Alternatives
Products that do what LumenIQ does
AI-powered S/4HANA migration — DQ report in 60 seconds
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Hey HN! I’m the founder of Unify, and we’ve just released our Model Hub, which provides a collection of LLM endpoints with live runtime benchmarks all plotted across time: https://unify.ai/hub A key finding is that static tabular runtime benchmarks for LLMs simply do not work. It’s necessary to take a time-series perspective, and plot the variations through time. We currently have 21 models provided by: Anyscale, Perplexity AI, Replicate, Together AI, OctoAI, Mistral AI and OpenAI, with more on the roadmap. We test across different regions (Asia, US, Europe), with varied…
2024
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Bring your app's data to Looker Studio, BigQuery, or AI
Mar 2026 · easyappreports.com
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We’re Robin, Louis, and Thomas. Pipelex is a DSL and a Python runtime for repeatable AI workflows. Think Dockerfile/SQL for multi-step LLM pipelines: you declare steps and interfaces; any model/provider can fill them. Why this instead of yet another workflow builder? - Declarative, not glue code: you state what to do; the runtime figures out how. - Agent-first: each step carries natural-language context (purpose, inputs/outputs with meaning) so LLMs can follow, audit, and optimize. Our MCP server enables agents to run pipelines but also to build new pipelines on demand. - Open…
Oct 2025 · github.com
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I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…
2025 · llmapitest.com
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Hi HN , I got tired of writing the same boilerplate over and over — DB setup, auth, routes, security — every time I built a backend. So I built Pipo360 — an AI-powered tool that generates production-ready backends in under 60 seconds, from just a plain-text description. How it works: Type what you need “Create a task management API with user auth and MongoDB” Hit Generate Get real, exportable code Auth (JWT) Database schema CRUD routes Deployable to Vercel, AWS, etc. No templates. No lock-in. Just code that works. Why it’s different: Built with Gemini AI + human supervision (to ensure real…
2025 · pipo360.xyz
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Your budget, mathematically guaranteed to be correct.
May 2026 · lums.ai
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2024 · github.com
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AutoMQ is a fully open-source, next-generation Kafka built on top of S3. Version 1.5.0 is a very important release. In March of this year, Confluent officially launched the commercial capability of Tableflow. Now, with the AutoMQ open-source software, you can also experience this killer feature. Simply put, for Kafka streaming data, AutoMQ can automatically store it in S3 in Iceberg Table format, so you no longer need to manage Flink Jobs and Spark Jobs yourself to perform ETL operations and convert Kafka data into table format. We believe this will be the new paradigm for Kafka stream data…
2025 · github.com
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I’d originally launched my app: Private LLM[1][2] on HN around 10 months ago, with a single RedPajama Chat 3B model. The app has come a long way since then. About a month ago, I added support for 4-bit OmniQuant quantized Mixtral 8x7B Instruct model, and it seems to outperform Q4 models at inference speed and Q8 models at text generation quality, while consuming only about 24GB of RAM[3] at 8k context length. The trick is: a) to use a better quantization algorithm and b) to use unquantized embeddings and the MoE gates (the overhead is quite small). Other notable features include many more…
2024
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SSRS to Power BI migration, 75–90% automated accuracy
29d ago · innovationalofficesolution.com
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Most mortgage processing delays aren’t due to risk — they’re due to manual workflows. We’ve been working on SimplAI, an AI-driven system designed for banking and financial services, starting with mortgage operations. The problem we kept seeing: 15–22 day processing timelines Heavy manual document handling (500+ pages per loan) Repetitive data entry + verification loops Underwriters spending hours on non-decision work So we built a set of AI agents that handle the operational layer: Document AI (IDP) → classifies + extracts data from loan docs in minutes Income analysis models → parse tax…
Mar 2026 · app.simplai.ai
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Dec 2025 · github.com
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