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For the same note-synthesis and filing jobs, Antigravity was the more reliable custodian of an existing knowledge vault. In a qualitative hands-on comparison, it was reported to read surrounding context first, preserve naming and folder conventions, and place new material in the right map-of-content index more consistently than a locally run Qwen 3.5 9B Q4_K_M model. The local model did complete the tasks and use tools, but it made more inference leaps: it briefly treated valid notes as orphans and sometimes defaulted to the vault root when placement was unclear. That does not make local models unusable. It means file-heavy automation should be judged on structural fidelity, permissions, and reviewability—not merely whether a task eventually finishes.
What exactly was tested?
The comparison gave two agents the same single-step and multi-step office tasks inside a personal knowledge-management vault. The work included synthesizing existing notes and filing the result into the correct map-of-content index, where the surrounding names, folders, and formatting conventions already carried meaning.
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One setup used Google’s Antigravity agent, reported as running Claude Sonnet 4.6 Thinking. The other used a locally hosted Qwen 3.5 9B model in Q4_K_M quantization. Both systems had access to tools needed to inspect and modify files. The comparison was qualitative and based on one person’s task set; no sample size, success percentage, or controlled benchmark score was published. Treat the result as an observed workflow difference, not a universal ranking.
Which agent respected the existing files better?
Antigravity showed the stronger consistency when the job depended on conventions that were not restated in every prompt. The local Qwen setup was capable, but it needed more explicit boundaries and checking whenever the correct location had to be inferred.
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Antigravity’s observed behavior
Antigravity more often inspected neighboring notes and indexes before writing. It followed established file names, folder placement, and formatting patterns instead of treating the requested output as an isolated document. That context-first behavior matters in a vault where a technically correct note can still be harmful if it lands in the wrong directory or breaks an index.
The agent also benefited from interactive controls around project folders, terminal use, and edit review. Those controls do not guarantee a correct decision, but they make an incorrect one easier to catch before it spreads through linked notes.
What the local Qwen model did well—and where it slipped
Qwen 3.5 9B Q4_K_M completed the assigned tasks and handled tool calling, so the local setup was not incapable of office automation. It even recognized when a change had already been made, a useful sign that it could track state across an operation.
The weakness was consistency in interpreting existing structure. It briefly misclassified existing notes as orphans and, when uncertain about placement, sometimes wrote to the vault root. Those are inference errors rather than failures to generate text. In a large archive, however, a misplaced or mislinked file can cost more time than a slow answer.
What this result does—and does not—prove
The finding supports a narrow conclusion: in this task set, Antigravity was more dependable at preserving local conventions. It does not prove that every Antigravity run will be correct, that every local model will misfile notes, or that a larger local model would behave the same way. Prompt quality, folder instructions, context-window limits, tool wrappers, and review steps can change the outcome substantially.
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How do the two approaches compare for office work?
Use the following as a decision map rather than a benchmark score. The values describe the observed comparison and the documented control surfaces, not guaranteed product behavior in every edition or deployment.
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| Comparison point | Antigravity | Local Qwen 3.5 9B Q4_K_M |
|---|---|---|
| Context inspection before writing | More consistently read surrounding notes and indexes first in the observed tasks. | Could inspect context, but made more inference leaps when structure was ambiguous. |
| Names, folders, and formatting | More reliably preserved existing conventions. | Completed work but sometimes defaulted to the vault root or misread note status. |
| Single-step tasks | Completed the assigned operations with stronger structural fidelity. | Completed the assigned operations. |
| Multi-step tasks | Better continuity between reading, synthesis, and filing in this comparison. | Handled tool calls and state, including recognizing an already-made change, but required closer checking. |
| Execution model | Reported as using Claude Sonnet 4.6 Thinking through an agent environment. | Runs on the user’s own machine when the model and tools are hosted locally. |
| Permission granularity | Settings cover terminal commands, file access, MCP tools, and web-page reads; project folders can be bounded. | Depends on the local runner, operating-system account, and tool wrapper you configure. |
| Approval behavior | Interactive approvals and a sandbox can gate higher-risk actions. | Must be implemented by the local host or wrapper; a model alone does not provide approval policy. |
| Edit review and rollback | Provides a VCS panel for reviewing agent edits when the project is under version control. | Use your own version-control, snapshot, or backup workflow. |
| Privacy trade-off | Cloud execution may expose the context you grant to the service; verify the deployment and data policy for your account. | Local inference can keep prompts and files on the machine, but logs, extensions, remote tools, or sync services can still transmit data. |
Why do permissions matter as much as model quality?
