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 three Google-controlled public surfaces on October 21, 2026: the dated in-car launch announcement, the Personal Intelligence product page and Google’s Connected Apps personalization help article. We recorded announcement date, current and promised availability, connected sources, example requests, setup and age notices, pricing and API omissions, data-use statements, human-review language, and disconnect and deletion behavior. All capability, availability, security and privacy descriptions are Google statements. AccessAllGPT did not enter a vehicle, sign in, receive the rollout, connect an app, issue a voice request, inspect an answer or source reference, test distraction handling, measure accuracy or latency, or verify account, language, vehicle or regional eligibility.
Reviewed: October 20 publication date; global Gemini availability in cars; Personal Intelligence rollout timing; connected Google apps; example requests; opt-in controls; Android Auto and Google built-in sequencing
Supports: Google says Gemini is available globally on Android Auto and cars with Google built-in. Personal Intelligence began rolling out to Android Auto on October 20, 2026, while availability in cars with Google built-in is promised for the coming months. The announcement lists Workspace, Photos, Maps, YouTube and Search as connected sources and says setup is optional.
Reviewed: Connected-app description; chat-history preferences; opt-in app selection; activity and settings controls; source-reference behavior; compatibility, availability, setup and age notices
Supports: Google describes Personal Intelligence as an optional personalization layer that can connect Gmail, Photos, Search and YouTube with chat-history preferences. The page says users can select connected apps, manage past-chat use and review activity; it also warns that responses require checking, setup is required, compatibility and availability vary, and users must be 18 or older.
Reviewed: Eligible-user scope; connected-app list; data categories; sensitive-data notice; service improvement and model training; human review; cross-service sharing; app disconnection; activity deletion; personalization settings
Supports: Google says connected data can include email, files, events, photos, videos, contacts, Wallet information and location, including sensitive or confidential information. It says data may be used to improve services and train generative AI, may be human reviewed under the Gemini Apps Privacy Notice, and is not automatically removed from Gemini Apps Activity when an app is disconnected or source data is deleted.
AccessAllGPT reviewed four project-controlled public surfaces on October 21, 2026: the IBM-authored October 20 report hosted by Hugging Face, the ALTK-Evolve repository at commit 5f76fd190bb9ad427dbac8f95e22f17cd8fddcb8, and the live τ²-bench retail and airline submission pull requests. We separated held-out test results from base-split leaderboard submissions; recorded task counts, trials, model roles, Pass^4, reported conversation cost, prompt modifications, train-task overlap, verification state, package version and license; and treated every performance result as author-reported. AccessAllGPT did not install ALTK-Evolve, call either OpenAI model, rerun τ²-bench, inspect all trajectories, reproduce costs or independently validate the leaderboard ranking.
Reviewed: October 20 publication date; guideline-learning method; held-out retail and airline design; mean accuracy and Pass^4 tables; token-cost and turn-count tables; base-split leaderboard comparisons; links to code and submissions
Supports: The IBM-authored report says prompt-appended guidelines learned from training trajectories improved GPT-4.1 held-out retail Pass^4 from 57.5% to 67.5% and held-out airline Pass^4 from 35% to 45%; it also reports higher input cost, domain-dependent guideline dosage, and separate base-split leaderboard comparisons.
Reviewed: README overview and October 20 update; source and PyPI installation routes; Python requirement; model-provider configuration; package version; Apache 2.0 license; local dashboard and API warning; available memory tools
Supports: The public repository provides ALTK-Evolve under Apache 2.0, lists Python 3.12 or newer, documents source and PyPI installation, and identifies core package version 1.7.1 at the reviewed commit. The README warns that the default local dashboard API has no authentication.
Reviewed: Open pull-request state; retail base-split task and trial counts; Pass^1 through Pass^4; average conversation cost; model and simulator; modified-prompts disclosure; train-task overlap; trajectory link; expected Unverified status
Supports: The still-open submission reports 67.54% Pass^4 over 114 retail base-split tasks and four trials at $0.1084 average cost per conversation. It discloses modified prompts, says guidelines were learned from the train split included in the base evaluation, and expects an Unverified label.
