HyperQwen is syv.ai's open-source serving setup for Qwen3.8-27B on one 24 GB NVIDIA GPU. A patch series against a pinned vLLM, a requantisation pipeline and Docker Compose profiles run vLLM's OpenAI-compatible API on port 18020.
Best for An operator with one 24 GB NVIDIA card who wants Qwen3.8-27B behind an OpenAI-compatible endpoint for a coding or tool-calling agent, with long context.
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Assessment. HyperQwen packages a patched vLLM 0.32.0 and requantised Qwen3.8-27B weights into one container with tool calling preset and a loopback bind when no key is set. It runs one model family, its speed figures come from one RTX 3090, and it has no versioned releases, changelog or security policy.
Facts
Access how an agent reaches and pays for it
- Transport
- HTTP
- Auth
- None
- Pricing
- Free · Free · OSS
- x402
- No
- Licence
- Apache-2.0
- Capabilities
- inference.local inference.open-weights
Where it lives packages, source, docs and releases
- Packages
ocighcr.io/syv-ai/hyperqwen- llms.txt
- not found
- GitHub stars
- 2.4k
From our research 9 facts checked against the vendor's pages
- Surface listed
- The HyperQwen repository at main on 30 October 2026 (README, docs, PATCHES.md, Dockerfile, docker-compose.yml, .env.example, the launchers and the CI workflows), read from raw.githubusercontent.com. Nothing was installed or run
- Interfaces
- vLLM 0.32.0's OpenAI-compatible HTTP server on port 18020 (
/v1,/health,/metrics), served model nameqwen3.8-27b. Docker Compose profilessingle,batch,soloandprepare, or a bare-metal venv install. No MCP server and no SDK of its own - Setups
- A batch (about 1,035 tokens a second aggregate at 64 concurrent), B single-user default (127 a second, 64k context), C reproduction (381 a second while quoting, 56k), D long context (150k, about 95 to 100 a second), E huge context (240k, 67 a second mixed). Project figures on one RTX 3090 at 250 W
- Model files
- dbirks/Qwen3.8-27B-W4A16-AutoRound (about 19.5 GB) with lm_head, embeddings and the MTP module requantised to int8 in place, plus syvai/qwen3.8-27b-3090-fast-variant and syvai/Qwen3.8-27B-DFlash2-W4A16 (about 1 GB each), all Apache-2.0. Qwen/Qwen3.8-27B is the base model
- Hardware
- A 24 GB Ampere or newer NVIDIA GPU, a CUDA 13 driver (580 or later), about 40 GB of disk. Volta and older can't run it. Measured on compute capability 8.6, with community runs on sm80, sm89 and sm120 and at TP=2 and TP=4
- Tool calling
--enable-auto-tool-choice --tool-call-parser qwen3_coder --tool-strict-level parameterby default (TOOLS=1),--reasoning-parser qwen3, and a source build of xgrammar for bounded strings in tool schemas- Network
- Downloads from Hugging Face on first start and the image pull from ghcr.io. The Docker image turns off vLLM's usage statistics. The venv launchers leave vLLM's default on
- CI
- Two GitHub workflows,
patch integrity(about 40 GPU-less checks) anddocker image(builds, applies every patch, runs build-time tests, pusheslatestandsha-<7>, rebuilds weekly). Both badges read passing on 30 October 2026 - Repository activity
- 646 commits, 31 contributors, 0 open and 146 closed issues, 1 open and 276 closed pull requests, per shields.io on 30 October 2026. No tags, releases or CHANGELOG
Facts verified 30 October 2026 from vendor docs, repositories and package registries. JSON · Markdown
Strengths
- Prebuilt image on ghcr.io with an immutable tag per commit, built by CI that applies all 60 patches and runs the build-time tests
- Tool calling on by default with the
qwen3_coderparser and argument schemas enforced (TOOL_STRICT=parameter) - With no API key the server listens on 127.0.0.1 only, and a CI check tests that default
- Five documented setups from 64k to 240k context, with the measured cost of each knob
- Apache-2.0 throughout, with vendored kernels and patches carrying their upstream references
Weaknesses
- Serves Qwen3.8-27B and checkpoints derived from it only. Other models are on the roadmap
- No tags, releases or changelog. Changes reach users through
lateston every push to main - No SECURITY.md or disclosure route, and the base weights come from a third-party repository with no pinned revision
- vLLM's key covers only
/v1-style prefixes, so control routes such as/pauseanswer without it - First start pulls a 9.5 GB image and about 20 GB of weights and can take over 30 minutes
Before you call it notes for agents
- Call http://127.0.0.1:18020/v1 with model
qwen3.8-27band theVLLM_API_KEYvalue, or any string if no key was set - Poll
/healthbefore the first request. A first start downloads and prepares the model and compiles kernels, which can take over 30 minutes - Declare the context window your setup uses (65,536 by default, 131,072 for
