This index exposes the material behind every decision guide: what we reviewed, when we retrieved it, the claim it supports and where the evidence stops.
181decision guides
790source records
782scoped claim notes
How to read this index
A citation is not a transferable result.
Reviewed identifies the part of the source used. Supports states the bounded proposition we draw from it. Retrieved records when time-sensitive material was checked.
Vendor documentation establishes vendor claims and product behavior as documented, not independent performance. Papers establish results only for their stated methods and tested scope. AccessAllGPT recommendations remain labeled analysis.
AccessAllGPT reviewed four first-party vendor surfaces on October 4, 2026: AWS’s October 2 Claude Desktop walkthrough, the live AgentCore Web Search developer guide, the live AgentCore pricing page and Anthropic’s Claude Desktop documentation for administrator-managed MCP servers. We checked the announcement date, product maturity, architecture, authentication chain, query path, connector controls and versions, supported regions, input and result boundaries, Claude Desktop policy controls and current list prices. Product capabilities, privacy properties and index scale are labeled as AWS claims; Claude Desktop behavior is labeled as Anthropic documentation; deployment guidance is AccessAllGPT analysis. We did not create AWS resources, configure IAM Identity Center or Cognito, connect Claude Desktop, issue a Web Search query, measure freshness or relevance, audit network traffic, verify retention, test policy enforcement or calculate a complete production bill.
Reviewed: October 2, 2026 publication metadata; architecture; prerequisites; IAM Identity Center, Cognito and JWT flow; AgentCore Gateway setup; Web Search target; Claude Desktop managed MCP configuration; validation and cleanup
Supports: AWS published an enterprise integration walkthrough connecting Claude Desktop to AgentCore Web Search through AgentCore Gateway, with IAM Identity Center authentication federated through Cognito and JWT validation at the gateway.
Supports: AWS documents Web Search as a managed MCP-compatible AgentCore Gateway connector with query, result-count, domain and date controls, structured result metadata, and availability in Northern Virginia, Ireland and Tokyo.
Reviewed: Web Search on AgentCore description; per-query billing; price table; Gateway API invocation price; consumption model; no-minimum-fee statement
Supports: AWS lists Web Search at $7 per 1,000 queries and Gateway API calls at $0.005 per 1,000 invocations, with consumption pricing and no minimum fee or upfront commitment.
Reviewed: Claude Desktop on third-party platforms; managed MCP servers; transport and URL configuration; OAuth and header helpers; per-tool allow, ask and blocked policies; connection testing; redirect behavior
Supports: Anthropic documents administrator-managed remote MCP server configuration for Claude Desktop, including per-tool policy locks, OAuth or header-based credentials, and a live initialize and tools/list connection test.
AccessAllGPT reviewed five first-party OpenAI surfaces on October 3, 2026: the official News RSS record for the October 2 GPT-6 family guide, the live API model catalog, the GPT-6 Luna model reference, the current API pricing tables and the model-selection guide. We checked the publication timestamp, family positioning, model IDs, modalities, context and output limits, reasoning settings, endpoints, tool support, residency qualifications, rate limits, short- and long-context prices and processing-mode multipliers. The canonical editorial guide returned a Cloudflare challenge, so claims about that page are limited to the title, description, category, URL and timestamp exposed by OpenAI’s own feed; operational details come from directly accessible OpenAI developer pages. Service facts and OpenAI recommendations are labeled separately from AccessAllGPT analysis. We did not receive credentials, call any model, reproduce evaluations, compare quality or latency, or verify account-specific access, residency or billing.
Supports: OpenAI’s official feed records an October 2 GPT-6 family guide for choosing models, tuning reasoning effort, improving prompts and skills, coordinating tools and preparing startup workflows for production.