Reviewed: Open pull-request state; airline base-split task and trial counts; Pass^1 through Pass^4; average conversation cost; model and simulator; modified-prompts disclosure; train-task overlap; trajectory link; expected Unverified status
Supports: The still-open submission reports 82.00% Pass^4 over 50 airline base-split tasks and four trials at $0.1690 average cost per conversation. It discloses modified prompts, says guidelines were learned from the train split included in the base evaluation, and expects an Unverified label.
AccessAllGPT reviewed three Google-controlled public surfaces on October 21, 2026: the dated small-business launch announcement, Google’s September Connected Apps announcement and the Gemini Apps Privacy Hub updated October 19. We checked what Google says is rolling out, intended users, named integrations, invocation paths, model reference, rollout timing, pricing omissions and documented data controls. Availability, speed, user-count and customer-outcome statements remain Google claims. AccessAllGPT did not sign in, receive the rollout, connect an account, upload a brand kit, generate an asset, analyze financial records, inspect permissions, observe retention, measure accuracy or latency, compare plans, or test Nano Banana 2.1.
Reviewed: October 20 publication date; business landing page; Google and third-party app connections; brand-style inputs and Nano Banana 2.1; finance analysis; named customer examples; global rollout timing and intended account scope
Supports: Google announced a Gemini business landing page, marketing and finance skills, brand-style retention, and additional connected apps on October 20, 2026. Google says the features are beginning to roll out globally in the coming days to all Gemini app users; the page does not publish a feature-specific price, fixed quota or developer API.
Reviewed: September 23 publication date; rollout wording; productivity, creativity and lifestyle app categories; named integrations; connection through Gemini settings; @-mention and direct-request invocation
Supports: Google previously documented a broader Gemini Connected Apps rollout, including Adobe, Zoho and other third-party services, and says users can connect apps in Gemini settings or invoke them through an @ mention or direct request. The announcement does not define one common permission or data contract for every provider.
Reviewed: October 19 update; information from Connected Apps; provider-specific data processing; Connected Apps controls; Keep Activity behavior; human review; temporary-chat retention; third-party service and custom-app warnings
Supports: Google says Gemini can exchange content with Connected Apps, third-party apps process shared data under their own policies, and users can connect or disconnect apps in settings. The notice says Keep Activity affects availability and improvement use, warns against entering confidential information users would not want reviewed, and describes separate retention behavior for temporary chats and reviewed data.
AccessAllGPT reviewed five first-party public surfaces on October 21, 2026: OpenAI’s dated Terminal X customer story, Terminal X’s live product page, OpenAI’s GPT-6.1 Sol model reference, current API pricing tables and original GPT-6.1 Sol launch article. We checked the publication date, evaluated task, 54-criterion rubric description, 500-judgment sample, agreement and mandate-drift figures, cost and speed comparison, platform availability, model ID, current limits and list prices. All performance, savings, usage and adoption figures are attributed to OpenAI’s customer story or Terminal X; they are not AccessAllGPT measurements. We did not receive the judgment set, rubric, predictions, reviewer labels, comparator identity, token logs, invoices, confidence intervals, error analysis or customer records; contact Terminal X, use its product, or call the OpenAI API.
Reviewed: October 20 publication date; product and deployment description; Capital Memory workflow; 54-criterion Eval Agent; 500-judgment agreement result; mandate-deviation detection; comparative cost and speed; customer savings; usage and adoption figures
Supports: OpenAI published a Terminal X customer story on October 20, 2026. It reports Terminal X-provided results for an investment-workflow Eval Agent using GPT-6.1 Sol, including 88.6% agreement with a senior reviewer across 500 scored judgments, 25% higher mandate-deviation detection and about 30% lower cost per query than an unnamed next-best model.
Reviewed: Current product positioning; Capital Memory; data, workflow, trace, editing, report, evaluation and delivery stages; on-premise deployment statement; booking-based access; real-time evaluation claims
Supports: Terminal X currently markets an investment-team AI platform that combines firm data, workflows and memory, exposes agent steps and sources, evaluates changes against answer keys from customer work, and is offered through a book-a-demo motion with on-premise deployment.
