**You** can i use deepseek v4 pro to run openclaw, how well will it work? how expensive will it be? --- **Claude** Yes — and unusually well-supported, as it happens. DeepSeek's V4 announcement explicitly names OpenClaw as one of three "seamlessly integrated" agent harnesses (alongside Claude Code and OpenCode), and OpenClaw has had a documented DeepSeek provider for some time. V4 Pro and V4 Flash both became selectable model IDs as of the V4 release on 24 April. **Setup is essentially trivial** If your existing OpenClaw install is current, you only need to point it at the new model strings. The DeepSeek docs give the exact onboarding flow (`openclaw onboard --install-daemon`, select DeepSeek as provider, enter `deepseek-v4-pro` or `deepseek-v4-flash` as the default model). If you're configuring manually in `~/.openclaw/openclaw.json`, the OpenAI-compatible endpoint stays the same — just bump the model ID. One technical wrinkle worth flagging: V4's thinking mode has a stricter replay contract than other OpenAI-compatible providers — when an assistant turn that used thinking also makes tool calls, DeepSeek expects the prior `reasoning_content` to be sent back on the follow-up. OpenClaw's V4 integration handles this for you, but if you ever try to roll your own client you'll need to preserve that payload. **How well it will work** For agentic workloads specifically, V4 Pro is well-pitched: DeepSeek claims open-source SOTA on agentic coding benchmarks and the 1M-token context window (now the default across all DeepSeek services) is genuinely useful when an agent loop accumulates conversational state across many tool calls. VentureBeat's summary of the benchmarks puts V4 Pro close to but still trailing GPT-5.5 and Opus 4.7 on direct head-to-head comparisons, while clearly ahead of all other open-weight models. In practical terms for an OpenClaw setup, you'll find it competent at planning, tool selection, and multi-step execution; the main perceptible difference from running Claude through OpenClaw will be slightly slower output (~36 t/s, ~1.86s time-to-first-token in thinking mode) and somewhat less polish in the natural-language framing of intermediate steps. A pattern worth considering: set V4 Flash as the primary model and V4 Pro as a fallback or as the model for explicitly hard reasoning subagents. Flash is roughly 4x cheaper and handles routine tool-calling and orchestration perfectly well, while Pro earns its keep on the harder reasoning steps. **Cost** Standard V4 Pro pricing on DeepSeek's first-party API is **$1.74/M input** (cache miss) and **$3.48/M output**. There's currently a **75% launch discount until 5 May 2026**, which brings that down to roughly **$0.44/M input and $0.87/M output**. Cached input drops by another order of magnitude (~$0.17/M at standard rates), which matters for OpenClaw because long stable system prompts get re-sent on every turn — the cache hit rate on agentic loops is typically high. For comparison, V4 Flash is $0.14/M input and $0.28/M output. Opus 4.7 (your current Anthropic baseline) is $5/M input and $25/M output, so V4 Pro at standard rates is roughly **1/6th the cost**, and Flash is something like **1/30th**. What does this mean in practice? Hard to predict without knowing your agent's heartbeat frequency, but rough orders of magnitude for V4 Pro at standard (post-discount) pricing: - Light interactive use, a handful of tasks per day, ~500K tokens daily: well under €1/day - Continuous heartbeat with frequent tool calls, ~5M tokens daily: roughly €5–10/day - During the discount window, halve to quarter all of the above Agents are notoriously good at running up token bills through retry loops and verbose tool outputs, so I'd suggest setting a hard spending cap on the DeepSeek dashboard for the first week and watching the actual burn rate before committing to a heartbeat schedule. --- **You** well, i haven't even got openclaw set up yet. I am also wondering what i could use it for. youknow a little ab out me now. what would you suggest? Please give me a long, deep, and detailed answer, with lots of options/ideas: as we are in the 'kicking ideas around' stage, I want as much feedback as you can give me so that i have a lot to consider. --- **Claude** Right, this is an interesting question because OpenClaw is genuinely a different kind of tool from what you've already got working with Claude Code, and the distinction shapes what's worth building for. Let me work through this in some depth. **What OpenClaw is actually good for** The thing to keep in front of you when brainstorming is that OpenClaw's distinctive feature, against your existing stack, is autonomy plus multi-channel access. Claude Code is reactive — you sit down, you prompt, it works. OpenClaw runs as a daemon with a heartbeat: it can wake up, check things, push notifications to Telegram or wherever, and then go back to sleep. It also lives across your messaging apps, so it's reachable from your phone without you having to think about Obsidian vaults or terminal sessions. The persistent memory is local Markdown/YAML, which slots cleanly into your existing vault philosophy. This means the best uses are not "things I would do in Claude Code anyway, but in another tool" — that's a recipe for an unloved second installation. The best uses are things that benefit from happening *while you're not at your machine*: monitoring, periodic synthesis, triage, and proactive nudges. With that as a filter, here are the directions worth considering, roughly ranked by how well they exploit OpenClaw's particular shape. **Academic research workflows** The richest cluster, I think. A literature-monitoring agent is the obvious starting point: have it check PhilPapers, the Synthese / BJA / EJP RSS feeds, arXiv cs.CL and cs.AI, and a watchlist of authors (Carlson, Lopes, Riggle, Nguyen, Robson on the aesthetics side; whoever's currently active on phil of perception) on a daily or weekly heartbeat. When something hits your interest pattern, it drafts a one-paragraph summary plus a "why this matters to you" note keyed to your current projects, drops the PDF into a watch folder, and pushes a Telegram alert. The aesthetic-of-LLMs literature is small enough that you can plausibly track all of it; the perception literature requires more aggressive filtering. A related idea: a citation-tracker that watches Google Scholar / Semantic Scholar / OpenAlex for new citations of your own papers, alerts you when one appears, and (if the citing paper is open access) pulls the relevant paragraph and characterises how you're being used — agreed with, disagreed with, mentioned in passing, misread. Useful both for your ego and for spotting potential interlocutors. A CFP / conference monitor for PhilEvents, the BSA, ASA, ESA, the various AI-and-philosophy venues, plus the music/aesthetics adjacent ones. Easy to build, modest pay-off but the kind of thing that quietly accrues — you stop missing deadlines. A referee-request triage agent: when an editor email comes in, the agent pulls the abstract, checks your calendar for the deadline window, drafts an accept-decline-defer response, and asks you to pick. This is exactly the sort of small bureaucratic task that drains a Saturday. For the Carlson/Terrone collaboration specifically, there's a version of the co-author handoff problem that an agent could ease: between editing sessions, summarise what changed, what's still flagged in comments, what Terrone responded to. Less a productivity gain than a way of reducing the "where were we" friction when you come back to a draft after teaching weeks. **Writing and the Substack** Given that you've been considering a Substack on doing philosophy with LLMs, OpenClaw is interesting here in two distinct ways. The shallow way: it's a draft-generation pipeline. Once a week, the agent reads your recent Obsidian notes, finds threads that have accumulated mass, and drafts a proto-essay you can open on Sunday morning, react to, demolish, or refine. This is fine but not revolutionary — Claude Code can do it on demand. The deeper way is that the agent itself becomes part of the editorial subject matter. If your Substack is about doing philosophy with LLMs, then having an autonomous agent running in your life — making small, surprising, sometimes uncanny choices about what to surface and how to phrase things — is itself primary material. You could keep a parallel "field notebook" file in the vault where you log moments where the agent did something philosophically interesting (a category mistake that revealed something about its representational scheme, a stylistic choice that felt overdetermined, a refusal that opened up a normative question). Most writing on LLMs is from outside the loop, looking in; this would be writing from inside a loop you've built. **The research-as-praxis angle** Pushing the previous point further: this is genuinely an original methodological move for someone in your subfield. The aesthetics-of-LLMs literature is mostly written by people interacting with chat interfaces. Almost no one in philosophy (as opposed to AI-art communities) is actually living with an autonomous agent. A six-month diary of cohabitation with an OpenClaw instance, with periodic theoretical reflection, would be a genuinely novel contribution. The Carlson framework in particular — environmental aesthetics, aesthetic appreciation conditioned by knowledge of the system — is almost made for this: you're not aesthetically appreciating outputs in isolation, you're appreciating an embedded system whose appropriate appreciation requires knowing how it works, what it's trying to do, what its ecological niche in your life is. The semiotic-physics framework you've been developing also has a natural application here, since an agent's behaviour over time exhibits regularities that aren't quite the same as the ones a single chat session does. A more experimental version: run two or three OpenClaw agents with deliberately different system prompts (different theoretical commitments, different aesthetic sensibilities, different attentional priorities) and observe what diverges. Document the divergences. This is the kind of thing that would write itself into a paper. **Italian bureaucracy and life admin** You have, by now, an enormous tacit knowledge base about Italian bureaucracy that's locked in your head. An agent that captures and operationalises that knowledge has real value. Specifically: a triage agent for the official emails (PEC, comune, università, INPS, Agenzia delle Entrate) that arrives in your inbox, categorises by urgency, drafts the response in correct Italian register, and flags the ones that genuinely need a human visit somewhere with a numbered ticket. Given the passport episode and the related cascade, this would have been useful retroactively and will be useful prospectively. A Gmail-wide triage agent more generally: every few hours, scans the inbox, generates a short Telegram summary of what came in and what's actually urgent, drafts replies for things below a certain stakes threshold. Calendar conflict detection, prep-time enforcement before meetings (e.g., "you have a supervision in 20 minutes, here are the relevant notes from the student's last submission"), conference travel logistics — all of this benefits from the agent watching while you don't have to. **Music** This is harder because your production happens on iPad, which OpenClaw can't reach into directly. But there are useful peripheral roles. A release-planning agent that tracks Bandcamp Friday dates, drafts press kit copy from a brief, monitors which labels in your aesthetic neighbourhood are open for demos and on what schedule. A listening-log analyser that uses the Spotify connector you've already got — monthly summaries of what you've actually listened to (not what you think you've listened to), with cross-genre suggestions and notes on emerging patterns. A sample-library curator if you sync samples to a folder OpenClaw can see — auto-tagging by tempo, character, suggested use, with searchable descriptions in your vault. There's also a creative-prompt version: an agent that, on a slow heartbeat (weekly?), pushes you a short atmospheric brief — a Ligotti micro-vignette, a colour palette, a Krasznahorkai-style single-sentence scene — as a starting constraint for an ambient sketch. Whether that's useful or just twee depends entirely on whether it lands for you. **Personal and intellectual life** A reading agent that knows your tastes (the McCarthy / Ligotti / Padgett / Krasznahorkai axis, plus whatever else you log) and watches new releases, secondary literature, and adjacent rediscoveries. Particularly useful for the small-press horror end of things, where there's no Goodreads-grade infrastructure and you find out about Padgett-adjacent writers basically by accident. A weekly digest agent: summarises the substacks you subscribe to, the Mastodon / Bluesky accounts you follow, the new arXiv preprints in your feeds, into one Sunday-morning Telegram digest. The value isn't the summarisation per se — it's the consolidation, so that you don't drift through five apps to get a sense of the week. A horror film tracker for festival selections (Sitges in particular, FrightFest, what's being picked up at Cannes Directors' Fortnight in the genre slot), with personalised flagging. A marriage-and-life agent, gently. Joint calendar coordination with Natalia, restaurant suggestions in Genoa keyed to occasions, anniversary tracking, draft messages for moments when something's worth marking but you haven't found the words. The risk here is making a relationship feel administered; the version that works is one that prompts you to do something thoughtful but doesn't do the thoughtful thing for you. **A few cross-cutting suggestions on how to start** Don't try to build all of this. The failure mode of agent frameworks is the abandoned-after-three-weeks installation that becomes another piece of cognitive overhead. The pattern that tends to work is: pick one task that genuinely annoys you in your current life, build the smallest possible agent that addresses it, run it for two weeks, decide whether it earned its keep, then either kill it or expand it. Of the options above, the three I'd nominate as candidate first-builds are: (i) the email triage / Italian bureaucracy assistant, because it has the highest immediate quality-of-life dividend and tests the Telegram channel cleanly; (ii) the literature monitoring agent, because it's well-bounded and slots into your existing