Software engineering

Auto Added by WPeMatico

Using Graphify and NetworkX to Map Python Codebase Structure with God Nodes, Communities, and Architecture Visualizations

In this tutorial, we build a fully offline Graphify workflow that turns a realistic multi-module Python application into a knowledge graph. We start by installing Graphify and supporting graph libraries, then generate a small but connected sample application with configuration, database, authentication, service, API, cache, model, and SQL layers. We extract the graph locally using […]

Using Graphify and NetworkX to Map Python Codebase Structure with God Nodes, Communities, and Architecture Visualizations Read More »

🔗

Nous Research Adds /learn to Hermes Agent’s Skills System, Capturing Workflows as Slash Commands Without Hand-Writing SKILL.md

Nous Research has expanded the Skills System inside Hermes Agent, its open-source self-improving agent. The new addition is /learn, a command that writes a reusable skill for you. Point it at a document page, a local SDK, a past conversation, or pasted notes. The live agent gathers the material, then authors a SKILL.md on your

Nous Research Adds /learn to Hermes Agent’s Skills System, Capturing Workflows as Slash Commands Without Hand-Writing SKILL.md Read More »

16 Best Generative AI Coding Tools in 2026 Compared: Features, and Best Fit

Generative AI has reshaped how software gets built. What began as line-by-line autocomplete now spans full application generation, multi-agent build pipelines, and natural-language interfaces to entire codebases. Large language models trained on code can read context, follow intent, and produce working frontends, backends, and infrastructure with little manual setup. For early-level AI engineers, software engineers,

16 Best Generative AI Coding Tools in 2026 Compared: Features, and Best Fit Read More »

DFlash Speculative Decoding Drafts Whole Token Blocks in Parallel for Up to 15x Higher Throughput on NVIDIA Blackwell

Autoregressive large language models generate text one token at a time. Each token waits for the one before it. This serial loop leaves modern GPUs underused and keeps inference slow. The cost grows worse with long Chain-of-Thought reasoning models. Their lengthy outputs make latency the dominant part of generation. Speculative decoding is the standard fix.

DFlash Speculative Decoding Drafts Whole Token Blocks in Parallel for Up to 15x Higher Throughput on NVIDIA Blackwell Read More »

Datalab Releases lift: A 9B Open-Weights Vision Model That Extracts Structured JSON From PDFs Using Schemas

Datalab has released lift, a 9B open-weights vision model for structured extraction. You pass it a JSON schema, and it returns a JSON object that matches. The model reads PDFs and images directly, then decodes against your schema. This is Datalab’s first model built purely for extraction. The team already ships open-source OCR tools: chandra,

Datalab Releases lift: A 9B Open-Weights Vision Model That Extracts Structured JSON From PDFs Using Schemas Read More »

↔

Prime Intellect Releases prime-rl 0.6.0 to Train Trillion-Parameter MoE Models on Agentic RL Workloads

Prime Intellect has released prime-rl version 0.6.0. The framework targets reinforcement learning on trillion-parameter Mixture-of-Experts (MoE) models. It focuses on heavy agentic workloads, like long-horizon software-engineering tasks. The research team trained GLM-5 on SWE tasks at up to 131k sequence length. Step times stayed under five minutes. The batch size was 256 rollouts. The run

Prime Intellect Releases prime-rl 0.6.0 to Train Trillion-Parameter MoE Models on Agentic RL Workloads Read More »

Sakana AI Launches Sakana Fugu: An Orchestration Model That Routes Tasks Across a Swappable Pool of Frontier LLMs

Today, Sakana AI launched Sakana Fugu. It is a multi-agent orchestration system that behaves like one model. You send a request to a single endpoint. Fugu decides how to handle it internally. It solves a task directly when that is enough. It also assembles and coordinates a team of expert models when needed. The complexity

Sakana AI Launches Sakana Fugu: An Orchestration Model That Routes Tasks Across a Swappable Pool of Frontier LLMs Read More »

MoonMath AI Open-Sources a HIP Attention Kernel for AMD MI300X That Beats AITER v3 on Every Shape and Rounding Mode

MoonMath AI team has released a bf16 forward attention kernel for AMD’s MI300X GPU. It is written in HIP, not hand-written assembly. The code is open-source under the MIT license. The MoonMath.ai team reports it beats AITER v3, AMD’s own optimized kernel, on every tested shape. Bare-metal access came from HotAisle, an AMD cloud provider.

MoonMath AI Open-Sources a HIP Attention Kernel for AMD MI300X That Beats AITER v3 on Every Shape and Rounding Mode Read More »

The 7 Types of Agent Memory: A Technical Guide for AI Engineers

Large language models are stateless by default. Each API call starts fresh. The model forgets your last message once the response returns. That is fine for a single question. It breaks the moment you build an agent. Agents plan, call tools, and run across many steps. They need to remember. Memory is the infrastructure that

The 7 Types of Agent Memory: A Technical Guide for AI Engineers Read More »

Nous Research Updates Hermes Agent With a Blank Slate Mode That Pins Toolsets via platform_toolsets.cli and disabled_toolsets

Nous Research has added a Blank Slate setup mode to its open-source Hermes Agent. It inverts the usual onboarding. Instead of a fully loaded default, you start with almost nothing. Hermes Agent is the self-improving agent framework from Nous Research. It runs on your own machine. The team announced the new mode on X. Blank

Nous Research Updates Hermes Agent With a Blank Slate Mode That Pins Toolsets via platform_toolsets.cli and disabled_toolsets Read More »