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AI & AgentsBest Open-Weight LLMs 2026: DeepSeek vs Qwen vs Kimi vs GLM vs Llama
DeepSeek V4, Qwen3.6, Kimi K3, GLM-5.2 and Llama 4 compared on architecture, context, license, self-hosting footprint and the workloads each family fits.
Business & RegulationAI Consulting in Austria 2026: An Honest Guide for SMEs
What AI consulting really covers, what it costs in 2026, which use cases pay off, what the EU AI Act asks today, and how funding works. An engineering view, not a sales pitch.
AI & AgentsThe Bottleneck Was Never Intelligence. It Was Context.
Rakuten ran a coding agent for seven hours on vLLM at 99.9% accuracy. The real lesson is not the hours. It is that context, not intelligence, was the bottleneck, and that directing agents became the new deep work.
AI & AgentsChatGPT Enterprise vs Copilot vs Custom RAG
Which should a DACH company buy? A neutral procurement guide: where your data lives, what each costs, and the decision rules. None of them is GDPR compliant on its own.
Business & RegulationEU Data Residency for AI APIs in 2026: Provider Guide
Compare EU storage and inference guarantees across OpenAI, Azure, Mistral, AWS and self-hosting. Current endpoints, transfer caveats and a procurement checklist.
Delivery & QALovable, Bolt, and Replit App Due Diligence
Before you raise or sell on a vibe-coded app, three questions decide whether it survives diligence: is it safe, do you own it, can anyone maintain it? The security, IP, and investor checklist.
Business & RegulationAI Funding in Austria 2026: aws, FFG, Premium, KMU.DIGITAL
The four AI funding routes in Austria for 2026, honestly assessed: aws for adoption, FFG for R&D, the 14% research premium, and KMU.DIGITAL. Plus the catch nobody mentions: open calls and budget.
Leadership & TeamsHow to Roll Out AI Internally in 2026
Most internal AI rollouts stall as shelfware. The order we apply: educate the team, map the real process, settle cost and compliance, ship one workflow to production, then hand it over so your team owns it.
AI & AgentsMCP vs RAG vs Agent Skills vs Custom GPTs
Not four answers to one question, but four layers: RAG for knowledge, MCP for connectivity, Skills for procedure, Custom GPTs as a packaged surface. A decision tree, and why you usually compose them.
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