Comprehension engine for long-form video

See the argument.
Not the summary.

Most AI tools flatten a three-hour podcast into bullet points that erase every nuance. Noeverse turns it into a navigable argument graph — every claim tagged for certainty, every blind spot named, every quote one click from the source.

See it in action ↓
Works withYouTubeX / TwitterPodcastsMP3 / MP4
SCROLL
91%of AI summaries can’t tell the difference between a speaker’s opinion and a cited fact
3–4distinct ideas your working memory can hold at once. A three-hour podcast surfaces 200+ claims.
70%of what you hear is gone within 24 hours. Without structure, the conversation evaporates.
The problem isn't time. It's structure.

You showed up to a three-hour Dwarkesh episode with full attention. You followed every word. And you finished it unable to say which claim depended on which, what was contested versus settled, or what the strongest counter-argument would have been — because nobody made it.

This is what speech does. The argument has a logical scaffold, but no one can see it. Standard AI summaries make it worse: they flatten the structure into bullet points and erase the speaker's voice along the way.

Noeverse renders the scaffold. So you can argue with it, learn from it, and remember it.

Live demo

This is what Andrej Karpathy
actually argued. All of it. Mapped.

Below is the full argument graph from Karpathy's interview with Dwarkesh Patel. Click any claim to see the moment in the video it was said. Toggle the Epistemic Terrain to see which claims are fact, which are contested, and which are speculation. Nothing here was paraphrased — every node links to a verbatim timestamp.