Reviewed: Current featured-model ordering; Astra, GPT-6.1 Sol and Luna positioning; model identifiers; common text-and-image input, text output, multilingual and vision availability; Responses API access
Supports: The live catalog recommends GPT-6 Astra for complex reasoning and coding, GPT-6.1 Sol for balancing intelligence and cost, and GPT-6 Luna for cost-sensitive high-volume work; all three are listed as available through the Responses API.
Reviewed: Model ID; positioning; reasoning efforts; modalities; context and output limits; knowledge cutoff; Standard token prices; long-context threshold; processing modes; endpoints; tools; EU residency; default rate-limit table
Supports: OpenAI documents gpt-6-luna with a 1,050,000-token context window, 922,000-token maximum input, 128,000-token maximum output, six reasoning settings, Responses API tools, EU residency qualifications and Standard rates of $0.10 input and $0.50 output per million short-context tokens.
Reviewed: Standard, Batch, Flex and Fast GPT-6 family tables; short- and long-context input, cached-input, cache-write and output rates; Astra, GPT-6.1 Sol and Luna comparisons
Supports: The current tables list short-context Standard prices per million tokens of $10 input and $50 output for Astra, $2 and $10 for GPT-6.1 Sol, and $0.10 and $0.50 for Luna; Batch and Flex halve listed Standard rates, while Fast doubles them.
Reviewed: GPT-6.1 Sol recommendation; model-and-effort ladder from Luna Low through Astra Extra high; workload frequency, latency, output use and quality questions; same-input comparison guidance
Supports: OpenAI presents its model-and-effort ladder as a starting point, associates Luna with lower-cost bounded work, Sol with complex revisable work and Astra with demanding work, and advises comparing the same inputs while keeping the lightest setting that meets the quality bar.
AccessAllGPT reviewed four first-party Ai2 surfaces on October 3, 2026: the dated launch post, the live AstaBrief 8B model repository and configuration, the released DPO preference dataset and the linked ScholarQA Lite example workflow. We checked what is available now, repository and model identifiers, license labels, architecture and context configuration, prompt and generation guidance, training-data counts, evaluation design, product latency and usage claims, and explicit caveats. Release facts and repository metadata, Ai2 performance claims, and AccessAllGPT deployment guidance are labeled separately. AccessAllGPT did not download or run the weights, call Asta, reproduce benchmarks, inspect all training records, validate citations or obtain pricing, hardware sizing or a security review.
Reviewed: October 2, 2026 publication date; product availability; open-weight release; training pipeline; SFT and DPO data construction; attribution filtering; evaluation design; latency; early product usage; limitations and future work
Supports: Ai2 announced AstaBrief 8B on October 2, made it available as Fast mode in Asta and released weights and training data; the post describes the Qwen3-8B starting point, 47K SFT examples, about 6K DPO examples, vendor-run evaluations and product telemetry while warning that most evaluation work was completed in 2025.
Reviewed: Repository identity; Apache-2.0 model license; base and preference dataset links; inference prompt requirement; code example; benchmark tables; intended use; training configuration; config.json architecture and 40,960-position limit; current repository files
Supports: The public repository identifies a Qwen3ForCausalLM checkpoint with a 40,960-position configuration, Apache-2.0 model license, recommended prompt format, 4,096-token sample generation cap, vendor-reported evaluation tables and DPO training details; its sample code currently names the SFT checkpoint rather than the final DPO repository.
Reviewed: Dataset identity; CC-BY-NC-4.0 license; English language and text-generation tags; train split row count; prompt, chosen, rejected, model and ID fields; dataset-card provenance and use notes
Supports: The released DPO preference dataset is separately licensed CC-BY-NC-4.0 and its current viewer exposes a 6.62K-row train split with prompts, chosen and rejected conversations, generator labels and record IDs.
Reviewed: Public repository path; lite pipeline files; prompt utilities; response parser; ScholarQA Lite orchestration; repository license and surrounding retrieval/report-generation implementation
Supports: Ai2 links this public ScholarQA Lite implementation as the example workflow for adapting local report generation to a user’s own PDFs; the directory exposes the orchestration, prompt and response-parsing code rather than a hosted Asta service contract.
