I've been testing the Canva integration. I need to make historic cards for people to read about some of the culture revival work I do so they learn the sources used.

Somewhere in August, I tested the plugin and I genuinely thought it was pointless. There were critical limitations such as the inability to create text boxes or basic shapes on a blank page. It couldn't even duplicate existing elements to work around it meaning the user had to seed the canvas manually and even then things like alignment were way off because it stacked objects.

Index of the below post:

• High Level Overview/Workflow
• Interesting Findings
• End Result

Today however, that change is quite substantial. Below are some findings/screenshots I took along the way:

High Level Overview/Workflow

https://preview.redd.it/o26brudwveth1.png?width=1472&format=png&auto=webp&s=56e07d50bced66e2eacb5c7d49d1e6af94814e48

Interesting Findings

- Does not use a URL to modify an existing template. I usually include the URL when I add the connector. In this case it made its own new template despite providing a square format.
Seems to be a generative workflow.

- Despite the connector enabled on the desktop app, the browser requests a login but also wanted a passkey (not happening). Luckily Canva works on the temp code.

https://preview.redd.it/xcmn88a3xeth1.png?width=1202&format=png&auto=webp&s=ec95aab6b3a5294729f1feb7df01689843565f74

- Designs are not exposed on Canva interface unless the user accepts/clicks the link.

https://preview.redd.it/u66ww3laxeth1.png?width=911&format=png&auto=webp&s=72b00e7cf03768bab1d4ee43579c2f68ead88a8e

End Result

(No, I am not using it. Too stylistic for what I want but still impressive)

Screenshot of Canva so the elements can be observed to show it isn't an image gen alone.

https://preview.redd.it/bxs4t9qlxeth1.png?width=1013&format=png&auto=webp&s=d4d14d9ea233f18cbef03fe72379d454e847ae0a

https://preview.redd.it/uudq5wp4q8nh1.png?width=1544&format=png&auto=webp&s=d2f75345631763474f7757f6128a6d635d84c0a5

Revisited security research I'd previously conducted with earlier models across OpenAI, Google/Gemini and Anthropic.

Rather than starting the research again, I gave the current model the existing evidence and analysis and had it audit the previous work.

Across the session it:

• challenged conclusions reached by earlier models and downgraded claims where the retained evidence didn't support them;
• separated observed evidence, reasonable inference, hypotheses and overreach;
• reconstructed disclosure timelines and incorporated evidence that emerged after the original investigations;
• searched the web to verify subsequent security research and continuing incident reports;
• retrieved existing research from connected Notion pages and compared it against the retained record;
• analysed screenshots and other visual evidence;
• reassessed risk classifications across the three vendors;
• maintained provenance distinctions between vendor statements, public reports, independently demonstrated findings and model inference;
• rewrote the public-facing incident records based on the resulting analysis.

https://preview.redd.it/16zn1jx9q8nh1.png?width=1553&format=png&auto=webp&s=db06ee6087bb11f01a4c981f925b8d16484c7088

Then we switched from research to implementation.

I showed it screenshots of the live registry when the layout broke. It diagnosed the HTML/CSS problems, rewrote the affected components, added tabbed incident navigation, built dynamic status information and incorporated dated source links for continuing reports.

So within the same piece of work it moved between long-context reasoning, model-over-model QA, connected-app retrieval, web research, vision, evidence analysis, risk assessment, writing, coding and visual debugging.

The interesting part for me wasn't any individual feature. It was being able to use them together against the same persistent body of work without turning each stage into a separate workflow.

With all the discussion around Astra and its security capabilities, I am keen to see how:

1. Can it identify where previous models overreached?
2. Can it cross-verify claims while processing inputs?
3. Can it find things previous models missed?
4. Does it maintain evidence/provenance boundaries better?
5. Does it recognise relationships across incidents without inventing causal links?
6. How does it handle processing limitations across large evidence sets? See below:

-------------------------------------------------------------------

Detailed multimodal usage:

