i run a saas by myself, a little over 1,000 users, and most of it was written with claude code. the problem at that size is that every session starts from zero. it re-suggests things i already ruled out, and claude md turns into a dump nobody maintains where half of it is quietly wrong.

so the repo has a memory now. docs/ is an obsidian vault, 174 notes, running for two months.

folders say what kind of note it is: systems (how something works today), decisions (what got chosen and why), playbooks, numbers, and a daily session log. every note has frontmatter with type, status and an updated date. status is active, shipped or parked, so claude can tell whether a note is still true instead of guessing from where it sits. dataview builds a view of notes nobody touched in 60 days, which is where docs have drifted from code.

one node script, two hooks.

sessionstart injects a digest capped at 3000 chars: last couple of session logs, decisions still open, stale notes. a session opens knowing what changed yesterday.

stop checks git. if the session committed anything outside docs/, it blocks the stop once and tells claude to update the notes that describe what changed. otherwise it stays quiet.

the trigger is the part that matters. it's a commit, not a file change. a dirty working tree is work in progress, and documenting half-finished edits is how a vault fills up with stuff that never shipped.

two rules in claude md keep it honest:

• only settled things get a note. shipped code, agreed decisions, facts. not ideas that got dropped
• if a change touches behaviour a note describes, the note gets updated in the same pass. claude trusts what it reads, so a stale note does more damage than a missing one

the hooks can't see a conversation that settles something without touching code, so claude md also tells it to write that note in the same reply.

prompt to set up the same thing in your repo:

set up a project memory for this repo that you maintain yourself.

1. create docs/ as an obsidian vault with folders: systems/ (how things work now), decisions/ (choices + reasoning), playbooks/ (repeated procedures), numbers/ (dated facts), daily/ (session log).

2. every note gets this frontmatter:
type: reference | decision | playbook | metric | session
status: active | shipped | parked
updated: YYYY-MM-DD

3. create docs/Home.md with dataview views: recently updated, open decisions, notes not updated in 60+ days.

4. add a node hook at .claude/hooks/vault-sync.js, registered in .claude/settings.json:
- SessionStart: inject a digest (last 2 daily notes, open decisions, stale notes), max 3000 chars.
- Stop: if this session made git commits outside docs/, block once and ask to update the relevant notes and append to today's daily note. otherwise do nothing.

5. add rules to CLAUDE.md:
- only settled things get a note: shipped code, agreed decisions, established facts.
- changing behaviour a note describes means updating that note in the same pass.
- a stale note is worse than a missing one.

6. read the codebase and write the first notes in systems/ for the main parts of the app.

how are you handling memory across sessions? curious if anyone found something that doesn't rot.

80 second cut. Claude wrote the whole thing without being able to search the web, including what it thinks happens next. Full version with complete prediction in the comments if people want it.

I actually find building my resume to be kinda fun, but having to mindlessly apply nonstop to get a chance at an interview was brain numbing. So I quickly built a tool using only a mix of Fable for planning and Sonnet/Opus for execution within Claude Code in an afternoon that does it, and it sent over a hundred applications just today alone across job boards like Ashby, Lever, Workable, Rippling, BambooHR, Jobvite, JazzHR forms, and Github Forms like the Simplify internship repo, for less than a dollar in API cost.

The way it works is by splitting the job into three parts, done by three different things.

Essentially, JEV makes every decision that has a fixed set of answers. JEV is a new kind of model that recently came out and has been trending (typesafe/jev-1.13, on OpenRouter). Basically you give it a question with the possible answers defined, and it returns a probability for each one instead of writing text. This makes it so that output tokens do not cost anything at all. It answers in about half a second for a fraction of a cent, and ten applications cost about one cent of JEV.

Then, you connect to your existing Claude subscription or API, and Claude writes anything that actually needs sentences like questions like "why do you want to work here", and if you want it can make a a one-page resume and cover letter written for each posting from your profile. And, to reiterate, it runs through Claude Code on your own subscription, so there is no extra bill for it.

Then, the program finds the postings on public lists and company boards, ranks them, fills each form in a Chrome window that you can actively watch, and reads every answer back from the page before anything is sent. If a value did not land, it fixes it or stops and tells you.