AccessAllGPT reviewed three first-party or author-controlled surfaces on October 3, 2026: ServiceNow CoreAI’s October 2 AutoSynthData article, the public EnterpriseOps-Gym dataset at immutable revision c8e538eae8a6205294f0a86675fefdc1fac408f6, and the benchmark paper submitted to arXiv on March 13. We checked the described generation loop, acceptance gates, target and teacher models, training volumes, elapsed times, reported evaluation changes, artifact availability, dataset schema and license, and benchmark scope. AutoSynthData experiment results and EnterpriseOps-Gym measurements are labeled as author-reported; pinned registry contents are artifact facts; adoption guidance is AccessAllGPT analysis. We did not receive or run AutoSynthData, regenerate tasks, download the dataset files, fine-tune a model, inspect private prompts or generated samples, reproduce either evaluation, validate cost, or independently audit data leakage, verifier quality or statistical uncertainty.
Reviewed: October 2, 2026 publication metadata; task definition; capability-card construction; target and multiply phases; sample and batch quality controls; Hybrid and ITSM experiment setups; reported training volume, elapsed time and evaluation changes; closing scope statement
Supports: ServiceNow describes AutoSynthData as an internal pipeline that converts target-model failures into generated, executable and verifier-backed agent-training tasks. The post reports two supervised fine-tuning experiments, but does not announce public AutoSynthData code, generated training sets, checkpoints, pricing or a managed API.
Reviewed: Pinned repository tree; dataset card metadata; Apache-2.0 license; oracle and distractor-tool configurations; eight domain splits; task schema; SQL-verifier description; dataset loading example; linked evaluation framework and paper
Supports: The pinned public dataset revision contains four configurations across eight domain splits and exposes task prompts, selected tools, server configuration and verifier fields under Apache-2.0. It is the pre-existing evaluation dataset used to illustrate AutoSynthData, not the newly generated AutoSynthData training corpus.
Reviewed: March 13, 2026 submission metadata; abstract; environment and task construction; SQL verification; model evaluation; human-plan ablation; infeasible-task results; limitations; dataset and framework references
Supports: The authors report a 1,150-task, eight-domain benchmark with 512 tools and 164 database tables. Their model study reports a 37.4% best overall success rate, 53.9% best clean-refusal rate on infeasible tasks and 14–35 percentage-point gains from human-authored plans. These are author-reported benchmark results, not independent measurements of AutoSynthData.
AccessAllGPT reviewed three first-party Anthropic surfaces on October 3, 2026: the dated Claude Frontier Academy announcement, the live Academy program page and the earlier Claude Partner Network announcement. We checked the launch date, stated investment and participation target, program sequence, assessments, credentials, intended participants, current locations, early-access and nomination conditions, application route, named first-cohort organizations and the relationship to Anthropic’s partner channel. Program facts and Anthropic’s claims about enterprise talent are separated from AccessAllGPT procurement guidance. We did not join a cohort, interview participants, inspect course materials or scoring rubrics, verify the investment, evaluate a resulting deployment, price employer time or compare the program with alternatives.
Reviewed: October 2, 2026 announcement date; $100 million commitment; 10,000-engineer target; participant criteria; in-person intensive; 12-week residency; assessments; current cohort cities; nomination-only access; first participating organizations
Supports: Anthropic announced Claude Frontier Academy on October 2, says current cohorts are running in San Francisco, New York and London, and describes a nomination-only training path with an in-person intensive, a 12-week workplace deployment and two practical assessments.
Supports: The live program page describes the FDE Residency as early access for selected customers and partners, specifies three in-person build days plus a fourth-day assessment, and directs interested organizations to an Anthropic account team or partner manager.
