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

What do I need to run it?

Accepted Answer

Node 20+ and (recommended) Docker for the bundled PostgreSQL + Ollama stack. The one-command installer does everything in about five minutes. A SQLite fallback runs with zero infrastructure.

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

Vai SecureContext sūta manu kodu vai datus uz mākoni?

Accepted Answer

Nē. Memorie, iegultie (Ollama nomic-embed-text), meklēšana, kopsavilkums un auditējuma ķēde darbojas lokāli. Tas darbojas pilnībā bez interneta (meklēšana var samazināties līdz tikai atslēgvārdiem, ja Ollama nav pieejams) un izmaksas ir $0, kad tas ir neaktīvs.

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

Kā es varu dot Claude Code pastāvīgu atmiņu starp sesijām?

Accepted Answer

Instalējiet SecureContext (vienu komandu — skatiet ātro startu), tad agenti izsauc zcremember, lai saglabātu lēmumus un trīšņus, un zcrecall_context({focus: "current task"}) sesijas sākumā, lai tos atjaunotu — šķiroti pēc nozīmes, ar dabīgā valodas laika vaicājumiem, kas atbildēti tieši no atmiņas. Atmiņa izdzīvo restartus, samazinājumus un pārstartējumus, jo tā glabājas PostgreSQL, nevis konteksta logā.

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

Kas ir SecureContext?

Accepted Answer

Ja jūs izmantojat Claude Code reālos projektos, jūs sastopaties ar šādām problēmām: 1. Katrs sesijas sākums ir auksts. Jūs atkārtoti ievietojat tos pašus failus, atkārtoti izskaidrojat tās pašas lēmumu pieņemšanas procesus, atkārtoti atklājiet tādas pašas griblas – un maksājiet tokenus par to pašu orientācijas darbu atkārtoti un atkārtoti. 2. Ilgdiem projektiem nav piegādēšanas stāvokļa. Kad orchestrating agents mirst pa vidu (aizkavēšanās, loga aizvēršana, konteksta iztukšošanās), tā aizvietotājs sāk bezredzē. Cilvēki kompensē ar roku rakstītiem CHECKPOINT failiem. 3. Nav ieraksta par to, ko faktiski darīja agenti. Ja kaut kas iziet greizi, Claude Code nav tamparēšanas pierādījumu par to, ko tika izpildīts. 4. Prasmju skripti darbojas ar jūsu atļaujām, neesot pārbaudīti. Ikviens (vai jebkurš agent) ar rakstīšanas piekļuvi ~/.claude/skills/ var iekāpt kodu, kas darbojas nākamajā izsaukumā. 5. Paralelās sesijas kļuvis viens otrā. Divi logs pieprasa vienu un to pašu uzdevumu; viens pārraksta cita izmaiņas. SecureContext ir MCP spraudnis, kas slēdz visas piecas problēmas. Instalējiet to vienreiz un jūsu agenti saņem: Memorie, kas izdzīvo restartēšanas – lēmumi, griblas un projektu stāvoklis atjaunots vienā izsaukumā; faila semantiskie kopsavilkumi, kas atbild uz jautājumu “ko šis fails dara”, ar 15–50 reizināšanas saspiestatīšanu, nevis brutālos lasījumus; laika meklēšana, kas zina, kad notika lietas, un atzīmē novecojušu informāciju. Piegādes slānis ilgu izpildes laika projektam – zcprogram izseko vairāku posmu programmas; pos

developer-documentation-instincts (Grade A)Drošības pārbaudīta datu-AI prasme Claude AI. Klase A. DOCUMENTATION INSTINCTS — izņemts no roles.json deepPrompt izstrādātāja.

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