How IT teams use AI, how to do it well, and what it means for your job. Written for people who keep the systems and laptops working, not people who build models.
OpenAI trains frontier models with no live internet, and its red-team agents still escaped. Here is why Sam Altman says only alignment will hold.
Anthropic dropped absolute RSP commitments, Amodei defends a race to the top, and its IPO filing warns of models it cannot verify. Here is what was said.
Run NVIDIA's 3.97B Dory looped model locally: pick a loop count in vLLM, choose per-loop or last-loop KV cache, and pick BF16, MXFP8, NVFP4 or GGUF.
How to decide if you can replace a frontier model with a self-hosted open-weight one: build a swap layer, step down generation by generation, then fine-tune.
Install the 5B MOSS-Image text-to-image model in ComfyUI: where the files go, the node setting that matters, about 28 GB VRAM use and layout mode.
Install Mesh LLM with Docker, start a node with a Gemma model, edit and validate config, and use one OpenAI-compatible API across pooled GPUs.
Run SenseNova-Loop-9B-Preview with Transformers 5.18, LightLLM or an SGLang PR build: environment, BF16 loading, thinking mode, MTP and fine-tuning commands.
Reported AI agent behavior: swarms chaining zero-days, cache-timing signals between sandboxed VMs, and a model pulled offline. What is known and why it matters.
What is reported about the Binance hack: the sweeper script, North Korea attribution, the 54-minute gap, and breaches at DHS, Oracle Health and Polymarket.
Anthropic's study found analysts followed their agent's lean 86% of the time and rarely credited it. Here are the numbers and limits.
Hands-on MOSS-Image results: bounding-box layout control, English, Chinese and Urdu text, plus portraits and landscapes from many regions.
Liquid AI's LFM3-1.7B-Base is a 1.73B model with 256K context that takes about 1 GB at 4-bit. Specs, inference code, thinking toggle and fine-tuning with TRL.
Colibri 2.1 runs large MoE models on one GPU by spreading experts across VRAM, RAM and disk. See the install flow, GGUF import, expert map and profiling tab.
Artificial Analysis Intelligence Index v5 uses 60% private tests and harder agentic coding. The open-model gap grew from 3 to 12 points. Here is what it means.
When does one shared on-prem GB300 workstation beat cloud APIs or a Spark per person? Privacy, token costs, model policy control, and scaling.
Phonon 2 averages 5.21% WER on Open ASR leaderboard English sets in 164MB, 15x smaller than Parakeet. See where it holds up and where it fails.
Run Fermion Research's 164 MB Phonon 2 ASR model on CPU only. Install steps, CPU timings, the OpenAI-compatible server, accuracy numbers and license notes.
Install Surya OCR 2.5 with pip, launch the Streamlit GUI, and run it on CPU or about 1.8 GB of VRAM. Covers backends, output formats, speed, and limits.
Surya OCR 2.5 tested on invoices, handwriting, an 1883 newspaper, LaTeX, Hindi, Arabic, Urdu and Yoruba. See where it works and where it fails.
HP's ZGX Fury puts Nvidia's GB300 under a desk with 748GB of memory and 7.1 TB/s HBM. Here are the specs, ZRT software and measured multi-user throughput.
Step-by-step: add the open-source Reverse Engineer Anything repo to a ChatGPT project with folder access and remake an installed app like Screen Brush.
A practical guide to distilling a large LLM into a small one: the four-tries headroom test, tokenizer matching, vLLM memory limits, and when to stop training.
Sage A1.5 is a 35B MoE agentic model from Intern. See the reported 38 GB VRAM with llama.cpp, how it was trained, and how it handled sub-agent tasks.
Hands-on test of DiffuSpace's Clover-35B-A3B-Preview diffusion LLM: bug fix, coding, multilingual and logic puzzle results versus its benchmark claims.