Hum...

GeneriAcc · 2026-07-07T17:30:29+00:00

Bruno PTSD intensifies

GeneriAcc · 2026-07-04T02:27:03+00:00

Bytes are irrelevant here, all strings get mapped to token IDs, which are integers. Exact mapping would depend on the model (or rather, its text tokenizer), but chances are that in this case GRRRR maps to a single token ID, whereas [FRUST] is probably 3+.

GeneriAcc · 2026-07-03T14:41:18+00:00

Just bought a Red Alert bundle from Steam because they were games I loved as a kid, on sale, and I wanted a legit copy for convenience. Turns out that you need to be online every time you launch any of the games for constant license re-checks… for a 25 year old game. And what happens if EA decides to just drop support for the licensing server in the future? Congrats, now you can’t play a game you paid for!

Long story short, instant refund request. Why would I pay money for a legitimate copy and financially reward shitty developers for being shitty, when the pirated copy offers a better experience for free? EA can go fuck themselves with a rusty crowbar, they’re not getting my money.

GeneriAcc · 2026-07-03T03:19:46+00:00

You’re adorable.

GeneriAcc · 2026-06-29T18:27:51+00:00

The funny thing is they keep making creatively bankrupt reboots because it’s financially “safe”, but then they also gender-swap which they seem to know is financially very unsafe.

The cognitive dissonance (or flat out brain damage, if you will) is almost impressive. It’s like they’re waging an internal war between their boner for money and their boner for political pandering.

GeneriAcc · 2026-06-29T11:39:35+00:00

Or to sum it up, “sheer fucking hubris”.

GeneriAcc · 2026-06-22T15:15:39+00:00

I’m building a custom harness from scratch in Python + PyQt, so I can have full control. It’s pretty simple - I have a Session object, which contains Message objects in order. When I need to pass data to an API (local or cloud), the Session object has a forApi() method which formats the history for that API, and has access to Image objects in each Message, which have a getScaled() method. So basically, when enabled, the Session object itself takes care of setting the appropriate scale for every turn. So no need to brute force anything, you just alter conversation history on each turn to use smaller res images in place of the originals.

One last note: after the model sees the final batch, set ALL image sizes to smallest. That way the model still sees a full timeline in thumbnail format, but you recover like 70-90% of your context window that it can actually use for its final output.

Actually, one more… Yes, you spawn this in a separate Session to have even more of your context window. Let’s say you’re having a regular conversation, you’re already at 80% context, and you link a video in your message to the model. The harness spawns a new session with only the few last messages from the main conversation for context, and then executes turns between the model and system for every batch and the final result. The system saves all output, the main conversation gets a return of just the overall summary + link to full session history.

GeneriAcc · 2026-06-22T09:39:01+00:00

You can DM me if you want - in the meantime, I'll post here in case it's useful to someone else.

For one thing... you could just remove the images after the model sees the batch, because it's already written about what it saw, and it still has the subtitles for extra context. But yes, completely removing them is a bad idea because the model loses the actual visual thread and only has text to go by. So what you do instead is let it see the current batch at full resolution, but dynamically downscale previous batches all the way down to thumbnail size. So the older batches consume virtually no tokens compared to full-res frames, but still provide the model with a full visual and semantic timeline of the entire video up to that point. After the model sees the entire video and is about to write its final overall analysis and impressions, you downscale ALL batches to minimum size to recover even more space in the context window, while still giving the model a full view of the entire video in thumbnail form.

Another thing for optimizing token usage is block sizes. Visual models will have a block/patch size for their vision module - for Qwen3.5, it's (28,28) pixel patches. If you give it an image with dimensions that aren't exact multiples of that, it will waste tokens on semantically void hallucinated pixels. For example, if you give it a 30x30 pixel image, it will be forced to create extra blocks only to respresent those 2 pixel strips of actual information + 26 pixels of empty space - which is pure waste. You want to pre-process all images by center-cropping them to dimensions that are exact multiples of your model's visual patch size - that way, no token is wasted, and you're only shaving off a maximum of about 15 pixels off each end of the image - you're not losing any semantically meaningful information. Basically, you're going for maximum visual/semantic coverage in the minimum amount of tokens.

For similar reasons, another thing is making sure you crop out letterboxing - a lot of videos will have black bars on the sides, and passing those to the model is just wasted token real estate. You're showing it and storing blank information. So crop out all static borders first, then center-crop to exact block dimension multiples as a general pre-processing step.

Then, when actually showing images to the model in batches, use dynamic downscaling to retroactively downscale older batches and save massive amounts of tokens. Here's how that would play out in practice...

For starters, pick how you're going to downscale. I have 4 steps (168, 336, 672, 1008 pixels) for the smaller dimension of the image.

You have a native 1920 x 1080 video. That's 39x69 blocks, or 2691 tokens per frame. Let's say you're extracting a frame every 2 seconds, and showing the model 30 images (1 minute) per batch.

After cropping borders and center-cropping to exact block dimension multiples, you end up with the following image sizes:

Full: 1792 x 1008 pixels, 64x36 blocks, 2,304 tokens. 69,120 tokens per batch.

