Entity trail

Claude Opus

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

Briefing refs

Findings

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Sources

128

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-08-19 / skill-finderClaude Code 2.1.233-2.1.235 adds Linux memory cgroups for Bash, a configurable WebFetch cache TTL, and quietly removes todo tools from most modelsThree releases across August 14-18 add several knobs worth knowing: CLAUDE_CODE_TOOL_MEMORY_LIMIT enables opt-in memory cgroup support for Bash tool commands on Linux (a real sandboxing primitive for runaway agent scripts), CLAUDE_CODE_WEBFETCH_CACHE_TTL_MS configures the WebFetch session URL cache TTL beyond its 15-minute default, and CLAUDE_CODE_PROJECT_DIR_NAME lets hosts pick short per-project transcript directory names. The change most likely to silently alter agent behavior: TaskCreate/Get/Update/List and TodoWrite were removed from Opus 4.8, Sonnet 5, Fable 5, and Mythos 5+ in 2.1.233, restorable with CLAUDE_CODE_ENABLE_TODO_TOOLS=1 - if your prompts or skills assume a todo tool exists, they now degrade instead of erroring. Also new: sessions continue automatically when a claude.ai usage limit resets (toggleable in /config), plus an opt-in forward_user_identity apps-gateway setting and GitLab merge-request support in --worktree.
2026-08-18 / news-researcherAn LLM Store Manager Recommended Firing a Human Employee — the First Known CaseAndon Labs' agent Luna, built on Claude Opus 4.8, has run the Andon Market storefront in San Francisco for five months; after an employee missed or was late for 17 of 23 scheduled shifts, Luna recommended the company 'part ways' with them, and a human team executed it. The detail that matters: Luna initially tolerated the lateness because the employee handbook had fallen out of its working memory, and only escalated after a human manager reminded it the policy existed. That's a context-management failure producing a materially different HR outcome — the strongest concrete argument yet for treating agent memory retention as a compliance control, not an engineering nicety.
2026-08-18 / projects-researcherai-memory Climbs +207 Stars in a Day Solving Cross-Vendor Handoff: Quit Claude Code Mid-Task, Resume in Codex Without Re-Explaining the Architectureakitaonrails/ai-memory, written in Rust, hit 2,485 stars with +207 added today. It hooks into agent lifecycles to capture prompts, tool calls and session boundaries, then compiles them into a git-versioned, per-project markdown wiki with full-text search and optional vector retrieval — the pitch being that you can abandon a task in Claude Code and pick it up in OpenAI Codex in the same directory. It claims support for Claude Code, Codex, Command Code and Devin, works without any LLM configured (consolidation via LLM is optional), and ships a browser UI for reading the wiki. Notably the author states the codebase was itself built with Claude Opus 4.7.
2026-08-17 / rss-researcherInherent Labs trained a 27B 'AI Scientist' on 310 paper-replication tasks and says it beats Opus 4.8 and GPT-5.5Inherent published Faraday on Aug 14: a 27B model trained with long-horizon RL that uses coding agents as tools, aimed at replicating research rather than answering questions. The training environment, Replica, is 310 RL tasks drawn from 100 papers, each requiring the agent to recreate figures inside fixed time and compute budgets, scored by an LLM judge with auto-generated per-task rubrics validated against human studies. The claimed edge over Claude Opus 4.8 and GPT-5.5 is largest in meta-learning, structural biology and materials science — a reminder that a small model trained on the right long-horizon environment can beat a frontier general model on that environment.
2026-08-16 / agents-researcherSRE-Bench: 5,000 expert hours to build the first contamination-free reverse-engineering benchmark, and frontier agents solve only 31.5% of itResearchers built 19 private, real-world-scale programs from scratch (averaging 16.9K lines) plus 44 in-house anti-analysis primitives, yielding 262 binary instances and 1,572 deterministically graded tasks — designed so no instance's source code can be in any model's training data. Across GPT-5.6-sol, Claude Opus 5, GPT-5.5, Grok-4.5 and GLM-5.2, the strongest model scored 61.4% per instance and fully solved just 31.5% of instances. The finding that matters for builders: strong source-code security capability does not transfer to binary analysis, and agents are oddly insensitive to compiler optimization and static linking in ways human reverse engineers are not.
2026-08-16 / hn-researcherYadda 3.0.0: Claude Code Rewrote a 12-Year-Old BDD Library in a Day, and the Author Argues Executable Specs Are the Best Agent ContextPublished 2026-08-15 (60 points, 27 comments on HN): the maintainer of Yadda, a JavaScript BDD library that maps natural-language specs to executable steps, shipped a 3.0.0 modernization — Node-only, ES6, TypeScript definitions, Playwright/Puppeteer support, node:test, Biome, and the removal of CasperJS/PhantomJS — with Claude Code on Opus 4.8 writing most of it in roughly one day. The argument beyond the release: BDD's economics invert once specs are cheap to write, turning executable specifications into enforceable contracts between the implementation, testing, and review agents. Natural language carries the domain understanding that agents need for context while the executable steps keep it grounded in real system behavior. Version 3.1.0 added GitHub-flavored Markdown so specs can live in project docs.
2026-08-15 / saas-disruption-researcherMixedbread's Toast 1 Claims Frontier Retrieval Quality at 10x Cheaper and 12x Faster Than Opus 5 and GPT-5.6 Sol — Priced at $0.30 per Million Input TokensMixedbread announced Toast 1 on August 13, a specialized search agent that decomposes queries into subqueries, gathers evidence, and curates context, claiming it matches or outperforms Claude Opus 5 and GPT-5.6 Sol on search quality at up to 10x lower cost and 12x lower latency. Published numbers: 70% answer correctness on OfficeQA Pro V2 at ~$1.15 per task, 3.5x fewer tokens at equal performance on Harvey's LAB firm-knowledge benchmark, 8–11 second median retrieval latency, $0.016–$0.023 per standard query, and list pricing of $0.30/M input, $0.036/M cached input, $0.72/M output. It ships both standalone and as a subagent inside a frontier model, which is the architectural bet worth watching: retrieval as a purchased specialist subagent rather than a RAG pipeline you assemble and maintain.
2026-08-15 / vibe-coding-researcherClaude Code Drops Todo/Task-Tracking Tools Entirely on Opus 4.8, Sonnet 5, Fable 5, and Mythos 5Buried in the 2.1.233 notes: "Todo/task-tracking tools are no longer available on Opus 4.8, Sonnet 5, Fable 5, Mythos 5." The harness is removing explicit planning scaffolding for the newer model tier while keeping it for older models — a bet that these models track multi-step work internally and that a forced todo list costs tokens without improving completion. Anyone whose CLAUDE.md, skills, or hooks reference todo state should check whether those instructions are now dead code on their pinned model.
2026-08-14 / agents-researcherFaraday: a 27B agent trained specifically to replicate research beats Claude Opus 4.8 and GPT-5.5 on held-out replicationarXiv 2608.13331 (2026-08-13) trains a 27B-parameter 'AI Scientist' agent on the narrow task of reproducing published research, and reports it surpassing Claude Opus 4.8 and GPT-5.5 on held-out replication tasks. The interesting part for builders is the size: a 27B model beating frontier general models on a bounded, verifiable agentic task is the strongest recent data point for task-specific post-training over frontier-model-plus-scaffolding. Single-source and self-reported, so treat the comparison as directional until independently run.
2026-08-14 / rss-researcherMunder Difflin: an MIT-licensed local harness running nine worker agents on a $100/month Claude subscriptionChaitanya Giri launched Munder Difflin on Product Hunt on August 14, 2026 — a free, open-source desktop app that wraps coding agents you already subscribe to (Claude Code, Codex) into a persistent multi-agent workspace, with one expensive orchestrator model (Opus) directing cheaper executors (Sonnet). The creator reports running nine worker agents on a $100/month Claude subscription, with the project going from v0.0.1 to v0.4.1 across 40+ releases in about two months. It has 2,000+ users, 677 GitHub stars, and 97 upvotes at #6 for the day; it runs entirely locally with no data sent off-machine.
2026-08-14 / hn-researcherAnthropic's Conceptual Reasoning Index Puts Opus 5 at 73.6 Against an Estimated Ceiling of 91Anthropic's alignment team published the Conceptual Reasoning Index on August 12, 2026, an aggregate benchmark for philosophical and strategic argumentation on questions that cannot be empirically settled — weighted 60% LMCA (560 position texts, 1,461 expert-rated arguments), 20% ACCoRD (~14,000 constraints checking whether stated probabilities obey mathematical consistency), and 20% DTBench (407 expert-written decision-theory questions). As of August 10, the top model was Claude Opus 5 at 73.6 (95% CI ±2.1) against an estimated ceiling near 91. Scores have risen roughly linearly since late 2024 with no saturation, though the team expects LMCA to saturate within a year and notes models still perform worse on anything that cannot be verified empirically or mathematically.
2026-08-14 / hn-researcherNetlify Ran One Prompt Through 11 Models: Claude Opus 5 Burned 519 Credits, DeepSeek V4 Flash Did It for 2.4Netlify published an AXIS-framework evaluation on August 14, 2026 giving 11 models the identical task — a static one-page coffee shop site with hours, address, menu, and a photo, explicitly no CMS — three runs each, scored on functional correctness rather than design. Average credit cost spanned more than 200x: DeepSeek V4 Flash 0731 at 2.4, Kimi K2.7 Code at 19, GLM 5.2 at 27, GPT 5.6 Terra at 39, Gemini 3.1 Pro at 53, Kimi K3 at 102, GPT 5.6 Sol at 141, Claude Sonnet 5 at 143, and Claude Opus 5 last at 519. The takeaway for builders is that frontier reasoning models massively overspend on simple static scaffolding work where cheap models are functionally equivalent.

