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Memplua is an open-source Windows-focused project for turning audio playing on a computer into draft knowledge-base notes without routing the audio to an external AI service. Its pipeline captures system audio, transcribes speech, processes the text with a local language model, and sends structured drafts to a person for review before Obsidian export. In an account published September 29, 2026, developer Ivan also described what changed after outside testers found failures his own setup had hidden.
How does Memplua turn computer audio into Obsidian notes?
The goal is to reduce the interruption involved in building a knowledge base: pausing to extract useful ideas from something being listened to, rewrite them, and organize them. Memplua treats system audio as a general input rather than creating a separate integration for each source. Development began on Windows because audio capture depends on the operating system and Windows was the developer’s own platform.
- Capture system audio: Take audio already playing on the computer as the input.
- Detect speech: Use voice activity detection (VAD) to identify voice and filter silence or noise before transcription.
- Transcribe: Use Whisper to convert speech to text.
- Chunk the transcript: Divide the text into pieces for processing while preserving enough surrounding context.
- Structure locally: Process transcript text with a local model through llama.cpp. Ivan named Qwen3-4B-Q4 as one model under test.
- Review and export: Generate structured note drafts, let the user edit or reject them, and export approved notes to Obsidian.
The model is asked for structured JSON rather than open-ended prose. The described concepts include terms with names and definitions, thoughts that connect multiple terms, and a draft set prepared for review. These are Memplua’s implementation choices, not evidence that this model combination is universally best or that it outperforms alternatives.
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The initial concept was “audio → LLM → Obsidian,” but Ivan decided that automatically writing model output into a knowledge base was not a safe default. A model might misunderstand a passage, create duplicate information, make a weak connection, or produce a technically accurate note that is not useful to the reader.
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Human review is therefore part of the design, not an optional cleanup step. The application prepares material to reduce repetitive drafting; the user retains judgment over what belongs in their notes. Drafts can be edited or rejected before saving.
How did transcript overlap create duplicate notes?
Chunk boundaries can split a sentence or an idea. To give the model enough context, Ivan first used overlapping, or “floating,” transcript windows. That improved continuity, but it also fed previously seen text back into the model. The model sometimes extracted that old material again, filling the approval queue with duplicate notes.
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The revised approach distinguishes between two parts of a chunk:
- Focus: The portion from which the model should extract new information.
- Context: Surrounding text that helps interpret the focus but should not prompt repeated entity extraction.
This is a targeted response to the duplicate-note failure, not a guarantee that duplicates are eliminated.
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What broke when outside users tested the app?
VAD rejected audio that appeared to contain speech
The most technically unresolved problem in Ivan’s account involved Silero VAD. One tester reported audio with peak amplitude of 0.55 and RMS of 0.24, yet the maximum VAD probability was only 0.0013. Memplua discarded the audio as if it contained no voice. When the tester disabled VAD, transcription and note generation worked normally.
Ivan narrowed the suspected problem to the VAD input path, with sample rate, normalization, window size, tensor shape, state handling, or a combination of factors still possible. He did not report a confirmed cause or a completed fix. The example is one troubleshooting case, not a benchmark or evidence about how often VAD fails.
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Installers and startup failed on other machines
Testers also exposed two failures that the developer’s own machine did not reveal: a build was missing a runtime DLL already present on his computer, and another build launched its process while leaving the user interface invisible. Ivan said he fixed both, rebuilt the installer to handle dependencies more reliably, and added online and offline installation paths. These are fixes he reported at the time, not independently verified claims about later releases.
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What was Memplua’s status in September 2026?
In his September 29, 2026 account, Ivan described Memplua as alpha software and said the main pipeline worked. He reported Windows system-audio capture, transcription, local inference, structured note generation, review and editing, Obsidian export, online and offline installers, corrected UI startup behavior, and early external testers.
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He identified easier first-run setup, more predictable VAD behavior, clearer model configuration, and local inference presets that require less manual tuning as next priorities. This is a dated account; it does not establish the project’s present release status, hardware requirements, support beyond its Windows focus, or security guarantees. The local-processing design expresses an intent to keep processing on the user’s machine, but the account does not independently establish broader security properties.
What did real-user testing change?
Outside testing surfaced environmental problems that were invisible on the developer’s machine: a missing dependency, a hidden interface, and a VAD path that could discard audio before transcription. It also provided a check on usability assumptions. Ivan wrote that testers “found bugs I had stopped noticing, questioned UI decisions that seemed obvious to me, and forced me to think more carefully about the boundaries of the project.”
That feedback shifted the near-term emphasis toward reliability and ease of setup rather than simply adding more AI features. As Ivan put it, “Memplua is not supposed to replace thinking. It is supposed to remove the repetitive work required to turn consumed information into a usable draft.”
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