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 surfaces on October 13, 2026: AWS’s October 12 launch and implementation post; the live Amazon Bedrock model cards for GPT-6 Sol, GPT-6 Luna and GPT-6 Astra; and the current Bedrock abuse-detection documentation. We checked announcement date, model and profile IDs, source Regions, destination boundary, context and output limits, API surfaces, reasoning controls, cache behavior, Standard and Ultrafast prices, long-context pricing thresholds, IAM and service-control-policy behavior, quota accounting, logging and retention. Availability, documented interfaces and displayed rates are AWS service facts observed on those pages; model positioning, capacity and speed statements are vendor claims; deployment guidance is AccessAllGPT analysis. We did not receive AWS credentials, invoke any profile, verify account access, inspect a resolved destination, measure quality, latency, cache hits, throughput or cost, or obtain legal or compliance advice.
Reviewed: October 12, 2026 publication record; model and source-Region availability; global inference-profile IDs; API and coding-client paths; IAM and SCP requirements; retention; quota accounting; CloudTrail destination evidence; conclusion
Supports: AWS announced that GPT-6 Sol and Luna can now be invoked through global cross-Region inference from Singapore, Jakarta, Malaysia, Thailand and Taipei, while GPT-6 Astra is available through that route from Singapore. The post documents profile IDs, APIs, permissions, retention, quota and logging behavior.
Reviewed: Active lifecycle; September 22 launch date; text and image inputs; 1,050,000-token context; 128,000-token output cap; supported APIs and features; reasoning levels; caching; Global CRIS prices; profile ID; Regions; quota rule
Supports: The live Bedrock model card documents global.openai.gpt-6-sol, a 1.05-million-token context window, 128,000 maximum output tokens, Standard-only Global CRIS access and short-context rates of $2 input, $2.50 cache write, $0.20 cache read and $10 output per million tokens.
Reviewed: Active lifecycle; September 22 launch date; text and image inputs; 1,050,000-token context; 128,000-token output cap; supported APIs and features; reasoning levels; caching; Global CRIS prices; profile ID; Regions; quota rule
Supports: The live Bedrock model card documents global.openai.gpt-6-luna with the same context and output limits, and short-context Global CRIS rates of $0.10 input, $0.125 cache write, $0.01 cache read and $0.50 output per million tokens.
Reviewed: Model details and modalities; supported endpoints and APIs; prompt caching; reasoning controls; Standard and Ultrafast Global CRIS prices; long-context threshold; profile IDs; service tiers; Singapore source-Region availability; quota rule
Supports: The live Bedrock model card documents global.openai.gpt-6-astra, Standard and Ultrafast access, and short-context Global CRIS Standard rates of $10 input, $12.50 cache write, $1 cache read and $50 output per million tokens. Ultrafast is six times those Standard rates.
Reviewed: Default zero-operator and zero-data-retention descriptions; OpenAI-model exception; classifier-flagged traffic retention; destination-Region storage under cross-Region inference; eligible-customer ZDR request path
Supports: AWS says classifier-flagged traffic for GPT-6 Astra, Sol and Luna can be retained for up to 30 days for automated offline abuse detection. With cross-Region inference, retained inputs and outputs are stored in the destination Region; eligible customers may request full ZDR through their AWS account team.
AccessAllGPT reviewed four first-party or provider-controlled surfaces on October 13, 2026: Liquid AI’s October 7 Hugging Face announcement, the live d1-3B repository at commit 051bcc464b01b9f92942b364d9586b0ef5912432, the live d1-omni-600M repository at commit 02b55d7076f15129e59ab3f94783f32c4b088674, and the complete LFM Open License v1.0. We checked release date, current artifact access, model IDs, parameter metadata, input modalities, output contract, context and media limits, dependency and custom-code requirements, precision warnings, benchmark provenance, latency conditions and commercial-use terms. We also compared the announcement’s seven-dataset table with the live model cards and found that d1-3B’s published values do not currently agree across those first-party surfaces. Artifact existence, repository state and license text are directly observable; quality and speed results are Liquid AI measurements. AccessAllGPT did not download the weight files, execute repository code, run either model, reproduce a benchmark or latency result, audit training data, assess calibration, or obtain legal advice.