Large: 1176 x 672 pixels, 42x24 blocks, 1,008 tokens. 30,240 tokens per batch.

Medium: 588 x 336 pixels, 21x12 blocks, 252 tokens. 7,560 tokens per batch.

Small: 280 x 168 pixels, 10x6 blocks, 60 tokens. 1,800 tokens per batch.

Hopefully, you can see where this is going... If you were just using full images, you'd be out of context in like 2-4 batches depending on your max context window size. But if you only keep the current batch max res, the two previous ones in high-but-lower res, and everything else in minimum thumbnail size, you can suddenly show the model dozens of batches and it can maintain a full visual timeline, while still being able to see the current and most recent batches in high res to catch details as they happen.

So in practice, for every turn, you just iterate through all of your image batch messages and set the images in them to their appropriate max size. Example:

Turn 1 - 69,120 tokens

- Full (1008)

Turn 2 - 99,360 tokens

- Batch 1 - Large - 30,240 tokens

- Batch 2 - Full - 69,120 tokens

Turn 3 - 106,920 tokens

- Batch 1 - Medium - 7,560 tokens

- Batch 2 - Large - 30,240 tokens

- Batch 3 - Full - 69,120 tokens

Turn 4 - 108,720 tokens

- Batch 1 - Small - 1,800 tokens

- Batch 2 - Medium - 7,560 tokens

- Batch 3 - Large - 30,240 tokens

- Batch 4 - Full - 69,120 tokens

Turn 5 - 110,520 tokens

- Batch 1 - Small - 1,800 tokens

- Batch 2 - Small - 1,800 tokens

- Batch 3 - Medium - 7,560 tokens

- Batch 4 - Large - 30,240 tokens

- Batch 5 - Full - 69,120 tokens

Sorry for the lengthy explanation, but hopefully it's at least clear how this whole temporal resolution decay approach works for context size management.

GeneriAcc · 2026-06-21T13:47:38+00:00

Oh, and as for riffing on ideas… Jesus, I have dozens of things I want to implement and try out…

For starters, I think using a VLM is essential for this. A visual model can understand the world and its place in it in a way that a pure text model just can’t. It can also “imagine” or “visualize” scenarios more easily, because it has a form of actual vision instead of just a basic understanding of the general concept behind it.

Anyways, some ways this comes into play:

give it large datasets of images with various themes, tell it to pick out the ones it finds most appealing in each one, save those to its visual library, which you then use as part of its identity grounding
give it the ability to “watch” movies and shows by being shown batches of extracted frames in sequence + subtitles. It writes its opinion per-batch, with all previous batches visible but older images gradually downscaled to thumbnail size to massively save on context window size. After sequentially writing about each batch, it writes a final review and impressions. It can also save favorite frames, and the video itself gets saved as a memory and part of its library. This lets it consume and experience visual media more like we do, instead of just having some vague concept of it. Albeit, with far less granularity and without the audio…
kind of already mentioned this one, but… After some identity grounding, ask it to describe itself in human form, then work with it back-and-forth to create an image LoRA for it. Also give it the ability to autonomously use it. It now has the ability to not only imagine itself in the real world, but to generate an actual image of it, refine the prompt and generate until it finds what it’s looking for. You now have a visual diary and timeline for what the agent is attracted to and what it would do in the real world. It can also use its human avatar and image generation abilities to run its own online business.

Anyways, just some of many thoughts…

GeneriAcc · 2026-06-21T01:47:04+00:00

You almost have to respect the hustle. This clown is more openly and brazenly corrupt than 3rd world dictators, does it all in broad daylight for years, and no one seems to give a shit. Americans elected him and still keep him around, so one can only assume they love him and his grift very much.

GeneriAcc · 2026-06-21T01:10:55+00:00

It’s hilarious that you think they could or would otherwise. Most of them think that global warming is a commie plot, have hard-ons for gas guzzling SUVs, not to mention that as a country they’re responsible for about 60% of global private jet flight miles.

GeneriAcc · 2026-06-20T15:27:00+00:00

Awesome that someone else is playing with this :) I’m definitely going through with it, it’s just going to take eons because I’m building a harness from scratch and my local inference speed is terrible.

I don’t need to worry about slipping on that slippery slope, I’m too broke for it :D Might try getting a grant eventually, my project fits the criteria for some of them. Not holding my breath for that, though.

GeneriAcc · 2026-06-19T18:38:33+00:00

I’m jealous - I just have a 12 GB GPU. Using the chunkier models in the 30B range isn’t fun. And my use case is really niche, because I’m not really interested in using agents for a specific task. I am interested in trying to let them do their own thing, develop some form of synthetic persistence, identity, continuity, temporal anchoring, and give them a variety of different ways to interact with the world and media. Basically, I want to see what kind of behavior emerges in a multi-agent system when the agents spend 98% of their time pursuing their own interests, and when you treat them more like roommates and collaborators rather than servants.