Reviewed: Current model identifier and positioning; text and image input; context and output limits; reasoning levels; Responses and Chat Completions support; tools; batch and residency availability; long-context and regional price qualifications
Supports: OpenAI currently documents gpt-6.1-sol as an available API model with a 1,050,000-token context window, 128,000 maximum output, five reasoning-effort levels, Responses API tools, Batch support, and US and EU data-residency options subject to documented constraints.
Reviewed: Standard GPT-6.1 Sol short- and long-context token tables; cache-write and cached-input rates; Batch, Flex and Fast processing; long-context threshold; regional-processing uplift
Supports: The current Standard short-context table lists GPT-6.1 Sol at $2 input, $0.10 cached input, $2.50 cache writes and $10 output per million tokens. Requests over 272,000 input tokens use higher rates for the full request, and processing modes and regional routing can change the price.
Reviewed: Model positioning; September 2026 launch; vendor capability claims; standard token prices; coding, professional-work and computer-use sections; pricing and availability
Supports: OpenAI introduced GPT-6.1 Sol in September 2026 for coding, computer use and professional work, positioning it between Astra and Luna. The launch article provides vendor-run model results and list-price context, not independent validation of Terminal X performance.
AccessAllGPT reviewed four Hugging Face-controlled public surfaces on October 20, 2026: the dated launch article, immutable AnyLanguageModel 1.0.0 GitHub release, tagged README, Package.swift and Apache-2.0 license, and the companion swift-huggingface and linked swift-xet 1.0 release notes. We checked the announcement and artifact chronology, installable version, platform requirements, provider and runtime list, shared features, package traits, license, credential guidance and breaking migrations. Capability and compatibility statements are project claims. AccessAllGPT did not install Xcode 26.4 or Swift 6.3, resolve the package, build an Apple or Linux application, download a model, invoke a hosted API, test tool calling or guided generation, measure binary size, latency, memory, energy, quality or cost, audit dependencies, or submit an app for Apple review.
Reviewed: October 20 publication date; 1.0 release announcement; drop-in API claim; local and hosted provider list; tool calling, guided generation and image support; Linux support; example local models; upgrade guidance; future-work statements
Supports: Hugging Face announced the AnyLanguageModel 1.0 milestone on October 20 and describes one Swift session API spanning Apple Foundation Models, local Core ML, MLX and llama.cpp backends, and multiple hosted model APIs. It also identifies swift-huggingface 1.0 and swift-xet 1.0 as companion releases.
Reviewed: Immutable 1.0.0 tag and commit; October 13 artifact date; breaking JSONValue and @Generable changes; swift-huggingface 1.0 requirement; JSON Lines streaming; prompt attachments; Linux cancellation; release comparison and source availability
Supports: The canonical repository exposes an immutable 1.0.0 release dated October 13, 2026. Its migration notes identify source and generated-content incompatibilities and require swift-huggingface 1.0, while the tagged code and release are publicly available.
Reviewed: Tagged installation and usage documentation; supported local and hosted providers; Swift and operating-system requirements; package traits and dependency-resolution workaround; credential guidance; tool-execution delegate; Apache-2.0 license; tagged Package.swift platform declarations
Supports: The tagged project requires Swift 6.3 and Xcode 26.4, supports iOS 17, macOS 14, visionOS 1 and Linux, and ships under Apache 2.0. Optional CoreML, MLX and Llama traits add their respective local runtimes; hosted providers retain their own credentials, usage prices and terms.
Reviewed: swift-huggingface October 9 release; swift-xet 1.0 dependency; public API changes; bucket downloading; provider and error-type migrations; linked swift-xet October 6 release; stable downloader API; error and configuration migrations; multipath default change
Supports: The companion download stack reached 1.0 before AnyLanguageModel: swift-xet on October 6 and swift-huggingface on October 9. Both releases stabilize public APIs but include breaking migrations that applications with direct dependencies must review.
