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Hi HN,

I'm the creator of BeyondComments ([https://beyondcomments.io]). It’s a tool that uses AI to analyze YouTube comments and extract actionable signals—content requests, brand deals, sentiment, and bug reports—that normally get buried.

The Problem If you run a successful YouTube channel (or publish tech tutorials), your comments section is a goldmine. Viewers literally tell you what they want you to build or film next. But YouTube Studio is mostly built for raw numeric stats. Finding high-intent comments manually means endlessly scrolling through "First!" and "Great video bro," which doesn't scale and leads to missed opportunities.

How it works You paste a YouTube URL and the application fetches the comments to run them through our pipeline:

Clustering: It groups hundreds or thousands of comments into major discussion themes. Intent & Signal Detection: Flags high-value signals like purchase interest, feature requests, or collaboration outreach. Virality & Next Steps: Calculates a "Virality Score" based on engagement velocity and provides concrete next steps (e.g., "Reply to UserXYZ about the Enterprise plan").

I'd love for you to try it out on your own videos (or your favorite creator's videos) and share feedback. I’m particularly interested in hearing how you find the accuracy of the topic clustering, and whether the AI insights feel actionable vs. generic.

You can try it out here: [https://beyondcomments.io]

Thanks!

Hi HN,

I'm a software engineer in IT support. I constantly need to transfer large log files and system images securely between machines. Existing tools either had size limits, required accounts, or lacked real encryption. So I built ShareNova.

How it works:

Live transfer: Server-relayed WebSocket streaming, chunked with bitfield tracking for automatic resume on disconnect. No account required.

Deferred storage: When the receiver is offline, files are chunked and encrypted client-side using AES-256-GCM (PBKDF2 key derivation, 200K iterations) before upload. The server only stores ciphertext. Keys never leave the browser.

Magic-byte file scanner: Every deferred file is scanned server-side by reading the first 16 bytes and comparing against known signatures. Detects disguised executables (MZ header in a .jpg), double extensions, and archives containing dangerous files. Results are shown as safety badges to the receiver.

In-browser preview: Images, video, audio, PDF, and code files can be previewed before download without breaking the security model.

The stack is Node.js + Express + ws, single-server, no external dependencies for the core transfer logic.

Try it: https://sharenova.io

I'd love feedback on the architecture and the scanning approach.

Hey HN. I evaluated three agent frameworks for a homelab project, one had 400K lines of code (and 42K exposed instances on Shodan), one was 9 days old, and the third was so thin I'd rebuild most of it anyway. None worked for me.

So I built my own in ~2300 lines of Python. No frameworks, 8 runtime dependencies, 106 tests.

What it does: - Persistent memory via SQLite (FTS5 keyword search + sqlite-vec embeddings + recency decay, fused with Reciprocal Rank Fusion) - MCP tool integration — add capabilities by editing a JSON file - Native tools with safety guardrails (bash blocklist, timeouts, output caps) - Discord interface with session isolation - Structured JSON logging for every operation - Conversation compression for effectively infinite context

Runs locally on 2x RTX 3090 with Qwen3-Coder-Next via llama-server. No cloud APIs.

The design philosophy was: don't build what you don't need, but don't block the insertion points. For example, my AI firewall isn't built yet, but all LLM traffic goes through a single configurable URL, swapping in a filtering proxy is a config change I'll do later.

DESIGN.md documents the reasoning behind every architectural decision. Tests mock the LLM client so you can run them on a laptop.

GitHub: https://github.com/nonatofabio/luna-agent Blog post with full technical deep-dive: https://nonatofabio.github.io/blog/post.html?slug=luna_agent

Happy to answer questions about any of the design tradeoffs.

That was the question I asked my self last week. So I decided to re-search a little bit. I went on subreddits, such as SideProject, SaaS, Solopreneurs, webdev, basically everything that has something to do with programming, and just lurked. What I found was interesting, there were news posts, almost every minute, of people asking for feedback, and not receiving anything. It was a flood of people spamming their projects wanting to gain attention, while giving none.

Then I spend two days, researching some of the projects, reading through their docs, signing up, using their service, and writing detailed feedback. The feedback took around 20 minutes of research and another 10-20 minutes of writing. After writing long comments, the response was almost always the same, one upvote, one thank you. However, after getting a response, I asked for feedback back, aand, was ignored, completely. Either a single upvote, or nothing at all. This actually confused me a bit. Developer might not give feedback anymore, but will they not even give feedback if they receive feedback themselves?

That was my next post. I asked developers, would you give feedback if someone gave it to you? Surprisingly, the answer was yet, in almost all cases. Then what was the problem? It was freedom, actually. Users had the choice of giving feedback or not giving feedback, and because not giving feedback is faster, people opted to not give it at all. I talked to a few fellow devs that responded, and they agreed. Devs gone lazy, they do not want to give feedback if they dont have to. An enforcement of some kind, like, 1 Feedback required per 1 Post, would encourage giving feedback slightly. But there was still one issue that a user pointed out. Devs would not give feedback, because it takes too long.

After a long discussion with the fellow redditor, we both agreed on the fact that asking for structured feedback would cut the time for writing and reading, as the user would not need to write as much, and the reader would not need to read as much.

But this was all still theory, so I had to put it to the test. I asked a few devs if they were ready to give feedback to someone if they knew they would get quality feedback back, I found a few devs that said they were ready, and paired them, without them knowing about it. I requested their website with structured questions, and then send it to the other person, and then did the same with the other person. The result? A massive improvement. Mind you, both of the users were ignored within their subreddits beforehand.

So what is the issue? Well, I am the middle man, and not a good one at that. So I decided to program a website that implements what I do. My goal is to build a community platform where developers exchange feedback together, making sure that everybody gets feedback, and nobody gets ignored.

What do you guys think though? The experiments seemed like a proof of concept to me. Can feedback be brought back?