quadrature

Does not look trustworthy

what are you implying specifically ?

best of luck to you, it will be great to see a rearchitected postgres.

Are you fixing the heap and table management ?. Postgres does not use an undo log and manages all table updates directly in table storage which slows MVCC.

also have you told Ben Dicken ? https://x.com/BenjDicken/status/2074326407795417435

A credible journalist would not entertain writing a story based on a screenshot some random user posts on social media.

Yes people frequently fake screenshots on social media. I'd want either a screenshot from a credible person, reporting from a journalist, trusted blogger, company statement etc.

Is there actual proof that they are doing this. Theres not much to go on in the tweet.

Is there something better than Swiss tables ?.

VHDL is not a language for spatial design. Its more akin to a programming language with circuit semantics.

thank you, how do they live like this.

Most people will make the lazy choice. But you can choose to do otherwise

I'd like for it to be a choice. AI is injected into search now, when you install vscode they have a prompt input sitting there and they nudge you to use it. Of course you can opt out of this stuff but it has become the default.

As someone teaching their nephew how to code i really want him to struggle and exercise his problem solving skills instead of having every touchpoint offer him an instant answer.

Does the model have capacity for in context learning ?, if we give it examples of patterns can it follow them ?.

"Equivocal describes something ambiguous, uncertain, or open to multiple interpretations, often used to intentionally mislead or evade."

do you mean equivalent ?.

In a traditional SLAM pipeline you do periodically fix drift by detecting when you've visited an area that you've mapped before this lets you align your sub maps so they are globally consistent.

In the areas you have visited previously you have two estimates of your position one from your frame-to-frame estimates and another from the map you built of the area the first time. You can then solve an optimization problem to bring those two estimates closer together.

In order to find out if you've already visited an area you store a description of the locations in a DB and search through them. The paper says they use a compressed representation of the "maps" and use test time training to optimize the global consistency between their sub maps.

Sure but the exploit here isn’t prompt injection, it is an edge case in their billing that isn’t attributing agent calls correctly.

Thats a completely different scenario than fully autonomous driving.

LLMs sample the next token from a conditional probability distribution, the hope is that dumb sequences are less probable but they will just happen naturally.

Had this same experience back when I first learned to program a PIC microcontroller. You really shouldn't be driving LEDs directly off IO pins anyways. I think the digitalness of IO pins also lends itself to not thinking about the underlying circuitry and coming at it from a software lens.

theres more information here https://docs.usetero.com/introduction/how-tero-works the link in the article is broken.

They determine what events/fields are not used and then add filters to your observability provider so you dont pay to ingest them.

So "stripe webhook" matches /services/stripe/webhook.handler.ts highly because the path literally contains both terms in logical positions

This sounds a lot like document search ontop of your specific attributes and you have a custom ranking algorithm.

For what it’s worth simdjson now has an on demand api that lets you skip over keys that you don’t need.

but I'd suggest doing some Deep Researches on the topic to get you a good lay of the land

thanks for the resources !. I've been trying to get a wide view by looking at different algorithms, but I was curious what was actually used in production systems especially for consumer products.

RTABMAP and Cartographer came up in my searches, will definitely give these a closer look to understand how they work.

Right now im starting off with filter based approaches like Particle filter and Kalman filter, but i'd also like to understand how the graph based approaches work.

Slightly offtopic but i feel like someone around here might be able to help. I've been learning how to do SLAM with LIDAR data and I was curious what algorithms robot vacuums use. I'm currently implementing a particle filter and will also try out EKF.

I’m building a planner that automatically plans employee shift schedules based on their availabilities and roles.

There’s many different solutions out there but I’m carving out a niche where we deal with complex shift assignment problems.

For example one of our customers has specific union rules that need to be followed when assigning work and we ensure that they are compliant.

Our backend relies on an MIP solver as well as heuristic search to refine plans.

What does the durability story look like for this single node offering ?.

There are a few realtime analytic DBs that let you query the in memory streaming buffer. That lets you have the best of both worlds but comes at the risk of inconsistency because you’re querying data that isn’t durable.

I'm not very well versed, but i believe that training requires more memory to store intermediate computations so that you can calculate gradients for each layer.

Not suprising that the hyperscalers will make this decision for inference and maybe even a large chunk of training. I wonder if it will spur nvidia to work on an inference only accelerator.

I really like the idea of crowd funding research or the opposite public funding of citizen science.

It would be interesting to also have a space where the community could propose research directions.

Is the problem mainly with tool use ? and are you using it through AI studio or through the API ?.

I've found that it hallucinates tool use for tools that aren't available and then gets very confident about the results.

I'm curious about how the perception works, how do you find correspondences between the arm camera and the stationary camera ?