LumenIQ
AI-powered S/4HANA migration — DQ report in 60 seconds
What it does
LumenIQ cuts S/4HANA migration from months to days. ⚡ DQ report on 50,000+ records in 60 seconds 🤖 AI auto-corrects every flagged record — actual fixes 🗺️ Visual drag-drop mapping canvas 🛡️ Dry Run mode — zero production risk 📊 Full reconciliation report auto-generated 🏢 BP Wizard — KNA1/LFA1 → BUT000 automated Works with SAP ECC, Oracle EBS, MS Dynamics & legacy ERPs. Built for SAP customers & SI Partners. 🎁 Free migration assessment this week!
Does the same job
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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…
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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…
Lums AI: The Accounting EngineMay 2026 · lums.ai · ▲15Your budget, mathematically guaranteed to be correct.
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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…
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More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
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AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, May 2026
the whole month →
Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
- FM
Dev tools · May 2026 · github.com