SemiLayer

What it does

Every AI search tool makes you copy data out and sync a second source of truth forever. SemiLayer flips it: an intelligence layer that bolts onto the database you already run — point at a table, declare a lens, get a typed client. Your data never leaves. Stands out: 4 features in one client (semantic search, similarity, drillable dashboards, personal feeds) where most tools do one; cross-source joins across Postgres/Mongo/ClickHouse; zero vector infra; airgap-capable Runners.

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  HelixDB – Open-source vector-graph database for AI applications (Rust)2025 · github.com · ▲237

  Hey HN, we want to share HelixDB (https://github.com/HelixDB/helix-db/), a project a college friend and I are working on. It’s a new database that natively intertwines graph and vector types, without sacrificing performance. It’s written in Rust and our initial focus is on supporting RAG. Here’s a video runthrough: https://screen.studio/share/szgQu3yq. Why a hybrid? Vector databases are useful for similarity queries, while graph databases are useful for relationship queries. Each stores data in a way that’s best for its main type of query (e.g.…

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  Layer2023 · ▲169

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  ShapedQLJan 2026 · ▲211

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  Basedash Semantic LayerJun 2026 · basedash.com · ▲102

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  Metabase Data StudioMar 2026 · metabase.com · ▲153

  Build the semantic layer that makes AI analytics trustworthy

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  DeepSQL – A self-hostable DBA agent for Postgres and MySQLJul 2026 · deepsql.ai · ▲52

  Hi HN - I'm Venkat, founder of Stayflexi (YC), CMU CS grad and Ex-Oracle Query Engine team (patents in core databases) DeepSQL started as an internal tool to stop our own databases from becoming the bottleneck they were becoming (13,000+ hotels in production). It worked well enough that we're releasing it. DeepSQL is an AI agent that operates a database the way a senior DBA and Data Engineer would 1. Fixes slow queries (we cutdown DB spend by 4x) 2. Fixes DB bloat (blocks unnecessary schema changes, in vibecoded setup) 2. BI dashboards(we removed spend on tableau, retool and appsmith) 3.…

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  I trained a 125M model to autocomplete piano on-device▲598

  I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.

  AI · 17d ago · simedw.com

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  AI · 26d ago · x.ai

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  Needle2: 14MB agentic LLM for phones, wearables, smart home and robots▲537

  Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…

  AI · 27d ago · cactuscompute.com

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  Like Claude design for email marketing

  AI · May 2026 · brew.new

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  Dev tools · May 2026 · kilo.ai

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  StoreClaw▲805

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  AI · May 2026 · storeclaw.ai

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  PollyReach▲796

  Give your agent a real number and voice to make calls.

  AI · May 2026 · pollyreach.ai

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  Needle: We Distilled Gemini Tool Calling into a 26M Model▲776

  Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…

  Life & fun · May 2026 · github.com

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  Files.md – Open-source alternative to Obsidian▲730

  Dev tools · May 2026 · github.com