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6 results for "Prompt Engineering"
Graph Engineering for AI Agents: From Prompts and Loops to Workflows
A viral debate over loops versus graphs points to a bigger shift in how we build AI systems. Here’s what graph engineering actually means, how it differs from prompt, context, and loop engineering, and why it matters. The post Graph Engi...
Designing AI agents to resist prompt injection
How ChatGPT defends against prompt injection and social engineering by constraining risky actions and protecting sensitive data in agent workflows.
Operator System Card
Drawing from OpenAI’s established safety frameworks, this document highlights our multi-layered approach, including model and product mitigations we’ve implemented to protect against prompt engineering and jailbreaks, protect privacy and...
Why are tiny models (<50M parameters) or swarms of specialised micro-models so rarely deployed in production?
I have been thinking about why we do not see more tiny, specialised models in production. It feels like it would be so much more efficient to use small, task-specific ones for certain things, but we always seem to end up with one massive...
Another Qwen3.8-27b Appreciation Post
I know I know, it's great, we know. I've been working on tweaking inference engines for a week now and it's been one shotting most of my vague prompts without any issues. It will even write tests and validate the changes without me askin...
Models & Releases
DeepSeek V4.1 Flash on 8× A40: ~40 tok/s Q2_K and ~32 tok/s Q4_K_M with TensorSharp
Hey everyone — I’m building TensorSharp, an open-source LLM inference engine. Here are the latest DeepSeek V4.1 Flash GGUF results using its native ggml_cuda backend. Setup: 8× NVIDIA A40, layer split, F16 KV cache, 65,536-token configur...