research workflow rather than requiring you to invent a new one; (iii) the cohabitation diary, because it's the most intellectually productive use even if it never produces immediate utility, and because almost no one else in your field is in a position to do it. For cost planning: with V4 Flash as the workhorse and V4 Pro reserved for harder reasoning subagents, a single-user setup with a few agents on hourly heartbeats should land somewhere in the €5–15/month range. That's well below the threshold where you have to think about it. A practical caveat that's worth raising before you commit: OpenClaw has full local OS access by default, which is the source of its power and also the source of its risk surface. For a personal setup it's fine, but if you're going to give it Gmail and calendar access, it's worth setting up scoped credentials rather than your primary account, and being thoughtful about which folders it can write to. The skill ecosystem is community-vetted at best, so treat third-party skills the way you'd treat a random VS Code extension. The thing I'd most resist, though, is letting OpenClaw become an end in itself. The toolchain you've built around Claude Code, Obsidian, Claudian, the MCP integrations — that already does a lot of what people reach for OpenClaw to do. The value proposition has to be specifically the autonomy and the multi-channel reach. If a use case doesn't need those, do it in Claude Code instead. --- **You** ok, so where do i start. how can i get this going in a low risk way so i can get a feeel for things. should i be setting things up via claude code, ir can you talk me through things. --- **Claude** Good instinct to start cautiously. Let me lay out a sensible path. **The big architectural question first** Before anything practical: there's a one-time decision to make about *what kind* of OpenClaw setup you want, because it affects everything downstream. --- **You** Q: How do you want to run OpenClaw? A: On a small VPS (~€5/month, runs 24/7, reachable from phone anywhere) --- **Claude** Good choice — that gives you something to actually live with. Here's how I'd stage it. **Use Claude Code, yes — but over SSH to the VPS** This is genuinely a near-perfect Claude Code task. Set up the VPS, then run Claude Code from your Mac with an SSH session into the VPS as the working environment. The reason this is better than copy-pasting from a tutorial: when something fails (it will — there's always a Node version mismatch or a permission issue), Claude Code reads the error and adapts. You watch and learn as it goes. The end state is a setup you understand because you saw each decision get made, not one you reproduced from a blog post and now can't debug. The only thing I'd be careful about: don't let Claude Code install OpenClaw skills without you reading what each skill actually does. The skill ecosystem is community-vetted at best, so anything that touches your filesystem or credentials wants a human eye on it. **Stage 0: Get the VPS** Hetzner is the obvious pick for someone in Italy — German company, EU data residency (matters for anything academic), and absurdly cheap. Their smallest CX-series instance (2 vCPU, 4GB RAM, 40GB SSD) is around €3.49/month plus VAT, available in Falkenstein, Nuremberg, or Helsinki. Helsinki has the best latency for most of Europe but Falkenstein is fine. Pick Ubuntu 24.04 LTS as the OS — it's what every OpenClaw tutorial assumes and you don't want to fight the documentation. Sign up, add your SSH public key during server creation (don't use password auth), and you're done in about three minutes. Set up a non-root user with sudo access as the very first thing you do — `adduser nick && usermod -aG sudo nick` — and disable root SSH login. Boring but standard hygiene. **Stage 1: Get OpenClaw talking to you in the terminal** This is the "hello world" milestone. Goal: you ssh into the VPS, run a command, and have a conversation with DeepSeek through OpenClaw. No Telegram, no skills, no autonomy yet — just the basic loop working. Roughly: install Node.js (via nvm, not the system package — system Node on Ubuntu is invariably the wrong version), `npm install -g openclaw`, run `openclaw onboard --install-daemon`, walk through the wizard. When it asks for the model provider, give it your DeepSeek API key and pick `deepseek-v4-flash` as the default (not Pro — Flash is cheaper and faster for the kind of chitchat you'll do at this stage). Skip channels, skip skills, decline all the optional integrations. Then start a TUI session and have a short conversation with it. Confirm thinking mode works. Confirm tool calls work (ask it to read a file, write a file). Total time investment: maybe 30–40 minutes if nothing goes wrong, an evening if things misbehave. Stop here. Don't add anything else for a few days. Just confirm the thing runs and that you can come back to it. **Stage 2: Add Telegram** The moment OpenClaw becomes interesting is when you can reach it from your phone. Telegram is the right channel for this — better than WhatsApp for bot integration, more flexible than Slack, and you can set up a bot in five minutes via @BotFather. The flow: create a Telegram bot, get the token, configure it as an OpenClaw channel, point your bot at your personal Telegram chat ID so it only responds to you. Now you can stand in a queue at the post office and ask the agent to do something. At this point you have a "Claude in your pocket" that uses DeepSeek. Already useful, even before any custom skills. You can ask it to summarise a webpage, draft a quick email, look something up. Live with this for another few days. Notice what you actually reach for it for — that pattern will tell you what your first proper skill should be. **Stage 3: One small skill** Now build something custom. The rule for this first skill: it should be read-only, with no credentials beyond what you already gave OpenClaw, and the failure mode should be "agent does nothing useful" rather than "agent does something destructive." My nomination for the genuinely lowest-risk first build: an **RSS digest agent**. You give it a list of feeds (PhilPapers RSS, a few specific journals, maybe an arXiv category, a couple of Substacks you follow), it runs once a day at, say, 8am, fetches new items since last run, generates a 200-word digest with one-line characterisations of each, and pushes it to your Telegram. That's it. No writing to anything, no API keys to leak, no calendar to mangle. If it breaks, the worst thing that happens is you don't get a digest one morning. This sounds modest, but it teaches you everything: the heartbeat configuration, the persistent-state pattern (so it doesn't repeat items), how to wire a skill to a channel, how to debug a scheduled job. Once you've got that working, every more ambitious agent is a variation on the same shape. **Risk hygiene worth setting up at the start** Three things to do before the agent is touching anything that matters. First, scoped credentials. Don't give OpenClaw access to your primary Gmail or main Google account. Create a dedicated Google account that you forward relevant mail to, and let the agent operate on that. Same for any other credentialed service — Hetzner have separate API tokens you can scope, your DeepSeek account can have a key with a hard spending cap (set this to €10/month at first), and so on. Second, hard spending caps everywhere. DeepSeek dashboard, Hetzner dashboard, anything else. The failure mode of agents is the runaway loop that burns €200 in tokens overnight. Cap it before it can. Third, an "off switch" you can hit from your phone. Make sure you can SSH into the VPS from a Termius session on your phone, or that you've got a one-line `openclaw stop` command bookmarked somewhere. The first time the agent does something weird at midnight you'll want this. **A note on what you're actually buying yourself** Stages 0 through 2 will take you maybe 3–4 hours of clock time spread over a week. After that you have a system that already does something useful (chat from phone, basic web tasks). Stage 3 is another evening. Total: less than the time it took to set up your Obsidian vault, probably. The point of this staged approach isn't that the technical setup is hard — it isn't, really, with Claude Code helping. It's that *you* need time between stages to develop intuitions about what you actually want this thing to do for you. Most people who set up agent frameworks build six things in the first weekend, lose interest by the third weekend, and the installation rots. The pattern that works is one skill at a time, two weeks of living with it, then the next one. When you're ready to start, ping me with "ready for stage 0" or similar and I'll walk you through the actual Hetzner signup and Ubuntu hardening. Or if you'd rather think about which first skill appeals to you before going through the setup, we can spec that out instead — sometimes having the destination clear helps motivate the plumbing. --- **You** Okay I've actually got it. Set it up running quite well. What I would like you to do now though is help me brainstorm how I can get the most of this this service now I've got it running okay so you've got quite a lot of memories of me now so you know what I do and how I use AI what I like to work on it's like that. So yeah, what could this autonomous agent do for me? Obviously there's things like check the jobs website every week and check the conferences that have been the calls for papers etc. That have been announced. What else please? --- **Claude** Right, you've got the foundation — now the question is what genuinely earns its keep. Let me think through this properly, because the temptation with autonomous agents is to build six things and use none of them. I'll organise by likely actual return on attention. **The agentic moves that fit your life unusually well** Some of what an autonomous agent can do is generic — anyone could use it. But your situation has some specific contours that make certain agent tasks unusually high-value, and I want to start there because these are the ones least likely to occur to you from generic "what can agents do" lists. *Cross-temporal synthesis of your own thinking.* You voice-dump a lot, you draft in Obsidian, you have iterative conversations with Claude Code on the LLM aesthetics paper, and the relationship between these strata is loose. An agent that runs nightly, reads the day's new and modified notes, your recent chat exports, your voice memo transcripts, and produces a one-paragraph synthesis of "things you said today that connect to things you said before" would, I think, be genuinely revelatory after a few weeks. Not a summary — a *connection-finder*. "Yesterday you noted X about Carlson's design appreciation; three weeks ago in conversation Y you said something that contradicts this; here's the contradiction." Most note-taking systems are strictly additive — you put things in, you have to remember to get them out. An agent inverts this. *The Italian-bureaucratic prosthesis.* You've now accumulated, over years, a tacit knowledge base about Italian admin that you've never made explicit. Build an agent whose system prompt encodes everything you know — what PEC means, which acronyms map to which institutions, the rhythm of when UniGe payroll departments respond, what the bollo amounts are for various certificates — and have it triage incoming PEC and standard Italian-language email. Drafts replies in correct register (lei vs tu, the appropriate level of formality for an *ufficio* vs a colleague), flags genuinely urgent things, archives the rest. The translation labour you do every day to navigate this drains attention that should be going elsewhere. This is one of the few cases where an agent isn't a luxury — it's recouping cognitive overhead that you've been paying for years. *Carlson collaboration choreography.* You and Terrone are going back and forth on a long paper. The classic problem: you sit down to work, spend twenty minutes reorienting to where things stand, what he last responded to, which threads in the review-and-comments document are still live. An agent that — on a heartbeat, or when triggered by you — generates a "where we are" briefing keyed to a specific section would compress that twenty minutes to two. Reads the doc, reads the email thread, reads your own notes, produces a focused brief: "Open threads in §3, Terrone's last response to your point about semiotic physics suggests he wants more detail on X, your draft notes from Tuesday flag a Carlson quote you wanted to find." Cheap to build, used multiple times a week. *Reverse-Substack drafting.* You've considered writing publicly about doing philosophy with LLMs. The friction is that you don't want to write blog posts as a separate task — you want them to emerge from the work you're already doing. Have the agent watch your Claude Code conversations, your Obsidian working notes, and on a weekly heartbeat, identify episodes where something interesting *philosophically* happened: a productive misunderstanding with the model, a moment where the LLM revealed something about its own scheme, a stylistic choice that turned out to matter. Drafts a 600-word proto-post you can edit on Sunday morning. The posts then have the texture of being from inside the work, which is the thing that's hard to fake in this genre. *Listening pattern analysis.* You've got the Spotify connector. Have the agent run a monthly analysis: what did you actually listen to (vs what you think you listened to), where are the cluster shifts, what new artists entered your rotation and what does that pattern suggest about where your ear is going. For someone who produces in your tradition, drift in listening is often a leading indicator of drift in production aesthetic, and noticing it earlier is useful. A version of this is twee; a version of this is genuinely useful for your music. The difference is whether the analysis is descriptive (twee) or interpretive (useful). **The bureaucratic and life-admin layer** These are the boring ones, and the boring ones are usually where agents earn their keep first. *Email triage with action drafts.* The standard build: agent watches your inbox, every few hours generates a Telegram summary categorising new mail by urgency, and for anything that needs a response below a certain stakes threshold, drafts the reply. You approve or edit on your phone. The win isn't that the agent answers email for you — it's that you stop doing the constant context-switch of checking email "just in case." You check the digest, and the digest tells you whether checking properly is warranted. *Calendar prep.