MarckDWN

joined 1 month ago
[–] MarckDWN@programming.dev 2 points 3 weeks ago

Just did it, thanks again... quite new user here didn't know it's possible😅

[–] MarckDWN@programming.dev 1 points 3 weeks ago (2 children)

good catch! it is indeed licensed under the MIT license. i made a mistake in the post title/description writing GPL-3.0, but the repo is correct. thanks for pointing it out!

Says MIT license on github. May want to fix that.

Contenuto

 

Hy,

I wanted to share a desktop client I've been building and recently open-sourced under the GPL-3.0 license: DWN.BRIDGE (C# WPF, targeting .NET 10). It is designed to solve the data privacy issue when using web LLMs (like Gemini) to analyze files or query local databases. It exposes a local orchestration bridge to your browser session via WebView2 automation, bypassing developer API paywalls while keeping your raw data offline. Key Features:

  • Zero-Knowledge Schema Mapping: It extracts table headers and column metadata locally. It sends only this structure to the web LLM. The LLM acts purely as a logic generator, and the client runs the SQL query locally using native providers (Dapper/SqlClient). Raw data rows never leave your hard drive.
  • Process Sandboxing & Consent: Every shell script execution, SQL write, or file modification requested by the agent is paused. The client prompts you with a WPF modal showing a git-style diff of the command/file for manual approval.
  • Coder Compilation Loop: If the agent generates scripts, it compiles and runs unit tests locally, feeding error logs (stderr) back to the context window until it builds successfully. The codebase is completely audit-friendly. I'd love to get feedback on the security consent model or contributions on expanding local DB support! 📺 Demo Video: https://www.youtube.com/watch?v=dCtOsXAuPgc 🔗 GitHub: https://github.com/MarckDWN/DWN.BRIDGE
 

I wanted to share a desktop client I've been building: DWN.BRIDGE (fully open-source C# WPF).

I love using AI for data analysis (like cleaning CSVs or querying databases), but there was no way I was going to upload my private/corporate files to cloud LLMs.

So I built a Zero-Knowledge local bridge:

  • When you point the client to a local database (SQL Server, SQLite, Excel, CSV), the client extracts ONLY the table schemas/headers locally.
  • It sends the schema metadata to the LLM (Gemini web UI) via a secure browser-automation bridge.
  • The LLM writes the SQL query, and the client executes it locally on your computer. Your raw rows and records never leave your hard drive.
  • All local file system access and system commands run locally and require explicit pop-up confirmation.

You can check out the source code or download the installer below. I'd love to get feedback on the local sandboxing model!

🔗 GitHub: https://github.com/MarckDWN/DWN.BRIDGE

 

Hey programmers,

I wanted to share a desktop client I’ve been building: DWN.BRIDGE (fully open-source C# WPF).

I love using AI for data analysis (like cleaning CSVs or querying databases), but there was no way I was going to upload my private/corporate files to cloud LLMs.

So I built a Zero-Knowledge local bridge:

When you point the client to a local database (SQL Server, SQLite, Excel, CSV), the client extracts ONLY the table schemas/headers locally. It sends the schema metadata to the LLM (Gemini web UI) via a secure browser-automation bridge. The LLM writes the SQL query, and the client executes it locally on your computer. Your raw rows and records never leave your hard drive. All local file system access and system commands run locally and require explicit pop-up confirmation. You can check out the source code or download the installer below. I’d love to get feedback on the local sandboxing model! watch the demo videos I uploaded on my youtube channel (links on github)

🔗 GitHub: https://github.com/MarckDWN/DWN.BRIDGE

[–] MarckDWN@programming.dev -3 points 1 month ago

That's exaclty what usually happens when you lead a group of developers, not all can be authonomous geniuses, that's why proper directions are the key. As you said we have an idiot (sometimes I see some Asperger-related behaviour) but that is a very efficient idiot that strictly follows your directions. And as a bonus it is incredibly fast

[–] MarckDWN@programming.dev 2 points 1 month ago

I wrote my first service worker, for a PWA application... Since then I could never write an app without. It's truly needed if you (as me) don't like useless store apps, e.g. for Android. But When you make a PWA for the Desktop taht truly makes sense !

[–] MarckDWN@programming.dev 1 points 1 month ago

The derived Elo is a great tool to isolate whether agent loops are actually "reasoning" or just brute-forcing the search space. It clearly proves that current agent scaling (via basic try-observe-reflect loops) quickly plateaus because it lacks the human capacity for abstract conceptual shifts and structural refactoring over long-horizon tasks. I believe the future of test-time compute in the agent era shouldn't just be about scaling trials or running more iterations; it should be about building architectures capable of hierarchical planning that can dynamically pivot their entire strategy when stuck.

[–] MarckDWN@programming.dev 2 points 1 month ago

Wow this is a resource! Got several older desktop and laptops to revive! Down with the programmed obsolescence!

[–] MarckDWN@programming.dev 2 points 1 month ago (1 children)

This is the inevitable consequence of the 'cloud-telemetry-first' approach to AI developer tools. If your AI coding assistants or workflow agents are logging your terminal history, active files, or screen activity back to a centralized cloud database for 'orchestration' or 'training,' you have essentially installed corporate spyware. It doesn't matter if it's Meta, Microsoft, or a startup—a single misconfigured pipeline or data breach will expose trade secrets, API keys, and private credentials. AI agents must have hard local boundaries. Tool execution (reading files, executing shell commands, querying local databases) must happen entirely locally under a zero-knowledge architecture, and every single execution must require explicit user-consent dialogs. Sending execution telemetry back to the cloud is a massive security hazard that corporations are only beginning to realize.

[–] MarckDWN@programming.dev 3 points 1 month ago

The surveillance landscape has shifted dramatically since 2016. Today, we don't just have passive state surveillance tapping cables; we have massive, voluntary corporate surveillance through the centralization of AI. Millions of developers and businesses are willingly uploading their proprietary source code, database structures, and internal spreadsheets to cloud LLMs (OpenAI, Google, Microsoft). All this data is logged, parsed, and stored in central cloud databases. We are essentially building the ultimate corporate intelligence database of all private technical infrastructure, completely voluntarily. If you care about privacy today, the absolute priority should be moving towards local-first execution. If you must leverage cloud LLMs, the only safe way is to use architectures that enforce local data isolation, keeping your actual database rows and files local, and sending only empty abstract schemas to the model for reasoning

[–] MarckDWN@programming.dev 19 points 1 month ago (19 children)

The problem isn't the tool; it's the lack of engineering foundations. Generalizing all AI-assisted development as 'vibe coding' is a massive oversimplification. There is a vast difference between a beginner blindly copy-pasting LLM output into a codebase they don’t understand, and a senior architect using LLMs as a high-powered assistant to speed up boilerplate, local schema generation, or parsing scripts. When you already know exactly how the underlying system operates, how memory is managed, and how to design clean software architectures, the LLM is just a productivity multiplier. You still design the data flow, audit the tool-use sandboxes, and review every single line of code. It doesn't replace thinking; it replaces tedious typing.

[–] MarckDWN@programming.dev 3 points 1 month ago* (last edited 1 month ago)

It’s frustrating how legislators still refuse to grasp the fundamental mathematics of cryptography. You cannot have a 'secure backdoor only for the good guys'. If a scanning pipeline is built into the client, the encryption is compromised by design. This isn't 'chat control' - it's the systematic dismantling of digital privacy under the guise of security.

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