For a long time, AI discussions were mostly about the model.
🤖 Which one is better?
📊 Which benchmark was beaten?
🚀 Which new release changed everything?
But only the model is not enough. The layer around it is just as important: That's the harness.
📖 https://t.co/O6zH0fJDF9
I really like it when tech CEOs skip the marketing fluff and just talk straight.
Sundar Pichai on the Hard Fork podcast, speaking openly about Google's current AI position:
"…when it comes to agentic coding… I think we are a bit behind at this moment."
https://t.co/ZGUHYECIuN
3️⃣ The Winner 🏆Gemma 4 26B MoE (NVFP4 via vLLM) is the winner. It achieved a 91.8% accuracy rate and clocked the fastest median inference speed.
Read our full architectural deep dive on how the validation schema and renderers handle this: 🔗 https://t.co/oJCsyZvrLz (4/4)
Using @CopilotKit & Google's #A2UI with local LLMs? 🤖
Following up on our article last week, here's the fully interactive, visual dashboard showcasing our benchmarks, created by @wolfmanfx:
👉 https://t.co/d4zTsUq3jc
The 3 key takeaways from our evaluation prompts 👇 (1/4)
2️⃣ Inference Engines Shift Accuracy ⚙️The engine matters. Running identical weights on different runtimes yielded completely different semantic accuracy. Qwen 3 14B hit 90.4% accuracy on vLLM, but dropped to 68.2% on llama.cpp. It's not just a performance choice! (3/4)
AI offers massive potential for UIs. The interface can be dynamically generated for each user.
A key technology is #A2UI, which is framework-agnostic.
Our new post covers:
✅ What is A2UI?
✅ How does it work?
✅ How to run it fully autonomously.
https://t.co/oJCsyZvrLz
RAG in a browser tab? No backend? Yes, it is possible.
@wolfmanfx spoke at AI India on "From the AI Jungle to RAG in a Tab." If you want to build full RAG pipelines that run entirely client-side for ultimate privacy and speed, the slides are live:
https://t.co/FZMInvvWG8