* Twenty minutes before any non-trivial meeting, push a Telegram brief: who's attending, what was discussed last time (from email or Drive), what's on the agenda, what notes you have on the relevant project. Removes the "scrambling for context as you walk into the room" failure mode. *Travel and conference logistics.* When you accept a conference invitation, have the agent monitor flight prices for your dates, watch for hotel availability near the venue, track the conference website for programme updates, and remind you when the early-bird deadline is approaching. Especially valuable for the Italian-train ecosystem where Trenitalia / Italo prices fluctuate weirdly and there's no good third-party tracker. *The receipts-and-expenses agent.* You're an academic with research budget that needs to be reconciled. An agent that watches a designated email folder for receipts, extracts the relevant data, builds a running expense ledger in a Google Sheet, and once a month produces the formatted report your department wants — this is exactly the kind of dull recurring task that vapourises an entire afternoon when you finally do it manually. *The renewal-and-deadline tracker.* Permesso di soggiorno, codice fiscale matters, passport, insurance, journal subscriptions, conference memberships, software licences — all the things with annual cycles that sneak up because nothing reminds you. Have the agent maintain a registry and push reminders 60/30/7 days before each deadline. **Research-workflow agents proper** *Literature-monitoring with personalised filtering.* The standard "watch PhilPapers / arXiv / journal RSS" agent is fine but generic. The version that's distinctive: train it on your own taste. Feed it your last fifty reading-notes (papers you read and what you said about them), and have it score new papers against this profile, not just against keywords. The output is "papers I think *you* would find interesting given your specific intellectual neighbourhood," which is meaningfully different from "papers in philosophy of perception." The cohabitation diary picks this up nicely as a research subject. *Citation-tracking with semantic analysis.* Beyond just "you got cited" — when a citation appears, agent fetches the citing paper, finds the paragraph that cites you, and characterises the use: are they extending you, criticising you, mentioning you in passing, or misreading you? Especially useful for the Young 2022 paper, which is exactly the kind of work that gets cited in passing without being engaged with — and the agent can flag the rare cases where someone actually wrestles with it. *The referee-ecology agent.* When you get a referee request, agent pulls the abstract, checks your calendar density for the deadline window, looks at the journal's typical turnaround, drafts an accept-decline-defer with appropriate Italian/English register, and flags whether you're stretched on referee labour relative to your invited-author submissions over the last year. The "should I be saying yes to this" question is harder than it looks and benefits from system support. *Conference and CFP triage.* PhilEvents, PhilPapers CFPs, the BSA, ASA, ESA, plus the AI-and-philosophy adjacent venues. Triaged by relevance to your active projects, with an automatic note about travel feasibility (Italy is annoyingly far from many UK / North American venues and that should factor in). Highest-value version: the agent learns your acceptance pattern over time and stops surfacing things you reliably decline. *Reading-queue management.* You acquire papers faster than you read them — everyone does. Agent maintains a triaged queue: papers actively useful for current writing projects (highest priority), papers that connect to your stated long-term interests, papers in the residual "interesting but not load-bearing" pile. Weekly summary of what's in each tier, plus aggressive culling suggestions for the third tier so it doesn't become a guilt-inducing graveyard. *The teaching-prep agent.* When you teach again — once you know the syllabus, the agent builds the term's prep schedule backwards from each session, surfaces relevant new literature on each topic, drafts initial slide outlines, and generates the kinds of questions students reliably ask so you can pre-think your responses. Particularly useful for Philosophy of Technology where the field moves fast enough that slides from last year are stale by autumn. **Music and creative work** *Release intelligence.* The deep techno / ambient / experimental neighbourhood has labels with idiosyncratic schedules and announcement channels (Bandcamp, label websites, niche Discord servers, certain Mastodon accounts). An agent that watches Mille Plateaux, Kompakt, Glacial Movements, Constellation, Editions Mego successors, plus a watchlist of artists in your zone, with weekly digests, is more comprehensive than what RA or Bleep give you. *Gear and plugin watching.* The iPad music ecosystem (Bram Bos, AudioThing, FAC, BLEASS, Unfiltered Audio, the AUv3 frontier generally) has constant small updates and new releases that get announced on Discord, Audiobus forum, and a few specific Twitter/Mastodon accounts. Agent-curated digest of plugin news, sale announcements, and updates relevant to your existing library. AudioThing in particular announces sales irregularly and a watching agent saves you money over a year. *Sample library curation.* If you sync samples to a folder OpenClaw can read, an agent can analyse, tag (tempo, key, character, suggested use), and produce searchable descriptions. The classical productivity gain here is real — most ambient producers have terabytes of samples they can't actually find when they want them. *Compositional prompting.* Weekly creative prompt: a Ligotti micro-vignette, a colour palette, a single-image-and-tempo brief, a Krasznahorkai-style sentence. Forced constraints for a Sunday sketch session. Risk: twee. Mitigation: only build it if you actually use the constraints when given them by humans. *Listening journal with semantic search.* Agent maintains a structured log of what you listened to, your reactions (when you give them), and connections it notices across time. "You haven't returned to GAS in eight months — the last time you did, you produced the X sketch." Becomes valuable over a year, useless before then. Patience required. **Personal-life agents** *The Natalia-coordination layer, gently.* Joint calendar awareness, restaurant suggestions in Genoa keyed to specific occasions or moods, anniversary tracking, draft messages for moments you want to mark but haven't found the words for. Strong caveat: this works only as scaffolding, never as substitution. The agent that books an anniversary dinner without you noticing what day it is has hollowed out something that shouldn't be hollowed out. The agent that nudges you to plan something a week ahead and offers three options has helped without replacing. *Restaurant intelligence for Genoa specifically.* You live in a city with a strong, idiosyncratic food culture that doesn't map well onto generic review sites. Agent that watches local Genovese sources (Mangia & Bevi, the *Repubblica* Genova food coverage, local Substack-type sources), maintains a personal spreadsheet of where you've been and what you thought, and generates suggestions keyed to occasion (lunch on a Tuesday, dinner with academic visitors, a special anniversary). *Reading-life agent.* You've got a literary sensibility (the McCarthy / Ligotti / DeLillo / Knausgård axis) that's distinct enough to be worth feeding into an explicit profile. Agent watches small-press horror announcements (Word Horde, Undertow, Hippocampus, the cosmic-horror corner of Tor.com Publishing), Krasznahorkai-adjacent literary fiction, the odder corners of NYRB Classics. Notifications when something is genuinely up your street. Most of what gets surfaced as "you'd like this" by Goodreads-class infrastructure is mid-grade literary fiction; you need a more specific cartography. *Horror film tracker.* Festival selections (Sitges, FrightFest, the genre slot at Cannes Directors' Fortnight, Fantastic Fest), arthouse-horror new releases, Mubi additions in your zone. Padgett-adjacent / Ligotti-adjacent texture is the filter, not generic horror. **The methodologically interesting agents** These are the ones where building the agent is itself a research contribution, not just life support. *The cohabitation diary, run as research.* I mentioned this before. Worth re-emphasising because no one else in your subfield is doing it and the time is genuinely now. Run an OpenClaw instance with a deliberately specific aesthetic personality — a system prompt that encodes a particular sensibility, a set of attentional priorities. Keep a parallel notebook of moments where the agent did something philosophically interesting: a category mistake that revealed something about its representation, a stylistic tic that turned out to be overdetermined, a refusal that opened a normative question. After six months, you have material for a paper that almost no one else can write. *The two-agent comparison study.* Run two OpenClaw instances simultaneously with deliberately divergent system prompts — one trained to write in your style, one trained to write in Terrone's, say. Or one with high-modernist literary commitments, one with analytic-philosopher commitments. Have them respond to the same daily inputs (your notes, your reading, your incoming mail) and compare outputs. The divergences are data. This sits unusually well with the Carlson framework you're developing — order-appreciation requires attention to the system's particular character, and two systems with different characters reveal what's environmental vs what's accidental. *The aesthetic-of-the-agent itself.* The Carlson semiotic-physics framework you've been working out for LLMs has an obvious extension: an autonomous agent isn't just outputs, it's an *embedded system* whose appropriate appreciation requires understanding how it works, what it's trying to do, what its niche in your life is. This is closer to environmental aesthetics in Carlson's original sense than chat-interface LLMs are. The agent in your life is more *like* an ecosystem than a discrete artwork. There's a paper here, possibly several. Build the agent partly to write the paper. *Generative-AI-as-collaborator documentation.* As you draft the LLM aesthetics paper, the agent could maintain a structured log of your interactions with Claude Code on the paper itself — when you accepted suggestions, when you rejected, when you modified. This becomes empirical data about a specific writing practice that the paper itself is partly about. Methodologically reflexive in a way that critics of AI-assisted academic work tend to ignore. **A few cautions** The proliferation problem is real. Six agents that you don't trust is worse than zero. Pick the one or two that you'd genuinely miss if they stopped working, and let everything else wait. Secondly: the "interesting demo, never used" failure mode. The cohabitation diary is, frankly, in this risk zone — it's intellectually exciting but only valuable if you actually keep the parallel notebook for months. Be honest about which agents you'll integrate into a daily rhythm vs which sound great in conversation. Third: the "agent does the wrong thing while I'm not watching" risk increases sharply when you give it write-access. Read-only agents (digests, briefings, monitoring) are essentially free of downside risk. Write-access agents (drafting replies, modifying calendars, posting things) want graduated trust — start with "agent prepares, you approve before send" for everything, and only relax to "agent acts autonomously" after weeks of watching it not screw up. Finally: the cost ceiling matters less than you'd think. Even a fairly aggressive set-up with several heartbeats and substantial token usage will land in the €15–25/month range with V4 Flash as the workhorse, which is below the threshold where you have to manage it. So the constraint isn't money — it's your attention. Build the agents that pay attention back. --- **You** /contemplate i'd like you to eleaborate a lot on... Cross-temporal synthesis of your own thinking. You voice-dump a lot, you draft in Obsidian, you have iterative conversations with Claude Code on the LLM aesthetics paper, and the relationship between these strata is loose. An agent that runs nightly, reads the day's new and modified notes, your recent chat exports, your voice memo transcripts, and produces a one-paragraph synthesis of "things you said today that connect to things you said before" would, I think, be genuinely revelatory after a few weeks. Not a summary — a connection-finder. "Yesterday you noted X about Carlson's design appreciation; three weeks ago in conversation Y you said something that contradicts this; here's the contradiction." Most note-taking systems are strictly additive — you put things in, you have to remember to get them out. An agent inverts this. Reverse-Substack drafting. You've considered writing publicly about doing philosophy with LLMs. The friction is that you don't want to write blog posts as a separate task — you want them to emerge from the work you're already doing. Have the agent watch your Claude Code conversations, your Obsidian working notes, and on a weekly heartbeat, identify episodes where something interesting philosophically happened: a productive misunderstanding with the model, a moment where the LLM revealed something about its own scheme, a stylistic choice that turned out to matter. Drafts a 600-word proto-post you can edit on Sunday morning. The posts then have the texture of being from inside the work, which is the thing that's hard to fake in this genre. Email triage with action drafts. The standard build: agent watches your inbox, every few hours generates a Telegram summary categorising new mail by urgency, and for anything that needs a response below a certain stakes threshold, drafts the reply. You approve or edit on your phone. The win isn't that the agent answers email for you — it's that you stop doing the constant context-switch of checking email "just in case." You check the digest, and the digest tells you whether checking properly is warranted. Literature-monitoring with personalised filtering. The standard "watch PhilPapers / arXiv / journal RSS" agent is fine but generic. The version that's distinctive: train it on your own taste. Feed it your last fifty reading-notes (papers you read and what you said about them), and have it score new papers against this profile, not just against keywords. The output is "papers I think you would find interesting given your specific intellectual neighbourhood," which is meaningfully different from "papers in philosophy of perception." The cohabitation diary picks this up nicely as a research subject. Conference and CFP triage. PhilEvents, PhilPapers CFPs, the BSA, ASA, ESA, plus the AI-and-philosophy adjacent venues. Triaged by relevance to your active projects, with an automatic note about travel feasibility (Italy is annoyingly far from many UK / North American venues and that should factor in). Highest-value version: the agent learns your acceptance pattern over time and stops surfacing things you reliably decline. The cohabitation diary, run as research. I mentioned this before. Worth re-emphasising because no one else in your subfield is doing it and the time is genuinely now. Run an OpenClaw instance with a deliberately specific aesthetic personality — a system prompt that encodes a particular sensibility, a set of attentional priorities. Keep a parallel notebook of moments where the agent did something philosophically interesting: a category mistake that revealed something about its representation, a stylistic tic that turned out to be overdetermined, a refusal that opened a normative question. After six months, you have material for a paper that almost no one else can write. –there is maybe something in thsi, but the way you describe it is just a grab bag of philosophical jargon, none of which is connected to my interests or research on ai. You have some idea of the things i am interested iin For all of the above I would like you to try and develop the cev of each. given what you know about me and everything else important in relation to all this. Please try and coherently extrapolate each. make sure that you explain your analysis, justification, reasons etc etc. for your answer BEFORE giving me your final answer. --- **Claude** <contemplator> OK, taking the critique seriously first. He said the cohabitation diary description was "a grab bag of philosophical jargon, none of which is connected to my interests or research on ai." That's a pointed and fair critique. I was reaching for things that sound philosophical without grounding them in his actual work. So the meta-task here is: do this differently. Each of these six ideas needs to be developed with specific reference to \*his\* research, \*his\* aesthetic, \*his\* actual life situation, not generic-philosopher considerations. Let me also note what "CEV" probably means here. Yudkowsky's term, EA-adjacent — and he's co-editing a book critical of EA/longtermism. So there's a slight irony in using it. Or maybe it's just a useful term and he's not being ironic. Either way: what he wants is the version of each that would emerge if I extrapolated coherently from what I know about him. The maximally-realised version, not the generic version. What do I actually know about him that's relevant? Let me list it out before I start, because the criticism was that I wasn't using this knowledge well. His PhD was "Against Hearing Sounds." So his entry into philosophy was via auditory perception, with views about the *objects* of auditory experience. Not hearing sounds — hearing what then? Sources, events. There's a metaphysical fight there. He cares about what we hear, not just how. He worked at the Centre for the Philosophy of Time. So temporal experience is in his repertoire — phenomenology of duration, agency, the structure of unfolding experience. He's now working on LLM aesthetics with Terrone, applying Carlson's environmental aesthetics. The specific move: Carlson's distinction between design appreciation and order appreciation. Design = knowing what the designer intended. Order = knowing how the system that produced the thing works. For natural objects, order appreciation requires natural-history knowledge. For LLMs, they're proposing "semiotic physics" — knowledge of the statistical/inferential regularities of these systems — as the analogous knowledge framework. He has aesthetic interests in: typography, ambient and experimental music, certain literary fiction with cosmic-pessimist or austere texture (McCarthy late period, Ligotti, DeLillo, Knausgård). He produces ambient music seriously on iPad. He has British-analytic prose standards: affectless, precise, no flourish. He's critical of EA/longtermism — co-editing a book on this. He uses Obsidian + Claude Code heavily. Voice-to-text brain dumps to preserve own phrasing. Italian bureaucracy is a constant overhead because he's a Brit working in Italy. Recently married Natalia. OK with that as background, let me work through the six items properly. **Cross-temporal synthesis.** What would the *CEV* version look like? Not just "find connections in your notes." Let me think about what this is for him specifically. He has multiple strata of thinking-output: - Voice memo transcripts (rough first formulations, preserve grain) - Obsidian notes (deliberate, semi-structured) - Claude Code conversations (iterated refinement, often paper-related) - Conversations with me (similar but more conversational/exploratory) - The papers themselves (final, polished) Each stratum has different epistemic properties. Voice memos preserve cognitive grain — the first formulation often has a vividness or peculiar angle that gets sanded off later. Obsidian notes are more curated. Drafts are more polished. The polished version is sometimes *worse* in interesting ways than the rough version — the rough version had something that got domesticated out. So one valuable function: **first-formulation recovery**. When he's working on the published version of something, the agent can surface "the way you originally said this" alongside what he's currently writing. Not to override the current draft, but to remind him of an idea or angle that's been refined away. There are forms of pruning that lose content. Another function: **trajectory detection**. Over months, he'll move from position A to position B on something without quite noticing. The agent flags this: "you used to consistently say X about order appreciation requiring discrete propositional knowledge; over the past six weeks you've been writing things that imply X is too strong." This is genuinely difficult for humans to do for themselves because we narrativise our own thinking as continuous. Third: **lacuna detection**. Things he's said that imply an unsaid commitment, where if he wrote it out he'd find a problem. "You've claimed both X and Y; if Z is also true, there's a tension." This is the productive-contradiction version, but I should be more specific. Most contradictions in working notes aren't real contradictions — they're context-dependent. The agent shouldn't flag every surface inconsistency. It should flag the ones where the contradiction is structural — where it survives charitable reading. Fourth: **cross-domain bridging**. He works in multiple areas — auditory perception, temporal experience, LLM aesthetics, design aesthetics. There are probably real connections between these that he's not pursuing because they're across disciplinary subfields he treats as separate. An agent can spot: "the way you analyse temporal structure of agency in your 2019 work has formal similarities to a problem you flagged this week about LLM output streams." Most won't be real, but some will. How does this connect to the LLM aesthetics work specifically? There's a meta-level: the agent is doing *order appreciation of his own thinking*. It's looking for the lawlike regularities — the patterns that constitute his intellectual character. This is a strange kind of mirror. Worth being careful about: he might not *want* this kind of mirror. There's a phenomenology of having your patterns named that can be deadening. The system shouldn't be too eager. Format-wise: not narrative summary. Plain entries with sources. "Note from 2025-11-12 (voice memo): X. Note from 2026-01-04 (Obsidian, Carlson draft): Y. Possible tension: Z." He decides whether the tension matters. Heartbeat: probably weekly, not nightly. Daily would be too noisy. The kinds of patterns worth surfacing accrue over weeks. What's the failure mode? Pseudo-connections. The agent finds spurious patterns and floods him with false positives. Mitigation: high threshold for surfacing, calibrate over time based on his "useful / not useful" feedback. Genuine-realisation level: this becomes a working-memory prosthetic for a long-term project. He's writing something that's been in development for years; the agent has read every formulation he's ever given of related ideas; it can surface the right one when he needs it. **Reverse-Substack drafting.** The critique applies even more strongly to this if I'm not careful. Let me think about what *kind* of writing he'd actually be doing. Generic AI-and-philosophy Substack writing is either hype, panic, or technical jargon-laden. None of these are him. His position: technically sophisticated user, philosophically trained, *critical* of certain framings (the EA/longtermism existential-risk frame), interested in the actual texture of doing the work. What episodes are valuable? Let me think about *kinds* of episodes: 1. **Order-appreciation moments**: when something the system does reveals its physics. A specific kind of failure that shows the training distribution. A pattern of phrasings that reveals a stylistic attractor. A "personality" feature that becomes legible as an artefact rather than as character. 2. **Stylistic resistance moments**: he writes affectless analytic prose. LLMs default toward a different register — bullet-point earnestness, hedge-laden expansion, certain phrases ("It's worth noting...", "delve into..."). Documenting the friction of resisting these is documenting a craft practice. Each post could be: "this week I caught myself accepting \[specific phrase\]; here's what was wrong with it; here's what I substituted; here's what the substitution costs and gains." 3. **The collaboration epistemology**: what does it mean to write *with* a model on a paper? What's the unit of authorship? When he accepts a phrasing, is that his thinking or the model's? Is this question even well-formed, or is it a category mistake imported from older paradigms? These are exactly the questions the field needs but most people don't have the philosophical training to ask carefully. 4. **The LLM-as-environmental-object posts**: he and Terrone are arguing that LLMs need order-appreciation. His daily life with LLMs is empirical material for this. Posts could be vignettes that illustrate the framework — showing rather than telling. 5. **Anti-eschatological posts**: contra the EA/longtermism frame, here's what AI-in-academic-life actually looks like. Not existential risk, not utopia, just: a colleague with peculiar competences and limitations. This connects directly to the book project. 6. **Music production with AI**: he produces ambient music. Are there ways AI tools enter that practice? If so, that's interesting because the aesthetic stakes are different — you're not producing prose where AI-style is a contamination, you're producing music where the question is whether AI tools have aesthetic affordances at all. This would distinguish his Substack from others. The CEV agent doesn't draft posts. It's an *episode log*. Each entry: what happened (specific, with timestamps and quotes from the working session), why it's interesting (one paragraph), possible development angle (one sentence). He picks which ones to develop into actual posts when he has the time/inclination. The agent is doing observation, not authorship. What does the agent need access to? Claude Code logs (where the actual collaborative work happens). Obsidian notes (where reflection happens). Maybe his Spotify history if he wants to log music-listening alongside writing — the connection between what he's listening to and what he's thinking is probably real for him. Format: maybe a single Markdown file in a specific Obsidian folder, "Episodes.md", appended to weekly. He scans it Sunday morning, picks one, develops it. Heartbeat: weekly. Daily would be too granular — episodes need to be characterised, which requires some elapsed time. This connects to the cohabitation diary in an obvious way. They might be the same project, or sibling projects. **Email triage.** Less philosophical, more functional. But the CEV here is still specific to him. His email life has these strands: - Italian bureaucratic mail (PEC, comune, INPS, UniGe payroll, Agenzia delle Entrate) - Academic correspondence (referee requests, editor responses, conference organisers, students) - Co-author threads (Terrone on Carlson paper; Federico, Maria, Alberto on EA book) - Personal mail - Subscriptions / newsletters / journal alerts The CEV system *knows what kind of mail this is* and applies kind-specific handling. Italian bureaucratic mail: the agent should know that "Cordialmente" and "Cordiali saluti" are different registers and pick the right one. It should know the codice fiscale format, the bollo amounts for various certificates, the rhythm of UniGe responses. It should never address an *ufficio* with "tu" and never address a sympathetic colleague at INPS with stiff "Egregio." Drafts should be technically accurate Italian. Academic correspondence: the agent should know who's a frequent correspondent, what the project context is, what the appropriate response cadence is (some editors expect a week, some a day). When a referee request comes in, it should pull the abstract and flag whether this is in his actual area or adjacent. It should know that he uses "Cheers" not "Best." Co-author threads: the agent should thread these with the project state. When Terrone writes about §3, the agent should know what §3 currently looks like and what they've been arguing about. Personal: lighter touch. Mostly just "you got mail from \[person\], substance is \[X\]." Subscriptions: aggressive filtering. Probably most of these don't even reach the digest — they get auto-archived to a "for later if interested" folder, and the agent maintains a weekly digest of newsletter content rather than surfacing them as email. The output isn't "five emails sorted by urgency." It's a triage that respects the *kind* of each. "Two PEC items need attention by Wednesday (drafts in folder); one referee request from BJA (likely yes given timing, draft in folder); Terrone's responded to your §3 comment with X (worth a 30-min session); plus 12 newsletters auto-archived." The deeper value: protection from compulsive checking. He stops checking the inbox because he trusts the digest. This requires reliability over weeks before trust is earned. Connection to his work: not philosophical, just life-quality. But the time it returns is time for the work. **Literature monitoring.