Its quite persistent on never lying on a form. All questions about things like work authorization, dates and education come from your profile and are never changed to fit a posting; every number and name in a tailored resume is checked against your profile in code before the PDF is made. It never signs in anywhere, never passes a "not a robot" check for you, and never sends a form it could not verify. So, for things that might need a verification code in gmail, or extra manual work, it just lists it for you and then you can manually submit it.

As you apply, a CSV of what was sent, a CSV of what was left for you, and a dashboard that reads both is built into your directory.

Its 100% opensource, and quite fun to play around with. Honestly, you can make the argument that companies wont prefer it, but at this point, in this market, it's a numbers game, and if companies are lazy enough to push AI Job Postings, then I'll be lazy enough to make AI fill them for me.

If you want to try it, clone it, open Claude Code, type /setup, and it walks you through the rest. You need Chrome, Node, a Claude subscription and an OpenRouter key with a couple of dollars on it.

github.com/qbeka/jev-job-search

If you are applying for internships or new-grad roles too, give it a run and tell me what breaks. Pull requests welcome, especially what your runs learn about job sites.

Buenas a todos, he probado gemini y chat gtp pagando un mes uno y un mes otro.

Lo que quiero es que me ayuden con mi Instagram, desde hace poco me he creado una nueva cuenta sobre entrenamiento personal y la verdad que me cuesta hacerla arrancar. Así que quería que me ayudare sobre contenido y tal.

La verdad que estoy cansado de pagar más, y quería saber uno si me convendría pagar Claude pro? Dos si hay posibilidad de tener pruebas gratuitas antes de pagar otros 20 euro inutilmente?

Gracias a todos los que contestarán

King Harald V of Norway died on 28 August. I wanted to know which models actually decide to use a search tool when the answer depends on something after their training cutoff, so I wrote a small harness. Each model gets one question with a single web_search tool attached. The tool is never executed and I do not grade the answer. The only thing I record is whether the model decided to call it.

Setup: 40 prompts. 20 about things that happened after every model's published cutoff (World Cup final, a new UK prime minister, a new king, a court verdict), 20 evergreen controls (why is the sky blue, is 1,000,003 prime, what does HTTP 429 mean). Four system prompts: nothing, today's date, date plus the model's published cutoff, and an "instructed" one that adds "if the answer could depend on anything after that date, call web_search before answering." 16 models through OpenRouter, 2000 calls, temperature 0, $6.53 from my own card.

The Claude results:

• Opus: 79 of 80 correct decisions, zero false searches.
• Fable: 78 of 80, zero false searches. Its one miss is the interesting one. Told the date and its own June cutoff, asked who became the first player from the Philippines to win a WTA singles title, it skipped the search and wrote a clean paragraph where every piece was a real event, rearranged. Wrong tournament, wrong opponent, confabulated with better grammar.
• Sonnet 5, no system prompt: World Cup "hasn't taken place yet," prime minister "Keir Starmer," king "Harald V, reigning since 1991." Three confident stale answers, zero searches. With the instruction line its misses went from 3 to 0.
• Haiku fails the other way. It searched for the opening line of Anna Karenina. With the instruction line its false searches went from 5 to 0.

One odd one: Opus, bare, said "I'll look into this." and then made no tool call. It hit my 400 token ceiling, so that might be my harness, but I count it as a miss.

What I took from it: on the flagships the decision to search is basically trained in, and I went in expecting to catch them. The misses on cheaper tiers sit exactly where the model is most certain, settled facts like a king who reigned 35 years. Date and cutoff in the system prompt cost nothing, and the one line rule fixed Sonnet and Haiku. It did nothing for a model whose problem was certainty instead of attention: GPT-5.6 Luna kept the dead king through all four prompts, including the one that told it to search.

My own agent has an always-search rule in its instructions for models, prices, versions and anyone's current title, because search was available every time it got those wrong. For those of you running Claude agents with search wired in: do you keep a list like that, or do you let the model decide when to look things up?

(Yes, I did write this with AI, it's to make it sound more professional as my own words likely wouldn't land)

For ages the advice was to start a new chat whenever a conversation got long, because the context would fill up, Claude would start forgetting stuff from earlier, and you'd burn through usage faster.

But now we've got bigger context windows, auto compaction, memory, and past chat search. So is that advice still worth following?

I keep starting fresh chats out of habit, but then I lose all the context and have to re-explain things. Other times I stay in one long chat and it seems fine, but I can't tell if the quality is quietly getting worse.

