Alternatives
Products that do what TamedTable, AI ETL in Natural Language does
Hi HN, TamedTable is an LLM harness for data ETL. And yes, it was developed using AI, meaning you can take the entire specification and recreate it to your desires: https://github.com/ZSvedic/TamedTable
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2017 · singer.io
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Hey HN — We're excited to share Trellis — a snowflake for unstructured data. We've built an AI engine that turns unstructured data into structured SQL-format based on the schema you define in natural language. We spent a lot of time building ML infrastructure and realized that most data warehouses and data pipelines are not designed for unstructured data (documents, PDFs, calls). While something like a Vector database and RAG are great at search tasks, they really struggle with aggregation and SQL type queries such as 1. How many emails in the past 6 months contain complaints about the…
2024 · demo.runtrellis.com
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Hi HN, we’re Dylan and Matthew, building sublingual (https://github.com/sublingual-ai/sublingual), an open-source LLM observability tool you can use with zero code changes. As developers focused on iterating and building features as fast as possible, we felt observability would’ve been a helpful tool to have, but we found existing solutions had too much overhead to set up. So we gave ourselves the challenge of building an observability tool that you can integrate without changing a single line of code in your project. How it works Run your python application as usual with…
2025 · github.com
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Hey HN, we’re Ross and Javier, co-founders of Engraph (www.engraph.ai). Our goal is to completely automate the process of building ETL pipelines, from ad hoc pipelines for question answering to fully fledged ETL pipelines within large organisations: For ad hoc pipelines, a question answering platform which enables users to ask questions in natural language about their organisation's data. Traditionally, access to data within organisations is limited to a handful of data-engineers. This means that if an employee needs access to some data, they have to go through a lengthy process of…
2023
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At testup.io we have been working for a while to bring artificial intelligence to the field of test automation. Just a few years ago, the primary challenge laid in accurately identifying UI elements following minor structural changes, such as updates to IDs or paths. The emergence of Large Language Models (LLMs) raised the bar for what it meant to be smart. Now, we anticipate the robot to do lots of things autonomously, such as retry in cases of unresponsiveness or handle minor error reports. A more challenging, but soon expected feature, would involve the test robot navigating your web shop…
2024 · github.com
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And you can try out the models live here: https://labs.refuel.ai/playground
2024 · huggingface.co
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I’ve been building LLM tooling for a small VC fund and found myself explaining the same mental model over and over to non-technical people around me: how a stateless LLM becomes a chatbot, how tool use works, what an agent is mechanically, and why context windows shape all of it. I never found a guide that covered that full chain at the level I wanted, so I wrote one. It’s nine short chapters, each building on the last. Deliberately simplified: the goal is a useful mental model, not a textbook. Feedback, corrections, and contributions welcome: github.com/ymyke/aiaiai
Apr 2026 · aiaiai.guide
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Hey HN, I've been building AutoAgents, an AI agent framework in Rust. Today I'm sharing a feature I haven't seen done well elsewhere: composable middleware layers for LLM inference pipelines. The problem Every agent framework lets you swap LLM providers. Almost none of them give you a structured way to enforce safety, caching, or data sanitization in the inference path itself. You end up with guardrails as application-level if-statements, caching bolted on as a separate service, and PII handling as a "we'll add it later" TODO that never ships. This gets worse with local models. Cloud APIs…
Mar 2026 · github.com
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2022 · weld.app
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2016 · tcl.wiki
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Every time I wanted to use LLMs in my existing pipelines the integration was very bloated, complex, and too slow. This is why I created a lightweight library that works just like scikit-learn, the flow generally follows a pipeline-like structure where you “fit” (learn) a skill from sample data or an instruction set, then “predict” (apply the skill) to new data, returning structured results. High-Level Concept Flow Your Data --> Load Skill / Learn Skill --> Create Tasks --> Run Tasks --> Structured Results --> Downstream Steps And the bast part: Every step can be saved and reused as…
2025 · github.com
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