More signal. Less noise. Better thinking.
One feed for everything you follow online.
Filter aggregates your RSS feeds, YouTube channels, Reddit communities, newsletters, and podcasts into a single stream — then helps you cut through the noise so you spend time learning, not triaging.
filter
Source health
41 healthy · 2 reconnecting
Open-source eval frameworks are converging on the same primitives
Consensus forming around dataset versioning, grader quality, and reproducible runs.
RAG performance drops came from indexing, not the model swap
Practical chunking and retrieval settings that outperformed architecture changes.
Building weekly research loops that produce writing output
A repeatable cadence for reading, tagging, and turning notes into drafts.
Heavy on retrieval and eval tooling today. Start with 3 high-signal reads.
3 items worth reading first
Matches your active themes: evals, retrieval, and writing systems.
5 items to batch-archive
Duplicate framing or low-depth takes already covered elsewhere.
Morning Triage
18
Unread · High signal · Last 24h
Build Inputs
24
Tooling · Product · AI infra
Weekly Synthesis
11
Saved · Tagged · Needs notes
The problem
Curious people are buried in tabs, apps, and inboxes.
Fragmentation
Context-switching between 5+ apps just to stay current.
Noise
No unified way to filter, rank, or deduplicate across sources.
Lost insight
No place to annotate, tag, or retrieve what you've read.
No learning loop
Your feed never gets smarter based on what you actually engage with.
How it works
From fragmented inputs to focused insight.
01 — Aggregate
Every source you care about, in one place.
Connect RSS feeds, YouTube channels, Reddit communities, newsletters, and podcasts. Import in bulk with OPML. Monitor source health so you always know what's flowing and what's broken.
- RSS, YouTube, Reddit, newsletters, podcasts
- Bulk add and OPML import
- Source health monitoring with retry and error reporting
- Canonical links always preserved
Sources
02 — Triage
Process your feed quickly and confidently.
Quality scoring surfaces the best items and suppresses noise. AI summaries tell you where to start. Saved views and keyboard shortcuts keep sessions fast and focused.
- Quality scoring to suppress noise and repetition
- AI daily summaries and "read next" recommendations
- Saved views and filter presets for focused sessions
- Keyboard shortcuts and bulk actions
Open-source eval frameworks are converging on the same primitives
Latent Space
RAG performance drops came from indexing, not the model swap
r/MachineLearning
Matches your active themes: evals, retrieval, and writing systems.
03 — Synthesize
Turn reading into durable knowledge.
Highlight, annotate, and attach notes to anything in your feed. When you're ready, export to Notion, Obsidian, or CSV — your insights leave with you.
- In-app reader with highlighting and annotation
- Notes attached to items, searchable and timestamped
- Export to Notion, Obsidian, CSV/JSON
- Browser extension for ad-hoc capture
Reader
Most teams over-focus on model swaps and under-invest in retrieval evaluation.
Benchmark indexing and chunk strategies before evaluating architecture-level changes.
Export to
Replace scattered tabs with one feed that learns what matters to you.
Connect your sources, triage the queue, and start turning information into insight.