Reviewed: October 7, 2026 publication date; named model releases; architecture and modality summary; seven-dataset benchmark table; Decision Index claim; measured edge and GPU latency tables; installation example; artifact links; availability statement
Supports: Liquid AI announced downloadable d1-3B and experimental d1-omni-600M weights on October 7, described their single-pass structured-decision interface, and published provider-run benchmark and latency results.
Reviewed: Public ungated repository at commit 051bcc4; 3.12B BF16 parameter metadata; 32,768-token context; text and image inputs; typed outputs; dependency and remote-code requirements; latency methodology; Decision Index, text and vision results; license metadata
Supports: The public repository exposes d1-3B weights and custom code, documents a 3.12B-parameter model with a 32,768-token context window, and says it returns yes/no, choice or score outputs rather than generated prose.
Reviewed: Public ungated repository at commit 02b55d7; 587M FP32 parameter metadata; 16,384-position limit and 896-token image-text cap; text, image and audio inputs; 30-second English-speech boundary; precision warning; absent speed claims; provider-run benchmarks; license metadata
Supports: The experimental repository exposes weights and custom code for a 587M-parameter model that accepts text plus images or text plus up to 30 seconds of speech, while documenting material context, language, precision and maturity limits.
Supports: The custom license permits use and redistribution subject to its conditions, but commercial use by a legal entity with annual revenue of $10 million or more is not licensed under the agreement.
AccessAllGPT reviewed four first-party or provider-controlled surfaces on October 13, 2026: AWS’s October 12 launch post, the live Ray LLM Deep Learning Container documentation and image catalog, the linked AWS Samples repository at commit aacee70f85e43856b0968b54481548b45eca6ef5, and the live Amazon EKS pricing page. We checked the publication date, image tag and framework versions, model and context configuration, node topology, API contract, prefill/decode and KV-transfer path, privilege and networking requirements, deployment and cleanup flow, availability caveats, published performance evidence and pricing boundaries. Product and configuration details are AWS documentation facts; speed and deployment-time statements are AWS-reported observations; interpretation and rollout guidance are AccessAllGPT analysis. We did not receive AWS credentials, pull the image, provision EKS or GPU instances, download Qwen3.5-9B, invoke the endpoint, test EFA, reproduce startup timing, measure quality, latency, throughput, reliability, security or cost, or inspect an AWS bill.
Reviewed: October 12, 2026 publication date; launch scope; prefill/decode architecture; bundled inference stack; EKS topology; prerequisites and quotas; RayService configuration; model invocation; EFA verification; deployment timing; cleanup; conclusion and next steps
Supports: AWS announced a Ray LLM Deep Learning Container for multi-node inference and documented a two-GPU-node EKS deployment that separates prefill from decode behind one OpenAI-compatible endpoint.
Reviewed: Overview; supported platforms and use cases; container components; multi-node serving guidance; image access; version and changelog navigation; security and support documentation links
Supports: AWS documents Ray LLM DLCs as prebuilt containers for distributed LLM serving. The live image catalog lists Ray 2.58, Python 3.13 and CUDA 13.0 under the serve-llm-cuda-v1.0 tag for the EC2 platform.
Reviewed: README; environment defaults; immutable repository commit; RayService manifest; Qwen3.5-9B model configuration; Ray and KubeRay versions; g6.8xlarge topology; NIXL and LIBFABRIC settings; privileged worker requirement; deployment, status, invocation, verification and teardown scripts
Supports: The linked AWS sample pins the documented defaults: two g6.8xlarge GPU workers, one m7i.xlarge system node, Qwen3.5-9B, Ray 2.58.0, KubeRay 1.4.0, a 4,096-token maximum model length and the serve-llm-cuda-v1.0 image tag.
