Hacker News

Article URL: https://ariadne.space/2021/06/05/actually-bsd-kqueue-is-a.html

Comments URL: https://news.ycombinator.com/item?id=47209899

Points: 1

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Article URL: https://docs.eventsourcingdb.io/blog/2026/03/02/the-snapshot-paradox/

Comments URL: https://news.ycombinator.com/item?id=47209870

Points: 1

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Article URL: https://indicator.media/p/this-ai-generated-podcast-network-publishes-11-000-episodes-a-day-it-s-also-ripping-off-media-outlet

Comments URL: https://news.ycombinator.com/item?id=47209868

Points: 1

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Article URL: https://www.youtube.com/watch?v=eh8bcBIAAFo

Comments URL: https://news.ycombinator.com/item?id=47209845

Points: 1

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Article URL: https://github.com/fumishiki/nabla

Comments URL: https://news.ycombinator.com/item?id=47209844

Points: 1

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Article URL: https://marshy-dew-d95.notion.site/Minecraft-Server-Setup-Tool-305cfa0d34eb8075bd91faba7f9e3ef7

Comments URL: https://news.ycombinator.com/item?id=47209841

Points: 1

# Comments: 1

Article URL: https://www.cantorsparadise.com/how-to-solve-the-rubiks-cube-8d5d3f8f3d80

Comments URL: https://news.ycombinator.com/item?id=47209837

Points: 1

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Article URL: https://daysift.com/

Comments URL: https://news.ycombinator.com/item?id=47209836

Points: 1

# Comments: 1

Umbra is built using a fork of fern.js, the ghostery browser build script. All telemetry and callouts are removed, AI is removed, Pocket is removed. Available as RPM, DEB, Flatpak. tar.xz, and exe.

Comments URL: https://news.ycombinator.com/item?id=47209835

Points: 1

# Comments: 0

The problem isn't that AI hallucinates. It's that we give it no reason not to.

I've been building production Magento 2 systems for years. Every time I tried using AI for complex enterprise work, it confidently generated code that looked right but violated runtime semantics, ignored concurrency constraints, or assumed facts about the codebase it had no right to assume.

So I stopped trying to make AI smarter. I built a reasoning harness instead.

Here's what that means concretely: # ENF-GATE system — AI cannot proceed without satisfying: Phase A: Explicit architectural declaration Phase B: Test-first enforcement (skeletons approved before implementation) Phase C: Static analysis gates per file Phase D: Operational proof traces (retry, DLQ, throughput modeling) # Halt conditions HALT: Missing context → declare before proceeding HALT: Uncertainty detected → surface, don't hallucinate HALT: ENF rule violation → stop slice, await review

I also built a deterministic Magento context compiler — a structured digest of the real codebase that AI reads before generating anything. Service contracts, repository patterns, runtime config, execution paths. No assumptions. No hallucinations about what exists.

The result?

Yesterday I built Custom_PartialCaptureInventory — a production-grade module with MSI-aware inventory reconciliation, async queue processing, concurrency safety, idempotent retry handling, REST + GraphQL APIs.

35 files. 6 approved slices. Zero ENF rule violations.

The AI didn't replace the senior engineer. It ran inside a reasoning architecture the senior engineer designed.

That's the part nobody is talking about.

Engineers who understand architecture, constraints, and system dynamics will decide whether AI-generated code should exist. The methodology is transferable to any complex platform — Salesforce, SAP, Shopify Plus, Laravel enterprise.

GitHub + writeup incoming. Happy to answer questions about the enforcement framework design. #ShowHN #AI #SoftwareEngineering #Magento #EnterpriseAI

Comments URL: https://news.ycombinator.com/item?id=47209822

Points: 3

# Comments: 0

Article URL: https://blog.natfu.be/validation-in-type-annotations/

Comments URL: https://news.ycombinator.com/item?id=47209813

Points: 1

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I built PraxisJS, a signal-driven frontend framework exploring what a more explicit and traceable architecture could look like.

PraxisJS started as a personal project. It reflects a single perspective on frontend design, not a committee decision, not a consensus. I wanted to see how far you can push explicitness before it becomes friction.

Most frameworks optimize for writing less. PraxisJS questions that tradeoff. @State doesn’t suggest reactivity, it is reactive, visible in the code. Signals reach the DOM without a reconciliation layer in between (the renderer is still evolving toward that goal).

It also became an AI-assisted experiment, not to automate thinking, but to pressure-test ideas. Some parts came from that collaboration. Some exist because it failed.

v0.1.0 beta, experimental, not production-ready. But the ideas are real.

Comments URL: https://news.ycombinator.com/item?id=47209808

Points: 2

# Comments: 0

Article URL: https://www.autodidacts.io/social-cache-busting/

Comments URL: https://news.ycombinator.com/item?id=47209800

Points: 1

# Comments: 0

Article URL: https://ianbarber.blog/2026/02/24/perplexed/

Comments URL: https://news.ycombinator.com/item?id=47209799

Points: 2

# Comments: 0

Hi HN! I built tensor.cx — a tool that turns your documents into a searchable AI knowledge base in seconds.

I built this because I kept running into the same problem: having a pile of documents (PDFs, DOCX) and needing to find specific information quickly without hallucinatory answers. Existing RAG solutions were either too complex to set up, didn't provide reliable inline citations, or made it impossible to share the actual search experience with others.

How it works: 1. Drop your files (PDF, DOCX, TXT, Markdown) 2. We chunk and embed them using OpenAI's text-embedding-3-small 3. Ask questions in natural language and get answers with exact inline citations

We give every workspace a shareable URL so you don't have to onboard your whole team just to share a document search. You just send them the link, and they can search the docs immediately. (Note: Since document uploads cost money to embed, I do require a quick login to create a workspace, but no credit card is needed for the free tier).

Live demo: https://tensor.cx Free tier: 3 workspaces, 5 docs each, 30 queries/day.

Under the hood: - The RAG pipeline: query -> embed -> pgvector -> top 5 chunks -> LLM (via LiteLLM) -> streamed via SSE - Backend: Django 6 + Django Ninja, PostgreSQL, Celery - Frontend: Next.js 16 + React 19 + Tailwind CSS 4 - Infra: Fly.io, Neon (DB), Cloudflare R2, Stripe, Clerk

I'd love to hear your feedback on the product, and I'm happy to answer any questions about the architecture, RAG pipeline, or anything else!

Comments URL: https://news.ycombinator.com/item?id=47209790

Points: 1

# Comments: 0

Article URL: https://techcrunch.com/2026/03/01/openai-shares-more-details-about-its-agreement-with-the-pentagon/

Comments URL: https://news.ycombinator.com/item?id=47209789

Points: 1

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Article URL: https://maurycyz.com/misc/c_files/

Comments URL: https://news.ycombinator.com/item?id=47209788

Points: 2

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Article URL: https://health.aws.amazon.com/health/status

Comments URL: https://news.ycombinator.com/item?id=47209781

Points: 4

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Article URL: https://github.com/Kevandrew/sophia

Comments URL: https://news.ycombinator.com/item?id=47209775

Points: 1

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Article URL: https://xcancel.com/cabsav456/status/2027937130995921119

Comments URL: https://news.ycombinator.com/item?id=47209773

Points: 9

# Comments: 5