@MrStickman

A viral fashion reel is a proven ad. Swap your products in — ~$10 a video, $200 pack ≈ 20 videos, no subscription.

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Edward Hasbrouck

Large Language Models (LLMs) have become increasingly effective at generating code when task descriptions are clear and precise. Yet, in practice, user-provided task descriptions are often ambiguous, incomplete, or contradictory, leaving critical aspects of the intended program behavior underspecified. In such cases, multiple behaviorally distinct interpretations may satisfy the description equally well, yet semantically differ in ways that matter/affect the user intent. A natural expectation, often assumed by researchers, is that prompt underspecification manifests as incoherence: When asked multiple times, an LLM produces multiple semantically distinct implementations reflecting the ambiguity of the task description. In this paper, we challenge this assumption. We find that LLMs frequently collapse onto a single incorrect interpretation of the task description, consistently generating coherent but behaviorally misaligned code. We term this failure mode detrimental semantic collapse and find that it affects over 10% of tasks in MBPP, 3% in HumanEval, and 32% of LiveCodeBench, all benchmarks assumed to be well-specified. By deliberately injecting underspecification issues in the benchmark prompts, the rate rises to over 5 times, exposing a fundamental blind spot in disambiguation and correctness estimation techniques that rely on incoherence as a proxy for prompt underspecification.

On buffalomaxxing

Implementing dark mode with minimal JavaScript and standard CSS

Two hundred and fifty years after the signing of the Declaration of Independence, a new commercial asks: What if the Founding Fathers had access to Google Workspace?

Open source isn't all freedom and community goodwill. Here are the real problems, controversies, and risks nobody talks about enough.

Decision-making has a real cognitive cost. Discover why accumulating decisions throughout the day leaves you with no energy for anything else.

I make the philosophical case for tax fairness and taxing billionaire wealth. I welcome an argument on the merits for this new social contract.

A letter from Three Rules Company on why agents move the economic constraint from doing the work to deciding what should be done.

Mailgun Routes is a filter-expression engine for inbound mail: match_recipient rules fire forward() and store() actions, and the parsed message arrives as form-encoded fields you decode and verify. MailKite (which we build) drops the rule DSL: point an address or catch-all at a webhook and the message arrives as one JSON payload with decoded text/html, SPF/DKIM/DMARC results, and signed attachment URLs. A fair comparison with working code for both sides.

TokenDiet reads your repo, finds where your code overpays for LLM calls, and opens a reviewed Pull Request that cuts the bill — with before/after proof the quality holds. You click merge.