Hacker News

Article URL: https://www.youtube.com/watch?v=7y_hbz6loEo

Comments URL: https://news.ycombinator.com/item?id=47226350

Points: 5

# Comments: 1

Article URL: https://zed.dev/blog/terms-update

Comments URL: https://news.ycombinator.com/item?id=47226341

Points: 7

# Comments: 5

Article URL: https://cdn.mc-weblink.sg-mktg.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

Comments URL: https://news.ycombinator.com/item?id=47226332

Points: 5

# Comments: 0

I wanted readers to actually engage with ideas in my posts, not just skim them. I know they're already using AI to go deeper — the friction is in the setup.

ThinqWith takes a blog post and generates prompt vectors readers can launch directly into Claude, ChatGPT, or Gemini (or ThinqBits) — no copy-pasting, no setup.

Comments URL: https://news.ycombinator.com/item?id=47226331

Points: 1

# Comments: 0

Article URL: https://doneyli.substack.com/p/the-3-layer-claude-code-configuration

Comments URL: https://news.ycombinator.com/item?id=47226319

Points: 2

# Comments: 0

Article URL: https://www.cnn.com/health/how-a-pacemaker-for-the-brain-could-help-treat-parkinsons-spc

Comments URL: https://news.ycombinator.com/item?id=47226154

Points: 1

# Comments: 0

Article URL: https://github.com/autospec-ai/playwright

Comments URL: https://news.ycombinator.com/item?id=47226151

Points: 1

# Comments: 0

Article URL: https://www.wsj.com/world/europe/france-floats-nuclear-deployment-across-europe-056a5cbc

Comments URL: https://news.ycombinator.com/item?id=47226141

Points: 3

# Comments: 0

I built a music discovery app. It's open source now.

BlackTape uses MusicBrainz and Discogs — open, community-maintained databases — to index artists and rank them by how unique they are within their genre. The more niche the artist, the higher they surface. It's the inverse of how Spotify's algorithm works. I got frustrated watching recommendation algorithms flatten discovery. The same artists keep surfacing out of 10+ million indexed. MusicBrainz has 2.6 million artists catalogued with rich genre tags, scene data, and regional metadata. Discogs has release metadata going back 80 years. Combine those two databases and score artists by distinctiveness rather than popularity, and the discovery space opens up completely. What it does: - Search by genre/scene with atomic tag combinations - Discover feed ranked by uniqueness score (rare = surfaceable) - Full artist pages: discography, tags, related artists, scene data - Spotify playback integration (optional) - Time Machine: browse artists by decade - Style Map: visual genre/scene navigation - Knowledge Base: genre relationship graph No tracking, no platform API dependency for the core discovery data. Desktop app built with Tauri + SvelteKit. GitHub: https://github.com/AllTheMachines/BlackTape Site: https://blacktape.org Happy to talk about the MusicBrainz pipeline, the uniqueness scoring, or the open-data approach to discovery.

Comments URL: https://news.ycombinator.com/item?id=47226140

Points: 2

# Comments: 0

Article URL: https://www.nytimes.com/2026/02/26/opinion/crypto-trump-bitcoin-clarity-genius.html

Comments URL: https://news.ycombinator.com/item?id=47226129

Points: 3

# Comments: 1

Article URL: https://matt.might.net/articles/what-cs-majors-should-know/

Comments URL: https://news.ycombinator.com/item?id=47226127

Points: 2

# Comments: 0

I built this over the weekend after reading about StrongDM's software factory (their writeup: https://factory.strongdm.ai/, Simon Willison's deep dive: https://simonwillison.net/2026/Feb/7/software-factory/, Dan Shapiro's Five Levels: https://www.danshapiro.com/blog/2026/01/the-five-levels-from...). OctopusGarden is an open-source implementation of the pattern StrongDM described: holdout scenarios, probabilistic satisfaction scoring via LLM-as-judge, and a convergence loop that iterates until the code works; no human code review in the loop.

