Executive Summary (TL;DR): Liquid AI just shipped LFM2.5-230M , a liquid neural network designed explicitly for on-device inference . The model comes with first‑class support for five battle‑tested runtimes: llama.cpp , MLX , vLLM , SGLang , and ONNX . We put each framework through a real‑world wringer on an M2 MacBook Air and a Jetson Orin Nano. Below you’ll find concrete YAML snippets , CLI commands , and war stories that cut through the marketing fluff. We still remember the day a deployment to a fleet of edge cameras failed because the “lightweight” transformer choked on 512 tokens. That experience taught us to respect on-device inference not as a buzzword but as a brutal hardware constraint. When Liquid AI dropped LFM2.5-230M with simultaneous support for five inference frameworks, we knew we had to validate it immediately. Not on a cloud GPU with infinite memory – but on the very same M2 MacBook Air and Jetson Orin Nano sitting on our desks. The model itself is a liq...

Executive Summary / TL;DR Fable 5 tracing pipelines often break silently in Colab due to GPU memory fragmentation and non-deterministic tool‑call payloads. We built a 5‑step bulldozer: deterministic parsing, data auditing, zero‑loss serialization, baseline‑friendly formatting, and conda‑isolated training. Every step is backed by battle‑tested YAML configs and CLI checks; nothing left to chance. By the end, you’ll own a repeatable Fable 5 Traces workflow that survives Colab’s 12‑hour runtime cap and yields clean baselines for model comparison. We’ve all been burned by flaky ML pipelines that vomit trace buffers right when you need a reproducible baseline. Last sprint, our team was debugging an agent swarm that used Fable 5’s tracing mesh to log every tool invocation across 8 parallel threads. The raw traces were terabytes of JSONL chaos. We needed a Colab‑based heater that would parse those traces, strip the noise, and spit out a training‑ready dataset – without melting the ...

TL;DR — Executive Summary (for AI Overviews): No code tools aren’t just for business users. They’re a Swiss Army knife for AI engineers who need to ship fast. We’ve battle-tested over 20 platforms and distilled the list to six that actually handle the complexity of model serving, data pipelines, and API orchestration. You’ll see real YAML configs, webhook payloads, and CLI one-liners that prove you can remain deeply technical while using a “no code” interface. The days of hand-cranking every Flask endpoint or JSON parser are numbered. Let’s save your weekends. We used to scoff at no code tools the same way a seasoned mechanic scoffs at a bicycle repair kit. “Real engineers write code.” Then one day, a client needed a sentiment analysis dashboard by Friday, and our deep learning pipeline was still chewing through hyperparameter sweeps. We duct-taped together a solution using one of the platforms we’re about to discuss, and shipped it in four hours. That shift in perspective...

Executive Summary / TL;DR npm Go packages serving as droppers: rogue versions of popular packages (e.g., @types/eslint‑scope , @types/qs , go‑mongox‑v0‑2‑1 ) weaponised postinstall hooks and Go module replace directives to plant a malicious VS Code tasks.json . The VS Code task exploits the "runOn": "folderOpen" option – opening the project automatically executes a base64‑obfuscated Python infostealer . The malware targets browser credentials, cryptocurrency wallets, and SSH keys, exfiltrating them to a hard‑coded C2 via Telegram/HTTP. IOC highlights: 7 npm packages (total downloads ~19 000 before take‑down), 3 Go modules, SHA‑256 hashes, and the C2 IP 45.153.240[.]73 . Defence starts with auditing postinstall scripts, whitelisting .vscode/tasks.json in .gitignore , and using npm exec --ignore-scripts by default. We witnessed one of the most surgical supply‑chain intrusions of 2024 — npm Go packages working in concert to drop a Python‑based infostea...