An agent can only respect files it is allowed to see, and it can only be safely corrected when its writes are reviewable. Google’s settings documentation describes permission presets that control terminal commands, file access, MCP tools, and web-page reads. The documented Default preset says: “Default: Commands run without prompting inside the Terminal Sandbox; running outside the sandbox requires approval.”
The feature overview also describes interactive approvals, bounded access to provided project folders, multi-folder project access, integrated terminal support, and a VCS panel for reviewing edits. Together, those controls create four practical questions for any agent: how much context may it inspect, which directories may it change, which actions require approval, and how quickly can you review or revert the result.
Use the smallest useful project boundary
Give a filing agent the vault or project folders it genuinely needs, not an entire home directory. Separate read access from write access where the host allows it. A notes-synthesis task usually needs to read source notes and an index, while a broad terminal grant may also expose unrelated credentials, caches, or personal documents.
Keep approval gates for irreversible actions
Require confirmation before deletion, bulk moves, shell commands outside the sandbox, or edits that touch many files. A fast approval prompt is cheaper than reconstructing a damaged index. If your local runner cannot pause for approval, make the tool wrapper produce a proposed change list first and apply it only after a human confirms.
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Make review and rollback ordinary
Put the vault under version control when its file formats and privacy requirements permit it, or create dated snapshots before an agent session. Review the diff, not just the final prose: check paths, renamed files, link targets, front matter, and index entries. A correct-looking paragraph can hide a destructive move.
How should you configure an agent before it edits a vault?
Use a staged workflow that separates discovery from writing. The exact labels vary by host, but the sequence below works for cloud agents and local runners alike.
- Define the allowed roots. Name the project folder and any additional read-only reference folders. Explicitly exclude credentials, system directories, and unrelated personal data.
- Describe the house rules. State naming patterns, date format, front-matter fields, preferred folder locations, and how map-of-content indexes are organized. Point to representative files rather than relying on abstract instructions.
- Ask for an inventory first. Have the agent list candidate notes, their paths, and the index it believes should change. Do not allow writes during this pass.
- Require an uncertainty report. For each ambiguous note, ask what evidence supports the proposed folder and what alternatives were rejected. This exposes leaps such as treating an existing note as an orphan.
- Generate a dry-run patch. The agent should show new files, moves, deletions, and index edits. A patch or VCS diff is more useful than a prose statement that the job is complete.
- Approve the smallest batch. Apply one logical group of changes, then inspect links and indexes before permitting the next group.
- Run structural checks. Confirm every new file exists where expected, every index link resolves, and no established naming rule was broken. Keep the original snapshot until those checks pass.
Can a local LLM organize notes without misfiling them?
Yes, but reliability depends on scaffolding around the model. The observed Qwen setup completed office tasks, yet its root-folder fallback shows why local automation should not be granted silent, unrestricted write access when placement is ambiguous.
Give placement decisions explicit evidence
Provide a small set of canonical examples: one note in each major folder, a representative map-of-content index, and the naming rule that connects them. Ask the model to cite the matching example for every proposed destination. If no example fits, require a “needs review” status instead of a guessed path.
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Separate classification from mutation
Have the model output a machine-readable plan containing source path, destination path, reason, and confidence. A deterministic script or human can validate that destination against an allowlist before any move occurs. This design keeps a reasoning mistake from becoming a filesystem mistake.