·Topology
Andrej Karpathy — AI coding, agents & the future of software
Type
Sort
36 of 36 claims
Opinion◆ primary
You are now the binding constraint
"I kind of feel like I was in this perpetual, I still am often in this state of AI psychosis, just like all th…
Agent WorkflowToken Economy+1
00:03:2010 conn.
Speculation◆ primary
Agents, not humans, are software's new customers
"There's this sense that these apps that are in the app store for using these smart home devices, et cetera, t…
Agent-First SWAgent UX+2
00:25:157 conn.
Opinion◆ primary
Remove human researchers from the optimization loop
"You have to arrange things such that they're completely autonomous. The more you can maximize your token thro…
Auto ResearchResearch Org
00:30:006 conn.
Contested◆ primary
Autonomous research loops beat decades of expert intuition
"I let auto research go for overnight and it came back with tunings that I didn't see. And yeah, I did forget …
Auto ResearchResearch Org+1
00:33:205 conn.
Opinion◆ primary
Models are simultaneously brilliant and 10-year-old incompetent
"I simultaneously feel like I'm talking to an extremely brilliant PhD student who's been like a systems progra…
Model LimitsRL Boundary
00:46:305 conn.
Contested◆ primary
Only RL-verifiable domains actually improve with scale
"The labs can improve the models in anything that is verifiable or that has rewards. So did you write the prog…
RL BoundaryModel Limits
00:48:404 conn.
Contested◆ primary
Cross-domain intelligence generalization is not happening
"I don't think that's happening. Maybe we're seeing like a little bit of that, but not like a satisfying amount."
RL BoundaryModel Limits
00:49:202 conn.
Opinion◆ primary
Digital transformation leads physical by years, maybe a decade
"Flipping bits and the ability to copy-paste digital information makes everything a million times faster than …
Digital vs PhysicalAgent-First SW
01:05:206 conn.
Fact◇ secondary
December 2024: the irreversible workflow flip
"In December, is when it really just... something flipped where I kind of went from 80-20 of like, you know, t…
Agent Workflow
00:04:303 conn.
Opinion◇ secondary
Agent failures feel like skill issues, not capability gaps
"It all kind of feels like skill issue when it doesn't work to some extent. You want to see how you can paraly…
Agent Workflow
00:08:451 conn.
Fact◇ secondary
Macro-actions: send features, not functions, to agents
"It's like here's a new functionality and delegate it to agent one. Here's a new functionality that's not goin…
Agent WorkflowAgent-First SW
00:10:203 conn.
Opinion◇ secondary
Token throughput is the new GPU utilization metric
"I feel nervous when I have subscription left over. That just means I haven't maximized my token throughput. S…
Token EconomyAgent Workflow
00:13:402 conn.
Fact◇ secondary
Claws: persistent autonomous loops that act while you sleep
"When I say a claw, I mean this like layer that kind of takes persistence to a whole new level. It's something…
Agent WorkflowAgent UX+2
00:18:305 conn.
Opinion◇ secondary
Agent personality is a genuine product innovation, not decoration
"He actually really crafted a personality that is kind of compelling and interesting. I feel like a lot of the…
AI PersonalityAgent UX
00:20:102 conn.
Opinion◇ secondary
Claude's calibrated sycophancy makes you earn its praise
"When Claude gives me praise, I do feel like I slightly deserve it… when it's a really good idea by my own acc…
AI PersonalityAgent UX
00:21:451 conn.
Fact◇ secondary
Three prompts: "find my Sonos" → music plays in study
"I'm like, yeah, can you try to play something in the study? And it does. Music comes out. I'm like, 'I can't …
Home AutomationAgent UX+1
00:22:554 conn.
Opinion◇ secondary
Write documentation for agents, not human readers
"I'm explaining things to agents. If you can explain it to agents, then agents can be the router and they can …
Education ShiftAgent-First SW+1
01:03:006 conn.
Speculation◇ secondary
Recursive self-improvement is every frontier lab's actual goal
"Every research organization is described by program.md… And you can imagine having a better research organiza…
Recursive AIAuto Research+1
00:34:005 conn.
Speculation◇ secondary
AI models will speciate like animal brains evolved
"I think we should expect more speciation in the intelligences. Like you know, the animal kingdom is extremely…
AI SpeciationFrontier Labs
00:53:453 conn.
Speculation◇ secondary
An internet swarm could out-research any single lab
"The Earth is much bigger and has a huge amount of untrusted compute. But if you put systems in place that dea…
Swarm IntelligenceAuto Research+1
00:58:304 conn.
Contested◇ secondary
Cheaper software creation paradoxically increases demand
"They made the cost of operation of a bank branch much cheaper, so there were more bank branches, so there wer…
Demand ElasticityJob Market+2
00:58:002 conn.
Fact◇ secondary
Frontier labs are all betting on monoculture models
"Currently my impression is the labs are trying to have a single sort of like monoculture of a model that is a…
AI SpeciationFrontier Labs+1
00:53:104 conn.
Fact◇ secondary
Auto-research only works where you can measure success
"This is extremely well suited to anything that has objective metrics that are easy to evaluate. For example, …
Auto ResearchRL Boundary
00:42:30
Opinion◇ secondary
Researchers at frontier labs are automating themselves away
"You guys realize if we're successful, we're all out of jobs. We're just building automation for SAM or someth…
Frontier LabsJob Market+1
01:10:304 conn.
Opinion◇ secondary
Centralized AI has historically poor precedent
"Centralization has a very poor track record in my view in the past… I almost wish like there were more labs. …
Frontier LabsOpen Source
01:12:004 conn.
Speculation◇ secondary
Next frontier: sensors and actuators at the digital↔physical boundary
"What's going to happen is, first, there's going to be a huge amount of unhobbling, and then actually, it's go…
Digital vs PhysicalAgent-First SW+1
01:06:452 conn.
Speculation◇ secondary
Compute (flops) may become the new wealth metric
"Like right now, for example, it's really hard to get compute, even if you have money… it almost seems like th…
Token EconomyFrontier Labs+2
01:02:403 conn.
Opinion◇ secondary
Open source AI will be to LLMs what Linux is to computing
"In operating systems, you have Windows and macOS… And there's Linux. And Linux is an extremely successful pro…
Open SourceFrontier Labs
01:14:303 conn.
Fact◇ secondary
LLM training is 200 lines; everything else is efficiency complexity
"If you don't need it to go fast and you just care about the algorithm, then that algorithm actually is 200 li…
MicroGPTEducation Shift+1
01:19:153 conn.
Speculation◇ secondary
Vibe coding is a transitional phase, not the destination
"I kind of feel like this kind of stuff that I just talked about, this should be... like in a year or two or t…
Agent WorkflowEducation Shift+1
00:27:103 conn.
Fact· supporting
No code typed personally since December 2024
"I don't think I've typed like a line of code probably since December, basically. Which is like an extremely l…
Agent Workflow
00:05:121 conn.
Fact· supporting
Auto Research found missed hyperparams after 20 years
"I let auto research go for like overnight, and it came back with like tunings that I didn't see. And yeah, I …
Auto ResearchResearch Org
00:35:401 conn.
Fact· supporting
Same 3 jokes for 4 years despite massive capability gains
"Why do scientists not trust atoms? Because they make everything up. This is the joke you would get three or f…
Model LimitsRL Boundary
00:47:551 conn.
Fact· supporting
Open source is ~6–8 months behind frontier, and that's healthy
"Basically, the closed models are ahead, but people are monitoring the number of months that open source model…
Open SourceFrontier Labs
00:46:003 conn.
Opinion· supporting
Markdown docs for agents, not HTML docs for humans
"Instead of HTML documents for humans, you have Markdown documents for agents. Because if agents get it, then …
Education ShiftAgent-First SW+1
01:03:452 conn.
Fact· supporting
Context windows are the only cheap model customization lever
"We don't have these primitives for actually working with the intelligences in ways other than just context wi…
Model LimitsAI Speciation
00:57:002 conn.
Sample · Andrej Karpathy on AI agents & the future of software · 36 claims extracted
Why summaries fail

