From the recent Local-First Conf in Berlin. Thanks for the shoutout @PVH
Try Composer today - https://dxos.org/composer
| Website | https://dxos.org |
| Github | https://github.com/dxos/dxos |
DXOS
@dxos_org
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A developer platform for building beautiful, local-first, collaborative applications.
Had an epic week hanging with industry leaders at the local first conf in Berlin.
Exciting times ahead. 🚀🚀
Thure#Seattle UXers! We’re gearing up for a great gathering on the topic of #UX for the #decentralized web (#DWeb) this Friday at 6pm near Pike Place Market!
If you haven’t registered yet but want to attend, drop us an RSVP: https://lu.ma/op4fc8ac
I’m going to talk about #InkAndSwitch’s Upswelling (peer cursors, the fishbowl effect, drafts and versions, ambient signals) and how related problems affect the space for @dxos_org’s Composer.
Join us next week for our monthly developer hangout.
It's the second Tuesday of every month in the DXOS Discord (https://dxos.org/discord)
Chad KohalykToday is Day 2 of #LoFiWKND. I was there until after 10 last night and there were many others staying to hack on things like Automerge and @dxos_org
Presentations will be later this afternoon.
Thanks to @hyphacoop @inkandswitch.com and @feathers.cloud
for supporting this community event.
In principle, this could be running any function and interacting with any data inside of Composer.
We're experimenting with AI use cases that leverage local data inside of documents, spreadsheets, tables and sketch/diagrams inside of agentic workflows.
New Year. New website. 🚀
Quick demo of running a custom function in Composer.
Showing a local-first collaborative chess board along with a custom function that listens for gameplay (changes in the local database) and moves the opposing piece as a response based on all possible legal moves.
Read more on our blog: https://blog.dxos.org/decentralized-schema-changes-and-data-migrations/
Decentralized schema changes and data migrations
As we deliver software in production, we often have to change the schema of our data. When we make new features, we add new data types and fields, and things get deprecated and removed as time goes on. In a centralized system, this is typically done by carefully moving data
We're looking into ways to make this better. Possible solutions include protocol-level policies for inactive peers and exploring new methods to rebase changes onto the canonical state.