AI Architecture · LeverageAI · The Wiki-Grounded Curator

A Newsfeed That Hunts Its Own Blind Spots: The Wiki-Grounded Curator

“Interesting” isn’t a property of a tweet — it’s the gap between the tweet and what you already know. Build a filter on your own explicit worldview and one property falls out for free: it knows what would falsify your claims, so it can go hunting for them. Which makes the echo-chamber objection exactly backwards.

Scott Farrell — LeverageAI · A field guide for builders with a second brain · ~12 min read

Here is the thing I actually want, and I suspect you want a version of it too. I want to know what the handful of people worth following are saying — Karpathy, a few researchers, two or three subreddits about agentic AI — without opening the apps they live in. I don’t want to read Twitter. I don’t want to spend an evening in a feed. But I do need to know what’s moving, because in this field a week is a long time. The naive fix is an AI that reads the feed and summarises it for me. I built toward that, and it was useless, and the reason it was useless is the whole point of this article.

A summary of a Karpathy thread tells me what he said. I can get that from the thread. What I can’t get from the thread — what no summariser pointed at the open web can give me — is what it means in my world: where it lines up with something I already argued, where it cuts against a claim I’ve staked, where it’s a genuinely new idea I should chase, and (most of the time) that it’s something I already know and can safely ignore. That judgment isn’t a property of the tweet. It’s a property of the gap between the tweet and me. And a summariser has no model of me, so it can’t compute the gap. It can only tell me what the tweet says, which I could already see.

Interesting is a diff, not an adjective

Start from the observation that reframes everything downstream: “interesting” is not a property of a tweet — it’s a relation between the tweet and what you already know. This is why generic AI news summarisers are useless and why this design isn’t. Filtering incoming information is a diff operation, and a diff is meaningless without something to diff against. The incumbent feeds all have a reference — but it’s the wrong one, and swapping it is the entire move.

I learned this from email, not Twitter. I gave an agent access to my Gmail early on and asked it to triage. It was hopeless — because “is this email worth interrupting Scott for?” is unanswerable without a model of what Scott knows, cares about, and has pending. Then I gave it a wiki compiled from a few years of my email, and the same agent, same model, became a genius at it. Nothing about its intelligence changed. What changed is that triage finally had something to diff against. (Judgment quality turned out to be mostly a function of worldview access, not model capability — a claim I’ll cash out elsewhere.) And the tell that it was working was counter-intuitive: it now hardly ever tells me anything, which is exactly right. Silence is the high-judgment output. Hold onto that, because a good news curator is quiet for the same reason.

So the design isn’t “an AI that reads Twitter.” It’s an AI that reads Twitter and diffs each item against an explicit, written-down model of my worldview — the same wiki my other agents already use.⁵ Once the reference is your compiled canon rather than the abstract notion of “newsworthy,” the scoring taxonomy stops being something you design and starts being something that falls out of the diff.

The four diff classes

Every incoming item — a tweet, a thread, a top comment in r/LocalLLaMA — lands in exactly one of four buckets, and each bucket has a predetermined disposition. This is the whole curator in one table.

Read the classes in order and notice they’re sorted by exactly one thing: how much this item should change what you do next. A contradiction should stop you now, because an unexamined counter-argument to a claim you’re building on is the most expensive thing to miss. A convergence is worth a note but not an interruption — it’s reassurance, and reassurance can wait. A novel idea is a lead to follow when you have a spare hour. And “already known” — the overwhelming majority of any feed — is precisely what an attention-optimised timeline would still serve you, dressed up as fresh, and what a worldview-grounded curator throws on the floor without a word. The wiki is what lets the agent tell these four apart, because only the wiki knows which of them you already wrote down.

One briefing, so this isn’t abstract

Here is what actually arrives — not the tweet, but the curator’s take on it. This is a convergence-class item that cleared the interrupt budget and reached my phone:

# BRIEFING — Tue 09:14 · 1 of 3 pushes left this week
# SOURCE: @karpathy, thread (link)   CLASS: converges-on-canon

He said:  agents should file their own answers back into a
          maintained wiki as new pages, so exploration compounds
          instead of evaporating into chat history.

