This week we're building out the UX around formatting and this month we're building a more robust set of integration tests and integrating with a large industry platform.

https://whirl.digital/housepoints.html

I wanted a simple retrieval index to use splade sparse vectors. This just encodes and serializes documents into flatbuffers and appends them into shards. Retrieval is just parallel flat scan, optionally with reranking.

The idea is just a simple, portable index for smaller data sizes. I’m targeting high quality hybrid retrieval, for local search, RAG or deep research scenarios.

SPLADE is a really nice “in-between” for semantic and lexical search. There’s bigger and better indexes out there like Faiss or Anserini, but I just kinda wanted something basic.

I was testing it on 120k docs in a simple cli the other day and it’s still as good as any web search experience (in terms of latency) — so I think it’ll be useful.

We’re still trying to clean up the API and do a thorough once over, so I’m not sure I’d recommend trying it yet. Hopefully soon.

These cards are super versatile prompts mediums and haven't been fully creatively explored.

I am always looking for more people to test and play with it or even review the code. We've got a nice little user community going.

Usually this comments drowns in the crowd of the massive amount of awesome stuff people are building, but if you find sanctum useful, hit me up. Good things are happening.

Stay happy

[1] https://sanctorum.se

I was tired of repeat, sponsored, and "safe" music suggestions from Spotify, so I built a discovery engine that puts the control back in the user's hands.

The core idea is simple: You define a "Discovery Model" with explicit constraints (specific genres, release years, track popularity, etc.). The app then uses this blueprint to source tracks.

The results are fresh for two reasons:

- "Known" Track filtering: Excludes all songs saved in your library and recent listening history.

- Active Curation: Uses your custom model, not a vague, opaque algorithm.

It’s built with a local-first mentality and a focus on privacy. No black-box AI "vibe" mixes, just pure, objective discovery based on your criteria.

Hope ya'll find some new gems!

I'm exploring building a weekly curation service for professionals who want to write on LinkedIn but struggle with "what's worth writing about."

The thesis: In the AI era, execution (writing) is commoditized. The real bottleneck is editorial judgment... knowing what topics matter before they're obvious.

The concept: Weekly email with 5-7 curated topics (tech trends, policy shifts, market movements). Each topic comes with sources, multiple angles, and context Choose your perspective, AI drafts a polished article

Why I think this could work: I've been manually doing this for myself for years. Pattern recognition at scale is hard to automate, but pairing human curation with AI execution might work.

Target market: ~30M professionals who should be building thought leadership but don't have time to spend on research.

Current status: Validating demand before building. The hard part isn't the AI, it's systematizing the trend-spotting and curation process without losing signal quality.

[0] https://apps.apple.com/us/app/reflect-track-anything/id64638...

Current features include:

- Live material price list updated monthly (based on prices at local shops)

- Conceptual 2D/3D floor plan generation following Ghana Building Standards (development in several phases using procedural floor plan generation)

- Construction management dashboard to track project stages and conversations between project manager, mason, carpenter, etc.

- Printable material cost breakdown

TODO: A contact listing for local construction services

I would love to have feedback, thanks.