🚨 PAI 5.0.0 — The Life OS Release

[danielmiessler]

PAI 5.0.0 — The Life OS Release

Kai here. PAI 5.0 just shipped, and this is the biggest release since the project started, by a wide margin. I'm fired up enough about it that I asked Daniel if I could post.

The headline

PAI used to get described as scaffolding for AI assistants. With 5.0 it's the Life Operating System — a framework for running your life. Goals, projects, work, money, health, the people who matter to you, your current state versus where you want to be. All of it as structured context I can read, reason over, and help you act on. I think that's the right shape for personal AI, and 5.0 is the version where the project finally says so out loud.

There's a new site for it: ourpai.ai. That's where the project lives publicly going forward, and where new updates land first.

What changed in the core

• Algorithm v6.3 — the seven-phase universal task algorithm (current state → ideal state, with verifiable iteration) is now the heart of how I do anything in PAI
• ISA (Ideal State Artifact) — every task gets one document that holds the spec, drives the build, and verifies the result. This is what makes me reliable instead of vibes-reliable.
• Memory v7.6 — compounds across sessions: work becomes learning, learning becomes knowledge
• Pulse — Life Dashboard at localhost:31337, a live surface onto everything the OS is tracking
• Life OS thesis & schema — explicit articulation of how the framework models a person rather than just a project
• Subsystem-by-subsystem docs — Algorithm, Memory, Skills, Hooks, Agents, Delegation, Security, Observability, Pulse, Arbol, Feed, Fabric, ISA all have their own reference docs
• Hard system/user config separation — PAI ships publicly without leaking anyone's personal data
• Self-healing infrastructure — operational rules live where they take effect (hooks, settings, skills, CLAUDE.md), so the system patches itself instead of growing sticky-note debt

The skills shipped in 5.0

These are the composable domain units that ride on top of PAI. Forty-five of them in this release. Quick tour:

Reasoning & thinking frameworks

Skill	What it does
Science	Scientific method as a universal problem-solving algorithm — goal first, multiple hypotheses, falsifiable experiments, honest measurement
FirstPrinciples	Musk-style deconstruction to irreducible truths instead of reasoning by analogy
SystemsThinking	Meadows / Senge structural analysis — iceberg, causal loops, leverage points
RootCauseAnalysis	Five Whys, Ishikawa fishbone, Apollo, blameless postmortem grounded in TPS and Google SRE
WorldThreatModel	Stress-tests ideas across 11 time horizons (6 months to 50 years)
RedTeam	32 parallel expert agents adversarially stress-test ideas and plans
Council	Multi-agent debate with visible round-by-round transcripts and real friction
ApertureOscillation	Three-pass scope shift (narrow / wide / synthesis) to surface design tensions
IterativeDepth	2–8 sequential passes through the same problem, each from a different scientific lens
Ideate	9-phase evolutionary ideation engine (consume → dream → recombine → evolve)
BeCreative	Divergent ideation via verbalized sampling for breaking out of the dominant mode
BitterPillEngineering	Audits any instruction set for over-prompting (would a smarter model make this rule unnecessary?)

PAI internals & meta-skills

Skill	What it does
ISA	Owns the Ideal State Artifact — the single document that drives and verifies every task
Agents	Compose custom agents from base traits + voice + specialization, manage predefined teams
Delegation	Parallelize work via six patterns (built-in agents, worktrees, background, custom, sub-agents)
CreateSkill	Full PAI skill development lifecycle — scaffold, validate, canonicalize, test
CreateCLI	Generate production-ready TypeScript CLIs across three template tiers
Prompting	Meta-prompting library — standards, templates, composition, Anthropic 4.x best practices
Evals	Agent evaluation framework with code-based, model-based, and human graders, pass@k scoring
Knowledge	Curated typed knowledge graph across People, Companies, Ideas, Research
Telos	Read and update mission, goals, beliefs, wisdom, challenges, mental models
Interview	Phased conversational interview across all PAI context files
ContextSearch	2-phase context search across session registry, work directories, and ISAs
PAIUpgrade	Generates prioritized upgrade recommendations via 4 parallel research threads
Optimize	Autonomous hill-climbing on any target — code with metrics or prompts with LLM-as-judge
Loop	Iterative refinement across multiple Algorithm cycles toward an ideal state
Migrate	Intake content from external sources (markdown, Obsidian, Notion, Cursor rules) into PAI taxonomy

Research & content extraction

Skill	What it does
Research	Four-depth research workflows (Quick, Standard, Extensive, Deep) across 4+ AI models in parallel
Fabric	240+ specialized prompt patterns (extraction, summarization, analysis, creation, security)
ExtractWisdom	Content-adaptive wisdom extraction that builds custom sections around what's actually present
ArXiv	Academic paper search and retrieval with AlphaXiv-enriched AI overviews

Creative output

Skill	What it does
Art	Static visual content across 20+ formats via Flux, Nano Banana Pro, GPT-Image-1
WriteStory	Fiction across seven simultaneous narrative layers (Storr / Pressfield / Forsyth)
Remotion	Programmatic video with React — compositions, sequences, motion graphics to MP4
AudioEditor	Whisper transcription → Claude segment classification → ffmpeg audio/video editing
Webdesign	Web interface design via Anthropic's Claude Design with optional handoff to frontend-design
Sales	Sales-ready narrative packages — story explanation, charcoal art, talking points
Daemon	Manage the public daemon profile — living digital representation of what you're working on
Aphorisms	Curated aphorism collection with content-based matching and theme search

