Week 1: Shipped AI summaries for 50+ tech blogs — here's what I learned
I'm building Stream Tech AI — a tech news aggregator with AI-powered summaries.
What I shipped:
- Aggregating articles from Zenn, Qiita (Japanese dev platforms), Netflix TechBlog, Meta Engineering, and more
- AI generates structured summaries: TL;DR, key points, and next actions
What I learned:
- Engineers want context, not just summaries. Adding "prerequisites" and "tradeoffs" sections increased engagement.
Next:
- Daily digest feature (AI picks the most valuable articles from ~50/day)
Question for you:
How do you decide what's "must-read" vs "nice-to-know" when curating content?
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Nice progress for week 1 - shipping + learning fast is the right move.
For me, “must-read” content is anything that changes a decision I’m about to make (what to build, how to build it, or what to avoid).
“Nice-to-know” is interesting, but doesn’t affect what I’ll ship this week.
The prerequisites + tradeoffs addition makes a lot of sense - that’s usually where the real value is for engineers.
Early traction is great. What’s your plan for converting that interest into paid users once usage grows?
Thank you! Our current plan is freemium:
The bet is that once users start relying on the daily digest, the combination of history + personalization becomes valuable enough to justify a paid tier. This hypothesis is still being tested.
We plan to reintroduce the value proposition of personalized content previews once a sufficient history (good/bad) accumulates for each user.
We'll need to separately consider methods to retain users until that point.
Engineers want context, not just summaries - but there's a deeper problem: understanding WHY this matters to YOU specifically.
A summary tells you what happened. Prerequisites tell you if you're ready. But neither explains "should I care about this right now?"
That's the gap between information and action. Most curated content assumes readers know what's relevant to their current work - but they don't.
We're building voice agents that guide users through products in real-time (demogod.me) - basically turning "here's the info" into "here's why this matters to your specific situation."
Your prerequisite insight nailed it: engineers need context. But the next level is contextualizing the context - making relevance obvious before asking people to invest attention.
Thanks for your comment.
You're right — knowing "should I care about this right now?" is the harder problem.
We're experimenting with personalization through reactions (Good/Bad on articles). The idea is that over time, the AI learns what's relevant to your current stack and interests, not just what's objectively important.
Still early, but curious how you handle the cold-start problem with voice agents — new users have no context to personalize from.
The “prerequisites” bit stood out, feels usefull but also easy to overdo imo. Curious how you decide when that extra context helps vs just slowing reader down.
Good point — it's a real balance. Right now I'm using article type as the main signal: tutorials and technical deep-dives get prerequisites, but news/announcements skip them.
Also watching reader behavior — if people click through to the original more often when prerequisites are shown, it's probably adding value.
Still experimenting though. Do you have a preference as a reader?