• Long-context reasoning: maintaining the OpenAI, Gemini and Anthropic cases simultaneously, comparing earlier conclusions with newer evidence, and keeping competing hypotheses separate.
• Critical analysis / self-audit: reviewing work produced by earlier models, identifying unjustified conclusions, and downgrading claims where the evidence did not support the original confidence level. This also included verification of previous bug-hunting work within code.
• Evidence classification: repeatedly separating observed fact → reasonable inference → hypothesis → unsupported claim / overreach.
• Risk assessment: reassessing technical security, privacy, governance, enterprise, systemic and disclosure risks as the available evidence changed.
• Temporal reasoning: reconstructing disclosure chronologies and evaluating later evidence without retroactively treating it as information available at the time of the original report.
• Cross-source synthesis: combining retained evidence, vendor responses, public reports, subsequent independent security research, and regulatory or assurance-framework material.
• Web search / browsing: locating and validating external evidence, including subsequent Google API-key research and continuing OpenAI billing/Codex reports.
• Connected apps / plugins: retrieving relevant material from connected workspaces and incorporating it directly into the analysis rather than requiring repeated manual transfer of source material.
• Notion retrieval: retrieving existing research and discussion material from Notion and auditing it against the retained evidentiary record.
• Vision: analysing screenshots of the incident registry, historical evidence, UI states and broken layouts, then incorporating visible details into technical and evidentiary analysis.
• Document / artefact interpretation: interpreting reports, timelines, disclosure correspondence, screenshots, tables and technical artefacts as structured evidence rather than treating everything as ordinary prose.
• Coding: producing and modifying HTML, CSS and JavaScript used to turn the underlying research into a working public incident registry.
• Debugging: both conventional software debugging and research debugging: identifying why a page had broken alongside why an earlier model had reached a particular conclusion.
• Information architecture: converting a large research record into a structured hierarchy of vendor → incident → chronology → evidence tracks → competing interpretations → risk → current status.
• Source attribution / provenance handling: maintaining distinctions between vendor statements, public allegations, independently demonstrated findings, retained evidence and model inference.
• Counterfactual / challenge reasoning: testing whether alternative explanations could account for the same evidence rather than simply constructing the strongest argument for the initial hypothesis.
• Cross-vendor comparison: applying broadly consistent evidentiary standards across OpenAI, Google/Gemini and Anthropic rather than assessing each case under different assumptions.
• Writing / editorial: converting technical analysis into defensible public-facing language while avoiding the conversion of hypotheses into statements of fact.
• Iterative visual QA: reviewing rendered output, identifying layout or presentation failures, diagnosing the underlying cause, modifying the implementation and validating the next iteration.
• Persistent-context use: maintaining continuity across an accumulated research programme rather than treating each interaction as an isolated prompt.
• Tool orchestration: selecting between supplied evidence, connected-source retrieval, public web research, image analysis, coding and debugging according to the requirements of each stage of the investigation.
• Model-over-model quality assurance: using the current model to audit work produced by earlier models, identify unjustified confidence, preserve supported findings, incorporate evidence that emerged later, and update the live research artefact accordingly.

Hi, big fan of literally everything you guys are putting out at the moment: https://www.reddit.com/r/ChituSystemsOfficial/s/MuhW9RTUYm

Few questions I had:

1. Is there a way to save an assistant chat within a project?

A majority of the prints I do are large and span across weeks. I can't have my machine running straight over those periods and leave the window open since it will contain images and discussions. Is there a way I can lock the chat to a project and catch up later?

2. Is there a way to integrate it to read the settings of a profile? I've made a few changes to my existing profile. Do I export it and share in each chat?

3. What is the context window or triggers to new chat? Using other AI I am vaguely aware that images and typically data traversing can exhaust capabilities.

4. Is there a beta user guide or best practices I can look at to work within limitations?

Not sponsored on an influencer. After 2+years of only using Lychee, I moved back to Chitubox this afternoon and my mind was blown at the variety of updates and innovations that took place.

I genuinely don't know where to begin and once I get a moment to sit, I hope I can do justice in a detailed write up.

-------------------------------

Software Design/Performance

• Speed

-Startup: roughly 40+ seconds in Lychee vs about 4 seconds in CHITUBOX (auto-login enabled).

-Slicing the same test file: ~21 seconds in Lychee vs ~2 seconds in CHITUBOX.

I actually recorded the comparison because I thought I'd measured it incorrectly along with CPU/RAM utilisation.

