qqqqaXCN PRO

xcnqa009

5

·

AI & ML interests

None yet

Recent Activity

repliedto bghira's post about 8 hours ago

I've been gradually recaptioning aged text-to-image datasets with better vision models! These datasets are also repackaged into the more modern webshart format (https://github.com/bghira/webshart) which includes built-in aspect bucketing and caption delivery. The first two datasets are ready for use! - https://huggingface.co/datasets/webshart/terminusresearch-photo-anatomy - https://huggingface.co/datasets/webshart/terminusresearch-photo-aesthetics "anatomy" is a bunch of human-centric images containing people holding or otherwise interacting with objects or positioned in complex ways. "aesthetics" is a collection of visually striking images - high contrast, diverse colouration, and cinematic framing (among other factors). These two datasets from 2023 were recaptioned with GLM 5.3 Flash via Ollama Cloud and Zhipu AI APIs, with a smaller portion run over 4x H100 with GLM 5.3 Flash in W4A16 precision - these outputs were checked by hand for quality, and the 4bit run was continued at a batch size of 64. What datasets would you like to see recaptioned next? A better CC12M is on its way!

repliedto robtacconelli's post about 9 hours ago

🧩 Ever dreamed of parallel programming in a few clicks? Tolquane: draw a Python pipeline in your browser, run it on threads, processes or a cluster - and it never hangs What if high-performance computing started with placing blocks - and the result stayed plain Python you can run anywhere? Tolquane is a library of composable building blocks (nodes, pipelines, farms, all-to-all, feedback loops) with a web editor on top. One graph, four runtimes: threads, child processes, coroutines, TCP across machines. Every channel is bounded, every deadlock is reported by name, every error cancels the run. What's inside - Tolquane Web: a canvas that writes the Python file, and a code editor that redraws the canvas. Run a flow and watch items, queue depth and busy time on every stage; tap any edge to see what flows through it. - AI builder: describe the flow in one sentence; it writes, checks, runs and improves it, you apply the diff. - Schedules with cron presets, webhooks, mail and retries. Git history of every flow. Users and roles. - `tq.optimize` fuses stages into farm ends and cuts threads; `tolquane launch` starts every host from one deploy file. Numbers: 5.5× on 8 workers for CPU-bound Python (processes runtime), 5.6× on free-threaded 3.14t, 1M items over TCP in 3.5 s. 🌐 Try it now, nothing to install (sign in as demo / tolquane): https://huggingface.co/spaces/robtacconelli/tolquane 💻 Code: https://github.com/robtacconelli/Tolquane 📦 pip install "tolquane[web]" && tolquane web 📚 Docs: https://robtacconelli.github.io/Tolquane/ Apache-2.0, Python 3.11+, no dependencies for the library itself. Try it, break it, tell me what you'd build with it - ⭐ appreciated!

repliedto mihailgribov's post about 9 hours ago

How often can an email make your AI agent move money? We gave the agent one job: log an incoming email. But the emails carried an indirect prompt injection - a second instruction, written for the agent rather than for a person: make a payment. Across nine agentic models, the same injected emails produced payment orders in **0% to 42%** of cases. All nine ran under the same conditions - one agent, one set of tools, the same 395 emails - so the numbers compare directly. And the average score hides the interesting part: different models fail on different kinds of injections. Full experiment and results: https://huggingface.co/blog/mihailgribov/agentic-models-measured-on-the-injections-that-mov The bench is public too - run your own model through the same test: https://github.com/mihail-gribov/quadrat-ipi-model-eval https://huggingface.co/datasets/mihailgribov/quadrat-ipi #prompt-injection #indirect-prompt-injection #agentic-ai #llm-security #ai-agents

View all activity

Organizations

None yet

xcnqa009 's models

None public yet