shaism

Awesome. Perfect for editing with agents and sharing with coworkers.

- Even works well on mobile.

- Embedding images and videos also works flawlessly.

What is your use case?

Have you considered moving to open source / Chinese models?

If gemini-2.5-flash-lite is good enough for your application, you will find even lower cost options with better performance outside of the Google ecosystem.

I worked in a startup whose value proposition was largely derived from using graph data and graph databases under the hood. The main benefit is, as the repo even states "Fast data processing: Links traversal is processed with O(1) complexity."

So, technically, you can do deep traversals quicker. A few notes:

1. Few use cases truly need low-latency deep traversal on realtime data (>5 hops). There are some well known ones like fraud detection in payment processing and, possibly, social media recommendation engines. But I am not even sure how latest social media engines work, and whether they still rely on graph DBs.

2. However, in practice the advantage is often marginal. With modern analytical databases, or even an optimized PostgreSQL (ltree, materialized views, pgrouting, pg_duckdb, etc), you often get more than good enough performance. In addition those traditional SQL DBs scale with hardware more easily than graph databases. So, you can always use the lever: "Throw more hardware at it."

3. Even Graph DBs don't get good traversal performance under all conditions without hand tuning. For example, there is the "super node" issue, a node with an abnormally high number of connections (edges). And if you still need hand-tuning, you might as well choose something more versatile.

4. The ecosystem of a PostgreSQL and other popular DBs is just unbeaten. With graph DBs, you often prematurely put yourself in a corner that you don't want to be in.

Hence, my recommendation. Unless you are really sure that a graph DB is the right fit for your use case, start with something else, and go the graph db way when you have established a true need.

It is unfortunate for the employees but it is necessary, and hopefully also a wake up call for other German companies and the government.

It’s time for leaner structures, smaller teams, faster decisions, and shorter development cycles. Volkswagen, like no other company, symbolizes the lethargy of the German economy and business environment.

Hopefully, the seven years with barely any economic growth will end soon.

> A small task that cost 121,000 tokens done directly cost 513,000 tokens when fanned out to two subagents, because every subagent has its own bootstrap cost, and the parent then consumes its transcript.

Is that true? My understanding is that the subagent only returns the result of the request without the main agent consuming its entire transcript.

Which provider do you use to access the MI300x?

Thanks! There is something magical about those symbolic approaches. But in practice, they seem to often fall short of expectations. Wonder whether there will be another "symbolic AI summer" one day.

Thanks for sharing!

I see. Cool. So did you generate the inference / reasoning rules to map those 500k events to 40 by hand or are there solutions to automatically discover those rules based on past incidences?

What would be a good use case for this?

"Halle is in the state of Saxony-Anhalt, which has an election in September. The far-right anti-immigrant Alternative für Deutschland (AfD), which is leading the polls with about 42%, has seized on the row."

That is the important part.

Yeah. Agree. The workload changes towards more orchestrating, editing, and reviewing rather than writing.

I see the same for slide decks. The bar has changed. Expectations are higher, but the high quality decks are still heavily edited, reviewed, and created by humans.