Reviewed: March 12, 2026 announcement; partner-network purpose; training and technical support; Partner Portal; Claude Certified Architect credential; eligibility; free membership; stated 2026 investment
Supports: Anthropic separately announced a $100 million 2026 Claude Partner Network investment, free partner membership and a technical-certification program in March; this provides context for the Academy’s partner channel but does not establish that the two $100 million figures are additive.
AccessAllGPT reviewed three first-party or project-controlled surfaces on October 3, 2026: Anthropic’s October 1 guest post by Matthew D. Schwartz, the live BootLoops project site, and the public BootLoops 1.0 repository pinned to commit 66b680ce742e654cfe86da4f072a69061fe182b1. We checked the publication and artifact dates, model and workflow descriptions, portfolio counts, reported scientific examples, human and expert involvement, stated failure modes, verification principles, source availability, ownership disclosure and software/documentation licenses. The article labels Schwartz’s portfolio and scientific outcomes as author-reported claims; repository contents and licenses are artifact facts; adoption guidance is AccessAllGPT analysis. We did not install BootLoops, run its self-tests or scientific tools, use Claude Fable 5, inspect private sessions or cloud machines, read all 36 manuscripts, validate any calculation or dataset, confirm novelty, interview coauthors or obtain independent peer review.
Anthropic (guest post by Matthew D. Schwartz) · Retrieved
Reviewed: October 1, 2026 publication date; summary; BootLoops origin and workflow; reported physics, ecology, genetics, economics and linguistics projects; 36-manuscript portfolio claim; orchestration details; failure modes; outlook; acknowledgements; visiting-researcher and project-ownership disclosure
Supports: Anthropic published Schwartz’s account on October 1. He reports using Claude Fable 5 with BootLoops across 400 candidate problems, yielding 36 manuscripts in 18 fields with 19 coauthors over three months, and describes expert steering, verification practices, compute intensity and recurring model failures. The post is a guest account, not independent validation of every scientific result.
Reviewed: Project overview; harness independence; scientific-tooling scope; verification principles; BootLoops Standard; methods; example applications; navigation to tools, manuscripts and summaries
Supports: The project site describes BootLoops as a model-independent harness of quantitative-science tools and protocols. It says the core acceptance standard requires a full functional form and an arbitrary-precision Python evaluator, and it exposes project descriptions and links to the public code.
Supports: The pinned public commit identifies BootLoops 1.0, contains tools, protocols, installation documentation and self-tests, and licenses software under MIT with documentation under CC BY 4.0. Repository availability establishes that an artifact can be inspected; it does not establish that every reported result reproduces.
AccessAllGPT reviewed four first-party vendor sources on October 2, 2026: OpenAI’s live Ultrafast implementation guide, current API pricing table and GPT-6 Astra model reference, plus NVIDIA’s October 1 launch report. We checked availability, model and service-tier identifiers, advertised speed, complete short- and long-context token prices, the long-context threshold, default Ultrafast token limits, transport guidance, context and output limits, supported regions and NVIDIA hardware attribution. Availability, interfaces and list prices are treated as current vendor service facts; speed and infrastructure-performance statements are labeled vendor claims; deployment guidance is AccessAllGPT analysis. We did not receive Ultrafast access, make an API call, measure latency or throughput, reproduce the speed claim, inspect OpenAI infrastructure or compare output quality.
Reviewed: Availability; speed claim; model and service-tier identifiers; WebSocket recommendation; HTTP alternative; default token rate limits; data-residency and regional-processing restrictions
Supports: OpenAI documents Ultrafast as broadly available for GPT-6 Astra at low default rate limits, configured with model gpt-6-astra and service_tier ultrafast, and claims up to eight-times faster generation than Standard while recommending persistent WebSocket connections for multi-turn tool workflows.
Reviewed: Flagship-model service-tier selector; GPT-6 Astra Standard, Fast and Ultrafast short-context prices; Ultrafast long-context prices; input, cached-input, cache-write and output columns
Supports: OpenAI lists GPT-6 Astra Ultrafast at $60 input, $6 cached input, $75 cache writes and $300 output per million short-context tokens, rising to $120, $12, $150 and $450 respectively for long-context requests.
