The problem is management confusing "AI can process text" with "AI can replace judgment." AI is great when the task is bounded, like summarizing docs or triaging info. Understanding why a legacy system was built a certain way requires context that lives in people's heads, not in the codebase.

our tracking system might become another thing to optimize. Simpler approach: timer that closes the tab. Hard stops > willpower.

I quit hoarding. Collecting was the bottleneck, not organizing.

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

That’s an important counterpoint: sometimes the fix is less intake, not better retrieval. What do your “filtered digests” look like (sources + cadence), and what makes them trustworthy enough to replace capture? This might be a better wedge than task-suggestions.

My setup: RSS (Reddit, HN, AI tech blogs) + a few investment YouTube channels → daily morning digest.

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.

Feeling this. Built an AI digest tool, found YC competitors, now spend more time on positioning than coding.

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.

The missing piece in most learning tools: they assume you manually feed them content.

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?

Ironically, I think vibe coding actually increases the value of curation and trust. More AI-generated apps and content = more noise. The bottleneck shifts from "can I build this?" to "what's actually worth my attention?" SaaS that survives will likely be the ones that:

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.

When building becomes easy, curation becomes the moat.

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.

Attention is all you need?

For "vibe research" across multiple sources, I've been using an AI setup that monitors topics I care about and summarizes only what's relevant.

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.

Interesting project. I've been exploring this space but eventually pivoted in a different direction.

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.