Your field, briefed every morning.

AI reads the papers overnight. You listen to the briefing.

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Sample briefing

An audio briefing from one of our researchers.

0:000:00 left

A real briefing · played in your browser

Hear what one morning sounds like.

Two hosts. A few papers. Eight minutes. Nothing scripted by hand — every word was written and voiced overnight from the actual papers that landed in this stream.

Sample briefing · loading

An audio briefing on what moved in this stream overnight.

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Live transcript

What it does

An engine for staying current. Not another feed.

Four pieces, built to compound. The briefings carry continuity. The anchor answers your follow-ups. The graph traces the context behind every claim.

On air

Two-host audio dialog briefings.

One host introduces each paper by author and institution. The other probes. Written and narrated overnight, before you sit down.

Mech Interp · Today · 5 papers

Anthropic’s sparse autoencoders scale to GPT-4.

▶ Played
A

So this one’s out of Anthropic — Henighan and team. They’re showing sparse autoencoders actually scale to GPT-4 size.

B

Wait, GPT-4 size? That’s the result everyone was waiting for.

Interrupt

A voice anchor that talks back.

Mid-briefing, ask anything. The anchor pauses, answers from the papers it just read, and resumes.

Live · 4:32
🎙 Hang up

Youhow does this build on what we covered last week?

AnchorGood question — Aoki’s May paper proposed dictionary learning at this scale was infeasible. This refutes that directly.

Continuity

It remembers what it already told you.

Each briefing keeps takeaways. Tomorrow’s episode references them when today’s papers extend or refute earlier work.

3 weeks ago

Aoki et al. argued dictionary learning didn't scale past 1B params.

Last week

DeepMind's interim result on sparse coding at 7B sized.

Today

Anthropic refutes Aoki — full SAE at GPT-4 scale, all features stable.

Trace

The citation neighborhood behind every claim.

Open any paper to see what it cites, what cites it, and the labs publishing at the frontier.

Seed

Anthropic SAE @ GPT-4

Seed
Cited
Cites it

How it works

From prompt to briefing, overnight.

01

Chart what you follow.

Write what you care about like you'd brief a new research assistant. Sciport runs a literature review on your topic, surfaces sub-topics, seed papers, and key researchers — you pick what to follow.

Step 1 of 4 · New stream

Tell Sciport what to follow.

I’m researching mechanistic interpretability of large language models, especially sparse autoencoders and dictionary learning at scale.

Follow:Anthropic interpretability team, Olah’s recent work, anything that refutes or extends...
Sparse autoencodersDictionary learningActivation patchingSAE scaling lawsFeature steering
02

Sciport reads and writes the briefing.

Each night it scans new papers across your sources, ranks for relevance, synthesizes the ones that matter, and writes a two-host dialog. Catchy headline. Key takeaways. Audio rendered before you wake up.

2:14 AM · Overnight

Scanning sources
arXiv · bioRxiv · OpenAlex
Ranking by relevance
48 → 5 papers
Synthesizing
claims · context · why it matters
Writing dialog
Host A & Host B
Rendering audio
ElevenLabs · 7m 14s
Headline + takeaways
pending
Ready by 6 AM
03

Listen, ask, explore.

Hit play whenever — commute, walk, between meetings. Interrupt your anchor by voice for follow-ups. Tap any paper for its claim, its references, and the citation neighborhood behind it.

▶ Now playing

Mech Interp · today’s briefing

03:18 / 07:50
You · listening

“Pause — who else is publishing in this area right now?”

🎙 Ask the anchor⏭ Skip paper📑 Open paper⌬ Citation graph

For working researchers

Built around how you actually read.

No fixed taxonomy. Write your stream like a brief — topics, authors, what to skip — and Sciport runs the lit-review, picks the threads, and brings you a briefing every morning.

PhD student

Stay current on three subfields without losing a Tuesday to feed-checking.

I'm researching: in-context learning in transformers, especially induction heads and how they emerge during training. Follow Anthropic interpretability, Olah's group, and the Stanford CRFM team. Skip anything pure-prompt-engineering.

Your streams

In-context learningInduction headsAnthropic interp

Postdoc

Track the people you used to share a hallway with — without LinkedIn-stalking.

Follow these authors: Jacob Steinhardt, Aditi Raghunathan, Chris Olah, Tatsu Hashimoto. Surface their new papers + anything that cites their work on robustness and alignment.

Your streams

Followed authors · 12Cites alignment workRobustness frontier

Principal Investigator

Know what's moving in your space before your lab meeting, every Monday.

Lab agenda: Weekly digest of: protein language models, cryo-EM at sub-3Å, AlphaFold-3 follow-ups, and anything from DeepMind, Baker Lab, or AI2 in structural biology. Flag preprints that contradict Jumper '24.

Your streams

Structural bio frontierAF3 follow-upsBaker Lab

Early access

Get one of these for your field.

Sciport is in private beta. Drop your email and we’ll open seats to researchers as fast as we can keep up.

Private beta · No spam · One email when your seat opens