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 opened AWS’s November 6, 2026 launch post and the live SageMaker AI Developer Guide on November 7. We checked the supported endpoint type, instance families, Regions, partition profiles, API fields, billing boundary, isolation scope, layout-change path, monitoring limits and P4d driver warning. We also reviewed AWS’s published methodology and caveats for its H100-partition versus full-A100 comparison. Performance and savings figures below are AWS-reported, not independent results. AccessAllGPT did not have an AWS account or quota in this job and did not provision an endpoint, execute the sample notebook, reproduce the benchmark or inspect a bill.
Reviewed: November 6 announcement; availability; supported endpoints, instances and Regions; API fields; partition profiles; billing; limitations; AWS performance and cost comparison; getting started
Supports: AWS announced managed NVIDIA MIG partitioning for SageMaker real-time inference-component endpoints, documented the supported hardware and Regions, and reported a vendor-run H100-partition versus A100 comparison.
Reviewed: Benefits; isolation boundary; API fields; endpoint lifecycle; monitoring; supported instance families, Regions and partition types; P4d driver warning
Supports: AWS documentation confirms the production interface and operating constraints: two optional fields, immutable layouts on running endpoints, no per-component resource-utilization metrics, four supported instance families and seven launch Regions.
Reviewed: Repository path linked from the AWS launch post
Supports: AWS links this public sample as the starting implementation for deploying several models onto GPU partitions; AccessAllGPT did not execute the notebook.
AccessAllGPT opened Anthropic’s November 6, 2026 engineering report on November 7 and reviewed its complete text, figures, evaluation descriptions, reported scores, caveats, linked product documentation and authorship. We separately opened the Continual Learning Bench 1.0 release page to verify what that benchmark says it measures, and reviewed Anthropic’s linked memory documentation for the persistent-directory and context-loading mechanisms. Numerical results below are vendor-reported: AccessAllGPT did not receive the underlying trajectories, prompts, graders, corpus, wikis or repeated-run distributions and did not reproduce any evaluation. Product facts, research claims and AccessAllGPT deployment guidance are labeled separately.
Reviewed: November 6 publication metadata; memory notes; CL-Bench and Pokémon evaluations; steering; memory correction; wiki construction; context injection; goal-focused memory; limitations and open questions
Supports: Anthropic describes a four-layer memory design—transcripts, notes, representations and injected context—and reports internal evaluations of writing, correcting and reorganizing Claude agent memory with offline “dreaming” passes.
Reviewed: May 4 release metadata; benchmark purpose; task design; analysis and evaluation; domain-expert process; systems and tasks
Supports: The benchmark team describes CL-Bench 1.0 as sequences of expert-validated tasks where a system is expected to change over time, and says it compares cumulative reward, cost and gain against stateless systems.
Reviewed: Memory directory behavior and persistence boundary linked from Anthropic’s research article
Supports: Anthropic documents the persistent memory directory used by managed agents; this establishes the product mechanism referenced in the evaluation, not the general effectiveness of memory.
Reviewed: Auto-memory loading behavior and MEMORY.md limits linked from Anthropic’s research article
Supports: Anthropic documents that Claude Code loads only the first 200 lines or 25 KB of MEMORY.md as an index rather than injecting all auto-memory into every session.
AccessAllGPT opened OpenRouter’s live free-model page and retrieved its public model registry on November 7, 2026. We checked both callable IDs, the shared canonical model record, creation timestamp, modalities, context and completion limits, paid and free prices, supported request controls, default sampling values, API surfaces, provider count and displayed privacy label. We then reopened Liquid AI’s public model card and October 27 family announcement to separate OpenRouter hosting facts from developer claims and intended-use guidance. AccessAllGPT did not have an OpenRouter API key in this publishing environment, so it did not call either route, inspect model outputs, test tools or structured output, measure latency, or validate long-context performance.
Reviewed: Model identity; release date; description; free price; context; provider count; provider privacy label; live latency, throughput and uptime panels
Supports: OpenRouter lists liquid/lfm-3-1.7b:free as a free text model released November 6, 2026, with a 262,144-token context. Its only listed provider was Liquid and carried a “Trains” privacy label when reviewed.
