Hi last time i tried using paradedb my pg cluster stopped replicating (I am fine with search queries being run on only the primary). Had to create my own fork.
I feel like there is an excessive push towards enterprise plans even before people are able to integrate pg_search.
Regardless I appreciate the work your team has been doing related to dev exp like native drizzle orm support and bringing in vectors along side bm25 search. Complete pg search is looking promising by the day!
I like that a lot of ASR models now offer native context biasing capabilities. If I have enough volume, I would probably just self-host my model and do my own biasing but good enough to roll out custom solutions.
I think "deliberately" is really hard to prove because very few lawmakers have an understanding of how agentic large language models work
I would say pointing an "internal research" model at a task with zero monitoring clearly is deliberate behaviour. And the team involved should be prosecuted to the full extent of law.
What should be introduced in AI safety bills are clear ways of monitoring and curtailing model behaviour.
It is not hard to get an AI model to use an append only log and that can be cryptographically secured with PQ security. And we will know when a model is being deceptive.
I am not sure why all AI safety arguments have to start with prominent people insinuating that ai will kill us. I think that it is pretty evident that there is a significant possibility.
What all of them fail to explain why we shouldn't/couldn't already molotov cocktail the datacenters. I think all these argument always greatly underestimate the ability of humans to coordinate against ai.
Very rarely in human society have a select group held substantial power over the masses without uprisings, coups or wars.
Sure I can pay an bio lab to build my bioweapon for a million but they sure won't if we have proper consequences against bio labs making frivolous decisions (death penalty for terrorism). Or say in another scenario where a million dollars only buys me a sandwich.
turns out its also very easy to create slides and floor plans and yes its easier to train for those.
I think you may also be incorrect on your notion that training data is proportional to good models if anything the opposite is true. what ai models are ultimately trying to be are a universal theorem prover and encode all human knowledge into "theorems" and rulesets
not trying to come off too harsh here but i think most of the work is just innovating on the frontier or people pleasing
i think nats jetstream is superoverlooked in this space. the amount of throughput is more then enough for any medium scale enterprise as long as you know what you are doing
I can imagine advancements on making smaller models work together better instead of a generalized core. Imagine a community or city having a https://pirateface.co/ so that the shard of the model that you need can be streamed in with minimal latency with your box only holding the minimal version (say deepseek v4 flash as orchestrator) of the model that you use on day to day basis.
We have overcome split brain problems before so this wont be our first
I feel like there is an excessive push towards enterprise plans even before people are able to integrate pg_search.
Regardless I appreciate the work your team has been doing related to dev exp like native drizzle orm support and bringing in vectors along side bm25 search. Complete pg search is looking promising by the day!
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