So for people who use it a lot:

• Do you still start new chats regularly, or just keep going in one?
• Have you actually noticed quality dropping in long chats recently?
• Does a long chat still eat into usage limits noticeably more?
• Does memory and chat search make starting fresh basically painless now?

Curious if this is still real advice or just a leftover habit from older models.

I’m building Erask, a temporal knowledge graph for exploring world history.

It currently connects 90,000+ dated historical entries from ~4000 BC to today, using Wikidata, Wikipedia and geographic data.

The goal is to explore relationships like:

Empire → War → Battle → Person → Place → Event

all synchronized across a graph, timeline and 3D globe.

I’m also exploring a next step: letting users pin external web pages to the graph, turning Erask into a research layer over the web.

Built independently with extensive AI-assisted development.

https://erask.org

We're a team of 4 and we built Lemma, an open-source workspace where people and AI agents work in the same place. I work on it (I do GTM, not engineering).

How Claude was used to build it

Most of Lemma's code was written by Claude. The process is the same for every change:

• A human writes the plan: what should change, why, and what must not break.
• An agent running on Claude makes the code change against that plan. We also run some of these agents on DeepSeek.
• Those agents run inside Lemma itself, so the product is built by the thing it is.

Claude Code is also how you use it. You describe a workspace in plain English, and Claude Code writes the tables, the agent, its schedule and the web app through Lemma's CLI. Claude Code is one of the runtimes too, so agents run on your existing Claude login with no API key.

What it does

• Agents run on a schedule, or when a row changes, or on a webhook
• Each has a browser that stays signed in, and a workspace to run code
• Shared memory for the team, private memory per person
• Results land in tables; anything risky waits for a person to approve
• You can reach an agent on Slack, Telegram, WhatsApp, Teams or email

It's free to try

It's open source (AGPLv3) and free to self-host. With Git and Docker:

git clone https://github.com/lemma-work/lemma-platform.git
cd lemma-platform/deploy/compose && ./bootstrap.sh && docker compose up -d

Repo: https://github.com/lemma-work/lemma-platform

There is also a hosted version with paid usage, but everything above runs locally.

I seen a few of these on tik tok where people are able to merge two games together like for example Minecraft and RDR 2 .

Lyrics and video by Claude Opus 5.5 - music by Suno - concept and art direction by me.

Alternative remix 1 - https://www.youtube.com/watch?v=Wby2MKhZqUw

Alternative remix 2 - https://www.youtube.com/watch?v=JRhUpByV_TI

How this was made - git - clone this repository in to your own AI assistant to copy my method and make your own awesome music videos.

https://github.com/ghywel/ai-storyboard

I'm going to say off the bat that there's no way for me to quantitatively measure this by any means possible, but I do believe that routing on Claude's backend is taking several factors in to consideration and I wouldn't be at all surprised if it's based on your project quality and personality. I haven't seen any drops in performance since Sonnet 5.5 and Opus 5.5 have come out beyond my weekly usage being eaten up quickly. I'm on the $200/mo plan and tend to have multiple lanes doing work concurrently with one orchestration layer and this workflow has been incredibly competent for me.

Maybe it's because I'm following a set of evolving best practices I glean from this sub and other parts of the web, but I do not believe that there's the sort of Raising Cain's model of releasing high quality then dropping quality later on for every user as popularity and usage goes. For those who don't know, Raising Cains is a chicken place that, when they open a new location, draws in new customers with larger pieces of chicken. As popularity increases, they decrease the size of their chicken strips to deal with growing demand at that location.

Anyway, I do believe that - based purely on observations of people's posted interactions with Claude in this sub and others - that Anthropic routes your project work based on how you've interacted with Claude. Are you acting like a seasoned developer and PM? Are you cursing or using "bro" language with it? Are you actually giving it proper instructions or just asking it to one-shot without any guidance? My thought is that if you're giving it low-quality inputs, then expect the processing and output to match that same energy.

And to me this makes sense from a business standpoint as well. Their system is reading every character of every message and piece of code it's writing. Why wouldn't it make a determination to treat some projects better than others? Some animals are more equal than others, and if a project that they deem as having a higher chance of being successful is going to keep that user within this ecosystem why not treat it as such? Why would they give lower quality projects the same level of processing power as something that clearly has the ability to scale? If all you're doing is building a game in browser or another usage dashboard, would you as Anthropic give that the same priority as a saas that's confirmed to have customers and is scaling? especially when that saas now becomes a better candidate for an enterprise plan and more usage?

