samsullivan

Cardog | Toronto, ON | Remote/Hybrid (Canada) | Full-time AI-powered automotive platform — we’re building the infrastructure layer for vehicle data, from VIN decoding to market intelligence, powering government and public-sector vehicle programs.

Looking for: Automotive experience, Full-Stack, AI/ML, Data Engineers (public-sector/govtech experience a plus)

Stack: React Native/Expo, Next.js, Hono, Cloudflare Workers, PostgreSQL, TypeScript everywhere

https://cardog.app/careers

Cardog | Toronto, ON | Remote (Canada) | Full-time AI-powered automotive platform — we're building the infrastructure layer for vehicle data, from VIN decoding to market intelligence.

Looking for: Automotive experience, Full-Stack, AI/ML, Data Engineers

Stack: React Native/Expo, Next.js, Hono, Cloudflare Workers, PostgreSQL, TypeScript everywhere

https://cardog.app/careers

Cardog | Toronto, ON | Remote (Canada) | Full-time

AI-powered automotive platform — we're building the infrastructure layer for vehicle data, from VIN decoding to market intelligence.

Looking for: iOS/Mobile, Full-Stack, AI/ML, Data Engineers

Stack: React Native/Expo, Next.js, Hono, Cloudflare Workers, PostgreSQL, TypeScript everywhere

https://cardog.app/careers

not sure what PocketOS does or why your whole dataset would be a single volume without a clear separation between application and automotive data. how are you decoding VINs?

Cardog | Toronto | Full Stack Engineer | In-person

We build automotive software. We work with dealerships on ad-hoc projects, websites, and pricing tools. We also run consumer facing products - a web app for vehicle shopping, research and market data, and a mobile app.

Right now we're building EV battery health diagnostics - connecting to a car's BMS over CAN bus, pulling cell voltages and state of health, generating reports. Mix of hardware and software.

Small company with real clients and real revenue. One engineer. Looking for a second who wants to own things e2e.

hello[at]cardog{dot}app

The need for proxies in any legitimate context became obsolete with starlink being so widespread. Throw up a few terminals and you have about 500-2k cgnat IP addresses to do whatever you like.

why do we insist on bringing the worst tech from sci-fi to reality? is our collective intelligence still not enough to thwart the social and market dynamics?

all of these problems are better articulated at the level you just explained them. the code for these issues is convoluted and is only of use when an entity (human or not) can actually manipulate the symbolic text that achieves that task. a random oauth stub is of 0 use to the most skilled programmers without documentation as to what contracts and invariants are. bits in a file is just a means

Trying to make anything car related easier - Cardog.app

Buying, researching and analyzing automotive data is broken. Trying to fix that bit by bit

Imagine a world where the code we write for humans would actually integrate with other computers

The battery uncertainty is real, but I think the bigger issue is information asymmetry.

Looking at actual market data, the spread on used EVs is wild - a 2022 Tesla Model S ranges from $57 to $112k depending on trim/condition (https://cardog.app/tools/valuation/tesla/model_s/2022). That's a $60k spread on the same year vehicle. Compare that to ICE vehicles where the range is typically much tighter.

When buyers can't confidently price an asset, they discount heavily. The depreciation problem might actually be a data problem - we just don't have the standardized battery health reporting and historical comps that exist for ICE vehicles yet.

this is a claude output verbatim, wonder what happens to reddit over the next 3-6 months, we really destroyed the commons.

answering correctly is completely dependent on the attention blocks to somehow capture the single letter nuance given word tokenization constraints. does the attention block in kimi have a more receptive architecture to this?

Thats why they need to pay 300k for a slide designer https://openai.com/careers/creative-lead-presentation-design...

"figure out how your employer makes money and position your ass directly in-between the corporate bank account and your customers' credit card information."

we're going to learn why companies were invented the hard way this decade aren't we

Im building https://cardog.app out of Toronto

Trying to make car research, buying and ownership easy

MCP feels overengineered for a client api lib transport to llms and underengineered for what ai applications actually need. Still confuses the hell out of me but I can see the value in some cases. Falls apart in any full stack app.

But meeting at some random parking lot without knowing what you're actually buying seems to be the only option. Dealers charge a huge premium for the trust layer, just like hotels did before Airbnb. It's an adversarial market that desperately needs a trusted intermediary, but current players exploit this information asymmetry rather than solve it.

Cardog guides users through the complete vehicle ownership cycle. Start by researching models using our comprehensive specs database, owner reviews, and curated video content. When ready to buy, analyze real-time listings from sellers across North America, with data organized by exact trim and feature configurations.

After purchase, the garage becomes your ownership hub - tracking maintenance, storing documents, and monitoring market values. Get proactive alerts for service needs, recalls, and registration renewals. When it's time to sell, use historical price data to time the market and determine optimal listing price.

