gavinray

I spent years in prison and homeless, lol

Being denied housing or employment was not just a regular occurrence but the norm for me for a large portion of my life

Unfortunately they have to actually execute and have identical semantics to the target source

I recall seeing a project based on DuckDB parser and engine which emulated dialects for one of these but because the sortinh and other behavior is different it wasn't usable

I think we're on the same page:

  > "a just society should generally tolerate the intolerant, reserving self-preserving actions for only when intolerance poses a concrete threat to liberty and stability."

Freedom of speech and self-expression applies to just that: words

If intolerance or hate poses a physical threat, or in any way infringes on the liberty and personal autonomy of others, a line has been crossed and must be swiftly dealt with.

Sure, it's a dick move. But it's important to distinguish "shouldn't" from "legally can't".

Should you be racist? No. Should you call someone fat and ugly? No.

But personal liberty and freedom require we allow people to make choices and have opinions we don't personally agree with.

I'm curious to hear other people's perspectives on why they think they should be illegal?

For example: Suppose you hire someone to help you move furniture.

You can decide you dont like the color of their shoes and tell them to kick rocks.

You dont owe anyone a job, and labor for pay is a contractual agreement, nothing more.

Because those tests are the only ones that prove the connector is working as intended

Our connectors receive a generic query IR from a Datafusion-based engine

Each connector is then responsible for faithfully translating the query IR into the semantically equivalent query for the underlying data source

For a SQL DB this would be a SQL query, for MongoDB a JSON predicate query, for csv/json files it would be an in-memory predicate engine, etc

Running the generated query against the target data source and asserting that this doesn't throw an error and that the records returned are both correct and in the right order is the true test

In American English, poem is pronounced as rhyming with "tome"

I could imagine in British English it might be a two-syllable "poh-ehm" though

I'll publish a few of my convos

1. Sleep debts effect on muscle size and strength gain

https://share.gemini.google/Gvdb5Q02INrl

2. Fish oil and extra cellular vesicles

https://share.gemini.google/fem9EBCe4mfy

3. Pharmacology question about human-equivalent dose

https://share.gemini.google/RWNYJ2eiNjcU

Gemini, for whatever reason, has consistently been the best for explaining scientific concepts for me.

Especially with the recent addition of graphic visualization widgets

It manages to strike the perfect balance between depth and style of prose that other Frontier models just can't quite seem to match

  > "Nix is lazy, so the emitted code wraps an expression in nix.thunk(() => ...)"

Why not use Generator functions rather than manually wrapping everything?

On-demand value materialization is what Generators are for

  > pgVolvra records every row change on the tables you choose. For each change, it keeps the row as it was, and the row as it became, along with who made the change, when, and in which transaction. When something goes wrong, it uses that record to put the affected rows back, and it touches nothing else.

You've essentially replicated the WAL s functionality

I'd imagine you could actually build this reusing a lot of the existing WAL code if you were Inclined

The English version is found in the second half of the page

Second line is 7 syllables though?

  > "add_or_sub_big: Qwenputer scores 97.5%, the same as with a Python tool, and 87.5% for base Qwen."

More than one of the tests in the post show sub 100% accuracy with the embedded computer.

I'm sorry but I'm not sure I understand how a calculator that isn't 100% accurate is useful?

If this actually works, this is such a huge boon. Let me share why:

I work on connectors to various datasources, mostly SQL DB's

The most useful tests are the integration tests, sending actual queries to the source

For most DB's we have a giant Docker Compose which runs a local instance

But things like Snowflake, Databricks, Redshift, Athena suck to test because we need an external hosted instance

I would kill to be able to run test versions of all of these locally

I used Gemini 4 Argon (High) from the Android app with the original image and following prompt:

  > "Edit this image to color code the benchmarks scores using a gradient scheme that is immediately obvious"

Here's the direct Gemini chat link:

https://share.gemini.google/g6LC7spTH0cO

  > are they essentially holding back sort of unweighted parameters built in to the model architecture which they can then populate with new context during 'sleep' and then using the techniques outlined in the 'dream' section to reinforce...

Sort of. As the model is interacted with, short term "memories" are accumulated in a sort of buffer/cache. During "sleep" these "memories" are given a ranking/score, and the useful bits are integrated into the weights.

They say the weight update can be done by computing a transform where the end state is the old weights with the addition of the useful semantic knowledge contained in memories. Rather than just additively chucking the entire memory concepts into weights. That's pretty neat.

The subtext for the memory architecture diagram sums the whole thing up pretty tidily I think:

  > Input streams from the active phase are stored in short-term memory before entering the sleep cycle, which bifurcates into Consolidation (transferring fragile short-term tokens into expanded long-term parametric memory using low-rank structures and MoE) and Dreaming (autonomous self-improvement via synthetic routing with random experts and RL refinement).

Benchmark scores recolored for readability:

https://i.imgur.com/IDFGuJU.jpeg

Spending up to 4.9% of their wealth to avoid a 5% tax is the fiscally logical thing to do

Oof. Well, in any case: https://sci-hub.su/https://doi.org/10.1086/601720

  > Bio is one of our sparsest regions 

Ah, that explains the topic distribution on the page. Can't fault it if the data just isn't there.

It does seem like one of the best uses of AI -- digging through existing literature to make novel connections/ideas.

