As I posted before, the unemployment rate for recent college graduates has been significantly higher than the labor market taken as a whole across all workers, according to Federal Reserve Bank of New York data.
Anyone that doesn’t think that rampant over hiring during Covid and AI is actively reshaping the entire programming industry is fooling themselves. Especially since so many people are calling software a ‘cooked’ field now.
Back in April this video came up in my feed – and the whole series are some of the best talks on the state of engineering software in the age of AI; but this particular talk by Matt Pocock was particularly good.
Square One TV was a math oriented education show during the late 80’s and early 90’s on PBS. I loved catching the show whenever I could after school. It was a show full of short sketches – one of the regular ones was Mathnet – a mathematical parody of Dragnet.
In one episode – they hit 2 of my favorite things: math and spooky mysteries. They parodied Agatha Christie’s “And Then There Were None” in the episode “The Case of the Mystery Weekend“.
Fish Audio S2 Pro is one of (if not the) best text-to-speech solutions. Getting it installed locally and working, however, isn’t so straightforward on Windows 10. There are at least 2 different ways to get this working. One of which is to download/run
Ensure you’re logged into Huggingface, and you should see the ‘Run Locally’ option Go up in the link
Ensure Docker is installed on the Windows desktop and WSL support is enabled in the Docker options.
Open a WSL session running Ubuntu 24.04 or similar.
Enter the docker command:
docker run -it -p 7860:7860 --platform=linux/amd64 --gpus all \
registry.hf.space/artificialguybr-fish-s2-pro-zero:latest python app.py
6. You’ll see the docker container download along with the models and start up:
(base) me@DESKTOP:/mnt/c/fish-audio-s2$ docker run -it -p 7860:7860 --platform=linux/amd64 --gpus all registry.hf.space/artificialguybr-fish-s2-pro-zero:latest python app.py
Cloning into 'fish-speech'…
remote: Enumerating objects: 6605, done.
remote: Counting objects: 100% (1088/1088), done.
remote: Compressing objects: 100% (292/292), done.
remote: Total 6605 (delta 905), reused 796 (delta 796), pack-reused 5517 (from 2)
Receiving objects: 100% (6605/6605), 28.21 MiB | 10.42 MiB/s, done.
Resolving deltas: 100% (4328/4328), done.
Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.
Fetching 13 files: 100%|████████████████████████████████████████████████████████████████| 13/13 [02:19<00:00, 10.72s/it]Fetching 13 files: 62%|████████████████████████████████████████ | 8/13 [02:19<01:13, 14.78s/itYou are using a model of type fish_qwen3_omni to instantiate a model of type `. This may be expected if you are loading a checkpoint that shares a subset of the architecture (e.g., loading asam2_video checkpoint intoSam2Model), but is otherwise not supported and can yield errors. Please verify that the checkpoint is compatible with the model you are instantiating. Download complete: : 11.0GB [02:19, 79.0MB/s] 2026-07-03 18:59:16.787 | INFO | fish_speech.models.text2semantic.llama:from_pretrained:504 - Injected Semantic IDs into Config: 151678-155773 2026-07-03 18:59:16.787 | INFO | fish_speech.models.text2semantic.llama:from_pretrained:520 - Loading model from /home/user/.cache/huggingface/hub/models--fishaudio--s2-pro/snapshots/1de9996b6be38b745688de084d87a5633f714e4e, config: DualARModelArgs(model_type='dual_ar', vocab_size=155776, n_layer=36, n_head=32, dim=2560, intermediate_size=9728, n_local_heads=8, head_dim=128, rope_base=1000000, norm_eps=1e-06, max_seq_len=32768, dropout=0.0, tie_word_embeddings=True, attention_qkv_bias=False, attention_o_bias=False, attention_qk_norm=True, codebook_size=4096, num_codebooks=10, semantic_begin_id=151678, semantic_end_id=155773, use_gradient_checkpointing=True, initializer_range=0.01976423537605237, is_reward_model=False, scale_codebook_embeddings=True, audio_embed_dim=2560, n_fast_layer=4, fast_dim=2560, fast_n_head=32, fast_n_local_heads=8, fast_head_dim=128, fast_intermediate_size=9728, fast_attention_qkv_bias=False, fast_attention_qk_norm=False, fast_attention_o_bias=False, norm_fastlayer_input=True) 2026-07-03 18:59:46.228 | INFO | fish_speech.models.text2semantic.llama:from_pretrained:552 - Loading sharded safetensors weights 2026-07-03 18:59:46.717 | INFO | fish_speech.models.text2semantic.llama:from_pretrained:588 - Model weights loaded - Status: <All keys matched successfully> 2026-07-03 18:59:48.707 | INFO | fish_speech.models.text2semantic.inference:init_model:366 - Restored model from checkpoint 2026-07-03 18:59:48.708 | INFO | fish_speech.models.text2semantic.inference:init_model:371 - Using DualARTransformer/usr/local/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py:144: FutureWarning:torch.nn.utils.weight_normis deprecated in favor oftorch.nn.utils.parametrizations.weight_norm`.