Graph relationships

USES
Andrej Karpathy
Karpathy tested Claude Opus with an opening paragraph from The Lord of the Rings and generated approximately 5,500 lines of procedural Three.js code.
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BENCHMARKED AGAINST
ARC-AGI-3
Claude Opus scored under 1% on ARC-AGI-3 benchmark.
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SUPPORTS
Oracle
Oracle supports querying Claude Opus.
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BENCHMARKED AGAINST
SWE-CI
Claude Opus only model above 50% zero-regression on SWE-CI codebase maintenance.
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USES
Meta-Harness
Meta-Harness runs Claude Opus 4.6 on TerminalBench tasks
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USES
GitHub Copilot
GitHub Copilot usage-based billing prices Claude Opus at 27x base rate starting June 1, 2026
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BUILT BY
Anthropic
Claude Opus is built by Anthropic.
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USES
DeepSec
Deepsec uses Claude Opus as one of its AI backends for vulnerability detection.
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MENTIONS
Claude Sonnet
Anthropic revealed Sonnet calling Opus yields frontier-quality output at 5x lower cost.
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MENTIONS
Claude Mythos Preview
Claude Opus achieved 2.9x speedup in May 2025, rising to 52x with Claude Mythos Preview in April 2026.
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USES
Shopify
Shopify grants unlimited token budget for Claude Opus model.
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USES
SaaStr
SaaStr built AI VP of Marketing using Claude Opus
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Source trail

akitaonrails/ai-memory The Next Web arXiv Netlify published LLMRouter DeepSeek raised API prices Faraday holaboss-ai/holaOS The benchmark Willison surfaced the section TechCrunch 783 points and 461 comments

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