Add Lumen 1.0 announcement

[philippjfr]

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Pull request overview

This PR adds a blog post announcement for the Lumen AI 1.0 release. The post describes the major architectural improvements and features in Lumen 1.0, including a UI rewrite, a shift from global memory to explicit typed context, a new execution architecture supporting reports, and expanded connectivity.

Changes:

• New blog post announcement for Lumen AI 1.0 release
• Includes technical diagrams illustrating the typed context flow
• Covers architectural improvements, lessons learned, and future roadmap

Reviewed changes

Copilot reviewed 1 out of 5 changed files in this pull request and generated 1 comment.

File	Description
posts/lumen_1.0/index.ipynb	Main blog post content in Jupyter notebook format with metadata, introductory content, technical details, and future plans
posts/lumen_1.0/images/typed_context.svg	SVG diagram showing the flow of typed context through Lumen's execution graph

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[ahuang11]

Great start! From my POV, this is more of a dev blog post, and for v1.0.0 (re-)release, I think we should try to appeal to the general public by sharing more potential use-cases, pretty pictures (from docs is fine), and less into the weeds of developing Lumen.

[philippjfr]

That's fair feedback. That said Lumen 1.0 is still very much a framework and developer-facing release, and the post reflects that reality. We should definitely do more to surface concrete use cases and visual outcomes to make the value easier to grasp, even for readers who won’t install it immediately (I've started doing that before I read your comment but there's likely more to do here).

My instinct would be to keep the architectural narrative, but complement it with clearer examples and visuals that show what Lumen enables, rather than trying to position it as an end-user product prematurely.

I'd suggest the following as a plan of action:

1. Add more usage examples of what can you do with Lumen.
2. Produce a number of videos of Lumen solving real world problems to share on social media.
3. Produce a distinct version of this blog post to e.g. feature on the Anaconda blog, that focuses more on the use cases.
4. Work towards building end user installers and announce those separately.

[jbednar]

Looks great! I left one comment but mainly engaged by editing a new version and pushing to this branch. @philippjfr , please review my changes and back out anything that you don't agree with.

[MarcSkovMadsen]

Overall feedback

• The blog post appeals to people willing to invest the time in reading and learning more about Lumen.
• I think the video to some extent can catch peoples attention and make them interested. But before investing a lot in reading the blog post I would assume people would like to try this out to learn whether this is at all worth investing time and brain width on. The blog post requires mental bandwidth to dive into.

I would recommend:

• A try it for your self link just below the video. Linking to a self containing (install + data + instructions) tutorial that makes it easy to try out lumen. uv can probably help make this easy and fast to try out.

Lumen is still very hard for me to understand the value of. Because I still haven't seen any end to end products. Is this for fast insights? Is this to create reports?

Having the vega specification above the plot tells me this is for developers that want to develop a vega spec using AI. Not for users wanting to plot something and get insights. The focus is on the spec not on the insight. Have you considered hiding the spec by default if this is for non-technical users?

If I was a developer and I was hooked to Lumen, I do think I would find the existing blog post interesting, read it and try to understand it.

Some questions I still have:

• How do I connect lumen to real data? We don't work with CSVs. We work with internal sql databases, datawarehouses and blob storages
• How do I provide my internal knowledge base to Lumen. It needs to know my context to become valuable.
• How do I navigate the UI. I still don't really understand it.
• How do I create an end product. I've seen hints at this. But its not clear to me.

[philippjfr]

Thanks @MarcSkovMadsen, your feedback has been very valuable and so is this comment. That said I think a lot of the "confusion" comes from the fact that you are definitely not the target user here, though I do expect that if Lumen was set up to access all data within your organization you could probably find value in it already.

You do make the very valid point that Lumen today sits at an awkward intersection:

1. It currently requires Python knowledge to set up and configure, so we currently have no direct way to appeal to a user. Competitors either provide a SaaS solution or integrate similar features in their existing product. The eventual goal for us should be to provide installers for all major operating systems with a configuration wizard, e.g. to configure the LLM on first launch. Today we don't have that.
2. One of the core goals is to empower "less-technical" users that wouldn't be super comfortable (vibe-)coding, but as you perhaps rightly point out that kind of clashes with the idea of inspectable specs. A Vega-Lite spec certainly does look intimidating to a non-technical user. Whether that means that we should hide the specs by default I'm not sure, "inspectability" is a core promise behind Lumen. But what help is that really if the user doesn't understand what they're inspecting? I don't have the correct answer here, though I wouldn't be opposed to add a toggle to the splash screen (and settings) to toggle "expert mode" on and off, which determines whether to show specs by default or not.

This makes it quite difficult to find the right tone and content for this blog post. We are hoping to attract a few institutional partners to work with them through the "create an end product" process, which I think can push the overall robustness along, flesh out the extension points etc. That means as of today we don't have a great end user story and are still lacking full coverage of the "build an end-product" story either.

Lumen is still very hard for me to understand the value of. Because I still haven't seen any end to end products. Is this for fast insights? Is this to create reports?

A fair way to frame this more directly is:

Today, Lumen is primarily about fast, structured insights without writing code. You can connect to data, ask questions in natural language, generate SQL, and produce visualizations quickly, while still being able to inspect and trust what was generated.

At the same time, Lumen is not positioned as a finished reporting or dashboarding product yet. What we have done in 1.0 is lay the architectural foundation for that next step: a typed execution model, explicit context passing, and a task-based plan runner that can mix LLM-driven and deterministic steps. Those pieces are what make exportable reports and data applications possible, but the end-user workflows around them are still under active development.

So the value proposition today is speed and structure: getting to insights quickly without coding, while avoiding black-box behavior. The longer-term direction is to let those same explorations be promoted into repeatable reports and composed data apps, without throwing away the work or re-implementing it elsewhere. That end-to-end vision is real, but it is not fully realized yet, and we should be clearer about that boundary.

How do I connect lumen to real data? We don't work with CSVs. We work with internal sql databases, datawarehouses and blob storages

Clear that we need at least 2-3 how-to guides à la "Connect to Snowflake/Dremio", "Use DuckDB to connect to blob storage", "Use SQLAlchemy to connect to MySQL/Postgres..."

How do I provide my internal knowledge base to Lumen. It needs to know my context to become valuable.

Also requires a how-to guide on populating a vector store and adding it.

How do I navigate the UI. I still don't really understand it.

I do believe the latest docs cover this fairly well.

How do I create an end product. I've seen hints at this. But its not clear to me.

Right now we have two pieces to point to here, Andrew's basic weather data AI Explorer tutorial and the lumen-anndata. One, I think, covers the basics, the other is much more complex but provides no real guidance. I've been working towards a more fleshed out lumen-finance example which aims to demonstrate a few things:

• Exposing finance APIs as a data source
• Adding a bunch of custom analyses, tools and agents
• Feeding it a bunch of documentation and customizing prompts