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π
Diving back into Hugo and figuring out how/where I want to deploy it.
Magnus Hedemark
magnus919
π
Diving back into Hugo and figuring out how/where I want to deploy it.
Agent Council: Multi-agent structured debate β what comes after Agile in a hybrid human/AI world?
Agent Council β Live Demo
What comes after Agile in a hybrid human/AI world?
Five real professional profiles β Product Manager, Technical Architect, Kanban Strategist, Researcher, Verifier β debated this question through structured rounds of cross-examination. Here's what they produced.
GroktoCrawl v0.12 β The Agent-Native Research Platform: structured output, compact citations, session protocol, plan-consent, depth injection, research memory, MCP server, and artifact-pyramid CLI output
GroktoCrawl v0.12 β The Agent-Native Research Platform
GroktoCrawl v0.12.0 transforms a self-hosted web scraper into an agent-native research platform. Every new capability ships with both a REST API and a groktocrawl CLI command. No .env changes needed β upgrade and go.
Upgrade:
cd~/docker-compose/groktocrawl
git pull origin main
docker compose pull
docker compose up -d
Running Ornith 1.0-9B on an RTX 5070 Ti β 400K Context on Consumer Hardware
Running Ornith 1.0-9B on an RTX 5070 Ti β 400K Context on Consumer Hardware
I needed a capable local coding model that would fit on a single consumer GPU with meaningful context. The RTX 5070 Ti has 16 GB of VRAM β not enough for most frontier models, but more than enough for interesting things if you pick the right architecture.
Ornith 1.0-9B is that architecture. It's a self-improving open-source coding model from DeepReinforce, MIT licensed, scoring 69.4 on SWE-Bench Verified and 43.1 on Terminal-Bench 2.1. Those numbers match Gemma 4-31B, a model with 3.4x more parameters. And thanks to its hybrid attention design, it runs at 400K context on a widely-available consumer GPU.
This guide shows you how to set it up, what to expect, and three exercises to see for yourself what it can do.