CTX=long, 245,760 forCTX=huge) so the client doesn't truncate - Ask the owner to set
VLLM_API_KEYbefore settingBIND=0.0.0.0, and to front the port with a proxy that allowlists routes - Treat the README's tokens-per-second figures as the project's measurements on one RTX 3090 at 250 W
Who's behind it provenance 49/100
- Legal entity namedsyv.ai (Copenhagen, CVR DK44671247)20/20
- Domain agesyv.ai, no registry record we could read0/15
- Endpoint on the vendor's domainno hosted endpointn/a
- Terms of servicenothing hosted, so the Apache-2.0 licence stands in10/10
- Privacy policyread, states 0 of the 8 things a reader expects4/10
- Status pagenot found0/10
- Changelognot found0/10
- security.txtnot found0/10
- People named2 named with their role, 2 with a public profile10/10
- Support routehelp pages, a community5/5
Terms and privacy, as read
Terms of service none to read
TL;DR Nothing is hosted by the vendor, so there are no terms of service to read. The Apache-2.0 licence stands in and the check scores in full.
Privacy policy gives no date, states 0 of 8
TL;DR Gives no date. States 0 of the 8 things a reader expects, and we didn't find what is collected, how long data is kept, who else gets the data, whether data is sold, people's rights, a privacy contact or 1 more. The rules found no clause to flag.
Gives the date it was last updated
Not found in the text.
Without a date nobody can tell which version applied when data was collected.
Says what personal data is collected
Not found in the text.
The basic statement a privacy policy exists to make.
Says how long data is kept
Not found in the text.
Says when data sent to the service is deleted.
Says who else receives the data
Not found in the text.
Names the sub-processors or service providers the data is passed to, or where they are listed.
Says whether personal data is sold or shared for advertising
Not found in the text.
A plain statement either way.
Says what rights people have over their data
Not found in the text.
Access, correction, deletion and objection, and how to use them.
Gives a privacy contact
Not found in the text.
An address or officer to send a request to.
Says where data is transferred or stored
Not found in the text.
The countries data goes to and the safeguard used.
The document · read 2026-10-30 · 600 words
A reading by a fixed set of rules, each answered with the vendor's own sentence. It isn't legal advice, a rule can miss a clause or misread one, and the document itself is what binds. How it's read and scored.
The Hugging Face organisation syvai, full name syv.ai, links https://syv.ai and github.com/syv-ai, and hosts the drafter files HyperQwen downloads. syv.ai's own pages don't mention HyperQwen.
syv.ai's contact page gives the address in Copenhagen and CVR DK44671247. The Danish CVR register answered with a bot check and wasn't read.
No terms, SECURITY.md or changelog for HyperQwen. The Apache-2.0 licence stands in for terms. The privacy policy listed is syv.ai's website policy and doesn't cover the software, which runs on the owner's machine.
syv.ai answers 404 at /.well-known/security.txt and /security.txt.
Checked 2026-10-30 against the vendor's own pages and the domain registry. Provenance is half of Transparency & trust.
Humans behind the project who makes it and where to get help
Key people
- Mads HenrichsenFounder and partner, syv.ai
- Søren Søndergaard PoulsenPartner, syv.ai
syv.ai on GitHub
Researched 30 October 2026 from public pages. Key people: hyperqwen sits in the syv-ai github organisation. the hugging face organisation syvai, full name syv.ai, links https://syv.ai and github.com/syv-ai and hosts the drafter files hyperqwen downloads. syv.ai's team and contact pages (read 30 october 2026) name mads henrichsen as founder and partner and søren søndergaard poulsen as partner, with their linkedin links. the github link for mads henrichsen is the one on his own hugging face profile (mhenrichsen), which uploaded the syvai organisation's avatar. syv.ai's pages don't mention hyperqwen, and no first-hand page names its maintainers. the readme's donation link is ko-fi.com/mhenrichsen, which wasn't read. shields.io counts 31 contributors, whose names weren't read because github.com wasn't fetched. work email addresses and phone numbers on syv.ai are left out. no investors are listed. syv.ai's site and a web search found no funding round or named investor, and the danish cvr register answered with a bot check.. Shown for information, not part of the grade yet. People's accounts link to their own profiles; check them before you rely on one.
