Hacker News

Article URL: https://www.youtube.com/watch?v=_k22WAEAfpE

Comments URL: https://news.ycombinator.com/item?id=47134384

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Article URL: https://www.qrexpress.org/#en

Comments URL: https://news.ycombinator.com/item?id=47134375

Points: 1

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Article URL: https://www.shakti.com

Comments URL: https://news.ycombinator.com/item?id=47134371

Points: 1

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Article URL: https://www.anildash.com/2026/02/23/taking-action-ai-harms/

Comments URL: https://news.ycombinator.com/item?id=47134355

Points: 1

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Article URL: https://github.com/folospior/grom

Comments URL: https://news.ycombinator.com/item?id=47134353

Points: 1

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Article URL: https://old.reddit.com/r/cscareerquestions/comments/1rd3x3v/what_happened_to_the_jobs_that_were_supposed_to/

Comments URL: https://news.ycombinator.com/item?id=47134350

Points: 1

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Article URL: https://agilitza.com/blog/no-good-engineer-is-arrogant/

Comments URL: https://news.ycombinator.com/item?id=47134319

Points: 1

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Hi HN,

I built VVMList for cybersecurity learners to easily find specific techniques used on specific machines.

A few years ago, I originally created VVMList to track the machines I completed and the techniques I used to solve them. After some time, I semi-abandoned the project.

At the end of 2024, I revisited the idea and decided to expand it into something bigger. More like a structured "ctf bible" for learners who want to study techniques in a more organized way.

For this, I read through many machine writeups from Vulnhub, HackMyVM, VulNyx and HackTheBox. I extracted and categorized the techniques used in each machine.

This process led to VVMList v3. when I first started, the project included around 250 machines. With v3, it grew to over 1200 machines, which allowed me to reach the original scope I had in mind.

In the 2026 annual update (v3.2), I added newly released machines from the platforms, integrated ctfmirror.com (my another project) for all machine images and integrated archive.org for all writeups. The goal was to provide a more centralized solution and reduce the risk of resources disappearing over time.

I'd really appreciate feedback from the community. Does the structure make sense? Are there techniques or machines that you think are missing? Any suggestions to improve usability or make it more useful for learners are very welcome.

Thanks for taking a look.

Comments URL: https://news.ycombinator.com/item?id=47134315

Points: 1

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I built a system that reads research papers across unrelated domains and tries to surface hypotheses that neither field would have generated on its own. The Discover page is where it publishes findings: https://aegismind.app/discoveries It's very early — only three discoveries so far — but the core idea is what I want feedback on. The problem it's trying to solve: Science is siloed. A breakthrough in mycology might have direct implications for network routing. A discovery in chronobiology might reframe how we think about database consistency. Nobody reads across all of it, and even when they do, the connection is usually accidental. How it works:

The autonomous "Right Brain" service ingests papers across domains continuously Multiple models (GPT, Claude, Gemini, Mistral, Grok) analyze papers in parallel A synthesis layer looks for structural or mechanistic similarities across domain boundaries Hypotheses are only published when they pass a novelty and coherence threshold — it won't surface things either field already knows

What I'm genuinely uncertain about and want feedback on:

How do you evaluate whether a cross-domain hypothesis is actually interesting vs. superficially pattern-matched? This is the hardest part of the novelty filter. Which domain combinations would you most want to see? (We're currently not curating this — it's whatever the system finds structurally similar.) Should findings link directly to the source papers?

Only three discoveries published so far, so the page is sparse — but I'd rather share the idea early than wait until it looks polished.

Comments URL: https://news.ycombinator.com/item?id=47134309

Points: 1

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Article URL: https://blog.rust-lang.org/2026/01/14/what-does-it-take-to-ship-rust-in-safety-critical/

Comments URL: https://news.ycombinator.com/item?id=47134305

Points: 1

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Article URL: https://www.hpc-ai.com/blog/Embodied-AI-Explained

Comments URL: https://news.ycombinator.com/item?id=47134300

Points: 2

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Hey HN,

I built ImgPakt because every image compressor I found uploads files to a server. TinyPNG, Compressor.io, iLoveIMG — they all require sending your images over the internet.

ImgPakt compresses images entirely in the browser using Canvas API and Web Workers. There's no fetch(), no XMLHttpRequest, no upload endpoint in the code. Files literally cannot leave your device.

Technical details: - FileReader → offscreen Canvas → toBlob() at target quality - Web Workers keep the UI thread free during batch processing - Supports JPEG, PNG, WebP, AVIF - 5 preset quality profiles instead of a manual slider

Free tier: 5 images per batch, all profiles. Pro ($3.99/mo): unlimited batch, format conversion, AVIF.

I'd appreciate any feedback on the compression approach or UX. Happy to answer questions about the architecture.

Comments URL: https://news.ycombinator.com/item?id=47134298

Points: 1

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Article URL: https://www.aipanic.news/p/the-ai-existential-risk-industrial

Comments URL: https://news.ycombinator.com/item?id=47134278

Points: 1

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Article URL: https://wonderwhy-er.medium.com/local-llms-are-finally-beating-the-cloud-but-are-they-51fc0ad0dbd7

Comments URL: https://news.ycombinator.com/item?id=47134267

Points: 2

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There's been a clear shift from 2023's you need RAG to 2026's agentic search outperforms RAG.

We made the switch at Cosmico when building AI-native applications with Claude Code. Agentic search gives us better accuracy but at higher cost per query.

For those running production systems:

1. What prompted your switch (or decision to stick with RAG)? 2. What broke during the transition? 3. What's your hybrid approach (if any)?

Specifically curious about code search vs. document retrieval use cases, and how you handle the latency/cost trade-offs.

Our context: building custom software with AI agents in weeks, not months. Accuracy matters more than speed for our workflow, but that's not universal.

What's working (or not working) for you?

Comments URL: https://news.ycombinator.com/item?id=47134263

Points: 1

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Article URL: https://forum.modular.com/t/duckdb-mojo-mojo-bindings-for-duckdb/2314

Comments URL: https://news.ycombinator.com/item?id=47134257

Points: 2

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There's been a clear shift from 2023's you need RAG to 2026's agentic search outperforms RAG.

We made the switch at Cosmico when building AI-native applications with Claude Code. Agentic search gives us better accuracy but at higher cost per query.

For those running production systems:

1. What prompted your switch (or decision to stick with RAG)? 2. What broke during the transition? 3. What's your hybrid approach (if any)?

Specifically curious about code search vs. document retrieval use cases, and how you handle the latency/cost trade-offs.

Our context: building custom software with AI agents in weeks, not months. Accuracy matters more than speed for our workflow, but that's not universal.

What's working (or not working) for you?

Comments URL: https://news.ycombinator.com/item?id=47134239

Points: 1

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Article URL: https://gutenberg.org/cache/epub/78017/pg78017-images.html

Comments URL: https://news.ycombinator.com/item?id=47134192

Points: 1

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Article URL: https://www.cnbc.com/2026/02/20/openai-resets-spend-expectations-targets-around-600-billion-by-2030.html

Comments URL: https://news.ycombinator.com/item?id=47133958

Points: 1

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Jeffrey Epstein’s case is absolutely real, horrifying, and proves that powerful people can commit and hide systemic abuse for years. The full extent of the control of powerful figure is not yet clear but we can see it is massive

Comments URL: https://news.ycombinator.com/item?id=47133946

Points: 1

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