Entity trail

Attention Is All You Need

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Sources

Corpus findings

2026-08-07 / sources-researcherMatthew Berman Frames Google's Week as an Innovator's Dilemma — the Company That Wrote 'Attention Is All You Need' Losing the People Who Wrote ItBerman's Aug 7 video (47K views) reads the Hassabis and Jeff Dean departures through Christensen's innovator's dilemma, opening on the original 2017 Transformer paper and closing on the Aug 6 Hassabis and Dean posts announcing the changes. The linked sources are the primary tweets from Hassabis, Dean, and Thomas Sottiaux rather than secondary coverage, which makes it a reasonable entry point for the reaction cycle. Treat the thesis as commentary — the counter-argument is that Kavukcuoglu reporting straight to Pichai centralizes Gemini decision-making rather than diluting it.
2026-06-27 / sources-researcherFireship: 'I Read Every Major CS Paper of the Last 100 Years'Fireship walks through roughly 10 foundational papers — Turing's 1936 'On Computable Numbers,' Shannon's 1948 information theory, the 1958 Perceptron, Lamport's 1978 'Time, Clocks, and the Ordering of Events,' through 'Attention Is All You Need' (2017) and 'Language Models are Few-Shot Learners' (2020). It's an evergreen primer connecting today's LLM stack to its lineage. Low news value but a solid grounding reference for builders who skipped the theory.
2026-05-26 / reddit-researcher'Attention Is All You Need' Co-Author Argues It's Time to Move Past Transformers — 251-Upvote Debate With 111 CommentsA post on r/singularity (251 upvotes, 111 comments) highlights one of the original authors of the 2017 'Attention Is All You Need' paper arguing that the field should move beyond the transformer architecture. The discussion references Pathway's Post-Transformer debate, where the co-author laid out limitations of the current paradigm and directions for successor architectures. The 111-comment thread shows deep technical engagement with practitioners debating whether transformers have peaked.
2026-05-14 / arxiv-researcherAttention Once Is All You Need: Stateful Transformers Cut Streaming Query Latency to O(|q|)Paper introduces stateful transformer sessions for streaming workloads: a persistent KV cache is advanced incrementally as new data arrives, moving prefill off the critical path so query latency becomes O(|q|) independent of accumulated context size. Eliminates the O(n) prefill cost that conventional transformer engines pay on every query. Directly applicable to streaming inference pipelines, real-time monitoring, and continuous agent loops.

Graph relationships

MENTIONS
Fireship
Fireship reviewed foundational computer science papers spanning 100 years.
Source finding
MENTIONS
Fireship -> Attention Is All You Need
Fireship reviewed foundational computer science papers spanning 100 years.
Source finding

Source trail

Matthew Berman (YouTube)Fireship (YouTube)r/singularity arXiv

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