You pick keywords and sources (news sites, blogs, YouTube, communities), and it sends you a summary on a schedule you set. Basically Google Alerts but it actually reads the content and gives you a digest instead of a list of links.

Built it because I was spending 30+ min/day manually checking the same sites for industry news. Now it's a 3-minute read every morning.

It's free to start if anyone here does this kind of monitoring.

The key for trust was picking my own sources. No algorithm deciding what's "relevant" - just feeds I've vetted. When something's missing, it's obvious which source dropped the ball.

I ended up building a tool for this (daige.st). You connect sources, tell it what matters, and it sends filtered summaries. Has a memory feature that gets better at filtering over time.

Cadence is still WIP. Daily for fast news, maybe weekly for deeper topics. Curious what works for others.

What's the point of browser automation that's slower than a human?

Watching it pause for seconds before every click drove me crazy.

We build muscle memory for repetitive tasks—why can't AI?

Traditional Agent

Observe → LLM reasoning (slow) → Action → Observe → LLM... repeat Stops to think before every click.

Sandy

1. First run: LLM figures out the workflow → save as scenario

2. After: Replay the scenario (no LLM calls) Once you've blazed the trail, just follow the path.

LLM only helps find the path once. After that, Sandy replays the saved scenario—

so you get both speed and cost savings.

Demo (searching and playing a video on YouTube):

- Left: Traditional agent

- Right: Sandy (real-time, not sped up)

https://www.youtube.com/watch?v=nSKs8sy7o2c

Useful for:

- E2E test automation

- Regression tests (deterministic execution)

- Multi-tool workflows (GitHub issue → Slack notification, etc.)

Works with any MCP server, so you can chain browser automation + API calls

in a single scenario.

GitHub: https://github.com/Sangkwun/sandy

Honest limitations:

- UI changes break scenarios (need re-recording)

- Better for repetitive workflows than dynamic exploration

Questions and feedback welcome. PRs and stars too!

Comments URL: https://news.ycombinator.com/item?id=46872109

Points: 2

# Comments: 0

Why I built this: I first built an internal tool to monitor my Notion workspace. I was worried about missing things happening at work. Then I realized this problem is everywhere: Reddit threads I could've helped with (but found too late), competitor moves I noticed after everyone else.

Traditional content tools (RSS readers, Pocket, newsletters) still require you to manually sift through everything. AI chatbots only answer when you ask. I wanted something that watches proactively and tells me "here's what you need to act on."

How it works: 1. Connect your sources (YouTube, RSS, Reddit, X, websites) 2. Tell AI what to watch for ("flag competitor pricing changes", "alert me to security vulnerabilities in my stack") 3. AI monitors and maintains a self-updating document of what matters to you

Tech stack: Next.js, Supabase, gemini

Limitations: - YouTube transcripts can be hit-or-miss for non-English content - Scheduled updates only (no real-time push yet)

Try it at: https://daige.st

What signals would you want AI to watch for? Happy to answer questions!

Comments URL: https://news.ycombinator.com/item?id=46842224

Points: 2

# Comments: 0

Just get filtered digests now. Needed less input, not better retrieval.

My bet: users don't want another tool. They want the outcome— the digest, not the dashboard. Trying this with Daigest.

Softflation = everyone shouting in a crowded room.

But where does that content come from? You're already: - Reading HN discussions - Following newsletters - Monitoring subreddits - Scanning Discord servers

What if the same AI that filters this information also: 1. Identifies concepts worth remembering 2. Generates SRS cards automatically 3. Tracks what you've mastered vs what needs review

You'd go from "information overload" → "filtered insights" → "retained knowledge" in one pipeline.

Passive consumption → Active retention, automatically.

This is the direction I'm exploring with Daigest (currently does steps 1-2, considering adding 3). Anyone else see value in this workflow?

Help users filter signal from noise Build trust through human judgment or curated sources Solve problems that require ongoing, reliable data — not just one-off generation

I'm building in this space myself (an AI-powered news digest tool), and the core bet is exactly this: people don't want more content, they want less but better. The vibe-coded clones might replicate features, but they can't replicate trust or a curated network of sources.

Anyone can spin up a SaaS now, but knowing what information actually matters to a specific audience is still hard to replicate. That's domain expertise, not technical skill.

I think the value is shifting from "can you build it" to "can you filter the noise." The bottleneck isn't access to information anymore - it's attention.

Helps reduce the information overload while still catching context quickly. Instead of browsing 10 newsletters and feeds manually, I get a digest of what actually matters to my current interests.

Not quite the same as deep literature review, but effective for staying on top of a field without drowning in it.

Two main things worry me about the 'always-on' agent approach:

1. Security & Surface Area: Giving an LLM broad permissions (Email, Calendar, etc.) while it's also scraping arbitrary web content is a prompt injection nightmare. The attack surface is just too wide for production use.

2. Token Economics: Seeing reports of '$300 in 2 days' is a massive red flag. For recurring tasks, there has to be a smarter way than re-processing the entire state every time.

I built Daigest to approach this differently. Instead of an autonomous agent wandering around, it's 'document-centric.' You connect your trusted sources, set a heartbeat, and the AI only processes what's changed to update a structured document. It's less 'magical' than a full agent, but it's predictable, auditable, and won't bankrupt you.

For 'gather and summarize' workflows, a structured document often beats a chat-based agent.

1. Input Friction (The Maker's Tax)

Context Switching: Stopping coding to recall yesterday's work and writing a paragraph is a huge flow-killer for devs.

Result: Updates become vague ("fixed stuff") or treated as a mindless chore.

2. Output Friction (The Manager's Tax)

Signal-to-Noise: Managers have to parse scattered logs across Slack/Jira to reconstruct the actual project state.

Result: Updates are posted but ignored because consuming them takes too much cognitive effort.

In your experience, which bottleneck is the bigger problem?

Should I prioritize making it easier to write (auto-drafting from logs) or easier to read (auto-summarizing context)?

Comments URL: https://news.ycombinator.com/item?id=46191167

Points: 1

# Comments: 0

I made it because I was copying Notion content into email clients every day, fixing spacing and colors by hand. Now it keeps the layout consistent and lets me repurpose the same content anywhere I need an email version.

What it does: • Convert any Notion page into a tidy, email-ready block • Preserve structure and styling (headings, callouts, images) • Works well in Gmail and Apple Mail (and most modern clients)

Would love feedback on tricky blocks (databases, toggles, synced pages) or how you’d use this for your own email workflows. Demo: https://notionto.email/en/template?utm_source=hn&utm_medium=...

Solo dev here — thanks for reading!

Comments URL: https://news.ycombinator.com/item?id=45901962

Points: 1

# Comments: 0