• Saved workflows. Whoever designed this deserves a raise. When printing larger pieces that are split, I've certainly forgotten to hollow some parts out.

https://preview.redd.it/hrax8ptvaygh1.png?width=995&format=png&auto=webp&s=2093fa22b99e5429d9d18483f36cf28977330c61

• The interactive videos to give an idea of the process was solid. When it comes to most tech, getting on the same terminology can be difficult. The videos? Insanely helpful

https://preview.redd.it/tc0442dwbygh1.png?width=892&format=png&auto=webp&s=8c39540f2376f424edeb0e068a46312accafb2b7

https://preview.redd.it/ygvaouuibygh1.png?width=1002&format=png&auto=webp&s=7341512623a9170835d4d621976a50344db5bb6a

• Standard Parts. I am used to primitives but.. bases?! and parts?!

https://preview.redd.it/6ropm2rwdygh1.png?width=2525&format=png&auto=webp&s=a4eba51ab66e923d39290b94b342d3803ec14798

The AI Assistant

I worked in tech for a long time and support a lot of AI companies and startups. This has to be the best and smoothest integrations that isn't a basic agent/public LLM agent bolted on to fit in with the crowd.

• I like to know the base models because I already maintain a list of constraints with existing ones. The assistant refused to discuss its underlying model and consistently redirected me back to the printing workflow was refreshing! I had to look up a user manual or something to verify if this was shared publicly. The team behind this assistant's development have done a SPECTACULAR job.
• It immediately defines its scope instead of pretending to know everything. It asks for the printer, resin, settings and workflow, then stays in that lane without trying to hallucinate but at the same time isn't a standard chatbot following a script. It actually reasons and took into consideration the effects of temperature (winter in AU) when we began resin calibration.

https://preview.redd.it/d7ppmju5fygh1.png?width=726&format=png&auto=webp&s=69ca222c80d26c08504472acdbfca1284d242e0c

• It is multimodal. It can analyse screenshots directly inside CHITUBOX. The built-in screenshot and annotation tools (boxes, arrows, text, etc.) make asking questions incredibly easy. A majority of public models still require the image is saved separately and attached. The built-in screenshot and annotation tools mean I never had to leave the application or manage dozens of screenshot files.

https://preview.redd.it/t3ud9yn2fygh1.png?width=819&format=png&auto=webp&s=661d1fa593eb9b09774eea2bdcd1b555e5c44e79

• It guides you through the interface rather than dumping documentation or URLs
• It tells you when it can't verify something instead of making it up. In my case it pointed out it couldn't see the Advanced tab and asked me to check it rather than hallucinating the setting.
• It explains why settings matter. I ended up learning more about lift stages and peel forces in one evening than I had in months of watching random YouTube videos.
• It even recommended using a dedicated filament slicer rather than trying to convince me CHITUBOX should do everything. That increased my trust immediately.

I also discovered saved workflows, ChituManager, SDCP, and the SDK, which makes the whole ecosystem feel much more cohesive than I realised and I am very excited to dig into this.

If anyone from the CHITUBOX team is interested in detailed user feedback from a production workshop perspective, I'd be happy to share more detailed notes after I've had more time with it.

Couldn't find a Questions tag so hope this works.

https://preview.redd.it/br4qiu2mg5gh1.png?width=791&format=png&auto=webp&s=92202b8d1824a534ffb1ffe33f033a78498391a0

Is there a way to edit the @ manus.bot email to use my domain instead?

Right now my workaround is setting up mail forwarding and aliases+groups but I was hoping we could get an option for additional DNS records or something.

If I missed an existing feature, please let me know.

CONTEXT

I run Arcanium Studios where we do 3D modelling, printing, and painting with a focus on making historically interesting objects affordable and continuing traditions.

This session was one of the more ambitious things we've attempted, and I want to document it properly because I think it shows something genuinely different about how AI collaboration can work.

What we built

A resin printed coin: 42mm, both faces hand-painted with an NTAG 213 NFC chip embedded inside it.

One face replicates the cursed Aztec gold coin from Pirates of the Caribbean. The other face carries the concentric ring structure of the Mexica Sun Stone (Piedra del Sol) a real archaeological object currently held at the Museo Nacional de Antropología in Mexico City.

Tap the coin with your phone. It opens a full interactive web exhibition: a 3D model viewer built from the actual STL file, hotspot annotations on the coin surface, an editorial layout comparing the film design to the historical artefact, sourced analysis of what Disney kept and what they changed, and links to the museum, the Google Arts & Culture source material, and the store.