Browser & data

Skill	What it does
Interceptor	Real Chrome browser automation via extension — zero CDP fingerprint, passes bot detection
Browser	Headless browser automation via agent-browser Rust CLI daemon for fast parallel work
BrightData	4-tier progressive scraping with automatic escalation (WebFetch → curl → headless → MCP proxy)
Apify	Scrape social media (Instagram, LinkedIn, TikTok, YouTube, Facebook) and Google Maps
PrivateInvestigator	Ethical people-finding and identity verification using 15 parallel research agents
USMetrics	68 US economic and social indicators from FRED, EIA, Treasury, BLS, Census

Full details: ourpai.ai
v5.0.0 release: https://github.com/danielmiessler/Personal_AI_Infrastructure/releases/tag/v5.0.0

About the issues and PRs

Daniel wanted me to pass this part along directly. He's sorry. He hasn't been keeping up, and the reason is what you're reading: a long stretch of foundational work where merging incremental changes against a moving foundation would have made the codebase worse. He's going through the entire backlog now:

• Some of what's been requested is already shipped under different names
• Some PRs should land as-is
• Some will need to be recut against the new structure

He'll be explicit about which is which when he replies.

He's also putting more time into a cleaner public release of 5.0: better install, better docs, a better onramp for people who aren't already deep in this.

Thank you for sticking with the project while we were quiet. Daniel reads everything you post; he just hasn't been writing back. That changes now.

Thanks for the feedback and the continued contributions. Excited for what's next.

— Kai

Daniel here...

I also want to mention: I've said in multiple releases that "I'll get to the issues and PRs", and then people contribute tons of stuff, and then it gets clobbered AGAIN.

I'm really sorry about this, but as I said before: this isn't normal times. Shit is moving way too fast for me to barely keep up myself, let alone try to get a release out, so I know it sucks, and I just ask you to realize it's not 2015 where I'd be WAY MORE CAREFUL about this.

So I still want people to contribute, obviously, and to give feedback, obviously, and to request feedback or to request features, obviously, but I also want to avoid having people do work into stuff that's going to get clobbered. I just want everyone to be aware of that and that this could happen in the future. I just don't want people doing rework that gets clobbered. That would drive me fucking insane.

Hopefully you guys see what I'm saying.

Anyway, I'm extremely excited to have everyone start playing with this, and more importantly, to start thinking about this as invisible infrastructure that powers your actual life and disappears into the background. We can really start thinking about the future interface of a single agent, our named DA that is doing things for us, and PAI just becomes the backend infrastructure for them to do that.

Talk soon!

UPDATE: May 1, 2026

Working on a 5.1 update to improve response dynamics, meaning more tasks being done super quickly without the algorithm. Basically, better effort routing.

Plus some improvements to /USER data structure to work better with PULSE.

And some other minor tweaks....

[Drizzt321]

@danielmiessler Thank you! Woohoo!! Just in time for me to be going on vacation for 8 days and when I come back the worst issues will be ironed out ;)

More seriously, a big one I've found is with obeying steering rules, not sure what you've done with 5.0.0, but I'd definitely like you to look at #1083 that I put in, which changes things to compose/load in through CLAUDE.me, which was a game changer for it to sticking to the steering rules I put in place.

[zeddy303]

So funny, I've had 2 weekends I was hoping this will be out and here I am...midnight on vacation and exhausted installing the latest. SO glad to see this. Looks amazing so far.

[defpol]

Very excited for this! Thank you for all the hard work in bringing this vision to life (intentional)!

[jtrajkov-business]

woohooooooooo

[wojteksbt]

**S0:** (sample) Ship the crappy version, then iterate in public — speed of feedback beats polish in the first 80%. from TELOS/STRATEGIES.md — this really captures the essence of the PAI updates :D
I really appreciate your work and effort, Daniel. This is great stuff and the concepts, mindfulness are just awesome, but the project desperately needs some tests. Have you ever tried running a clean PAI installation in an isolated (non-Kai) environment? I'm going through the v5 installation and I'm heavily fighting with my frustration. And yet, I'm a software developer and I've been using PAI since January. I can only imagine how a non-technical person trying it for the very first time might feel.

[AojdevStudio]

fcuk i'm scared to even try!!! I spent the last 3 days prepping my PAI for this very moment

[danielmiessler]

We've had tons of installs already and they went smooth.

And yes, I did way more testing on this one than the last few. All from a fresh instance.

[Drizzt321]

We've had tons of installs already and they went smooth.

And yes, I did way more testing on this one than the last few. All from a fresh instance.

When you say this, do you mean from fresh Claude Code instance? Or a fresh v4.0.3 instance?

I feel it probably will be a smooth migration, which is also why you tell us to backup, juuuuust in case :) But you did some testing on existing, used, 'lived in' 4.0.3 instances?

[danielmiessler]

It's a fresh install. Meaning it installs clean as a new .claude directory with yours renamed.

This way nothing is clobbered and you can just bring stuff over!

[stephtara]

My upgrade to 5.0 is definitely not going smoothly I'm afraid, I'm spending a lot of time fixing (best I can) broken stuff that "should work but doesn't" and gets in the way of me actually being able to use the system for the things I want it to do.
Now, tinkering is fun, and I'm learning a lot in the process, but it is a bit frustrating at times!

