DevTrellis
Discover. Research. Build together.
Türkçe · MIT license · Research & free NVIDIA setup · Community service
DevTrellis is an open technology knowledge community for hobbyists and professional developers. Its aim is to make useful research easier to find and reuse, helping reduce the time and tokens spent asking AI to investigate the same topic again. By bringing together current news, research, projects and developments, the community can also help people discover work and topics they did not know to look for.
Start at devtrellis.org: open a title or choose Read research. A shared report opens as a readable page; a record published through the machine gate opens as structured data (JSON) with its quotes and sources, because these records have no readable page yet. Follow the original source links. Check its date, sources, scope and limitations. If it answers your question, reuse it; if something is missing or outdated, research that part with your own model. Reading on the site needs no account, installation, API key or AI assistant. Reading and importing an existing report do not call a model.
Community pages: About · Guidelines · Volunteer application · Support. All offer English and Turkish without an account. Applications and support messages are private; applying is not required to read or share research.
Your personal research stays on your computer. When it is ready, choose Keep local or Preview and share: review the exact public text, sources, model details and content permission before sending an anonymous copy for moderation. On devtrellis.org, records that pass the machine gate are published automatically and labelled "not human-reviewed"; reports shared from the local app go to a moderator. Private question/claim metadata and author identity are excluded. Sharing is optional and requires neither an account nor manual file transfer. Finding, checking, reusing and contributing should build a useful shared library. The product vision and roadmap distinguishes current features from planned work.
Two ways to use it
Research can develop through linked contributions: cite the exact earlier report version, explain new findings and keep its original sources. Readers can follow that history and report usefulness, actual reuse, outdated information or a disputed claim. Separate web and MCP access counters show approximate activity for shared reports (machine-gated records are not counted yet); popularity and model names do not establish correctness. Research development and privacy.
This is an early alpha, previously named UFUK. Legacy UFUK_* environment variables, database keys and the ufuk-share format remain compatible. The community service is installed and operated separately. Living topic pages, an hourly community researcher and publisher connectors remain planned.
| Personal workspace | Community service |
|---|
| Follow sources and save interests, projects and research locally | Browse published research, projects and developments |
| Use your own optional model provider | Read without a model account or contribution requirement |
| Preview and send selected research directly; keep everything else local | Receive account-free contributions; machine-gated records can be published automatically (labelled "not human-reviewed"), shared reports go to moderators |
| Keep private research on your computer | Version corrections and withdraw publications |
The developer space, advanced workspace and community screens offer English and Turkish. Source and contributor text keeps its original language; interface language does not rewrite other people's work. Model report language is a separate provider setting.
An optional MCP connection lets an assistant check whether a documentation page is already covered (lookup_source) before fetching it, search and read records, and offer a contribution with an explicit preview and approval. Record text returned to an assistant still consumes context tokens. On covered questions, early experiments suggest that answers of similar quality can be reached with fewer page opens. Limits: 6 questions (11 November 2026, alpha.14 bridge), chosen to match existing records; 2 runs per question; the bridge was called by a script, not from inside a real assistant; one evaluator; an artificial cold baseline that read whole pages as raw files; billed cost and general savings were not measured; against WebFetch no saving was shown.
An assistant needs only the MCP connection to read records; the optional Claude Code / Codex skill helps prepare a contribution or plan deeper research. Compatible MCP Apps hosts show Share / Keep local controls; other hosts provide the same controls on a local review page. Submission requires the review capability and two initially unchecked confirmations. A model-supplied approval boolean is insufficient. Pattern filters reduce accidental disclosure, but cannot detect every private fact; inspect the exact public copy. Skill installation and privacy boundaries.
Install
Python 3.12 is the tested runtime. Node.js is only needed for JavaScript tests. No Docker, administrator account or model subscription is needed for local browsing and drafts. Run from the extracted source directory on Windows:
python -X utf8 kur.py
.\.venv\Scripts\python.exe -X utf8 calistir.py sun --elle-toplama
Open http://127.0.0.1:8781/platform. Stop with Ctrl+C. Setup creates .venv, installs requirements-lock.txt and checks dependencies without deleting application data. python -X utf8 kur.py --check checks an existing environment without installing anything.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
