output limit long before the job is finished. You get corrections for the first 20 or 30 pages. Then it stops. You ask it to continue. Now it’s working from a fragmented context. Quality drops with each continuation.

Formatting gets in the way. Word documents carry metadata: styles, headers, footers, comments, tracked changes. The AI confuses structural elements with content. Edits break your formatting. Sections get skipped entirely.

The result: maybe 15% of your book gets reviewed, inconsistently, with no guarantee the same standards applied throughout.

How the Second Brain makes this work

Instead of feeding Claude one massive .docx file, I just imported the book into the knowledge base. Each page became one row in a database table.

Now Claude wasn’t processing a 200-page Word document. It was looking at a table with 200+ rows of clean text. No formatting noise. No embedded images. No metadata. Just the words.

The real power showed up in cross-document queries. “Find every instance where I used ‘which’ instead of ‘that’ in a restrictive clause.” “Flag pages where the tone shifts noticeably from the pages around them.” “List every sentence longer than 35 words.” These consistency checks only work when the full text is structured and queryable. They’re impossible with a monolithic file.

The proofreading quality stayed high from page 1 to page 211. The AI never had to manage a large document. It just did what it’s good at: analyze text, one piece at a time, with the ability to reference everything else when needed.

Why This Keeps Happening

These two use cases look completely different. One is data merging. The other is prose editing. But the failure mode was the same: the AI had the skills for the job, but the input was in a shape it could not work with.

AI models are excellent at analyzing, comparing, and transforming organized data. They struggle when the first task is organizational.

The Second Brain fills that gap with two layers: structure and Skills.

Structure is the road. Your raw data gets normalized into a queryable database so AI has something to work with.

Skills are the rules of that road. A Skill is a set of instructions attached to your Second Brain that tells Claude how to behave with your specific data. Which tables exist. How sources relate to each other. What to never guess about. What to ask before doing. It’s the rulebook for your knowledge base.

This is why “upload your file and ask AI” keeps disappointing people. No structure, no rules. You’re asking a powerful engine to drive without a road or a map. The Second Brain provides both.

Where This Is Going

We started building the Second Brain to solve our own problems at Hexact. Since then, we’ve been using it to store and query across all of our published articles, track customer and lead data in one place, and connect ideas across different knowledge domains.

It works with the tools we already have. Hexomatic collects and enriches the data. The Second Brain stores and structures it. Claude analyzes it on demand. The combination is more useful than any of those pieces alone.

We’re still early in development, but if you’re dealing with scattered data across multiple sources, documents too large for AI to handle, or knowledge that needs structure before it becomes useful, this is what we’re building toward.

Want to see how it works? Book a call with us and we’ll walk you through the current setup, from data import to your first cross-source query.

Already using Hexomatic for data collection? The Second Brain adds the storage and analysis layer that turns scraped data into a working knowledge base.

Reality was the opposite. Every tool I use keeps its own version of reality. Stripe knows who paid. Substack knows who reads my newsletter. Google Calendar knows who I met with last Tuesday. My CRM knows... whatever someone remembered to type into it six months ago.

Now multiply that by several businesses, each with its own Stripe account, its own calendar, its own contact list, its own CRM. The data doesn’t just live in different tools. It lives in different worlds. A customer in one business might also be a client in another, and I’d have no way of knowing unless I happened to remember their name.

None of these tools talk to each other. And I got tired of it.

So when we built Second Brain, the first thing I did was dump everything into it. Every contact, every transaction, every subscriber, every meeting. All into one local database on my laptop. Then I connected it to Claude and started asking questions I’d never been able to ask before.

I finally know the relationship between my newsletter and customers

Here’s a question I couldn’t answer until I put both datasets in the same database: what’s the relationship between reading the newsletter and becoming a customer?

Not just the overlap. The sequence.

Did people subscribe to the newsletter first and then become customers? Or did they buy first and subscribe later? How long between subscribing and paying? Are long-time readers more likely to stay on a paid plan?

No single tool gives you this. Your newsletter platform tracks opens and clicks. Your payment processor tracks charges. But the timeline across both? That only exists when the data is in one place and you can actually ask questions across it.

In a traditional CRM, you’d need to connect your payment system to your analytics platform to your email tool, set up automations, build reports. In Second Brain, I asked the question and got the answer in about five seconds.

I actually know who I’m talking to

You’re about to get on a call. You check the CRM. You see a name, a company, maybe a deal stage. That’s it.

What you don’t see: this person’s payment failed twice last month. They filed a support ticket that’s still open. They unsubscribed from your newsletter.

You walk into the call thinking everything is fine. It’s not.