Curious if you've heard of Don Swanson's "Public Knowledge Problem" paper from the 1980's, which was roughly this line of thought?

I'm an engineer by trade, but spend quite a bit of time in pharmacology and molecular biology research in my free time.

One of the proposal papers caught my eye:

https://lacuna.tiptreesystems.com/hypothesis/why-receptor-hu...

  > " ... a ligand that binds tightly and stays bound a long time should produce a strong intracellular signal, a weak binder a faint one."

When a ligand binds to the ligand binding pocket, the contact points it makes with the proteins inside (called "cofactors") and the strength of each contact point (measured in van der Waals force) modulate the distinct transcriptional effect.

This is called "conformation": the shape it takes in the ligand binding pocket.

So it is not necessarily residence time and overall binding affinity that modulate transcriptional effect.

A very good example of this is the androgen Trestolone/MENT, which is an order of magnitude more potent than Testosterone but displays similar relative binding affinity for the androgen receptor:

https://en.wikipedia.org/wiki/Trestolone

This potency is due to the specific conformational shape it takes within the AR binding pocket.

Family has all the traditional cardiac symptom clusters (hypertension, elevated lipids/triglycerides, diabetes) and family history of heart attacks.

My biomarkers were good, but combined with family history and a past history of drug abuse (primarily stimulants), it's sort of a wonder it didn't happen sooner.

The MI itself was unusual. When they placed the stent in my heart, the coronary angiogram was able to image my arteries. I had almost no measurable stenosis in my arteries: a minor 30% in one inferior artery.

I was at the gym when it happened, the prevailing theory is that I had the one artery with vulnerable plaque and it ruptured from arterial pressure.

So on the one hand, it sucks that it happened at all. But on the other hand, I'll take "you didn't die and the rest of your heart looks clear" with no complaints.

This is a micro-implementation of what research calls a "Neural Computer", in which the entire UI itself is generative and driven by user actions.

Neural Computers provide the illusion you are interacting with an underlying operating system/application, but are actually a recurring (user input) -> (predicted pixel output) loop.

In theory, it would be impossible to tell a perfect "Neural" computer apart from a "real" one. Which is kind of a wild thought.

https://arxiv.org/abs/2604.06425

  > who already have a great estimator for 10-year heart disease mortality risk in the coronary calcium score

Heart attack at age 29 survivor here.

CAC scores only show calcified plaque, not vulnerable plaque, which is the sort likely to rupture and cause an event.

You can go get a scan and receive a CAC score of 0, walk away thinking you're perfectly healthy, and then die tomorrow because the scan couldn't detect the massive amounts of non-calcified plaque lining your arteries.

The only non-invasive way to measure vulnerable/soft plaque is via CCTA

See, for example, "Atherosclerotic Plaque in Patients with Zero Calcium Score at Coronary Computed Tomography Angiography"

It measured nearly 1 in 10 patients with a 0 CAC score as having vulnerable plaque:

https://pmc.ncbi.nlm.nih.gov/articles/PMC5967134/

  > "The frequency of atherosclerotic plaque in the coronary arteries in 367 patients with zero CS was 9.3% (34 individuals)"

I didn't leave anything off, this is a direct paste from OpenAI's docs

https://developers.openai.com/api/docs?lang=curl

The code examples from their own website show the use of cURL and this CLI are roughly as verbose. Not sure I understand the need for maintaining a CLI.

    openai responses create \
      --model gpt-6-astra \
      --input "Write a short bedtime story about a unicorn." \
      --raw-output \
      --transform 'output.#(type=="message").content.0.text'

Versus

    curl https://api.openai.com/v1/responses \
      -H "Content-Type: application/json" \
      -H "Authorization: Bearer $OPENAI_API_KEY" \
      -d '{
        "model": "gpt-6-astra",
        "input": "Write a short bedtime story about a unicorn."
      }'

  > Having kicked opiates cold turkey in the past, I felt nostalgic for three simple days of traditional nausea, sweatiness, and bone-deep ache.

I am one of few people stupid enough to have, at one point, been addicted to and subsequently withdrawn from, almost everything but alcohol.

There is no way to explain the hell on earth that is benzo/GABA-ergic drug withdrawal, and I would not wish it on my worst enemy. It is an order of magnitude more intense than opiate withdrawals, and the timeline is weeks/months rather than days.

Like the author, I managed to quit via a year-long Ashton protocol taper.

The irony is that benzos were the one class of drugs I wasn't abusing for recreation. I was using mega-doses of Etizolam to cope with PTSD flashbacks, and only discovered I had become physically addicted after flushing it all and winding up in the ER.

I am a highschool dropout, so I am perhaps not the best judge of this, but I later worked on-campus at a major university and befriended many students, most of whom struggled to find employment after graduation.

I would counter that education, at least in the USA, does not adequately prepare one for life, the real world, and effectively finding employment.

What ACTUALLY mattered in my life turned out to be:

1. Who you know, and by proxy, what kind of a person you are

2. The value of "just showing up", every day

3. How important personal relationships/networking are, and just how SMALL the world is for how vast its size

Knowledge and skills are things you can sort of just pick up as you go along, but being given any kind of opportunity in the first place is not quite so easy.

Smaller than Kokoro yet sounds just as good, impressive.

Makes a great option for mobile on-device or browser app TTS.