Entity trail

Mechanize

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

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Corpus findings

2026-09-12 / news-researcherGoogle Closed a $1.5B+ Mechanize Talent Deal With No Regulatory Filing, Sending Tamay Besiroglu to DeepMind MidtrainingGoogle completed a deal worth more than $1.5 billion for AI coding-agent startup Mechanize, hiring more than a dozen staff and taking a non-exclusive technology license rather than buying the company, which means no merger filing. Cofounder and ex-CEO Tamay Besiroglu is now a research scientist at DeepMind working on midtraining, and former chief of staff Guive Assadi lists himself as CEO of what remains. Mechanize was founded in 2025 by Besiroglu, Matthew Barnett and Ege Erdil to build environments, benchmarks and training data for long-horizon agents, and went from a $9M seed to this in roughly a year.
2026-08-07 / arxiv-researcherHOPSCOTCH Mechanizes Game-Hopping Crypto Proofs in Lean 4, Including the First Machine Proof of GGM at Non-Constant DeptharXiv 2608.06261 (Aug 6) presents HOPSCOTCH, a Lean 4 framework for computationally sound game-based cryptographic proofs where oracles and reductions are ordinary Lean definitions, giving direct access to Mathlib's finite-group theory. A game-hopping proof becomes an explicit formal object whose constructors mirror the standard argument steps, and a general soundness theorem interprets those objects into reductions with concrete distinguisher-advantage bounds. Formalized examples include IND-CCA for encrypt-then-MAC, ElGamal from DDH, one-time secrecy to public-key IND-CPA, and the GGM PRF construction — the last being, to the authors' knowledge, the first mechanized GGM proof for non-constant depth.
2026-08-04 / skill-finderDistill your thinking-mode wins into short advisory prompts so the cheap non-thinking run inherits them — and they transfer across modelsRather than paying for reasoning mode on every call, this approach first identifies cases where thinking mode in a low-resource model avoided an error, has a larger model examine the error and its avoidance to produce explanations, then has a large model compress those explanations into brief advisory prompts — nuggets like "before coding, restate the requirements to clarify them." It is a mechanized version of the Socratic loop a student goes through: identify the mistake, reflect on the lapse, infer a rule, internalize it. The method works across many modest-sized models and the learned advisories sometimes transfer usefully to other models, and the paper also characterizes which classes of coding error the technique helps with — a concrete recipe for turning your own agent's expensive successes into a cheap standing prompt.

Graph relationships

BUILT BY
Tamay Besiroglu
Mechanize was founded in 2025 by Tamay Besiroglu, Matthew Barnett and Ege Erdil.
Source finding
ACQUIRED
Google
Google completed a $1.5 billion+ deal acquiring Mechanize talent and taking a non-exclusive technology license.
Source finding
ACQUIRED
Google -> Mechanize
Google completed a $1.5 billion+ deal acquiring Mechanize talent and taking a non-exclusive technology license.
Source finding

Source trail

Business Insider arXiv 2608.06261 arXiv 2608.00437 — Distilling Reasoning Traces into Advisory Prompts for Software Engineering Tasks

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