activescott's Notes

While on-time mortgage payments are known to increase one’s credit score, many renters don’t have any history of credit. Esusu reports on time rent payments to credit bureaus so renters can build their scores. Over 50 million Americans lack a credit history with the three major credit bureaus: Experian, Equifax and TransUnion.

continually updating a model's parameters with new data, often leads to “catastrophic forgetting” (CF), where learning new tasks sacrifices proficiency on old tasks. Researchers traditionally combat CF through architectural tweaks or better optimization rules. However, for too long, we have treated the model's architecture (the network structure) and the optimization algorithm (the training rule) as two separate things, which prevents us from achieving a truly unified, efficient learning system.

By defining an update frequency rate, i.e., how often each component's weights are adjusted, we can order these interconnected optimization problems into "levels." This ordered set forms the heart of the Nested Learning paradigm.

We observed that many standard optimizers rely on simple dot-product similarity (a measure of how alike two vectors are by calculating the sum of the products of their corresponding components) whose update doesn't account for how different data samples relate to each other. By changing the underlying objective of the optimizer to a more standard loss metric, such as L2 regression loss (a common loss function in regression tasks that quantifies the error by summing the squares of the differences between predicted and true values), we derive new formulations for core concepts like momentum, making them more resilient to imperfect data.

In a standard Transformer, the sequence model acts as a short-term memory, holding the immediate context, while the feedforward neural networks act as long-term memory, storing pre-training knowledge. The Nested Learning paradigm extends this concept into what we call a “continuum memory system” (CMS), where memory is seen as a spectrum of modules, each updating at a different, specific frequency rate. This creates a much richer and more effective memory system for continual learning.

"Nested Learning" extends the traditional two-tier memory concept of "attention layers" (short-term memory / context window) and "feed-forward network layers" (long term memory) into a spectrum of modules that update at different rates, some very frequently (like attention), some rarely (like FFNs), and others at various points in between.

That’s the New York Times, CNN, CNBC, NBC, and the Guardian all confidently telling their readers that Trump can magically override state sovereignty with a memo. These aren’t fringe blogs—these are supposedly serious news organizations with actual editors who apparently skipped the day they taught how the federal government works. They have failed the most simple journalistic test of “don’t print lies in the newspaper.”

Executive orders aren’t laws. They’re memos. Fancy, official memos that tell federal employees how to do their jobs, but memos nonetheless. You want to change what states can and can’t do? You need this little thing called “Congress” to pass this other little thing called “legislation.” Trump can’t just declare state laws invalid any more than he can declare himself emperor of Mars.

But here’s where this gets kinda funny (in a stupid way): that “interstate commerce” language could backfire spectacularly. Almost all state laws trying to regulate the internet—from child safety laws to age verification to the various attempts at content moderation laws—might run afoul of the dormant commerce clause by attempting to regulate interstate commerce if what the admin here claims is true (it’s not really true, but if the Supreme Court buys it…). Courts had been hesitant to use this nuclear option because it would essentially wipe out the entire patchwork of state internet regulation that’s been building for years, and a few decades of work in other areas that hasn’t really been challenged. Also, because they’ve mostly been able to invalidate those laws using the simple and straightforward First Amendment.

The real story here isn’t that Trump signed some groundbreaking AI policy—it’s that the entire mainstream media apparatus completely failed to understand the most basic principles of American government. Executive orders aren’t magic spells that override federalism. They’re memos.

Saturday's attack happened on the Palestinian-controlled side of the so-called Yellow Line which has divided Gaza since an unstable US-led ceasefire came into effect on 10 October.

Whalebrew creates aliases for Docker images so you can run them as if they were native commands. It's like Homebrew, but with Docker images.

Average Price of Electricity to Ultimate Customers

The measles, mumps and rubella vaccine is safe and provides 97% protection against the disease after two doses. Most children in the U.S. are required to get the shot to attend school. But vaccination rates have declined as more parents waive the shots or have fallen behind on recommended vaccination schedules.

Starting today, self-service access to Reddit’s public data API will be closed. Anyone looking to build with Reddit data, whether you’re a developer, researcher, or moderator, will need to request approval before gaining access.

WTF?

Sphinx is cray cray:

Inside Python object description directives, reStructuredText field lists with these fields are recognized and formatted nicely:

param, parameter, arg, argument, key, keyword: Description of a parameter.

type: Type of a parameter. Creates a link if possible.

raises, raise, except, exception: That (and when) a specific exception is raised.

var, ivar, cvar: Description of a variable.

vartype: Type of a variable. Creates a link if possible.

returns, return: Description of the return value.

rtype: Return type. Creates a link if possible.

meta: Add metadata to description of the python object. The metadata will not be shown on output document. For example, :meta private: indicates the python object is private member. It is used in sphinx.ext.autodoc for filtering members.

Trump said at the roundtable, adding "this money would not be possible without tariffs."

Another way to spend the money that’s already been spent many times over.

MLPerf Client is a benchmark developed collaboratively at MLCommons to evaluate the performance of large language models (LLMs) and other AI workloads on personal computers–from laptops and desktops to workstations. By simulating real-world AI tasks it provides clear metrics for understanding how well systems handle generative AI workloads. The MLPerf Client working group intends for this benchmark to drive innovation and foster competition, ensuring that PCs can meet the challenges of the AI-powered future.

Common Expression Language (CEL) is an expression language that’s fast, portable, and safe to execute in performance-critical applications. CEL is designed to be embedded in an application, with application-specific extensions, and is ideal for extending declarative configurations that your applications might already use.

We introduce the Berkeley Function Calling Leaderboard (BFCL), the first comprehensive and executable function call evaluation dedicated to assessing Large Language Models' (LLMs) ability to invoke functions. Unlike previous evaluations, BFCL accounts for various forms of function calls, diverse scenarios, and executability.

In 2024, SWE-bench & SWE-agent helped kickstart the coding agent revolution.

We now ask: What if SWE-agent was 100x smaller, and still worked nearly as well?

mini is for

Researchers who want to benchmark, fine-tune or RL without assumptions, bloat, or surprises
Developers who like their tools like their scripts: short, sharp, and readable
Engineers who want something trivial to sandbox & to deploy anywhere

Here's some details:

Minimal: Just 100 lines of python (+100 total for env, model, script) — no fancy dependencies!
Powerful: Resolves >74% of GitHub issues in the SWE-bench verified benchmark (leaderboard).
Convenient: Comes with UIs that turn this into your daily dev swiss army knife!
Deployable: In addition to local envs, you can use docker, podman, singularity, apptainer, and more
Tested: Codecov
Cutting edge: Built by the Princeton & Stanford team behind SWE-bench and SWE-agent.