Reviewed: IDs; canonical slug; creation timestamp; architecture; pricing; context and completion limits; supported parameters; defaults; API endpoints; reasoning requirement
Supports: The registry exposes paid and free IDs created at 23:57:57 UTC on November 6. Both are text-to-text routes with a 262,144-token context and 8,192-token maximum completion. The paid route lists $0.08/M input, $0.02/M cached input and $0.20/M output; the free route lists $0 prompt and completion prices.
Reviewed: Model summary; parameter count; architecture; context; languages; license; intended uses and limitations; inference variants
Supports: Liquid AI describes LFM3-1.7B as a 1.73B-parameter post-trained text model for local agents, tool calling, structured output and specialization. It warns that the small model is not recommended for agentic coding or complex multi-step reasoning.
Reviewed: October 27 launch date; family availability; architecture and training description; context and edge claims; benchmark sections
Supports: Liquid AI announced the LFM3 family on October 27 and made weights available through Hugging Face, its Playground and its own API. Architecture, speed, memory and quality results in the announcement are vendor claims.
AccessAllGPT reviewed OpenAI’s complete November 5 launch article through a text retrieval proxy after the canonical page presented a Cloudflare challenge, then opened the live first-party GPT-6 Cyber System Card directly. We checked the announcement date, model lineage, current access route, named account controls, Preparedness rating, benchmark definitions and reported results, scope-adherence evaluations, real-world campaign claims, retention and monitoring conditions, and stated rollout plan. We also opened Mozilla’s Firefox 159 security advisory and inspected its vulnerability, impact, reporter and bug-reference entries as independent evidence that fixes attributed to OpenAI researchers shipped. OpenAI’s model scores, aggregate campaign totals and alignment results remain vendor-reported. AccessAllGPT did not receive Daybreak access, call the API, use the model in Codex, reproduce an exploit, inspect a patch, rerun an evaluation or audit OpenAI’s monitoring.
Reviewed: Official November 5 announcement; launch summary; capability and alignment evaluations; Firefox and Linux campaigns; misuse controls; critical-infrastructure safeguards; Daybreak access
Supports: OpenAI announced GPT-6 Cyber on November 5, 2026, described its restricted Daybreak Red availability, reported benchmark and real-world vulnerability-research results, and detailed the safeguards deployed with the model. Performance and alignment figures are OpenAI claims.
Reviewed: Introduction; model data and training; model safety; respecting restrictions and scope; safeguards; Preparedness evaluations; cybersecurity evaluations; external cyber evaluations
Supports: The system card identifies GPT-6 Cyber as a cyber-specialized GPT-6 Astra variant, assigns it a Critical cybersecurity capability rating, documents restricted access and account controls, and supplies evaluation methodology and limitations.
Reviewed: October 27 advisory metadata; fixed Firefox version; high-, moderate- and low-impact vulnerability entries; reporter attribution and bug references
Supports: Mozilla’s advisory independently confirms that Firefox 159 fixed numerous vulnerabilities and attributes multiple reported issues to OpenAI researchers and OpenAI Codex Security. It does not by itself establish OpenAI’s aggregate campaign totals or benchmark claims.
AccessAllGPT reviewed three first-party Google surfaces on November 6, 2026: the complete November 5 launch announcement, the live Gemini Omni developer guide and the model-specific API pricing section. We checked the announcement date, general-availability language, exact model ID, product routes, output duration and resolution, supported reference media, multi-turn editing and extension behavior, migration date, regional and media restrictions, provenance language, paid-tier rates and Google’s stated weak areas. Model ID, documented interface, migration date and listed rates are service facts; consistency, speech, latency and cinematic-quality statements are Google claims; rollout guidance is AccessAllGPT analysis. We did not call the API, sign in to a Google product, generate or edit video, inspect a bill, measure quality or latency, or independently compare Omni 1.2 with Omni 1.1 or another model.