AccessAllGPT reviewed three Google-controlled public surfaces on October 20, 2026: the October 19 co-creation announcement, the current Gemini Notebook plan comparison and Google’s August 28 compute-limit notice. We compared the exact creation sequence, review controls, optional fast path, rollout language, plan positioning and limit mechanics. Capability and availability statements remain vendor claims. AccessAllGPT did not sign in, receive the rollout, upload sources, generate or edit a deck or video, inspect citations or prompts, identify the underlying model, compare account tiers, exhaust a quota, measure rendering time, or test factual accuracy, source fidelity, visual quality, accessibility, export behavior or pricing.
Reviewed: October 19 publication date; source-based topic suggestions; Review toggle; outline and producer-note review; feedback loop; direct-to-review prompting; optional fast path; global rollout timing and intended user scope
Supports: Google says Gemini Notebook is adding a guided co-creation flow for Slide Decks and Video Overviews. Users can approve suggested topics, review outline or producer notes, request changes before rendering, or skip review. Google says the capabilities are rolling out globally to all users during the week following October 19.
Reviewed: Standard and paid plan comparison; Slides and artifact generation; usage-limit multipliers; model-access descriptions; feature-access labels; source limits; regional plan caveat; organization and education routes
Supports: Google’s current plan page includes Slide generation in the standard Gemini Notebook plan, lists higher limits and access levels for AI Plus, Pro and Ultra, and says paid plans are region-dependent. It does not assign a separate price or quota to the new co-creation controls.
Reviewed: Compute-specific limits; five-hour refresh; prompt, chat, source and feature factors; alternative-output suggestions; deferred Slide Deck and Video Overview generation; notification option; consumer web and mobile rollout
Supports: Google says consumer limits refresh every five hours and vary with prompt complexity, chat length, source count and feature choice. Slide Decks and Video Overviews can be deferred when an account reaches its current allowance, so the new review flow does not imply unlimited or immediate rendering.
AccessAllGPT reviewed four first-party or project-controlled public surfaces on October 20, 2026: the PyTorch launch post, Hugging Face integration post, live AWS Neuron 2.33 documentation and canonical AWS GitHub repository. We reconciled announcement and availability dates; release status; supported hardware and PyTorch line; execution, distribution and profiling surfaces; library integration; published coverage claims; known gaps; license and artifact availability. Vendor and project claims are labeled as such. AccessAllGPT did not provision a Trainium instance, install Neuron 2.33, run a model, reproduce the 100,000-model sweep, inspect its fixtures, measure fallback frequency, compile time, throughput, latency, numerical parity or cost, or compare Trainium with a GPU.
Reviewed: October 19 announcement and October 20 public-beta chronology; Trainium2 and Trainium3 support; eager, adaptive eager and torch.compile modes; distributed APIs; NKI and profiling; Hugging Face and TorchTitan integrations; model-coverage methodology; upstream-test skips; install and open-source availability
Supports: The launch post announces TorchNeuron as an Apache-2.0 native PyTorch backend for Trainium, scheduled for public beta on October 20. It documents eager and compiled execution, standard distributed APIs, profiling and NKI, while identifying unsupported operations and features in the first release.
Reviewed: October 19 publication and October 20 availability statement; migration from Optimum Neuron; Transformers inference and training; supported-library list; architecture-coverage claim; static-shape, async-loading and compilation constraints; Diffusers and Hub kernel examples
Supports: Hugging Face says Transformers, Diffusers, Accelerate, PEFT and TRL now use Trainium through the native backend. It reports out-of-box support for 89% of the top 100 text-generation architectures versus 46% with Optimum Neuron 0.4.5, and documents important static-shape and asynchronous-loading caveats.
Reviewed: Neuron 2.33 public-beta status; Trn2 and Trn3 scope; PyTorch 2.14 autoloading; execution modes; operator fallback and compile-failure behavior; supported data types; cache and recompilation behavior; inference, distributed training and NKI support; open-source and legacy-version guidance
Supports: The live AWS documentation confirms public beta in Neuron 2.33 on Trn2 and Trn3, identifies PyTorch 2.14 as the native-backend line, explains CPU fallback in eager mode versus default compile failure for unsupported lowering, and recommends TorchNeuron for new PyTorch 2.14 workloads.