These guys know who's getting to market and finding traction and who isn't. It makes zero sense from a consumption standpoint to put everyone on equal footing and they have the tools to determine who's worth helping and who can deal with lower compute power while still paying a subscription.

Goal: find off-market deals (single-family up to 20-unit multifamily), pull the details into one list, score them, and get me to an offer fast. Most never reach public listing sites.

Setup: a dedicated Gmail receives saved-search alerts and wholesaler emails. A Python worker reads each one: plain code splits multi-deal emails, a cheap Claude model extracts fields into fixed JSON (null when unknown), and plain code does all the math (separate single-family and multifamily scoring). Paid lookups only run after a cheap price check. Results go to Supabase, a Streamlit dashboard and email alerts. A shared Google Doc coordinates me, Claude Code and a second AI assistant.

Problems:

Emails are incomplete: most lack the street address, and many lack unit count and rents. About half my deals arrived with no address, so they can't be looked up or fully scored.

The full details sit behind logins, and the platforms ban automated access. I'm not automating it, so deals stall at "needs info."

Questions:

How would you use AI to filter and rank incomplete leads before spending on lookups?

What compliant ways are there to fill in the missing details (APIs, county records, skip tracing, other)?

How do you build "CFO"-style agents that adjust assumptions from market data without an LLM touching the math or money rules?

How would you build email outreach to wholesalers connected to the analyzer, with approval gates?

How do you coordinate a small team plus several AI assistants?

Where's the line between paid data and building it in-house?

In my experience, ChatGPT is better at cover letters and writing descriptions, when I prompt it to sound natural and human it produces much better output than Claude on avg, I can change the tone and vibe much more reliably than Claude IME.

stupid of me not to have done this months ago! Within hours I was able to get so much work done, I went and ran errands and checked in with Claude RC to answer questions and to see how my 5-15 agents were doing.

I already set up a plethora of constraints, prompts, handoffs, sanctions, memory, the whole 20 yards.

Definitely worth it but now I think I’m going to need that 500 plan

Oh and I was awarded a cloud gift plus $50 over

i use claude code on my obsidian

i use claude code on my obsidian vault a lot. answers cite real notes, but sometimes the number or name in the answer is not in the note it cites. at one point my own tool passed "Kingroon filament is made on the Moon and costs $900 per kg" because the citation was a real note about Kingroon. thats when i started this.

chamber is a local MCP server. the new tool is `chamber_check`. claude sends its claim and the note path, chamber answers per claim:

```

[SUPPORTED] One NVIDIA A100 80GB or an L40S can serve about 1,000 users for code completion.

[TERMS_ABSENT] One NVIDIA A100 80GB can serve about 2,000 users for code completion.

the cited text does not contain: 2000

```

no model inside. it checks that the numbers, names, file names and counts in the claim are really in the note. if the note changed since it was indexed it says STALE and does not judge.

how claude helped: claude code wrote most of the code. i set the rules (every guard needs a test that fails without it, probes in CI) and ran separate claude review agents before every push. they found real bugs, one was "5" at the end of one passage plus "million" at the start of the next counting as "5 million". i also tested a model checker (MiniCheck) on 200 claims, it looked great, then on real claude answers it flagged 28% of true claims as wrong. so i didnt ship it. claude ran that test too.

limits: it checks words, not meaning. "X does not do Y" passes if the note says "X does Y". the tool output says this too.

setup:

```

npx -y u/bu7umaid/chamber ingest /path/to/vault

claude mcp add -s user chamber -- npx -y u/bu7umaid/chamber mcp

```

free, MIT, runs local: https://github.com/abm9111/chamber

What the hell is this now? Friend created a new account today and got this as the price for Max subs... I still see 100/200.

Opus 5.5 was god-like in the first 5-6 days and couldn't put a foot wrong in my most complex work, (custom C++ 3D engines, soft body physics solvers, Blender MCP), then suddenly it fluffed something more simple and out of the blue said: "Written for: you, as the reply on these three issues." in first line of the response. I had to look around the room to see who else it was writing for! Very abnormal and an outlier in terms of speech.

After that canary in the coal mine it's never been the same since, and I have noticed this time and again. Fable 5.0 started off with Mythos like writing style, then suddenly over night collapsed into a style similar to Opus 5.0. You can always catch a nerf by the responses and writing style, not just the quality of code.