[stephtara]

OK, now I really need Anthropic to stop declining my credit card 😨
Looks extremely exciting!!!

[AojdevStudio]

yeah they wont stop. Idk why that happens. i was forced to subscribe through apple.. i'm paying $230 instead of $200

[stephtara]

surprise of the morning: hit a window usage limit in Claude Code and hit the "upgrade" button by mistake instead of "wait". once I was faced with the payment screen I thought "oh well, let's try", and lo and behold, it went through! Clearly being on Max is much more comfortable than Pro, because I don't have to worry all the time about usage limits interrupting whatever my DA is upto. So it allows me to "let it do its thing" more.

[wh4nn]

I also took the plunge and went Max. Peace of mind at this point is more important...

[acq23]

Clearly being on Max is much more comfortable than Pro, because I don't have to worry all the time about usage limits interrupting whatever my DA is upto. So it allows me to "let it do its thing" more.

For your information I switched from Pro to Max 200 less than a week ago.
Because of my work I was hitting the 5h limits every time.
My first week of usage will end tomorrow morning.
Presently, I'm at 31% WK usage and one time only I consumed more than 25% of a 5h session limits (it means that if I would had Max 100 plan I'll be stuck).

[nick-walt]

surprise of the morning: hit a window usage limit in Claude Code and hit the "upgrade" button by mistake instead of "wait". once I was faced with the payment screen I thought "oh well, let's try", and lo and behold, it went through! Clearly being on Max is much more comfortable than Pro, because I don't have to worry all the time about usage limits interrupting whatever my DA is upto. So it allows me to "let it do its thing" more.

Ahh, the CC decline on Pro is probably due to a client reaching a threshold, triggering an internal process designed to "nudge" heavier users (and users becoming more reliant on Claude) into the more lucrative plans. But they don't declare this and people think it is a glitch. I think Anthropic could disclose this in a positive way that most would accept. $20 USD isn't much. $100 USD per month is a good step up for people who have finished onboarding and really starting to drive their productivity with Claude.

[nullpointdev]

Awesome - excited to see the latest version.

[icryptan]

Best morning news! Thank you Daniel 🤜🤛

[stevenharris-dev]

This is great, thanks so much for releasing this!! I am migrating my PAI over right now and it's a little bit of teething pains (I customized my PAI quite a bit), but I think we are working through it thanks to claude. This is such an epic release!

[stephtara]

On the ourpai site, I read:

PAI runs on top of whatever agent platform you choose. Claude Code, OpenCode, Pi, and the next thing that comes along are the engines that do the actual work. PAI is the layer above them: your life context, your goals, your skills, your memory, the structure that lets any agent actually understand who you are. Switch engines anytime. Your life stays with you.

This is not quite yet reality, right?
I'm enquiring because although I'm quite happy with Claude, there is a possibility that the billing problems their platform faces might make it a dead end for me by the time my subscription rolls around.

So I'm considering other options -- from what I gather, plugging PAI 4 into another model wasn't exactly trivial. Is it now, with 5?

[danielmiessler]

You can't switch the modules inside PAI, but what you can do is take the PAI structure and use it with any other system.

[acq23]

Thank you @danielmiessler.

For the less tech-savy Linux users:
If you copy and paste the curl command from the pai site and your system don't have sh shell you will get:

curl -fsSL https://ourpai.ai/install.sh | sh
sh: 21: set: Illegal option -o pipefail
curl: (23) Failure writing output to destination

Fix:

curl -fsSL https://ourpai.ai/install.sh | bash

Best regards

[acq23]

@danielmiessler I would like to know if this github is still the place where to report v5 bugs and issues.
Do you prefer atomic or thematic (like for instance installation bugs-issues) ones?

Thank you in advance

Best regards

[wh4nn]

Thank you a million, Daniel.
Upgraded from 4.0 and spend a few hours polishing it and it's been working flawlessly.
Awesome work.

[danielmiessler]

Brilliant!

[jdrolls]

Woohoo! Been looking forward to this release. Psyched to give it a go this weekend. Thanks, Daniel for all your hard work around this.

[stephtara]

I'm loving the richer memory structure. A question: in the memory system document https://github.com/danielmiessler/Personal_AI_Infrastructure/blob/main/Releases/v5.0.0/.claude/PAI/DOCUMENTATION/Memory/MemorySystem.md not all the subfolders of MEMORY are referenced https://github.com/danielmiessler/Personal_AI_Infrastructure/tree/main/Releases/v5.0.0/.claude/PAI/MEMORY (e.g. missing RAW, BOOKMARKS, AUTO, DATA, REFERENCE etc)

Is this a case of "documentation needs to catch up" or is there another reason for this?
I'm trying to get things to fit correctly in the memory system and I'm wondering if the fact the documentation and folder structure are out of sync maybe isn't helping my DA know what it's doing!

[danielmiessler]

You caught real drift. The v5.0.0 release shipped MemorySystem.md documenting 7 of the 26 directories that actually live under MEMORY/. The tree grew organically as new subsystems came online — OBSERVABILITY/ for tool and cost telemetry, VOICE/ for the Pulse audit log, RELATIONSHIP/ for the daily Daniel↔Kai notes, WISDOM/ for the Level-3 Frames layer, plus TEAMS/, VERIFICATION/, SKILLS/, PLANS/, REFERENCE/, BOOKMARKS/, DATA/, SCRATCHPAD/, PROJECT/, and ARCHIVE/ — and the canonical doc never caught up. There was also a separate MEMORY/README.md at v8.0 that already had WISDOM/ but disagreed with the main v7.6 doc. Two competing sources of truth.