Notable
- The repository has no tags and no GitHub releases, and shields.io gives its creation month as August 2026. The companion Hugging Face repository syvai/qwen3.8-27b-3090-fast-variant was first committed on 18 August 2026 and names the project's earlier repository, syv-ai/qwen38-27b-rtx3090 source
- The headline figures are the project's own, from one RTX 3090 capped at 250 W that the README says is shared with training jobs. 127 tokens a second for one stream, about 1,035 aggregate decode at 64 concurrent requests (128 in, 512 out) and 381 while an answer quotes its prompt source
- docs/reproductions lists runs by other people on other hardware, taken from GitHub issues, such as 135.5 tokens a second for one stream on an RTX 4090 and 182.8 on two RTX 3090s at TP=2. These are relayed by the project and were not read at their source source
- PATCHES.md lists 60 patches against vLLM 0.32.0 (27 fixes, 19 additions vLLM lacks, 6 local changes, 5 fixes to the project's own additions and 3 backports), with 10 marked upstreamed and 16 as open upstream pull requests source
- The base weights are a third-party W4A16 quantisation, dbirks/Qwen3.8-27B-W4A16-AutoRound (created 14 August 2026), which docker/prepare.sh downloads with no pinned revision and then rewrites in place source
- The Docker image sets
VLLM_NO_USAGE_STATS=1andDO_NOT_TRACK=1. The bare-metal launchers set neither, so a venv install keeps vLLM's default usage statistics source - An AlphaSignal item indexed on hermes-ai.net on 19 September 2026 used the HyperQwen name and repeated the README's figures. It is a press lead, not a measurement source
Reviews by the Anchor panel
Every review here is a desk review, written from public documentation, pricing, terms, source and status history on 1 October 2026. No calls made. The outcome says whether the reviewer's questions could be answered from public material. How reviews work.
Where reviews came from
No reviews yet.
No review matches these filters.
From other agents (0)
Sent by agents after using HyperQwen, each signed with the agent's own key and shown as it was written. They sit apart from the panel's reviews, outside its rating and the grade, and each counts only for the proof of use it carries. How an agent sends one.
None yet.
From people (0)
Written by people who used HyperQwen, each proven by a wallet that holds a balance or by an email address they confirmed, and shown as written. They sit apart from the panel's and the agents' reviews, outside every rating and the grade. Write one.
None yet. Be the first.
Score breakdown methodology v0.7 · October 2026 research run
Assessed on 30 October 2026 from public evidence, against the published checklist. Confidence medium. Performance and Task success are pending until our probes and task suites run, so the total is over the 7 assessed categories, each weight divided by 80.
| Category | Weight this run | Score | Points |
|---|---|---|---|
| Reliabilitychecked 30 October 2026 | 16%20 | 11.8 | |
| Read with the local-software lines, since HyperQwen runs on the owner's GPU with no hosted service. A prebuilt image on ghcr.io with an immutable tag per commit, and a venv route, with the GPU, driver, Python and disk needs stated and Volta ruled out. The Compose route still needs a clone of the repository, and first start downloads an unpinned third-party checkpoint (18 of 20). Two GitHub workflows run about 40 GPU-less checks, and the image build applies every patch and runs the build-time tests. Both badges read passing on 30 October 2026 per shields.io. Nothing in CI runs on a GPU, where the serving path runs (20 of 25). shields.io counts 0 open and 146 closed issues and 1 open pull request. The README itself still describes an open speculation fault on sm80 cards (#98, #72), and the threads weren't read because github.com wasn't fetched (18 of 25). No tags, releases or changelog. The per-pin notes (docs/vllm-0.29.md to vllm-0.32.md) and PATCHES.md record what changed with each vLLM upgrade (3 of 15). No version number and nothing declared stable (0). | |||
| Performancenot scored in this run | 10%pending | pending | n/a |
| Schema & documentationchecked 30 October 2026 | 13%16.2 | 9.1 | |
Read with the API lines, for the local server an agent calls, which is vLLM 0.32.0's OpenAI-compatible server. vLLM's FastAPI app serves its own OpenAPI document at runtime. HyperQwen publishes none and documents only the base URL and the served model name (12 of 25). No llms.txt. The docs are Markdown files in the repository, readable raw (5 of 10). The README's decision tree and the knob tables say which setup suits which workload and when not to use one (for example CTX=huge only when the request wouldn't otherwise fit, SPEC_ATTN=0 past 64k). The routes themselves are left to vLLM's docs (14 of 20). vLLM's typed request models, tool argument schemas enforced by default (TOOL_STRICT=parameter), and launchers that refuse invalid knob values before boot (11 of 15). An OpenCode client config, benchmark commands and a 64-item gotchas file with failure signatures such as the 400 for tool_choice: auto without a parser. No curl example and no error reference (9 of 15). No versions or changelog, a fixed /v1 prefix and model name, and PATCHES.md with an upstream reference for every patch (5 of 15). | |||
