That’s a really interesting split: daily “historical log” for recall (3 years later), plus a separate work vault where docs are essentially a living project index. That’s a very sane way to avoid turning everything into one giant, overfit system.
Two things I’m curious about:
1.When you say you “mainly slip up when I write something in the wrong document” — is that mostly a friction/UX issue (too many similar places), or a missing “active project” surface that tells you where you are right now?
2.In grad school mode, what changes fastest for you: the set of active projects, or the kinds of inputs (papers/notes/emails/reading list) you’re trying to connect?
I’m exploring a goal-first workflow where you keep a small number of active targets/projects and let that drive re-entry and resurfacing (details in my HN profile/bio if you’re curious).
Thanks for your support! And you’re right, what you described is very much the enterprise version of this problem (curated corpora + Jira/Confluence + compliance).
Just to clarify my scope: I’m starting with a personal, individual workflow (toC) where you control the sources end-to-end — local files, bookmarks, email, personal docs, etc. I’m not assuming company integrations, approval flows, or “drop into Jira” as the primary surface (those are a different product/compliance game).
That said, your “vetted docs + provenance + surface into the place you already work” framing is still useful in the personal setting too: a small trusted set of sources, always show citations/snippets, and a low-friction output surface (e.g. a task/project note you already use).
If you were applying the same idea personally, what would your “output surface” be — a todo app, calendar, a project doc, or just a weekly review note?
On “notes are all actions”: I think we’re using the word “action” differently. I don’t mean “notes should become todos”. I mean “notes should resolve into an explicit state”: either it becomes a commitment (calendar/todo), or it becomes reference with a clear home, or it gets discarded on purpose. Your system already does that digestion, which is why it works.
On your last question: the “smallest xyz that abc” phrasing isn’t a special framework or guru thing. It’s just a way to force a constraint so the answer becomes practical instead of aspirational. Did I use it here?
Weekly is often the sweet spot because it gives ideas/time to “gel” into something worth acting on, without turning your life into a constant triage loop.
And +1 on not rewarding tool-churn. The goal isn’t a more elaborate system, it’s a simple ritual that reliably produces real output. If a pen-and-paper journal plus a weekly check-in works, that’s already the whole game.
What does your weekly check look like in practice: are you mainly pruning (delete/ignore), distilling (rewrite what matters), or committing (pick 1–3 actions for the next week)?
You’re basically describing “search that expands context”: start from the matches, then follow outbound links (and maybe one more hop), index the linked pages/snippets, and treat that neighborhood as the result set. The killer feature is the re-entry part: “take me back to the same cluster of places I ended up last time I looked into this.”
Two quick questions:
1. Are your links mostly local files, web URLs, or a mix?
2. When you say “end up in those places again”, do you want it to save a trail/session automatically (like a breadcrumb graph), or just learn “these links co-occur with this query” over time?
I’m exploring a similar “context neighborhood” retrieval loop (more context in my HN profile/bio if you want to compare notes).
This is super helpful, thanks. The “search as a context switch” point is real, and the 80/15/5 split (Jira/Slack/Confluence) matches what I’ve seen too.
On the compliance point: totally fair. To clarify, I’m not assuming a company-wide deployment — I’m primarily thinking about a personal tool/workflow where you control what it can read (and for many people that means local-only or only non-sensitive sources). Your environment is a good reminder that “enterprise-ready” integrations are a different game.
If you could improve your personal workflow, what would save you the most time: pulling the right Confluence page when a Jira task is active, extracting a short “what’s the current state + next step” from scattered Slack threads, or something else?
More context on what I’m validating is in my HN profile/bio if you’re curious.
I think we might be talking past each other a bit. I’m not trying to build “a better personal library manager” or “AI search over your notes”. The core idea is goal-first: you set a small number of active targets/projects, and the system uses whatever sources you already have (even a small amount) to propose concrete options you can choose from: a plan, a checklist, tradeoffs, or a next experiment that moves that target forward. The value isn’t “organizing”, it’s reducing the thinking overhead of turning scattered inputs into an executable next step.
