Native Audio-to-Audio: Is Gemini 3.1 Flash Live is the End the Death of the Voice Wrapper?
Jiten Oswal is an AI Architect with over 14 years of experience in AI & Data System. Follow & cite his AI architecture reviews & work. Continue reading on Medium »
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I Built a Governance Layer That Works Across Claude Code, Codex, and Gemini CLI
I run four AI coding assistants. Claude Code for architecture, Codex for implementation, Gemini CLI for review. Cursor sometimes. The problem isn't that any of them are bad. The problem is that none of them remember what the others did. Every time I switched models, I was re-explaining context, re-establishing decisions, and discovering that the previous model had silently reverted something. On a real API migration last month, Codex deleted an endpoint that Claude had marked as "preserve for 6 months" two sessions earlier. There was no shared record. No handoff. Just vibes. So I built Delimit to fix it. What actually breaks when you switch models Three things, consistently: Context amnesia. Claude drafts a v2 schema with nested address objects. You close the session. Open Codex. Codex has
How we turned a small open-source model into the world's best AI forecaster
tldr: Our model Foresight V3 is #1 on Prophet Arena, beating every frontier model. The base model is gpt-oss-120b, training data was auto-generated using public news. Benchmark Prophet Arena is a live forecasting benchmark from UChicago's SIGMA Lab. Every model receives identical context, so the leaderboard reflects the model's reasoning ability. OpenAI's Head of Applied Research called it "the only benchmark that can't be hacked." We lead both the Overall and Sports categories, ahead of every frontier model including GPT-5.2, Gemini 3 Pro, and Claude Opus 4.5. Data Generation Pipeline Real-world data is messy, unstructured, and doesn't have labels. But it does have timestamps. We turn those timestamps into labeled training data using an approach we call future-as-label. We start with a so
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I have an older laptop from ~2018, an Asus Zenbook UX430U. It was quite powerful in its time, with an i7-8550U CPU @ 1.80GHz (4 physical cores and an Intel iGPU), 16GB RAM and an additional NVIDIA MX150 GPU with 2GB VRAM. I think the GPU was intended for CAD applications, Photoshop filters or such - it is definitely not a gaming laptop. I'm using Linux Mint with the Cinnamon desktop using the iGPU only, leaving the MX150 free for other uses. I never thought I would run LLMs on this machine, though I've occasionally used the MX150 GPU to train small PyTorch or TensorFlow models; it is maybe 3 times faster than using just the CPU. However, when the 1-bit Bonsai 8B model was released, I couldn't resist trying out if I could run it on this GPU. So I took the llama.cpp fork from PrismML, compil
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Ignoring the 8 bit size of Nvidia’s marketed 4 bit quantization of the dense model… The dense model KV cache architecture uses 3x or more the memory than what I have seen with other models. It seems like the big choice was 256 head dim instead of 128. I am looking at 490KB per 8 bit token of KV cache versus 128KB on Qwen3. I am running the nvidia weights at 4 bit on an rtx pro 6000 with 96GB of RAM and 8 bit kv cache and still only have room for 115k tokens. I was surprised is all. The model scales well in vllm and seems quite smart. submitted by /u/IngeniousIdiocy [link] [comments]
Built a zero allocation, header only C++ Qwen tokenizer that is nearly 20x faster than openai Tiktoken
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