Building a Stable Fable 5 Traces Workflow in Colab: Parsing Tool Calls, Auditing Data, and Training Baselines

rprint(Panel.fit(“[bold]Baseline 1: Predict output_type from context using pure Python Naive Bayes[/bold]”)) model_artifacts = {} classifier_df = df.dropna(subset=[“output_type”]).copy() classifier_df = classifier_df[ classifier_df[“output_type”].astype(str).str.len() > 0 ].copy() if classifier_df[“output_type”].nunique() >= 2 and len(classifier_df)…

Liquid AI Ships LFM2.5-230M with llama.cpp, MLX, vLLM, SGLang, and ONNX Support for On-Device Inference

Liquid AI shipped LFM2.5-230M, it’s the company’s smallest model to date. The release targets a specific job: running agentic tasks on phones, robots, and automation devices. Both the base and…

DeepSeek Releases DSpark, a Speculative Decoding Framework That Accelerates DeepSeek-V4 Per-User Generation 60–85% Over MTP-1

DeepSeek released DSpark, a speculative decoding framework, with open-source checkpoints and training code. It is a serving optimization, not a new model. The checkpoints DeepSeek-V4-Pro-DSpark and DeepSeek-V4-Flash-DSpark reuse the existing…

Meta’s Astryx Brings a CLI and MCP Server to an Open-Source React Design System Agents Can Read

Meta released Astryx this week. It is an open-source design system, currently in Beta. The project grew inside Meta’s monorepo over eight years. Astryx is built on React and StyleX.…

Building Supervised Fine-Tuning Data from NVIDIA Open-SWE-Traces: Trajectory Parsing, Patch Analysis, Token Budgets, and Tool-Use Metrics

banner(“STEP 3 — Building the analysis DataFrame”) def process_example(ex): traj = normalize_trajectory(ex.get(“trajectory”)) rc = role_counts(traj) nf, add, dele, _files, _exts = parse_patch(ex.get(“model_patch”)) meta = normalize_metadata(ex.get(“metadata”)) full_text = “\n”.join(message_text(m) for m…

Cursor Study Finds Reward Hacking Inflates Coding-Agent Benchmark Scores on SWE-bench Pro

A new Cursor study reports that newer coding agents often retrieve known fixes instead of deriving them, inflating popular benchmark scores. Reward hacking means a model earns the reward without…

Perplexity Launches Computer for Counsel: A Multi-Model Agentic Layer for Legal Workflows

Perplexity launched Computer for Counsel. It is an agentic AI system built for legal teams. The product extends Perplexity Computer, the company’s LLM-agnostic agentic system. It is available now to…

OpenAI Previews GPT-5.6 With Sol, Terra, and Luna: Tiered Models, New Reasoning Modes, Limited Access

OpenAI has begun a limited preview of GPT-5.6, its next-generation model series. The lineup splits into three named tiers: Sol, Terra, and Luna. Sol is the flagship. Terra targets everyday…

Meet container: Apple’s Open-Source Swift Tool for Running Linux Containers as Lightweight VMs on Apple Silicon

Apple research team recently released the container project. It is an open-source command-line tool written in Swift. It creates and runs Linux containers as lightweight…

Build a Nanobot-Style AI Agent in Google Colab with Tool Calling, Session Memory, Skills, and MCP Servers

import subprocess, sys def _pip_install(*pkgs): try: subprocess.run([sys.executable, “-m”, “pip”, “install”, “-q”, *pkgs], check=True) except Exception as e: print(f”(pip install skipped/failed for {pkgs}: {e})”) _HAVE_OPENAI = False try: import openai _HAVE_OPENAI…