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Article URL: https://www.openculture.com/2024/11/how-magician-david-copperfield-made-the-statue-of-liberty-disappear-1983.html

Comments URL: https://news.ycombinator.com/item?id=42186170

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Article URL: https://www.kaggle.com/code/chandgoti/simulated-consiousness-a-small-step-towards-agi

Comments URL: https://news.ycombinator.com/item?id=42186152

Points: 1

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Article URL: https://perthirtysix.com/tool/birthday-paradox

Comments URL: https://news.ycombinator.com/item?id=42186146

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Article URL: https://www.tomlooman.com/unreal-engine-5-5-performance-highlights/

Comments URL: https://news.ycombinator.com/item?id=42186126

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Article URL: https://www.youtube.com/watch?v=R-dgMFm8Gro

Comments URL: https://news.ycombinator.com/item?id=42186110

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Article URL: https://www.bloomberg.com/news/features/2024-11-18/kraft-mac-cheese-sales-fall-as-shoppers-ditch-classic-us-brands

Comments URL: https://news.ycombinator.com/item?id=42186082

Points: 1

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Article URL: https://www.stripe.press/boom

Comments URL: https://news.ycombinator.com/item?id=42186067

Points: 1

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Article URL: https://github.blog/news-insights/research/does-github-copilot-improve-code-quality-heres-what-the-data-says/

Comments URL: https://news.ycombinator.com/item?id=42186054

Points: 1

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Hi Everyone,

Any leads on how to connect with the account behind whoishiring ?

Comments URL: https://news.ycombinator.com/item?id=42186052

Points: 1

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Article URL: https://unin.io/about/blog/why-is-it-still-2005

Comments URL: https://news.ycombinator.com/item?id=42186051

Points: 2

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Article URL: https://betterexplained.com/articles/the-rule-of-72/

Comments URL: https://news.ycombinator.com/item?id=42186042

Points: 1

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Article URL: https://www.fillout.com/blog/what-is-servicenow

Comments URL: https://news.ycombinator.com/item?id=42185337

Points: 2

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Article URL: https://nightsky.hctr.dev

Comments URL: https://news.ycombinator.com/item?id=42185336

Points: 1

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Article URL: https://www.voanews.com/a/world-s-largest-coral-discovered-in-solomon-islands/7863036.html

Comments URL: https://news.ycombinator.com/item?id=42185332

Points: 1

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Article URL: https://cedardb.com/blog/pagination/

Comments URL: https://news.ycombinator.com/item?id=42185328

Points: 2

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Article URL: https://www.reuters.com/world/middle-east/iran-offers-cap-sensitive-uranium-stock-avoid-iaea-resolution-2024-11-19/

Comments URL: https://news.ycombinator.com/item?id=42185327

Points: 1

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Article URL: https://journalofsportsmedicine.org/full-text/280/eng

Comments URL: https://news.ycombinator.com/item?id=42185322

Points: 1

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Article URL: https://www.cnbc.com/video/2024/11/13/ai-doesnat-stand-a-chance-against-actors-or-shakespeare-ben-affleck-says.html

Comments URL: https://news.ycombinator.com/item?id=42185314

Points: 1

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Hey all, excited to pitch you Garam, my B2B SaaS start-up idea. Looking forward to some roasting here :)

Product

What is it: an out-of-the-box data warehouse. In other words, a single “enterprise-glue” platform that contains ETL/ELT, a data warehouse and a semantic layer specifically tailored to B2B SaaS companies (eg., includes templated code for MRR, ARR, subscriptions, sales pipelines, etc).

Use case: Garam simplifies the data journey for start-ups. At the moment, start-ups need to pay for an expensive data team and a fragmented data stack. It takes ages to build a data warehouse. Worst of all, the data modelling only starts then, and the chaotic nature of this work results in inaccuracies.

Most of the code written by data engineers is similar across business models, and thus can and should be productionised. This templated semantic layer is the USP of Garam.

Who would want it: B2B SaaS start-ups that have scaled to the point that they cannot rely on spreadsheets for their data infrastructure anymore.

Product analysis

*Competitors:*The fact that the direct competitors have launched only recently suggests two things to me: the problem shows validation and the timing could not be better for me to launch. Happy to be challenged on this.

A few recently-launched start-ups offer something similar (eg., pliable.co, agiledata.io, arch.dev). However, they focus on no-code interfaces or they cater to data consultancies. To me, these are solutions that will not scale for start-ups, the same way that a no-code website builder hits its limits very soon.

Traditional data stack tools which are too technical, need to be “glued together” and offer no modelling (cube.dev, DBT, etc.)

Data consultancies (their offer does not scale and is a lot more expensive).

Key difference: two things: enterprise glue and templated metrics. In addition, this semantic layer comes with built-in data quality checks and automated documentation (once again, in 99% of companies order IDs should not be duplicate or NULL. This is business logic that can be templated; data engineers should not replicate this work across companies).

Customer conversion strategy: probably SaaS events, startup accelerators, etc. This is something to work out once the MVP is ready. Advice welcome.

Stage: designing the MVP, very early stages.

Why me?: 7+ years of experience as a head of analytics (incl. leading +20 data engineers and data analysts) at both start-ups and scale-ups. I have led several data modelling initiatives, built full-fledged data warehouses from scratch, etc. These experiences have helped me realise that there is a gap in the market, and I want to contribute to productionising this process.

Comments URL: https://news.ycombinator.com/item?id=42185310

Points: 1

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Article URL: https://www.readmargins.com/p/technology-and-grieving

Comments URL: https://news.ycombinator.com/item?id=42185309

Points: 1

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