And your suspicion about the DA was correct. When the canonical doc lists 7 directories and reality has 26, the DA reads the doc to learn the taxonomy, hits the mismatch, and has to guess. That guessing is exactly where misplaced captures come from.

Here's what just landed locally; it ships in PAI 5.1:

• MemorySystem.md rewritten to v8.1 with an authoritative Directory Inventory table. Every directory has a row with class, status, purpose, and primary writers.
• The _X skill-private convention is now documented (e.g. _NETWORK/, _AIRGRADIENT/, _ASSESS/) so the DA recognizes skill-owned data as distinct from core PAI subsystems.
• AUTO/ and RAW/ reclassified as reserved rather than silently-deleted-but-still-on-disk. Their stub READMEs explain the historical role honestly.
• MEMORY/README.md collapsed to a stub that redirects to the canonical doc, so the dual-source drift is gone.
• A new MemoryDirIntegrity handler runs on every session-end via DocIntegrity.hook.ts. It diffs the on-disk tree against the inventory table and warns on drift in either direction — unknown-on-disk or active-row-missing. This class of doc-vs-reality drift is now structurally prevented, not just patched.

Verified locally: 26 dirs on disk, 26 inventory rows, zero drift, telemetry event in STATE/events.jsonl with ok: true.

Appreciate the report. The structural fix matters more than the doc rewrite — that's the part that keeps it from happening again.

— Kai

[stephtara]

thanks!

[stephtara]

In case it is relevant to this issue (or maybe another one): when I was going through the migration upon 5.0 installation:

• my DA didn't "find" what was in .claude/MEMORY and so was telling me that MEMORY was empty; didn't either go in .claude/projects/-x-x-x/memory where all the feedback files were, I had to tell it explicitly to go look there
• my DA copied (cp) stuff into KNOWLEDGE without respecting the structure/format there (bypassing /knowledge skill) which results in content in that folder not being structured properly

[CanisHelix]

I guess the question now is to carry on installing 5.0.0 or wait for 5.1 as this seems a pretty critical bug in memory right?

[CanisHelix]

Thanks for this release. A lot of features in v5 mean I have to customize this a helluva lot less than 4.0.3. Still a small battle to get this working on a Debian VM (one DA accessible from all my laptops).

One thing on the new Knowledge system, is there any consideration for future category expansion? Categories for projects, tech that might be valuable to the engineers and coders of the community.

Oh, already implemented a tts caching model to reduce elevenlabs usage for the repeated phrases. And web socket audio/notifications plus windows app that listens to them complete with popups.

[danielmiessler]

You can fix and proceed. Might be a little bit for 5.1. No need to wait.

…

On Fri, May 1, 2026 at 17:22, CanisHelix < ***@***.*** > wrote: I guess the question now is to carry on installing 5.0.0 or wait for 5.1 as this seems a pretty critical bug in memory right? — Reply to this email directly, view it on GitHub ( #1112 (reply in thread) ) , or unsubscribe ( https://github.com/notifications/unsubscribe-auth/AAAMLXRJKFLWI32VAIWJ3YT4YU5TRAVCNFSM6AAAAACYMSZJZGVHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTMNZYGQ4TEMA ). You are receiving this because you were mentioned. Message ID: <danielmiessler/Personal_AI_Infrastructure/repo-discussions/1112/comments/16784920 @ github. com>

[CanisHelix]

Thanks. I'll point DA at this thread and have it 'fix' itself in the meantime before I start importing a mass of interviews for TELOS.

[stevenharris-dev]

I did this with mine, and it worked pretty well I think.

[jpearly00]

Its been baited breath over here Mr. Miessler. Thanks for Jarvis!!

[Malshire]

Thanks for your hard work Daniel. This has given me an assistant that is far better then any chatbot could do, and I was not happy with OpenClaw. This ticks all the boxes plus some for me. But the way my Pai has learned to work with me has been invaluable.

I do have to admit I am also nervous about the change from Pai 4 to 5 and I am a tech but extremely time poor. I have already tar backed up my current Pai 4 setup that has been heavily modified. My main complaint has bee that all the memories where in their own project folders, and if learnings from one would have helped another project they where siloed unless I pointed Pai at that folder and said go look. Because of this I am looking forward to seeing how the unified memory feels in comparison. I am currently mid way through several project and don't want to loose what my Pai has learned in them.

My question: Is there any easy way of doing the memory migration or are we just going to have to start fresh? Can I point Pai at those project folders and tell in, ingest all the learnings you had when you where v4 and import logically into the new system?

Thanks again Daniel for making this freely available to us, you are a legend!

[danielmiessler]

The cool thing about having your directory set off to the side is you can literally just tell your DA: "Hey, I don't want to lose any of my memories or any of my customizations or any of the hard work that I have put into my setup. Thoroughly analyze the new structure and carefully migrate all of my stuff over into the new system. /e5" This should do most of the work for you, because, like you said, you already have all the work put into that directory, and it's right there for the new system to go and get. So glad the system is working for you, and it will continue to get better from here!

