Hacker News

With GitHub's recent instability, I'm seriously looking into alternatives. But the one piece of the puzzle I haven't figured out yet is GitHub Actions. I've used both TeamCity and Jenkins in the past, and have no desire to go back to either one; GHA, for all its flaws, was just so much better of an experience than TeamCity or Jenkins, for me at least. I've read about multiple alternatives to GHA for CI, but it's hard for me to tell just from the documentation what they feel like to use.

So, for those of you who have used left GitHub Actions and are using a different CI solution, I'd like to ask: what did you settle on? Why? Are you happy with it? And how does it feel to use it? Do you feel like it's invisible and gets out of the way, or do you feel like you're constantly having to massage its configuration all the time?

Comments URL: https://news.ycombinator.com/item?id=46954054

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Article URL: https://github.com/AGDNoob/squish

Comments URL: https://news.ycombinator.com/item?id=46953779

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Article URL: https://www.japantimes.co.jp/culture/2026/01/23/film/black-ox-tetsuichiro-tsuta/

Comments URL: https://news.ycombinator.com/item?id=46953742

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Hi HN, I'm new here, wanted to share a little lib I've been working on. It's a cache library that lets you declare dependencies instead of classic TTL invalidation.

Basic usage:

from zoocache import cacheable, invalidate, add_deps @cacheable def get_product_page(pid: int, sid: int): product = db.get_product(pid) add_deps([f"product:{pid}"]) stock = db.get_stock(sid, pid) add_deps([f"store:{sid}:stock"]) return render(product, stock) get_product_page(42, 1) # computes and caches get_product_page(42, 1) # cache hit invalidate("product:42") # product info changed invalidate("store:1") # clears ALL store:1:* (stock, prices, etc) Tags are hierarchical, invalidating a parent like "store:1" clears everything under it. No scanning or pattern matching needed.

Internally it's a prefix trie with HLC for distributed consistency, a SingleFlight to avoid thundering herds, and MsgPack+LZ4 serialization. The core is Rust via PyO3. Supports in-memory, LMDB, and Redis backends.

It's a fairly niche tool, if simple TTL or lru_cache covers your use case, you don't need this. It's more for when you have complex data relationships and need precise, immediate invalidation.

Source: https://github.com/albertobadia/zoocache

Hope you find it useful. Happy to receive feedback, PRs, or discuss design tradeoffs.

Comments URL: https://news.ycombinator.com/item?id=46953736

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Article URL: https://www.reuters.com/science/musk-says-spacex-prioritise-building-self-growing-city-moon-2026-02-08/

Comments URL: https://news.ycombinator.com/item?id=46953730

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Article URL: https://flaneur.ink

Comments URL: https://news.ycombinator.com/item?id=46953632

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Article URL: https://github.com/championswimmer/env.sync.local

Comments URL: https://news.ycombinator.com/item?id=46953617

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Article URL: https://princeea.com/mexican-scientist-eva-ramon-gallegos-is-the-first-to-eradicate-hpv-in-29-women/

Comments URL: https://news.ycombinator.com/item?id=46953616

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Article URL: https://apocryphos.com/post/windows_wlan-report/

Comments URL: https://news.ycombinator.com/item?id=46953593

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Article URL: https://zenodo.org/records/18571935

Comments URL: https://news.ycombinator.com/item?id=46953585

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Adobe recently announced the end-of-life for Adobe Animate, then walked it back after community backlash.

Regardless of what Adobe decides next, the message was clear: animators who depend on proprietary tools are one corporate decision away from losing their workflow.

2D animation deserves an open-source option that isn't a toy. We've been working with a professional animator to guide feature priorities and ensure we're building something that actually fits real production workflows - not just a tech demo.

Github Repo: https://github.com/17twenty/inamate

We're at the stage where community feedback shapes the direction. If you're an animator, motion designer, or just someone who's been frustrated by the state of 2D animation tools — we'd love to hear:

- What features would make you switch from your current tool?

- What's the biggest pain point in your animation workflow?

- Is real-time collaboration actually useful for animation, or is it a gimmick?

Try it out, break it, and tell us what you think.

Built with Go, TS & React, WebAssembly, PostgreSQL, WebSocket, ffmpeg (for video exports).

Comments URL: https://news.ycombinator.com/item?id=46953567

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Article URL: https://technobabble.bearblog.dev/fine-ill-try-ai/

Comments URL: https://news.ycombinator.com/item?id=46953557

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Article URL: https://github.com/AgriciDaniel/claude-seo

Comments URL: https://news.ycombinator.com/item?id=46953527

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Article URL: https://www.iankduncan.com/engineering/2026-02-09-what-functional-programmers-get-wrong-about-systems/

Comments URL: https://news.ycombinator.com/item?id=46953491

Points: 2

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Article URL: https://github.com/Waraq-Labs/review-for-agent

Comments URL: https://news.ycombinator.com/item?id=46953473

Points: 1

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We published the first public benchmark for insurance AI agents on HuggingFace.

What it contains: - 510 real insurance scenarios - 10 categories across 9 insurance lines - Train/val/test splits (357/76/77) - 4 routing decisions per scenario: AI handles, AI with verification, human handoff, hybrid collaboration - 3 evaluation metrics: intent accuracy, routing accuracy, action completeness

Why it matters: Insurance is precision work. A wrong routing decision costs money and trust. Most AI benchmarks miss this. They don't test what matters in production.

This data came from a real voice AI system. Years of customer calls. Actual insurance decisions. The scenarios are messy. They're real.

Open source: Apache 2.0 license. Ready to use.

Implementation: https://github.com/pavelsukhachev/hybrid-orchestrator Paper: TechRxiv (IEEE) - "The Hybrid Orchestrator: A Framework for Coordinating Human-AI Teams"

Comments URL: https://news.ycombinator.com/item?id=46953463

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I built AI systems for banking and insurance. They failed too often. So I created a framework for human-AI teams.

The Hybrid Orchestrator has four patterns: (1) session state that survives context window limits, (2) multi-channel communication routing, (3) activity monitoring with triggers, and (4) human escalation pathways.

These patterns come from a production voice AI system for insurance applications. The code is Python, 97 tests, Apache 2.0.

I also published a research paper on TechRxiv (IEEE) describing the architecture.

Feedback welcome. What patterns do you use for reliable AI agents?

Comments URL: https://news.ycombinator.com/item?id=46953458

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Article URL: https://trends.google.com/trends/explore?date=today%205-y&geo=US&q=learn%20python&hl=en

Comments URL: https://news.ycombinator.com/item?id=46953437

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Article URL: https://www.ssp.sh/brain/data-modeling-is-changing/

Comments URL: https://news.ycombinator.com/item?id=46953428

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Article URL: https://twitter.com/rauchg/status/2020616857561284848

Comments URL: https://news.ycombinator.com/item?id=46953420

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