AI summaries flatten the things
you need most.

91%

can’t distinguish the speaker’s opinion from a cited fact

64%

miss contradictions within the source material

79%

miss the insight, context, or nuance that changes the meaning

Noeverse was built so this list would not apply to it. Every claim is tagged for certainty. Every contradiction is mapped as an edge. Every quote links to the moment it was actually said.

How it works

From video link to full argument map
in under five minutes.

01

Drop a link

Paste any YouTube URL or search directly from the app. Talks, interviews, podcasts, documentaries — anything with speech.

02

We transcribe & extract

Full transcript pulled, then a frontier AI model identifies every distinct claim, who said it, and how each one logically relates to the others.

03

Choose a lens

Argument graph for structure. Epistemic Terrain for certainty. Steelman for what the strongest critic would say. Switch freely — the underlying data is the same.

04

Explore the map

Every claim links to the ideas it supports, challenges, or qualifies — and to the verbatim timestamp where it was said. No paraphrase. No interpretation.

The ten lenses

Ten lenses. Same conversation.
A different question each time.

A summary answers "what did they say?" Noeverse answers ten harder questions — each rendered as its own visual mode over the same underlying transcript.

LENS 01

Argument Topology

The logical scaffold no one can hold in their head.

Every claim becomes a node. Every dependency becomes a typed edge — supports, attacks, qualifies, depends. Load-bearing claims highlighted: pull them out and the argument collapses.

LENS 02

Epistemic Terrain

Fact, consensus, contested, or speculation — color-coded across the whole conversation.

The certainty heatmap the rationalist community has been hand-writing as “epistemic status” headers for a decade — now generated automatically, per claim.

03

Blindspot

The perspectives, populations, and considerations the speakers never addressed.

04

Principles

The underlying rules the speakers operate from — even when they don’t name them.

05

Timeline

Every claim in the order it was said, each anchored to a verbatim timestamp.

06

Power

Who benefits if these claims are accepted? What incentives are speaking?

07

Prerequisites

Every concept you needed to know to fully understand this conversation.

08

Implications

The downstream consequences of each claim — what follows if they’re true.

09

Steelman

The strongest version of the counter-argument they should have heard.

10

History

How this debate has been argued before — and what’s been settled.