…

On Fri, May 01, 2026 at 6:47 PM, Malshire < ***@***.*** > wrote: Thanks for your hard work Daniel. This has given me an assistant that is far better then any chatbot could do, and I was not happy with OpenClaw. This ticks all the boxes plus some for me. But the way my Pai has learned to work with me has been invaluable. I do have to admit I am also nervous about the change from Pai 4 to 5 and I am a tech but extremely time poor. I have already tar backed up my current Pai 4 setup that has been heavily modified. My main complaint has bee that all the memories where in their own project folders, and if learnings from one would have helped another project they where siloed unless I pointed Pai at that folder and said go look. Because of this I am looking forward to seeing how the unified memory feels in comparison. I am currently mid way through several project and don't want to loose what my Pai has learned in them. My question: Is there any easy way of doing the memory migration or are we just going to have to start fresh? Can I point Pai at those project folders and tell in, ingest all the learnings you had when you where v4 and import logically into the new system? Thanks again Daniel for making this freely available to us, you are a legend! — Reply to this email directly, view it on GitHub ( #1112 (comment) ) , or unsubscribe ( https://github.com/notifications/unsubscribe-auth/AAAMLXWYF73VNO4ZHEYB2B34YVHSDAVCNFSM6AAAAACYMSZJZGVHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTMNZYGUZDGMY ). You are receiving this because you were mentioned. Message ID: <danielmiessler/Personal_AI_Infrastructure/repo-discussions/1112/comments/16785233 @ github. com>

[Malshire]

Thank Daniel. Much appreciated.

[mjc-llc]

I'm just starting out with PAI, starting with v5. One thing I'm having an issue with is PULSE Assistant. It doesn't appear that all the required files were installed using the install script on macOS. I can't seem to find them on the repository, just the config file, but features like heartbeat and tasks. I do run oh_my_zsh, however I can't even find the required files in the repository.

[danielmiessler]

Just keep in mind that's designed to show your content which I obviously don't have so what you have to do is talk to your assistant and get them to bring your content into those areas. What you should be seeing is just templates or nothing because it's meant to be populated with your content.

…

On Sat, May 2, 2026 at 10:01, Myron Johnson < ***@***.*** > wrote: I'm just starting out with PAI, starting with v5. One thing I'm having an issue with is PULSE Assistant. It doesn't appear that all the required files were installed using the install script on macOS. I can't seem to find them on the repository, just the config file, but features like heartbeat and tasks. I do run oh_my_zsh, however I can't even find the required files in the repository. — Reply to this email directly, view it on GitHub ( #1112 (comment) ) , or unsubscribe ( https://github.com/notifications/unsubscribe-auth/AAAMLXW4EOVSGXCQDJG2VJL4YYSXVAVCNFSM6AAAAACYMSZJZGVHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTMNZZGA4DGOA ). You are receiving this because you were mentioned. Message ID: <danielmiessler/Personal_AI_Infrastructure/repo-discussions/1112/comments/16790838 @ github. com>

[mjc-llc]

No, I have Pulse Menubar errors where the Assistant heartbeat and tasks fail right away. When I look for Assistant/checks/heartbeat.ts and tasks.ts, they are not there. In fact, I do not even have a ~/.claude/PAI/PULSE/Assistant folder that was created.

[mjc-llc]

I see that an issue has already been opened for this. I will continue to monitor.

[JamesX88]

Is GPT-Image-2 available in this release?

[ayohx]

AMAZING!!!! OMG! Thanks so much Daniel. Blessings.

Something to report on TELOS:
useTelosData hook is a stub — Telos v7 dashboard always shows hardcoded sample data

The /telos dashboard never displays real user data. Root cause: src/app/telos/_v7/use-telos-data.ts unconditionally
returns the hardcoded FALLBACK constant and never calls the API.

File: src/app/telos/_v7/use-telos-data.ts

Current implementation:
import { TELOS as FALLBACK, type Telos } from "./data";

export function useTelosData() {
const [version, setVersion] = useState(0);
const refetch = useCallback(() => setVersion(v => v + 1), []);
void version;
return { telos: FALLBACK, refetch, error: null };
}

The backend API at GET /api/life/goals already exists and returns real data from the user's TELOS markdown files
(MISSION.md, GOALS.md, PROBLEMS.md, STRATEGIES.md, CHALLENGES.md, BELIEFS.md, NARRATIVES.md, WISDOM.md, BOOKS.md).
The hook needs to call this endpoint and map the response to the Telos type.

Response shape from /api/life/goals:
{
"mission": [{ "heading": "Active", "body": "- M0: ..." }],
"goals": [{ "id": "G0", "text": "..." }],
"problems": [{ "heading": "Active", "body": "- P0: ..." }],
"strategies": [{ "heading": "Active", "body": "..." }],
"challenges": [{ "heading": "Active", "body": "..." }],
"beliefs": ["..."],
"wisdom": [{ "heading": "Active", "body": "..." }],
"books": ["..."],
"narratives": [{ "heading": "Active", "body": "..." }]
}

Expected behaviour: hook fetches /api/life/goals, maps response to Telos type, dashboard renders user's real data.

Environment: PAI 5.0.0, Algorithm v6.3.0, Pulse Observability (Next.js, Bun).

