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 five primary release surfaces on October 7, 2026: OpenAI’s official News RSS record, the public openai/math repository at immutable commit adc7f1241b42e322a6451854ab7e4b4c146bf78a, its README, the complete manuscript map, and its Lean/formalization materials. We checked the announcement timestamp, repository identity, catalogue counts, stated generation procedure, model-availability status, verification warnings, reasoning-summary list, release-history policy, license, and the distinction between manuscripts, result families and formalized artifacts. We cloned the exact initial commit and counted 722 preprint directories, 372 numbered result-family headings, ten reasoning-summary PDFs, 235 result-family documentation pages marked for Lean, 162 source records in the formalization catalogue and 405 JSON Comparator configurations. Those are repository-inventory observations, not proof validation. The canonical OpenAI article returned a Cloudflare challenge, so claims unique to its inaccessible body are excluded. We did not review all manuscripts, compile Lean, run Comparator, validate any theorem, assess novelty or priority, reproduce the generation process, identify independent referees, or use the unreleased model.
Reviewed: Official OpenAI News RSS title, description, canonical URL, Research category and October 6, 2026 12:00 UTC publication timestamp; the canonical article itself returned a Cloudflare challenge
Supports: OpenAI’s official feed says it published new results on open mathematics problems from an internal frontier model and shared Lean formalizations and research details on GitHub.
Reviewed: Collection purpose; verification warning; catalogue size; navigation; reasoning summaries; production procedure; exceptions; versioning and citation policy
Supports: The immutable release README reports 722 manuscripts in 372 families, says the vast majority came from one unreleased internal model using about three hours of ChatGPT Pro thinking compute per result across roughly 4,000 posed problems, and warns that some unformalized results could have issues.
Reviewed: Catalogue header and map structure; result-family summaries; manuscript abstracts; linked preprints; Lean markers; representative number theory, geometry, analysis and theoretical-computer-science entries
Supports: The immutable map exposes all 372 claimed result families and 722 linked manuscript directories, including high-consequence claims across number theory, geometry, analysis, complexity theory and mathematical physics.
Reviewed: Lean library README; formalization.yaml schema and source catalogue; result documentation; Comparator challenge README; tool prerequisites; cache and per-challenge verification commands
Supports: The release includes a Lean 4 library, a formalization catalogue and Comparator challenge configurations, while OpenAI explicitly says many—but not all—manuscripts have formalizations and recommends compiling small portions rather than the entire library at once.
Reviewed: Repository identity; public creation and push timestamps; initial commit hash and timestamp; default branch; Apache 2.0 license identification; complete initial-release tree
Supports: GitHub records the public openai/math repository and its initial commit on October 6, 2026; the reviewed commit is adc7f1241b42e322a6451854ab7e4b4c146bf78a and the root repository identifies Apache 2.0 licensing.
AccessAllGPT reviewed four first-party or primary release surfaces on October 7, 2026: Google DeepMind’s October 6 launch post, Google’s full model card, Google’s developer guide and the live official Hugging Face repository. We checked release timing, model identity, license, weight availability, architecture, modality and context contracts, task prompts, numerical-precision warnings, vendor benchmark tables, memory claims, integrations, safety notes and limitations. All quality, benchmark and device-memory results are vendor-reported; availability and repository facts were directly observed; deployment guidance below is AccessAllGPT analysis. We did not download the weights, run inference, reproduce a benchmark, measure latency or memory, evaluate retrieval quality, inspect training data, test fine-tuning, or price hardware and operating costs.
Reviewed: October 6, 2026 launch date; architecture and parameter counts; modality, context and vector-size claims; Pixel memory figures; benchmark summary; availability and supported tooling
Supports: Google launched EmbeddingGemma 2 as a 740M-parameter Apache 2.0 model for text, code, image, video and audio embeddings, with downloadable weights and a modular 270M text-only configuration.
Supports: Google provides the model ID google/embeddinggemma-2, Sentence Transformers loading examples and effective parameter counts for text-only, text-plus-vision, text-plus-audio and full-multimodal configurations.
Reviewed: Live repository identity and availability; publisher namespace; Apache 2.0 license label; Transformers and Sentence Transformers tags; model card and files navigation
Supports: The official Google repository was live under google/embeddinggemma-2 when reviewed and labeled for Apache 2.0, feature extraction, Transformers and Sentence Transformers.
AccessAllGPT reviewed four first-party surfaces on October 6, 2026: AWS’s October 5 launch post, the live Amazon Bedrock GLM 5.3 model card, the live Bedrock pricing table and Z.ai’s official Hugging Face model card. We checked launch date, customer eligibility, profile and base-model IDs, endpoints, APIs, modalities, context and output limits, supported Bedrock features, prompt-cache conditions, service-tier multipliers, token prices, model-scale descriptions and benchmark labels. AWS service facts and prices, Z.ai capability claims and AccessAllGPT deployment guidance are separated below. We did not obtain an eligible AWS account, invoke the model, inspect cross-Region routing, reproduce benchmarks, measure latency or cache hits, run Strix, download weights or verify an invoice.
Reviewed: October 5, 2026 publication date; eligibility statement; cross-Region profile IDs; supported APIs; authentication example; prompt-caching behavior; service tiers; Strix integration caveat; availability and cleanup notes
Supports: AWS announced GLM 5.3 on Amazon Bedrock for eligible enterprise customers, with Global and US cross-Region inference profiles, four API surfaces, implicit and explicit prompt caching, and Standard, Priority and Flex service tiers.
Reviewed: Model details; October 5 launch date; lifecycle; context and output limits; input and output modalities; APIs and endpoints; supported Bedrock features; caching minimum and retention; profile IDs; service tiers; regional availability; quotas and sample code
Supports: The live Bedrock model card documents a text-input/text-output model with a 1M-token context, 128K maximum output, profile IDs us.zai.glm-5.3 and global.zai.glm-5.3, cross-Region-only access, four API surfaces and at-least-30-minute explicit-cache retention.
Reviewed: GLM 5.3 On-Demand pricing table; Global and US CRIS input, output, cache-read and 30-minute cache-write rates; Standard tier basis; Priority premium; Flex and Batch discount
Supports: AWS lists Global Standard rates per million tokens of $1.68 input, $5.28 output, $0.312 cache read and $2.10 30-minute cache write; US CRIS rates are $1.848, $5.808, $0.3432 and $2.31 respectively. Priority adds 75%, while Flex and Batch are 50% below Standard.
Reviewed: Model overview; base-model relationship; benchmark table and run notes; 753B parameter label; coding and cyber claims; reasoning settings; deployment links; license label
Supports: Z.ai describes GLM-5.3 as a 753B-parameter open-weight model derived from the GLM-5.2 base through post-training, and reports coding and cyber benchmark results under disclosed but vendor-controlled harnesses. The card establishes publisher claims, not independently reproduced performance.
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.