SShortSingh.
Back to feed

Egregor Platform Uses Multi-AI Consilium to Audit Smart Contracts and Code

0
·7 views

Developer Vladislav Shter has built Egregor, a local multi-AI system designed to perform comprehensive audits of smart contracts and software code. The platform runs multiple AI models in a consilium structure to reduce echo chambers and algorithmic blind spots that can occur with single-model analysis. Egregor is positioned as part of Shter's broader Sovereign Ecosystem project. The tool supports data privacy and is capable of analyzing large code archives locally, without sending data to external servers. Source code and demos have been published on GitHub and GitVerse, with additional details available on the Sovereign Ecosystem website.

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 ·

Developer Builds Edu-Insight Assistant Using Gemini AI and Snowflake for DEV Challenge

A developer has submitted a project called Edu-Insight Assistant as part of a DEV Community Passion Challenge. The tool leverages Google's Gemini 3.5 Flash model to translate natural language queries into SQL and interpret the results. The project also features a data layer built for compatibility with Snowflake, designed to handle production-scale analytical workloads. The submission targets two prize categories: Best Use of Google AI and Best Use of Snowflake.

0
ProgrammingDEV Community ·

Developer builds index basket app on Robinhood Chain using standard Web3 tools

A developer created a simple index basket application on Robinhood Chain, a custom Arbitrum-based, EVM-compatible blockchain, to explore its end-to-end development workflow. The app allows users to deposit Stock Tokens — ERC-20 tokens representing real equity exposure — and receive a basket share priced from live feeds, which can later be redeemed. Despite running on a custom chain, the developer used familiar tools including Solidity, Foundry, viem, and React with wagmi, finding the experience comparable to standard Ethereum development. Stock Tokens follow the ERC-8056 Scaled UI Amount extension to handle corporate actions like stock splits, keeping raw contract balances stable while adjusting display values via a UI multiplier. The project's source code and a live demo have been made publicly available on GitHub and Vercel respectively.

0
ProgrammingDEV Community ·

Mid-Level Backend Engineer Cuts AI Reliance to Rebuild Core Skills

A software engineer with a CS degree and roughly three years of experience shared on DEV Community that they recently recognized stagnation in their backend development skills. The engineer, who has worked across financial, tourism, and client-based companies since 2022, realized during a job search that over-reliance on AI tools had hindered genuine skill growth. In response, they decided to reduce AI assistance on daily tasks and instead challenge themselves through hands-on personal projects. They plan to build a stock exchange engine and a collaborative music production system, documenting progress and mistakes along the way.

0
ProgrammingDEV Community ·

Dev Tutorial: Iceberg Partition Overwrite Solves Gold Table Duplication on Reprocessing

A developer on DEV Community published a technical walkthrough addressing a data reprocessing problem in a manufacturing data platform mini-project. The core issue was distinguishing between retrying the same input versus processing a corrected source for the same business date, where a simple skip strategy causes duplicate rows in the gold table. The solution uses Apache Iceberg's partition overwrite feature via DataFrameWriterV2.overwritePartitions(), which atomically replaces only the targeted business_date partition without affecting other dates. A local PySpark 3.5.8 and Iceberg 1.11.0 walking skeleton was built to validate that corrected rows replace the old partition while snapshot metadata serves as an audit trail linking each pipeline run to a specific Iceberg commit. The implementation passed all tests and deliberately avoids whole-table overwrite and append strategies to prevent data loss or duplication.