Gauge | Founding Engineer | San Francisco, CA | ONSITE | Full-time

Gauge helps companies show up in more AI answers. Search traffic is shifting away from Google into AI chat, and companies are figuring out how to adapt right now. Our system delivers real-time analytics derived from millions of LLM answers, while providing an intuitive, powerful UX. We also generate optimized articles to improve our customers’ visibility, and offer an agentic chat interface over their data (think ‘Why was there a spike on 12/10?’ or ‘Which of my pages should I refresh?’).

I'm Evan, the CTO, and we're looking for engineers with strong programming fundamentals, creative problem solving skills, and a high-agency working style. You might be a good fit if you are an ex-founder, ex-founding engineer, or have worked at a Seed/Series A startup.

We primarily use Django, React, Postgres, Celery, and Clickhouse.

Contact me with your resume (and mention HN) if you're interested: evan[at]withgauge.com

More details/JD also here: https://www.ycombinator.com/companies/gauge/jobs/qemTjv7-fou...

Very cool!

Yup I definitely see confusion in our responses around the product and brand names. We do another pass through an LLM specifically aimed at ‘canonicalizing’ the names, but we’ll need to get more sophisticated to catch most issues.

In that case you mentioned, the brand confusion is what accounts for the top three omission for QBO. Both OpenAI and Perplexity rank it #1, but Anthropic ranks the slightly different “Quickbooks” product as #1. Our overall ranking prioritizes products that appear in all three responses, so both are dropped down.

Interesting, I thought it might be something like that.

Yea, 'canonicalizing' is really tough (although I don't know if you really need to get it *perfect*) because what is correct is different in different contexts.

Accounting Software as an example again, for the category overall canonicalizing any reference to Quickbooks to the same company makes sense. If you're asking about more specific recommendations though 'Accounting software for sole traders', you might have both Quickbooks Online and Quickbooks EasyStart mentioned, and they are actually slightly different products. Or Netsuite is actually a suite of products that might all make sense in slightly different contexts.

That nuance is really important/hard to piece apart. Have you found any good techniques to solve for it?

To be honest not really!

I get the output from the LLMs, compile into a report, and then pass it back through an LLM to sense check the result with the added context of what's been requested in the report, but I'm not super happy with the outcome still, some different categories still come out a bit of a mess.

No specific reason, just started with these three, will add Gemini soon!

It's from previous searches actually, we have an 'enrichment' step after the initial rankings come back which helps with semantic deduplication and tries to give us a canonical website domain. We store the Product and tag all matching rankings: https://productrank.ai/product/microsoft and use a 3rd party to map website <-> brand logo.

We are using sonar-pro

These are structured results from explicitly asking the LLM for a ranking in the given category, and we provide guidance in the prompt telling the LLM to 'use best judgment' when the topic doesn't clearly include products.

Also we include the 'key features' from each answer - you can see this by clicking the cell containing the rank (e.g. '1st' in the Anthropic column)

In this case, Anthropic said of 'Death during sleep':

Anthropic Analysis for Death During Sleep

    Painless and unaware experience
    No anticipatory anxiety
    Common with certain cardiac conditions
    Often described as 'peaceful'
    No suffering

I actually made that change in response to their comment! It used to be a live GIF with no narration.

Thank you for the change then. I liked the video with narration.

Yes!

Here is an example project that is configured as if it were a uv workspace: https://github.com/gauge-sh/tach/tree/main/python/tests/exam...

In that project, `tach check-external` would handle between-workspace dependencies, while the core `modules` and `interfaces` config would handle within-workspace dependencies.

Soon these will be better unified, we kept the 1st-party/3rd-party distinction separate while we learned what the UX should be.

This should definitely be supported out-of-the-box, we can take a closer look in Discord or through GH Issues! But generally I would expect:

```

[[modules]]

path = "foo"

depends_on = []

[[modules]]

path = "bar"

depends_on = ["foo"]

```

would do the trick, assuming both are within a configured source root, and their children are _not_ also marked as modules. If the children are marked as modules, their dependency rules are treated separately and wouldn't automatically inherit from a parent.

Ok great, I indeed just tried, I might just have been confused last time! Maybe you could mention in the doc that a module can be a "package"? (Even though I suppose a package is also a module, I always find the Python terminology a bit confusing there.)

I always used to think of it as a module is a file `example.py`, a package is a directory `example` with a module `__init__.py`.

Co-author here - We do provide a web platform (https://www.gauge.sh/platform) which we have been developing with design partners. The fundamental difference between using Tach alone vs. the platform is that the platform provides incremental enforcement at the pull request level.

We're always happy to chat about adding more design partners! email: [email protected]