Vince ErkadooAI researcher & engineer

case files

Five things, built and measured.

Each file covers the problem, the constraint, the decision I made and what came of it. The numbers come from the products' own pages, the paper or my own records, and the source is named under each file.

Nº 01 · macos · released 2026

A searchable memory of your Mac. You describe it, Trawl finds it.

Sole builder: product, engine, brand, checkout, release

problem

I kept losing things I knew I had. I remembered what they were about, never what they were called. Spotlight finds file names, and it misses the video I half watched, the thing I copied last week and the moment someone said it on a call.

constraint

Nothing was allowed to leave the Mac. I built it alone, and it had to run well on my own 8 GB MacBook Air.

decision

  1. 01I kept everything on the device: Electron, with native Swift helpers that read text in images, recognise objects and sounds and turn speech into text.
  2. 02Search blends keywords with 384 dimensional embeddings through reciprocal rank fusion, then a small quantised ONNX model reranks the top 14.
  3. 03It costs $20 once, with a signed offline licence. No account, no subscription, nothing to phone home to.

result

files searchable on the test Mac
4.6Mfiles searchable on the test Mac
per query across your own files
2–60 msper query across your own files
to index 11,165 photos, videos and sounds, in about 210 MB
11 minto index 11,165 photos, videos and sounds, in about 210 MB
files uploaded
0files uploaded

Measured on a MacBook Air M3 with 8 GB on macOS 26, using the maker's own files.

ElectronSwiftONNXEmbeddingsRank fusionApple VisionSoundAnalysissource · usetrawl.vercel.app/benchmarks

Nº 02 · platform · cto since 2025

Nourishubs

visit ↗

Meals and nutrition for schools, early learning centres and organisations, in one place.

CTO on a consulting contract, leading 10 engineers, designers and QA

problem

Parents, schools, early learning centres, organisations and vendors were ordering and preparing meals across separate tools. And the platform holds children's data, so every shortcut has a cost.

constraint

It's live in several countries, each with its own privacy law. There are apps in both stores, and every release has to stay shippable.

decision

  1. 01I ran security as engineering work. I led the ISO 27001 implementation, covering the risk assessment, access control and audit evidence, and mapped it to each national privacy law.
  2. 02I own the architecture and give the final approval on every feature, across Next.js, Python, Node.js and .NET services.
  3. 03I grew it from school meals into early learning centres and organisations. That release is the newest one, and it ships alongside the React Native app on iOS and Android.

result

Preferred Supplier of Early Learning Australia, 2026–27
GoldPreferred Supplier of Early Learning Australia, 2026–27
app version shipped to both stores
1.24app version shipped to both stores
people on the team I lead
10people on the team I lead
ISO 27001React NativeNext.js.NETKubernetes on EKSFluxCDsource · nourishubs.com and my own records

Nº 03 · storefront · client

The Wardrobe

visit ↗

A Mauritian boutique's rail, online and delivered island wide.

I built the storefront for the boutique

problem

The boutique needed a real shop with categories, search, a wishlist and delivery across Mauritius, without sending customers through messages to order.

constraint

Customers in Mauritius pay by Juice or bank transfer rather than card. Delivery has to reach Rodrigues too, and the owner manages the product photos.

decision

  1. 01A Next.js storefront, with Supabase holding the catalogue and the product images at responsive sizes.
  2. 02Checkout is built around Juice and bank transfer, and delivery is free over Rs 2,500.
  3. 03I kept the type quiet and editorial, so the clothes carry the page.

result

categories live, from new in to sale
11categories live, from new in to sale
free delivery threshold, island wide
Rs 2,500free delivery threshold, island wide
Next.jsSupabaseVercelsource · the-wardrobe-nu.vercel.app

Nº 04 · saas · founder since 2025

Bienzoli

visit ↗

Free point of sale, invoicing and stock for Mauritian shops.

Founder and solo builder: product, code, brand, films and sales

problem

Most small shops around me run on WhatsApp, paper and Excel. Invoices get lost, stock runs out without warning, and nobody remembers who still owes money.

constraint

I built it alone. Shop owners won't buy POS hardware, so it has to run on the phone they already carry, in English, French and Kreol.

decision

  1. 01One app covers counter sales, barcode stock, invoices and customers, as a PWA on the phone.
  2. 02Invoices go out as a WhatsApp link or a PDF. It takes cash, card and MCB Juice at the counter.
  3. 03I launched it free, with no card and no trial clock. It runs on Next.js, Prisma and PostgreSQL, with cookie free analytics.

result

to send an invoice
30 sto send an invoice
to use the whole app
Rs 0to use the whole app
languages in the product and the sales work
3languages in the product and the sales work
Next.jsPrismaPostgreSQLPWAsource · bienzoli.com and my own records

Nº 05 · research · draft, oct 2026

Living Base Models

What a language model says about itself once the assistant script is gone.

Independent researcher, with Claude as research assistant

Living Base Models, drawn in blue stipple

problem

Ask an assistant model what it is and you get a trained script. I wanted to know what is underneath once the system prompt, the chat template and the instruction tuning are gone.

constraint

I had one day, an 8 GB laptop for the 3B model and one rented A100 80 GB for the 32B. The samples were small, so the paper could not claim more than it shows.

decision

  1. 01I used base Qwen2.5 models only, with no system prompt and no chat template.
  2. 02I gave the model a life: memory of every event, its own surprise as a signal, self set goals, read only senses and LoRA learning on its own writing.
  3. 03I ablated internal directions and recorded every failure alongside the results.

result

words per reply, going from 3B to 32B
5 → 18words per reply, going from 3B to 32B
answers claimed feelings after the self denial direction was removed
0 / 8answers claimed feelings after the self denial direction was removed
self prediction vs 0.79 for an outsider model, p = 0.51, not significant
0.88self prediction vs 0.79 for an outsider model, p = 0.51, not significant

I found no evidence that any configuration has experiences, and the paper says so plainly. I'm still looking.

Qwen2.5MLXPyTorchLoRAActivation steeringsource · Living Base Models, draft of 14 October 2026