The coin is the key. The exhibition is what's inside it.

https://preview.redd.it/sjpxoy5wrdeh1.jpg?width=754&format=pjpg&auto=webp&s=3901d0890474cb3c7d001786e32c613c576281d6

https://preview.redd.it/lgktadwwrdeh1.jpg?width=1080&format=pjpg&auto=webp&s=8cb9c725710b6a4cb608c37478666ab6a0fb7dca

How it started: a bad brief

I was working with GPT+Codex for this project but due to outages and performance degradation, on the final part of this build what came back was a high-level product requirements document. Technically structured, zero sources, no acknowledgment of the IP complexity, no actual art direction analysis of what we did or the set prop makers. It stated things like "the coin references Aztec iconography" without explaining what that iconography actually is or where it comes from.

I brought it to Manus with a simple instruction: read this, and let's discuss before we do anything.

What followed was a proper adversarial review. We broke down data by verifiable, debates and inferences we could provide only with a high %.

https://preview.redd.it/xq93ja9zrdeh1.jpg?width=1080&format=pjpg&auto=webp&s=66b30f61af0bf6a2120086f437933fbb9d8f829f

We didn't touch any code until the research was done.

The research pass

Manus and I worked to pulled and synthesise data. I've been working with historic data for a while, so having Manus to collaborate with has been phenomenal. Especially since I may have misread information.

The most useful find: the MNA's own description of the Sun Stone centre face states it has the lower half fleshless and the upper half normal, symbolising life and death in a single image. Disney's coin is a full skull. In the movie, the curse transforms living men into skeletons. The life/death duality of the source material was deliberately pushed entirely into death to serve the narrative. That's a defensible art direction observation with a primary source behind it.

Everything went into a content skeleton with confidence labels: verified / film lore / adapted / debated / UNKNOWN. No synthesis without a source.

The build

Once the research was solid, the interactive exhibition build moved fast.

3D model: I had the STL file for the coin. Manus converted it to GLB, applied a PBR bronze material (metallic/roughness, dark recesses, bright raised edges), and decimated the mesh from 786k faces to 80k. Down from 15MB to 1.5MB, still sharp at screen resolution. Embedded using Google's model-viewer web component with three hotspot annotations placed at actual mesh surface coordinates.

EXHIBITION STRUCTURE: Six sections, alternating left/right editorial layout, sticky anchor nav, comparison table (Sun Stone vs Film Coin across five design elements), pull quotes from MNA source material, link cards to the museum, Google Arts & Culture, and the Arcanium store.

DEPLOYMENT PIPELINE: GitHub repo (ArcaniumStudios/BTA-BehindTheArtefact) → Netlify auto-deploy. Every push redeploys in under a minute. The Squarespace URL Mapping redirects the branded domain path to the Netlify app. The NFC chip encodes the Squarespace URL. Tap coin → branded domain → silent redirect → exhibition.

https://preview.redd.it/o5qoc3c0sdeh1.jpg?width=1080&format=pjpg&auto=webp&s=c76046f807f359941f4b4b4166f4fa0231d0cf33

NFC: NTAG 213. 144 bytes is more than enough for a URL. Programmed with NFC Tools on Android, then locked so it can't be overwritten. No app required on the visitor's end. Works on any modern iOS or Android.

What "code you can touch" actually means

This phrase came up during the session and I think it's the most accurate description of what this project is.

The coin is simultaneously:

•A replica of a fictional prop from a 2003 film

•A reference to a real historical artefact that is 500+ years old

•A functional piece of technology with embedded data

•A handmade object with documented craft decisions

•A physical key to a digital exhibition

That's five layers in a 42mm disc. Most replicas are made to look like the thing they reference. This one was made to ask questions about it.

The NFC chip is invisible. The interaction is instinctive. You tap a coin and something happens. The result is a digital experience triggered by a physical one. That's not a gimmick. It's a demonstration of how physical and digital layers can coexist in an object.

History you can hold. Code you can touch.

Thank you team Manus and developers!

I have a few logical operations saved that gpt has been able to reference and flawlessly use in the past. For the past few days I've noticed it is unable to execute the calculations, let alone apply reasoning to it.

For example:

I saved my pricing logic on to gpt.

Size range:

25-40mm

45-80mm

85-100mm

Base+25%+10.95+10%

Usually,if I gave it a request for a price guesstimation on a 55mm, it would apply the above formula and take context into consideration which can impact the pricing but also time to manufacture or considerations.