And you’re right that many people don’t collect enough personally — that’s why I’m also considering a hybrid where your own saves provide personalization, but a shared/managed collection (or public sources) fills the gaps.
In your case, would you find this useful if the output was “one good plan/next step per target” even when your personal saves are sparse, or do you prefer it to be entirely web-driven unless you opt in?
One pattern that may help (without heavy re-org) is to treat those snippets as first-class items with a stable home, and only link them out:
- keep an “inbox / snippets” note (or a single folder) where every orphan snippet goes
- give each snippet a short, searchable handle (one line title)
- add 1–2 lightweight links: related topics, and optionally “why it matters / when I’d need this”
Then when you’re in a top-level doc, you can embed/query “snippets linked to this topic” instead of trying to decide the perfect location for each one.
In your case, are those “things to remember” mostly time-bound (follow up, renew, schedule), or more like evergreen reference (commands, ideas, reminders)?
A fixed daily slot really can turn into another obligation (and then the backlog becomes guilt again), whereas a lightweight “I’m in curiosity mode” toggle matches how attention actually works. The driving “radio” use case also suggests the output should be low-friction: short, self-contained, and not demanding follow-up right now.
If I were to design around your constraints, it would look like:
* a manual toggle for “curiosity mode”
* a queue that plays 1–3 small “snack” insights (not full summaries)
* and a single “save this to revisit” action that you can do in 1 second, so you don’t lose it while driving
One question: when you hear something interesting in that mode, what’s the most natural next step for you later—open the original link/video, add it to an “active project/topic”, or capture a single note like “try X / look up Y”? (More context on the direction I’m validating is in my HN profile/bio if you want to compare.)
I think (2) is the hardest: even if you saved it, it only feels “great” when it matches your current context and mental model.
When you tried the twin brain/second mind approaches, what specifically failed for you?
Was it capture overhead, inconsistent tagging, not knowing where to put things, or simply that nothing resurfaced at the right moment without you searching?
Also, what did “tagged meaningfully” look like in your system — topic tags, project tags, or “why I saved this” tags?
I’m exploring an approach centered on “active targets/projects as the context signal” to improve resurfacing without more organization work (more context in my HN profile/bio if you want to compare).
I like your “I’m not sure, but there might be a solution…” phrasing because it sets the right expectation and invites exploration without pretending certainty. One thing I’m thinking about is making every suggestion come with a short confidence signal plus the specific evidence it’s based on (so you can sanity-check fast), and keeping the interaction pull-based so it never interrupts you.
Re pricing: one-time purchase or BYO model makes a lot of sense for this kind of personal workflow tool.
More context on the direction I’m validating is in my HN profile/bio if you want to compare notes.
I appreciate the framing. I think a lot of “second brain” attempts fail the cost/benefit test unless there’s a very specific, narrow use case.
The “git docs dir / pkm fragment” idea is exactly the kind of wedge that feels realistic to me: a small, scoped corpus with clear boundaries, where an LLM can be useful as a collaborator (RAG, summarizing, filling gaps) without you committing your whole life to a system.
If you were to try a small fragment, what would you pick as the smallest useful scope: a single project docs folder, meeting notes for one team, or a personal “decisions log”?
In Concerns the “action” isn’t generic, it’s anchored to a target/project you set first.
The flow I’m exploring is: you define a small number of active targets (e.g. “ship feature X”, “prepare for interview Y”). Then when you save/read something, the system searches your existing library (notes/links/email/posts/etc.) against that target and suggests a few candidate next steps or plans that are specifically useful for that target. You pick one (or dismiss them), so it’s more “menu of options” than “AI tells you what to do”.
Example 1 (technical): target = “build a small Kotlin app”. From a Kotlin article + your saved repos, it might suggest: “start with template A”, “try library B for state management”, or “do a 30-min spike to validate architecture C”.