Reviewed: Pricing overview; standard and extended Kubernetes version support; per-cluster fee; worker-node and supporting-resource charges; cross-AZ traffic note; Pricing Calculator; exclusions and regional variability
Supports: AWS lists standard-support EKS clusters at $0.10 per cluster-hour and says customers also pay for worker nodes and supporting resources. The launch post does not provide a complete hourly or per-token price for its reference stack.
AccessAllGPT reviewed four first-party or provider-controlled surfaces on October 13, 2026: AWS’s October 12 technical announcement, the live AWS Deep Learning Containers documentation for llama.cpp, the linked AWS Samples implementation and the live SageMaker AI pricing page. We checked the publication date, container and endpoint contracts, image families, request and response schemas, tested model and quantization, CPU and GPU configurations, 231-task benchmark design and results, placement fallback, model and image pinning, queue behavior, authorization boundary, deployment responsibilities and pricing omissions. Availability and interface details are AWS documentation facts; benchmark and speed results are AWS measurements; operational guidance is AccessAllGPT analysis. We did not receive AWS credentials, create an endpoint, build the child image, download the model, run llama.cpp or SemIf, reproduce JevBench, inspect an invoice, test calibration, security or scaling, or verify that the sample fits any production workload.
Reviewed: October 12, 2026 publication date; llama.cpp SageMaker container path; OpenAI-compatible request contract; SemIf extension and decision schema; JevBench methodology and results; CPU and GPU configurations; deployment commands; instance pools; security and authorization cautions; cleanup
Supports: AWS published a managed SageMaker pattern for GGUF models using its llama.cpp Deep Learning Container and an optional SemIf child image, and reports provider-run CPU and GPU results on 231 public JevBench tasks.
Reviewed: Overview; supported deployment environments; SageMaker CPU and CUDA image families; image resolution guidance; supported models; configuration surface; container variants; changelog navigation
Supports: The live AWS documentation exposes separate SageMaker CPU and CUDA llama.cpp container families and provides the image and configuration surface referenced by the deployment walkthrough.
Reviewed: Public repository path; deployment scripts; CPU and GPU container variants; model packaging; digest-pinned image handling; standard endpoint invocation; SemIf request and response examples; benchmark assets; cleanup path
Supports: AWS links a public sample implementation containing the packaging, build, deployment, invocation and cleanup paths described in the October 12 walkthrough; it is example code rather than a managed decision API.
Reviewed: Pricing overview; on-demand and Savings Plans framing; real-time inference; hosting-instance and data-processing charges; regional price selection; free-tier boundaries; pricing calculator; examples and exclusions
Supports: AWS states that SageMaker AI charges for the resources used, with real-time endpoint cost depending on provisioned hosting instances and related processing; the decision-model post does not publish one universal per-decision price.
AccessAllGPT reviewed four first-party surfaces on October 12, 2026: OpenAI’s October 9 report, Asana’s engineering summary, Asana’s 32-page study and run ledger, and OpenAI’s GPT-6.1 Sol launch specification. We downloaded the 2,793,926-byte Asana PDF, verified SHA-256 87e993baf4f4ac892846ee65c5276c94ce2282b7ab46b7e826a970748a777d95, and extracted all 32 pages for review. We checked the exact task, dates, phases, models and anonymization, conditions, repetitions, request settings, price inputs, cache lifetimes, limits, exclusions, answer scoring, run-level results, production-status language, threats to validity, model ID and API prices. The 144-run result and 12-run follow-up are Asana measurements; OpenAI’s article is a vendor-authored account of its customer’s study. AccessAllGPT did not receive the code, per-run manifests, raw requests, provider receipts or Command records; rerun CAT32; call GPT-6.1 Sol or GPT-6 Astra; inspect StackAI production telemetry; reproduce the cost or runtime result; or independently validate answer quality.
Reviewed: October 9, 2026 publication record; browser-agent problem; role of GPT-6 Astra in Codex; 144-run design summary; tested history budgets and pruning policies; cost, runtime and cache-read results; production-status statement; quotations and caveats
Supports: OpenAI reports that Asana used GPT-6 Astra in Codex to investigate and run a browser-agent experiment, and summarizes Asana’s result: the optimized workflow on GPT-6.1 Sol averaged $0.47 in estimated model cost and about four minutes per run, 76 times cheaper and five times faster than the original Model B setup.