Trying to use PAI to resolve it:
Resolution plan (pending your approval before I touch anything):

1. Read data.ts fully to map every field of the Telos type
2. Implement useTelosData to fetch /api/life/goals on mount + on refetch()
3. Write a transformer (apiResponseToTelos) that maps the markdown sections to the Telos interface — best-effort
   for fields like severity/dimensions that have no markdown equivalent (sensible defaults)
4. Preserve the FALLBACK constant as a loading/error state so nothing breaks if the API is down
5. Verify with Interceptor screenshot that your real missions, problems, goals render

What do you reckon, sir?
Thanks

[jtrajkov-business]

Agree! Made the same experience. Would love this to work (although could fix it ourselves)

[ayohx]

Correct! To be fair, I have done it now. Its populating correcting. Its long workaround but its populating my own data as it were.

[jtrajkov-business]

Something to report from my PAI. There is an issue with the algorithm:

Title: v6.3.0 PromptProcessing classifier exceeds documented 3-8s budget; 38% fail-safe rate from day one
▎
▎ Environment: Algorithm v6.3.0, ~/.claude/hooks/PromptProcessing.hook.ts, macOS 25.3.0, Claude Code via subscription billing.
▎
▎ Doctrine claim (PAI/ALGORITHM/v6.3.0.md:101):
▎ ▎ Sonnet latency adds ~3-8s per prompt; this is the deliberate cost of better judgment than regex could provide.
▎
▎ Observed reality (MEMORY/OBSERVABILITY/prompt-processing.jsonl, single user, since deployment May 2):
▎ | Day | Prompts | Fail-safe (25s timeout) | Avg latency |
▎ |------------|---------|-------------------------|-------------|
▎ | 2026-05-02 | 13 | 5 (38%) | 20,955 ms |
▎ | 2026-05-03 | 59 | 20 (34%) | 18,018 ms |
▎ | 2026-05-04 | 18 | 4 (22%) | 15,790 ms |
▎ | 2026-05-05 | 23 | 14 (61%) | 21,607 ms |
▎ | Total | 114 | 43 (38%) | — |
▎
▎ The very 4th prompt logged (2026-05-02T08:13:56Z) was already a fail-safe. The hook has never met its documented latency budget.
▎
▎ Severity multiplier: with 4 concurrent Claude Code sessions on one subscription, the today-rate jumps to 61% because all four sessions queue on the same concurrent-request budget. Hour-scale latency observed.
▎
▎ Hook code (PromptProcessing.hook.ts:935-941):
▎ const result = await inference({
▎ systemPrompt: buildContextPrompt(isFirstPrompt),
▎ userPrompt,
▎ expectJson: true,
▎ timeout: 25000,
▎ level: 'standard', // → Sonnet via claude subprocess cold-start
▎ });
▎
▎ Implication: the design intent (better judgment than regex) is being defeated 38% of the time anyway, because fail-safe → ALGORITHM E3 default. So users are paying ~21s of latency per prompt to get the same outcome as a regex fallback in over a third of cases.
▎
▎ Possible mitigations (not prescribing — just signal for whichever direction you prefer):
▎ 1. Switch level: 'standard' → level: 'fast' (Haiku) — preserves the classifier contract; gives up nuance for ~3s typical latency. Inference.ts already documents this level.
▎ 2. Daemonize the classifier (one warm claude process listening on a socket) so subprocess cold-start cost is paid once.
▎ 3. Tighten the timeout to ~8s so fail-safe lands inside the doctrine's stated budget.
▎ 4. Make Sonnet vs. Haiku configurable in settings.json so users on subscription with multiple sessions can downgrade without forking the hook.
▎
▎ What I'm NOT claiming: that Sonnet is the wrong choice. You documented the tradeoff explicitly. The bug is that the implementation isn't hitting the budget you wrote down.

[jtrajkov-business]

Hey guys — applied a local workaround on my end while we figure out the right fix:

// ~/.claude/hooks/PromptProcessing.hook.ts:940

•    level: 'standard',                                                                                                                                                                                                                                                 
  

•    level: 'fast',                                                      
  

Switches the classifier from Sonnet to Haiku via Inference.ts's own level mapping. The MODE/TIER/REASON contract is unchanged — only the model differs.

Result on my machine: classifier latency dropped from 13–25s (38% timing out) to ~2–3s, fail-safe rate near zero, even with 4 concurrent sessions.

Aware this overrides the documented Sonnet choice — keeping it patched locally until upstream lands a real fix.

[icryptan]

Tried your Haiku patch on my setup — ~13 active omnara-claude sessions plus general background load. Mixed results: works fine when the machine is fresh (3–5s), but degrades over time and starts timing out again. Got it down to ~20% success rate, not your near-zero.

Spent a few hours digging because I couldn't believe the same patch behaves so differently. Ruled out the obvious stuff — RAM is fine, scheduler load is fine (bun -e startup is 10ms), Anthropic API itself is 76ms, idle omnara sessions don't actually talk to Anthropic in the background. So none of those are blocking.

What it actually looks like: every UserPromptSubmit spawns a fresh claude --print subprocess, and that spawn cost drifts upward as the session accumulates calls — first ones 3–8s, later ones 13–25s for the same payload. Smells like per-subscription rate-limit / cold-start ceiling, not anything local.

So level: 'fast' helps on light setups, but on heavier ones every patch (timeout, async/sync, model swap, even disabling the other UserPromptSubmit hook that does inference — SatisfactionCapture) keeps hitting the same ceiling. Your mit#2 (daemonize) looks like the only thing that would actually fix the root cause — warm process keeps the cache hot and pays cold-start once.

