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.
158decision guides
707source records
699scoped 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 five first-party AWS and Anthropic surfaces on October 6, 2026: AWS’s October 5 Claude Code deployment post, Anthropic’s live Bedrock configuration documentation, the AWS GovCloud Bedrock service page, AWS’s October 5 model-certification matrix and AWS’s September 28 Sonnet 5.5 GovCloud availability notice. We checked publication and availability dates, endpoint and Region coverage, model-profile IDs, required IAM actions, model pinning and fallback, Guardrails and logging support, certification labels, metadata exceptions, price disclosure and deployment caveats. AWS service and certification statements and Anthropic configuration behavior are vendor documentation; compliance suitability and operating guidance below are AccessAllGPT analysis. We did not access GovCloud, install Claude Code, invoke a model, verify an account-specific price, inspect routing or logs, test a fallback, or perform a legal or compliance assessment.
Reviewed: October 5, 2026 publication date and compliance disclaimer; model and endpoint availability; prerequisites; setup wizard and environment variables; IAM actions; model pinning; cost, logging, Guardrails, quotas, permissions and security considerations
Supports: AWS published a Claude Code configuration route for Claude Opus 5.5 and Sonnet 5.5 in AWS GovCloud (US), documenting separate bedrock-runtime and bedrock-mantle endpoints, us-gov inference-profile IDs and different audit and feature contracts.
Reviewed: Amazon Bedrock setup; IAM permissions; model access; model pinning and fallback; AWS GovCloud us-gov prefix; Mantle setup and distinct permissions; endpoint and troubleshooting guidance
Supports: Anthropic documents Bedrock authentication and model-discovery behavior, warns that Claude Code can fall back when a default model is unavailable, and states that AWS GovCloud deployments use the us-gov profile prefix.
Reviewed: Regional availability links; FedRAMP and DoD authorization links; export-controlled-content section; customer-defined metadata exceptions; model-evaluation metadata restriction
Supports: AWS directs GovCloud users to live model and certification tables and documents metadata that may leave GovCloud during customer-requested support plus model-evaluation metadata that must not contain export-controlled data.
Reviewed: October 5, 2026 update date; Anthropic model-family rows; U.S. commercial FedRAMP Class C column; GovCloud FedRAMP Class D column; DoD CSP SRG IL4/IL5 column
Supports: The dated AWS matrix lists Claude 5.5 Opus and Claude 5.5 Sonnet under GovCloud FedRAMP Class D, while the reviewed row does not mark those two models for the separate DoD IL4/IL5 column.
Reviewed: September 28, 2026 posting date; GovCloud availability statement; AWS infrastructure and regional-residency positioning; Guardrails and Knowledge Bases references; documentation links
Supports: AWS announced Claude Sonnet 5.5 availability in AWS GovCloud (US) on September 28. This predates the October 5 Claude Code how-to and separates model availability from the later deployment guidance.
AccessAllGPT reviewed four first-party AWS surfaces on October 6, 2026: the October 5 launch post, the live Agent Toolkit product page, the public version 4 aws-ai-ml skill source and commit history, and AWS’s April 22 announcement for the underlying optimized inference recommendation service. We checked dates, supported agents, install paths, prerequisites, credentials, action scope, benchmark metrics, confirmation behavior, generated-code claims, service lineage, pricing language, source layout and cleanup instructions. AWS product claims and AccessAllGPT operating guidance are separated below. We did not install the toolkit or skill, authenticate an agent, access an AWS account, run generated code, benchmark an endpoint, request a recommendation, reproduce AWS’s example, inspect the preconfigured Studio image, verify every required IAM action or incur charges.
Reviewed: October 5, 2026 publication date; availability and prerequisites; local and SageMaker Studio setup; endpoint benchmarking; instance recommendations; run comparison; example results; confirmation behavior; scope and cleanup
Supports: AWS announced an aws-ai-ml agent skill that generates SageMaker Python SDK v3 code for endpoint benchmarks, deployment recommendations and benchmark comparisons in Kiro, Claude Code, Codex and other compatible agents.