I thought it was a one off but the past three days I have had to explain the logic and why selling things at $4 instead of $95 makes absolutely no sense. That $4 isn't even referencing raw value.

Does anyone else have custom workflows or calculations you've been using for a long time that suddenly don't work?

https://preview.redd.it/mkilh8clik9h1.png?width=2553&format=png&auto=webp&s=8692cf471e59486de8cc450a7138261e4c4e8592

I've been using Manus for a lot of my business aspects for a while. I had been prepping for an event for 2+ months to showcase products. Unfortunately, 3 days before the event my dad had a heart attack (LAD, also known as widow maker with a survival rate of 10-15%).

As I rushed to the hospital, I wanted to take my mind off concerning thoughts. At the time, all we knew was he had a heart attack with no pre-existing heart conditions.

I first ran my original request past Manus. I needed to build an offline portfolio of my work to display on an iPad. I also stated that I needed to work off my phone so it had context and understood constraints.

https://preview.redd.it/71u6ciinhk9h1.png?width=994&format=png&auto=webp&s=653d5223bb0ce3f8b731d961896ef057ce8164c5

While this is just the original message, I want to highlight the level of empathy to still address the needs. We do expect it from humans but through the entire process, it considered my workload. I work with AI on a daily and usually stop the care behaviour but this was actually the kindness I probably needed at the time.

First pass:

Objective: Build a portfolio app to run offline on an iPad.

Specs: # Core Use Case

A table-mounted iPad portfolio for Arcanium events.

Visitors should be able to:

- browse examples of previous work
- understand what Arcanium can make
- see before/after or process comparisons
- express interest in future/custom work
- access order/contact pathways

The kiosk should feel like an **interactive archive / portfolio object**, not a checkout terminal.

# Hardware Context

Device:

- iPad 7th gen
- 10.2 inch

Stand direction:

- low wedge / slanted table display
- preferably custom FDM printed
- integrated signage possible
- modular front plate preferred

Potential printed signage labels:

- Portfolio: Custom jobs, custom pet models, corporate builds, diorama builds
- contact: where people can drop their details and request 
- Behind the build
- About our theme
- Vote Next Project

Manus gave me some decent push backs in regards to constraints of the build and attempted to fill gaps:

https://preview.redd.it/z10yuto4jk9h1.png?width=1100&format=png&auto=webp&s=0c72878ab6a741235b865508e89eba4182e07d6b

Final and revised plan:

• Static PWA (offline-capable, installable to home screen)
• Content driven by a local JSON file you can edit between events
• Sections matching your physical signage labels
• Interest/contact capture stored locally, exportable as CSV
• Voting stored locally, resets on demand
• Theme page is a swappable JSON block

I am unable to provide screenshots of the build because my dad still has my tablet. I didn't want him bored during non-visiting hours. Manus was loaded up as well so he could ask any questions or take his mind off things. Sorry, he chose Netflix :)

------------------------------

Second pass

Since my dad had my tablet and was recovering fine, I now had to think of another strategy. Manus helped me build a mobile touch screen based solution.

I was still making hospital visits daily. During transit (45mins), I would get it to work in the background by giving it folder access to create this locally hosted solution with handover/left over items I can complete when I get back home.

Since I was offline, Manus created mockups for my review first:

https://preview.redd.it/jcumy04kkk9h1.png?width=1624&format=png&auto=webp&s=16e0904dc7d3ca0f5fdfd9e733f8f422a56a3c2c

Some additional things it did out of scope that I truly appreciated:

1. Created a shortcut for me to launch the kiosk instead of navigating to folder and running npm dev.

2. Reviewed my images and actually wrote detailed sections for the kiosk based on what we've done in the past

https://preview.redd.it/yjrg4nffmk9h1.png?width=1121&format=png&auto=webp&s=e894e4930d0a6d236a7b15d4e61b2a3ea016c455

https://preview.redd.it/vju560s2lk9h1.png?width=2427&format=png&auto=webp&s=91dcd9c9dd09a0ce6d7d5571150b1b558aee0bef

https://preview.redd.it/9hsl3bm6lk9h1.png?width=1657&format=png&auto=webp&s=12c88ab70eba9ba3e7811b237aa05e75b7fe3280

https://preview.redd.it/rlyajwralk9h1.png?width=2129&format=png&auto=webp&s=31606e95eebc24ddf56e7f938d6ddeb0a42b8042

