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The newish SBCL parallel gc is fantastic and uses no additional memory during gc

Its interesting that they don't even mention the key to human intelligence, concepts, in this list.


Incredible! Is this ready for at scale production use?


No. I have yet to propose the patches formally. The SBCL maintainers are reviewing the high-level proposal (on my blog) first. You can try the implementation, however. There's a pointer to the repo/branch on my blog. I need to build a proper benchmarking framework and publish some real numbers that people can reproduce before I am confident enough to submit the patches for review.

Let me know if you try it out. I would love some feedback (via github)


Would it work with the parallel GC feature?


I haven't really looked into it, but I'm hopeful it can be made to work.


Systems Twin Intelligence, where a Pod represents the full space-time information for part of the world, using Solid Protocol: https://graphmetrix.com/trinpod-server

The W3C Linked Web Storage (LWS) working group is transforming Solid into a web standard: https://www.w3.org/groups/wg/lws/


A key error is there literally are no where close to billions of concepts. Its a misunderstanding of what a concept is as used by us humans. There are an unlimited number of instances and entities, but the concepts we use to think about them is very limited by comparison.


The 18,000 people who lost their jobs may disagree.


California created nearly one in five of the nation’s new jobs - https://www.gov.ca.gov/2024/08/16/california-created-nearly-... - August 16th, 2024

> California’s job expansion has continued into its 51st month, with Governor Gavin Newsom announcing that the state created 21,100 new jobs in July. Fast food jobs also continued to rise, exceeding 750,000 jobs for the first time in California history.

> “Our steady, consistent job growth in recent months highlights the strength of California’s economy – still the 5th largest in the entire world. Just this year, the state has created 126,500 jobs – solid growth by any measure.”

This is slightly out of date; California is now the world’s fourth largest economy as of April 2025, passing Japan. I assert the data shows the state does not have a job creation issue.

https://www.gov.ca.gov/2025/04/23/california-is-now-the-4th-...


These 18,000 are most likely employed somewhere else at 20-25% wage increase. Note that a different study didn't see a rise in unemployment: https://www.nbcbayarea.com/investigations/california-minimum... which means that these people affected actually got a better living standard.


This group is well known for bias, over and over through the years. Nothing they report should be taken at face value.

"A considerable amount of financial support for the Center comes from labor unions: According to federal reports, over the last 15 years it has received nearly $1.2 million in labor funding."

"The IRLE’s highest-profile researcher is Michael Reich, who co-chairs its Center on Wage and Employment Dynamics. Reich made a name for himself at a young age co-founding the Union for Radical Political Economics, with the stated goal of supporting “public ownership of production and a government-planned economy.”"

https://us.fundsforngos.org/news/nonprofit-accuses-uc-berkel... https://epionline.org/release/biased-uc-berkeley-research-te... https://epionline.org/release/biased-uc-berkeley-research-te...


And the nation is currently ruled by somebody who orders rewriting past papers on climate science:

https://phys.org/news/2025-08-rewrite-national-climate.html

So why are we taking at face value that study from nber which is increasingly staffed by Trump loyalists?


Our[1] solution to that is to use a hierarchical semantic systems approach such that you can give access to a subsystem or entire biological systems.

[1] https://graphmetrix.com/trinpod-server


Actually, here is an example enterprise internet scale solid solid server: https://graphmetrix.com/trinpod-server


Thats a terrible ontology (the relations) - needs to be much lower level to understand anything important.


The current demo shows a simplified view, but the tool can handle much more granular relationships. I have some glioblastoma and pancreatic cancer networks with protein-protein interactions, phosphorylation events, and pathway cross-talk that show the lower-level detail. The challenge is balancing accessibility with scientific rigor.


hey you made it to the front page but looks like its down


It's back up, thanks for checking it out!


Does it hallucinate with the LLM being used?


Sometimes. I just fed the huggingface demo an image containing some rather improbable details [1] and it OCRed "Page 1000000000000" with one extra trailing zero.

Honestly I was expecting the opposite - a repetition penalty to kick in having repeated zero too many times, resulting in too few zeros - but apparently not. So you might want to steer clear of this model if your document has a trillion pages.

Other than that, it did a solid job - I've certainly seen worse attempts to OCR a table.

[1] https://imgur.com/a/8rJeHf8


The base model is Qwen2.5-VL-3B and the announcement says a limitation is "Model can suffer from hallucination"


Seems a bit scary that the "source" text from the pdfs could actually be hallucinated.


Given that input is image and not raw pdf, its not completely unexpected


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