WeightNorm.apply(module, name, dim)
* Running on local URL: http://0.0.0.0:7860, with SSR ⚡ (experimental, to disable set ssr_mode=False in launch())
* To create a public link, set share=True in launch().
It feels like the American industrial and manufacturing landscape has been left behind in the digitalization revolution. But recent changes demonstrated in both Ukraine and a small robot company in Pittsburg may be pointing to the coming revolution.
Gecko is a scrappy robot company founded by a college senior that saw workers spending hours putting up dangerous scaffolding to check and fix pipes in a power plant. What if he could build robots to scale around the pipes and check and fix them? It turns out they could – and it is revolutionizing maintenance in refineries and energy infrastructure across the country. The robots now no longer can crawl and inspect/repair – but they can create new digital maps of a plant’s infrastructure. Inspections are taking orders of magnitude less time. Plants can have their actual systems instantly remapped/re-diagrammed instead of relying on out of date schematics.
It turns out someone else has the same problem: the military. Systems like Gecko allow the navy to build and repair ships faster. Gecko’s small robots reduced nuclear submarine inspection times from 300 hours to just 6 hours. But this revolution is bigger than just repair of existing systems.
The war in Ukraine is now being won not by ‘exquisite’, complex, and exorbitantly expensive weapons systems. Instead, it’s being won by swarms of low-cost drones. Million dollar tanks are being disabled by $200 drones carrying explosives. Military experts around the world are watching Ukraine and re-thinking everything. Even before the Ukraine war, the US navy was already started the move from big capital ships to cheaper, faster to build modular ships.
Anduril wrote a paper in 2024 that goes a step further. They claim that these low-cost robotics and AI systems are making existing weapons systems vulnerable and outdated. Modern, 1st world weapon systems are too complex and hugely expensive. They take too long to make in quantities required for something beyond a short war. What is needed is to establish fast, cheap, commercial manufacturing of these systems that can be built and deployed rapidly. This is leading to a revolution of automated manufacturing.
The future is not going to belong to giant, expensive, monolithic systems – but fast, easy to build, capable systems built in large numbers.
After 18 months, graphics features like multi-frame generation are working really well, but only a handful of games support path tracing to use it. How few? 7 titles. Even worse, the Steam list of top 100 most anticipated titles – only 1 has path tracing that allows multi-frame generation.
And yet, the price of 5090’s continues to remain sky high and selling better than ever; but almost certainly because of the 32gb of RAM for AI usage. I know that’s why I got my 5090.
With the huge implosing of the game development world, the rise of faster and cheaper to develop indie titles, its no wonder top-end graphics is taking a back seat.
UI trends are called ‘morphisms’. We’ve gone through glass-morphism, squircle-morphism, and lots of other morphisms. Morphism changes by UI designers chasing the latest cool-looking thing is why the web site that was working just fine for you gets re-designed every 6 months.
Malewicz talks about some of these trends and the design languages used to describe and classify them.
4090 graphics cards came with 24gb of memory. As the AI boom sucked up everything on the market, some modders (mostly in China) learned you could upgrade the VRAM on a 4090 from 24GB to 48GB. These mods were often done poorly and had high failure rates; but people were desperate for more VRAM for AI uses. Soon, better quality modders come online – including these guys in Michigan that seem to be reputable and stand behind their modding.
Still, it’s an odd world. The world is quickly aligning to standards of fitting their models into 32, 96, 128, or 256 of ram – so these 48gb variants might not be as interesting as they once were. Also add to the fact that the processor in the 4090’s isn’t nearly as fast as a 5090, and while expensive, 5090’s are now generally available. Still, someone locally was selling one of these cards recently. (Update: still hasn’t sold in 6 weeks)