Unlike search engines providing scattered information, Cardog unifies vehicle data with practical ownership tools. Whether you're researching your next car or managing your current one, everything stays organized and updated automatically. The AI interface makes this comprehensive dataset accessible, while the garage helps track your vehicles through their entire lifecycle.

Each phase feeds into the next: research informs purchase decisions, purchase details populate your garage, and garage history helps optimize your eventual sale - creating a seamless ownership experience.

The LLM interface isn't the main feature - it's the combination of vehicle-specific data sources and tools:

Vehicle maintenance history aggregation Automatic quote retrieval from sellers Registration/insurance document management Market value tracking across North America VIN-specific recall and service bulletin monitoring Manual parsing and model-specific guidance

The LLM makes this data accessible, but the value is in having everything about your vehicle centralized and contextualized. Claude/Deepseek can give general car advice, but they can't track your specific vehicle's history or provide personalized maintenance alerts.

Would you find value in having all your vehicle's data and documents in one place?

With all the news about scraping legality you'd think a multi billion dollar AI company would try to obfuscate their attempts.

You should consider focusing on intercepting network requests. Most if not all sites I scrape end up fetching data from some api. Like others have said, if you instead had the LLM create an ad hoc script for the scraping task and then use the feedback loop to continuously improve the outputs it would be really cool. I'd pay between $5 - $50 for each working output script.

We're going to need some sort of knowledge graph for all the different vehicle models, imagine how vague dealers will be when your looking on the lot. Wonder how crazy this will be once they start adding in-car ads.

It would have to in order to produce the outputs, our brains have crazy physics engines though, F1 drivers can simulate an entire race in their heads.

shadcn/ui and chatgpt makes it pretty easy to pull off some nice design in hours compared to days / weeks

Yeah it fetches the vehicles daily and removes sold listings by diffing the existing inventory w/ the recent scrape data.

Hi HN,

I'm excited to share Cardog, a platform I've built from the ground up, aimed at simplifying the car searching process. Every day, Cardog scrapes over 250,000 vehicles, consolidating a vast amount of information to make finding the right car easier than ever.

*What Cardog Offers*: - Aggregates data from thousands of sources daily. - Provides comprehensive search filters to narrow down options based on various criteria. - Designed to offer a streamlined, user-friendly experience.

*My Journey*: As a developer and car enthusiast, I noticed the challenges in the online car search process and decided to tackle them head-on. Cardog is the direct result of this. My goal is to make finding a car online as easy as ordering lunch.

*Future Features Discussion*: I'm exploring the idea of scraping car reviews from various publications, blogs, and YouTube videos to create a comprehensive overview of pros and cons for each vehicle. I'd love to hear your thoughts on this:

- How valuable would you find a feature that compiles reviews and ratings for each car model? - Are there specific sources of reviews or types of information you trust more when researching cars? - What other features or data points would you like to see integrated into an online car search platform?

Your insights and suggestions would be incredibly helpful in shaping the future of Cardog.

If you could check out Cardog and let me know what you think it would really mean a lot, any feedback would be immensely helpful.

I'm currently building cardog.io for this, its currently only in Canada. It works by scraping 3000+ dealerships daily for their inventories, I'm working on adding all the more fine grained filters (fuel economy, days on lot, etc), but most of the common filters (make, model, price, year, etc) are working. Still working out some bugs but looking for any feedback.

SEEKING WORK | North America, Canada | REMOTE

Hi HN! I'm Sam Sullivan, a Fullstack Developer with over 2 years of experience in web development and IT consultancy, I specialize in creating robust, scalable web applications and AI-driven UX designs.

Here's what I offer:

- Web Development: Expert in Svelte & Sveltekit, React & Nextjs, with experience in package building (UMD, npm, pnpm), browser testing (Playwright), and various integrations (Shopify, Stripe, Plaid).

- DevOps: Skilled in cloud infrastructure automation, proficient with Vercel and Digital Ocean.

- Web Design: Following strict design guidelines (e.g., Hyundai, Kia, Audi) and creating custom component libraries.

- Web Scraping: Experienced in browser automation, traditional scraping, and setting up comprehensive scraping pipelines.

- AI UX Development: Proficient with OpenAI Functions, usage billing, and Vector Databases.

- Database Management: Well-versed in PostgreSQL, MS SQL, and more.

I thrive in projects that require rapid learning and adaptation to new technologies. Whether it's developing a comprehensive web suite or integrating complex APIs, I deliver high-quality, efficient solutions.

Email: mail@samsullivan.dev

GitHub: https://github.com/samsullivandelgobbo

My Resume: https://samsullivan.dev/resume.pdf