CONTEXT ENCODING & COMPRESSION SYSTEM

Every session.
Zero context lost.

ContextCore compresses entire conversation histories, codebases, and decision logs into structured CECS format — preserving 100% of the decisions while eliminating 90%+ of the tokens.

97%
TOKEN REDUCTION (S1-14)
22,000
TOKENS REPLACE 2M+
8.5/10
USEFULNESS RATING
14 sessions
VALIDATED ACROSS
REAL NUMBERS FROM SOVEREIGN PLATFORM

14 sessions. 97% reduction.

Raw session tokens (S1-S14)99% compression · 2,000,000 → 22,000 tokens
Architecture decisions (80 DECs)91% compression · 45,000 → 4,200 tokens
Signal corpus (420+ rules)93% compression · 120,000 → 8,000 tokens
Bug fix history (14 sessions)96% compression · 80,000 → 3,600 tokens
97.3%
AVERAGE COMPRESSION RATIO
DEC[]
NUMBERED DECISION SYSTEM
YAML
KCC DIALECT FOR GEMINI/KIMI
100%
DECISION PRESERVATION
THE CECS PROTOCOL

Structure that survives compression.

01
SESSION ENCODING
Every conversation compresses to a numbered decision log, hypothesis list, and priority-ordered context block. Nothing ambiguous survives — only decisions.
02
CROSS-SESSION SYNC
CECS files merge across instances. Start a new session with any Claude, Gemini, or Kimi instance — all previous decisions are present from line one.
03
DEC[] DECISION LOG
Every architectural decision gets a permanent DEC[N] identifier. Claude never re-suggests settled questions. The number is the memory.
04
DIALECT SUPPORT
CECS native format for Claude. KCC YAML dialect for Gemini and Kimi. Same compression algorithm, output adapts to the receiving model.
05
HYPOTHESIS TRACKING
Scientific method built in. HYP[N] tags track unresolved questions across sessions. When they resolve, they convert to DEC entries automatically.
06
API ENDPOINTS
Compress via POST, load via GET, merge libraries via the admin endpoints. Ship CECS as infrastructure alongside your existing AI stack.
LIVE COMPRESSION

Compress anything. In seconds.

Claude Sonnet reads your text and returns a structured CECS file. Try the example or paste your own session content.

CECS COMPRESSION TERMINAL
INPUT0 tokens
COMPRESSED OUTPUT
Output appears here after compression
Powered by Claude Sonnet · outputs CECS format
CECS FORMAT

Designed for
machine memory.

CECS isn't a summary. It's a structured decision ledger with hypotheses, blockers, and priority-ranked context — designed to be machine-readable at the start of every new session.

◈DEC[] numbered decisions — never re-discuss settled questions
◈HYP[] hypotheses — track unresolved questions across sessions
◈PRIORITY ordering — most critical context loads first
◈BLOCKER flags — surface what's blocking progress immediately
◈DECISION LOG — chronological record of all architectural choices
example.cecs
CECS-VERSION: 2.0
PROJECT: Sovereign Platform
SESSION: 14

━━ DECISIONS ━━
DEC[77]: validator home/away fix
DEC[78]: checkingUnlock starts true
DEC[79]: pre-check before charge

━━ HYPOTHESES ━━
HYP[04]: Signal backtester at 50+ picks

━━ STATUS ━━
Win rate: 57.1% (12W-9L)
F&G: 11 EXTREME FEAR

END SESSION 14
DEPLOY CONTEXTCORE

Open source. Self-hosted. Free.

ContextCore is MIT licensed and ships as a single Python module deployable on any Render free tier in under 5 minutes.

OPEN SOURCE
FREE
Self-hosted
◈ contextcore.py single file
◈ /compress /load /status endpoints
◈ CECS + KCC dialect output
◈ Claude-powered compression
◈ MIT licensed
EMPIRE STACK
INCLUDED
With EmpireCore subscription
◈ Pre-connected to EmpireCore
◈ PHANTOM memory integration
◈ Agent 001 loop wired
◈ 14 sessions pre-loaded
◈ Sovereign Platform access