Instead of asking the model: "Here's this codebase, report any vulnerability." you ask. "Here's this codebase, report any vulnerability in module\main.c".

The model can still explore references and other files inside the codebase, but you start over a new context/session for each file in the codebase.

I upload edited gameplay vods of twitch streams on youtube, and use whisper-large-v3 to provide subtitles for accessibility reasons (youtube's own auto-subtitles suck, tho they've been getting better).

My checklist for a good ASR model for my use case is:

1. Have timestamp support.

2. Support overlapping speakers.

3. Accurate transcripts that don't coalesce half words/interrupted sentences.

4. Support non verbal stuff like [coughs], [groans], [laughs], [sighs], etc.

5. Allow context injection of non-trivial sizes (10k+ words)

1 is obvious because without it we can't have subtitles. Force alignment fails too often.

2 is crucial for real world scenarios because in the real world people talk over each other all the time, in my case it's a streamer talking over gameplay audio, or when the streamer has guests over. When 2 people speak the transcript either ignores one of them, or in the worst case, both of them.

3 and 4 are an accessibility thing, if you're deaf or hard of hearing having a more literal transcript of what's being said conveys better how the speaker is speaking. If all subtitles are properly "spell-checked" then it's clear your model is overfit to the benchmarks.

5 Is not a requirement per se, but more of a nice to have. In my use cause the streamer is often reading stream chat so feeding the model the list of users that recently talked, recent chat messages, text on screen, etc. Would make for more accurate transcripts.

I've tried many models, and the closest that fulfill my needs are LLM style models on top of forced alignment. It's too slow, so I've been sticky with whisper because with whisperx I can get a transcript in 5 minutes with just a single command.

One thing all these models do (including whisper) is just omit full sentences, it's the worst thing a model can do.

https://i.imgur.com/c0a3vUy.png

DLSS is also AI and people like it.

People don't like framegen because the manufacturers are not being honest about it and using it for deceptive hype marketing. Anyone with a brain knows that it introduces latency and is only useful if you're already 40+ FPS, we also know that companies will use it to pad benchmarks. NVIDIA themselves said that the 5070 had 4090 performance because it supports framegen.

This is obviously not true, you can use real data and common sense.

Just look up a similar sized open weights model on openrouter and compare the prices. You'll note the similar sized model is often much cheaper than what anthropic/openai provide.

Example: Let's compare claude 4 models with deepseek. Claude 4 is ~400B params so it's best to compare with something like deepseek V3 which is 680B params.

Even if we compare the cheapest claude model to the most expensive deepseek provider we have claude charging $1/M for input and $5/M for output, while deepseek providers charge $0.4/M and $1.2/M, a fifth of the price, you can get it as cheap as $.27 input $0.4 output.

As you can see, even if we skew things overly in favor of claude, the story is clear, claude token prices are much higher than they could've been. The difference in prices is because anthropic also needs to pay for training costs, while openrouter providers just need to worry on making serving models profitable. Deepseek is also not as capable as claude which also puts down pressure on the prices.

There's still a chance that anthropic/openai models are losing money on inference, if for example they're somehow much larger than expected, the 400B param number is not official, just speculative from how it performs, this is only taking into account API prices, subscriptions and free user will of course skew the real profitability numbers, etc.

Price sources:

https://openrouter.ai/deepseek/deepseek-v3.2-speciale

https://claude.com/pricing#api

According to this[1] site visual studio had a 35.6% marketshare, tied at #1 with notepad++.

[1] https://asterisk.dynevor.org/editor-dominance.html

https://www.youtube.com/watch?v=GMLB_BxyRJ4 - 10 AIs Play Mafia: Vigilante Edition

https://www.youtube.com/watch?v=OwyUGkoLgwY - 1 Human vs 10 AIs Mafia

I don't know what nightshade is supposed to do, but the fact that it doesn't affect the synthetic labeling of data at all leads me to believe image model trainers will have close to 0 consideration of what it does when training new models.

I don't disagree with the choice, but seeing how things turned out I can't just help but look at the greener grass on the other side.

Looking at performance numbers is important regardless if it's python, assembly or HDL. If you don't understand why your code is slow you can always look at how many cycles things take and learn to understand how code works at a deeper level, as you mature as a programmer things will become obvious, but going through the learning process and having references like these will help you to get there sooner, seeing the performance numbers and asking why some things take much longer—or sometimes why they take the exact same time—is the perfect opportunity to learn.

Early in my python career I had a python script that found duplicate files across my disks, the first iteration of the script was extremely slow, optimizing the script went through several iterations as I learned how to optimize at various levels. None of them required me to use C. I just used caching, learned to enumerate all files on disk fast, and used sets instead of lists. The end result was that doing subsequent runs made my script run in 10 seconds instead of 15 minutes. Maybe implementing in C would make it run in 1 second, but if I had just assumed my script was slow because of python then I would've spent hours doing it in C only to go from 15 minutes to 14 minutes and 51 seconds.

There's an argument to be made that it would be useful to see C numbers next to the python ones, but for the same reason people don't just tell you to just use an FPGA instead of using C, it's also rude to say python is the wrong tool when often it isn't.