Example 2 (research/learning): target = “write a short brief on topic Z”. From your saved posts, it might propose: “3 key claims + 2 counterpoints”, plus a short outline you can accept/edit.
So “action” = a target-linked next step or plan proposal, chosen by you — not turning every summary into a task.
It seems like most software now integrates LLM... Can't escape it, sigh.
The mymind you recommended has made significant strides toward tackling “information overload” and “organization fatigue.” However, I feel it remains fundamentally a storage solution—reducing the effort of organizing and facilitating retrieval—but doesn't directly align with my target.
It also reminds me of another product, youmind (https://youmind.com/), though it's primarily geared toward creation rather than PKM. Perhaps I could pay to try its advanced AI features.
This is a super interesting (and refreshingly candid) direction. You’re basically building a local-first “life event ledger” with delayed sync.
Actually, I'm not an expert in this area, but I feel the challenge may not lie in data collection itself, but rather in ensuring the data remains secure, usable, and easy to maintain over many years.
A custom binary format can work, but it could be a long-term maintenance commitment (schema evolution, tooling, corruption recovery).
As mentioned in my current Ask HN post, the product is indeed not yet finalized or launched. The envisioned product, Concerns, acts as a bridge, linking your current concerns and target tasks to your knowledge base/resource repository and action list (which could be to-do lists, calendars, etc.), forming an organic closed-loop system. Using target/active projects within Concerns as triggers, it retrieves relevant information from your resource repository. It proactively pushes solutions, plans, and suggestions for users to filter. Selected items then enter the user's action list. The goal is to enhance efficiency and effectiveness in a lightweight manner, without altering existing habits for using resource repositories or action lists.
This idea stems from my own pain points, and I genuinely hope that while solving my own issues, it might also address broader needs.
Regarding your response: It's interesting that AI tagging primarily aids by adding extra searchable keywords. However, I'd prefer broader content and semantic search/matching capabilities without relying solely on tags—though tagging remains a viable implementation approach.
Thanks for the mymind reference—I'll explore it.
PS. Did you perceive an AI-driven approach because I used translation software?
That’s another strong point, and I think it’s the pragmatic default: shrink scope, keep one source of truth, enforce a repeatable format, and prune on a cadence. It’s basically how you keep both retrieval and any automation predictable.
The tension I’m trying to understand is that in a lot of real setups the “corpus” isn’t voluntarily curated — it’s fragmented across machines/networks/tools, and the opportunity cost of “move everything into one place” is exactly why people fall back to grep and ad-hoc search.
Do you think the right answer is always “accept the constraint and curate harder”, or is there a middle ground where you can keep sources where they are but still get reliable re-entry (even if it’s incomplete/partial)?
I’m collecting constraints like this as the core design input (more context in my HN profile/bio if you want to compare notes).
That’s a really important definition of “zero setup”: no long-running indexing jobs, and no “crunch for 24 hours on my laptop” just to make search usable.
And I hear you on cross-network fragmentation — in a lot of real environments the hardest part isn’t search quality, it’s that data lives on different machines, different networks, and you only have partial visibility at any given time.
If you had to pick, would you rather have:
1.instant local indexing over whatever is reachable right now (even if incomplete), or
2.a lightweight distributed approach that can index in-place on each machine/network and only share metadata/results across boundaries?
I’m exploring this “latency + partial visibility” constraint as a first-class requirement (more context in my HN profile/bio if you want to compare notes).
That makes sense — treating it as a personal search engine is a real, high-ROI use case. Full-text search covers the “I remember the idea but not where I saw it” problem really well.
Out of curiosity, what’s the bigger win for you: full-text search itself, or the tagging/metadata layer that helps narrow results when your memory is fuzzy? And do you mostly search by keywords, or by “context” (project/topic you’re working on)?
I’m validating a similar retrieval-first angle (summarized in my HN profile/bio if you want to compare notes).