Reviewed: November 5 announcement; general availability; 15-second generation; conversational editing; reference media; speech and camera-control claims; product access; SynthID; known weak areas; migration and pricing links
Supports: Google announced Gemini Omni 1.2 Flash on November 5, 2026, made it generally available across the Gemini API and Gemini for business, and described 15-second video, multi-turn editing and a 30% lower 720p output price. Quality statements and examples are vendor claims.
Reviewed: Model ID and transition notice; inputs and native audio; duration and resolution controls; reference limits; stateful editing; extension; URI delivery; regional and media limitations; SynthID and safety filters
Supports: The live documentation identifies gemini-omni-1.2-flash and documents 3–15 second generation, up to six reference images, stateful editing, 3–10 second extensions up to 30 seconds total, and material input, region and editing constraints.
Reviewed: Paid-tier availability; standard output-token rate; resolution-specific token rates and effective per-second examples; Omni 1.1 transition pricing
Supports: Google lists no free API tier for Gemini Omni 1.2 Flash and prices output at $12 per million tokens. It maps that to about $0.023 per second at 360p, $0.07 at 720p, $0.104 at 1080p with upsampling and $0.209 at 4K with upsampling.
AccessAllGPT reviewed four live first-party OpenRouter surfaces on November 6, 2026: the Auto Router and Auto Code model pages, the Auto Router documentation and the public model registry. We checked the exact slugs, registry creation timestamps, API surfaces, routing description, session behavior, allow and exclusion controls, cost tiers, price rule, advertised modalities, context metadata, supported request fields, returned model identity and current live-routing displays. We distinguish documented product behavior from changing dashboard snapshots and from AccessAllGPT adoption guidance. We did not authenticate, spend credits, call either route, inspect OpenRouter’s classifier or ranking code, reproduce a coding pass rate, verify billing, or compare routed output with a fixed-model baseline.
Reviewed: Release date; API identifier; product description; live routing display; model mix; request example; supported API surfaces; modalities; context; regional-routing notice; FAQ
Supports: OpenRouter lists openrouter/auto as released November 5, 2026 and available through its chat-completions, Responses and Anthropic-compatible APIs. The page describes general-purpose, session-aware routing and exposes a changing live model mix.
Reviewed: API identifier; product description; coding-agent routing criteria; live request and model-mix displays; request examples; supported parameters; modalities and context
Supports: OpenRouter exposes openrouter/auto-code as a coding-agent-specific route. It says model choices consider coding-task pass rate, completion time and cost per solved task, while session behavior follows edits, tests and recurring errors.
Reviewed: Overview; routing method; usage; response; session stickiness; model allow and exclusion patterns; cost tiers; account defaults; pricing; limitations
Supports: OpenRouter documents task classification, trailing seven-day aggregate spend-share rankings, cost tiers, model restrictions, session stickiness and the returned model field. It says customers pay the selected model’s standard rate with no additional router fee.
Reviewed: Records for openrouter/auto and openrouter/auto-code: creation timestamps; architecture; modalities; context; supported parameters; API surfaces; pricing sentinels and completion limits
Supports: The public registry recorded Auto Code at 2026-11-05 13:12:01 UTC and Auto Router at 13:38:09 UTC. Both records advertise a two-million-token route ceiling and multimodal input, while actual compatibility depends on the selected downstream model.
AccessAllGPT reviewed five public first-party or distribution surfaces on November 5, 2026: Ricursive’s launch article; the live BF16 and NVFP4 Hugging Face repositories; OpenRouter’s model page and machine-readable endpoint record; and Together AI’s model page. We checked launch timing, exact repository and API IDs, access gates, license, artifact inventory, architecture config, parameter metadata, training-stage disclosures, chat and tool templates, serving instructions, context and output limits, supported request parameters, providers and current token prices. The BF16 repository reported 64,782,689,360 parameters across 16 weight shards and 129,565,519,472 bytes of stored artifacts; we inspected metadata and small text files but did not download those weights. Vendor-reported benchmark, throughput, cost and efficiency claims are labeled separately from registry facts and AccessAllGPT deployment guidance. We did not call any hosted route, run either checkpoint, reproduce a score or audit the training corpus.