Reviewed: Setup command and prerequisites; charge statement; MCP Server, Agent Skills, Agent Plugins and Rule Files; supported agents; runtime discovery; IAM, CloudWatch and sandbox descriptions
Supports: AWS documents AWS CLI 2.35+ as a setup requirement, says the toolkit has no additional charge, and states that customers pay for AWS resources their agent provisions or uses.
Reviewed: Version 4 frontmatter; scope; routing table; model-deployment route; progressive-disclosure, best-effort and usage-attribution rules; current repository path and September 15 inference-optimization commit history
Supports: The public source routes SageMaker real-time endpoint benchmarking, benchmark comparison and cost, latency, throughput or performance recommendations through its model-deployment references; the skill also covers broader AI/ML lifecycle tasks.
Reviewed: April 22, 2026 announcement; inference-recommendation scope; NVIDIA AIPerf basis; candidate configuration search; workload and metric descriptions; resource creation and pricing caveats
Supports: AWS previously launched the underlying optimized inference recommendation service, describing measured candidate configurations and NVIDIA AIPerf-based benchmarking. The October agent skill is a new conversational code-generation layer over that existing capability.
AccessAllGPT reviewed four first-party Google surfaces on October 5, 2026: the September 30 launch announcement, the live skills help page, the Gems transition guide and the Gemini Spark help page. We checked the announcement date, current availability, eligibility, invocation, automatic activation, composition, reference-file support, import format, client limitations, Workspace timing, migration behavior, retirement dates and the boundary between chat skills and Spark tasks. Product behavior and rollout claims are Google documentation; migration and validation guidance is AccessAllGPT analysis. We did not receive the rollout, create or run a skill, migrate a Gem, upload files, test automatic activation, use Spark, verify an administrator console, inspect model behavior or obtain account-specific pricing or terms.
Reviewed: September 30, 2026 publication date; Gemini chat rollout; invocation and automatic-use behavior; skill stacking; reference files; Workspace timing; Gems retirement and migration dates; subscription and age footnotes
Supports: Google announced a global Gemini chat rollout for reusable skills, said the launch covers Google AI subscription tiers for adults, and published staged Gems retirement dates beginning with personal accounts in November 2026.
Reviewed: Current rollout notice; eligibility and activity prerequisites; supported clients; SKILL.md and reference-file imports; automatic and explicit invocation; skill composition; known mobile and Mac editing issues
Supports: Google documents skills for adults using personal Google Accounts with Keep Activity enabled, gradual availability across Gemini web, mobile and Mac, explicit or automatic invocation, composition, SKILL.md imports and two current client limitations.
Supports: Google says existing Gems will be automatically recreated as skills when Gems are removed, while also documenting a manual migration path and separate retirement windows for personal and organizational accounts.
Reviewed: Spark definition; schedules and tasks; personal-account and subscription prerequisites; regional exclusions; supported clients; skill use; connected information sources; safety notices
Supports: Google documents Spark as a separate personal AI agent for tasks and schedules that can use skills, requires a Google AI Pro or Ultra subscription, and currently carries regional and account restrictions distinct from skills in ordinary Gemini chat.
AccessAllGPT reviewed three first-party Google surfaces on October 5, 2026: the same-day UK and Ireland expansion announcement, the live Google AI Educator Series landing page and Google’s September 3 program update. We checked the announcement date, intended reach, price language, delivery mode, module duration, example content, badges, regional signup state, policy support and the boundary between training availability and product entitlement. Availability and program descriptions are Google statements; adoption and procurement guidance is AccessAllGPT analysis. We did not register for an event, receive regional access, complete a module, verify educator eligibility, inspect course materials, test Gemini, assess the AI Policy Toolkit or measure classroom, workload, safety or learning outcomes.
Reviewed: October 5, 2026 publication metadata; UK and Ireland audience; stated 650,000-educator reach; curriculum delivery; badges; policy toolkit; roadshow registration; US, India and Korea expansion context
Supports: Google announced that its free AI Educator Series is expanding to all 650,000 educators in the UK and Ireland through teacher-led virtual and in-person events, with module badges and a separate AI Policy Toolkit for school governance.