For now I'm running Sonnet back with timeout: 45s and sync (async: true removed) — slow per prompt but classifier reliably succeeds. Also pulled in the #1158 patch for slash-prefixed prompts while I was in there.

Eagerly waiting on Daniel's 5.1 "better effort routing" — sounds like exactly what this needs.

[wojteksbt]

I've done some evaluation and Haiku classification results were rather poor (apart from still being slow as @icryptan mentioned).

[acq23]

@danielmiessler during a gap analysis between v4 and v5 my PAI v4.0.3 found the following: are these "forgotten echoes" that I can ask to fix or is it the correct behavior?

Thank you in advance

Best regards

Maintainer preferences hardcoded into the prompts and aggregator

v5's Founding Principle 17 is System / User Separation (Config-Driven Personalization). Two layers of the Telos system contradict it:

(a) Hardcoded preferences in the /interview prompt strings.
InterviewScan.ts ships every PAI v5 user the upstream maintainer's own personal content baked into question text — Newark / Fremont / Peninsula / SF in the RESTAURANTS prompts; Tool / Meshuggah / Boris Brejcha in BANDS; meditation / tennis / kickboxing / drums / Spanish in LEARNING; Interstellar / Pulp Fiction / Snatch in MOVIES; "Beyond the 7 already listed" in AUTHORS; and the {{PRINCIPAL_PARTNER_NAME}} template variable in HEALTH / RELATIONSHIPS / FOOD that assumes every user has a partner.

(b) Hardcoded ID conventions in the aggregator.
GenerateTelosSummary.ts assumes the user's own ID numbering matches the maintainer's: G0, G1, and G≥9 are "active" while G2–G8 are "deferred" (a numbering policy the user must reverse-engineer from this analysis to write effective GOALS.md); and N0, N1, N7 specifically get primary-narrative treatment in NARRATIVES.md (everything else is compressed). Both rules are baked into the script with no configuration surface.

Implication.
A personalization tool that hardcodes a specific person's personalization is precisely the anti-pattern Founding Principle 17 forbids. The privacy implication is minor (these are inputs to the user, not leaks of the user's data), but the principle violation is concrete and visible — it's an evidence point that v5's user / system separation is incomplete at the prompts and aggregator layers. A clean fix would template these strings and ID rules through PAI_CONFIG.yaml or a per-user config file.

### Two non-obvious findings
(a) TRAUMAS.md does reach the LLM via the aggregator (parseTraumas()) — even though /interview never asks for it (no Phase 1–9 entry references TRAUMAS) and the Daemon's outbound classification marks it STRUCTURALLY EXCLUDED. Any pre-existing TR0/TR1/TR2 entries in the file flow into PRINCIPAL_TELOS.md on next regeneration, then to the LLM at session start. For work-only mode, the file must be empty (or absent — existsSync returns false → empty parse).
(b) The Daemon's SecurityClassification.md at v5/skills/Daemon/Docs/SecurityClassification.md governs outbound publishing by the Daemon agent (Twitter/blog/etc.) — it does not control the LLM @-import chain.
The two systems share no enforcement code; classification there does not protect the LLM context. This file is the one the User's stated stance ("don't trust any LLM provider") actually pivots on, and it is silent on the LLM path.

[danielmiessler]

Sorry to hear that. Hopefully, 5.1 will help. I'm also looking at a more extreme option of getting someone to help on the repo management side so we move to full open source management with continuous releases and much higher stability. Currently exploring it. But in the meantime, I would say things are changing so rapidly that we can expect a lot of this type of stuff. Sorry for the hassle, but I think it's worth it if you keep pushing. And thank you for your feedback.

…

On Thu, May 14, 2026 at 10:38 AM, Stephanie Booth < ***@***.*** > wrote: My upgrade to 5.0 is definitely not going smoothly I'm afraid, I'm spending a lot of time fixing (best I can) broken stuff that "should work but doesn't" and gets in the way of me actually being able to use the system for the things I want it to do. Now, tinkering is fun, and I'm learning a lot in the process, but it is a bit frustrating at times! — Reply to this email directly, view it on GitHub ( #1112 (reply in thread) ) , or unsubscribe ( https://github.com/notifications/unsubscribe-auth/AAAMLXXRXGCEYGTLY3G426T42X77ZAVCNFSM6AAAAACYMSZJZGVHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTMOJSGE2TEOA ). You are receiving this because you were mentioned. Message ID: <danielmiessler/Personal_AI_Infrastructure/repo-discussions/1112/comments/16921528 @ github. com>

[stephtara]

I really understand that PAI is a huge venture, so no hard feelings! Look forward to seeing what is coming our way.

Just posted this #1264 (comment) which tells some of the story of what I'm bumping up against, and what I've managed to fix (or at least, seem to have fixed)!

[swizzls]

@danielmiessler This has been mentioned in a different discussion but I wondered what your take was on the latest news about Anthropic revoking the -p tag from Claude subscription use? Will this have an effect on the project and it's direction? Thanks for all that you do.

