Question 1

Which local models should I run, and how does hardware affect SecureContext?

Accepted Answer

SecureContext degrades gracefully by hardware tier — every LLM-powered layer fails closed (the feature quietly contributes nothing) rather than breaking ingest or search. What changes with hardware is how much of the intelligence stack is active: | Tier | Hardware | Models that fit | What you get | |---|---|---|---| | Minimum | Any CPU, ~2 GB RAM free | nomic-embed-text (embeddings only) | Hybrid BM25+vector search, working memory, audit chain, skills gating — the core. LLM layers (event extraction, entity extraction, contradiction adjudication) stay dormant. | | Mid | 8–16 GB GPU (or Apple Silicon 16 GB+) | + qwen2.5-coder:14b or phi4:14b (one at a time) | + Event-fact extraction at ingest (temporal reasoning), LLM contradiction adjudication, entity extraction, L0/L1 semantic file summaries. | | Full | 24 GB+ GPU (e.g. RTX 4090/5090) | + phi4:14b and gpt-oss:20b resident together | Everything, concurrently, at interactive latency — plus a strong local generator for QA/benchmarks. This is the configuration our published benchmark deltas were measured on. | Model-choice guidance (all measured, see bench/): Embeddings: nomic-embed-text — required, tiny, runs anywhere. Event extraction (ZCEVENTEXTRACTMODEL, default phi4:14b): our bakeoff scored phi4:14b at 100% event recall / 100% date accuracy, tying gpt-oss:20b at 2× the speed. On smaller GPUs qwen2.5-coder:14b is close behind (84.6% recall). Coder models ≠ better: qwen2.5-coder:32b scored worst (69.2%) despite being the large

Question 2

Bunu çalıştırmak için neye ihtiyacım var?

Accepted Answer

Node 20+ ve (önerilen) Docker, paketlenmiş PostgreSQL + Ollama yığını için. Tek komutlu kurulum, her şeyi yaklaşık beş dakikada yapar. Sıfır altyapı ile çalışan bir SQLite yedekleme seçeneği de mevcuttur.

Question 3

Can multiple Claude Code sessions work on the same project without conflicts?

Accepted Answer

Yes — parallel sessions atomically claim tasks from a work-stealing queue (zero double-claims at 50 agents × 100 tasks in testing), coordinate through typed broadcasts (ASSIGN/STATUS/MERGE/REJECT), and keep private per-agent memory namespaces plus a shared pool. Department-style hierarchies (heads + workers, N-tier escalation) are supported for larger agent teams.

Question 4

Are Claude Code skills safe to install?

Accepted Answer

Filesystem skills bundle scripts that run with your permissions, and Claude Code's native loader does not scan them. SecureContext adds the missing gate: AST scan at admission, HMAC verification before every execution, automatic quarantine on failure or post-admission change, and a verifiable chained log of every admission decision.

Question 5

Does SecureContext send my code or data to the cloud?

Accepted Answer

No. Memory, embeddings (Ollama nomic-embed-text), search, summarization, and the audit chain all run locally. It works fully offline (search degrades gracefully to keyword-only if Ollama is down) and costs $0 when idle.

Question 6

Can it manage a long, multi-week project?

Accepted Answer

Yes — that's what the delivery layer is for. Define a program with phases (zc_program), and the open phase's acceptance checklist travels with the project state. Closing a phase requires an evidence table and generates a checkpoint document automatically. If the orchestrating agent dies mid-phase, a fresh one reconstructs the full delivery state from two calls — verified live.

Question 7

How much does SecureContext reduce Claude Code token costs?

Accepted Answer

Measured across a 10-session project: ~87% less context-overhead (session restore is ~1.5–4k tokens instead of 20–50k of re-pasted files). On long-lived projects with hundreds of accumulated memories, the recall budget keeps each recall at ~4k tokens where an unbounded dump would be ~47k. File questions are answered from 15–50× compressed semantic summaries instead of raw reads.

Question 8

How do I give Claude Code persistent memory across sessions?

Accepted Answer

Install SecureContext (one command — see Quick start), then agents call zcremember to persist decisions and gotchas and zcrecall_context({focus: "current task"}) at session start to restore them — ranked by relevance, with natural-language time queries answered directly from memory. Memory survives restarts, compactions, and reboots because it lives in PostgreSQL, not in the context window.

