1,463 karma · joined November 5, 2020
It can sync from audio in the video or from another srt. Target srt can be in different language than reference.
EDIT: Two forks, but I haven't tried them as I'm still using the original as it still works well even though it has been archived.
Loading vs loaded difference needs to be clear.
Subsidies to manufacturers in Germany are much smaller than both: subsidies to consumers in Germany, and subsidies to manufacturers in China.
So, there are different subsidy types with different subsidy rates which all impact the market. Higher rates usually have a higher impact, but type of subsidy can also change what kind of an impact they have. Any comparison quickly becomes complex.
I posted some numbers in an other post [0], but the numbers are not exact.
Subsidy is also one of many factors. Of course you also need capable people and manufacturing capabilities which China has. So, subsidy alone is not enough, but if needed capabilities exist, subsidy at high rates can help tip the scales.
Yes, but those are two separate things. Lower Saxony owning part of VW doesn’t mean VW gets extra public money because of it.
High subsidy rates alter market dynamics. Can we agree on that?
First, the subsidies to consumers for electric vehicles in Germany apply to all cars, not just those built in Europe. This effectively subsidizes the competition from China.
For subsidies to manufacturer, as I didn't quickly find any source making the direct comparison, I asked LLM to research it (I apologize for for this, but doing it manually would take too much time).
BYD: ~15% producer-focused economic benefit under the Commission's methodology; 17.0% including the legacy NEV fiscal scheme.
VW Europe: ~0.2–0.7% is my best public-data-based estimate of currently observable and allocatable producer support; roughly 0.7–1.3% if we deliberately make aggressive assumptions favorable to VW.
VW extreme stress test: ~2.5–3.2%, obtained by implausibly allocating essentially all VW Group grants and tax credits to European BEVs.
https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=17382497...
That's how I understand it.
Cloud compute means its work VM is isolated from your devices, so it can't destroy your local data as easily (unless it has access to your devices) and it's on 24/7. A big negative is that you are giving access to even more of your services and data to a third party and the lock-in into a walled garden happens once you start depending on it.
See section 3.6:
https://ec.europa.eu/justice/article-29/documentation/opinio...
Let's check what "Opinion 04/2012 on Cookie Consent Exemption" [0] says under section 3.6:
"""
3.6 UI customization cookies
User interface customization cookies are used to store a user’s preference regarding a service across web pages and not linked to other persistent identifiers such as a username. They are only set if the user has explicitly requested the service to remember a certain piece of information, for example, by clicking on a button or ticking a box.
...
These customization functionalities are thus explicitly enabled by the user of an information society service (e.g. by clicking on button or ticking a box) although in the absence of additional information the intention of the user could not be interpreted as a preference to remember that choice for longer than a browser session (or no more than a few additional hours). As such only session (or short term) cookies storing such information are exempted under CRITERION B. The addition of additional information in a prominent location (e.g. “uses cookies” written next to the flag) would constitute sufficient information for valid consent to remember the user’s preference for a longer duration, negating the requirement to apply an exemption in this case.
"""
See that you need to provide provide "information in a prominent location (e.g. “uses cookies” written next to the flag)" to be able to store user preferences in persistent cookies. You don't need consent banner for that (which I didn't say you need), but you need to clearly inform the user. The act of setting a preference together with clear information about persistence counts as a valid consent.
[0] https://ec.europa.eu/justice/article-29/documentation/opinio...
Your answer just throws in anything that enables programs to communicate somehow, ignoring all the differences and tradeoffs to what is being discussed here. Many of your solutions lock you in to a specific platform, a language or add a non-trivial overhead like message serialization or add an unnecessary complexity to a program. Also, IR does not solve the same problem as ABI.
In the end there is always some long lived secret. What changes is just where and how it is stored, secured and used.
I bet we can generalize to say that data shows that you will likely fail to properly secure any secret (including the ones used in OAuth2).
EDIT: An example: https://news.ycombinator.com/item?id=37973937
For example, you if you instruct a model to create decoder for some data type users will upload to your website. The intelligent model without notions will retrieve information about that data type and build a working decoder, but it might miss from context that users uploading to a website means untrusted input and thus won't even try to gather information about what it needs to be done to securely handle such uploaded data.
Or if you give it a task to translate text to a language it didn't encounter during training. You can provide it with grammar rules and a dictionary for information retrieval, but I guess it won't perform as well as inteligent model that already has some fundamental notions of that language and only needs a dictionary to expand its vocabulary.
Gpt-4.1 only knows a lot of patterns, but doesn't have reasoning intelligence that would help it properly use that knowledge. So, a small reasoning model can easily beat it in a lot of tasks. The question is how will, 14 months from now, new small reasoning models compare to current big reasoning models.
How much information needs to be embedded is not yet clear, but currently, bigger reasoning models are still better at complex tasks than small reasoning models. Either sweet spot of embedded notions is higher that what current small models have or information retrieval ability needs to improve.
Model doesn't know what it doesn't know.
"The react in react stands for reactivity, however it is not." [because] "Its entire state management is not reactive"
React is primarily an UI library, not full state management library. And its UI is reactive.
I think the mayor win for coding was reasoning. That's why such a small model can match GPT-4.1 in coding, but I suspect that GPT-4.1 still wins in general world knowledge due to bigger size.
This piqued my interest on how it does it and after briefly checking the project it seems it only has two features for automatic photo categorization. 1) it can group photos by date and 2) It has face detection and recognition that uses trained weights (so ML "intelligence").
Databasus seems to be taking somewhat similar approach to Barman, but (at this time) does not appear to use pg_receivewal, which makes it less efficient than Barman.
For PG v17+, Barman seems to be the most efficient backup solution based on PG native tools, that is able to do low-RPO or even zero-RPO (if configured as a synchronous receiver).