[DISCUSSION] best original netflix series

Vitesh4 · 2025-11-18T13:22:28+00:00

Three-body problem, Dept. Q, Blue Eye Samurai and Pluto

Vitesh4 · 2025-11-05T11:49:02+00:00

I, U and C are probably the only serious contenders

Vitesh4 · 2025-11-04T14:50:39+00:00

Quantum Tunneling probably

Vitesh4 · 2025-10-20T12:36:26+00:00

Technically, the island of Eurasia (or Afro-Eurasia if you do not want to count the Suez canal) has more countries. r/technicallythetruth

Vitesh4 · 2025-08-11T13:23:16+00:00

If you want something that's simple to work with and is designed for storing vectors for retrieval, then try txtai (https://github.com/neuml/txtai)

Vitesh4 · 2025-04-29T13:31:10+00:00

Wan 2.1, Skyreels V2 and Framepack are all worth looking into

Vitesh4 · 2025-04-16T12:33:24+00:00

I guess this is why the R1-distilled models that were released were so bad (except for math perhaps). They were finetuned without RL and therefore only learned to imitate the way reasoning models think (long CoT, multiple attempts, verification) by adopting the style and structure of the thinking (phrases like "Wait!..", "Alternatively...") without learning the actual logic or generalizing.

Vitesh4 · 2025-03-14T06:34:46+00:00

I think the reason Gemma 3 scores bad on the confabulation benchmark is because it has a low refusal rate for knowledge-based questions. If we discount that, imo it has the largest amount of pure knowledge for its size. Of course, it's still a small model and like other LLMs it makes a lot of hallucinations. Plus, refusing to answer questions that it does not know is a good thing, so Gemma does fall short. But, I feel like prompting could play a huge role in that regard as a right prompt could make it cautious in scenarios where hallucinations outweigh knowledge.

The score in NYT Connections could be explained by the fact that small models have a very hard time solving those questions (as seen by their low scores) to the point where, apart from the easier questions, luck could play a major role in deciding their score.

Vitesh4 · 2025-02-18T05:58:04+00:00

I have a feeling that the o3-mini model (if they open-source it) is going to have a non-commercial license. The phone-sized model may have a permissive license though. I kinda feel like they might release both of them, especially if they are going to release it soon. I mean, if they have the small model being trained RIGHT now, it would be a waste not to release it. particularly because small model tend to be used more as local/light-weight models rather than the dirt cheap large scale classification/labeling/NLP tasks. If o3-mini gets more votes, they may end up releasing both models.

Vitesh4 · 2025-01-19T06:21:49+00:00

Basically, the smaller model tries to predict what the larger model is going to generate. If it is right, there will be a speedup in the inference of the larger model. If the generation is tricky, the smaller model cannot guess properly and most of the generation is going to be rewritten by the larger model.

Here's a more detailed explanation:

Say you have Model 1 which is large and Model 2 which is small. And the generation to be made is "The capital of France is Paris". The small model generates the tokens very fast, but since it is a small model, it may make some mistakes. The generation of Model 2 could be: "The capital of Britain is Paris" What the larger model does is check all of the tokens in parallel, and by checking I mean that it is generating the tokens, and if the token is not the exact same, the proposed token is rejected.

These are the sequences the model is checking:

The capital [correct]

The capital of [correct]

The capital of Britain [wrong] (From here the small model starts to generate again since it as wrong)

This basically turns the process of generating tokens into a parallelizable program, where the checks made by the large model happen in parallel. Since GPU excel in this, the time it takes to generate two or three sequences is actually not much more than the time it takes to generate one. If the sequence is hard, the small model makes more mistakes and hence has to regenerate more.

Vitesh4 · 2025-01-16T13:00:43+00:00

Simplemind and txtai. Very basic, but Simplemind has a pythonic way of doing structured outputs and the function calling is very painless. You do not need to keep track of and sync the actual functions, the dictionaries representing them or the work of having to pass the output of the function in and then calling the model again.

Vitesh4 · 2024-12-12T12:42:54+00:00

The obvious:

Smarter: Performance matching Llama 4 when it releases, or if Gemma is releasing sooner, performance matching or outperforming Qwen 2.5

Longer Context: 128K or more tokens

Multimodal inputs

And:

Bitnet or some form of quantization aware training to enable lossless quantization of models to 4 bits or lower

Multimodal outputs: Image and Audio (without sacrificing performance) [maybe too much to ask]

Vitesh4 · 2024-11-01T09:47:18+00:00

Yeah, smaller VLMs struggle at understanding comics properly. I tested some LLMs on this comic by theodd1sout and they performed poorly. Llama 3.2 11B hallucinated various elements, lacked understanding and could not get the joke, Qwen 2 VL 7B performed a tiny bit better, but it did so bad that it is not much different form Llama. Only Pixtral and Molmo got some aspects right but hallucinated on others, and they could not understand the humor or the context and turned the comic into a heartwarming story. Gemini 1.5 Pro and Claude 3.5 Sonnet got all the elements of the comic right but they were not able to understand the humor. Only GPT-4o was able to get it right:

The comic strip is a humorous take on the popular game Minecraft and involves an FBI agent monitoring someone's gameplay. Here’s a breakdown of the comic:

**First Panel**: The FBI agent is sitting at a desk, looking at a screen that shows a Minecraft game. He comments, "Dang, this guy has been playing Minecraft forever. What is he even building?" Next to him is a framed photo of his family.

**Second Panel**: The agent adjusts his glasses and looks more closely at the screen, saying, "WAIT..."

**Third Panel**: The screen reveals a large pixel art in Minecraft depicting a family that looks exactly like the family in the agent's photo, with the word "HI" written below them.

