For any task, whether code or a legal document, immediately asking "What can be done to make it better?" and/or "Are there any problems with this?" typically leads to improvement.

Comments URL: https://news.ycombinator.com/item?id=42342670

Points: 1

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Comments URL: https://news.ycombinator.com/item?id=41943623

Points: 2

# Comments: 0

"key differentiator" and not necessarily easy to pull off or pay for

After years of raising 3 kids, you would think if I ask to add diapers to the cart, it would know something. But no, it would just go with whatever is the top recommended, or first in a search, or something like that. Nothing using the brand or most recent sizes we purchased.

There was no serious attempt to drive real commerce. Instead, Alexa became full of recommendation slots that PMs would battle over. "I set that timer for you. Do you want to try the Yoga skill?"

On the other hand, they have taken on messy problems and solved them well, but not using technology, and for no real financial gain. For example, if you ask for the score of the Tigers game, Alexa has to reconcile which "Tigers" sports team you mean among both your own geography and the worldwide teams, at all levels from worldwide to local, across all sports, might have had games of interest. People worked behind the scenes to manage this manually, tracking teams of interest and filling intent slots daily.

You work around it with post-processing and retries. But it’s still a bit brittle given how much stuff happens downstream without supervision.

- Generate targeted LLM micro summaries of every record (ticket, call, etc.) continually

- Use layers of regex, semantic embeddings, and scoring enrichments to identify report rows (pivots on aggregates) worth attention, running on a schedule

- Proactively explain each report row by identifying what’s unusual about it and LLM summarizing a subset of the microsummaries.

- Push the result to webhook

Lack of JSON schema restriction is a significant barrier to entry on hooking LLMs up to a multi step process.

Another is preventing LLMs from adding intro or conclusion text.

The real problem is that most people don’t want to pay for things.

If the API is being used to support something new -- i.e., not a replacement for a user on the site, but as a new revenue source -- it will be priced to maximize overall API revenue, or overall revenue as an indirect result of growth supported by the API. (Maybe it's one massive customer using it or 100,000 freemium developers.)

The idea that a company "must" do something doesn't make any sense. And the assertion that the company is harming itself is ultimately something the Board, acting on behalf of shareholders, is responsible for.

(FWIW, I agree Reddit is shooting itself in the foot.)

I asked ChatGPT to reformat it into a CSV, noted the useful breaks, requested some transformations and filtering, and specified a delimiter. After 5 minutes of experimentation, it worked like a charm. Absolutely amazing.