The contact page was VERY interesting because data is stored locally for my view only meaning I could advise users their data was not sold or misused.

https://preview.redd.it/39mv7q4vlk9h1.png?width=1804&format=png&auto=webp&s=db87f0920b37510fd99d562e7bf2fb390cbe1fba

https://preview.redd.it/1p3pqao6mk9h1.png?width=1449&format=png&auto=webp&s=76014c950eacd83bcb19b61d814bf41b75311e05

Total usage stats:

https://preview.redd.it/78jpmigkmk9h1.png?width=397&format=png&auto=webp&s=efd803424625f481d8f616303325969ea48cc15f

Thank you Manus team :)

Work still goes on no matter what the emergency is. Your team allowed me to focus on my dad's recovery and removed the stress of planning and building the kiosk.

Is anyone else experiencing scheduled task issues? What I am seeing and unsure if its only region specific or something else.

It was perfectly fine before the made a major post about it. The issues I am facing:

1. It does not alert me or create a new thread any more.

2. None of my scheduled tasks appear in the interface but happily run and pop up at random times of the day but only on my phone notification. For example, if it was a one off scheduled for 8am AEST on the current day, it executes the next day and not even at 8am. Might be picking another time zone - my assumption is US since its usually at night for me.

3. I can click on the task and delete it but at the moment I don't know if it still appears or not. I've checked with GPT and it has a different and much older list of tasks.

4. Even my scheduled list shows nothing at all in completed, paused or active.

https://preview.redd.it/wi3cthbql49h1.png?width=1193&format=png&auto=webp&s=207fd28df281c8357e486c01f148ccf5dc1530b7

Hi there,

I am working on a project and was hoping I could get an idea of what people use GPT for.

The part 1 of this project is HERE.

Summary: Pro users got a pen from OpenAI. While I didn't receive or want one, I didn't think it captured the journey of a user "Since'24".

In my opinion, it should be about the user and showing them why they are valued. If this is a thank you gift, I would want to make users feel like they know that I understand the multiple faceted usage. Just standard tech and vendor behaviour from the past.

https://preview.redd.it/459i3um7qq8h1.png?width=800&format=png&auto=webp&s=bf6bd7739ca4f165068d290e6ec7475c503fe9a6

Please note, I do not work with OpenAI. Nor am I affiliated with them. This is also not an aim at criticising them.

I just like making stuff.

-----------------------------

The responses will be condensed into this list as part of the card insert.

The current card talks a lot about the pen and actually uses HMM slogans instead of OpenAI messaging. You can find the card here.

https://preview.redd.it/ainr3qaxqq8h1.png?width=501&format=png&auto=webp&s=2039a913d74b864842c670eeb2ac6b89cef63c8d

TL;DR tell me your story or you as a user :)

https://preview.redd.it/o8onspnfdk8h1.png?width=887&format=png&auto=webp&s=2aa9c6fa6504810b71f7b2011bc6a7d24989e8c6

Context

I recently received an email highlighting ChatGPT's ability to work with notes and uploaded content. While useful, this isn't a particularly new capability.

Back in August 2025, shortly after GPT-5 launched, I used the model to build a searchable paint database from over 200 miniature paints and create a repeatable colour-matching workflow for customer projects.

The goal wasn't automation for its own sake. It was to save time, improve consistency, and provide evidence-based colour recommendations rather than relying on subjective judgement.

Background:

At the time, I had access to more than 200 paints across multiple brands.

Customers would often request colours that matched existing branding, marketing material, logos, or previous work. Achieving these colours typically required mixing multiple paints together, making consistency difficult.

To support this process, I wanted a system that could:

• Identify and catalogue every paint I owned.
• Create a searchable inventory.
• Restrict recommendations to paints actually available in the workshop.
• Support colour matching against reference images.
• Improve repeatability across projects.

Planning

Phase 1: Photograph and transcribe 200+ paint bottles with aged labels

Phase 2: Determine the optimal bottle spacing, stacking arrangement, and image quality required to maximise extraction accuracy and minimise transcription errors.

Human verification remained part of the process to ensure the final inventory was accurate.7

Phase 3: Once the inventory existed, convert paint information into a structured dataset and record colour attributes including:

• Hue
• Chroma
• Lightness
• LAB colour values

This allowed future colour recommendations to be based on measurable colour relationships rather than paint names alone.

