Cognilumin
Visualize topology. Trace reasoning. Illuminate complexity
What it does
Deterministic AI infrastructure tools. The Illuminator synthesizes complex technical topics into high‑signal explanations. The Topology Visualizer renders infrastructure scenarios as live network state maps with flow fields and diagnostic logs. Execution Logs provide verifiable, step‑by‑step agentic reasoning traces with hex‑formatted state transitions. Built on a zero‑dependency vanilla stack with a multi‑provider AI backend (Gemini → Groq → OpenRouter auto‑failover). No sign‑up. Free to use.
Does a similar job
all alternatives →
- CCCogency – Cognitive Architecture for AI Agents2025 · github.com · ▲19
Yesterday I built something that probably shouldn’t exist yet. In 9 hours, I created a cognitive architecture demonstrating emergent reasoning. It follows a 5-step loop: Plan → Reason → Act → Reflect → Respond. Adding a WebSearchTool to test extensibility, the agent initially failed its first search, reflected on poor results, adapted its query, and then succeeded. This behavior wasn’t programmed; it emerged naturally from the architecture. Five hours later, I integrated a FileManagerTool — it worked on the first try. Like code compiling first time, except this was intelligence composing…
ElevenCreative FlowsMay 2026 · elevenlabs.io · ▲84Node-based creative pipelines, now with real-time collab
- MRMorph Reflexes – Multi-head classifiers for agent tracesJun 2026 · ▲20
The most common failures for production agents are behavioral: looping, reasoning leakage, user frustration, and more. Using a frontier model like GPT or Sonnet to judge every turn is too expensive and slow to run at scale. To solve this, we built Reflexes: semantic signals from agent traces, served fast and cheap over API. Built on custom kernels and a custom inference engine forked from vLLM. Under the hood, it is a small LLM architected around multi-head inference. Small models need to be trained for specific tasks, but running 50 separate small models on the same input for 50 tasks makes…
- IYIf You Want Coherence, Orchestrate a Team of Rivals: Multi-Agent "Jan 2026 · arxiv.org · ▲10
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
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, 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