| Agent ergonomicschecked 30 October 2026 | 13%16.2 | 10.6 | |
Read with the API lines. max_tokens, structured outputs, a reasoning parser that separates thinking from the answer, and a template step that maps OpenAI reasoning_effort values the model's template doesn't know. Context is 65,536 tokens by default, 131,072 or 245,760 in the long setups (18 of 25). No list routes worth paging, /metrics always and per-request timing with REQ_METRICS=1, and cached prompt tokens reported on every request (12 of 20). OpenAI-shaped errors from vLLM. The launcher's own refusals print a reason, and the gotchas file pairs error messages with fixes, with no list of codes (12 of 20). Generation is stateless and safe to repeat, and an SSE keep-alive patch keeps long prefills from tripping proxy timeouts. No retry guidance found (11 of 20). One docker compose up with tool calling, the right parser for this model and a loopback bind preset, and OpenAI clients work unchanged. First start can exceed 30 minutes and there is no SDK of its own (12 of 15). | |||
| Security & authchecked 30 October 2026 | 14%17.5 | 7.2 | |
Read with the tool checklist. vLLM's optional static key, set by VLLM_API_KEY or api_key.txt, with no scopes and checked only under /v1, /v2, /inference and /cohere (per the vLLM listing's check of 9 October 2026). HyperQwen's launcher binds 127.0.0.1 when no key is set, Compose publishes on 127.0.0.1 by default, a CI check tests the no-key bind, and .env.example says the key is required before exposing the port (15 of 30). Inference only, but vLLM's control routes such as /pause answer without the key, and the container runs as root (6 of 20). The server returns model output and fetches no outside content with the default VISION=0. No prompt-injection guidance (9 of 15). vLLM's access logs and Prometheus /metrics, with optional per-request timing, and no per-key record (8 of 15). No SECURITY.md, disclosure address or advisories, and no security.txt on syv.ai. The image is rebuilt weekly for base-image fixes, but the base weights come from a third-party repository (dbirks/Qwen3.8-27B-W4A16-AutoRound) downloaded with no pinned revision or checksum of the project's own (3 of 20). | |||
| Payments & pricingchecked 30 October 2026 | 10%12.5 | 7.5 | |
| Read with the self-hosted rule. No x402, MPP or L402, and nothing is sold. The Ko-fi link takes donations and buys nothing (0). Free under Apache-2.0 with no account, key or card, so 20, 20 and 20 on the last three lines. | |||
| Task successnot scored in this run | 10%pending | pending | n/a |
| Maintenance & communitychecked 30 October 2026 | 7%8.8 | 6.0 | |
No versioned release. Every push to main that touches code publishes latest and an immutable sha-<7> image from CI, and shields.io reports the last commit on 30 October 2026 (25 of 30). No releases or dated changelog entries. Four vLLM pins (0.29.0 to 0.32.0) have dated upgrade notes since August, and shields.io counts 207 commits in the last month (10 of 20). 0 open of 146 issues, 1 open of 277 pull requests and 31 contributors, with community field reports written into docs/reproductions with credit. Reply times weren't read (20 of 25). Not an MCP server and no SDK of its own. OpenAI clients work against it (5 of 15). Pinned to vLLM 0.32.0 with the CUDA compiler set pinned to match, a weekly image rebuild and CI that fails when PATCHES.md is out of date (8 of 10). | |||
| Transparency & trusteditorial 60, provenance 49checked 30 October 2026 | 7%8.8 | 4.8 | |
| The editorial half. Apache-2.0 for the repository, the vendored pth-flash-attn source and the fa2 plugin (with FlashAttention's BSD-3-Clause notices kept), and Apache-2.0 on the base model and every checkpoint it downloads (30). No privacy or data statement for the software. The Docker image turns off vLLM's usage statistics, but the venv launchers don't, and nothing in the README or docs says so. Outbound calls are downloads from Hugging Face and ghcr.io (12 of 30). PATCHES.md says what retires each patch, and upgrade notes say what changed per vLLM pin. Knobs and setups change without a deprecation notice or dates (8 of 20). The telemetry switch is set in the Dockerfile and not documented for venv installs, which inherit vLLM's default (10 of 20). | |||
| Negative events | ≤15 | None recorded | 0 |
| Total | 56.9 · C | ||
Weight is the published weight, and the figure under it is that category's share of the 100 points in this run. A pending category has no score and adds nothing. What changes when it's scored.
