I built a local data lake for AI powered data engineering and analytics
I got tired of the overhead required to run even a simple data analysis - cloud setup, ETL pipelines, orchestration, cost monitoring - so I built a fully local data-stack/IDE where I can write SQL/Py, run it, see results, and iterate quickly and interactively. You get data lake like catalog, zero-ETL, lineage, versioning, and analytics running entirely on your machine. You can import from a database, webpage, CSV, etc. and query in natural language or do your own work in SQL/Pyspark. Connect to local models like Gemma or cloud LLMs like Claude for querying and analysis. You don’t have to setup local LLMs, it comes built in. This is completely free. No cloud account required. Downloading the software - https://getnile.ai/downloads Watch a demo - https://www.youtube.com/watch?v=C6qSFLylryk Check the code repo - https://github.com/NileData/local This is still early and I'd genuinely love your feedback on what's broken, what's missing, and if you find this useful for your data and analytics work.
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Microphone
If you are an aspiring founder, any VC will ask you this question: “why are you the only person who could solve this”. If you want to generate passive income with your side idea, get ready to enter a crowded market as everyone and their mother is shipping. Unless you have an active X account or you’re a TikTok sensation distribution is going to be tough. I just launched the trie.dev microphone beta to help folks find their edge. You yap into your phone about your ideas; Trie turns the rambling into hypotheses, then prioritizes them based on your experience and your realistic ability to distribute in that idea space — surfacing the problems only you can solve. From there you can generate creative and run Meta ads against your hypotheses straight from your phone, with zero setup, to see how real people respond. I built it initially for myself and friends as an “intake form” for running paid ads to help validate our side gig ideas. Happy to chat about how it works or the stack. Joining the waitlist will send you an email to join via TestFlight.
Adaptive Recall, persistent memory for AI assistants over MCP
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We beat Gemini Embedding 2 by training only 16M params (open weights)
Show HN: We beat Gemini Embedding 2 by training only 16M params (open weights)
I built a free app for New Yorkers to save money on groceries
I built this because I see that grocery savings are achievable in NYC. People usually just go to the store they're used to going to, and it's rarely worth the effort of combing through card cashback, weekly coupons, CPG rebates. Most people leave real money on the table by not stacking them, and even more don't even know that these deals are out there.... so I built a way to automate it. You can use it for free, no login, currently NYC-only with ~690 stores. I built it so that you just search whatever you want (use commas if you want to search multiple items). Or - use the AI tool to help shop for you. If you're curious, it's powered by a trained LLama model. Honest limitations are coverage and freshness. Id love some feedback on where the data looks wrong or is stale. Question for the room - what to prioritize if you're working with messy, multi-source retail/pricing data? Is freshness or coverage the top priority if you cant get a uniform response from every source? curious on what to prioritize here.