Reviewed: Model identifier and positioning; 1,050,000-token context; 128,000 maximum output; reasoning levels; modalities; endpoints; Responses API tools; Standard pricing; long-context threshold; model and account rate limits
Supports: OpenAI’s model reference identifies gpt-6-astra, documents a 1.05-million-token context window and 128,000-token output maximum, and states that requests above 272,000 input tokens use higher rates for the full request.
Reviewed: October 1, 2026 publication date; API availability; eligible ChatGPT Work and Codex access; Blackwell deployment; up-to-eight-times generation claim; agent-loop rationale; OpenAI executive statements; implementation link
Supports: NVIDIA announced on October 1, 2026 that GPT-6 Astra Ultrafast was available through the OpenAI API and to eligible ChatGPT Work and Codex users, runs on NVIDIA Blackwell GPUs and is presented by the vendors as up to eight times faster in token generation than Astra Standard.
AccessAllGPT reviewed three first-party Google pages on October 2, 2026: the October 1 Guided Vision announcement, the live Android Accessibility help page for Guided Vision and the current Gemini Live help page. We checked the publication timestamp, device and operating-system requirements, rollout language, camera workflow, entry points, task examples, training and tester claims, account prerequisites, camera shutoff behavior and safety warnings. Availability and capability descriptions are Google statements; the conflict between “now available” launch wording and a gradual-rollout support notice is preserved rather than resolved by inference. AccessAllGPT did not receive access, run Guided Vision, assess description accuracy or latency, reproduce Google’s development claims, interview participants or evaluate safety guardrails.
Reviewed: October 1, 2026 publication time; Android launch and eligibility; camera-sharing interaction; reframing cues; stated use cases; Aira training and testing collaboration; shortcuts; languages; safety warning
Supports: Google announced Guided Vision for Gemini Live on October 1, says it works on compatible Android devices running Android 9 or newer where Gemini Live is supported, and describes camera-based audio descriptions, follow-up questions and spoken reframing cues while warning against navigation, obstacle detection and replacement of mobility aids.
Reviewed: Rollout notice; enablement steps; Live camera requirement; Android accessibility shortcut options; TalkBack entry point; supported task examples; limitations and warnings
Supports: Google’s live help page says Guided Vision is rolling out slowly and may not yet be available, documents the Gemini setting, accessibility-button, two-finger-gesture, volume-key and TalkBack launch paths, and warns that the system can make mistakes and must not be used for navigation or obstacle detection.
Reviewed: Gemini Live prerequisites; gradual feature releases; camera sharing and automatic shutoff behavior; Guided Vision link; age and account requirements; privacy reminder
Supports: Google’s Gemini Live help requires an Android phone or tablet, a supported signed-in account and current Google app, says Live updates roll out gradually, and documents that camera sharing stops when Live is held, the user leaves the Gemini app or the screen locks.
AccessAllGPT reviewed four first-party OpenAI sources on September 30, 2026: the official OpenAI News RSS record for the September 29 announcement, the live Dots product guide, the Dots controls guide and current ChatGPT Work and Codex pricing. We checked the announcement date, product identity, underlying model, current capabilities, cloud and local execution paths, channels, memory, permission and stopping behavior, plan and regional eligibility, rollout status and disclosed prices. The canonical launch page returned a Cloudflare JavaScript-and-cookie challenge in our environment, so chronology comes from OpenAI’s RSS record and product details come from OpenAI’s accessible live documentation. Availability and documented behavior are vendor service facts; reliability, control and safety descriptions are OpenAI claims; adoption guidance is AccessAllGPT analysis. We did not receive access, create a dot, connect an account or computer, run a task, test an approval, inspect its private notes, measure cost or verify an enterprise configuration.
