vault backup: 2025-12-23 17:41:04

content/.obsidian/workspace.json

@@ -14,13 +14,13 @@
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content/z-Tech/Mega Mega.md

@@ -0,0 +1,142 @@
+
+Got it. Here’s the “project-level” design (still high-level, but with your technical choices and the AI piece called out clearly).
+
+Project goals with your technical choices baked in
+
+Guiding constraints
+	•	Django + templates + HTMX as the primary UI model (no SPA).
+	•	CodeMirror as an “editor island” for Markdown.
+	•	Markdown is the internal format everywhere (notes, chunks, explanations, prompts) to keep import/export trivial.
+	•	PostgreSQL from day 1 because search is a core feature, not a nice-to-have.
+	•	Background work must exist early (OCR/transcripts/indexing/summarization).
+
+⸻
+
+The four main product areas
+
+1) Course content hub
+
+Goal: capture all course material in one place and make it searchable + referenceable.
+
+What “done” looks like:
+	•	You can attach/organize:
+	•	slides/PDFs, målbeskrivning, instudieringsfrågor
+	•	lecture resources + your own lecture notes
+	•	YouTube links (+ subtitles/transcripts)
+	•	Everything becomes text (OCR/transcripts) and is searchable via PG FTS.
+	•	Every processed/derived thing points back to origin (page number, timestamp, file).
+
+Key UX goal:
+	•	“I remember something was mentioned somewhere” → search finds it fast → open at the exact page/timestamp.
+
+⸻
+
+2) Incremental writing (SuperMemo-style) on top of Markdown
+
+Goal: turn raw material into durable knowledge in small steps.
+
+What “done” looks like:
+	•	You can extract passages from sources into your Markdown note system as stable chunks.
+	•	Each chunk has a lifecycle:
+	•	extracted → rewritten → distilled → prompted → maintained
+	•	When you fail a review or miss a tentamen question, the system routes you back to the exact chunk/source context to improve the text/prompt (the “SM loop”).
+
+Key UX goal:
+	•	You never rewrite the whole note; you process a queue of small “next actions”.
+
+⸻
+
+3) Old exams (tentamen) training
+
+Goal: train the exact skill that matters: solving the course’s exam-style questions.
+
+What “done” looks like:
+	•	Import exam material (from DISA exports, PDFs, scans).
+	•	Run exam sessions (timed/untimed), log:
+	•	correctness, time spent, confidence, common wrong option, etc.
+	•	Link questions to:
+	•	concepts/chunks
+	•	source locations (slides/pages) that explain them
+
+Key UX goal:
+	•	After an attempt, you get a short list of specific fixes: which concept/chunk to improve, which sources to revisit.
+
+⸻
+
+4) Spaced repetition (FSRS) across both concepts and exams
+
+Goal: one review system for everything that’s worth remembering.
+
+What “done” looks like:
+	•	FSRS schedules:
+	•	knowledge prompts derived from chunks (conceptual memory)
+	•	exam questions (performance memory)
+	•	A unified daily queue mixes:
+	•	concept prompts (depth)
+	•	exam drilling (transfer / exam performance)
+
+Key UX goal:
+	•	Your daily queue reflects both “what you need to understand” and “what you need to score”.
+
+⸻
+
+AI integration goals (the missing piece)
+
+You want AI as an operator inside the workflows, not a separate chatbot page.
+
+AI should be able to do (first-class features)
+	1.	OCR improvement / cleanup
+	•	de-noise OCR text, fix hyphenation, headings, tables “good enough”
+	2.	Summarization at multiple granularities
+	•	per page/segment, per lecture, per section
+	•	generate: key points, misconceptions, “exam traps”, candidate prompts
+	3.	Teacher substitute Q&A grounded in your sources
+	•	ask a question and get an answer that cites your sources (page/timestamp)
+	4.	Validation of free-text answers
+	•	you type an answer to an exam question / prompt
+	•	AI grades it against rubric + your sources, and suggests what’s missing
+
+Non-negotiables for AI in this system
+	•	Provenance: AI answers must cite the exact source chunks they used (your extracted text units).
+	•	Separation of concerns: AI generates suggestions; your stored knowledge remains Markdown artifacts you control.
+	•	Repeatability: store prompt/version/meta for generated outputs so you can regenerate or compare later.
+
+AI architecture choice (still high-level)
+	•	A provider-agnostic “AI service layer” with capabilities:
+	•	summarize(source_segment)
+	•	extract_concepts(source_segment)
+	•	generate_prompts(chunk)
+	•	answer_question(query, retrieved_context)
+	•	grade_free_text(answer, rubric, context)
+	•	Retrieval should be PG-driven (FTS + ranking) and the AI is fed only the top-k source segments (RAG).
+
+⸻
+
+Background tasks: important correction about Django 6 tasks
+
+Django 6 adds a built-in Tasks framework (definition/queueing/results), but it does not ship a production-ready worker/backend that executes tasks.  
+
+So your project goal should be:
+	•	use Django’s Tasks API for a standard interface
+	•	pick an execution mechanism:
+	•	simplest: django-tasks reference implementation (community)  
+	•	or keep Celery/RQ if you already like them
+
+Either way, the system must run these jobs out-of-request:
+	•	OCR, transcript fetching, indexing, summarization, embedding generation (if you add it), exam parsing.
+
+⸻
+
+Success criteria (project-level)
+	•	You can ingest a course’s full material set in a day and search it effectively.
+	•	Your incremental queue (“next chunks to refine / prompt”) always has the right next step.
+	•	Exam attempts automatically generate a focused remediation plan.
+	•	Your FSRS daily queue stays small but high-yield.
+	•	AI outputs are grounded and traceable (no “hand-wavy teacher”).
+
+⸻
+
+If you want the next doc to be even more “project charter style”, I can produce:
+	•	a phased roadmap (MVP → V1 → V2)
+	•	success metrics per phase (e.g., “time-to-find”, “review load”, “exam delta”)
+	•	and a list of “risky assumptions” (OCR quality, exam parsing variability, answer keys availability, etc.).