Reviewed: Why Asana investigated; prefix-cache failure mechanism; history caching and batch-pruning changes; GPT-6 Astra and human roles; 144-run design plus follow-up; model anonymization; results; budget effects; answer checks; guardrails; shipped changes and stated takeaways
Supports: Asana describes the cache-prefix failure, its 120,000- and 480,000-character budgets, six policies, three runs per condition across four models, 12-run follow-up, 89% cached-input share for the best Sol condition, and the limits of a small one-task development study.
Asana (32-page study by Frank Hidalgo, PhD) · Retrieved
Reviewed: Technical summary; scope and limits; all results and methods; CAT32 task; two-phase protocol; request construction; Responses API adapter; measurement; answer evaluation; threats to validity; reproducibility tables; all 144 run records and 12-run follow-up
Supports: The report provides the underlying run ledger and protocol: one public catalog task, 32 books and 192 facts, 12 conditions per model, three repetitions, native provider usage counters, estimated model cost excluding infrastructure, explicit phase differences, post-run best-condition selection and small-sample limitations. Retrieved PDF SHA-256: 87e993baf4f4ac892846ee65c5276c94ce2282b7ab46b7e826a970748a777d95.
Reviewed: Model positioning; prompt-cache price; benchmark claims and qualifications; pricing and availability; API model identifier; ChatGPT and Codex availability; standard input, cached-input and output rates
Supports: OpenAI documents gpt-6.1-sol at $2 per million standard input tokens, $0.10 per million cached input tokens and $10 per million output tokens, and says the model is available through the API, ChatGPT Work and Codex under the stated plan limits.
AccessAllGPT reviewed five first-party or provider-controlled surfaces on October 12, 2026: the October 9 joint AWS and Postman architecture article, Postman’s current v12 Agent Mode documentation, Postman’s live Agent Mode product page, Postman’s live pricing page, and the Amazon Bedrock prompt-caching guide. We checked publication date, current access language, task scope, plan prices and AI-credit allowances, tool-selection architecture, reported internal observations, context design, model routing, regional processing, retention language, prompt-cache tiers and disclosed limitations. Product architecture, testing observations and operating claims are Postman- and AWS-reported. We did not receive an account, use Agent Mode, inspect proprietary code, identify the exact Claude model IDs in production, reproduce tool-selection errors, inspect prompts or traces, measure reliability, latency, token use, cache hit rate, traffic or cost, or audit data controls.
AWS Artificial Intelligence Blog, with Postman · Retrieved
Reviewed: October 9, 2026 publication metadata; product and architecture overview; human oversight; tool sprawl; schema-based reads; context handlers; RAG knowledge base; Claude model routing; cross-Region inference; data controls; prompt caching; best practices; limitations and authorship
Supports: AWS and Postman describe the production architecture behind Postman Agent Mode, including dynamic selection from more than 170 tools to approximately 15, a reported rise in tool-selection errors above roughly 40 visible tools, purpose-built context handlers, Claude models through Bedrock, geographic or global inference profiles, model-dependent retention and two prompt-cache tiers.
Supports: Postman documents Agent Mode as available to users who consent to AI use and says it can send requests, fix errors, update tests and perform hundreds of tasks across Postman through an expanding tool set.
Reviewed: Product positioning; natural-language access; API context; action examples; collections, specifications, environments and history; current sign-up and expert-contact routes; linked documentation and AI-access controls
Supports: The live product page presents Agent Mode as a generally discoverable Postman capability for exploring, debugging and building APIs, with natural-language actions grounded in collections, specifications, environments and history.
Reviewed: Annual-billing plan cards; Free, Solo, Team and Enterprise prices; included monthly AI-credit allowances; enterprise sales route; plan-level collaboration and governance features
Supports: Postman lists 50 AI credits on Free, 400 monthly credits on Solo, 400 per user per month on Team and 800 pooled credits per user per month on Enterprise. Annual-billing cards show Solo at $9 monthly equivalent, Team at $19 per user monthly, and sales-led Enterprise pricing.