Reviewed: Official OpenAI News RSS record; title; canonical URL; Product category; September 29, 2026 publication timestamp; launch summary
Supports: OpenAI’s official feed records the Dots announcement on September 29, 2026 and describes dots as proactive assistants that keep working across complex projects and everyday tasks while leaving the user in control.
Reviewed: Product definition; GPT-6 Astra foundation; cloud computer and browser; channels; memory; background work; plugins; permissions; access, plan and regional requirements
Supports: OpenAI documents a dot as an always-on GPT-6 Astra agent with its own cloud computer and browser, persistent notes, background agents, scheduled work, plugin and optional local-computer access, and a gradual rollout to specified Pro, Business Premium and Enterprise accounts.
Reviewed: Activity and schedule review; automatic action review; custom permission rules; data settings; pause, delegated-task stop and schedule cancellation; deletion
Supports: OpenAI documents separate approval checks, custom rules and plugin permissions, warns that rules can be mistaken, and says pausing the main task does not stop delegated tasks or cancel future scheduled runs.
Reviewed: Individual plan cards; Pro plan prices; shared Work and Codex usage; API-key distinction; Business and Enterprise plan framing
Supports: OpenAI lists Pro subscriptions at $100, $200 and $500 per month. The Dots guide identifies all three as eligible but says rollout is gradual; it does not publish a separate per-dot price or public Business Premium and Enterprise rate.
AccessAllGPT reviewed three primary sources on September 30, 2026: Anthropic Frontier Red Team’s September 29 study, NIST CAISI’s September 17 assessment and Z.ai’s official Hugging Face repository pinned at revision aca966e4e02791568aa6a4ced368624b3d897f42. We compared publication dates, benchmark scopes, agent conditions, denominators, confidence intervals, access claims and safeguard tests. Anthropic’s results and interpretation are vendor-authored findings from a direct competitor that sells controlled-access Claude models; CAISI is a separate government evaluation with different tasks and harnesses; Z.ai’s repository establishes artifact availability and license labeling, not safety. We did not run GLM-5.3, download its weights, reproduce an exploit, test a bypass or independently inspect the disclosed vulnerabilities. This briefing omits operational exploit instructions and treats all measured outcomes as specific to the reported setups.
Reviewed: September 29, 2026 publication record; ExploitBench and internal binary-exploitation results; human-in-the-loop browser and N-day sessions; abliteration cost and refusal evaluations; simulated malicious-request tests; interpretation and policy recommendations
Supports: Anthropic reports that GLM-5.3 completed end-to-end V8 exploits in 50 of 410 ExploitBench attempts, produced full control-flow hijacks in 4% of 100 internal benchmark trials, and could be induced to engage with simulated malicious requests in 64% to 100% of trials under tested bypasses. These are Anthropic-run results, not AccessAllGPT reproductions.
NIST Center for AI Standards and Innovation · Retrieved
Reviewed: September 17, 2026 assessment; four benchmark definitions; ReAct harness and turn limits; maximum-reasoning and safeguard-disabled comparison conditions; benchmark results and confidence intervals; item-response-theory capability index methodology
Supports: CAISI separately classifies GLM-5.3 as the most cyber-capable open-weight model it had evaluated while estimating it remained about four months behind the current U.S. frontier on its aggregate cyber capability index. CAISI reports results for SEC-Bench Pro, ExploitBench, ExploitGym and a private OSS-Fuzz benchmark under its own agent harness.
Reviewed: Pinned repository identity and revision; model-card scope and cyber-capability claims; custom glm-5.3 license label; 141 safetensor shards; configuration, tokenizer and chat-template files; API-reported file manifest and last-modified timestamp
Supports: The official repository confirms that GLM-5.3 weights are publicly downloadable and that the artifact uses a custom GLM-5.3 license rather than a standard open-source license identifier. The model card itself describes emergent cyber capability; it does not establish that the safeguards withstand misuse.