But it does have a new helper -- LSP.
But it occurs to me that Claude Code is maybe best at small repos that were themselves built with claude code. I would have to think about whether I have experienced exactly this... But it makes sense because without semantic search, claude code just greps based on keywords and folder names it deems standard.
Minevra Jobs, Minevra Quizzes, Minevra Consulting... What do these products and services have in common. For one, they have no paying customers (though we do have our first few users and pro bono consulting clients!). But, no, what I wanted to say was: It names product lines like Anthropic seems to: Note that the way anthropic names its products and original standard: Claude Code and Claude Skills. It uses such generic and commonplace words that you are forced to say the full thing. You must say Claude. And you must capitalize both because otherwise people will misunderstand what you are referring to. Their naming strategy makes you repeat their main brand - Claude - and makes you write it in a well formed way. Though this trend is not the case for their models -- opus, haiku, sonnet ... so they might not have thought about it the way I mentioned, given that it is only a partial trend.
I love these types of things.
Classics:
By Suzanne Vega. Due to this song, she has been called "The mother of the MP3". It was used by the engineers that worked on mp3 compression the perfect "I was ready to fine-tune my compression algorithm...somewhere down the corridor, a radio was playing 'Tom's Diner.' I was electrified. I knew it would be nearly impossible to compress this warm a cappella voice."[9] It is also a nice short length, which is ideal when engineers wanted to test it
I am sitting in the morning at the diner on the corner
I am waiting at the counter for the man to pour the coffee
And he fills it only halfway, and before I even argue
He is looking out the window at somebody coming in
`curl -o suzanne_vega.mp3 https://museumofportablesound.com/wp-content/uploads/2020/07/mopswaxcylindersuzannevega.mp3`
Here is a video that I like, because it is 2 and a half minutes long, and because it has no words. https://www.youtube.com/watch?v=4-ISLpKhQJI
AI Specific:
Pelican on a Bicycle
Will Smith Pasta Video
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I
I have a few of my own:
Like all followers of the main AI bloggers, I saw Simon Willisons endorsement of DSPy. It reminds me of MLFlow. Early on in my pivot to AI, I recall many a langchain + Mlflow tutorial. It is premature. Evals. Evals. Have i ever actually created a good evals system. Nope. A few things needed within DSPy. First of all, it has to be able to better handle complete garbage output from the student models (And the teacher models, for that matter -- not to mention the judge reflection models). What i mean is suppose I am creating an AI pipeline that does something. The example doesnt matter but lets say it is categorizing emails in bulk. I should be able to use Dspy to come up with a prompt that works through an optimizer like MIProV2. And if i pick a model that at DSPy's first attempt at a prompt for the task returns something, The optimizer should not only be expecting it to not be the correct category but structurally, not at all something that is what the system should be able to handle. When evaluating this library, I guess the problem i see is that it seems useful in very unusual, specific circumstances. It is not robust enough t needs easy ways to have the optimizer first solve an easy problem before solving the harder problem. it needs easy ways to start with smart models and move to dumb models, and vice versa. The dspy signatures dont solve problems. They are for simple tasks.
The coolest thing about this stream of consiousness article was that when writing it had the links filled out . Kind of neat.
Mantegna argues that copyright law is the wrong tool for governing generative AI, especially for text, because copyright was never designed to regulate systems that learn statistical structures rather than copy expressive works. Attempts to stretch copyright to cover AI training and outputs risk (1) failing to meaningfully protect authors, (2) entrenching corporate intermediaries, (3) destabilizing fair use, (4) shrinking the public domain, and (5) degrading AI quality through defensive data practices. She insists that ethical harms (consent, attribution, labor displacement) are real—but conflating “unethical” with “illegal” produces bad law and ultimately worsens outcomes for creators and society.