What stood out to me was that this architecture largely rhymes with the coding workflows I and others already do with coding agents. It's basically automating the connective tissue between the workflows I was already doing in Claude Code, and then brute-forcing a result. In the dark factory model, a spec goes in, code gets generated, built in Docker, validated against scenarios the agent never saw, scored, and failures feed back until it converges.

I've tried it with mostly standard CRUD/REST API apps and it works. I haven't tried anything with HTML/JS yet. You can try the sample specs in the repo.

Some raw notes from the experience:

1. I don't want to maintain the code these factories generate. It works. The phenotype is (largely) correct, but the genotype is pretty wild and messy. I did not use OctopusGarden to build OctopusGarden (you can tell because it uses strict linting and tests). I know the point of these systems is zero human in the loop, but I think there's a real opportunity to get factories to generate code that humans actually want to maintain. I'm going to work on getting OctopusGarden there.

2. Compliance might be a nightmare. In my day job I think a lot about ISO 27001 and SOC 2 compliance. The idea of deploying dark-factory-generated projects into my environments and checking compliance boxes sounds painful. That might just be the current state of OctopusGarden and the code it generates, but I think we can get to a point where generated code is completely linted, statically checked, and tested inside the factory. That's not OctopusGarden today, but maybe it will be there next week? I can see this moving fast.

3. These dark factory apps will be hard to debug. There was a Claude outage today and I couldn't run my smoke tests or generate new apps. I don't want to maintain services that can't be debugged and fixed by a human in a pinch. We're already partially there with AI-assisted code, but this factory-generated code is even more convoluted. Requiring AI to create a new app version is probably worth it...but it's still yet another thing between you and quickly patching an urgent bug.

4. Security needs a better story. These things need real security hardening. Maybe that's just better spec files and scenarios, maybe it's something more. I'm going to drink a strong cola and think about this one.

5. The unit of responsibility keeps growing. Last year we said code must come in PR-sized bites — that's how we manage risk. Now we're talking about deploying meshes of services created and deployed with no humans in the loop (except at creation). AI-generated services could really push the scale of what people are willing to accept responsibility for. Most SRE teams manage 1-5 services at big companies. Will that number increase per team? How much GDP is one person willing to manage via agents? Just a shower thought.

6. I was surprised this works. I'm surprised at how easy it was to make. I'm surprised more of these aren't out there already. I only did a couple of GitHub searches and didn't find many. I'm bad at searching. Sorry if I didn't find your project.

Comments URL: https://news.ycombinator.com/item?id=47226107

Points: 2

# Comments: 0

Article URL: https://crackhead.technology/contact-sales-for-dimensions/

Comments URL: https://news.ycombinator.com/item?id=47226104

Points: 2

# Comments: 0

Hi everyone,

I’ve developed a minimalist 2FA tool (TOTP) for CLI. My goal was to create something with the widest possible reach across Unix-like systems without the typical dependency bloat.

Key features:

- Pure ANSI C: No external dependencies beyond libc.

- Wide Portability: Tested and running on Linux, FreeBSD, OpenBSD, and NetBSD.

- Security focused: Fixed-width types, endianness-aware, and compiled with stack protection flags.

- Lightweight: Fast execution, ideal for integration into scripts and legacy infrastructure.

- Build for final users is gmake based but without autotools.

The project is hosted on Codeberg under the BSD-3-Clause license. I’m currently in the final polishing stage and would love to hear your thoughts on the code structure and portability.

