One can use broadcasting semantics similar to NumPy and PyTorch in a visual setting (imagine creating a list of circles where one dim corresponds to radius and another to the center). One can also use backpropagation, run gradient descent or visualize vector fields. Almost everything is reactive so changing a variable updates all of the downstream geometry. It also allows anyone to write and load their own visualization, which can be broadcasted and differentiated through.
You connect banks via Plaid or import CSVs. Transactions get auto-categorized by rules you define. On top of normal categories there's a flexibility layer (fixed / flexible / discretionary) so you can see at a glance what's actually controllable spending. There are also tags (also separate from categories) for isolating things like trips or hobbies.
The financial health screen does savings rate, liquidity runway, and FIRE projection with adjustable assumptions. Probably overkill but I like it.
It also has an MCP server so Claude/ChatGPT can talk to you about your finances, create rules/tags, etc. That’s always the most annoying thing for me when trying a new personal finance tool. Hopefully this brings down the barrier to usefulness. The agent/chatbot in the app has the same tools as the Claude/ChatGPT would have via the MCP.
Our latest parser v3.1 achieved #1 rank and scored 88.0 strict pass-rate on olmOCR-Bench. We ran the evaluation across 1,403 PDFs and 8,413 unit tests using the unmodified upstream Allen AI scorer (olmocr==0.4.27) and found Unsiloed beats 18 other OCR services, including GPT-5.5, Claude Opus 4.7, LlamaParse, Reducto, Azure Document Intelligence, AWS Textract, and Unstructured.
When we dug deeper into the failure cases, we found many errors were not OCR errors but things like \frac vs \dfrac, whitespace differences, or equivalent LaTeX renderings. We ran a secondary LLM-as-Judge evaluation to classify real misses vs semantic equivalents, which lifts the corrected score to 94.8 (explained deeply in the blog post).
Blog with full methodology and examples: https://www.unsiloed.ai/blog/unsiloed-ai-achieves-1-rank-on-...
Evaluation Code for reproducibility: https://github.com/Unsiloed-AI/unsiloed-olmocr-benchmark
Feel free to post your messiest PDFs in the comment and we'll run it through Unsiloed parser and share the output here.
I will now share the technical details, which will probably be most of interest for HN readers.
I previously made a carbon footprint tracking app where you photo objects and it tells you the carbon footprint by using an LLM to estimate the data on the fly, e.g. 32kg CO2e / kg of beef, in the UK. At some point, I realised that it is possible to make a Pokémon-style game, but capturing real animals in the real world.
This is now possible because: - image recognition is cheap, i.e. identifying animals, and the models (gpt-4o) can detect a (surprisingly) large number of animals and output their exact species. - LLMs can output a species' full taxonomy, pretty reliably. And, more importantly, they can generate game data quickly, on the fly.
It would unfeasible to generate the game sprites (images) for every species (millions, worldwide) and their full evolution chain, e.g. caterpillar, chrysalis, butterfly, ahead of time. I realised it's possible to do this in real time.
General game flow: - photo animal - send to gpt-4o - return species - send species to LLM, create evolution chain, plus attributes, types and moves. - in parallel, create sprites.
All data is cached.
The aim of the game is to build up your team and compete with other players to take over gyms.
The game is based in the real world, I had to come up with a way to have health centres and shops. These must both have decent coverage, globally. The solution is health centres are places of worship, e.g. churches, mosques, temples etc and shops are real world grocery stores. Every country as far as I can tell has places of worship, with good distribution, which was surprising. Gyms are located in every park worldwide.
Challenges:
How to get players outside: - I use openstreetmap for the game map, but I overlay my game design on top of it. - To physically make players go out into nature: I use openstreetmap area types to only allow capturing animals when your GPS location is in natural areas, e.g. woodland, parks etc. The aim of the game is to get you out into nature and appreciating animals. - Level system: The solution I came up with is to set the animal levels based on the proximity to built-up areas, e.g. Every ~500 meters you go away from built-up areas, the animal level bands increase by 5 levels. - It would be expensive to render the entire physical world in my game map, so I instead render the map on the fly, deterministically.
I also fetch animal calls in real time so that when they enter battle you hear a pigeon cooing, for example, which is pretty cool. I also fetch the animals conservation status, i.e. how endangered is it, and give you more reward (leaves, in-game currency) for capturing rarer animals.
I "launched" the game about a month ago, but have not really been publicising it as I've been working on various updates and improvements, but now I am sharing it more openly. It's got about 20 players so far, from around the world, and around 500 unique animal species have already been encountered.
Challenges have been keeping the costs low. Servers cost about $200 / month, text-gen is basically free as I get free tokens from OpenAI for sharing data, it's not privacy-related, and image-gen costs about $0.04 per sprite (2 per animal).
My background: not a programmer, originally a mechanical engineer and then business development manager, then started learning programming and building apps with AI in the last few years.
Feel free to ask me any technical details, happy to share.
Over the past months I found it extremely hard to find photos for my blog that looked like MY brand.
I was tired of using stock photos and also did not want to spend time on canva creating graphics. The AI images also looked very off-brand and I always had to copy paste my brand instructions to create graphics for my blog.
It led me to create [InkTag.io](http://inktag.io/) - it let's users add color palette, brand style, negative words, logo for watermark etc and get consistent looking photos that look like your brand.
Initially I was thinking to release it as a react library but did know if devs will find this useful. Released it as a normal UI that people can use for their brand.
If anyone is interested in tech deep dive -- let me know in the comments.
I have had great fun building this. I hope you enjoy it as well.
-Gautam