I Built a Private AI Assistant That Queries My Git History and Project Management Data — Using Only Local LLMs
A web developer created a private AI assistant that queries Git history and project management data using local large language models (LLMs). The assistant operates entirely on the user's machine, ensuring data privacy while providing instant answers to project-related questions. By utilizing a structured data approach with SQLite and Text-to-SQL, the assistant efficiently links Git commits to project tasks.
- ▪The AI assistant does not require API keys or cloud services, keeping all data local.
- ▪It uses a SQLite database to store structured data from Git and project management platforms.
- ▪The assistant translates user questions into SQL queries to retrieve relevant information.
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Story provenance
Source · retrieval · rights · ranking — open for full record
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Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/pouria_zand/i-built-a-private-ai-assistant-that-queries-my-git-history-and-project-management-data-using-only-39mn |
| Publication time | Thu, 21 May 2026 22:14:40 +0000 |
| Retrieval time | 2026-05-21T22:31:35.997Z |
| Last seen | 2026-05-21T22:31:35.997Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | YW1KhWcAFlSt |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3943899) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Pouria Zandakbari Posted on May 21 I Built a Private AI Assistant That Queries My Git History and Project Management Data — Using Only Local LLMs #llm #rag #privacy #python No API keys. No cloud. All data stays on my machine.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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