activescott's Notes

The 2019 Trump–Ukraine political scandal arose primarily from the discovery of U.S. president Donald Trump's attempts to coerce Ukraine into investigating his political rival Joe Biden and thus potentially damage Biden's campaign for the 2020 Democratic Party presidential nomination. Trump enlisted surrogates in and outside his administration, including personal lawyer Rudy Giuliani and Attorney General William Barr, to pressure Ukraine and other governments to cooperate in supporting and legitimizing the Biden–Ukraine conspiracy theory and other conspiracy theories concerning U.S. politics.[1][2][3][4][5] Trump blocked payment of a congressionally-mandated $400 million military aid package, in an attempt to obtain quid pro quo cooperation from Ukrainian president Volodymyr Zelenskyy. Contacts were established between the White House and government of Ukraine, culminating in a phone call between Trump and Zelenskyy on July 25, 2019.

The White House corroborated allegations raised by the whistleblower. A transcript of the Trump–Zelenskyy call confirmed Trump requested investigations into Joe Biden and his son Hunter Biden, as well as a conspiracy theory involving a Democratic National Committee server, while urging Zelenskyy to work with Giuliani and Barr on this.

Former acting chief of staff Mick Mulvaney said one reason why Trump withheld aid to Ukraine was Ukrainian "corruption related to the DNC server", referring to a debunked theory that Ukrainians framed Russia for hacking into the DNC system.[12] Trump has publicly urged Ukraine and China to investigate the Bidens.[13] The Trump administration's top diplomat to Ukraine, Bill Taylor, testified he was told aid to Ukraine and a Trump–Zelenskyy White House meeting were conditional on Zelenskyy announcing investigations into the Bidens and alleged Ukrainian interference in the 2016 U.S. elections.

Trump was impeached on charges of abuse of power and obstruction of Congress,[18] but was acquitted by the Senate.

Instead of using equity to fund sales and marketing spend, General Catalyst provides structured growth capital tied directly to customer acquisition and recurring revenue. The goal is to let startups like Chainguard preserve equity while using outcome-based financing to scale efficiently.

Microsoft is running one of the largest corporate espionage operations in modern history.

Every time any of LinkedIn’s one billion users visits linkedin.com, hidden code searches their computer for installed software, collects the results, and transmits them to LinkedIn’s servers and to third-party companies including an American-Israeli cybersecurity firm.

The user is never asked. Never told. LinkedIn’s privacy policy does not mention it.

Because LinkedIn knows each user’s real name, employer, and job title, it is not searching anonymous visitors. It is searching identified people at identified companies. Millions of companies. Every day. All over the world. This is illegal and potentially a criminal offense in every jurisdiction we have examined.

LinkedIn loads an invisible tracking element from HUMAN Security (formerly PerimeterX), an American-Israeli cybersecurity firm, zero pixels wide, hidden off-screen, that sets cookies on your browser without your knowledge. A separate fingerprinting script runs from LinkedIn’s own servers. A third script from Google executes silently on every page load. All of it encrypted. None of it disclosed.

Every time you open LinkedIn in a Chrome-based browser, LinkedIn’s JavaScript executes a silent scan of your installed browser extensions. The scan probes for thousands of specific extensions by ID, collects the results, encrypts them, and transmits them to LinkedIn’s servers. The entire process happens in the background. There is no consent dialog, no notification, no mention of it in LinkedIn’s privacy policy.

This page documents exactly how the system works, with line references and code excerpts from LinkedIn’s production JavaScript bundle.

See https://browsergate.eu/how-it-works/

Rough country tubular bumper with hoop and skid plate. ACC worked in the front grille and the skid plate sets under the shrould that lets in air

Woodpecker uses docker containers to execute pipeline steps.

.woodpecker/build.yaml

steps:

• name: build image: debian:stable-slim commands:
  • echo building
  • sleep 5

.woodpecker/deploy.yaml

steps:

• name: deploy image: debian:stable-slim commands:
  • echo deploying

depends_on:

• lint
• build
• test

.woodpecker/test.yaml

steps:

• name: test image: debian:stable-slim commands:
  • echo testing
  • sleep 5

depends_on:

• build

.woodpecker/lint.yaml

steps:

• name: lint image: debian:stable-slim commands:
  • echo linting
  • sleep 5

Codeberg is a non-profit, community-led effort that provides services to free and open-source projects, such as Git hosting (using Forgejo), Pages, CI/CD and a Weblate instance.

nanochat is the simplest experimental harness for training LLMs. It is designed to run on a single GPU node, the code is minimal/hackable, and it covers all major LLM stages including tokenization, pretraining, finetuning, evaluation, inference, and a chat UI. For example, you can train your own GPT-2 capability LLM (which cost ~$43,000 to train in 2019) for only $48 (~2 hours of 8XH100 GPU node) and then talk to it in a familiar ChatGPT-like web UI. On a spot instance, the total cost can be closer to ~$15. More generally, nanochat is configured out of the box to train an entire miniseries of compute-optimal models by setting one single complexity dial: --depth, the number of layers in the GPT transformer model (GPT-2 capability happens to be approximately depth 26). All other hyperparameters (the width of the transformer, number of heads, learning rate adjustments, training horizons, weight decays, ...) are calculated automatically in an optimal way.