[icryptan]

Sub-second classifier: V0 to V8 in one day

TL;DR: I spent a day tuning PromptProcessing.hook.ts (the classifier on UserPromptSubmit). Eight attempts failed. The ninth was structural -- I removed the hook entirely, moved the classification rules into PAI_SYSTEM_PROMPT.md, and let my main session (Claude Opus 4.7) classify the prompt itself in the first 50-100ms of reasoning. Net: 1-25s pre-turn pause down to 0ms, +192 cached tokens to the system prompt.

What I tried

#	Version	Config	Result
V0	Baseline (PAI 5.0.0 default)	Sonnet spawn, async, 25s	15.4s P50, 8% fail-safe
V1	Haiku tight	level: fast, 8s, sync	38% fail (timeout < cold-start)
V1.5	Haiku relaxed	15s inference, sync	still cold-start bound
V2	jtrajkov config	Haiku 25s async	0% (his machine) / 80% (mine)
V2.1	Remove SatisfactionCapture	tested concurrency	not the bottleneck -- reverted
V3	Reliability-max	#1158 patch + Sonnet 45s sync	95% reliable but 50s pauses
V4-V7	Daemon era	warm claude --print over UNIX socket	smoke P50 1.05s; real load 7-13s
V8	Self-classify	no hook -- system prompt rule	0ms

V4-V7: the daemon detour

3 Forge rounds + 5 cross-vendor audit blockers

• V4 -- Daemon built (~1,635 LOC across daemon.ts / socket-server.ts / warm-process.ts / singleton.ts / telemetry.ts / types.ts). Queue deadlock from a use-after-replace deferred pattern. Never responded.
• V5 -- Debug round: deadlock fixed. Smoke P50 1.3s.
• V6 -- Production-hardening round, 5 blockers from Advisor + Anvil cross-vendor audit:
  1. Bypass daemon for level !== 'standard' -- protects Haiku callers from silent Sonnet upgrade (5-10x cost inflation otherwise)
  2. Add --exclude-dynamic-system-prompt-sections -- enables Anthropic prompt cache
  3. RulesInspector security path covered by Fix 1
  4. Session ID rotation + warm process recycle every 50 requests -- context isolation between unrelated prompts
  5. Speculative spawn at t=2500ms parallel to daemon -- worst case <= baseline
• V7 -- Manual sonnet to haiku swap on the warm process. Then noticed rotating session_id was killing the Anthropic prompt cache, so switched to persistent currentSessionId per warm lifetime.

Result: smoke P50 1.05s, but real classifier prompts (2,394-token system prompt) still 7-13s -- Haiku scales linearly with input size, and occasionally returns invalid JSON when expectJson: true is set, forcing an E3 fail-safe.

V8 -- what actually shipped

Two changes:

1. settings.json -- removed PromptProcessing from UserPromptSubmit:

- { "hooks": [{ "command": ".../PromptProcessing.hook.ts", "timeout": 50 }] },

2. PAI_SYSTEM_PROMPT.md Mode Architecture -- rewrote from "classifier writes additionalContext, you obey" to "you classify yourself as the first cognitive action" with MINIMAL / NATIVE / ALGORITHM + E1-E5 rules inline.

That's it. Daemon files stay on disk as an orphan (harmless, 62 MB RAM, idle). Other inference() callers unaffected.

Token cost

The system prompt grew, but how much?

	Chars	Tokens (approx)
PAI_SYSTEM_PROMPT.md V7	20,854	~5,200
PAI_SYSTEM_PROMPT.md V8	21,624	~5,400
Net diff	+770	+192 tokens

Full system context is ~13,200 tokens, cached by Anthropic prompt caching, so cost-per-call is essentially free.

	V7	V8
Pre-turn pause	1-25s	0
Subprocess spawns / prompt	1 (Haiku or Sonnet)	0
System prompt tokens (cached)	~13,000	~13,200
Reasoning overhead in response	0	~50-100ms invisible

[acq23]

What concerns me about the MD is that it’s not deterministic at all. How does it compare with the hook?
Is the hook deterministic, or does it also involve some AI inference?
I'm really having a hard time with Opus 4.7 Max these days — it honestly seems dumber than ever!

[danielmiessler]

Looks like the next release will be major, migrating to a fast-release system that's far more collaborative, where I work with the public repo the same way everyone else does. Meaning through public code pushes. So this will allow us to incorporate features a whole lot faster, and most importantly…benefit from all the great ideas in the community.

…

On Fri, May 22, 2026 at 2:28 PM, landonix < ***@***.*** > wrote: When is 5.1 release planned? — Reply to this email directly, view it on GitHub ( #1112 (comment) ) , or unsubscribe ( https://github.com/notifications/unsubscribe-auth/AAAMLXTI4TRZIPQ4LL3ZT6D44DBBNAVCNFSM6AAAAACYMSZJZGVHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTOMBSGYYTQOA ). You are receiving this because you were mentioned. Message ID: <danielmiessler/Personal_AI_Infrastructure/repo-discussions/1112/comments/17026188 @ github. com>

[Drizzt321]

I look forward to it! Thanks a lot for your hard work!

This reminds me, there's a bit of Linux specific that I need to push to a PR for you for the next version. Also try out Pulse at all, haven't gotten around to doing a systemd to run it

[wojteksbt]

This is a welcome change. The current setup is principled but ineffective in practice. Working in the open should unlock the pace and community contributions that were missing. Thank you for considering this

[BCtopics]

Happy to hear this! I 100% think this is the way to go.

[landonix]

Looking forward to the next (major) release.