In your world:  this is your own "the index is the data"
          argument, and the write-back move in your scout/senior
          loop. Nearest pages: concept.write-back, framework.wiki-graph.
          Edge added: karpathy-2026 --[converges-on]--> write-back.

The extension he didn't take:  he keeps the *answer*. You also
          keep the *path* — the scout transcript — which turns the
          wiki into telemetry about its own missing edges. That half
          is still yours; nobody's tweeted it.

✓ signals log: theme "wiki-write-back" flagged — won't re-alert.

Look at what the delivery is. Not “Karpathy said X.” Instead: here’s what he said, here’s where it already lives in your work, here’s the edge I added to your graph, and here’s the one rung he stopped short of that’s still open ground for you. That last line — the extension he didn’t take — is the thing you cannot buy from a summariser, because producing it requires diffing his claim against your whole corpus and finding the seam. A wiki-grounded senior produces exactly that. A wiki-less summariser produces a paraphrase of a tweet you could already read.

Two disciplines that keep it honest

A curator with no constraints becomes an enthusiastic intern who forwards everything. Two disciplines stop that, and both are cheap.

An explicit interrupt budget. Give it a hard cap — say, three Pushover⁴ notifications a week — and let everything else wait for the batched briefing or die silently. A budget forces genuine ranking. Without it, the agent’s natural failure mode is to mistake its own enthusiasm for your priorities and ping you nine times a day, at which point you mute it and you’re back to reading the feed yourself. The scarcity is the feature: when the agent can only spend three interrupts, it has to decide what a real contradiction looks like, and that decision is where the value lives. Two or three genuinely earned interrupts a week beats a smart-sounding trickle that trains you to ignore it.

Briefing write-back with a signals log. Every briefing is filed back into the wiki as a derived page, and every theme it raises goes into an append-only signals log. So the next time three more people tweet about wiki-write-back, the curator checks the log, sees the theme is already flagged, and stays quiet — it doesn’t re-alert you on a drum it already beat. This is what makes the curation compound rather than loop: the system remembers what it has already told you, so its silence gets smarter over time, not just its noise. The briefings are cache, not canon — typed as derived, ranked below your source-backed claims, regenerable and disposable — but while they live they stop the agent repeating itself.

You don’t build a new engine — it’s a fourth role

Here is the part that surprised me, and the reason this is worth building rather than just admiring: the curator needs almost no new machinery. If you’ve already got the pattern I’ve written about before — a cheap, fast scout that walks the wiki and a smart senior that finalises the judgment — then the curator is that same rig pointed somewhere new. The incoming tweet or thread is just a source. The scout walks both wikis — your frameworks canon and your dev-project wiki — anchored on that source, exactly as it would when ingesting a new document. The only difference is the ending: instead of the senior emitting a mutation doc that changes the wiki, it emits a briefing that changes you. Same conversation-handoff loop, same map, same tools. A fourth role that differs from the triager, the ingester, and the janitor only in its North Star and its terminal tool.⁵

That’s the pattern I keep arriving at from every direction: one scout, one senior, one map, many North Stars. The triager files your email. The ingester grows the wiki. The janitor keeps it clean. And now the briefer reads Twitter so you don’t have to. They’re the same machine aimed at different destinations. Which means the marginal cost of the entire news-curation capability is roughly: write one North Star, grant one output tool (Pushover instead of a write-to-wiki call), and point the cron at the feeds. The reason I can build a personal newsfeed in an afternoon is that I’m not building a newsfeed. I’m adding a destination to a machine that already runs.

The prior art rhymes with this without quite reaching it. Karpathy’s own LLM-wiki work already treats the agent’s explorations as durable — answers filed back as pages so exploration compounds instead of evaporating into chat history¹ — and there are open-source skills that let a coding agent maintain an interlinked wiki for you.² What none of them centre is the diff: the idea that the wiki’s highest use on inbound information isn’t storage, it’s ranking against what you already believe. That’s the turn that makes a knowledge base into a curator.

The echo-chamber objection is exactly backwards

Now the objection everyone raises, because it’s the right one to raise: isn’t a feed filtered through your own worldview the ultimate echo chamber? A filter tuned to what you already think, enclosing you in a bubble that only ever confirms you — the filter-bubble worry, named fifteen years ago and truer than ever.³ If that’s what this were, it would deserve the objection. It’s the opposite, and the reason is structural, not aspirational.

