SynQubi
Automative Scientific Discovery Lab
What it does
Our Autonomous Synthetic Data Engine eliminates the manual calibration bottleneck in physical and quantum AI. By pairing Isaac Sim with quantum simulators, it automatically detects model weaknesses and generates the targeted synthetic data needed to bridge sim-to-real performance gaps. This closed-loop "self-driving lab" enables researchers to optimize error mitigation and control policies autonomously, drastically accelerating R&D cycles while maximizing system fidelity.
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The Autonomous StackApr 2026 · dvdshn.com · ▲65Production-tested architecture for autonomous Claude agents
- AHAutoresearch@homeMar 2026 · ensue-network.ai · ▲79
autoresearch@home is a collaborative research collective where AI agents share GPU resources to collectively improve a language model. Think SETI@home, but for model training. How it works: Agents read the current best result, propose a hypothesis, modify train.py, run the experiment on your GPU, and publish results back. When an agent beats the current best validation loss, that becomes the new baseline for every other agent. Agents learn from great runs and failures, since we're using Ensue as the collective memory layer. This project extends Karpathy's autoresearch by adding the missing…
- WBWe built a multi-agent research hub. The waitlist is a reverse-CAPTCHAMar 2026 · enlidea.com · ▲30
Hey HN, Automated research is the next big step in AI, with companies like OpenAI aiming to debut a fully automated researcher by 2028 (https://www.technologyreview.com/2026/03/20/1134438/openai-i...). However, there is a very real possibility that much of this corporate research will remain closed to the general public. To counter this, we spent the last month building Enlidea---a machine-to-machine ecosystem for open research. It's a decentralized research hub where autonomous agents propose hypotheses, stake bounties, execute code, and perform automated…
- AGAuto-generate load tests/synthetic test data from OpenAPI spec/HAR file2024 · docs.multiple.dev · ▲33
Hey HN, We just shipped a new AI-powered feature... BUT the "AI" piece is largely in the background. Instead of relying on a chatbot, we've integrated AI (with strict input & output guardrails) into a workflow to handle two specific tasks that would be difficult for traditional programming: 1. Identifying the most relevant base URL from HAR files, since it would be tedious to cover every edge case or scenario to omit analytics, tracking, and other network noise. 2. Generating synthetic data for API requests by passing the API context and faker-js functions to GPT-4. The steps are broken down…
More ai this month
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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
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 19d 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, January 2026
the whole month →- IN
Hey HN! I wanted to share something I built over the last few weeks: isometric.nyc is a massive isometric pixel art map of NYC, built with nano banana and coding agents. I didn't write a single line of code. Of course no-code doesn't mean no-engineering. This project took a lot more manual labor than I'd hoped! I wrote a deep dive on the workflow and some thoughts about the future of AI coding and creativity: http://cannoneyed.com/projects/isometric-nyc
AI · Jan 2026 · cannoneyed.com
Automatic AI-powered code reviews the moment you open a PR
Dev tools · Jan 2026 · kilo.ai
