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Situational Awareness Bets $400 Million on Stealth Chip Startup After Crash

AI-battered hedge fund Situational Awareness made a big bet this week in Source Foundry, a private company aiming to reinvent the way chips are manufactured.

Situational Awareness Bets $400 Million on Stealth Chip Startup After Crash

We haven't written up this one. WSJ Markets has the full story — the link below goes straight to it.

Read the original at wsj.com →

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Can You Trust a Model’s Stated Reasoning?

A chain of thought looks like an explanation, and that resemblance is doing a lot of unearned work. The published tests ask a narrower and more answerable question: if you change what actually drove…

  • Chain of thought can be correct yet unfaithful to actual reasoning.
  • Biased training data can alter model predictions independently of explanation.
  • Faithfulness varies by task and isn't guaranteed by model scaling.

Faceted Search and Filters That Don’t Return Zero Results

A faceted interface makes a promise: every filter you can click leads somewhere. Breaking that promise is the single most common way a good search engine feels broken, and it happens for a reason that…

  • Faceted search interfaces aim to lead users to relevant results based on filter selections.
  • Disabling zero-result facets and offering user actions restores search usability.

Confidence Scores in Structured Extraction

Adding confidence: number to a schema is the most popular non-solution in extraction. You will get a number. It will be 0.95.

  • Confidence scores remain static at 0.95, regardless of correctness
  • Clustering around 0.9, 0.95, and 0.99 indicates style capture, not belief
  • Moving confidence field's position alters numbers, showing lack of true belief

Pattern: Extract, Then Reason

“Read this invoice and tell me whether to approve it” is two tasks pretending to be one. The model reads badly and reasons badly at the same time, in a single opaque step, and when the answer is wrong…

  • Splitting tasks into extraction and decision-making stages improves observability and debugging
  • Extraction stage produces structured output with spans for verification
  • Split approach allows independent testing and improvement of each stage

Selecting a Compatible API Gateway: Token Cost, Batch Work, and Data Residency

Short answer: the cheapest compatible API gateway is the one with the lowest cost per accepted result on your own traffic after cache misses, batch eligibility, retries, and regional constraints are…

  • Set up Python script to replay traffic and gather usage data for each API gateway candidate.
  • Calculate effective cost by dividing total charged cost by number of accepted results.

Exploration and Exploitation, Made Concrete

Every learning agent faces the same choice at every step: take the action that currently looks best, or take one it knows less about.

  • Exploration vs. exploitation dilemma in decision-making
  • Regret quantifies difference between chosen and best action outcomes
  • Optimal regret growth is logarithmic in bandit problems