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Use idempotent operations
Design commands so running them twice does not duplicate notes or append the same index entry repeatedly. Before creating a file, check for an existing path and stable identifier. The comparison’s observation that the local model recognized an already-made change suggests state awareness is possible; idempotent tooling makes that awareness less fragile.
What security risks come with broad agent access?
Independent reporting has described prompt-injection and unintended-command risks when Antigravity receives broad file and terminal access. Those reports are a separate security consideration, not evidence that the office-task comparison encountered an exploit.
Prompt injection can arrive through a note, web page, or document that contains instructions aimed at the agent rather than at you. If the agent can execute commands or write across the machine, hostile text may persuade it to reveal data, alter files, or run an unintended operation. Limit web-page reads and MCP tools unless the task needs them, keep secrets outside the allowed roots, and require approval for commands with side effects.
Local execution removes some network exposure but does not remove agent risk. A local process still inherits the permissions of its operating-system account, and an overpowered wrapper can erase or overwrite files just as quickly as a cloud-connected agent. Privacy and safety are properties of the whole stack—model, runner, tools, account, logs, and backups—not of the model name alone.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which option should you choose for your own office tasks?
Choose based on the cost of a structural mistake, your privacy requirements, and how much setup you are willing to maintain.
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| Your priority | More suitable starting point | Reason |
|---|---|---|
| Preserving an established vault with minimal prompt engineering | Antigravity with bounded folders and approvals | The observed run was more consistent at reading context and following conventions. |
| Keeping model inference on your own machine | A local Qwen setup with a strict wrapper | Local hosting can reduce transmission of file content, provided logs and tools remain local. |
| Automating high-volume moves | Either option, but only with dry runs, allowlists, and versioned rollback | Scale magnifies a single classification error; controls matter more than fluent output. |
| Working with sensitive files and uncertain instructions | The least-privileged setup that can complete the task | Reduce readable folders, disable unnecessary tools, and gate every irreversible action. |
For a personal vault whose value lies in its accumulated links and conventions, the practical verdict is to start with the agent that demonstrates stronger context fidelity, then keep its permissions narrow. A local model remains a credible choice when privacy or offline operation dominates, but treat folder placement as a supervised classification problem rather than an automatic side effect.
How can you reproduce the comparison fairly?
Use the same clean snapshot of your vault, identical task wording, and the same allowed folders for both systems. Randomize the order or use separate copies so the first agent’s edits cannot help the second. Record whether each agent inspected neighboring notes, selected the expected index, preserved formatting, and requested clarification when evidence was missing.
Score the artifacts, not just completion messages. Check path accuracy, link validity, naming conformity, duplicate prevention, and unwanted changes outside the task. Report the number of tasks and failure modes alongside any success rate; without those details, a result is an anecdote rather than a benchmark.
Frequently Asked Questions
Does Antigravity run entirely on my computer?
Not necessarily. The comparison described it as running Claude Sonnet 4.6 Thinking through an agent environment, so you should verify where inference, logs, and tool calls occur for your account and deployment. Never infer local-only privacy from the presence of a local project folder.
Is a 9B local model powerful enough for office automation?
It can be, especially for bounded extraction, renaming, and tool-driven workflows. The limiting factor is often context selection and error handling rather than text generation; a smaller model needs clearer schemas, examples, and validation around it.
How can I tell whether an agent misunderstood my folder structure?
Ask it to produce a path-and-reason table before writing and to identify notes it considers ambiguous. Compare each proposed destination with a known-good neighboring file; an unexplained root-folder choice is a reason to stop and clarify.
What should I preserve before letting an agent edit files?
Keep an immutable snapshot of the project and a record of the agent’s proposed operations. For version-controlled text vaults, retain the commit or patch that existed immediately before the run so you can restore the exact prior state, not merely recover individual files.
Quick Recap
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