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I'm Anders, co-founder of Shoehorn (https://shoehorn.dev/).

For about a year I was genuinely excited about Backstage. Saw the Spotify demo, thought "I want that too". Turned out it wasn't as easy. But to be fair, Backstage is a great piece of software. Powerful, flexible, and if you have a dedicated developer team to run it, you can build something amazing on top of it.

The catch is exactly that: you need a dedicated team. Most companies don't have one. Or they do, and that team would rather be solving product problems than maintaining the platform that helps everyone else solve theirs. We wanted something that gave teams 90% of the value with 10% of the operational overhead.

We built Shoehorn out of frustration.

Goals from the start:

1. A great UI, API first, Terraform and CLI: That's what most users actually want.

2. Surface problems clearly: No silent failures, no "why didn't my service show up." If something's wrong, you see it.

3. Automate the boring stuff: The data is already in GitHub, Kubernetes, your identity provider. Use it. Don't make humans retype it into manifests.

4. Maintainable by one person: Deploy and use it. No team required.

Shoehorn is an event-driven developer platform — service catalog, scaffolding, scorecards, fuzzy search, multi-tenant by default. Same category as Backstage, Port, and Cortex. The difference: Backstage is a framework you assemble into a portal. Shoehorn is a platform you turn on. Auto-discovery from Kubernetes, GitHub, Cloud vendors etc.

Auto-discovery from Kubernetes and GitHub crawler for markdowns and (manifests if desired). One install, sensible defaults (we think), prebuilt hardened images from (https://docs.docker.com/dhi/), gives you an working catalog inside within 15 min.

Architecturally its a small set of Go microservices, its backed by Postgres(RLS), Meilisearch, Valkey and Redpanda. Authentication is OIDC, we currently support: Zitadel and Okta.

We released Beta yesterday, there's probably bugs and some parts might be rough. Would love to hear any thoughts, comments, or ideas of how we could make this better, and if there are obvious things we’re missing etc. Are you using backstage or any other internal developer portal today?

Demo page here: https://demo.shoehorn.dev

Comments URL: https://news.ycombinator.com/item?id=48077324

Points: 1

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Article URL: https://isthisanart.substack.com/p/death-of-a-software-developer

Comments URL: https://news.ycombinator.com/item?id=48077317

Points: 1

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Article URL: https://www.copahost.com/blog/dirty-frag-cve-2026-43284/

Comments URL: https://news.ycombinator.com/item?id=48077314

Points: 1

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Article URL: https://x86.lol/generic/2022/11/26/lanzaboote.html

Comments URL: https://news.ycombinator.com/item?id=48077291

Points: 1

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Article URL: https://github.com/jordepic/icestream

Comments URL: https://news.ycombinator.com/item?id=48077256

Points: 1

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Article URL: https://xunroll.com/thread/2053047748191232310

Comments URL: https://news.ycombinator.com/item?id=48077231

Points: 14

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Article URL: https://www.echooff.dev/blog/developer-experience-is-a-performance-feature

Comments URL: https://news.ycombinator.com/item?id=48077205

Points: 2

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Article URL: https://github.blog/news-insights/policy-news-and-insights/why-age-assurance-laws-matter-for-developers/

Comments URL: https://news.ycombinator.com/item?id=48077199

Points: 2

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Article URL: https://redstonemarkets.com/

Comments URL: https://news.ycombinator.com/item?id=48077183

Points: 1

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Article URL: https://www.ft.com/content/d2136091-9fd2-4923-b168-50539e5b27ab

Comments URL: https://news.ycombinator.com/item?id=48077176

Points: 2

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Article URL: https://get168.app/blog/money-is-renewable-time-isnt

Comments URL: https://news.ycombinator.com/item?id=48077175

Points: 1

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Article URL: https://trinkle23897.github.io/learning-beyond-gradients/

Comments URL: https://news.ycombinator.com/item?id=48077130

Points: 1

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Article URL: https://www.nytimes.com/2026/05/08/technology/meta-ai-employees-miserable.html

Comments URL: https://news.ycombinator.com/item?id=48077126

Points: 5

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ML is known to be good at interpolating between points in the training set, but does much worse at extrapolating. Both can produce innovation when applied to science. For example, a lot of innovation is about transferring existing ideas from one domain to another.

We start seeing examples of LLMs proving novel math theorems: https://news.ycombinator.com/item?id=48071262. But those could be “interpolation”. Is there any strong evidence for or against LLMs being capable of extrapolating?

Comments URL: https://news.ycombinator.com/item?id=48077122

Points: 1

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Article URL: https://hbr.org/2026/03/researchers-asked-llms-for-strategic-advice-they-got-trendslop-in-return

Comments URL: https://news.ycombinator.com/item?id=48077117

Points: 3

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Article URL: https://www.youtube.com/watch?v=y894m7joEBc

Comments URL: https://news.ycombinator.com/item?id=48077110

Points: 1

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Article URL: https://arkoinad.com/posts/zsh_speedup.html

Comments URL: https://news.ycombinator.com/item?id=48077108

Points: 1

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Article URL: https://github.com/GiuseppePuleri/jimphp/blob/main/README.md

Comments URL: https://news.ycombinator.com/item?id=48077106

Points: 1

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Article URL: https://marginalrevolution.com/marginalrevolution/2026/05/the-uap-report-so-far.html

Comments URL: https://news.ycombinator.com/item?id=48077092

Points: 2

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