Question 9

How it's different from competitors

Accepted Answer

Most "memory for Claude Code" projects do one of three things: Wrap the conversation in JSON. Saves the messages, replays them. No semantic compression, no audit trail, no security model. Sync to a cloud service. Better UX but now your agent's tool history (potentially including secrets it touched) lives on someone else's server. Drop in vector search. Adds RAG over your docs but doesn't help with the actual problem — orchestrating what the agent does, verifying what ran, surviving session compaction. SecureContext is none of those: | | SecureContext | Cloud memory services | Vector RAG plugins | Native Claude Code | |---|---|---|---|---| | Persistent across restarts | ✅ Yes, local Postgres | ✅ But on their server | ⚠️ Only docs, not state | ❌ Lossy auto-compact | | Delivery programs + evidence-gated checkpoints | ✅ zcprogram | ❌ | ❌ | ❌ | | HMAC audit trail | ✅ Per-agent subkey | ❌ | ❌ | ❌ | | Per-agent identity isolation | ✅ HKDF + RLS | ❌ | ❌ | ❌ | | Skill admission gate (HMAC + AST) | ✅ | ❌ | ❌ | ❌ | | Tamper-evident chain + session replay | ✅ | ❌ | ❌ | ❌ | | Work-stealing queue for parallel sessions | ✅ FOR UPDATE SKIP LOCKED | ❌ | ❌ | ❌ | | Runs offline | ✅ Local Postgres + Ollama | ❌ | ⚠️ Depends | ✅ | | Cost when idle | $0 | Monthly subscription | $0 | $0 | Versus the well-known memory products: Mem0 and Zep are excellent hosted memory APIs for LLM apps you build; Letta (MemGPT) is an agent framework with self-editing memory. SecureContext targets a different job — me

Question 10

What's new (v0.46.1)

Accepted Answer

Temporal retrieval round 2 + the delivery-tool kit. Compound temporal questions decompose into event clauses with rank fusion; temporal searches render a chronological Timeline block and stale results carry age warnings. zcprogram adds multi-phase delivery programs with evidence-gated, auto-checkpointing phase closure — live-verified with an orchestrator handoff test (kill the orchestrator mid-phase; a cold replacement resumes from two calls). The embed lane now has a watchdog with budgeted self-healing. Recent releases: v0.46.0 — team memory (per-user keys, shared workspaces, write attribution), session replay with cryptographic provenance, OTel Gen-AI spans, CI/headless memory CLI, chunked embeddings, compliance report export. v0.44.0 — temporal fact supersession, durable PG task graph, first public LongMemEval numbers. v0.43.0 — token-budgeted recall digest (fixes the 47k-token recall firehose on mature projects). v0.41–0.42** — memory-quality program: focused recall, consolidation, bi-temporal windows, multi-hop graph retrieval, TTL memories, multimodal ingestion. Full details in CHANGELOG.md; architecture notes in ARCHITECTURE.md.

Question 11

SecureContext nedir?

Accepted Answer

Claude Code’u gerçek projelerde kullanıyorsanız, şu duvarlarla karşılaşacaksınız: 1. Her oturum soğuk başlar. Aynı dosyaları tekrar yapıştırırsınız, aynı kararları yeniden açıklarsınız, aynı tuzakları yeniden keşfedersiniz — ve aynı yönlendirme işini tekrar tekrar token ödeyerek yaparsınız. 2. Uzun projelerde teslim durumu yoktur. Bir orkestratör ajanı, bir özelliğin ortasında ölürse (crash, kapalı pencere, bağlam tükenmesi), yerine geçen ajanın baştan başlaması gerekir. İnsanlar bunu el yazısı CHECKPOINT dosyalarıyla telafi ederler. 3. Ajantların gerçekte ne yaptığının kaydı yoktur. Bir şey yanlış gittiğinde, Claude Code, çalıştırılan şeyin bozulmaya dayanıklı bir geçmişini tutmaz. 4. Beceriler (skill) betikleri, izinlerinizle çalışır, taranmaz. Herhangi bir kişi (veya ajan) ~/.claude/skills/ dizinine yazma erişimi varsa, sonraki çağrıda çalışacak kod gömebilir. 5. Paralel oturumlar birbirlerine çarpar. İki pencere aynı görevi talep eder; biri diğerinin düzenlemelerini üzerine yazar. SecureContext, bu beş sorunu çözen bir MCP eklentisidir. Tek kurulumdan sonra ajanslar aşağıdakileri elde eder: Yeniden başlatmalardan kurtalan hafıza — kararlar, tuzaklar ve proje durumu tek bir çağrıda geri yüklenir; dosya başına semantik özetler, “bu dosya ne yapar?” sorusuna 15–50× sıkıştırma ile yanıt verir, ham okuma yerine; zamanlı arama, olayların ne zaman gerçekleştiğini bilir ve eski bilgiyi işaret eder. Uzun süreli projeler için bir teslim katmanı — zcprogram, çok aşamalı programları izler; bir aşama

designer-cognitive-instincts (Grade A)Güvenlik test edilmiş veri-ai yeteneği için Claude AI. Mükemmellik derecesi A. BÜYÜK ZEPİR — rol.json dosyasından deepPrompt'ın tasarımdan çıkarıldığı cognitive instincts-

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