2025年11月22日 の Show HN

This started as a reaction to a conversational trope. Despite being a tranquil place, even conversations at my yoga studio often start with, "Can you believe what's going on right now?" with that angry/scared undertone.

I'm a news avoider, so I usually feel some smug self-satisfaction in those instances, but I wondered if there was a way to satisfy the urge to doomscroll without the anxiety.

My hypothesis: Apply a 40-year latency buffer. You get the intellectual stimulation of "Big Events" without the fog of war, because you know the world didn't end.

40 years creates a mirror between the Reagan Era and today. The parallels include celebrity populism, Cold War tensions (Soviets vs. Russia), and inflation economics.

The system ingests raw newspaper scans and uses a multi-step LLM pipeline to generate the daily edition:

OCR & Ingestion: Converts raw pixels to text.

Scoring: Grades events on metrics like Dramatic Irony and Name Recognition to surface stories that are interesting with hindsight. For example, a dry business blurb about Steve Jobs leaving Apple scores highly because the future context creates a narrative arc.

Objective Fact Extraction: Extracts a list of discrete, verifiable facts from the raw text.

Generation: Uses those extracted facts as the ground truth to write new headlines and story summaries.

I expected a zen experience. Instead, I got an entertaining docudrama. Historical events are surprisingly compelling when serialized over weeks.

For example, on Oct 7, 1985, Palestinian hijackers took over the cruise ship Achille Lauro. Reading this on a delay in 2025, the story unfolded over weeks: first they threw an American in a wheelchair overboard, then US fighter jets forced the escape plane to land, leading to a military standoff between US Navy SEALs and the Italian Air Force. Unbelievably, the US backed down, but the later diplomatic fallout led the Italian Prime Minister to resign.

It hits the dopamine receptors of the news cycle, but with the comfort of a known outcome.

Stack: React, Node.js (Caskada for the LLM pipeline orchestration), Gemini for OCR/Scoring.

Link: https://forty.news (No signup required, it's only if you want the stories emailed to you daily/weekly)

Hi HN - My name is Andrew, and I'm a high school student.

This is a write-up on StratoSpore, a payload I designed and launched to the stratosphere. The goal was to test if we could estimate physical altitude based on algae fluorescence (using a lightweight ML model trained on the sensor data).

The blog post covers the full engineering mess/process, including:

- The Hardware: Designing PCBs for the AS7263 spectral sensor and Pi Zero 2 W.

-The biological altimeter: How I tried to correlate biological stress (fluorescence) with altitude.

- The Communications: A custom lossy compression algorithm I wrote to smash 1080p images down to 18x10 pixels so I could transmit them over LoRA (915 Mhz) in semi-real-time.

The payload is currently lost in a forest, but the telemetry data survived. The code and hardware designs are open source on GitHub: https://github.com/radeeyate/stratospore

I'm happy to answer technical questions about the payload, software, or anything else you are curious about! Critique also appreciated!

Too often I find myself being lazy with commit messages. But I don't want AI to write them for me... only i truly know why i wrote the code i did.

So why don't i get AI to help me get that into words from my head?

That's what i built: smartcommit asks you questions about your changes, then helps you articulate what you already know into a proper commit message. Captures the what, how, and why.

Built this after repeatedly being confused 6 months in a project as to why i made the change i had made...

Would love feedback!

I created vibescaffold.dev. It is a wizard-style AI tool that will guide you from idea → vision → tech spec → implementation plan. It will generate all the documents necessary for AI coding agents to understand & iteratively execute on your vision.

How it works: - Step 1: Define your product vision and MVP - Step 2: AI helps create technical architecture and data models - Step 3: Generate a staged development plan - Step 4: Create an AGENTS.md for automated workflows

I've used AI coding tools for awhile. Before this workflow (and now, this tool), I kept getting "close but not quite" results from AI coding tools. I learned that the more context & guidance I gave these tools up front, the better results I got.

The other thing I have found with most tools that attempt to improve on "vibe coding" is that they add abstraction. To me, this just adds to the problem. AI coding agents are valuable, but they are error-prone - you need to be an active participation in their work. This workflow is designed to provide a scaffolding for these AI agents, while minimizing additional abstraction.

Would love feedback on the workflow - especially curious if others find the upfront planning helpful or constraining.

I built PolyGPT to solve a problem I had: constantly tab-switching between ChatGPT, Claude, and Gemini to compare their responses.

  It's a desktop app (Mac/Windows/Linux) that lets you type a prompt once and see all three AI models respond
  simultaneously in a split view. Useful for:
  - Comparing technical explanations
  - Getting multiple perspectives on code problems
  - Fact-checking answers across models

  The app is free, open source, and runs locally - your credentials stay on your machine.

  Download: https://polygpt.app
  Source: https://github.com/ncvgl/polygpt

  Would love feedback from the HN community. What other features would make this more useful?