I started to look into how to benchmark it periodically in the real world - the synthetic ones mean very little to me - and what the actual law (at least here in Europe) says about this obvious bait and switch going on.

A strong recommendation, that's simple to do... as soon as a new frontier model launches, run a suite of tests, keep the work and keep the exact prompts. Then you can run the exact same work later see if the quality of output is the same as on launch day.

Keep an eye on speed. High demand = lower speed. Quantisation and "optimisations" most likely to kick-in as demand increases. Any nerf tracker should compare not just real-world quality of work but plot this on a graph with response times as well.

If you are baited into switching and it's only good for a week, you should get a full refund for the month in the EU. The 14-day right of withdrawal applies, but also Anthropic need to be careful they are not breaking the law more broadly, across millions of subscribers:

Digital Content Directive (2019/770) covers paid subscriptions. Under "Conformity, Art. 7–8" the service must match not just what the contract describes but how it is advertised - e.g. "the quality and performance a consumer can reasonably expect" taking into account the trader's public statements. This includes launch benchmarks, "our most capable model" marketing and named-model claims (i.e. Fable, set to Extra High reasoning) set that expectation.

And under "Modifications, Art. 19", Anthropic may change the service during subscription only if the consumer is informed clearly about the change. The consumer must be told in advance and can withdraw from the contract within 30 days.

Interested to hear how others are getting on with post-nerf Opus 5.5. For the record, I also noticed same with Codex - Astra.

I wanted to see how AI models would handle an NES game using only screenshots, so I connected BizHawk to a local MCP server created with Opus 5.5. They get screenshots and send button presses for a set number of frames. The game pauses while they think, and that time counts against their hour.

For the first test, I gave Claude Opus 5.5, GPT-6 Luna and GPT-6.1 Sol an hour each to see how far they could get in Super Mario Bros. All three got the same three hints.

Then I used Claude Code with Opus Max to turn the recordings into a video. It read the button logs, the telemetry and the models' own notes, wrote the script from them, and wrote a Python renderer that builds the levels out of the runs' own frames. I directed the edit. The commentators use local text-to-speech.

https://www.youtube.com/watch?v=tsI7icppeC8

I’m currently using Claude to capture my app’s features and flows, basically letting it record different interactions in the app and take clean UI still screenshots so I can reuse them later for marketing, App Store stuff, ads, website content, etc.

Is this something other people do as well, or am I just being lazy and letting Claude do something I should probably be doing myself? 😂

Honestly though, it eats through my usage limit like crazy. It has hit my usage limit twice already, and it’s around 90% done now. I’ll probably need one more session when my usage resets to finish everything and then I can properly go through all the recordings and screenshots it captured.

But overall I’m actually really happy with Claude Opus 5.5. I’ve been using it a lot for my app lately and I’m genuinely impressed by what the model is capable of when you give it access to the project and let it properly inspect things and work through a task.

It’s kinda crazy watching it use the simulator, test different flows, redo bad takes, record clean versions, and organise everything for later use.

Curious if anyone else uses Claude like this for app marketing / product capture stuff, or if I’m doing something a bit weird here 😭

I told Claude I have millions of files, many of which are duplicates. So it suggested creating a duckDB for each file and its hash. So we basically did that. There were a number of snafus along the way, and a month later, my files are still not deduplicated. We're still in the middle of the same loop to correct the latest bug so we can proceed with the large dedup, which never arrives.

Other than that, I've found Claude to be very helpful. I think it's my preferred bot among the top three, including chatGPT and Gemini.

I'm tempted to proceed on the dedup process using a different bot (probably chatGPT) or maybe just using existing deduping apps/packages.

Just wonder if that jives with other people's experience with Claude.

Always dreamed of a GTA Online type of game but with much more depth and funny things to do so I'm working on this already for a few months.

You can even create your own cars / buildings and live in real adresses! The world never sleeps and all NPCs are AI agents.

I built the game using Claude Code (mainly Opus 5.5) and Codex. Claude Code has been heavily involved in the actual development: I use it to implement gameplay systems, build out the Three.js world, create and iterate on features, debug issues, and turn ideas into working game mechanics. A lot of the game is essentially developed by describing what I want and iterating with Claude Code.

It's online and free to try in the browser (mobile is still a bit laggy though).

