I noticed a lot more AI crap in Bing with a Duck. It’s still my default for simple searches but I now use ChatGPT or Google for more advanced searches.
As I understand the LGPL - not a lawyer - you have to somehow enable all your users to relink your application against a different version of Skip (4.d.0 since 4.d.1 isn't possible on iOS). This means that your application must do something like include a copy of all the files that went into linking the application and convey that to the users along with your application, with scripts to build the application against a different version of Skip...
I can't imagine the app store would be particularly amused with this during app review... though I've never tried.
The license file linked provides an exception for 4d and 4e:
As a special exception to the GNU Lesser General Public License version 3 ("LGPL3"), the copyright holders of this Library give you permission to convey to a third party a Combined Work that links statically or dynamically to this Library without providing any Minimal Corresponding Source or Minimal Application Code as set out in 4d or providing the installation information set out in section 4e, provided that you comply with the other provisions of LGPL3 and provided that you meet, for the Application the terms and conditions of the license(s) which apply to the Application.
Cloud service providers deprecate services all the time. To be honest, why are you still on llama 3.3 70B in January 2026? Are you in the Strava AI team?
Yes, every single Enterprise customer that was unfortunate enough to be subscribed to LLama 3.3 has had their plans terminated. Everyone would have gladly migrated to OSS or something, but they never gave us the option.
my guess is they are aiming for the high rollers and not shrimp like you even though you were "Enterprise". I worked for a company that wanted to get rid of the lower tier customers to focus the money makers.
I had the second highest tier. But yes, that makes sense.
Regardless, I just don't see them as a stable business partner. I know the same cycle will continue with the next round of deprecation. Perhaps the highest tier enterprise users will form a different opinion.
My understanding is that for most residential heat pumps, the temperature needed to make the heat pump less efficient than resistive heating is so low that it enters a range that the pump doesn't even work anymore.
However, that's only a measure of efficiency. It could still be that the throughput isn't enough. A 30 kW resistive heater can ALWAYS output 30 kW of heat. But my 7 kW heat pump could produce anywhere from 14 to 30 kW depending on outside temperature.
Does that mean the heat pump gets less efficient as the outside warms? Because that would be fine. 7kW to make you home a constant temperature seems wonderful.
Accenture managed to build a data platform for my company with Elasticsearch as the primary database. I raised concerns early during the process but their software architect told me they never had any issues. I assume he didn’t lie. I was only an user so I didn’t fight and decided to not make my work rely on their work.
I worked in a company that used elastic search as main db.
It worked, company made alot of money from that project. It was a wrong decision but helped us complete the project very fast. We needed search capability and a db. ES did it both.
Problems that we faced by using elastic search:
High load, high Ram usage : db goes down, more ram needed. Luckily we had ES experts in infra team, helped us alot.(ecommerce company)
To Write and read after, you need to refresh the index or wait a refresh.
More inserts, more index refreshes. Which ES is not designed for, inserts become slow. You need to find a way to insert in bulk.
Api starts, cannot find es alias because of connection issue, creates a new alias(our code did that when it cant find alias, bad idea). Oops whole data on alias is gone.
Most important thing to use ES as main db is to use "keyword" type for every field that you don't text search.
No transaction: if second insert fails you need to delete first insert by hand. Makes code look ugly.
Advantages: you can search, every field is indexed, super fast reads. Fast development. Easy to learn. We never faced data loss, even if db crashed.
Databases and search engines have different engineering priorities, and data integrity is not a top tier priority for search engine developers because a search engine is assumed not to be the primary data store. Search engines are designed to build an index which augments a data store and which can be regenerated when needed.
Anyone in engineering who recommends using a search engine as a primary data store is taking on risk of data loss for their organization that most non-engineering people do not understand.
In one org I worked for, we put the search engine in front of the database for retrieval, but we also made sure that the data was going to Postgres.
> Anyone in engineering who recommends using a search engine as a primary data store is taking on risk of data loss for their organization.
It is true that Elasticsearch was not designed for it, but there is no reason why another "search engine" designed for that purpose couldn't fit that role.
