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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

# Comments: 0

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

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

Points: 2

# Comments: 0

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

# Comments: 0

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

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

Points: 1

# Comments: 0

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

# Comments: 1

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

# Comments: 0

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

# Comments: 0

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

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

Points: 1

# Comments: 0

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

# Comments: 0

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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Article URL: https://www.theguardian.com/news/ng-interactive/2026/feb/21/under-water-in-denial-is-europe-drowning-out-the-climate-crisis

Comments URL: https://news.ycombinator.com/item?id=47133895

Points: 1

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Article URL: https://bobrubbens.nl/post/speeding-up-html-generation-2000/

Comments URL: https://news.ycombinator.com/item?id=47133885

Points: 1

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Article URL: https://github.com/4ier/notion-cli

Comments URL: https://news.ycombinator.com/item?id=47133849

Points: 1

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Article URL: https://www.wsj.com/tech/apple-plans-to-manufacture-mac-mini-in-houston-c9b4c23c

Comments URL: https://news.ycombinator.com/item?id=47133848

Points: 1

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Article URL: https://rows.com/blog/post/rows-is-joining-superhuman

Comments URL: https://news.ycombinator.com/item?id=47133842

Points: 1

# Comments: 0

Coding agents are addictive and I stay up too late playing with them.

This hook makes Claude code nudge me to go to sleep when it's past my bedtime. It works! Goodnight! :)

Comments URL: https://news.ycombinator.com/item?id=47133829

Points: 1

# Comments: 0

A Terraform apply, a DB migration, a secret rotation, a break glass access grant. These are the kinds of mutations that can cause real damage if they go wrong, or if you later need to explain exactly what happened.

In many environments, evidence of these changes ends up scattered across logs, CI systems, ticketing tools, and control planes. Reconstructing a single change months later can mean querying multiple systems that may not retain state indefinitely.

I am exploring a simple idea: treating each privileged mutation as something that emits a portable, signed receipt. The receipt would tie together what was authorized, who approved it, what ran, and what the resulting state was. The goal is that it could be verified offline, without querying the original system.

There is a draft spec here: https://github.com/reciprium/spec

It defines a receipt format using JSON Schema, CDDL, and Protobuf, with test vectors and worked examples for Terraform, GitOps workflows, database migrations, secret rotation, and break glass access.

I would appreciate perspective from people who operate infrastructure at scale:

1. How do you currently record that a privileged change was authorized, executed as approved, and verified afterward?

2. Could you reconstruct a complete audit of a specific change from six months ago without relying on your CI or control plane?

3. During audits or incidents, how do you demonstrate that a change matched its approval?

4. Would a signed, portable receipt for each mutation materially simplify anything for you, or does this problem already feel solved?

5. Where in your workflow would something like this naturally attach, if at all?

I am trying to understand whether the problem exists in the form described, or whether practitioners have converged on better patterns. Critical feedback is especially useful.

Comments URL: https://news.ycombinator.com/item?id=47133826

Points: 1

# Comments: 0