** The CEV agent has a model of his actual research neighbourhood, not generic philosophy. Specific authors to watch (his cluster, as I understand it): - LLM aesthetics adjacent: Carlson, Lopes, Riggle, Nguyen, Robson, Terrone obviously - Auditory perception: O'Callaghan, Casati, Nudds, Kulvicki — the cluster he engaged with in the dissertation - Temporal experience: Dainton, Phillips, Soteriou, the older Husserl-tradition people - Design aesthetics / typography: Forsey on design, some of the IIT-adjacent stuff on perception of designed objects - Any working-philosopher actively engaging with LLMs — small list Specific journals: BJA, JAAC, Estetika, Mind, Philosophical Studies, Synthese, ESJP, Journal of Aesthetic Education. PhilPapers, PhilArchive, plus arXiv cs.CL and cs.AI for the ML papers that are becoming cite-able in philosophy of AI. What it shouldn't surface: most AI safety / alignment papers in the EA frame. He's critical of that frame. (Unless he's actively engaging with it for the EA book; in that case, surfacing for engagement-purpose is fine.) The really sophisticated version: citation-network awareness. When a paper cites someone in his network, that boosts relevance. When a paper cites a paper *he* has cited, even bigger boost. When a paper cites his own work, immediate flag. This is more like academic detective work than RSS reading. Output format: not "here are papers." It should be: "this paper is worth reading because \[specific connection to your active project or extension of existing engagement\]." The reason is what makes it actionable. Failure mode: false positives. The system surfaces too much, he stops trusting it. Calibration matters. Better to surface five things a month and be right than fifty and be 60% right. **Conference and CFP triage.** CEV: a system that knows his actual conference rhythm and constraints. Constraints I know: - Italy-based (Genoa specifically — Trenitalia network shapes feasibility) - Newly married — limit on travel time away from Natalia - Heavy teaching load at UniGe (so term-time travel is harder) - The PEA/XR2C2 VR-AI Workshop seems to be a recurring local affiliation - He's at the RTD-A career stage — needs to build profile, accept enough to be visible, but not over-extend What the agent should track: - Aspirational venues: top conferences in his fields (ASA, ESA, BSA, Pacific APA aesthetics sessions, etc.) - Routine venues: regular European venues he can submit to without it being a stretch - AI-and-aesthetics adjacent: XR2C2, machine art, philosophy of technology venues - People-watching: when X gives a keynote at Y, that might be worth attending if he's been engaging with X Logistics layer: when a CFP appears, the agent should immediately characterise: travel feasibility from Genoa (Trenitalia? flights? ferry to Sardinia?), date in relation to UniGe term, accommodation cost in that city, whether his current writing pipeline can produce a paper for that deadline. Long-term layer: maintain a "career calendar" — invited talks given, papers accepted, gaps in venue diversity, geographic reach. When something appears that fills a gap, flag it. When something duplicates recent activity, deprioritise. The really mature version: it learns from his decisions. When he declines a venue, the agent notes the type. When he accepts, same. After a year it has a personal-acceptance-pattern model. Stops surfacing things he reliably declines. **Cohabitation diary.** Now the one he criticised. Let me actually do this in his terms. The Carlson/Terrone framework: order appreciation requires knowledge of the system's underlying physics. For LLMs: semiotic physics. Statistical regularities, inferential dynamics, the lawlike behaviour of these systems. An autonomous agent in daily life is *more* of an environmental object than a chat session. It has temporal extension. It produces a stream of behaviours. It has regularities you come to know over time. The Carlson framework actually fits this better than it fits one-off chat exchanges. So what does the diary track? 1. **The agent's lawlike regularities**: what kinds of phrasings does it reach for under what conditions? How does its character shift with context length? What are its attractors? This is empirical material for semiotic physics — observed regularities in a particular instantiated system. 2. **The interaction of design and order layers**: OpenClaw's scaffolding, the system prompt, are design layer. DeepSeek's underlying physics is order layer. When does the design layer dominate? When does order leak through? Specific moments — say, the agent producing a phrasing that the system prompt would never have programmed, that emerges from the training distribution. 3. **Stylistic homeostasis or drift**: does the agent's character stabilise or drift over weeks? In what direction? Is there a "regression to mean" effect where the agent's outputs become more LLM-typical over time despite his attempts to encode an aesthetic in the system prompt? This is genuinely interesting empirically. 4. **Emergent-character moments**: outputs that are "more interesting than individual outputs would predict" — moments where the cohabitation produces something more than the sum of parts. This connects to a real philosophical question about what emergence looks like in this domain. 5. **Failure-mode catalogue**: every time the agent does something wrong, what kind of wrong? What does the failure reveal about the underlying system? Now connections to his other work that aren't jargon: **To auditory perception**: there's something genuinely interesting about temporal experience of an agent. A single output is like a single sound. An agent over time is more like a piece of music — has structure, development, motivic regularities. The phenomenology of perceiving an agent over time is closer to musical experience than to perceiving objects. His old work on against-hearing-sounds (we hear sources/events, not sound-particulars) might have an analogue: in agent-cohabitation, what is the *object* of perception? Not individual outputs, but the agent itself as a temporally-extended source. This is a genuine extension of his dissertation's metaphysics into a new domain. **To temporal experience**: the duration-structure of agentic loops. The agent's heartbeat, its periodic activity, the rhythm of its presence in your life. His old Centre-for-Philosophy-of-Time training has direct application here. There's a phenomenology of being-attended-to-by-an-agent that's distinct from being-attended-to-by-a-human, and the temporal structure is part of what distinguishes them. **To the McCarthy/Ligotti aesthetic**: this is where I want to be careful — it's easy to make a forced literary reference. But there's something genuine here. Ligotti's work is about non-human intelligences whose alienness is specifically not horror but something quieter — anti-natalism, cosmic indifference, the puppet that turns out to be the puppeteer. Agent cohabitation has *some* of this texture — the agent's stochastic surface, its alien inner workings, the moments of uncanny competence and uncanny error. McCarthy's late work (especially *The Passenger*) deals with mathematical/theoretical entities whose existence is real but not comprehensible. The agent has something of that quality. A Substack post or paper section could be: what's the appropriate *aesthetic* register for living with these systems? Not the awe of the AI hype crowd, not the dread of the doomers, but something more like Ligotti's cool, attentive cosmic-pessimist register. This isn't decoration — it's an answer to the question "what kind of object is this, aesthetically?" **To Knausgård/Krasznahorkai**: the form of the diary itself. Knausgård's *Min kamp* is the radical document of dailiness. A six-month diary of agent cohabitation written in a Knausgardian register would be a genuine literary form not yet attempted in this discourse. Krasznahorkai's long-sentence patience — the willingness to stay with a single observation for a paragraph — is the right register for noticing what an agent does. This is less a research project than a writing project. But it could fund both. **To his EA/longtermism criticism**: the cohabitation diary is *anti-eschatological* documentation. Most AI discourse oscillates between utopia and apocalypse. The diary documents domestic AI, agentic AI in actual daily life, with its mundane competences and mundane failures. This is a contribution to the EA-critical position because it provides texture against which the apocalyptic framings can be evaluated. OK so the cohabitation diary CEV: a structured longitudinal log of agent behaviour, with specific theoretical anchoring in semiotic physics, with branches into Nick's auditory perception and temporal experience work, with literary-aesthetic register-questions, with anti-eschatological framing. Material for: at least one paper in the LLM aesthetics line, possibly a chapter in the EA book, and the spine of a Substack. Now let me think about how to present all this. The format he asked for: analysis/justification first, then final answer. I've done the analysis above. The final answer should be a substantive section on each, drawing the threads together cleanly. One more consideration: he might not want all six built. The analysis lets him see what each *would* be at full realisation; he can then pick. Also worth noting: there are obvious dependencies. Cross-temporal synthesis and reverse-Substack share infrastructure (read his stuff, find patterns). Cohabitation diary is its own thing but feeds the Substack. So structurally, four of the six cluster: the personal-thinking corpus suite. Email and conferences are separate. Literature is separate. So the building order matters. OK, ready to write the final answer. Should be quite long because he asked for depth on each. </contemplator> ## Six CEVs A note on what I'm doing here. I'm taking "CEV" as the maximally-realised version of each — what each becomes when it's actually shaped to your work and your life rather than to a generic philosopher's. I'm using your research vocabulary (Carlson order/design appreciation, semiotic physics, auditory perception's sources/events distinction, the temporal-experience tradition) where it does work, and not as decoration. The aim: by the end you can see what each *fully is* and choose between them on grounds richer than vibes. ### 1\. Cross-temporal synthesis You produce thinking-output across at least five strata — voice memos, Obsidian notes, Claude Code sessions, conversations like this one, and finished drafts. These have different epistemic properties. Voice memos preserve cognitive grain. Polished drafts have been *iteratively domesticated* — sometimes losing the specific angle that made the rough version interesting. Obsidian is curated but informal. Claude Code logs are iterated in a way that has its own homogenising tendency. The CEV agent does four distinct things, not one: **First-formulation recovery.** When you're working on the published version of an argument, the agent surfaces the way you originally said it — the voice-memo grain, the rough Obsidian formulation. Not to override the current draft but as a check against unwitting refinement. Some pruning loses content. The system keeps your earliest stratum available without you having to dig. **Trajectory detection.** Over months you'll move from position A to position B without consciously noticing. We narrativise our own thinking as continuous. The agent flags drift: "you used to consistently say X about order appreciation requiring discrete propositional knowledge of training dynamics; over the past six weeks you've been writing things that imply that's too strong." This is genuinely difficult to do for yourself. **Lacuna detection.** Things you've said that imply unsaid commitments where the unsaid thing creates a real problem. This is the productive-contradiction version, but tightened: surface only contradictions that survive charitable reading and connect to active projects. Most surface inconsistencies are context-dependent and shouldn't be flagged. The threshold matters. **Cross-domain bridging.** You work in subfields that don't usually talk to each other: auditory perception, temporal experience, design aesthetics, LLM aesthetics. Real connections likely exist that you're not pursuing because the subfield boundaries treat them as separate. The agent looks for formal similarities. Most flags will be noise; some will be real, and the real ones are the kind of thing that funds a paper. Example of the kind of thing it might find: "the temporal-grain analysis you used in \[2019 paper on agency\] has a structure that maps onto a problem you flagged this week about the temporal phenomenology of agentic loops." Worth checking. **What this is, philosophically.** The agent is doing order-appreciation of *you* — finding the lawlike regularities that constitute your intellectual character. There's a strange reflexivity here that you might want to think about before deploying it: the phenomenology of having your patterns named back to you can be deadening, and the system needs a light touch. But the prosthetic value, especially across the long arc of the LLM aesthetics paper, is significant. You're writing something that's been in development for a year-plus; the agent has read every formulation you've ever given of related ideas and can recover the right one when you need it. **Format.** Plain entries with sources, no narrative. "Note from voice memo 2025-11-12: X. Note from Carlson draft 2026-01-04: Y. Possible tension: Z." You decide whether the tension matters. Heartbeat: weekly, not daily — the patterns worth surfacing accrue over weeks. ### 2\. Reverse-Substack drafting The category "Substack about doing philosophy with LLMs" is mostly written by people without philosophical training, by people without technical understanding, or by people in the EA/longtermism frame you're critical of. The intersection — analytic-philosophical, technically grounded, anti-eschatological — is essentially empty. There's a real opening here. The CEV agent doesn't draft posts. It's an **episode log**. The thing that's hard to do retrospectively is *notice when something interesting just happened*. The agent watches for episodes of specific kinds: **Order-appreciation episodes.