SpeakesQuery – Splunk-style search over local Parquet, with LLM pipes
SpeakesQuery is a local-first search tool that enables Splunk-style queries over local Parquet and SQLite data using a custom language called SPQL. It integrates optional LLM capabilities with strict budget controls and zero telemetry to ensure data privacy and cost management. The software is designed to be transparent and non-rent-seeking, offering a comprehensive suite of connectors and tools that run entirely on user-owned hardware.
- ▪SpeakesQuery operates as a local-first application with zero cloud dependency and no telemetry, ensuring user data remains on their own machine.
- ▪The tool utilizes a custom query language named SPQL to search local Parquet and SQLite indexes with features like semantic ranking and pipe-based processing.
- ▪Optional AI features allow users to integrate LLMs via the Anthropic API or local models, with mandatory cost ceilings and dry-run options for every billable operation.
- ▪The platform includes 131 data connectors, 57 SPQL commands, and a visual query builder, all available for free without artificial restrictions or gated capabilities.
Hacker News (AI / LLM) files mainly under ai. We currently carry 7,129 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
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 | GitHub |
| Canonical URL | https://github.com/13alvone/SpeakesQuery |
| Publication time | Thu, 01 Oct 2026 08:51:22 +0000 |
| Retrieval time | 2026-10-01T08:57:31.835Z |
| Last seen | 2026-10-01T08:57:31.835Z |
| 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 | JnVeVyLwbdeY · 1 stories |
| 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
Pipe-powered search over everything you ingest - on hardware you own. Local-first · zero telemetry · zero cloud dependency · AI when you want it, budget-gated when you do Quick Start • Features • Docker • Query Syntax • Application Guide • Email Setup One query language - SPQL - over local Parquet and SQLite. Ingest anything on a schedule, search it with pipes, rank it semantically, hand it to an LLM mid-pipeline, and get analyst briefs in your inbox. Everything runs on your machine: no accounts, no telemetry, no cloud dependency.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.