Reviewed: Public repository status; main branch; 2.33.0 release commit and tag; repository description; source tree; Apache-2.0 license; README installation and platform scope
Supports: The canonical repository is publicly readable and exposes the TorchNeuron source, documentation, tests and 2.33.0 release under the Apache-2.0 license. This verifies that the promised open-source artifact is live rather than merely announced.
AccessAllGPT reviewed four AWS-controlled public surfaces on October 20, 2026: the October 9 availability notice, AWS’s October 19 technical walkthrough, the live Pegasus v1.5 Bedrock model card and the current Bedrock pricing page. We reconciled the model, availability and explainer dates; model IDs; inference paths; input and output limits; supported modes; regional routing; billing dimensions and documented caveats. These are vendor documents, not independent performance evidence. AccessAllGPT did not obtain AWS or TwelveLabs credentials, submit video, compare Pegasus 1.2, reproduce timestamps or JSON, test reference images, measure latency or accuracy, inspect data handling, or receive a bill.
Reviewed: October 19, 2026 publication date and October 9 availability chronology; new capabilities; runtime model ID and APIs; analysis modes; input methods and limits; request and response examples; stop reasons; segmentation definitions; multimodal prompting; pricing and cleanup notes
Supports: AWS documents Pegasus 1.5 on Bedrock as an on-demand video-language model for general analysis and time-based metadata. The post identifies two-hour video input, JSON segment output, up to 30 reference images, S3 or base64 input, InvokeModel and streaming invocation, and model-specific request fields.
Reviewed: October 9, 2026 availability announcement; video-to-text and time-coded metadata description; whole-video analysis; user-defined event types; audiovisual and on-screen-text processing; no-pre-indexing statement; US and Global cross-Region access
Supports: AWS announced Bedrock availability on October 9, 2026 and described Pegasus 1.5 as reading an entire video to return timestamped, structured metadata through US and Global cross-Region inference profiles.
Reviewed: Model details and October 8 launch date; active lifecycle; context and output limits; endpoint guidance; unsupported Bedrock features; Marketplace billing; base, US and Global model identifiers; cross-Region-only restriction; source and destination Regions; S3 co-location requirement; quota caveat
Supports: The live model card lists a 261,120-token context, 98,304 maximum output tokens, bedrock-runtime access, US and Global inference IDs, no in-Region on-demand support, Marketplace billing, and an active lifecycle. It says the request S3 bucket must match the source Region.
Reviewed: Model Pricing introduction; TwelveLabs on-demand section; Global and Geo cross-Region tabs; Pegasus 1.5 billing dimensions; video-input seconds; output-token units; applicability to US and Global CRIS
Supports: The current pricing surface bills Pegasus 1.5 using video-input seconds and output tokens and says Pegasus pricing applies to US and Global cross-Region inference. Numeric values are client-rendered pricing tokens and did not materialize in the reviewed public response, so this article does not quote an unverified rate.
AccessAllGPT reviewed three AWS-controlled public surfaces on October 20, 2026: AWS’s October 19 announcement, the live Amazon Bedrock structured-outputs user guide and the current Bedrock pricing page. We compared availability language, API surfaces, schema rules, tool strictness, compilation and cache behavior, exception paths, compatibility caveats and price disclosure. Availability and behavior described by AWS remain vendor documentation. AccessAllGPT did not use AWS credentials, invoke a Claude model, compile a schema, measure latency or token savings, reproduce schema compliance, test tool calls, validate every model or Region, or inspect an AWS bill.
Reviewed: October 19, 2026 announcement date; runtime and Mantle availability; JSON Schema output and strict tool use; constrained decoding; grammar compilation and caching; API examples; stop reasons; supported and unenforced schema constraints; conclusion and general-availability statement
Supports: AWS announced general availability of structured outputs for Anthropic Claude models where available on bedrock-runtime and bedrock-mantle. It documents JSON Schema responses, strict tool inputs, a Draft 2020-12 subset, a 24-hour grammar cache, first-use compilation latency, and refusal or max-token exceptions.
