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Video Lecture Notes

Sun, 06 Sep 2026 00:00:00 +0000

A lecture series can contain several explanations of the same idea, spread across videos with unhelpful titles. Video Lecture Notes downloads the audio, transcribes it, and combines related lectures into study notes that can be searched alongside textbooks and handwritten notes. The combination step matters: it tries to turn a sequence of recordings into one explanation rather than leave a separate transcript for every file.

One trial exposed a surprisingly basic limit. A three-video group produced a note reflecting only one video’s content because the local model was receiving only the end of the combined transcript. The shared model helper now requests enough context for the prompt. Rechecking showed better coverage and structure, although reliable links from each concept to the exact video moment remain unfinished.

From recording to study note

Audio download, speech transcription, and note synthesis run locally. The transcript retains timed segments; the synthesis model receives them when drafting the combined note. Registering that note with the uses the existing classification and embedding API calls, so the full searchable workflow is not entirely offline.

Grouping first uses playlist context and recognizable lecture-series titles. When that context is insufficient, it can compare transcript contents. This saves manual sorting, but shared vocabulary is not the same as belonging to the same lecture: the recorded trial grouped some differently numbered videos together and split another lecture’s two parts.

For an illustrative output, a note might organize “Sets and functions” into definitions, examples, and a comparison of related ideas. That describes the intended format, not an excerpt from the recorded run. To check an explanation, I still need the recordings and transcripts; a general video link at the end is less useful than a precise reference beside the claim.

What the trial covered

The September 2026 record covers eight videos from a 105-video Math Camp playlist, across two passes. They produced five indexed notes. That establishes a working download-to-note path on a subset, not full-playlist coverage or complete retention of every explanation.

Long groups remain expensive to synthesize on CPU hardware, and the remaining timestamp and grouping issues affect practical review. The and linked status record distinguish the implemented workflow from those open checks.

Journal Discovery Pipeline

Wed, 02 Sep 2026 00:00:00 +0000

A search for “disaster resilience” can return papers about infrastructure, recovery, public health, or business continuity. Journal Discovery lets me specify the angle I want, then compares that description with candidate abstracts before trying to download anything. The comparison runs locally, helping narrow a broad literature search without asking a language model to judge every paper.

The October 15, 2026 trial makes the stages concrete: a disaster-research query retrieved 60 candidates, accepted 15 as relevant, and downloaded seven PDFs. Eight accepted papers still needed manual access. Finding a candidate, obtaining its full text, and reading or evaluating it are separate accomplishments; the pipeline handles the first two only within the available access routes.

Grow a library around a question

The starting point can be a topic or a researcher’s name. OpenAlex, an academic works catalog, supplies candidates and bibliographic information. A locally computed representation of each abstract is compared with my relevance description. When an abstract is unavailable, title-based scoring is a weaker signal; names and subject labels also need judgment.

A citation-following mode offers another route: find newer papers citing material already in the library, review the proposed list, then fetch the selected papers. A citation alone never authorizes that download step or establishes relevance.

The downloader tries open-access sources and the configured institutional route. If a publisher requires ordinary browser access, the paper goes onto a linked manual-download list. I use my library access in the browser and save the PDF; the tool does not solve publisher access challenges. Downloaded files are checked as PDFs and written through temporary files so an interrupted transfer does not leave a partial final file.

Discovery is upstream of reading

separately converts and indexes the files. Reconciliation can recognize manually saved papers by their converted contents, rather than depending on filenames.

Two maintenance operations have different authority. The older metadata audit can automatically reconcile mechanical folder and tag differences against its reference data, while flagging title or author mismatches. The newer classification mode uses stored OpenAlex Topics to propose folder and tag decisions for human acceptance. It is not an automatic reading of each paper’s argument.

Direct inspection found the 15 accepted disaster papers on topic in that trial; a separate author query accepted six and downloaded one. Those are limited runs. Converted chart descriptions may also omit plotted values, so later search results still need checking against the PDF. The and linked October status record give the current workflow and evidence.

