OliverGuy

Got a source for that?

It's location tracking more than anything, so yes they would most likely be as the use all the same networks as a normal device

Probably a bit niche at the moment really. The only people interested in that are us nerds, and the product segment is very adhoc - especially for the local crowd where an epyc, with a bunch of pcie riders and some 3090s on a steel frame is considered optimal

Curl is one of the most audited code bases, so that's not surprising.

Put it in front of a common-ish Python or NPM package and see what it finds, likely going to be a lot more.

To be fair SRAM are particularly a pain to bleed. Shimano, Magura etc are much easier

It's comical too, jumpy and takes way to long.

Sure it's kinda cool as a gimmick, but it should be like 600-800ms at most if you do want one

Ampere make some big 32-128 core server ARM chips that are somewhere around Zen4c ish in performance, although they aren't commonly used outside of the server context. But they do at least exist.

Ferrari have had indicator buttons in all their cars since about 2010

Have you got a source for the s3/glue/redshift bit?

AFAIK youtube will stretch the player window to match the aspect ratio of the source media, lots of cinematic content that's a wider than normal (21:9 I think?) ratio that youtube adjusts the player window to fit around without black bars.

They won't ever squash or stretch video though, so this means the original uploader stretched the 4:3 content to 16:9 at some point before upload

I get this is a pretty nice product that they won't sell in huge volume, but that is really steep, was expecting something <20k

I would love to have a shit load of small (27B dense. 35B MoE) agents running locally and looking at and ingesting every bit of data about me, my life and what I get up to see what sort of correlations it finds. Give a coding agent access to a data lake of events and let it build up its own analytics tooling to extract and draw out information from that data, and present it to me as daily/weekly/monthly summaries.

What's the HA plan?

Sounds like from the requirement to live migrate you can't really afford planned downtime, so why are you risking unplanned downtime?

Just started this weekend on https://gitlab.com/get-otter/otter-sdk

Its dbt inspired stream ETL tool (or maybe just the TL?), it currently just has a dev mode that does RabbitMQ to local Parque files while I'm getting the core of it to a place I'm happy with.

It runs SQL models against the incoming messages and outputs the results to one or more output tables. Has a local WAL so you can tune it to have sensible sized output files (or not, if you need regular updates but at the expense of query perf.)

Planning on adding Protobuf messages, Kafka as a source and S3 and Iceberg tables as sinks this week.

Lightly inspired by a some projects at work where a lot of time and effort was spent doing this and resulted in something not very reusable without a lot of refactor work. Feel like the stream -> data lake pattern should be something that is just SQL + Config, same way dbt is for transformations within a data warehouse.

No plans on adding any cross message joins or aggregations as that would require cross worker communications and I explicitly want to keep the workers stateless (minus the WAL of course)

Would really appreciate any feedback on the core concept, esp. if this is something you'd actually use in prod (if it were finished!) Not sure if there is something that does this already that I don't know about, or if this genuinely fills some sort of hole in the exisitng tooling

And you know Nvidia can't be constent with one format for FLOPs within a single graph, 1,000,000x faster but comparing FP32 to FP8 or NVFP4 and acting like it's the same.

settings.json -> global config Env vars -> settings different to your global for a specific project Slash commands / chat keywords -> need to change a setting mid chat

Edit, it looks like the paper does

TPUv5e with 16 tensor cores for 2 days for the 200M param model.

Claude reckons this is 60 hours on a 8xA100 rig, so very accessibile compared to LLMs for smaller labs

Wish they gave some numbers for total GPU hours to train this model, seems comparatively tiny when compared to LLMs so interested to know how close this is to something trainable by your average hobbyist/university/small lab

Interested to know more about your inference start up? How you guys operating, do you own hardware or use the cloud?

https://gitlab.com/usecaliper/caliper-python-sdk

An LLM observability SDK that let's you store pre and post request metadata with every call in as lightweight an SDK as possible.

Stores to S3 in batched JSON files, so can easily plug into existing tooling like DuckDB for analysis.

It's designed to answer questions like; "how do different user tiers of my services rate this two different models and three different systems prompts?". You can capture all the information required to answer this in the SDK and do some queries over the data to get the answers.

That's still the same crawler system though. And it's lazy engineering to not build in something to track when you last requested a url.

And it's quite a trivial feature at that.

Tbh, for most companies/orgs the cost/complexity of multi region just isn't worth it.

The cost of a work days worth of downtime is rarely enough to justify the expense of trying to deploy across multiple regions or clouds.

Esp if you are public facing and not internal. You just go 'well everyone else was down to because of aws' and your customers just go 'ah okay fair enough'

Cisco etc have truly insane pricing on optics, like $1000 for something generic that cost $20-50 from fs.com etc. The only difference is how it presents itself to the switch (ie, says its a Cisco optic), not actual difference in performance.

Often Cisco/etc will refuse support cases if you aren't using their optics, if the switches/routers even work with them in the first case, which isn't a given as often they'll refuse to work with non branded optics.

Really just a money grab by the big network vendors.

This box allows you to flash the firmware on the optic to say its from whatever brand you want (Cisco, Dell, Aruba, Juniper etc) so that you can get it to work in that companies switch/router.

For most SMEs, the brand of optics makes no difference. Maybe keep a few legit branded ones around for debugging and when you need to raise a support case. But otherwise, the generic ones flashed to look like branded ones work just fine.

Their latest update on the status page says it's a Dynamodb DNS issue

How representative is this platform of the bigger GB200 and GB300 chips?

Could I write code that runs on Spark and effortlessly run it on a big GB300 system with no code changes?

The NetApps are just disk shelves, can plug it into a SAS controller and use whatever software stack you please.

Aren't those netapp shelves pretty old at this point? See a lot of people recommending against them even for homelab type uses. You can get those 60 drive SuperMicro JBODs for pretty cheap now, and those aren't too old, would have been my choice.

Plus, the TCO is already way under the cloud equiv. so might as well spend a little more to get something much newer and more reliable

I can put some AWS Creds in my terminal and Claude Code is perfectly happy writing AWS CLI commands (or whole python scripts if necessary) to work out what it needs to about my infrastructure.

SageMaker have serverless inference endpoints

If you are trying to get facts out of an LLM you are using it wrong, if you want a fact it should use a tool (eg we search, rag etc) to get the information that contains the fact (Wikipedia page, documentation etc) and then parse that document for the fact and return it to you.