The bottles+patchy labels

The Process: A bit over 1 hour's work.

1: Image prep. Paint bottles were arranged in groups and photographed under consistent conditions.

The objective was to maximise label visibility while keeping enough bottles in frame to make the workflow efficient.

2. Based on observations of the results, the workflow appeared to operate roughly as:

Image > Vision Processing >Multimodal Understanding >Structured Extraction

The model was able to:

• Recognise paint brands.
• Read bottle labels.
• Identify paint names and codes.
• Reconstruct partially obscured or degraded labels using context.
• Return structured results suitable for spreadsheet import.

Rather than acting as traditional OCR alone, the system appeared to combine image understanding, text recognition, contextual reasoning, and structured output generation.

Image transcription

3. Verification: The extracted results were reviewed and corrected where necessary before being imported into Excel.

This human verification step was important because the objective was a trusted inventory rather than an unverified AI-generated list.

4. Colour Classification

The inventory was then enriched with colour information including:

• Hue
• Chroma
• Lightness
• LAB values

This transformed the inventory from a simple list of paint names into a colour reference database.

5. The final dataset was imported into Notion and organised into a searchable paint library.

This allowed future colour recommendations to be constrained to paints that actually existed in the workshop.

Colour Matching Workflow

When provided with a reference image, the workflow was able to:

1. Extract dominant colours from the image.
2. Cluster and analyse those colours.
3. Convert colours into LAB colour space.
4. Compare colours using Delta E measurements.
5. Estimate colour weighting within the composition.
6. Generate recommendations based on available paints.

The result was a more repeatable and measurable approach to colour matching than visual estimation alone.

Outcome

Today's result because I don't seem to have a screenshot of what it did back in the day:

https://preview.redd.it/h8evpzqitk8h1.png?width=1802&format=png&auto=webp&s=cf6cdfc18c8630458eb0676f6599fe03321552e3

What the notion DB looks like:

https://preview.redd.it/nkgickomtk8h1.png?width=1420&format=png&auto=webp&s=0873f39defe681e6ff229fcbcec729bfcfe0f4ee

I've recently gone down the rabbit hole of how looking into how OpenAI does swag.

My final stop was the HMM pen some pro users received. Source.

I'm going to redesign the pen AND the card from my perspective of what PRO users should have got based on my experience of working in tech orgs. I do not work or represent OpenAI. I just like making stuff.
___________________________

Part 1: The timeline

One thing I noticed was the "since '24".
Most of the images I saw online didn't feel like they did much to celebrate the users who were part of their most expensive subscription or that walk through memory lane. Source list will be in comments.
I chose to share the timeline because the fact pro users were part of such an extensive journey is insane.

What would YOU the pro users like to see in Part 2?

• The design concept?
• The research needed to make an optimal pen?
• The Card design that should have been attached?
• What should have been commemorated?
• Something else?

Stay tuned :)

https://preview.redd.it/tq1i7b66ke8h1.png?width=1080&format=png&auto=webp&s=cbc882e92a2edfe1cd4bbb03624e58b2994ea4e6

Majority of Widgets are now interactive and still missing from OpenAI documentation (https://help.openai.com/)

In addition, the widget calls now employ better reasoning capabilties and there are variations on desktop vs mobile app. For example, maps is now with a filter on desktop app.

Widgets/apps can pull or display live/external information or perform utility functions. Often connected to external data and update over time. Some of these have navigation/search and some launch maps, websites, stores, etc.

Learning blocks are interactive educational material that are self contained, slider based, teach a concept and not tied to live external data.

https://preview.redd.it/9dqb5onszx7h1.png?width=1277&format=png&auto=webp&s=c2051cc1cc04c8fec09de85effe733a781a2563e

Learning Blocks:

These appear to allow interaction from within the interface where you can adjust as you go through it. The difference I am seeing is widgets are based on information where as learning blocks allow you to change variables as needed.

Follow a pattern of Category> Type IDs. So you should be able to see:

1. Physics learning blocks (Torque, hookes law, OHMS law)
2. Probability statistics learning block (Bayes theorem, Variance, venm diagram)
3. Algebra Functions (Graphable, slope intercept, taylor series)
4. Geometry Measurement (Pythagorean theorem, sphere volume, distance formula)
5. Trigonometry (Unit circle, euler, component_x)
6. Chemistry (Molarity moles per litre, Charles law, Mass density volume ratio)
7. Biology (Mitosis, DNA transcription, Virus life cycle)
8. Finance and economics (Compound Interest, Economic order quantity

I have identified a few more IDs and these are just examples. You should be able to extract a fair amount with exception to biology which is quite limited to the things that tend to be harder to understand.

