[1] https://www.morningstar.com/stocks/anthropics-leaked-financi...

[2] https://www.wheresyoured.at/anthropics-profitability-swindle...

[3] https://fortune.com/2026/06/16/openai-financials-leaked-loss...

[4] https://www.forbes.com/sites/paulocarvao/2025/12/06/why-open...

I think the power savings and efficiencies (that the large companies have also benefited from) have come from 2 areas:

1. open source and local AI enthusiasts -- think things like llama.cpp, quantization, etc.

2. Chinese labs and other smaller/research companies like Mistral that are using constrained hardware -- see the various advancements in the various models to reduce compute complexity such as mixture of experts [1], sharing key/value data between a group of layers, etc.

[1] Though the original idea for mixture of experts comes from a 1991 research paper (https://huggingface.co/blog/moe), so maybe a third area is research from Universities, etc.

GPUs are designed to process a large number of calculations at once (so they can process triangles in 3D graphics). This makes them good at ML applications as they can process many of the matrix calculations at once. A 4090 has 16,384 CUDA cores (general compute ALUs) and 512 Tensor cores (dedicated matrix compute ALUs); a 5090 has 21,760 CUDA and 680 Tensor cores.

The other issue when training models (and running larger models) is the amount of VRAM (or RAM for CPUs) available. GPUs are limited in this aspect, whereas CPUs can have a lot higher memory. This affects things like batch size and the size of model that can be trained or fine-tuned.

https://unsloth.ai has guides for how to fine-tune existing models like Qwen 3.8 27B, memory requirements, etc.

https://medium.com/@kailaspsudheer/the-transformers-arithmet... has some information on training a base model. A 7B llama model is estimated at taking ~34GB memory for inference at F32, but was observed requiring 96GB memory when training (for the model weights, gradients, activations, and optimizer states).

Note: you can reduce the memory required for training by recomputing the gradients, at a cost of performance/time. You can also do other tricks like performing a QLoRA/LoRA pass on the model then merging that into the model to create a checkpoint.

I don't know what sized model you could train on 64GB/128GB RAM via a CPU.

It's a number of things like:

1. not searching for exact quotes/text, only words in the query;

2. including other lexical forms in the results (e.g. searching for things like "how do you open the run dialog" returns results for running (the sport/exercise) as well as run (an application));

3. including synonyms, typos, etc.

It seems like while each change makes sense and can improve results in some cases, over time the combined effect has made Google's search near unusable.

The behaviour/output of an LLM is not like that. Ask an LLM to create a dashboard to show games by genre and it will generate different results with each run, and each model/model version produces wildly different results.

1. the linux kernel has patches created by and security vulnerabilities identified by Claude/Anthropic;

2. same with other software like SQLite and rsync.

1. a particle/anti-particle pair is created at the event horizon;

2. the particle is on the outside edge of the event horizon, so "escapes" the black hole;

3. the anti-particle is on the inside edge of the horizon, so decreases the size of the black hole due to particle/anti-particle annihilation (with a corresponding particle on the inside of the black hole).

If you're after quality then Qwen3 TTS is a very good model esp. if you take some effort to craft a voice file. It is slow, so isn't practical for real-time voices (like assistants). It can also occasionally switch to a different voice to the one provided, so you may want to break up the text being processed.

I've not yet tried other recent/recentish models.

If you are after performance then two options from older models are:

1. flite with a HTS (Hidden Markov Model) voice like cmu_us_rms (male) or cmu_us_slt (female);

2. espeak/espeak-ng with an MBROLA (an Overlapped Add model) voice (mb-us1, mb-de5-en, etc.).

Alternatively, you could try using Qwen3 TTS or over voice changing model with the CMU Arctic (http://www.festvox.org/cmu_arctic/) voice data which includes audio for the rms and slt voices among others.

If you're feeling adventurous you could also try fine tuning one of the TTS models on that data to create a custom voice, though the data is likely to be in the training data for the voices, so using an audio sample may be sufficient depending on the TTS model.

The YouTube channel https://www.youtube.com/@doranchak/videos by David Oranchak, one of the people who solved the Z340 cypher, has some more details on this as well as how the Z340 cypher was cracked.

I started with writing a correct recursive descent parser. I then extended it to detect, report, and recover from common syntax errors as I encountered them so that the parser is robust. And adding a parser test case for each of these (e.g. one test for each branch through an EBNF construction).

Some examples are:

1. missing keywords when the keyword can be detected from the current context (e.g. missing semicolon at the end of a statement);

2. using the wrong token (e.g. `:` instead of `::` in a C++ namespace qualified name);

3. detecting and ignoring whitespace in a whitespace-sensitive qualification (e.g. in XML QNames);

4. keeping in the prolog state (where functions are defined) when there are errors so that functions after the error don't get lost;

5. lexing incomplete literals like `10e` so they can be handled as integers in the parser and emitting an error for them.

When I tried using agents for something as a test it immediately started doing things instead of being part of a conversation. I want to be actively involved in the process, not sit back and let AI agents write/generate stuff that I'd have to/end up rewriting/modifying anyway when it goes against the design I have in mind.

The other thing I don't like about agents is their ability to run any command [1]. That seems like a nightmare w.r.t. the potential for leaking secrets (signing/access keys, etc.) or doing damage (deleting files, database tables, etc.).

[1] You can set the option to review every command it runs, but you're then just hand-holding the agent.