A new Google model is nearly perfect on automated handwriting recognition

Historical & practical use cases

• Several commenters are excited about strong handwriting recognition, especially for:
  • 16th–18th century archival material (Conquistador accounts, colonial Spanish files, ledgers, local town records).
  • Genealogy, Renaissance Neo-Latin texts, family diaries, and children’s handwriting.
• People describe current LLMs (Gemini 2.5 Pro/Flash, Claude, o3) already being very useful for:
  • Transcribing handwritten notes and food logs with few errors.
  • Searching, summarizing, and translating scanned historical documents.
  • Acting as research assistants via custom tooling and agents.

Skepticism about OS clones and “wild capabilities”

• Many doubt claims that the model “codes full Windows/Apple OSes, 3D software, emulators” from one prompt:
  • Most likely outputs are web-based UI clones (HTML/CSS/JS) that resemble OS desktops, not kernels.
  • With abundant open-source OSes and emulators on GitHub, such results may be remixing or near-copying, not deep novelty.
• Some see this as classic social-media hype and suspect astroturfing and engagement farming around new model launches.

Novelty, reasoning, and “stochastic parrots”

• Long debate over whether LLMs:
  • Only interpolate from training data vs. genuinely extrapolate and create novel solutions.
  • Are “just next-token predictors” vs. systems that necessarily build internal world models to predict well.
• Examples used on the “they reason” side:
  • Math Olympiad-style problem solving.
  • Material-physics intuitions (“can X cut through Y?”).
  • Multi-document code or research synthesis.
• Critics respond that:
  • Impressive feats often align with dense training coverage (e.g., NES emulators, sugar loaves, ledgers).
  • There are no clear signs yet of breakthroughs comparable to relativity or the transistor.

Handwriting example and trust issues

• The sugar-loaf ledger case that impressed the author is heavily debated:
  • Alternatives: the model may have simply seen the space (“14 5”), recognized period notation, or drawn on prior examples of typical loaf weights.
  • Regardless, it violated the explicit instruction to transcribe “exactly as written,” which some see as a reliability red flag.
• Historians worry about:
  • Being subtly biased by AI “guesses” in ambiguous passages.
  • Using models on primary sources without strong provenance and error-characterization.

Concerns about hype, regressions, and evaluation

• Many find the article hyperbolic, with marketing-style language about “emergent abstract reasoning.”
• Several users report:
  • Earlier Gemini 2.5 Pro previews feeling stronger than later releases, possibly due to cost optimizations.
  • Models that once worked well for research later hallucinating sources or references.
• There’s interest in standardized handwriting benchmarks; some are surprised none are widely cited.