Personal assistant · 2026-09-12

A quiet local personal assistant on a two-machine setup

A contributor uses a quiet Qwen 3.5 9B assistant on an RTX 5060 laptop for planning, documentation, research and private questions, while a Radeon desktop handles heavier work.

An experienced software developer works under NDAs strict enough that a data leak could end their career, and does not even trust Mistral with the data. They use a local Qwen 3.6 35B-A3B on a machine with 16GB of VRAM, and are clear that it is capable of small tasks but not autonomous ones: you have to walk it through step by step, and sometimes they feel they could have done the work faster themselves. To them the real skill is understanding the knobs — temperature, KV cache, mmap — and each model's limits, the way a photographer's eye matters more than a bigger lens.

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SETUP HISTORY

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CURRENT2026-09-12A quiet local personal assistant on a two-machine setup

A contributor uses a quiet Qwen 3.5 9B assistant on an RTX 5060 laptop for planning, documentation, research and private questions, while a Radeon desktop handles heavier work.

Personal assistant1 machineQwen 3.6 35B-A3BRuntime unspecified

ChangedDescriptionHardwareModelsTools / workflow

Compare with revision 4

DescriptionPreviousAn experienced software developer works under NDAs strict enough that a data leak could end their career, so privacy matters for sensitive questions and work. ## What it is used for Local AI covers task planning, documentation, summarizing, translation, fact checking, research and learning, avoiding cloud token costs for everyday assistance. ## Stack - **Most-used model**: Qwen 3.5 9B in Q4_K_M, on an RTX 5060 laptop at roughly **40-60 tok/s** with about 50K context - **Second machine**: a desktop with a Radeon 7800 XT, reserved for heavier coding, RAG and small-model training (including Qwen 3.6 35B) - **Workflow**: Unsloth Studio and a Pi Agent with customized system prompts ## Limits Larger agentic tasks need careful step-by-step supervision and tuning, long-running tasks remain limited by context, and heavier work still uses a Mistral Pro subscription and JetBrains AI Assistant.This snapshotAn experienced software developer works under NDAs strict enough that a data leak could end their career, and does not even trust Mistral with the data. They use a local **Qwen 3.6 35B-A3B** on a machine with 16GB of VRAM, and are clear that it is capable of small tasks but not autonomous ones: you have to walk it through step by step, and sometimes they feel they could have done the work faster themselves. To them the real skill is understanding the knobs — temperature, KV cache, mmap — and each model's limits, the way a photographer's eye matters more than a bigger lens.

HardwarePreviousEveryday assistant laptop: RTX 5060 / Heavy-work desktop: Radeon 7800 XTThis snapshotDeveloper machine: GPU unspecified

ModelsPreviousQwen 3.5 9B, Qwen 3.6 35B-A3BThis snapshotQwen 3.6 35B-A3B

Tools / workflowPreviousUnsloth Studio; Pi Agent with customized system promptsThis snapshotNot specified

This is the currently published snapshot.

REVISION 42026-09-12A quiet local personal assistant on a two-machine setup

A contributor uses a quiet Qwen 3.5 9B assistant on an RTX 5060 laptop for planning, documentation, research and private questions, while a Radeon desktop handles heavier work.

Personal assistant2 machinesQwen 3.5 9B, Qwen 3.6 35B-A3BRuntime unspecified

ChangedDescription

Compare with revision 3

DescriptionPreviousThis experienced software developer works under NDAs strict enough that a data leak could end their career, so privacy matters for sensitive questions and work. They use local AI for task planning, documentation, summarizing, translation, fact checking, research and learning, avoiding cloud token costs for everyday assistance. The most-used model is Qwen 3.5 9B in Q4_K_M, running quietly on an RTX 5060 laptop at roughly 40–60 tokens per second with around 50K context. A second desktop with a Radeon 7800 XT is reserved for heavier coding, RAG and small-model training workloads, including Qwen 3.6 35B. The workflow uses Unsloth Studio and a Pi Agent with customized system prompts, and larger agentic tasks require careful step-by-step supervision and tuning. The contributor reports that long-running tasks remain limited by context, while larger work still uses a Mistral Pro subscription and JetBrains AI Assistant.This snapshotAn experienced software developer works under NDAs strict enough that a data leak could end their career, so privacy matters for sensitive questions and work. ## What it is used for Local AI covers task planning, documentation, summarizing, translation, fact checking, research and learning, avoiding cloud token costs for everyday assistance. ## Stack - **Most-used model**: Qwen 3.5 9B in Q4_K_M, on an RTX 5060 laptop at roughly **40-60 tok/s** with about 50K context - **Second machine**: a desktop with a Radeon 7800 XT, reserved for heavier coding, RAG and small-model training (including Qwen 3.6 35B) - **Workflow**: Unsloth Studio and a Pi Agent with customized system prompts ## Limits Larger agentic tasks need careful step-by-step supervision and tuning, long-running tasks remain limited by context, and heavier work still uses a Mistral Pro subscription and JetBrains AI Assistant.

