Debugging an AI agent that uses multiple steps: reading files, calling APIs, writing code, and updating the output, is not the same as debugging a normal task. Not a single stack trace can be read. Instead, developers are left staring at hundreds of lines of raw JSON, trying to reconstruct what the model was actually … Read more
Hugging Face is released ml-internis an open source AI agent designed to automate end-to-end workflows for large-scale linguistic models (LLMs). It is built on the company smolagents framework, the tool can automate literature review, dataset acquisition, scripting training, and iterative testing – tasks that typically require significant effort from ML researchers and developers. What does … Read more
Training powerful AI models relies on one resource that is running out of steam: specialized data. While the Internet has provided a seemingly endless supply of text and images to train today’s standard models, the next wave of AI breakthroughs – in cybersecurity, forensics, healthcare, and other niche domains – requires data that is not … Read more
class QwenChat: def __init__(self, model, processor, system=None, tools=None): self.model, self.processor = model, processor self.tokenizer = processor.tokenizer self.history: list[dict] = [] if system: self.history.append({“role”: “system”, “content”: system}) self.tools = tools def user(self, content): self.history.append({“role”:”user”,”content”:content}); return self def assistant(self, content, reasoning=””): m = {“role”:”assistant”,”content”:content} if reasoning: m[“reasoning_content”] = reasoning self.history.append(m); return self def tool_result(self, name, result): self.history.append({“role”:”tool”,”name”:name, … Read more
import subprocess, sys, os, shutil, glob def pip_install(args): subprocess.run([sys.executable, “-m”, “pip”, “install”, “-q”, *args], check=True) pip_install([“huggingface_hub>=0.26,<1.0”]) pip_install([ “-U”, “transformers>=4.49,<4.57”, “accelerate>=0.33.0”, “bitsandbytes>=0.43.0”, “peft>=0.11.0”, “datasets>=2.20.0,<3.0”, “sentence-transformers>=3.0.0,<4.0”, “faiss-cpu”, ]) for p in glob.glob(os.path.expanduser( “~/.cache/huggingface/modules/transformers_modules/microsoft/Phi-4*”)): shutil.rmtree(p, ignore_errors=True) for _m in list(sys.modules): if _m.startswith((“transformers”, “huggingface_hub”, “tokenizers”, “accelerate”, “peft”, “datasets”, “sentence_transformers”)): del sys.modules[_m] import json, re, textwrap, warnings, torch warnings.filterwarnings(“ignore”) from … Read more
Cybersecurity has always had a dual use problem: the same technical knowledge that helps defenders detect vulnerabilities can also help attackers exploit. In AI systems, that tension is sharper than ever. Restrictions intended to prevent harm have historically caused conflict in the fiduciary duty of security, and it can be really difficult to say whether … Read more
It’s a scary and exciting concept: the possibility that aging may not be the inevitable end of humanity, as it was thought. It seemed like the kind of thing that only exists in science fiction and flying cars and teleportation in the foreseeable future. Now, we have a new and rapidly growing tool to do … Read more
For years, the way major language models handle inference has been stuck inside the box – literally. The high-bandwidth RDMA networks that enable modern LLM deployments cover both prefill and trim in the same data area, sometimes even the same rack. A team of researchers at Moonshot AI and Tsinghua University make the case that … Read more
Anthropic has never published a technical paper on the Claude Mythos. That hasn’t stopped the research community from theorizing. A new open source project called OpenMythosreleased on GitHub by Kye Gomezit attempts something ambitious: a first-principles theoretical reconstruction of what the Claude Mythos might be, built entirely on PyTorch and based on peer-reviewed research. The … Read more
!pip install magika openai -q import os, io, json, zipfile, textwrap, hashlib, tempfile, getpass from pathlib import Path from collections import Counter from magika import Magika from magika.types import MagikaResult, PredictionMode from openai import OpenAI print(“🔑 Enter your OpenAI API key (input is hidden):”) api_key = getpass.getpass(“OpenAI API Key: “) client = OpenAI(api_key=api_key) try: client.models.list() … Read more