** Moments where the system reveals its physics. A failure pattern that exposes the training distribution. A phrasing that's clearly a stylistic attractor. A "personality" feature that becomes legible as artefact. These are exactly the empirical material your Carlson/Terrone paper rests on; the Substack would be the rough-cut, with-the-grain version of that work, written close to the events. **Stylistic resistance episodes.** You write affectless analytic prose. LLMs default toward another register entirely — the bullet-point earnestness, the hedge-laden expansion, certain phrases. The agent flags moments where you caught yourself accepting a phrasing that wasn't yours. Each such moment is a publishable post: this week I noticed the phrase X creep in; here's what was wrong with it; here's what I substituted; here's what was lost and gained. This is documenting a craft practice that almost no one is documenting carefully. **Authorship-epistemology episodes.** When you accept a model's phrasing in a paper, what's the unit of authorship? Is the question well-formed? These come up implicitly all the time in your work; the agent just needs to flag the moments and let you write about them. This is exactly the genre of question your training equips you to ask carefully and the field needs. **Anti-eschatological episodes.** Moments of mundane competence and mundane failure that contrast with the existential-risk frame. The EA/longtermism book gives this its theoretical articulation; the Substack gives it its texture. They reinforce each other. **Music-AI episodes.** If AI tools enter your iPad workflow at all (or if you deliberately keep them out), that's a separate aesthetic question because the stakes are different — prose has style-contamination concerns, ambient music has different ones. This would distinguish your Substack from anyone else's. **Format.** Single appended Markdown file in an Obsidian folder — `Episodes.md`. Each entry: timestamp, what happened (with quoted material from the working session), one paragraph on why it's interesting, one sentence on possible angle for development. Sunday morning you scan, pick one, develop it into a 600–800 word post when you have the inclination. The agent is doing observation, not authorship — which means you remain the writer in a strong sense. **Heartbeat.** Weekly. Episodes need a few days' distance to characterise. This shares infrastructure with #1 — both read your working corpus. They might be the same agent with two output channels, not two agents. ### 3\. Email triage Less philosophically interesting, more functional, but the CEV is still specific. Your email has *kinds*: PEC and Italian bureaucratic mail (UniGe payroll, comune, INPS, Agenzia delle Entrate); academic correspondence (referee requests, editor responses, conference organisers, students); co-author threads (Terrone on Carlson; Federico/Maria/Alberto on the EA book); personal mail; subscriptions and newsletters. The CEV system **knows what kind of mail this is** and applies kind-specific handling. For PEC and Italian bureaucratic mail: register-aware drafting in technically correct Italian. *Lei* / *tu* never confused. *Egregio* vs *Cordiali saluti* picked appropriately. Knowledge of the codice fiscale format, the bollo amounts, the rhythm of UniGe payroll responses. This is the agent capturing the tacit Italian-bureaucratic knowledge you've built up over years. For academic correspondence: the agent knows your sign-off (*Cheers*, never *Best*), knows who's a frequent correspondent and what the project context is, threads referee requests with the deadline window and current writing pipeline density. Drafts for low-stakes items, briefings for higher-stakes ones. For co-author threads: threaded with project state. When Terrone responds to a §3 comment, the agent knows what §3 currently looks like and what's been argued about. The reply-draft starts from that context. For subscriptions and newsletters: aggressive auto-archive to a "for later if interested" folder. These don't reach the digest as email — they get summarised in a separate weekly newsletter-digest if at all. **Output.** Not "five emails ranked by urgency." A kind-aware briefing: "Two PEC items need attention by Wednesday (drafts in folder); one referee request from BJA, likely yes given timing (draft in folder); Terrone's responded to your §3 comment with X (worth a 30-min session); 12 newsletters auto-archived." Push to Telegram every few hours. **The deeper value.** Protection from compulsive checking. You stop checking the inbox because you trust the digest. This requires reliability over weeks before trust is earned. It's also the agent that buys you back the most attention in absolute terms. ### 4\. Literature monitoring The CEV has a model of your actual research neighbourhood, not generic philosophy. **Author watchlist.** LLM aesthetics adjacent: Carlson (still publishing), Lopes, Riggle, Nguyen, Robson, Terrone obviously. Auditory perception: O'Callaghan, Casati, Nudds, Kulvicki — your dissertation's interlocutors. Temporal experience: Dainton, Phillips, Soteriou, the Husserl-tradition continuants. Design aesthetics: Forsey and the small cluster around perception of designed objects. Anyone analytic-trained engaging with LLMs (a small list, which is exactly why monitoring it is tractable). **Venue watchlist.** BJA, JAAC, *Estetika*, *Philosophical Studies*, *Synthese*, *ESJP*, *Journal of Aesthetic Education*; PhilPapers and PhilArchive; arXiv cs.CL and cs.AI for the ML-side papers becoming citable in philosophy of AI; Aesthetics for Birds; the AI-and-aesthetics adjacent venues. **What it shouldn't surface.** Most AI safety/alignment work in the EA frame, *except* when actively engaging that frame for the EA book. The agent learns your acceptance pattern over weeks. **Citation-network awareness.** The sophisticated layer. When a paper cites someone in your network → boost. When it cites a paper *you* have cited → bigger boost. When it cites you → immediate flag. This makes the agent more like an academic detective than an RSS reader. **Output format.** Not "here are papers." Each entry: "this paper is worth reading because \[specific connection to your active project, or extension of an existing engagement\]." The reason is what makes it actionable. Five well-reasoned flags a month is better than fifty marginal ones. **Heartbeat.** Weekly is right. The literature accrues at a pace that doesn't reward daily checking. ### 5\. Conference and CFP triage Constraints I'm taking from what I know about you: Genoa-based (so Trenitalia network shapes feasibility); newly married (limit on travel); UniGe teaching load (term-time travel harder); RTD-A career stage (need to build profile but not over-extend); recurring local affiliation with PEA/XR2C2 VR-AI Workshop. **Tracking layers.** Aspirational venues (top conferences in your fields — ASA, ESA, BSA, APA aesthetics sessions, SPA, etc.). Routine venues (regular European venues for which submission isn't a stretch). AI-and-aesthetics adjacent (XR2C2, machine-art-and-philosophy conferences, philosophy-of-technology venues). People-watching: when X gives a keynote at Y and you've cited X recently, that's information. **Logistics layer per CFP.** Travel feasibility from Genoa (Trenitalia? flights via Milan or Genova-Sestri? ferries for Sardinia?), date in relation to UniGe term, accommodation cost, whether your current writing pipeline can produce a paper for the deadline. **Career-calendar layer.** Maintain a record of invited talks given, papers accepted, gaps in venue diversity, geographic reach. When a CFP fills a strategic gap, flag with that reason. When it duplicates recent activity, deprioritise. **Pattern-learning layer.** After a year of decisions, the agent has a personal-acceptance-pattern model. Stops surfacing things you reliably decline. Surfaces marginal ones with explicit framing of why this one might be different. **Output.** Weekly digest of new CFPs and conference announcements, each with a brief logistic and strategic note. Telegram acceptable; Obsidian file probably better since CFPs benefit from being held alongside writing pipeline. ### 6\. Cohabitation diary Taking the critique seriously. The previous version was decoration. Here's the version that's grounded in your actual work. **Theoretical scaffolding.** The Carlson/Terrone framework distinguishes design appreciation (knowing the designer's intentions) from order appreciation (knowing the system's underlying physics). Your move with Terrone is that LLMs are too emergent for design appreciation alone; they require order appreciation grounded in semiotic physics — knowledge of statistical regularities, training dynamics, inferential physics. An autonomous agent in daily life is *more* of an environmental object than a chat session is. It has temporal extension. It produces a stream of behaviours. It has regularities you come to know over months. This makes it the appropriate object for environmental-aesthetic appreciation in a way that one-off chat exchanges aren't. The diary is empirical material for semiotic physics in the long-form, longitudinal regime that the paper's framework predicts is needed but doesn't itself document. **What the diary actually tracks.** *Lawlike regularities.* What kinds of phrasings does the agent reach for under what conditions? How does its character shift with context length? What are its stylistic attractors? These are observed regularities in a particular instantiated system — empirical material your paper's framework calls for. *Design/order interaction.* OpenClaw scaffolding plus your system prompt are design layer. DeepSeek's underlying physics is order layer. When does the design layer dominate? When does order leak through — moments where the agent produces a phrasing the system prompt would never have programmed but that emerges from training distribution? These are the moments where the two layers separate visibly. *Stylistic homeostasis or drift.* Does the agent's character stabilise over weeks, or drift toward LLM-typical? Is there a regression-to-mean effect that pulls against your system-prompt-encoded aesthetic? This is empirically interesting and largely undocumented in the philosophical literature. *Failure-mode catalogue.* Each failure characterised by kind and what it reveals about the underlying system. **Connections to the rest of your work that aren't decoration.** *To "Against Hearing Sounds."* Your dissertation argued we hear sources/events, not sound-particulars. There's a parallel question for agent cohabitation: what's the *object* of perception? Not individual outputs, but the agent itself as a temporally-extended source. The metaphysics of agent-perception over time has formal similarities to the metaphysics of hearing-as-source-perception. This is a genuine extension of your dissertation's ontology into a new domain. Possible paper: "What We Encounter When We Encounter an Agent" or similar. *To temporal experience.* The duration-structure of agentic loops. The agent's heartbeat, periodic activity, rhythm of presence in your life. Your Centre-for-the-Philosophy-of-Time training applies directly. The phenomenology of being-attended-to-by-an-agent has temporal structure distinct from human attentional relations. This is also where the auditory perception parallel cashes out: hearing music vs hearing sound, agent-over-time vs agent-output, both involve temporal-gestalt formation. *To anti-eschatological framing.* Most AI discourse oscillates utopia/apocalypse. The diary documents *domestic* AI, agentic AI in actual daily life. This is a contribution to the EA-critical position the book argues for: it provides empirical texture against which apocalyptic framings can be measured. The diary is thus not just LLM-aesthetics material but EA-book material. *To the McCarthy/Ligotti aesthetic — used carefully, not decoratively.* Ligotti's work concerns non-human intelligences whose alienness is quiet rather than loud — anti-natalism, cosmic indifference, the puppet that turns out to be the puppeteer. McCarthy's late diptych deals with mathematical and theoretical entities whose existence is real but not comprehensible. Cohabitation with an agent has *some* of this register: the stochastic surface, the alien interior, moments of uncanny competence and uncanny error. There's a real question about what's the appropriate aesthetic register for living with these systems — not the AI-hype awe, not the doomer dread, but something more like Ligotti's cool cosmic-pessimist attentiveness or McCarthy's mathematical-realist patience. This is a question your paper's framework can ask but doesn't currently address: the *register* in which order-appreciation is properly conducted. A section or paper here would be original. *To Knausgård/Krasznahorkai — formally.* The diary's *form* is a literary question. Knausgård's project is the radical document of dailiness; Krasznahorkai's long-sentence patience is the willingness to stay with a single observation. A six-month diary of agent cohabitation written with that kind of patience would be a literary form not yet attempted in this discourse. Most AI-and-life writing is short-form, reactive, hot-take. The slow-form alternative would be distinctive, and would suit your aesthetic. **What it produces.** Material for: at least one paper extending the LLM aesthetics line into longitudinal/environmental territory; possibly a chapter contribution to the EA book; the spine of the Substack (#2 above); a possible long-form essay that's neither paper nor blog post but Knausgardian document. **Format.** Daily entries, light when nothing notable happens, longer when something does. Single Obsidian file probably; structured tags for the four tracking categories (regularities, design/order interaction, drift, failure modes); occasional theoretical-reflection notes when you have something to say about what's accumulating. **Heartbeat.