Plus the basics you'd expect

Minutes, not hours

A two-hour video, analyzed in under five minutes.

Joint Projects

Combine multiple videos into one cross-referenced map.

Shareable by link

Every analysis is publicly shareable. No account needed to read.

YouTube search

Find and confirm videos without leaving the app.

Sort by what matters

Order by centrality, provocativeness, or argument flow.

How Noeverse compares

The intersection is empty.
That's where we live.

Summarizers extract from video but flatten the structure. Argument mappers preserve structure but make you build it by hand. Research notebooks chat over sources but don't map them.

AI summarizer (Eightify, Glasp, BibiGPT)
AI research notebook (NotebookLM, Recall)
Argument mapper (Kialo, Rationale)
Noeverse
Auto-generated from video
Argument graph with typed edges
yes (manual)
Per-claim epistemic certainty
Steelman & blindspot lenses
partial (debate audio)
Every claim → timestamp
partial
partial
Ten interpretive lenses

Categories named for clarity. Feature presence as of May 2026.

Who it's for

For people who don't want a shorter podcast.
They want a deeper one.

01

Power listeners of Lex, Dwarkesh, Tyler, EconTalk, 80,000 Hours

If you finish a three-hour interview wanting to argue with it, not summarize it, you are the target user.

02

The rationalist & forecasting community

Every claim tagged for certainty. Every counter-argument steelmanned. The “epistemic status” header you’ve been writing manually for a decade — now automatic.

03

Researchers & academics

Map the argumentative structure of an entire lecture series. Compare how different speakers handle the same claims.

04

Journalists & analysts

Every claim, traced to its source. Every assumption, made explicit. Fact-check at the level of individual assertions.

05

Founders & operators

Watch a 3-hour investor talk in 10 minutes. Extract signal from keynotes, panels, and podcasts without sitting through the runtime.

What Noeverse will not do

We picked a side.
These are the things we're not building.

×

No AI hostspretending to discuss your podcast in two voices that say “deep dive” forty times before they get to anything.

×

No paraphrased summariesthat read like ChatGPT compressed the speaker’s voice into a textbook table of contents.

×

No claims that don’t link to a sourceevery node in every lens points to the exact timestamp where it was said. If we can’t cite it, we don’t surface it.

×

No “you might also like”Noeverse is a comprehension tool, not a recommendation engine. You bring the video. We render its structure. That’s it.

For fifteen years, argument-mapping tools failed because building the map was the user's job. Noeverse generates the map automatically from the transcript — that's why it works.
Why this is different
Pricing

Ten lenses per video. One credit per video. No throwaway summaries.

Each video analysis costs one credit and unlocks all ten lenses. Get credits monthly via subscription, or buy a pack that never expires.

Monthly credits used first · Pack credits never expire · Cancel anytime

Monthly subscriptions

BASIC
2.99/mo

10 credits/month · €0.30 per analysis

Ideal for occasional deep-dives into long-form content.

  • 10 video analyses / month
  • All ten lenses on every video
  • Verbatim timestamps on every claim
  • Shareable links
  • Joint Projects
Best valuePRO
8.99/mo

40 credits/month · €0.22 per analysis

For researchers, journalists, and knowledge workers who live in video.

  • 40 video analyses / month
  • All ten lenses on every video
  • Verbatim timestamps on every claim
  • Shareable links
  • Joint Projects (unlimited)

Or buy credits once — they never expire

STARTER PACK
5.99

20 credits · €0.30 each

PopularVALUE PACK
11.99

45 credits · €0.27 each

POWER PACK
29.99

120 credits · €0.25 each

Need just a few? Top up at €0.35/credit — buy any amount ≥ 5, no subscription needed.

Refund any analysis that doesn't pass the verbatim test — every claim must link to a real timestamp in the source.

Try it on the next Dwarkesh episode.
See what you missed.

Paste a video URL. See the argument graph, the certainty heatmap, and the steelman they should have heard.
Basic plan from €2.99/mo · Credits never expire.