An engagement feed structurally can’t prioritise productive contradiction, because being challenged isn’t reliably engaging. A filter that knows your claims explicitly knows what would falsify them — and can hunt for it. A bubble that targets its own weak points isn’t a bubble; it’s peer review.

Sit with why the incumbents can’t do this even if they wanted to. An engagement-optimised feed selects for what holds your attention, and being told you’re wrong is not reliably attention-holding — it’s often the thing you scroll past. So the optimiser learns to feed you affirmation and outrage, because those keep you there, and it has no principled way to distinguish “a contradiction that would improve your thinking” from “a contradiction that just annoys you.” It doesn’t know your claims. It only knows your dwell time.

My curator knows my claims — they’re written down as explicit pages with edges. Which means it can do the one thing the engagement feed can’t: it can look at my canon, work out what evidence would break a given claim, and go looking for exactly that. “Contradicts the canon” isn’t an accident of the taxonomy; it’s the top class, the one thing most likely to earn a same-hour interrupt. The feed that knows me best is the feed most able to hunt my blind spots, because it’s the only one that knows where they are. That’s not an echo chamber with better manners. It’s a category flip: a personalised filter whose highest priority is disconfirmation is doing peer review, and peer review is the opposite of a bubble.

“The algorithm” versus “my lens”

Underneath the echo-chamber flip is an ownership story, and it’s the cleanest way I know to name what’s actually different here. Think about how my daughter finds out about the world: she asks TikTok. Not Google, not an AI — TikTok. It’s her whole news system, other people’s information pushed at her. And TikTok has a model of her, a very good one. But that model is behavioural, implicit, proprietary, and optimised against her attention. It learns what she lingers on, which drifts toward whatever captures, and she cannot open it, read it, or edit it. She doesn’t own her filter. She is the thing being filtered for.

My curator’s model of me is the inverse on every axis. It’s explicit — a wiki of pages I can read. It’s inspectable — when it decides a tweet is interesting, I can see which of my claims it diffed against. It’s owned — the pages sit on my disk, in markdown, and I can edit the filter by editing the canon. And it’s optimised for my growth, because I set the North Star, and my North Star is “make me think,” not “keep me here.” It knows what I think, not what I click. That’s the difference between the algorithm and my lens, and it’s a difference in kind. Which is also why this is a genuine marketplace-of-one proposition rather than a better recommender: the product isn’t a feed, it’s your feed, curated against a worldview only you own — and the moat is that compiled worldview, which takes months of ingestion to build and gets sharper every time it’s used. The honest cost is the cold start: the curator is only magic once the wiki knows you, and a stranger’s wiki starts empty. Onboarding a new person is ingesting their exhaust — their mail, their notes, their own writing — until there’s enough of a lens to diff against.

The delivery is the take, not the tweet

Step back to the reader’s original question — how do I follow the influential people without reading social media, and get takes better than the originals? — and the answer is now precise. You follow them through a filter that holds an explicit model of what you know, ranks each item by how it changes you, interrupts you at most a handful of times a week and only for things that should genuinely stop you, and delivers not the tweet but what the tweet means in your world, where it already surfaced in your work, and the extension the author didn’t take. That last part is why the take can be better than the original: the original is one person’s point; your version is that point diffed against everything you’ve ever argued, which is a thing only your corpus can compute.

There’s an obvious mirror to all this — the same engine pointed outward, publishing your take on his tweet instead of filing it to your phone — but that’s a distribution story, and it’s the next article. Here we stay inbound.

What I keep coming back to is that I didn’t set out to build a news product. I set out to stop drowning, kept a wiki of what I actually think, and discovered that a filter built on an explicit worldview does something no engagement feed can: it gets quieter as it gets smarter, it hunts the arguments that would prove me wrong, and it hands back other people’s ideas already metabolised into mine. The feed everyone warned would trap me in a bubble turned out to be the only one capable of finding the holes in it. The blind spots were always there. Now something reads the whole world looking for them, three times a week, while I’m doing something else.