Reddit sues AI search engine Perplexity for scraping its data - Financial Times
<a href="https://news.google.com/rss/articles/CBMihAFBVV95cUxNd2U5Y3E2UXNVb044WWdZcnJiZFNGVVdkVVRTeEhlMzFzSFdIM2d6VjhfMWRvY0F5N3JaaHIxM1dSUjFQRjhqQWZPRF8tMl9ubHdCWWs0WEtaaGNIdjVhb2NSRi1KRUt2QjNrSG84djNydF9aZDNLVnljdnhjM2MxTjFCVWI?oc=5" target="_blank">Reddit sues AI search engine Perplexity for scraping its data</a> <font color="#6f6f6f">Financial Times</font>
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Meta-Programming and Macro capabilities of various languages
Meta-programming = the broad idea of “programs that manipulate or generate programs” . It can happen at runtime (reflection) or compile-time (macros). Macros = one specific style of meta-programming, usually tied to transforming syntax at compile time (in a pre-processor or AST-transformer). It takes a piece of code as input and replaces it with another piece of code as output, often based on patterns or parameters. Rule‑based transformation: A macro is specified as a pattern (e.g., a template, an AST pattern, or token pattern) plus a replacement that is generated when that pattern is matched. Expansion, not function call: Macro use is not a runtime call; the macro is expanded before execution, so the final code is the result of replacing the macro invocation with its generated code. Here
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I've been experimenting with Bonsai 8B — PrismML's true 1-bit model (every weight is literally 0 or 1, not ternary like BitNet). I realized that since weights are bits, the diff between two model behaviors is just a XOR mask. So I built a tool that searches for sparse XOR patches that modify model behavior. The basic idea: flip a row of weights, check if the model got better at the target task without breaking anything else, keep or revert. The set of accepted flips is the patch. What it does on held-out prompts the search never saw: Without patch: d/dx [x^7 + x] = 0 ✗ With patch: d/dx [x^7 + x] = 7x^6 + 1 ✓ Without patch: Is 113 prime? No, 113 is not prime ✗ With patch: Is 113 prime? Yes, 113 is a prime number ✓ 93 row flips. 0.007% of weights. ~1 KB. Zero inference overhead — the patched
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