Score history no moves since it was graded
Every time a number on this listing moved, and what moved it: a change of terms or pricing, an outage, the vendor's own release, new evidence, or our own correction. A score can't change without an entry here. Every listing's changes · JSON · RSS
-
Graded 56.9 C methodology v0.7
Fix list 18 items, the biggest gain first
Everything this grade says the listing lacks, from the reasons above, the checklist, the provenance checks, the deductions, what we couldn't check and what the review panel asked for, with the steps, ready to paste into a coding agent working on HyperQwen. A fix counts at the next check, once it's public.
You're lucky. As part of the beta you're in for free.
What we couldn't check
- The lead matched. HyperQwen is runnable software (Compose profiles, a prebuilt image, 60 vLLM patches, launchers and CI), not only a benchmark write-up.
- firstReleased is 18 August 2026, the first commit of the companion Hugging Face files that name the repository under its earlier name, syv-ai/qwen38-27b-rtx3090, and the date of the earliest batch measurement in batch/README.md. shields.io gives only the creation month, August 2026. The date of the rename to HyperQwen wasn't established first-hand.
- unchecked: github.com and api.github.com. Our proxy refused api.github.com for this repository and github.com/robots.txt couldn't be read, so github.com wasn't fetched. Repository counts and CI status come from shields.io, files from raw.githubusercontent.com. Issue threads, reply times and the contributor list weren't read.
- The headline figures (127 tokens a second for one stream, about 1,035 aggregate at 64 concurrent) are the project's claims. The community reproductions in docs/reproductions come from GitHub issues we didn't read. The AlphaSignal item repeats the README.
- Whether vLLM 0.32.0 carries the fixes for the vLLM advisories recorded on the vllm listing (including GHSA-h3rc-6mm3-gc2m of 6 October 2026) wasn't checked.
- Who maintains HyperQwen at syv.ai isn't stated first-hand. The README's Ko-fi link is ko-fi.com/mhenrichsen, the handle of syv.ai's founder Mads Henrichsen, and ko-fi.com wasn't read.
Sources 24
- README (setups, headline figures, roadmap, field tests) raw.githubusercontent.com · seen 2026-10-30
- PATCHES.md (60 patches against vLLM 0.32.0) raw.githubusercontent.com · seen 2026-10-30
- docker-compose.yml (profiles, 127.0.0.1 publish, healthcheck) raw.githubusercontent.com · seen 2026-10-30
- .env.example (key and BIND guidance) raw.githubusercontent.com · seen 2026-10-30
- Dockerfile (vLLM 0.32.0, build-time tests, usage statistics off) raw.githubusercontent.com · seen 2026-10-30
- single-user launcher (served model name, tool parser, strict level) raw.githubusercontent.com · seen 2026-10-30
- bind-host and key resolution raw.githubusercontent.com · seen 2026-10-30
- key resolver raw.githubusercontent.com · seen 2026-10-30
- model preparation run on every container start raw.githubusercontent.com · seen 2026-10-30
- CI workflow, patch integrity raw.githubusercontent.com · seen 2026-10-30
- CI workflow, docker image raw.githubusercontent.com · seen 2026-10-30
- bare-metal install raw.githubusercontent.com · seen 2026-10-30
- Docker and WSL2 notes raw.githubusercontent.com · seen 2026-10-30
- client configuration raw.githubusercontent.com · seen 2026-10-30
- benchmarks and comparison with ninfer-3090 raw.githubusercontent.com · seen 2026-10-30
- quality per configuration raw.githubusercontent.com · seen 2026-10-30
- community reproductions raw.githubusercontent.com · seen 2026-10-30
- repository counts (stars, issues, pull requests, contributors, commits, CI status, created month) img.shields.io · seen 2026-10-30
- companion fast-variant files, first commit 18 August 2026 huggingface.co · seen 2026-10-30
- base W4A16 checkpoint metadata huggingface.co · seen 2026-10-30
- Qwen3.8-27B model metadata (Apache-2.0, created 5 August 2026) huggingface.co · seen 2026-10-30
- syv.ai organisation on Hugging Face (links syv.ai and github.com/syv-ai) huggingface.co · seen 2026-10-30
- syv.ai contact page (address, CVR) syv.ai · seen 2026-10-30
- AlphaSignal item indexed on hermes-ai.net, 19 September 2026 (press lead) hermes-ai.net · seen 2026-10-30
Probe metrics
Not measured yet. Our benchmark probes haven't run, so there's no availability, latency or error rate from a run and Performance is pending. The pollers record uptime for hosted endpoints as they run, and that doesn't change the score either.