Check it out here: https://codeberg.org/rafael-santiago/pinhead

Comments URL: https://news.ycombinator.com/item?id=47226103

Points: 2

# Comments: 0

Article URL: https://singh-sanjay.com/2026/02/18/dns-the-silent-killer-of-distributed-systems.html

Comments URL: https://news.ycombinator.com/item?id=47226101

Points: 2

# Comments: 1

Article URL: https://www.politico.com/news/2026/03/02/netflixs-wake-up-in-trumps-washington-00807853

Comments URL: https://news.ycombinator.com/item?id=47226089

Points: 1

# Comments: 0

Hey HN,

I built Decision Guardian — an open-source GitHub Action and CLI that automatically surfaces architectural decisions as PR comments when code touches protected files. The problem it solves:

Spotify published a great post in 2020 about when to write Architecture Decision Records. I followed the advice. My team wrote ADRs. They sat in docs/adr/. Nobody read them before opening a PR.

https://engineering.atspotify.com/2020/04/when-should-i-write-an-architecture-decision-record

The gap isn't documentation — it's surfacing. The right moment to surface a decision isn't onboarding or sprint planning. It's when someone is actively editing the code the decision protects.

How it works:

1) Write decisions in a Markdown file (compatible with existing ADRs) 2) Add the GitHub Action to your workflow 3) When a PR modifies protected files, Decision Guardian posts the relevant decisions as a comment automatically

Decision format (plain Markdown):

## Decision: Use Postgres for Billing

*Status*: Active *Severity*: Critical

*Files*: - `src/db/*/*.ts`

### Context We evaluated Postgres vs MongoDB. Billing requires ACID compliance. MongoDB was rejected — no transaction guarantees.

Features worth calling out:

1) Severity levels (Critical / Warning / Info) — can block PRs on critical violations 2) Advanced matching: glob patterns, regex, content-based rules, boolean logic 3) CLI works with any CI (GitLab, Jenkins, CircleCI, pre-commit hooks) 4) Handles PRs with 3000+ files without OOM 5) Idempotent comments — no spam, updates in place 6) Zero external network calls — nothing leaves your GitHub runner 7) 109 tests, ReDoS protection, path traversal protection

vs. CODEOWNERS: CODEOWNERS assigns reviewers. This explains why the review matters. Best used together. vs. Danger.js: Danger requires code. This requires Markdown. Non-JS engineers can own their decisions.

It's MIT licensed, single-step GitHub Action or npx decision-guardian CLI.

Happy to answer questions.

Repo: https://github.com/DecispherHQ/decision-guardian

Comments URL: https://news.ycombinator.com/item?id=47226046

Points: 5

# Comments: 0

Article URL: https://www.neowin.net/news/report-lenovos-new-ai-assistant-ad-portrays-humans-as-useless/

Comments URL: https://news.ycombinator.com/item?id=47226042

Points: 1

# Comments: 1

Article URL: https://arxiv.org/abs/2602.24286

Comments URL: https://news.ycombinator.com/item?id=47226028

Points: 2

# Comments: 0

I'll be graduating high school in a few months and I have no real plan for my life. I applied to electrical engineering at various universities because hardware and computing systems seemed interesting enough, and it's a practical choice. In school I got high nineties in physics, calculus, chemistry and biology class, but none of it really excited me or made me want to keep going on my own time.

What does pull me is the idea of research itself: actually discovering new stuff, adding to knowledge instead of just using what's already there. The problem is I don't have a field that excites me yet. I feel like I haven't seen enough of any discipline to know if it's worth years (or a lifetime) of focus.

For people who ended up in research careers, how did you figure it out? Was there a specific moment, project, class, paper, or random conversation that inspired you? Did you mostly wander through undergrad, switching majors or labs or taking odd electives until something stuck, or did it come earlier for you?

How do you know when something is more than a passing interest, when it's the kind of thing that could actually sustain you long-term? Is it the questions that keep nagging at you years later, or the daily grind of experiments/debugging/reading feeling surprisingly okay (or even good), or some other signal? Any stories from when you were directionless at my age but eventually found your thing would be great. Low-stakes ways a freshman could poke around different areas without locking in too soon? Summer programs, random labs, self-reading that helped?

Comments URL: https://news.ycombinator.com/item?id=47226020

Points: 1

# Comments: 0