ES should be thought of as a json key value store and search engine. The json key value store is fully consistent and supports read after write semantics, refresh is needed for search api. In some cases it does make sense to treat it as a database provided the key value store semantics is enough.
I used it about 7 years ago. Text search was not that heavily used, but we utilized the keyword filter heavily. It's like having a database where you can throw any query at it and it would return a response in reasonable time, because you are just creating an index on all fields.
agree with comment. We use ES quite extensively as a database with huge documents and touchwood we haven't had any data loss. We take hourly backups and it is simple to restore.
You have to get used to eventual consistency. If you want to read after writing even by id, you have to wait for the indexing to be complete (around 1 second).
You have to design the documents in such a way that you shouldn't need to join the data with anything else. So make sure you have all the data you need for the document inside it. In an SQL db you would normalize the data and then join. Here assume you have only one table and put all the data inside the doc.
But as we evolved and added more and more fields into the document, the document sizes have grown a lot (Megabytes) and hitting limits like (max searchable fields :1000 can be increased but not recommended) search buffer limits 100MB).
My take is that ES is good for exploration and faster development but should switch to SQL as soon the product is successful if you're using it as the main db.
I’ve either been involved with or adjacent to dozens of Accenture projects at 5 companies over the last 20 years, and not a single one had a satisfactory outcome.
I’ve never heard a single story of “Accenture came in, and we got what we wanted, on time and on budget.” Cases of “we got a minimum viable solution for $100m instead of $30m, and it was four years late” seem more typical.
Just like IBM, they are big enough that no one ever got fired for buying them.
I've also found they do a good job of getting cadre of executives that float between companies hiring them when they move between companies while they get wined and dined.
It's just that they're only seeing money to build and a place to make excuses on being late.
If you hire your own people you can make them feel how well the business is doing and get features out the door tomorrow and build to the larger thing over time.
I've seen some mess-ups in my life, but they started sticking out like a sore thumb long, long, long, long before anywhere close to $30 million was spent on it.
Teams of consultants on site, some remote, and many offshore. Tons of documents are created and many environments and DevOps pipelines are stood up. First code release is when the people who push buttons touch the system for the first time. It is crap. Several more code releases attempt to make the system usable. Eventually another consultant or two are brought to evaluate the project and they say the project violated every best practice and common sense rule. Most egregiously the internal stakeholders who voiced serious concerns at the beginning of the project were dismissed or forced out etc.
So much the same as what I've seen before, except instead of abandoning ship when the mess was clear and present, doubling down to see how far of a hole one can dig? Sunk cost must be one hell of a drug for the aforementioned CTO.
"A $30 million mess-up" can look like (at least) two things. It can be $30 million was spent on a project that earned $0 revenue and was ultimately canceled, or it can look like $x was spent on a project to win a $30 million contract but a competitor won the contract instead.
You could focus on having positive projects for the society, and a good reputation. That works.
I don’t think I ever seen a CV from an ex Pal*ntir employee though. Perhaps they are automatically filtered or working for good morals doesn’t attract them.
I think they might be a little desperate for new employees since I haven’t worked in about ten years and both Palantir and Anduril contacted me with cold calls in past year.
In a country with many huge companies selling oil, cigarettes, weapons, etc. there is no shortage of people willing to deal in morally questionable trades for money. I might even boldly suggest that Palantir is arguably far from the worst.
Is there any usefulness with the small large language models, outside perhaps embeddings and learning?
I fail to see the use-case on a Pi. For learning you can have access to much better hardware for cheaper. Perhaps you can use it as a slow and expensive embedding machine, but why?
A natural language based smart home interface, perhaps?
Tiny LLMs are pretty much useless as general purpose workhorses, but where they shine is when you finetune them for a very specific application.
(In general this is applicable across the board, where if you have a single, specific usecase and can prepare appropriate training data, then you can often fine-tune a smaller model to match the performance of a general purpose model that is 10x its size.)
I think there's a lot of room to push this further. Of course there are LLMs being used for this case and I guess it's nice to be able to ask your house who the candidates were in the Venezuelan presidential election of 1936, but I'd be happy if I could just consistently control devices locally and a small language model definitely makes that easier.
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