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Your company knowledge base, on autopilot. Find out more about us at

[Justin Butlion @justin_butlion]

I just found a great solution to a frustrating issue I've had for years.

I like to subscribe to educational newsletters to pick up tips and learn from entrepreneurs far more successful than myself.

I've been really enjoying the 30 day growth series by @chenellco.

Here's the issue: I might get a newsletter on Monday but only want to go through it on Friday. Until then it sits unread in my inbox taking up space.

I like inbox zero.

The solution: I forward the email to an inbox my virtual employee, Viktor (@get_viktor_com), has access to and I ask him to turn the email into a document in @slitehq, my AI-friendly knowledge base.

Why does this work so well?

1. The content is organized in a knowledge base so I can find it easily (Slite has a great search function).

2. I can ask Viktor to scan through Slite and use the info saved from the newsletters to solve problems, draft plans, or make suggestions.

3. I keep my inbox clean.

4. I can ask Viktor to create a digest of the lessons that were saved in the knowledge base in the previous week. I can then scan through them and zoom in on the items which catch my attention.

By the way, the above flow can be semi-automated by using multiple inboxes, labels and a scheduled task set up in @get_viktor_com.

DM me if you'd like the exact steps for setting up this flow.

[Femke Plantinga @femke_plantinga]

Me whenever I use Notion instead of Slite.

[Femke Plantinga @femke_plantinga]

late to the party? 🪩

@slitehq

[Femke Plantinga @femke_plantinga]

Everyone's building a personal Knowledge Base.

For one person. But what about teams?

Obsidian. Notion. Claude. Readwise. It looks clean. It feels smart. It works great.

Here's what nobody shows you when you try to do the same for your team ↓

Permissions & access control
Verification & expiry cycles
Owner accountability
Staleness detection
Multi-source ingestion (Slack, GitHub, Jira, Linear, Drive...)
Conflict resolution (who's right when two docs contradict?)
Onboarding flows
Search at scale
AI that knows what's trusted vs. someone's Slack opinion
Guest management
Audit logs
SSO / SCIM provisioning
Cross-tool search
Content gap detection

A personal KB needs one thing: you to keep feeding it.

A team KB needs all of this, and it needs to run without anyone remembering to do anything.
That's not a note-taking problem. That's an infrastructure problem.

With @slitehq, we're building a knowledge base that maintains itself. More coming in June! 👀

[Slite @slitehq]

Your codebase is your best knowledge base.

You just can't search it in plain English. (Until now)

Most AI tools search the docs your team wrote.
But docs go stale. Code doesn't lie.

Last week, a support agent asked Super: "What happens when a blocked user tries to sign up?", something no one had documented.

𝗠𝗲𝗲𝘁 𝗦𝘂𝗽𝗲𝗿, 𝗼𝘂𝗿 𝗔𝗜 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁:
→ browsed the codebase
→ walked the relevant code paths
→ returned the exact behavior

No ticket. No engineer pulled away from their work. No two-day wait.

This is what @Christophepas, our CEO, shared for @Newsweek's AI impact newsletter last month: "The code has always been the real source of truth."

Super connects to Slack, Intercom, your help center, and your codebase. Ask anything in plain English. Get a cited answer from wherever the truth really lives.

Read the full post: https://t.co/t2DwvIrztL

[Femke Plantinga @femke_plantinga]

Your AI agent is only as smart as your worst doc.

And your worst doc is probably 18 months old.

Everyone's deploying AI agents right now. Cursor. Claude. Internal copilots. Support bots crawling your company wiki.

But almost nobody is asking the obvious question:
𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝗿𝗲𝗮𝗱𝘀 𝗮 𝘀𝘁𝗮𝗹𝗲 𝗱𝗼𝗰?

It acts. Confidently. At scale. One wrong answer to one customer becomes one wrong answer to five thousand before anyone notices.

A stale doc used to be a human inconvenience. Now it's an infrastructure risk.
So what does a team KB actually need to support AI agents?

Four things:

1️⃣ Continuous ingestion: Your knowledge doesn't live in one place. It lives in the Slack thread where a decision got made, the support ticket that revealed a product gap, the meeting where the policy changed. A real team KB pulls from all of it — automatically, continuously.

2️⃣ A verification layer: A verified policy doc and a half-baked Slack thread shouldn't carry the same weight when your AI is answering questions at scale. The system needs to know what's been reviewed, approved, and trusted, and treat sources accordingly.