While I do not have a technical background, I am very fortunate to live in the era of Andrej Karpathy's nanochat, a very simple harness for training LLMs, and Claude Code, a tool for those who, like me, know just enough Python to know how to break things but not enough to know how to fix them. I am not a machine learning expert or AI lab with gobs of money. My only co-worker can't speak English and spends most of the day sleeping on my lap or cleaning her fur. I'm just a man with a laptop, Claude Code, and a dream of the 1890's.

happened to stumble across the British Library Books dataset, a dataset of digitized books dating from between 1500 and 1900

This left me with 28,035 books, or roughly 2.93 billion tokes for pretraining data

I settled on using a Vast.ai instance that used PyTorch. Renting a NVIDIA H-100 GPU ran me between $1.50 and $2.00 per hour.

Using Claude Code, I trained a BPE tokenizer from scratch on the corpus, ending up with a vocabulary of about 32,000 words. Using a modern tokenizer wouldn't capture the unique Victorian morphology and orthography of the corpus.

However, my method for dealing with most other problems was to nicely ask Claude Code to fix them once identified, and it was able to without too many issues.

the final pre-trained model came out to about 340 million parameters, and had a final validation bpb of 0.973. The pretraining process took about five hours on-chip, and cost maybe $35. I had my pretrained model, trained in 6496 steps

but it lacked the spark of intellect that would allow such a creation to engage in discourse. I needed to develop some kind of dataset to teach it the art of conversation

Fortunately, I already had a corpus of 28,000 books, so I set Claude Code to work extracting dialogue pairs from the books. I ultimately ended up with 190,000 or so training pairs. So, when one person said X, I had an example of another person saying Y. The art of conversation!

I needed to rewrite these corpus pairs so that the input question was in modern argot. This task was more than I could possibly do by hand, so Claude Code suggested, helpfully, that I used Claude Haiku to rewrite the input questions

Totally useless. This model—which I will call Model #1—had learned to emit Victorian-sounding novelistic gobbledygook in response to user inputs, not how to answer user queries. I had assumed my pre-written QA pairs were good enough, when they clearly weren't. It was back to the drawing board

I decided to start including fully-synthetic data in the mix. Working with Claude Code, I asked it to write a script that would direct another LLM to write a .jsonl file of fully-synthetic scenes. In them, a user greeted the LLM, queried about Victorian topics, and the LLM responded in a period-appropriate manner for 2-4 turns. We

Or $496.66 all together.

Blocks, Elements and Modifiers Block Standalone entity that is meaningful on its own.

Examples header, container, menu, checkbox, input

Element A part of a block that has no standalone meaning and is semantically tied to its block.

Examples menu item, list item, checkbox caption, header title

Modifier A flag on a block or element. Use them to change appearance or behavior.

Examples disabled, highlighted, checked, fixed, size big, color yellow

uhashring implements consistent hashing in pure Python.

Consistent hashing is mostly used on distributed systems/caches/databases as this avoid the total reshuffling of your key-node mappings when adding or removing a node in your ring (called continuum on libketama). More information and details about this can be found in the literature section.

This full featured implementation offers:

a lot of convenient methods to use your consistent hash ring in real world applications. simple integration with other libs such as memcache through monkey patching. a full ketama compatibility if you need to use it (see important mention below). all the missing functions in the libketama C python binding (which is not even available on pypi) for ketama users. possibility to use your own weight and hash functions if you don't care about the ketama compatibility. instance-oriented usage so you can use your consistent hash ring object directly in your code (see advanced usage). native pypy support, since this is a pure python library. tests of implementation, key distribution and ketama compatibility.

Solo founders and small teams are shipping real products faster than ever.

Launching is the easy part. Once you're live, the hard part starts -- growth, monetization, security, stability — that's where most apps stall out or die.

We've scaled products to millions of users and hundreds of millions in revenue. Now we're building the tools we wish we had when we started.

Think of us as the cofounder you wish you had.

Argo Workflows is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes.

Define workflows where each step in the workflow is a container. Model multi-step workflows as a sequence of tasks or capture the dependencies between tasks using a graph (DAG). Easily run compute intensive jobs for machine learning or data processing in a fraction of the time using Argo Workflows on Kubernetes. Run CI/CD pipelines natively on Kubernetes without configuring complex software development products.