I used to do this all the time. Now before any important call, I ask Claude: “What do I know about this person?” It pulls their payment history, subscription status, newsletter engagement, calendar meetings, any notes I’ve saved. All in one answer.

It takes ten seconds and it completely changes how I approach the conversation. I’m not guessing anymore. I’m prepared.

You can ask questions nobody built a dashboard for

This is the thing that changed my thinking the most.

Dashboards are great, but they only answer questions someone already thought to ask. They’re static. Someone designed them last quarter based on what seemed important then.

That shift matters more than any feature. You go from consuming pre-made reports to interrogating your own business in real time. You become curious about your data because the cost of asking a question (before it was a new custom report) dropped to zero.

It works both ways

Everything I’ve described so far is about asking your database questions. But you can also tell it things.

After a call, I say “save this ….. about…..” It goes straight into the database as a note, linked to the contact. I can paste a screenshot of a chat and ask Claude to create a records based on that. I can paste a meeting summary and say “add this to the customer’s record.” I can say “this person is now the CEO of Acme Corp” and it’s done.

This is the part that makes it feel like that movie scene I mentioned at the top. You’re not filling out forms. You’re not clicking through dropdown menus. You’re having a conversation with your database, and it works in both directions. Ask and get answers. Tell and it remembers. Meanwhile still having the direct access to your data using Second Brain interface.

It keeps getting better

We ship updates constantly. Right now Second Brain has 21 import sources: Stripe, PayPal, Notion, ChatGPT conversations, Evernote, Asana, Jira, Trello, Obsidian, Google Calendar, Google Contacts, Substack, email archives, CSV uploads, and more.

We just added zip file imports (up to 200+ doc files at once), and improved database cleanup tool. More import sources are coming.

Here’s what I’ve learned building this: each new import source makes everything else more valuable. Connecting your Stripe data is useful. Importing your Stripe data, CRM and subscribers is powerful. Import your Google Calendar data on top of that and now you can see the correlations.

It compounds. The more data you bring in, the more questions you can ask, and the more patterns you find that were always there but invisible.

Try it

Download the latest version at brain.hexact.io. Free trial, no credit card.

Start with one import. Whatever you have. A contacts CSV, your transactions, your newsletter subscriber list, multiple spreadsheets. Get it in, connect Claude, and ask your database something you’ve always wanted to know but never could.

You’ll be surprised what’s been hiding in your own data.

Database management tools

You can now edit and delete data directly from the app. Browse your entries, fix a typo, remove something outdated, all from within Second Brain. This was one of the most requested features since launch, and it’s live now.

OneNote import

If you’ve been sitting on years of OneNote notebooks, you can now bring them into Second Brain. Export from OneNote, import into the app, done. Your notes land as structured entries in your local database, searchable and ready to connect with Claude.

Bug fixes

Several stability fixes across the board. Smoother imports, fewer edge cases on large datasets, better error handling. Nothing dramatic, but the kind of work that makes the app feel more solid every week.

Updated: How to connect with Claude

The setup instructions for connecting Second Brain with Claude Desktop have been updated. If you tried connecting before and hit friction, it’s worth revisiting.

Here’s the thing most people don’t realize: Claude Desktop’s free plan works with Second Brain. You get a limited number of prompts per day, but the MCP connection works the same way. You don’t need a paid Claude plan to start using your personal database as context. Install Second Brain, connect it to Claude Desktop, and you’re running.

The paid plan obviously gives you more prompts and longer conversations. But if you want to test the workflow before committing, the free option still works.

We also published a skills archive on GitHub with ready-to-use skills like weekly review, meeting prep, and quick lookup.

How to actually start

I keep hearing the same question: “What should I import first?”

Stop thinking about it as building an archive. Think about it as eliminating repetition.

What files do you upload to ChatGPT or Claude every week? What context do you re-explain every time you start a new conversation? Product specs, client data, meeting notes, project documentation, your own writing. That’s your starting point.

Import the stuff you’re already feeding to AI manually. Once it’s in your Second Brain database and connected via MCP, Claude has that context automatically. No more copy-pasting the same brief into every conversation.

You don’t need to import everything on day one. Start with what you use today. Add more as you go.

What’s coming next

We’ve gotten a lot of requests and ideas from early users (keep them coming). Two things we’re thinking about first:

Custom database location. Right now the SQLite database lives in a default directory. We want to give you the option to choose where it sits. Your NAS, a synced folder, an external drive. Your data, your choice of where it lives.

More tools to keep your Second Brain current. The initial focus was on importing historical data. The next step is making it easier to keep things up to date. Live syncs, scheduled refreshes, and smarter ways to capture new information as it happens, not after the fact.