Reviewed: Benefits; JSON Schema and strict-tool mechanisms; request workflow; compilation and cache behavior; supported and unsupported JSON Schema features; supported APIs; citations incompatibility; geographic-profile caveat; model-support lookup
Supports: The live Bedrock guide confirms support across Converse, InvokeModel, streaming, cross-Region inference, batch inference and the Anthropic Messages API on Mantle. It warns that citations are incompatible for Anthropic models and that some geographic profiles do not support every model.
Reviewed: Model Pricing overview; provider and model dependency; Standard, Flex, Priority and Reserved tiers; batch-inference discount statement; Anthropic provider selector; separate capability tabs
Supports: AWS says Bedrock model pricing depends on modality, provider and model, and identifies multiple service tiers. The reviewed pricing page does not state a separate structured-output feature fee; account, model, Region, tier, token use and any surrounding Bedrock services still determine cost.
AccessAllGPT reviewed four NVIDIA-controlled surfaces on October 19, 2026: the October 15 launch post, the public Super VL NVFP4 and Bolt 74B-A9B 24GB Hugging Face repositories, and NVIDIA’s technical article. We checked exact model identifiers, announced and scheduled availability, public repository access, parameter counts, active parameters, precision, context limits, supported modalities, license labels, deployment recipes, runtime requirements, displayed benchmarks and throughput methodology notes. We also queried the public Hugging Face model API to confirm that both reviewed repositories were ungated and to record repository revisions 59b8721646c38111a242da18218859fb2b454322 for Super VL and c6cc2e8ffa353403213d6b9b18aaf8eb684d705a for Bolt at review time. AccessAllGPT did not download either checkpoint, accept separate service terms, run a model, inspect every weight shard, reproduce a benchmark, validate a hardware recipe, measure memory or power, or test NVIDIA NIM, vLLM, SGLang, TensorRT-LLM, llama.cpp or an agent harness.
Reviewed: October 15 announcement; model names and availability; parameter and memory positioning; Super VL and Bolt descriptions; benchmark and throughput claims; customization examples; deployment links; Bolt E4B timing
Supports: NVIDIA announced Nemotron 3.5 Super VL and Bolt 74B-A9B as available on October 15, 2026, while Bolt E4B was scheduled for October 23. The page positions the family for multimodal and local agents and makes the 5x throughput, benchmark-rank and deployment claims cited here; those are vendor claims.
Reviewed: Public repository status; model summary; OpenMDW 1.1 license; inputs and outputs; architecture; context; hardware and runtime matrix; benchmark table and methodology notes; release date; training stages; quick-start limitations
Supports: The public, ungated NVFP4 repository identifies a 120B-total, 12B-active vision-language MoE with text, image and video input, text output, up to 1M context and OpenMDW 1.1 terms. NVIDIA lists deployment recipes for DGX Spark, H200, two H100s and GB200, and reports benchmark results that AccessAllGPT did not reproduce.
Reviewed: Public repository status; description; 74B-total and 9B-active architecture; IQ2 precision; 24GB packed size; 256K context; multimodal inputs; supported NVIDIA architectures; reasoning and speculative-decoding controls; license
Supports: The public, ungated model card describes Bolt 74B-A9B 24GB as a 74B-total, 9B-active multimodal model with native IQ2 quantization, up to 256K context and OpenMDW 1.1 terms. Its 24GB packed-model claim does not establish total application memory, speed or compatibility on every 24GB system.
Reviewed: October 15 publication date; architecture and use cases; Artificial Analysis throughput figure; multimodal agent positioning; post-training workflow; deployment guidance; limitations of stated comparisons
Supports: NVIDIA describes Super VL as a customizable multimodal agent model and reports more than 600 output tokens per second on a Baseten endpoint at a 100K-token input in its displayed Artificial Analysis comparison. This is a vendor-selected serving result, not a universal local or production throughput guarantee.