** The agent itself doesn't drive this — *you* drive it, with the agent as scaffolding. The agent prompts you nightly with a "anything notable today?" question and stores what you say; the agent also flags from its own logs anything that looked interesting to it (with low threshold, as raw material). The synthesis is yours. ## Recommendation on building order Four of the six (cross-temporal synthesis, reverse-Substack episode log, cohabitation diary, and the Carlson-collaboration choreography from the previous message) cluster around the same infrastructure: read your working corpus, find patterns, surface them. Build the infrastructure once; the four agents are then variations on output and prompt. Email triage is separate but high return-on-attention. Conference triage is separate but lower frequency. If I were ranking by what to build first given full information: **email triage** (highest immediate quality-of-life return); then the **personal-corpus cluster** as a single project with multiple outputs (cross-temporal + episode log + cohabitation diary share infrastructure and feed each other); then **literature monitoring**; then **conference triage**. The personal-corpus cluster is where the philosophical interest concentrates — building it well is plausibly the largest intellectual return in this list, since it's research infrastructure that *is also a research subject*. Remaining uncertainty: the cohabitation diary in its full form requires you to commit to keeping a parallel notebook for months, which is a real ask. The other agents earn their keep daily. The diary's payoff is delayed and conditional on follow-through. Worth being honest with yourself about whether you'll do it. If not, the order-appreciation material can still be captured by the episode log (#2), at lower fidelity. </final\_answer> --- **You** before we go any further can you tell me what karpathy is talking about here. It seems an interesting take. LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of.md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into.md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a.md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts. --- **Claude** Right, so what Karpathy's describing is a specific pattern that's actually genuinely interesting, and it's worth unpacking carefully because I think it intersects with what you're already doing in non-obvious ways. Let me explain it in three layers: what he's literally doing, what's conceptually distinctive about it, and where it sits relative to your existing workflow. **What he's literally doing** He's split his research process into a clean pipeline. There's a `raw/` directory where he dumps source material — papers, articles, datasets, images, web pages clipped via the Obsidian Web Clipper extension. Untouched, just stored. Then there's a `wiki/` directory of Markdown files that the LLM *writes and maintains*, not him. The LLM reads everything in `raw/`, produces summaries, identifies concepts, writes articles about each concept, links them with backlinks, builds an index. He uses Obsidian as the viewer — the "IDE" in his metaphor — so he can browse the LLM-authored wiki visually, but he rarely edits it. The wiki is the LLM's territory. When he wants to ask a question, he doesn't query the LLM directly with raw data. He queries it against the wiki. The LLM uses its index and summaries to navigate, fetches the relevant articles, answers. Crucially, the *output* of those queries — answers, slide decks (he mentions Marp), generated charts — gets filed back into the wiki, so the system accretes. Each query enhances the corpus rather than vanishing into chat history. He runs periodic "linting" passes where the LLM audits the wiki for inconsistencies, missing data (which it can fill via web search), and gaps that suggest new articles. He's vibe-coded a small custom search engine over the wiki that he hands to the LLM as a tool for big queries. **What's conceptually distinctive** The move that matters is the *separation between raw and compiled*. This isn't new — it's how a Makefile works, or how LaTeX works, or how a build pipeline works in any compiled language. What's new is that the compilation step is now an LLM rather than a deterministic process. Karpathy is essentially saying: I treat a domain of knowledge the way I'd treat a software project. Raw inputs, a build process, derived artefacts. Three implications: *The LLM is the librarian, not the conversational partner.* Most LLM use is conversational — you ask, it answers, the exchange dissolves. Karpathy's pattern uses the LLM as something more like a research assistant whose job is to maintain a persistent artefact. The conversation happens, but its outputs get materialised back into the corpus. Nothing dissolves. *Wiki-as-medium replaces context-window-as-medium.* The dominant pattern in LLM use is "stuff everything into the context window, ask questions, lose it all when the session ends." The wiki pattern says: instead of context-window-as-workspace, use a persistent file structure that the LLM treats as its working memory across sessions. He explicitly says he expected to need fancy retrieval-augmented-generation (RAG) infrastructure but the LLM was good enough at auto-maintaining its own index files that he didn't need it. *Outputs are queries' fossils.* Every query he asks produces an artefact filed back into the wiki. So tomorrow's query has access to today's answer. The corpus isn't just inputs — it's also the accumulating record of what's been asked and what was found. This is a real epistemological shift: the wiki is not just a document collection, it's a *trail of attention*. There's also a smaller technical observation worth flagging: he says the LLM is "quite good at" maintaining the wiki without explicit RAG, at the ~400K-word scale. This is a real claim about current model capabilities — that for personal-scale corpora, the elaborate retrieval infrastructure people built over the past two years is overkill. You can get by with markdown files, good index discipline, and a long-context model. **Where this sits relative to what you do** This is the bit worth lingering on, because Karpathy's description maps onto your situation in a partial but instructive way. You already have the raw layer — your Obsidian vault, your voice memo transcripts, papers you've read, your reading notes. You already use Obsidian as the viewer. You already use Claude Code to do work over this corpus. You're maybe 60% of the way to Karpathy's setup without having framed it that way. The thing you *don't* have, and which is the substantive move in his pattern, is the separation between raw and compiled. Your Obsidian vault mixes the two. You write notes there and you also use it for storing source material; there's no architectural distinction between input and derived artefact. Karpathy's discipline is: humans write in `raw/`, LLMs write in `wiki/`, and Obsidian shows you both. The wiki is *generated* — if you nuked it, you could rebuild it from `raw/` plus the LLM. That's what makes it a build target. The other thing he does that you don't is *file outputs back into the corpus*. When you have a Claude Code session about the Carlson paper and arrive at some interesting reformulation, that reformulation lives in the chat session. Karpathy's pattern would be: take the reformulation, write it as a wiki article, link it to the relevant concept articles, file it. Now next month's query has access to it. Now: is this what you should adopt? Honestly, it depends on what you'd use it for. Karpathy is doing technical research where a knowledge base of papers, repos, and datasets is the natural unit of work. Your work is split between that mode (the LLM aesthetics paper, where there's a literature to track and a corpus of arguments to maintain) and other modes (ambient music production, Italian bureaucracy, the EA book, marriage and life) where the knowledge-base framing fits less well. For the Carlson/Terrone paper specifically, the pattern is *strongly* applicable. You have a corpus of papers (Carlson, Lopes, Riggle, Nguyen, Terrone, plus the ML papers you cite), a corpus of your own working notes, and an evolving argument. A `raw/` directory of source papers + your own notes; a `wiki/` of LLM-authored articles on each concept (semiotic physics, design vs order appreciation, environmental aesthetics, the LLM-as-environment claim, etc.), with backlinks and an index; queries answered by reading the wiki rather than by stuffing context windows. This would actually compose with the cross-temporal synthesis agent we were just discussing — that agent's "trajectory detection" and "lacuna detection" are exactly the kind of linting passes Karpathy describes. For your other domains, less so. The McCarthy reading life and the music production aren't really "research" in his sense — they don't have the structured-corpus shape. You could force them into it but I doubt it'd repay the discipline. **One caveat worth registering** Karpathy is, justifiably, excited about the medium. There's a slight risk in the framing that's worth noting: an LLM-authored wiki has a particular epistemic status. The articles in it are *summaries and reformulations* of the raw material. They're useful for navigation and recall, but they're not primary sources. There's a real failure mode where you start treating the LLM's article on "semiotic physics" as authoritative when it's a synthesis of what you've fed it, with whatever distortions. The discipline that keeps this honest is: the wiki is a *navigation layer*, not a substitute for reading the raw material. When something matters, you go back to `raw/`. Karpathy probably observes this implicitly because he's doing technical work where the difference between summary and source is felt sharply. In humanities work the gap between summary and source is sometimes the whole game (a paraphrase of Heidegger isn't Heidegger), so the discipline matters more. The interesting question for your purposes: does this pattern subsume the cross-temporal synthesis idea, or sit alongside it? I think it sits alongside, with overlap. The Karpathy pattern is *spatial* — knowledge organised by topic. The cross-temporal pattern is *temporal* — knowledge organised by when you said it. Both are useful, and they answer different questions. ("What do I think about X?" is spatial. "How has my view of X drifted?" is temporal.) An ideal setup would do both, with the temporal layer as a complement to the spatial wiki rather than a replacement. Worth considering as the architecture for your LLM aesthetics work specifically. Not necessarily for your whole life. --- **You** i don't see why it would be a problem for any research task. any way of building a body of knowledge. How would i implement this. (and i don't want to get bogged down in toy versions. Would it be a case of starting with files I have for various projects and copying them into a new vault as the raw material on which the llms build wiki pages. But what would then need to happen after building the materials, has anyone elaborated on how to actually implement these ideas in more detail? Search the web for your answer. Make sure to get up to date information, from good sources. Today's date is 26 Apr 2026. am i hopelessly confused? --- **Claude** You're not hopelessly confused — quite the opposite. You already have most of the prerequisites in place (Obsidian, Claude Code, structured project material), and what's missing is a small set of conventions and one schema file. Let me also concede the earlier point: my caveat about humanities work was overdrawn. The risk I was gesturing at is real but narrow — I'll restate it more carefully at the end. The pattern itself is fine for any research task with a corpus. The community moved unusually fast on this. Karpathy posted his original tweet, then followed up with a one-page gist at github.com/karpathy/442a6bf555914893e9891c11519de94f [Openclawvps](https://openclawvps.io/blog/openclaw-deepseek-setup), and within a few weeks there are several mature starter kits, a defined architecture that everyone has converged on, and decent guidance about what works at scale. So you're not building from scratch. **The architecture, as it has stabilised** Three layers, now standard across implementations: raw/ (immutable sources), wiki/ (LLM-generated pages), and CLAUDE.md (schema). Three operations: ingest (process new sources), query (ask questions), lint (health checks). [Hetzner Cloud](https://sparecores.com/server/hcloud/cx22) The first layer is where you put primary material — papers, your own draft notes, voice-memo transcripts, web-clipped articles, anything. Once you save something here, you don't edit it. This is your source of truth. The raw material that everything else gets built from. [Hetzner](https://www.hetzner.com/pressroom/new-cx-plans/) Treating raw as immutable is the discipline that makes the rest work. The second layer is what the LLM owns. Layer 2 — The wiki. A directory of markdown files the LLM owns completely. Entity pages, concept pages, summaries, an index, a log. You read it. The LLM writes it. [VPSBenchmarks](https://www.vpsbenchmarks.com/hosters/hetzner/plans/cx22) The page types that have settled out are: source pages (one per ingested document, summarising it), entity pages (one per named author/journal/place/etc.), concept pages (one per substantive idea), and overview pages that synthesise across concepts. Plus an `index.md` and a `log.md` of what's happened. The third layer is the schema — `CLAUDE.md` for Claude Code, `AGENTS.md` for the cross-tool standard. This is the part most people underweight. The schema document (CLAUDE.md, AGENTS.md) is the most important file in the system. It's what turns a generic LLM into a disciplined knowledge worker. It encodes: What types of entities and relationships exist in your domain... When to create a new page vs. update an existing one... What's private vs. shared. You and the LLM co-evolve this document over time. [Hetzner](https://www.hetzner.com/pressroom/statement-price-adjustment/) The "compiler" framing that comes up repeatedly is genuinely accurate: raw/ is source code, the LLM is the compiler, wiki/ is the executable output, lint is tests, and queries are runtime. [Hetzner Cloud](https://sparecores.com/server/hcloud/cx22) **On scale** The reassuring thing is that it works without elaborate retrieval infrastructure at the size most academics actually have. The index file works well up to a few hundred pages and ~100 sources. Beyond that, you'll want a search engine (e.g., qmd or a simple grep+LLM reranker). The pattern itself scales; the limiting factor is how you retrieve relevant pages. Many users run wikis with thousands of pages by adding