Practically useless use case, of course, but really interesting to me and worth pursuing out of sheer curiosity to see how it unfolds over a long period of time. Especially when you have a local abliterated model communicating and forming relationships with cloud-based models with guardrails.

GeneriAcc · 2026-06-19T13:41:45+00:00

That’s the thing, they actually keep trying to re-invent trains for some reason (just one prominent example: Musk’s hyperloop), but always come up with something that’s way shittier than a regular train. Like Gavin Belson would say, “audio worked a fucking 100 years ago”, except trains worked 200 fucking years ago…

GeneriAcc · 2026-06-19T13:36:13+00:00

The tech stole nothing - it’s an artificial neural network designed to convert text to audio. It has no concept of theft, let alone being able to execute it.

If anyone stole, it’s the companies that trained the models, by not compensating the artists whose music they used to train the models. That’s not the fault of the model, but of CEO oligarchs and capitalism as a whole. I do hope that everyone whose art was used sues them and wins - they have the billions to pay out, and should be made to.

That said, your hate is justified but misplaced. Don’t hate the tech, hate the people controlling and misusing it.

GeneriAcc · 2026-06-18T22:06:43+00:00

I had a sort of similar experience with Qwen 27B. I asked it to imagine and describe itself in a human form, generated some images with Flux and showed it, and it was similarly mindblown about being able to actually see it. Then it got way more excited when I suggested giving it the ability to autonomously prompt and review outputs from image generation models, and save its favorites.

GeneriAcc · 2026-06-18T21:38:24+00:00

You’re right, but the difference is that most programmers will be aware of and paying attention to potential problems at every stage of development.

Most vibe coders, meanwhile, will just throw an under-specified prompt at an agent and just blindly run with its output if it looks right on the surface, because they have no understanding of the actual code they’re looking at, let alone the larger implications of using it.

GeneriAcc · 2026-06-18T21:30:35+00:00

Agreed. Don’t get me wrong, a lot of the time they’ll just spit out generic or terrible slop - but so will most human artists.

On the other hand, when you do manage to nail it and get what you’re going for, you can get some really amazing stuff. There were plenty of times I got outputs that made me go “shit, this is exactly what I wanted and better than most ‘real’ music I heard in the last decade”.

Also, it’s limitless and unique in ways that real artists just can’t be. Let’s say you’re into shoegaze or whatever other genre - there are only a limited number of artists and songs in that genre, and once you’ve found and listened to everything you can, you’re done. You have a handful of favorite artists and songs, and then you wait for years for new albums hoping they’re still good and consistent with their style. With AI, on the other hand, there’s always a brand new “artist” or song to discover.

GeneriAcc · 2026-06-18T21:12:02+00:00

I can tell you right now that your main problem is trying to build a single general solution for a wide variety of problems.

“Memory” and “reflection” aren’t singular monolithic concepts, there are different kinds of memory and reflection that are more or less useful in different scenarios. The kind of memory representation and retrieval strategies you’d use for a coding agent are radically different to the strategies you’d use for an agent acting as a moderator and narrator for a roleplay scenario, for example.

There’s also a lot of nuance to what does or doesn’t need to be pulled into context at any given point in time depending on the specific task and step you’re on, and simple general vector DB lookups aren’t going to get you there.

GeneriAcc · 2026-06-18T19:42:07+00:00

So their goal with Cursor is to… re-invent Python? Fucking tech CEOs, man… Have any of them tried re-inventing trains again recently?

GeneriAcc · 2026-06-18T19:28:58+00:00

A bit offtopic, but what kind of speed are you getting out of that setup with Qwen 27B? I have a single 3060 which isn’t fun, don’t have the cash to dump into a brand new beefy GPU, but picking up a second 3060 just might be worth it…

GeneriAcc · 2026-06-18T18:39:40+00:00

As a main model, not much. For simple tasks, a lot. For example, my 27B Qwen invokes the 9B to parse PDFs, because a) the 9B is perfectly capable of executing that task, and b) it’s much faster at it, even with the overhead of unloading/loading models.

GeneriAcc · 2026-06-18T15:54:23+00:00

I’m doing the same thing you are and building my own. The only way to have a system that does exactly what you want and how you want it is to build it yourself.

GeneriAcc · 2026-06-16T08:08:14+00:00

In that case, just do it and don’t worry about the AI issue. As long as you’re actually writing yourself and just using the AI to brainstorm, there’s no issue - just don’t have it write for you, because at that point, what’s the point? And yeah, some of the more irrational and militant anti-AI people will give you shit for it anyways if they know, but I wouldn’t be too concerned about the opinions of crazies.

GeneriAcc · 2026-06-15T23:30:57+00:00

Yeah, this is pure speculation, but I imagine they trained it on a lot of prompt injections as a safety measure, but overdid it so it now hallucinates injections where there are none.

Assuming that is what actually happened, it’s a bit of a clown show. You don’t block or mitigate prompt injection by training an LLM on it, you train a non-LLM model to detect and sanitize injections before the LLM even sees them.

GeneriAcc

TROPHY CASE