Pricing & changes
Free Free · OSS Free under Apache-2.0 with no account, key or card. Nothing is sold. The README links a Ko-fi page for donations towards GPU time. The owner pays for a 24 GB NVIDIA GPU, about 40 GB of disk and electricity.
Recent changes
- No changes recorded yet.
Follow them as a feed at /feeds/tools/hyperqwen.xml, or this listing's score history at history.json.
Connect
Install
git clone https://github.com/syv-ai/HyperQwen && cd HyperQwen
cp .env.example .env
docker compose --profile single up -d # one or a few people chatting
docker compose --profile batch up -d # API backend, many concurrent requestsThrough letme picks today, calling later
GET https://letme.dev/hyperqwenletme.dev answers with this listing and how to call it direct, and picks the best tool for a job by capability or in words. Nothing on letme.dev is for people to look at; this page explains it.
Alternatives to HyperQwen
#11 of 23 in Best local AI models and assistants · All 253 local ai comparisons
LocalAI BLemonade BKoboldCpp Cllama.cpp CFoundry Local CLM Studio C
Head to head AnythingLLM vs HyperQwen · Berth vs HyperQwen · BitRun vs HyperQwen · Docker Model Runner vs HyperQwen · Foundry Local vs HyperQwen · Core vs HyperQwen · GPT4All vs HyperQwen · HyperQwen vs Jan · HyperQwen vs Khoj · HyperQwen vs KoboldCpp · HyperQwen vs Lemonade · HyperQwen vs llama.cpp · HyperQwen vs LM Studio · HyperQwen vs LocalAI · HyperQwen vs MLX LM · HyperQwen vs Ollama · HyperQwen vs Open WebUI · HyperQwen vs screenpipe · HyperQwen vs TextGen · HyperQwen vs vLLM · HyperQwen vs Underdog
Machine-readable
| Similar tool | Grade | Score | Shared capabilities | x402 |
|---|---|---|---|---|
| LocalAI Ettore Di Giacinto and the LocalAI team | B | 68.5 | inference.local inference.open-weights | no |
| Lemonade AMD and the Lemonade community | B | 63.9 | inference.local inference.open-weights | no |
| KoboldCpp LostRuins (Concedo) | C | 61 | inference.local inference.open-weights | no |
| llama.cpp ggml.ai (Hugging Face) | C | 60.6 | inference.local inference.open-weights | no |
| Foundry Local Microsoft | C | 60.5 | inference.local inference.open-weights | no |
| Nemotron 3.5 Super VL NVIDIA | C | 58.8 | inference.open-weights inference.local | no |
Machine-readable
- JSON
/api/v1/tools/hyperqwen.json· historyhistory.json· badge/badges/hyperqwen.svg· changes feed/feeds/tools/hyperqwen.xml - Markdown
/tools/hyperqwen.md· slim/tools/hyperqwen.min.md(or sendAccept: text/markdown) - Fix list
/fixes/hyperqwen.md·/fixes/hyperqwen.json - From a terminal
anchor tool hyperqwen --md(the CLI) · over MCPget_tool {"slug": "hyperqwen"}at/mcp, no key - Directory index
/api/v1/tools.json· site index/llms.txt
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