Both are in active development. No dates yet, but these are top priority.

Try Second Brain: brain.hexact.io. 7-day free trial, no credit card required.

Want a quick walkthrough? Book a 15-min demo.

github.com/HEXACT-INC/second-brain-claude-skills

A skill is a markdown file that teaches Claude how to handle a specific task with your data. Drop it into your skills folder, restart Claude, and it works.

The repo currently have five skills:

second-brain is the core skill. It gives Claude safe, permission-first access to your local database. Read anything, write only when you say so. This one should be installed first. Everything else builds on top of it.

quick-lookup answers “what do I know about X?” questions. Ask about a person, a company, a deal, and it pulls everything from your KB, synthesizes it into a short answer. Useful before calls, before signing contracts, before replying to an email you’re not sure about.

weekly-review generates a summary of everything you captured in the last 7 days. Notes, meetings, saved ideas, new contacts. Instead of scrolling through your database wondering what happened last week, you get a concise brief.

meeting-prep builds a pre-call brief for a specific person or company. It pulls contact history, past meetings, relevant notes, payment data if it exists. You walk into the call knowing what matters.

save-and-categorize handles “save this” requests. Share a thought, a link, a meeting takeaway, and the skill figures out the right entry type, writes a clean title, and stores it properly. No manual tagging.

Tell Us What’s Missing, What’s Blocking You, or What You’d Change

We shipped the app and the skills based on what we already know. But you’re using it differently. You loaded different data, you run a different business, you ask different questions. That means you’re hitting walls we haven’t hit yet.

What feature would make the biggest difference for you right now? What’s confusing? What workflow are you trying to build but can’t? What skill would save you time if it existed? A daily journal, a deal tracker, an invoice reviewer, a hiring pipeline brief, a content calendar. If it involves structured data and a repeatable task, it’s probably a good skill. Reply to this email or leave a comment.

If you've built a skill that works for you and you're willing to share it, the repo is open. Fork it, add your skill folder with a SKILL.md and a README.md, and open a pull request. Same goes for improving the existing ones. One rule: keep each skill focused on one job.

Any feedback that points to a product gap - we’ll build it. That’s how the new import formats, database management tool and global search happened. Users asked, we shipped.

New Release: Download the Latest Version

We also shipped a new version of Second Brain today. If you already have the app installed, download the latest build from brain.hexact.io. Here’s what changed:

Database management tool. You can now edit any table in your database manually, right from the app. No SQL required. This was the most requested feature after launch.

Global search. Search across all your local records from one search bar. Contacts, notes, articles, transactions. Type a name or a keyword and see results from every table.

Bug fixes. Stability improvements across import pipelines, dashboard rendering, and MCP connectivity.

If you’re on an older version, the update is worth it. The database tool alone changes how you interact with your data day to day.

Use Cases That Make This Click

I’ve been using Second Brain daily since before the launch. Here are some real workflows that show what this setup can do once your data is loaded.

Pre-meeting intelligence. Before any call, I ask Claude: “What do I know about this person?” It pulls their contact record, any past emails, payment history if they’re a customer, notes from previous meetings. I walk in prepared without spending 15 minutes digging through apps.

Customer health check. “Show me all customers who paid more than $500 but haven’t logged in since 2024.” That query runs against my actual database. Real data, real answers. No dashboard needed.

Content research from your own writing. “What have I written about data ownership?” Claude searches my 168 published articles and pulls the relevant ones. I see my own arguments, positions, and examples. Useful to see how things change compared to what I said six months ago.

Supplier and vendor comparison. If you use Hexomatic to scrape pricing pages or product catalogs, that data flows into Second Brain automatically. Ask Claude to compare suppliers by price, minimum order, or any other detail. The comparison runs on your scraped data, not on Claude’s general knowledge.

Trend spotting across scraped data. Scrape job postings in your industry weekly. After a few months, ask Claude: “Are companies hiring more for AI roles compared to three months ago?” You get trend analysis from your own data, not a generic report.

The pattern is the same in every case. Your data is already in the database. You ask a question in plain language. Claude reads your actual records and answers. No exports, no spreadsheets, no switching between six apps.

Get Started

1. Download the latest version at brain.hexact.io

2. Install the second-brain skill, then add whichever specialized skills you want

3. Browse the full repo at github.com/HEXACT-INC/second-brain-claude-skills

Three things you can do right now: update the app, try a new skill, and tell us what you’re building with it. Reply to this email with your use case, open a pull request with your skill, or just star the repo so we know you’re watching. We’re building this in the open and your input shapes what comes next.