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I used to have one Brother mode, sold it for $50 after long battle with ink drains. It will sneakily drain the ink cleaning the print head at midnight. I had to keep it off. Then they have chips inside the cartridge to prove authenticity of cartridge!! When the printer itself is cheaper than the cartridge, its almost always is a scam in my personal experience.
Get an EPSON tank printer, or a laser..
I used to write a shell script to do all this. Then the number of scripts started adding up, got overly complex. This local app here is what evolved from all that, but with more reliable query running, compute management, spark environment, and above all UI and AI that can make this process seamless than a cluttered CLI UX.
Yes, absolutely. You can turn off wifi and still work on your data, with AI to help you out. LLM runs locally on your box.
Everything including actual data, schema and transform is versioned and tracked at job run level.
You will get job run level lineage for any datasets created in the system.
Yes. I would recommend a model with 16gb ram at least but I was able to run it on a MacBook air 8gb but it lagged for LLM assist.
You don't need to setup LLM locally, the tool does that. You can choose which model to go with. It has Gemma and Qwen supported now.
Two things:
1. You may not want to expose bits and pieces of your data and metadata to an LLM, you dont want your data to be used for training. If you are using LLM running on your machine, as in this case, you are covered there.
2. Claude can do a lot of stuff, but doing multi step analysis consistently and reliably is not guaranteed due to the non-deterministic nature of LLMs. Every time it may take a different route. Nile local offers a bunch of data primitives like query, build-pipe, discover, etc. that reduces the non-determinism and bring reliability and transparency (how the answer was derrived) to the data analysis.
Lineage-aware. Versioned. Trustworthy Data - for Engineers and AI.
Your engineers waste up to 40% of their time monitoring, investigating and fixing data. Even then you don’t trust the accuracy, source, or freshness of metrics on your dashboard. You wish AI can answer your data questions but it cannot show you proof, or where it came from. AI helps software engineers to move fast and break things, because they can always rollback, with git. But you cannot do that for data. Bad data entering the system, spreads across the company before spotting, and takes weeks to clean up.
DV changes this, giving you lineage-aware, versioned data. It records data-lineage when data is captured, transformed, and committed, at commit/snapshot level. So when things break, DV knows what other data is impacted downstream, and it can rollback the whole chain to the previous state, instantly - no data copy/restore needed. It can also backfill the data across the chain automatically.
With DV, both your team and your AI agents can finally see: - where data came from - how it was transformed - how to revert safely with a single click
Your engineers can move fast on data, without breaking trust. Your analysts can build pipelines by simply describing business questions to AI.
DV is Git for data, so you can focus on your business, putting analytics on auto pilot.
-- Please contact me if you are interested in preview program.
Hi HN,
For the past few months, we were working on a financial transparency tool for governments and non-profits. I left my job to make this software a reality. I and a couple of my friends have worked hard to get this working and we need some serious review from experts on this. Please take a look and give us feedback.
Elevator Pitch: Allow organizations to publish financial information online.
Looking for honest, brutal feedback. Thank you for your time!
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Clickable links:
Website - http://openreporting.com
Demo report - http://sampleorg.openreporting.com/Report/Category-198-Yearl...
I already junked my desktop for my office laptop last week. Thinking of junking the laptop too...