Notes Post-Processing

Fri, 28 Aug 2026 00:00:00 +0000

A square-root sign once became the letter p in my extracted notes. The text still looked printable and ordinary, so the initial checks let it through. Notes Post-Processing was built to catch that kind of plausible-looking error after transcription, then compare it with the original page before making a correction.

Context on both sides of a symbol mattered. Looking only at the text before the corrupted character missed evidence that appeared afterward; a second local model could use that surrounding context to judge which positions deserved attention. The models flag candidates rather than deciding what the source must have said.

The recorded motivating run repaired that square-root error on page 6 and left nine other flagged candidates unchanged. The notation below illustrates the difference:

Extracted text	Corrected reading
`p(h² + k²)`	`√(h² + k²)`

The tool first identifies suspicious text using structural checks and local language-model scores. Surviving candidates go to an image-based comparison with the PDF page. It records whether the page was changed, left alone, or could not be verified.

Testing across more notes exposed another problem: legitimate mathematical language can look unusual to a general language model. Joined words from an upstream spacing error also produced many false alarms. Fixing the spacing and tuning the checks reduced unnecessary flags, but did not eliminate them.

False positives consume review time and image-checking calls. Comparing with an image supplies additional evidence; the model can still misread that evidence or make a bad change. Corrected material therefore needs spot checks rather than being treated as automatically clean.

This tool follows and helps improve material used by the . The describes the reproduced error and broader precision limits.

Notes Transcription Pipeline

Thu, 27 Aug 2026 00:00:00 +0000

A question scribbled beside a lecture slide is easy to lose in a folder of drawings. Notes Transcription turns handwritten canvases and short course PDFs into searchable text, keeping references to the original material. It also separates pasted slides from handwriting, so later study tools can tell which explanation came from the lecturer and which note I added.

Typed PDFs need care too. A small raised character can carry the difference between R² and R2, yet ordinary text extraction may flatten them into the same line. The pipeline uses character size and vertical position to recover exponents and subscripts locally before deciding whether a page needs AI transcription. That preserves useful structure without paying a model to reread every clean page.

Once the output is registered with the , its passages can be found alongside textbook material. That is what lets the draw on a handwritten explanation, or cite course notes behind a proposed connection. Transcription prepares the material those tools read.

Choose how much of the source needs rereading

The PDF path uses three levels of work. Clean digital text can be extracted locally. If only a few pages look defective, those pages receive image-based repair with neighboring text for context. Handwriting, difficult exports, or documents with widespread defects need fuller transcription. Some note-taking exports can omit entire equation regions from their text layer, so an apparently clean extraction is insufficient evidence for trusting them.

The recorded August 2026 comparison checked recovered exponents and subscripts across 13 pages from two documents against existing transcriptions. In one 43-page problem set, the local improvements reduced the detected defect rate from 63% to 5%, leaving two pages for repair. Those results concern the checked notation and routing decisions, not the accuracy of every word in the collection.

Drawings and PDFs take different paths

Excalidraw saves its SVG images automatically. The canvas path cuts a tall drawing at whitespace gaps, transcribes the pieces with some preceding context, and produces both a raw transcript and an expanded note for search. Expansion adds prose and can introduce interpretation; the raw version and source drawing remain important for checking it. Handwritten questions can be marked for a separate answering tool.

The current command-line workflow can prepare page images and task cards for a subscription agent, then validate the submitted cards before writing the usual outputs. This is a manual handoff. The metered API path is also available, and search indexing has its own API calls; running the scripts locally does not mean all processing stays on the device.

Supported sources here are PDFs and Excalidraw scenes with exported images. Word-produced PDFs fit the PDF route; native Word documents are a separate conversion task. Most importantly, plausible-looking errors can survive transcription. checks suspicious text against source images afterward, while the explains the available routes.