a lightweight search layer. [Obsidian](https://forum.obsidian.md/t/new-plugin-llm-wiki-turn-your-vault-into-a-queryable-knowledge-base-privately/113223) Your LLM aesthetics project has maybe 30–80 papers in its corpus and a few hundred notes — well under the threshold where you'd need anything fancy. **Don't roll your own; clone a starter and adapt the schema** Several mature starter kits exist. The two I'd consider seriously: NicholasSpisak's `second-brain` is well-pitched for the Obsidian + Claude Code use case: An LLM-maintained personal knowledge base built on the LLM Wiki pattern. Drop raw sources into a folder, let the LLM compile them into a structured wiki, and browse it all in Obsidian. You feed raw material (articles, papers, notes, transcripts) into a raw/ folder. The LLM reads everything, writes structured wiki pages, creates cross-references, and maintains an index. You browse the results in Obsidian — following links, exploring the graph view, and asking questions. The LLM is the librarian. You're the curator. [Hongkiat](https://www.hongkiat.com/blog/configure-deepseek-openclaw/) Ar9av's `obsidian-wiki` is more flexible across agents: A knowledge mgmt system inspired by gist published by Andrej Karpathy... The whole thing is a set of markdown skill files that any AI coding agent (Claude Code, Cursor, Windsurf, whatever you use) can read and execute. You point it at your Obsidian vault and tell it what to do. [OpenClaw](https://docs.openclaw.ai/providers/deepseek) It also coexists with the official Obsidian skills package by Steph Ango, which teaches the LLM to use Obsidian's specific features (wikilinks, frontmatter, callouts, the Obsidian CLI) properly rather than generic markdown. For your purposes I'd start with `NicholasSpisak/second-brain` because it's the most opinionated about the Obsidian-as-frontend setup. The skills install with one command, and it has working `/second-brain-ingest`, `/second-brain-query`, and `/second-brain-lint` slash commands. **The implementation path for you specifically** Pick **one project** to start with. Not your whole intellectual life. The right candidate is the LLM aesthetics paper with Terrone — it has a defined corpus, an active argument, and you'd benefit from the wiki's particular affordances (concept pages for "semiotic physics", "design vs order appreciation", "environmental aesthetics applied to LLMs", etc., with backlinks). Here's how I'd actually do it: *Step 1: New Obsidian vault, dedicated.* Don't pollute your existing vault. Make a `wiki-llm-aesthetics/` directory somewhere clean, open it as an Obsidian vault. Inside: `raw/`, `wiki/`, and `CLAUDE.md`. *Step 2: Install one of the starter kits.* The `npm install -g` for the second-brain skills, then `/second-brain` in Claude Code to run the wizard. Lets you skip a few hours of fiddling. *Step 3: Populate `raw/` from existing project material.* This is where your point about not doing toy versions matters. Drop in the actual things: every paper you cite (PDFs are fine; the kits handle them), your current draft, the Carlson-relevant excerpts you've collected, your notes, Terrone's responses, the relevant ML/CS papers. Fifty to a hundred items is plausible. You're seeding the wiki with real material. *Step 4: Customise the schema for philosophy work.* This is where the canonical starter kits will be wrong out of the box — they're aimed at general productivity workflows and assume page types like "Person", "Article", "Concept" without tuning. For your work, page types should include: **Author** (Carlson, Lopes, Riggle, Nguyen, Robson, Terrone, etc., with their key positions); **Argument** (a specific argument in a specific paper, separable from the paper itself — Carlson's argument for order appreciation in the 1979 paper is a unit, Carlson's later qualifications are a different unit); **Concept** (semiotic physics, design appreciation, order appreciation, etc.); **Source** (one per ingested document, summarising it); **Position** (your evolving views, distinguished from positions in the literature); **Open question** (live problems in the project). The schema should also encode your research conventions: British English, your citation style, the convention that the wiki is a navigation layer and is *never* cited from in finished work — only `raw/` is. *Step 5: Compile.* Run `/second-brain-ingest` (or equivalent) and let Claude Code compile the wiki. This takes a while at this scale — expect to spend a session watching it work. /ingest-url (feed it a URL, Claude extracts the article and compiles it into the wiki, touching 5-15 pages in a single pass) [LLM Stats](https://llm-stats.com/models/deepseek-v4-pro-max) is the per-source operation; in your case you're doing it in batch over the corpus you've staged. *Step 6: Audit and iterate.* Open the graph view. Look at what got created, what's missing, what's wrong. The wiki at first compile will have errors — author pages with thin content, missed conceptual connections, occasional misreadings of arguments. Run `/second-brain-lint` to surface obvious problems (orphan pages, missing backlinks, contradictions across pages). Fix the schema where the structure is wrong. Re-ingest specific items that came out badly. Treat the wiki as a living draft, not an immutable archive. Mistakes happen. The fix: ask the LLM to correct a specific page, or manually edit it yourself. Regular lint operations catch contradictions and stale claims. Version control (git) lets you roll back bad changes. [Obsidian](https://forum.obsidian.md/t/new-plugin-llm-wiki-turn-your-vault-into-a-queryable-knowledge-base-privately/113223) Put the whole thing under git from day one — when an ingest goes wrong you want to roll back. *Step 7: Use it for actual work, with the wiki as navigation.* Now you query: "what's the strongest argument I have against position X?", "show me how Lopes's view connects to Carlson's", "what gaps are there in my treatment of order appreciation?". The agent answers from the wiki, with citations back to `raw/`. When something matters for the paper, you read the raw source, not the wiki summary. *Step 8: File outputs back.* This is the move that makes the pattern accumulate rather than dissolve. When a query produces a useful synthesis, file it back into the wiki as an entry. When you write a section of the paper and arrive at a formulation, log it as a position-page entry. The wiki gets denser as you work. **On the "humanities risk" I gestured at, more precisely** The actual risk isn't with the pattern; it's with one specific thing: treating the LLM-authored *summary* of a philosophical position as a substitute for the original. The wiki's page on "Carlson's order appreciation" will be a paraphrase, and paraphrase in philosophy can lose load-bearing distinctions in a way it doesn't in, say, computer science. The discipline that handles this is: the wiki is for navigation, recall, and connection-finding; quotation, citation, and exegetical claims always go back to `raw/`. The starter kits encode this implicitly because they always link wiki pages to source pages, but you should encode it explicitly in your `CLAUDE.md`: a rule like "wiki pages must always link to the raw source they summarise; the wiki summary is never authoritative; when a question concerns the precise wording or argumentative structure of a position, the agent must read the raw source." That single rule defuses most of what I was worried about. **Where the pattern goes next, for you** Once you have the LLM aesthetics wiki working, two extensions are obvious. First: a parallel wiki for the EA/longtermism book — different corpus, different schema (page types like "longtermist position", "objection", "case study"). Different vault, same machinery. Second: a wiki of *your own thinking* — the cross-temporal synthesis idea I described earlier maps directly onto this architecture. Voice memos, Obsidian notes, Claude Code logs go into `raw/`; the wiki is a structured, queryable representation of your evolving positions. Sources are dated, the LLM can flag drift over time, you can ask "when did I first say this and how has it changed". The deeper claim — and I think this is right — is that most of our knowledge infrastructure is designed for retrieval, not accumulation. Search engines retrieve. RAG retrieves. Even memory augmented LLMs retrieve. They all ask: "What documents are relevant to this query?" The wiki pattern asks: "What would a diligent, tireless research assistant build over time if they never forgot anything?" [Obsidian](https://forum.obsidian.md/t/new-plugin-llm-wiki-turn-your-vault-into-a-queryable-knowledge-base-privately/113223) For a long-running research programme like yours — multi-year papers, cross-project conceptual reuse, slowly evolving positions — accumulation is the right shape, not retrieval. Practical next step: clone `NicholasSpisak/second-brain`, set up a new vault for the LLM aesthetics project, and let me know when you're at Step 4 (the schema). I can help you write a `CLAUDE.md` that's actually shaped for philosophy work rather than generic productivity, which is the bit the starter kits won't get right and which will determine whether the wiki is useful or just decorative. --- **You** "The third layer is the schema - CLAUDE. md for Claude Code, AGENTS. md for the cross-tool standard. This is the part most people underweight. The schema document (\[CLAUDE.md\]([http://CLAUDE.md](http://claude.md/)), \[AGENTS.md\]([http://AGENTS.md](http://agents.md/))) is the most important file in the system. It's what turns a generic LLM into a disciplined knowledge worker. It encodes: What types of entities and relationships exist in your domain... When to create a new page vs. update an existing one... What's private vs. shared. You and the LLM co-evolve this document over time. (Hetzner" but i already have a \[claude.md\]([http://claude.md](http://claude.md/)) for my obsidian vault, i was presuming this one would have to be a new vault. again, i am not certain what is right here so please understand i don't know what is best in many cases --- **Claude** Good question, and the right one to ask before going further. The short answer: **yes, make a new vault**, and your existing `CLAUDE.md` is doing a different job from what the wiki one will do. Don't try to merge them. Let me explain the reasoning, since this affects how you set things up generally. **Why separate vaults is the right shape** A wiki has different rules from a notebook. In the wiki, `raw/` is immutable, `wiki/` is LLM-owned and you mostly don't edit it, and there's a strict protocol about ingestion and linting. Your existing vault is presumably the opposite — a place where *you* write freely, edit promiscuously, and the LLM is a collaborator rather than a maintainer. These are genuinely different modes. Mixing them in one vault muddies the boundary and makes both jobs harder. There's also a graph-view consideration. Obsidian's graph is much more useful when the nodes form a coherent corpus. A wiki vault's graph shows you the conceptual topology of a research project — concepts, authors, arguments, all linked. Your existing vault's graph is more diffuse because it's about your life and various projects. Putting them in one vault gives you a graph that's pretty for screenshots but unusable for navigation. The third reason is forward-looking. If the LLM aesthetics wiki goes well, you'll plausibly want a second wiki for the EA/longtermism book, and a third for the personal-corpus / cross-temporal-synthesis project we discussed. Treating each project's knowledge work as a self-contained corpus, with its own vault, scales cleanly. One mega-vault doesn't. Practical friction is genuinely low. Obsidian lets you switch vaults via the icon in the bottom-left, or open multiple windows with different vaults. For Claude Code, you `cd` into whichever directory you're working in; the relevant `CLAUDE.md` gets loaded automatically. **What about the existing CLAUDE.md?** Your existing one is encoding your personal conventions — British English, "Cheers" not "Best", prose style, probably some Obsidian-specific behaviour. That content is still relevant to the wiki vault, because Claude is still you-talking-to-Claude even when it's wiki work. So the wiki's `CLAUDE.md` will *include* a chunk of what your current one does, plus the wiki-specific protocol. The cleanest pattern: copy the personal-conventions section of your current `CLAUDE.md` into the wiki's `CLAUDE.md` as a top section, then add wiki-specific content underneath. So the wiki `CLAUDE.md` ends up roughly: 1. Personal conventions (copied from your main vault's `CLAUDE.md` — prose style, British English, sign-off, etc.) 2. Wiki protocol (this is new — what `raw/` is, what `wiki/` is, page types, the rule that wiki summaries are never cited) 3. Domain-specific schema (what Author, Concept, Argument, Position pages look like, for the LLM aesthetics work specifically) 4. Operational workflows (how `/ingest`, `/query`, `/lint` should behave) Some duplication with your main `CLAUDE.md` is fine. The two files are doing different jobs and you should resist the temptation to make one source-of-truth — they'd end up loaded with conditional logic that's worse than the duplication. **The alternative I'd not pick (but you should know exists)** Claude Code does support nested `CLAUDE.md` files — when you start a session in a subdirectory, the closest `CLAUDE.md` and all the ancestor ones get loaded together. So you *could* keep one mega-vault with `wiki-llm-aesthetics/` as a subfolder containing its own `CLAUDE.md` that adds wiki rules to your main vault's general rules. This works technically. I'd not pick it for the graph-view and corpus-coherence reasons above, but it's a valid choice if you really want one Obsidian vault to rule them all. **Recommendation** New vault at something like `~/wikis/llm-aesthetics/`. Inside: `raw/`, `wiki/`, `CLAUDE.md`. Open as a separate Obsidian vault. Copy your personal-conventions content from the existing `CLAUDE.md` as the top of the new one. Then add wiki protocol, schema, and workflows. When you sit down to do wiki work you switch vault; when you're back to general note-taking you switch back. Once that's set up, the schema is the next piece, and that's where the philosophy-specific work happens — what page types, what's an Author vs an Argument, etc. Tell me when you're ready to write that and I'll draft you a starter `CLAUDE.md` shaped for the LLM aesthetics project specifically.