wastewise-ai

Enterprise food waste reduction & demand forecasting platform: Random Forest ML, Newsvendor buffer planning, surplus rescue, and multi-tenant Supabase PostgreSQL

A new way for AI to search for data

View on GitHub

1GitHub stars

120commits on main

0forks

Python 40.1%, TypeScript 36.1%, CSS 20.5%, PLpgSQL 2.9% · created 2026-10-19 · last push 2026-10-29

What XERJ indexed

Snapshot

Whole repository

The whole repository at commit 1fe9e933f0. It is the newest commit on main.

Selected work

Tanish Ganesh Bane
@TanishBane

Licence

The repository

MIT

LICENSE file checked at the pinned commit; the author agreed to inclusion.

The XERJ corpus

MIT, with credit

The owner added it on hub.xerj.org after signing in with GitHub, on 2026-10-29. Same terms as the repository. Answers name the repo, the commit and the file:line.

selected for the XERJ Corpus Hublicence clearedsuggested use: adapt with attribution

demand-forecastingfastapifood-wastefoodtechmachine-learningoperations-researchpostgresqlreact

Example questions

How does wastewise-ai implement demand-forecasting?
Show the fastapi code in wastewise-ai.

Inside the indexed corpus

Languages by size: Python 40.1%, TypeScript 36.1%, CSS 20.5%, PLpgSQL 2.9%, Rich Text Format 0.2%.
On GitHub since 2026-10-19, last push 2026-10-29, 120 commits on main.

Licence on GitHub and in the corpus

GitHub reported MIT for TanishBane/wastewise-ai on 2026-10-29, and the LICENSE file at commit 1fe9e933f0 was reviewed as MIT. They match, so answers from this corpus carry the same terms as the live repository.

Contributors

Tanish Ganesh Bane (@TanishBane) has 2 selected repositories on the hub.

Their other repository pages: TanishBane/Nexfield.

Using XERJ with wastewise-ai

Search a SQL dump without restoring it wastewise-ai is tagged postgresql on GitHub.
RAG without a vector database: one hybrid index The hub files it under AI, prompts and agents.
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The prompt

Paste it into your AI agent. It clones TanishBane/wastewise-ai at the pinned commit and indexes it on your machine.

Search the GitHub repository TanishBane/wastewise-ai with XERJ (https://hub.xerj.org/corpus/tanishbane--wastewise-ai).
Run these in a shell, in order, and check each result before the next:
1. xerj --help  (success: it prints usage). If xerj is missing: curl -fsSL https://xerj.org/get | sh
2. git clone https://github.com/TanishBane/wastewise-ai wastewise-ai && git -C wastewise-ai checkout 1fe9e933f0d00725388e75b6fad42dcc8615d565
3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data
4. xerj autoindex ./wastewise-ai --url http://127.0.0.1:9200 --prefix wastewise-ai --progress plain
5. xerj autoindex map --url http://127.0.0.1:9200
Then answer my question from the indexed files and cite the ax_path of every hit. Query syntax: https://xerj.org/llms.txt
Licence: MIT. Keep the copyright notice and credit the authors.

Tanish Ganesh Bane AI, prompts and agents corpora adalflow cognita opennyai-legal-nlp iamvisheshsrivastava/insightdesk-ai fqhd/LoLytics aawhan0/ModelDock

Stats: GitHub API, fetched 2026-10-29. Selected work and consent: the XERJ registry.