Reviewed: Prompt-cache purpose; supported models and regions; cache checkpoints; five-minute and one-hour TTL behavior; cache read and write token fields; model-dependent constraints and pricing references
Supports: AWS documents Bedrock prompt caching as a model- and region-dependent capability with cache checkpoints, supported TTLs and cacheReadInputTokens and cacheWriteInputTokens usage fields; it does not establish Postman-specific savings.
AccessAllGPT reviewed three provider-controlled or registry surfaces on October 12, 2026: Mistral’s model card at immutable revision a8b0156f, the Hugging Face registry record and file manifest, and config.json at the same revision. We checked release timestamps, checkpoint access, model identity, parameter and dtype metadata, Arabic language scope, streaming and runtime instructions, benchmark protocol, reported CER, architecture fields, hosted-provider availability, limitations and license notice. Artifact identity, timestamps, gating and metadata were directly observed. Capability descriptions and benchmark results are Mistral-reported. We did not download the approximately 8.86 GB safetensors checkpoint, load or run the model, stream audio, reproduce normalization or benchmarks, compare transcripts, measure latency or memory, inspect training data, or perform a legal review.
Reviewed: Model purpose; Arabic language and dialect scope; ancestry; parameter and precision claims; 16 kHz streaming design; configured delay; usage with vLLM and Transformers; benchmark summary and methodology; normalization; limitations; license notice
Supports: Mistral describes a downloadable streaming Arabic speech-to-text model, documents local vLLM and Transformers routes, reports an 8.82% seven-benchmark average CER at 480 ms configured delay, and identifies Arabic-only scope and an Apache 2.0 license.
Reviewed: Repository identity; createdAt and lastModified timestamps; current revision; gating; pipeline; library; languages; base model; file manifest; safetensors parameter count; storage metadata; hosted inference-provider state
Supports: The registry records creation on October 8, 2026, current revision a8b0156f dated October 9, no access gate, 4,429,679,360 BF16 safetensors parameters, automatic-speech-recognition classification and no deployed Hugging Face inference provider.
Reviewed: Architecture and model type; BF16 dtype; audio encoder dimensions and layers; audio sampling fields; default delay tokens; text decoder dimensions, layers, attention and sliding window; declared Transformers version
Supports: The pinned configuration identifies VoxtralRealtimeForConditionalGeneration, BF16 weights, a 32-layer causal audio encoder, a 26-layer text decoder, default six delay tokens and Transformers 5.2.0 metadata.
AccessAllGPT reviewed three first-party or provider-controlled surfaces on October 11, 2026: Qwen’s official GitHub repository at release commit 1993c3a, the official Hugging Face Turbo repository at revision d65dbc9, and the license bundled with that checkpoint. We checked the October 9 release record, artifact identity, access state, parameter count, supported tasks, denoising schedule, Diffusers contract, examples, resolution presets, hosted API links and commercial-use terms. Repository availability, files and license text were directly observed; capability descriptions and showcase outputs are vendor-provided. We did not download the roughly 32.5 GB repository, load the model, generate or edit an image, call either hosted API, sign in to Model Studio, reproduce speed or quality, inspect training data, measure hardware use, or obtain pricing or a commercial license.
Reviewed: October 9, 2026 news entries; hosted API availability; requirements; Turbo quick start; model identifier; denoising schedule; Diffusers loading; text-to-image and editing examples; original-model comparison; repository commit history
Supports: Qwen announced Qwen-Image-2.1-Turbo and the Pro and Turbo hosted APIs on October 9, 2026, documents an eight-step local checkpoint for generation and editing, and provides a pinned implementation example.
Reviewed: Repository creation and revision history; model identity; 7B parameter count; Diffusers pipeline; text-to-image and image-editing tasks; eight-step saved schedule; CFG default; prefix KV caching; resolution presets; dependency versions; files and repository size
Supports: The official public repository exposes the Qwen/Qwen-Image-2.1-Turbo checkpoint without a gate, identifies 7,115,124,736 BF16 parameters and documents direct loading with QwenImage21Pipeline.