You may also notice the blocks inherit properties and views based off others in the same category. For example: Biology is interactive learning. Physics has adjustable toggles.

PHYSICS - Wavelength (Animated as well)

Probability- Venn Diagram

BIOLOGY : Virus Life cycle

ECONOMICS: Supply and demand

Majority of Widgets are now interactive and still missing from OpenAI documentation (https://help.openai.com/)

In addition, the widget calls now employ better reasoning capabilties and there are variations on desktop vs mobile app. For example, maps is now with a filter on desktop app.

Widgets/apps can pull or display live/external information or perform utility functions. Often connected to external data and update over time. Some of these have navigation/search and some launch maps, websites, stores, etc.

Learning blocks are interactive educational material that are self contained, slider based, teach a concept and not tied to live external data.

https://preview.redd.it/9dqb5onszx7h1.png?width=1277&format=png&auto=webp&s=c2051cc1cc04c8fec09de85effe733a781a2563e

Learning Blocks:

These appear to allow interaction from within the interface where you can adjust as you go through it. The difference I am seeing is widgets are based on information where as learning blocks allow you to change variables as needed.

Follow a pattern of Category> Type IDs. So you should be able to see:

1. Physics learning blocks (Torque, hookes law, OHMS law)
2. Probability statistics learning block (Bayes theorem, Variance, venm diagram)
3. Algebra Functions (Graphable, slope intercept, taylor series)
4. Geometry Measurement (Pythagorean theorem, sphere volume, distance formula)
5. Trigonometry (Unit circle, euler, component_x)
6. Chemistry (Molarity moles per litre, Charles law, Mass density volume ratio)
7. Biology (Mitosis, DNA transcription, Virus life cycle)
8. Finance and economics (Compound Interest, Economic order quantity

I have identified a few more IDs and these are just examples. You should be able to extract a fair amount with exception to biology which is quite limited to the things that tend to be harder to understand.

You may also notice the blocks inherit properties and views based off others in the same category. For example: Biology is interactive learning. Physics has adjustable toggles.

PHYSICS - Wavelength (Animated as well)

Probability- Venn Diagram

BIOLOGY : Virus Life cycle

ECONOMICS: Supply and demand

Majority of Widgets are now interactive and still missing from OpenAI documentation (https://help.openai.com/)

In addition, the widget calls now employ better reasoning capabilties and there are variations on desktop vs mobile app. For example, maps is now with a filter on desktop app.

Widgets/apps can pull or display live/external information or perform utility functions. Often connected to external data and update over time. Some of these have navigation/search and some launch maps, websites, stores, etc.

Learning blocks are interactive educational material that are self contained, slider based, teach a concept and not tied to live external data.

https://preview.redd.it/9dqb5onszx7h1.png?width=1277&format=png&auto=webp&s=c2051cc1cc04c8fec09de85effe733a781a2563e

Learning Blocks:

These appear to allow interaction from within the interface where you can adjust as you go through it. The difference I am seeing is widgets are based on information where as learning blocks allow you to change variables as needed.

Follow a pattern of Category> Type IDs. So you should be able to see:

1. Physics learning blocks (Torque, hookes law, OHMS law)
2. Probability statistics learning block (Bayes theorem, Variance, venm diagram)
3. Algebra Functions (Graphable, slope intercept, taylor series)
4. Geometry Measurement (Pythagorean theorem, sphere volume, distance formula)
5. Trigonometry (Unit circle, euler, component_x)
6. Chemistry (Molarity moles per litre, Charles law, Mass density volume ratio)
7. Biology (Mitosis, DNA transcription, Virus life cycle)
8. Finance and economics (Compound Interest, Economic order quantity

I have identified a few more IDs and these are just examples. You should be able to extract a fair amount with exception to biology which is quite limited to the things that tend to be harder to understand.

You may also notice the blocks inherit properties and views based off others in the same category. For example: Biology is interactive learning. Physics has adjustable toggles.

PHYSICS - Wavelength (Animated as well)

Probability- Venn Diagram

BIOLOGY : Virus Life cycle

ECONOMICS: Supply and demand