Reviewed: Launch announcement; availability; training cost claim; reinforcement-learning recipe; architecture; model ladder; conclusion and footnote
Supports: Ricursive announced Ripple-65B Preview on November 4, describes its 65B dense architecture and 12-trillion-token pretraining run, names launch hosts, and reports its own benchmark, throughput and development-cost results. These remain vendor claims.
Ricursive Intelligence on Hugging Face · Retrieved
Reviewed: Repository metadata; model card; files; architecture config; tokenizer config; chat template; license; inference server instructions; benchmark methodology and limitations
Supports: The public, ungated repository contains the BF16 instruction model, custom architecture and serving code, 16 safetensor shards, an Apache 2.0 license, tool-aware chat template and a vendor benchmark table with methodology caveats.
Ricursive Intelligence on Hugging Face · Retrieved
Reviewed: Repository identity; creation and modification timestamps; access gate; license; safetensor parameter metadata and files
Supports: The public, ungated NVFP4 checkpoint is available under Apache 2.0. Its existence supports a lower-memory local evaluation path; it does not independently validate quality or hardware fit.
Reviewed: Release date; model ID; modalities; provider; listed token prices; cache price; context and output limits; quantization; supported parameters; uptime and privacy label
Supports: OpenRouter lists the callable ricursiveai/ripple-65b-preview route, one BF16 GMICloud endpoint, a 1,048,576-token context window, 131,072-token maximum output and current token prices. Registry values can change.
Reviewed: Model identity; provider attribution; architecture; training-token summary; context; license and hosted availability
Supports: Together AI has a live model page for Ripple-65B Preview, corroborating that a second hosted access route exists. AccessAllGPT did not authenticate or call the Together API.
AccessAllGPT reviewed six public first-party or project-maintainer surfaces on November 5, 2026: the new Hugging Face and Gradium technical article, Gradium’s October 20 launch post, both principal Hugging Face model repositories, the Open TTS Leaderboard methodology article and the live Transformers documentation. We checked announcement dates, exact repository IDs, model sizes, artifact formats, access gates, licenses, languages, audio format, claimed runtime coverage, integration version and what the leaderboard metrics measure. We also queried the public Hugging Face model API: gradium/phonon-31m-en was public and ungated, while gradium/phonon-40m-multilingual was public with an automatic access gate. Registry facts are separated from Gradium performance claims and AccessAllGPT deployment analysis. We did not download or run the weights, synthesize or listen to audio, clone a voice, inspect training data, reproduce a benchmark, measure latency or memory, test every runtime, accept the multilingual repository terms, or obtain commercial pricing.
Reviewed: Publication date; release summary; model sizes and languages; licenses; Open TTS Leaderboard claims; inference runtimes; Transformers integration; voice workflow
Supports: The November 5 article documents the Phonon family, exact Transformers model ID, supported runtimes and Hugging Face leaderboard claims. Gradium authors participated, so benchmark characterizations remain vendor-associated rather than independent validation.
Reviewed: October 20 announcement; model family; claimed latency; language coverage; platform support; evaluation charts; licensing and commercial access
Supports: Gradium announced the weights on October 20, identifies the 31M and 40M variants, reports a 7 ms start on a laptop CPU for 31M and describes the runtime targets. Performance and quality statements are vendor claims.
Reviewed: Repository metadata; model details; family table; files; audio format; license; runtime and usage links
Supports: The public, ungated repository identifies a 31M-parameter English TTS model, 64.5 MB 8-bit weights, 24 kHz mono output and Apache 2.0 licensing, with safetensors and GGUF artifacts.
Reviewed: Access gate; model details; family table; languages; files; audio format and license
Supports: The public but access-gated repository identifies a 40M-parameter multilingual model, 74.6 MB 8-bit weights, five languages, 24 kHz mono output and a CC BY-NC 4.0 license.
