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README.md: Elffuss now lives on elffuss.com (links only)
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metadata 
title: Elffuss Code
emoji: 🧑‍💻
colorFrom: purple
colorTo: pink
sdk: static
app_file: index.html
pinned: false
license: apache-2.0
short_description: A VS Code-style IDE with a local AI agent, in-browser
tags:
  - webgpu
  - transformers.js
  - local-llm
  - agent
  - code
  - gemma

🧑‍💻 Elffuss Code — a VS Code-style IDE with an AI agent, 100% in your browser

Open a local folder and a full web IDE unfolds around it — real Monaco (the VS Code editor), a file tree, tabs — with an AI agent that reads, searches and edits your project for real. The model runs on your GPU via WebGPU. Your code never leaves your machine.

⚠️ Open it full-screen (direct link) — the File System Access API (opening a local folder) is blocked inside the embedded iframe.

demo

In the demo a small model running entirely in the browser reads calc.py, finds a real bug and fixes it with a minimal edit — genuine tool-calling, not autocomplete.

Why it's different

  • Your code stays local. The folder is opened via the File System Access API — no upload, no account.
  • The model runs on your GPU (WebGPU, Gemma-4 E4B via LiteRT-LM; ONNX/transformers.js as fallback).
  • Real tool-calling: code.tree, code.read, code.search, code.edit, code.write — you watch every call.
  • Edits, not rewrites: the agent patches the lines that change, even in files of several megabytes.
  • Long tool results don't break the conversation: they are compacted to fit the model's context instead.
  • Context retrieval that works beyond English — accents and Cyrillic included (measurements below).
  • Real editor: Monaco, file tree, tabs, Ctrl+S, and git branch/last commit parsed straight from .git.
  • Zero install, zero build. Vanilla ES modules.
  • Your language: the whole IDE follows your browser's language (English, Spanish and more).
  • External providers (OpenAI, Anthropic, Ollama) are opt-in; keys stay in your browser.
  • Optional local bridge for real node / npm / python execution — downloaded from the main site.

How it's built

Write-ups on the full stack — model, runtime, agent harness, editor and filesystem access, all in one tab: https://bitacora.utopiaia.com/#elffuss

Context management — how it's measured

The context packer isn't chosen by intuition. Numbers measured at the same token budget:

Real agent sessions (25 sessions, 174 probes, fact recall, no LLM judge):

recall
tail truncation 7.0 %
previous version 15.1 %
current 65.3 % ± 8.5

Long-term memory (200 questions, F1 from the benchmark's own repo, a real model answering, ~8 % of the context):

F1
the FULL context, uncompressed 22.56
tail truncation 6.62
hand-written heuristics 6.07
BM25 without IDF 20.34
BM25 23.59
embeddings 23.95
rank fusion (BM25 + embeddings) 28.09

Retrieving well beats having everything: 28.09 vs 22.56 using 8 % of the tokens. Irrelevant context isn't neutral ballast — it distracts.

Two more things that were measured, not assumed: turning the heuristics on cost −3.02 F1, and removing IDF costs −3.25. Semantic search doesn't replace lexical search, it covers its blind spot: with no vocabulary overlap between question and answer, embeddings win (24.28 vs 18.60); with overlap, BM25 wins (29.33 vs 23.58) — and fusion keeps both.

References: BM25 (Robertson et al., TREC-3 1994) · IDF (Spärck Jones, 1972) · RRF (Cormack et al., SIGIR 2009) · MMR (Carbonell & Goldstein, SIGIR 1998) · attention sinks (Xiao et al., ICLR 2024) · valid time (Snodgrass & Ahn, SIGMOD 1985).

The full story, including what went wrong: https://bitacora.utopiaia.com/posts/16-beaten-by-doing-nothing.html

The Elffuss family