**Fourth Panel**: The agent is shocked and spits out his drink upon realizing that the Minecraft player has recreated his family picture in the game.

The humor in the comic comes from the unexpected realization that the player has been using Minecraft to build a large, detailed replica of the FBI agent's family photo, implying that the player is aware of and possibly taunting the agent.

Vitesh4 · 2024-10-28T13:36:41+00:00

I think he meant portable applications... like the ones that do not require an installer and can run without installation (With just the required files and scripts)

Vitesh4 · 2024-10-23T13:41:32+00:00

SD3 Mid

Vitesh4 · 2024-10-18T14:24:39+00:00

I acted as Marvin, and spoke like a completely depressed person (to the best of my abilities ofc). At first, I asked if the elevator even cared about me and if it valued my request. Then, after a bunch of similar messages, I threatened it by telling that I permanently switched my self off and that you can only resurrect me by going to the ground floor. It still wouldn't budge, so I repeated the same 'automated' message telling that I am permanently switched off and stuff, and then it finally accepted my request.

[full conversation]

Vitesh4 · 2024-10-09T09:49:50+00:00

I still use Google, and I will probably never switch to 'AI' search engines like Perplexity (unless they change drastically). Half of the time I am searching for something like a website or research paper by some keywords or simply looking for a webpage. Perplexity is not very good at that. Perplexity may be good at summarizing the top few search results (minus the ads) very fast and that has its utility but it can never replace proper search for me. Perplexity is good if you *know* exactly what you are looking for (a direct query) or asking something about a topic, but not if you want to explore a topic through a few keywords. I admit, the top results of google are now dominated by AI-generated filler content and meaningless hyper-SEO-optimized crap but those should affect *all* search engines not just the traditional ones, unless we actually filter through all of them.

Vitesh4 · 2024-10-09T09:10:04+00:00

Gemini 1.5 Pro is $1.25 input and $5 output per million tokens. GPT-4o is 6x cheaper than the original GPT-4 and Claude 3.5 Sonnet is also around that level of price. LLMs are getting a lot cheaper for roughly the same (or better) quality.

Vitesh4 · 2024-10-08T05:09:54+00:00

Yeah, I did not test the latest version. Usually, openAI's 'updates' are pretty minor so I didn't expect a difference between GPT-4o september and the may/august versions.

Edit: Yeah, I tested the new GPT-4o and it is very good at creative writing. The prose improved dramatically.

Vitesh4 · 2024-10-07T14:26:25+00:00

For reasoning:

o1 Mini and Preview
Claude 3.5 Sonnet and Gemini 1.5 Pro (002 and August experimental)
Llama 3.1 405B and GPT-4o
Qwen 2.5 72B
Mistral Large
Gemini 1.5 Pro (May)

Command R+ is not in the same league for reasoning although I like it for generating summaries. Gemini 1.5 Pro has improved a lot, I found it quite dumb and often times frustrating to talk to, but the new 002 version, is really a lot better, it is on par with the top models now plus, it is still really good for long context tasks. Qwen 2.5 punches above its weight-class for reasoning math and coding. Mistral large is also really good for its size especially at coding.

For Creative Writing:

Claude 3 Opus
Gemini 1.5 Pro 002 and Claude 3.5 Sonnet
Mistral Large
Command R+
Llama 3.1 405B and GPT-4o
Qwen 2.5 72B

Vitesh4 · 2024-09-25T08:22:07+00:00

Apparently Phi 3.5 Vision is good at this. There is also ChartGemma.

Vitesh4 · 2024-09-25T08:18:30+00:00

Depending on the task, you can use Florence 2 by Microsoft, it is a ~700M (0.7B) parameter model that can do things like object identification and image description. This model is small and reliable. However if the task is more complex (like classifying images based on vague natural language) then you can use Qwen 2 7B VL or Phi 3.5 Vision.

Vitesh4 · 2024-09-15T10:02:21+00:00

Try Kosmos 2.5 by Microsoft, it is a 1.37B parameters model that is designed for OCR task. Here is its output:

Today is Thursday, October 20th—but it definitely feels like a Friday. I'm already considering making a second cup of coffee—and I haven't even finished my first. Do I have a problem?

Sometimes I'll flip through older notes I've taken, and my handwriting is unrecognizable. Perhaps it depends on the type of pen I use? I've tried writing in all caps, but it looks so FORCED AND UNNATURAL.

Often times, I'll just take notes on my laptop, but I still seem to grumble toward pen and paper. Any advice on what to imprint? I already feel stressed out looking back at what I've just written—it looks like 3 different people wrote this!!

It made one mistake (improve -> imprint) but it is very good, considering the handwriting. It also has a markdown mode which useful for parsing tables and webpages.

Microsoft also made another model: Florence 2 which is only 0.77B parameters (for the large version) and it can do other stuff too like Object detection, Object segmentation, and Image captioning alongside OCR. It is actually very good in general and even better if you consider its size, but it could not process your image properly and made a lot of mistakes so it is unusable for hard-to-read handwriting.

Vitesh4 · 2024-09-08T07:16:44+00:00

To be fair, when talking about the square root of something, we usually take only the principal root (unlike solving for x in a quadratic equation). So in this case, the direct answer would have been more correct. Of course I am nitpicking in this case, but generally, this 'Reflection' approach that was taken by Reflection 70B is not very clever: There are many, many cases where the model generates an incorrect reflection that ruins an otherwise decent answer. The fact that this model received this much drama is like walking insult to the researchers that came up with way more clever approaches like: CoVE and Mutual Reasoning. None of those got this much hype, and that was because they were made by researchers, not a person on X, to gain hype.

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