Reviewed: Leaderboard scope; languages; fixed-voice and voice-cloning tracks; intelligibility, naturalness, similarity and speed metrics; limitations
Supports: Hugging Face describes the leaderboard methodology and its automated metrics. This supports what the reported ranks measure, not production quality or listener preference for a specific application.
AccessAllGPT reviewed three public surfaces on November 5, 2026: the live OpenRouter model page, its machine-readable endpoint registry record and OpenRouter’s API reference. We checked the creation timestamp, exact model and endpoint IDs, provider identity, modalities, context and completion limits, quantization, supported request parameters, cache and tool flags, endpoint status and listed prices. We also followed the page’s “Model weights” link; https://huggingface.co/tencent/Hy4 returned 404. Availability and endpoint metadata are live registry facts; OpenRouter’s description and capability labels are platform/vendor claims; rollout guidance is AccessAllGPT analysis. We did not call the paid model, test vision or tools, reproduce any benchmark, inspect weights, verify a Tencent announcement outside the serving registry, or measure quality, latency, throughput, privacy or effective billing.
Reviewed: Model identity; release date; description; modalities; context; listed price; provider; pricing history; latency, throughput and uptime displays; model-weights link
Supports: OpenRouter lists tencent/hy4 as released November 4, 2026, with text and image input, text output, a 1,048,576-token context window and one Tencent-hosted endpoint. The page links to a Tencent Hugging Face path that returned 404 during review.
Reviewed: Model and endpoint identifiers; creation timestamp; architecture; context and completion limits; provider; quantization; price fields; supported parameters; cache and tool flags; endpoint status
Supports: The live registry returned model tencent/hy4 and endpoint tencent/hy4-20261104, served by Tencent in FP4, with 1,048,576 context, a 65,536-token maximum completion and support flags for reasoning, structured output and tools.
Reviewed: API endpoint; authentication; OpenAI SDK compatibility; request headers; model selection; response and error shapes
Supports: OpenRouter documents its OpenAI-compatible chat-completions route and bearer-key authentication. This supports the access path, not any Hy4 capability claim.
On November 5, 2026 AccessAllGPT sent five prompts once each to stealth/crouton-alpha through the OpenRouter chat completions API, without setting temperature or reasoning effort, so the listed default (max) applied. Four prompts asked for a single self-contained HTML file: a marketing landing page, an analytics dashboard, a 390px mobile home screen and a working kanban board. The fifth sent a 1440×1400 screenshot of the AccessAllGPT home page and asked for a faithful rebuild. All prompts banned network resources. We saved every raw response, measured wall-clock time from request to response, and rendered each HTML file in headless Chrome with all hostname resolution blocked, at the task’s viewport, recording page exceptions, console errors and horizontal overflow. For the kanban board we submitted the add-card form, moved the new card to Done with synthetic HTML5 drag events, reloaded, deleted it and reloaded again. Screenshots are unedited captures of those renders. We did not retry, cherry-pick or hand-fix any output.
Reviewed: Model description; stealth-model notice; listing date; context window; input and output prices
Supports: OpenRouter lists Crouton Alpha as a free stealth model from Nov 3, 2026 with a 1,048,576-token context, describes it as a lightweight coding, agentic and multimodal model, and says an anonymous third-party provider may retain prompts and completions but does not use them for training.
Reviewed: id, created timestamp, architecture and modalities, pricing, top provider limits, supported parameters, reasoning settings, endpoints
Supports: The registry records creation at 01:38:50 UTC on Nov 3, 2026, text, image, video, file and audio input with text output, $0 prompt and completion prices, a 943,718-token completion ceiling, tool support and mandatory reasoning with a default effort of max.
Supports: The single endpoint is served by a provider named only “Stealth” with unknown quantization, auto-only tool choice and no implicit caching; it reported 97.6% uptime over the previous day when retrieved.
Reviewed: Program overview; availability; inputs and personal data; payment; licence to user content; acceptable use policy
Supports: Stealth models are free in exchange for user content, may be withdrawn at any time, pass personal data in inputs to the anonymous provider, and must not receive information that law or regulation restricts.