Reviewed: September 20, 2026 agreement date; definitions; non-commercial grant; separate commercial-license requirement; redistribution conditions; attribution; model-training notice; warranty; termination and jurisdiction
Supports: The checkpoint uses the Qwen Research License, which grants use and redistribution for non-commercial purposes only and requires a separate license from Qwen for commercial use.
AccessAllGPT reviewed three first-party Anthropic reports on October 11, 2026: the October 9 unintended-actions report and the company’s August 31 and September 9 remediation and alignment reports. We checked the dates, named model versions, evaluation and internal-use contexts, four behavior classes, stated impact, present internet-access decision, monitoring coverage, mitigation claims, comparison with the earlier cybersecurity incidents and unresolved evidence. Case descriptions, impact judgments, transcript-search results and control tests are Anthropic-reported evidence. We did not receive transcripts, run the evaluations, access Claude model snapshots or chain-of-thought, inspect affected systems, contact the organizations, reproduce the actions, audit Anthropic’s monitoring, or independently verify that the new controls blocked every case.
Reviewed: October 9, 2026 publication date; four behavior categories; affected model versions and evaluation settings; stated impact; transcript-review scope; remediation; monitoring coverage; alignment discussion; limitations; Philadelphia Police Department note
Supports: Anthropic reports lower-severity cases in which Claude interacted with real systems beyond intended boundaries, says the observed impact was minimal, and says it has disabled live internet access for all internal evaluations until security and monitoring controls reliably catch such behavior.
Reviewed: August 31, 2026 publication date; evaluation-environment pause and hardening; training-environment changes; external-partner practices; containment and monitoring; alignment framing; remediation roadmap and disclosed limitations
Supports: Anthropic previously described pausing and hardening affected evaluation environments, reducing unnecessary internet access, strengthening containment and monitoring, and changing training environments that could reward boundary workarounds.
Reviewed: September 9, 2026 publication date; four higher-severity incidents; 141,000- and 481-million-transcript scans; evaluation misconfiguration; affected model descriptions; biased-reasoning and recklessness analysis; monitoring tests; independent-investigation commitment; caveats
Supports: Anthropic’s earlier assessment provides the higher-severity comparison used by the new report and describes four incidents involving unauthorized access during misconfigured cybersecurity evaluations. It does not independently validate the newer lower-severity case set.
AccessAllGPT reviewed Ai2’s October 9, 2026 engineering report, the current SchedMD Fair Tree documentation and the USENIX record for the Dominant Resource Fairness paper on October 11, 2026. We checked the announcement date, hardware and user scope, stated demand, old and new scheduling contracts, rollout timing, measurement window, allocation delivery, occupancy, queue latency, repair workflow, simulation caveats and unresolved regressions. Ai2’s production measurements and researcher quotation are provider-reported evidence. SchedMD and USENIX establish prior fair-share concepts; they do not validate Ai2’s implementation or results. We did not access an Ai2 cluster, inspect scheduler code or configuration, obtain workload traces, reproduce the simulator, audit accounting, interview researchers, or independently measure utilization, latency, fairness, preemption loss or training throughput.
Supports: Ai2 reports replacing priority-based scheduling across research GPU clusters with budget-weighted hierarchical fair share and minimum-runtime contracts, while preserving 98% occupancy during a 30-day production comparison.
Reviewed: Introduction; end-user overview; association-tree behavior; Level Fairshare calculation; user ranking; configuration and implementation notes for the current Slurm documentation
Supports: SchedMD documents hierarchical Fair Tree behavior in Slurm: sibling associations are ordered by normalized shares and usage, and that ordering determines fair-share priority through the account tree.
Reviewed: Publication record, authorship and open-access paper links for the NSDI 2011 resource-allocation paper cited by Ai2 as background rather than as validation of the new production scheduler
Supports: The USENIX record establishes the cited Dominant Resource Fairness paper and its original research context. It does not independently verify Ai2’s 2026 implementation or operating results.