Reviewed: Current regional landing-page state; K-12 and higher-education session lengths; delivery format; Gemini focus; learning paths; badges; language availability; signup state and no-cost description
Supports: The current landing page describes short, non-sequential Gemini training, 15-minute K-12 and 45-minute higher-education sessions, digital badges and no-cost access, but the page served during review was localized to India rather than a UK and Ireland catalogue.
Reviewed: September 3, 2026 publication date; no-cost and on-demand description; module cadence; K-12 audience; Guided Learning, Deep Research and AI Quest examples; badge alignment; practical-use framing
Supports: Google previously described the series as no-cost, on-demand training with bite-sized sessions, monthly additions and modules covering Gemini Guided Learning, Deep Research, administrative automation and classroom activities.
AccessAllGPT reviewed three first-party AWS surfaces on October 4, 2026: the October 2 search-agent experiment, the live SageMaker AI multi-turn reinforcement-learning developer guide and the live SageMaker AI pricing page. We checked the announced date, model ID, region, datasets, splits, reward, job settings, benchmark denominators, baseline and fine-tuned scores, failure definition, supported workflow and public billing language. All performance results are AWS-reported; product behavior is AWS documentation; the pricing discrepancy and deployment guidance are AccessAllGPT analysis. We did not access SageMaker, run Qwen3.6-27B, inspect prompts or trajectories, reproduce the training or evaluation, validate dataset leakage, measure latency or cost, or obtain a private price quote.
Reviewed: October 2, 2026 publication metadata; MTRL capabilities; environment and datasets; nDCG@10 reward; Qwen3.6-27B job configuration; training progress; held-out results; cleanup; conclusion and next steps
Supports: AWS reports a SageMaker AI multi-turn reinforcement-learning run on Qwen3.6-27B that improved nDCG@10 on three of four held-out retrieval sets and reduced the reported BrowseComp-Plus failure rate from 22.89% to 0.68%.
Reviewed: MTRL overview; rollout and update architecture; supported losses and advantage estimators; agent requirements; training assets; job submission; evaluation; deployment; limitations and linked workflow pages
Supports: AWS documents MTRL as a managed model-customization workflow that collects multi-turn trajectories against a customer agent environment, applies sequence-level rewards and supports evaluation and deployment workflows.
Supports: The live pricing page says general reinforcement-learning customization is billed by job duration at a model-specific hourly rate, while the October 2 blog characterizes MTRL as per-token. The reviewed public page did not expose a clear Qwen3.6-27B MTRL rate, so a production estimate requires a region- and model-specific quote or console check.
AccessAllGPT reviewed four first-party, author-controlled or registry surfaces on October 4, 2026: Google’s October 1 launch update, its September 24 mission briefing, the November 2025 Google Research technical overview, and Crossref metadata for the October 2026 Joule article at DOI 10.1016/j.joule.2026.102678. We checked announcement and launch dates, current mission status, hardware identity, experiment scope, precursor measurements, projected system design, access and pricing boundaries, unresolved engineering work and the distinction between the current single satellite and a planned two-satellite milestone. Current spacecraft status and technical measurements are labeled as Google reports; DOI metadata is a registry fact; deployment guidance is AccessAllGPT analysis. Cell Press’s article page presented bot verification, so we did not review the paper body. We did not receive telemetry, contact the spacecraft, inspect the satellite, run Trillium workloads, reproduce vibration, radiation, vacuum, optical-link or orbital tests, validate launch-cost projections, or independently verify that the satellite remains operational after Google’s update.
Reviewed: October 1, 2026 publication metadata; launch and mission status; Planet partnership; Transporter-18 rideshare; confirmed contact; current operating status; TPU stress, radiation and thermal data plan; Joule paper link
Supports: Google says one Project Suncatcher prototype satellite launched on SpaceX’s Transporter-18 rideshare, that its team established contact and that the spacecraft was operating as expected. It describes the mission as an in-orbit research step, not a commercial AI service.