Archived published snapshot — kept for context and comparison.

REVISION 32026-09-12A quiet local personal assistant on a two-machine setup

A contributor uses a quiet Qwen 3.5 9B assistant on an RTX 5060 laptop for planning, documentation, research and private questions, while a Radeon desktop handles heavier work.

Personal assistant2 machinesQwen 3.5 9B, Qwen 3.6 35B-A3BRuntime unspecified

Archived published snapshot — kept for context and comparison.

REVISION 22026-09-12A quiet local personal assistant on a two-machine setup

A contributor uses a quiet Qwen 3.5 9B assistant on an RTX 5060 laptop for planning, documentation, research and private questions, while a Radeon desktop handles heavier work.

Personal assistant2 machinesQwen 3.5 9B, Qwen 3.6 35B-A3BRuntime unspecified

ChangedTitleDescriptionCategoryHardwareModelsTools / workflowCloud roleROI / limitations

Compare with revision 1

TitlePreviousNDA-bound development in small, supervised local tasksThis snapshotA quiet local personal assistant on a two-machine setup

DescriptionPreviousThis developer works under NDAs strict enough that a data leak could end their career, so sensitive work cannot be sent to cloud APIs. They use Qwen 3.6 35B for small, well-defined coding tasks rather than asking a local agent to plan and execute an entire project autonomously. The workflow requires going step by step and tuning memory, KV cache, mmap, temperature, and model selection to avoid loops on limited hardware. They consider that operational knowledge and the surrounding workflow as important as the parameter count.This snapshotThis experienced software developer works under NDAs strict enough that a data leak could end their career, so privacy matters for sensitive questions and work. They use local AI for task planning, documentation, summarizing, translation, fact checking, research and learning, avoiding cloud token costs for everyday assistance. The most-used model is Qwen 3.5 9B in Q4_K_M, running quietly on an RTX 5060 laptop at roughly 40–60 tokens per second with around 50K context. A second desktop with a Radeon 7800 XT is reserved for heavier coding, RAG and small-model training workloads, including Qwen 3.6 35B. The workflow uses Unsloth Studio and a Pi Agent with customized system prompts, and larger agentic tasks require careful step-by-step supervision and tuning. The contributor reports that long-running tasks remain limited by context, while larger work still uses a Mistral Pro subscription and JetBrains AI Assistant.

CategoryPreviousregulated-workThis snapshotpersonal-assistant

HardwarePreviousPrimary machine: GPU unspecifiedThis snapshotEveryday assistant laptop: RTX 5060 / Heavy-work desktop: Radeon 7800 XT

ModelsPreviousQwen 3.6 35B-A3BThis snapshotQwen 3.5 9B, Qwen 3.6 35B-A3B

Tools / workflowPreviousstep-by-step supervised workflowThis snapshotUnsloth Studio; Pi Agent with customized system prompts

Cloud rolePreviousfullThis snapshotpartial

ROI / limitationsPreviousLocal preserves confidentiality but can be slower than doing a small task manually and requires careful supervision and tuning.This snapshotQwen 3.5 9B is the most-used model because it is reliable, quiet and inexpensive to run locally. Long-running tasks remain limited by context, while larger work still uses Mistral Pro and JetBrains AI Assistant.

Archived published snapshot — kept for context and comparison.

REVISION 12026-09-12NDA-bound development in small, supervised local tasks

An experienced developer keeps sensitive work away from cloud APIs and uses a local Qwen model for narrow tasks, accepting that the workflow needs hands-on supervision.

A quiet local personal assistant on a two-machine setup

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