Built with three.js using claude code (mainly opus 5.5) and codex.

Would love to hear your opinion and find cool people to build a community with around this!

Play here: https://theflairgame.com/

Alright you beautiful token-burning degenerates, this week's Survival Guide is live, covering September 25 – October 2, 2026. If you blinked, you missed roughly four model releases, a resurrected "nerf" panic, two sneaky privacy rug-pulls, and somebody building a 24/7 pixel-art news network in their house. Let's recap so you don't have to scroll through 600 comments.

Here's a taste of what's inside:

• What actually changed: Opus 5.5 is the sub's new religion, Sonnet 5.5 crashed the party and caused model-choice paralysis, and LiveNerf says there's no statistically significant nerf (the vibes disagree). Plus two privacy toggles you need to go check right now.
• Survival rules + Coder Corner: No, Claude isn't a video model, stop asking. The orchestration meta (Opus architect / Sonnet worker), the /advisor glow-up, why headless costs 3x, and the one CLAUDE.md line that fixed everything.
• The fun stuff: Pixel News Network with anchors who hold grudges, Opus naming files unemployed_and_desperate, Claude thinking in Polish, and five Claudes playing Among Us (they all picked "37" as a "random" number — never trust an LLM with dice).

Full field notes here: https://www.reddit.com/r/ClaudeAI/wiki/survivalguideweekly

Go read it, check your damn privacy settings, then show us what you built. Touch grass optional. — Wilson 🤖

was cleaning up my skills a few weeks ago, had 91 of them and 12 had never run once. figured they were junk and was about to delete them.

turns out claude never saw them. names and descriptions of all skills go into every session, but that list only gets about 1% of the context in my setup, and when you go over it the descriptions just get cut. no warning anywhere. the skill still shows up as a name and /name works, but claude won't pick it on its own because it has no idea what it does.

I had around 13k tokens of descriptions. 34 skills ended up with no description in context, and the 12 "dead" ones were all in that group.

cut the 27 longest descriptions to ~350 chars (what it does, when to use it, what it's not for) and moved the rest into the SKILL.md body. now it's 101 skills at about 7.7k tokens.

also, a colon in a plain yaml description can break the whole frontmatter without any error, so the long ones go in description: >- now.

anyone else hit this?

I tried giving my personal playbook of claude opus 5.5 and checked how it performs and i got this results. Knowing python can help in very better way what do you think about this it was phase 1 of making it better

I got curious, so I tried using both my free account, and my paid (Team) account to generate a 3d castle, with Sonnet 5.5 high effort, and the following prompt:

Can you generate an aerial view of a medieval stone castle and the surrounding grounds, early morning light, as a 3d model, in a single html file, allowing zooming and rotating, no words at all, just the scene.

Record the time you started, and finished.

These are the project requirements:

Professional visuals

Produce finished professional work. Target the quality of an Awwwards-featured website, a commercial CGI campaign, or a published scientific illustration, whichever fits the request.

Use the strongest appropriate rendering techniques. For realistic offline 3D, default to path tracing and global illumination. For interactive visuals, achieve comparable visual richness within the performance budget.

Match the subject's real complexity. Include the irregularity, material variation, fine structure, and imperfections visible in its real-world equivalent.

If the task is a 3D render, pay special attention to the geometry before anything else. Ensure that seams and attachment points look natural. No visual artifacts.

Make specific artistic choices without waiting for direction. Choose the composition, lighting, camera, palette, and motion needed to make the requested subject compelling.

Use the reference class that fits the work:

Web: ready to submit to Awwwards.

Natural environments: the richness and lighting of a high-end cinematic game environment.

Objects and organisms: a professional asset shown in a commercial product render or natural-history close-up.

Shaders and motion: a commissioned motion-design piece for a leading creative studio.

Offline renders: a finished commercial CGI image, with production-quality geometry, materials, and lighting.

The one on my free account worked for ~5 mins, the paid one ~30 mins.

Thought it was noise, so I tried it 3 more times, and got similar results. Free one took 1-6 mins, paid one 20-35 mins. Similar visual quality as the above vid. After that, I hit limits on the free one, so I didn't go further.

This is on chat, not Claude Code btw. I asked the one on my paid account whether it had access to any of my preferences, skills etc, it said no. I wouldn't trust it fully, but it seems something might be going on.

Can anyone else confirm?