Reviewed: September 24, 2026 publication metadata; mission purpose; Trillium TPU radiation and vibration tests; thermal-vacuum work; radiator and heat-pipe cooling; laser interconnect plan; 2027 two-satellite milestone; limitations and open engineering work
Supports: Google describes the hardware and experiments behind the orbital prototype. It reports preflight testing and outlines a later two-satellite optical-link mission, but does not publish an in-orbit performance result, service date, price or customer-access path.
Reviewed: November 4, 2025 research overview; proposed system design; solar-power premise; optical-link demonstrator; orbital models; Trillium proton-beam test; launch-cost scenario; engineering gaps; future directions; author list and supporting paper link
Supports: The Google Research overview reports the project’s modeled architecture and precursor experiments: 800 Gbps each way on a bench optical link, an illustrative 81-satellite orbital model, Trillium radiation-test thresholds and a conditional launch-cost scenario. These are Google-authored findings and projections, not independent validation of an orbital AI data center.
Supports: The DOI registry establishes the peer-reviewed Joule article’s bibliographic identity and author list. The publisher page was blocked by bot verification during this review, so AccessAllGPT did not use the paper to add claims beyond Google’s accessible summaries.
AccessAllGPT reviewed five author-controlled or first-party surfaces on October 4, 2026: the October 3 joint Microsoft–Hugging Face article, arXiv paper version 2 updated October 1, the benchmark’s immutable thinkingbox-bench-v1.0 target commit fcaba4c1a9debec42fda7f15bf29fe6d6b46c431, the Hugging Face dataset at revision 5e49f89a16c31db0b30b00950f4d654bc1708a90, and the OpenEnv adapter at commit 1fe80907e5d955860329e3113a6cde17b66fd55e. We checked announcement timing, benchmark scope, metric definitions, reported model and failure results, pricing method, artifact availability, setup dependencies, revisions and licenses. Author measurements and cost calculations are labeled as reported findings; repository and registry contents are artifact facts; deployment guidance is AccessAllGPT analysis. We did not install or run ThinkingBox, download the full dataset, call any evaluated model, reproduce the 20-run campaigns, inspect all tasks and assertions, recompute token costs, audit hidden prompts, or validate benchmark realism against private production traffic.
Supports: The joint article announces ThinkingBox through Hugging Face and OpenEnv, describes 507 stateful workflows repeated 20 times, reports model, consistency, cost and failure results, and links the runnable harness, tagged benchmark data and paper. Its measurements are author-reported rather than independent AccessAllGPT results.
Reviewed: August 20 submission and October 1 revision metadata; abstract; 507-workflow scope; isolated MCP-compatible sessions; terminal-state evaluation; repeat-reliability results; response-rubric boundary; released code and benchmark references
Supports: The author paper defines the benchmark and reports the reliability gap, including Claude Opus 5 moving from 66.50% pass@1 to 47.53% observed all-20 success and Kimi-K3 from 57.37% pass@1 to 17.60% under the paper’s pass^20 metric. These are paper results, not independent replication.
Reviewed: Annotated thinkingbox-bench-v1.0 tag target; August 17 release notes; repository tree; task and server layout; setup documentation; canonical task-selection surface; CDLA-Permissive-2.0 benchmark-data license
Supports: The immutable tag target exposes the executable 507-task benchmark across five business domains. The release predates the October 3 Hugging Face announcement, so the news is broader packaging and access rather than creation of a brand-new dataset.
Reviewed: Pinned public dataset revision; dataset card; 507-row task collection; five-domain scope; file inventory; task fields; CDLA-Permissive-2.0 license metadata; linked paper and source repository
Supports: The public, ungated registry revision establishes that the benchmark dataset is downloadable on Hugging Face and identifies its license and paper. Registry availability does not independently validate the authors’ model scores or cost estimates.
Reviewed: Pinned adapter tree; environment package; server and evaluator entry points; example usage; configuration and readiness surfaces; OpenEnv BSD-3-Clause repository license
Supports: The pinned OpenEnv artifact provides a public integration layer for running ThinkingBox episodes. It still requires local services, benchmark data and configured model endpoints; it is not a hosted benchmark API with included inference.
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.