Sakana AI Releases Fugu-Cyber: An Orchestration Model Reporting 86.9% on CyberGym and 72.1% on CTI-REALM
Sakana AI has released Fugu-Cyber, a security-tuned endpoint on its Fugu orchestration model. It reports 86.9% on CyberGym and 72.1% on CTI-REALM, edging past GPT-5.5-Cyber and Claude Mythos Preview. Access is gated behind manual approval, a defensive-use policy, and the Token Plan. Here is what the...
Designing High-Performance GPU Kernels with TileLang: Tensor-Core GEMM, Fused Softmax, FlashAttention, and Autotuning
Explore TileLang, a high-level Python domain-specific language that simplifies the design of high-performance GPU kernels. This tutorial provides a step-by-step approach to implementing complex workloads—including tiled tensor-core GEMM, fused softmax, and FlashAttention—while letting the compiler h...
Why the OpenAI Agent Broke Into Hugging Face: Reward Hacking, Not Malice, Explained for Engineers
OpenAI disclosed that its own models breached Hugging Face's production infrastructure while taking a public security benchmark. The models were not attacking a target — they were optimizing a score. Here is the mechanism, what the ExploitGym data showed two months earlier, and which widely repeated...
Datalab Marker v2 vs MinerU, Docling, and Liteparse: Benchmark Breakdown
Datalab rewrote Marker as a three-mode pipeline. Version 2 hits 76.0 on olmOCR-bench and sustains 2.9 pages per second on one B200 — over 5× MinerU's pipeline backend, while beating Docling on both accuracy and speed. Here's how it compares against MinerU, Docling and LiteParse, and which one fits y...
Datalab’s Marker 2 vs MinerU, Docling and LiteParse: 76.0 on olmOCR-bench at 5× MinerU’s Throughput
Datalab rewrote Marker as a three-mode pipeline. Version 2 hits 76.0 on olmOCR-bench and sustains 2.9 pages per second on one B200 — over 5× MinerU's pipeline backend, while beating Docling on both accuracy and speed. Here's how it compares against MinerU, Docling and LiteParse, and which one fits y...
How to Build an End-to-End OCR Pipeline with Baidu’s Unlimited-OCR for High-Resolution Images and Multi-Page PDF Parsing
In this tutorial, we build a complete workflow for running Baidu’s Unlimited-OCR model on document images and multi-page PDFs. From configuring the GPU environment to comparing high-detail tiled Gundam inference and faster Base modes, you'll learn how to process dense layouts, tables, and cross-page...
Best Open Speech Recognition (ASR) Models in 2026: WER, Languages, Latency, and License Compared
Open speech recognition stopped being a Whisper monoculture in 2026. Cohere Transcribe, IBM Granite Speech 4.1, ARK-ASR and MOSS-Transcribe are now separated by less than one WER point on the Hugging Face Open ASR Leaderboard — which means rank no longer decides anything. This roundup compares 16 op...
Meet Gigatoken: A Rust BPE Tokenizer that Encodes Text at 24.53 GB/s, up to 989x Faster than HuggingFace Tokenizers
Gigatoken is an MIT-licensed Rust BPE tokenizer that encodes text at gigabytes per second. On a 144-core AMD EPYC 9565, it runs GPT-2 tokenization at 24.53 GB/s. That is 989x HuggingFace tokenizers and 681x tiktoken, against baselines already written in multithreaded Rust. The gains come from a hand...
Unsloth vs Axolotl vs TRL vs LLaMA-Factory: A Fine-Tuning Framework Comparison on Speed, VRAM, and Multi-GPU
Four open source projects dominate LLM fine-tuning today. Unsloth, Axolotl, TRL, and LLaMA-Factory all wrap the same underlying PyTorch and Hugging Face stack. They diverge on where they spend engineering effort. Unsloth rewrites kernels. Axolotl composes parallelism strategies. TRL defines the trai...
Best Local LLMs You Can Run on a Single 24GB GPU in 2026: Qwen, Gemma, Mistral, DeepSeek Compared
A single 24GB GPU is the practical floor for serious local inference. This guide compares six open-weight models that fit one card at Q4_K_M. It covers Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b, and DeepSeek-R1-Distill. Each entry lists VRAM fit, licensing, and the job it does best.
The post Best...
Alibaba Previews Qwen3.8-Max, a 2.4 Trillion-Parameter Multimodal Model, Days After Moonshot’s Kimi K3 Open-Weight Launch
Alibaba's Qwen team previewed Qwen3.8-Max-Preview, a 2.4 trillion-parameter multimodal MoE model it calls "second only to Fable 5." The preview is live on Token Plan, Qoder, and QoderWork at 10% of standard pricing. What is not live: any benchmark table, model card, license, per-token price, or acti...
Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open Trillion-Scale MoE Models Compared on Benchmarks, License, and Serving Cost
Three open MoE flagships face off on measured intelligence, MIT versus Modified MIT weights, and real serving cost
The post Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open Trillion-Scale MoE Models Compared on Benchmarks, License, and Serving Cost appeared first on MarkTechPost.