3️⃣ Freshness monitoring: A human skimming a doc might sense something feels off. An AI agent won't. Freshness monitoring flags content that hasn't been reviewed in months and surfaces docs that contradict newer information before they quietly become the source of truth.

4️⃣ Agentic maintenance:  The goal is to make human review scalable. Instead of waiting for someone to notice a doc is wrong, the system proactively identifies what needs updating, drafts a suggested fix, and puts it in front of the right person for a quick approve or edit.

Karpathy said there's room for "an incredible new product" for personal knowledge bases. The same is true 10x over for team knowledge.

[Christophe Pasquier 🇺🇦 @Christophepas]

Love this workflow, just editing my @slitehq doc on the right, leaving comments to claude, it runs, check, update, resolve the comment and pick the next one

[Femke Plantinga @femke_plantinga]

Your AI stack is only as good as its memory.

And right now, you are the memory.

Every session starts the same: re-explain the context, copy-paste the Slack thread, remind your agent who owns what and why the launch slipped. The moment you stop typing, it all goes stale.

@Christophepas posted this pretty cool article, where he finds that most teams are tangling three very different things into one messy layer:
Tools - that go deep into one domain.
Hands - that execute and coordinate.
A brain - that remembers everything and connects the dots.

My key takeaways from the article: the more capable your agents get (like Claude), the hungrier they are for context. So the bottleneck of your entire AI stack is you, re-feeding it every morning.

Keeping the layers separate means every improvement in any one of them benefits you immediately. Bundle them, and you're stuck upgrading everything or nothing.

This is the kinda problem we're building for → @superdotwork is the brain. @SliteHQ is where verified knowledge lives. Together, they shape the memory layer for your team and agents.

Full article: https://t.co/OBGUzBhjOD

[Femke Plantinga @femke_plantinga]

We're hiring a Technical Product Specialist at @Slitehq 🧡

Not a support role. Not just demos.

We're looking for someone who:
• Speaks API fluently
• Is obsessed with AI tools (like, actually tests them all)
• Has run real customer implementations end-to-end
• Makes complex things simple — in writing, in calls, in docs

20-person team. Remote. Profitable. YC-backed.

If that's you (or someone you know) @ them or apply here 👉 https://t.co/EZsVGkn73d

[Slite @slitehq]

inline LaTeX formulas are now in your Slite docs.

Type /LaTeX formula → mix math + text in the same line (exports work too!)

long-requested. finally shipped.

[Slite @slitehq]

Docs that look like spreadsheets are a crime.

That's why there are now 𝗠𝘂𝗹𝘁𝗶-𝗰𝗼𝗹𝘂𝗺𝗻 𝗹𝗮𝘆𝗼𝘂𝘁𝘀 in Slite.

Compare options. Build compact summaries. Stop writing walls of text.

Try it now: Type /column→ Pick 2, 3, or 4 columns → Done

[Slite @slitehq]

It’s not just about finding the right doc...
it’s about making sure the right people actually see it.

That’s how you turn @NotionHQ into more than a knowledge base.

You connect it to Super, and it starts working for you.
Try it today: https://t.co/ia3fbPpawk

[Slite @slitehq]

Notion holds a lot of answers.

But for most teams, those answers are hard to find.

That’s where Super comes in.

[Slite @slitehq]

And it's not just search.

With Super + Notion, you can:
- Track decisions across product specs
- Spot patterns in meeting notes
- Power digests with real updates from real docs
- Surface buried info before someone asks again

[Slite @slitehq]

@icanvardar 👋

[Andrew S. Rosen @Andrew_S_Rosen]

@icanvardar @SliteHQ every day.

[Christophe Pasquier 🇺🇦 @Christophepas]

Curious on how ChatGPT new connectors work?

I ran a small side by side comparison running queries on @linear data:
- w/ ChatGPT Deepresearch with connectors
- w/ Claude MCP
- and w/ @superdotwork

Conclusions:
- Claude has issues and MCP are very bad for retrieval
- Deepsearch kind of fixes it but it takes enormous time + tokens
- Super gives answers in 10" but will have less depth than deepsearch (which we'll add very soon)

Overall I feel deep search w/ connectors could be interesting for advanced analysts use cases... but also wonder if that wouldn't be the situations where experts want much more control on the retrieval and the steps.

The black box effect makes it hard to fully believe the results.

And once again shows the importance of having incredible retrieval on internal data for AI workflows, that's what we build.

[Slite @slitehq]

@lolcat_host Or if they do, it breaks 🥲