SShortSingh.
Back to feed

ERD Tool Schemity Now Flags Which Diagram Files Are Inside a Git Repository

0
·1 views

Desktop ERD tool Schemity has added a Git branch icon to its workspace list to indicate which diagram files reside inside a Git repository. Previously, all workspaces appeared identical regardless of whether they were version-controlled, making it easy for users to lose track of untracked diagrams. The tool detects repository status by walking up the directory tree, so deeply nested workspaces are also correctly identified. Imported workspaces receive a separate icon to distinguish them from those the app created automatically. The update requires no configuration and aims to prevent the silent data-loss scenario where a diagram is never recoverable because it was never tracked by Git.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

Enola AI Tool Uses Typed Fact Model to Extract Reliable Architectural Insights

Enola is an AI-assisted architectural analysis tool being developed to extract structured facts from source code before any AI reasoning begins. Rather than treating all relationships as generic graph edges, Enola uses a typed fact model that distinguishes between function calls, imports, route registrations, and type references. The system separates raw parsing from architectural extraction, so that framework-specific patterns — such as composed router paths in Go or Spring annotations — are correctly interpreted into meaningful facts. Repositories serve as independently addressable extraction scopes, meaning Enola can reason about a codebase even without loading its dependencies. Because architectural boundaries rarely align with source ownership boundaries, Enola assigns each entity a scoped identity that includes context like language, qualified name, and source revision.

0
ProgrammingDEV Community ·

Researchers Test LLMs to Identify Processors in Bare-Metal Binaries via Ghidra

A reverse engineering project is exploring automated methods to identify the processor architecture of undocumented bare-metal binaries, with the second strategy combining Ghidra disassembly across 177 architectures and LLM-based analysis. Test binaries were compiled from a single C source file for roughly 30 processors in both raw and ELF formats, with three firmware samples selected to cover supported, unsupported, and misformatted scenarios. Five local LLMs — including qwen2.5-coder, qwen3-coder:30b, gemma4:26b, dolphinMistral24b, and dolphin3-cyber — were benchmarked using an identical system prompt and a strict JSON response schema. For the first firmware, qwen2.5-coder flagged 69 candidate processors and qwen3-coder:30b narrowed it to 35, both correctly including the true target, while gemma4:26b largely failed due to severe formatting errors. The findings highlight significant variation in LLM reliability for structured reverse engineering tasks, informing which models are viable for building a scalable disassembly identification pipeline.

0
ProgrammingDEV Community ·

Google Renames NotebookLM to Gemini Notebook in Phased Rollout for Subscribers

Google officially rebranded NotebookLM as Gemini Notebook on July 16, 2026, expanding access to AI Ultra, Pro, and Plus subscribers. The rollout is staged and multi-phase, meaning not all eligible users receive access simultaneously across regions and platforms. Initial web access was prioritised for paying subscribers, while mobile availability and additional account types were scheduled to follow in subsequent weeks. Some UK-based Pro subscribers have reported delays, consistent with a rollout that is broad but not yet universally complete. Users and teams are advised to verify access within their own accounts before building workflows that depend on the new Notebook experience.

0
ProgrammingDEV Community ·

DeepMind Splits Robot Brain and Body With Three-Model Gemini Robotics 2 Suite

Google DeepMind released Gemini Robotics 2 on July 28, 2026, followed by Gemini Robotics ER 2 on July 30, introducing a modular architecture that separates high-level reasoning from real-time motor control. Rather than a single end-to-end network, the suite uses three specialized models: an embodied reasoning planner, a whole-body vision-language-action controller, and an on-device adaptation model. The reasoning model, ER 2, interprets scenes and natural-language instructions to break multi-minute tasks into sub-goals, while the core VLA model handles physical movement across humanoid bodies and robotic arms at high frequency. A third on-device model runs locally on robot hardware and can adapt to a new robot's mechanics using fewer than 200 demonstration examples and just a few hours of data. The architecture addresses a longstanding robotics trade-off where large context windows needed for reasoning conflict with the low-latency demands of precise physical control.