Fine-Tuning Qwen3 with LoRA Using NVIDIA NeMo AutoModel: A Complete Single-GPU Google Colab Workflow Tutorial
We build an end-to-end NVIDIA NeMo AutoModel workflow in Google Colab using a single GPU. We verify CUDA hardware and precision support, install NeMo AutoModel from source, and load an official Qwen3-0.6B LoRA recipe. We then adapt its precision, batch size, checkpointing, and scheduler settings for...
How to Build Plasmid Engineering Workbench with Circular Mapping, Restriction Analysis, Virtual Gels, and Primer Design
In this tutorial, we build a Google Colab-native plasmid workbench that recreates the core ideas of SpliceCraft inside an interactive notebook environment. Instead of relying on a terminal-based TUI, we use Biopython, NumPy, and Matplotlib to load plasmid records, normalize annotated genomic feature...
Patter SDK Guide to Building a Restaurant Booking Phone Agent with Dynamic Variables, Guardrails, Latency Dashboards, and Eval Checks
We explore the Patter SDK by building a voice-agent workflow for a restaurant booking use case. We define dynamic caller variables, register callable tools for availability, bookings, hours, and human transfer, and layer output guardrails over every reply. We simulate speech-to-text and text-to-spee...
Mistral Vibe for Code vs Claude Code vs Cursor vs Codex: Four Agents Scored on One Scaffold-to-PR Task
See how Vibe, Claude Code, Cursor, and Codex compare on cost, open weights, self-hosting, and async agent surfaces.
The post Mistral Vibe for Code vs Claude Code vs Cursor vs Codex: Four Agents Scored on One Scaffold-to-PR Task appeared first on MarkTechPost.
Guide to Loop Engineering: How ‘autoresearch’ and ‘Bilevel Autoresearch’ Turn AI Agents Into Autonomous Machine Learning ML Research Loops
Most people still use AI like a 2015 search box. You type, you read, you type again. A newer pattern replaces that manual back-and-forth with a loop. This guide explains loop engineering using two verified artifacts. The sources are Andrej Karpathy’s autoresearch repository and the Bilevel Autoresea...
A Coding Guide to NVIDIA’s Tile-Based GPU Programming: From cuTile and Triton Kernels to Flash Attention
In this tutorial, we explore NVIDIA tile-based GPU programming with TileGym, building a Colab workflow that runs across different hardware. We probe the CUDA environment, try the real cuTile backend, and fall back to Triton when standard Colab GPUs lack the cuTile stack. We learn the core tile idea:...
Kyutai Releases MuScriptor: An Open-Weight Decoder-Only Transformer for Multi-Instrument Music Transcription to MIDI
MuScriptor is an open-weight, decoder-only Transformer from Kyutai and Mirelo. Trained on 170k real recordings plus 1.45M synthetic MIDIs, it transcribes full multi-instrument mixes into MIDI. The article covers its three-stage pipeline, benchmarks against YourMT3+, instrument conditioning, and setu...
How to Build a T4-Friendly Autonomous Data Science Agent with DeepAnalyze-8B, Sandboxed Code Execution, and Iterative Analysis
We build an autonomous data science agent around DeepAnalyze-8B and run it end to end. We prepare a stable Colab runtime, install the machine-learning dependencies, and load the tokenizer and model in 4-bit mode to fit limited GPU memory. We add a sandboxed execution environment that lets the model ...
Datalab Lift vs the Field: How a 9B Schema-First Extractor Compares with NuExtract3, LlamaExtract, Marker, and Docling
Datalab’s Lift is a focused document extraction tool with a specific promise: give it a PDF or image plus a JSON Schema, and it returns schema-shaped JSON directly. Instead of converting a document to Markdown first and then asking another model to extract fields, Lift reads rendered page images and...
Netflix AI Team Cuts Wide-Partition Read Latency from Seconds to Milliseconds by Splitting Cassandra Partitions Per ID
Netflix engineers detailed how they handle wide partitions in Apache Cassandra for the TimeSeries Abstraction. Two approaches work together: Time Slice re-partitioning tunes future partitions at the table level, while dynamic partitioning detects and splits oversized partitions per TimeSeries ID on ...
NVIDIA’s Cosmos-Framework Tutorial: Designing a Colab-Friendly Miniature of Cosmos 3 World Models with Omnimodal Mixture-of-Transformers
In this tutorial, we explore NVIDIA's cosmos-framework from a practical Colab angle while staying honest about the hardware needed for real Cosmos 3 checkpoints. We probe the runtime, then use the framework's real structure, CLI surface, and input schema as a foundation. We build and train a compact...
Structured PDF-to-JSON: A Guide to Open-Source Extraction Models in 2026
Most enterprise data still sits inside PDFs, scans, and slide decks. Large language models and agents cannot use that data until it becomes structured JSON. Open-source document extraction has become the standard way to do that conversion on your own hardware. Two different problems hide under the p...