Again rookie question. All these world class athletes I’ve been watching. How do I continue to watch these guys when they go back to their club teams-Now that I’ve seen them I want to keep watching them-I’m sure this sounds ridiculous but how do you watch these guys on their club teams when you live in the US?
Are all these great players in the same league? When is their season? And how do you buy a package to keep watchin these ballers?
And are the crowds like this for club matches or just the WC?
The energy is what really draws me in (along with the crazy skill).
And lastly how do I find a team to follow?
Other than the obvious guys like Messi/Renaldo-Mbappé the other guy I love is #2 on Morocco Hakimi.
Who is his club team? Maybe that’s my team…he might be my favorite guy I’ve watched the entire tournament.
I’m sorry I’m clueless to all this-just completely uncharted waters for me but I’m hooked and am gonna want more when this is over in a few weeks.
Introducing Claude Tag, a new way for teams to work with Claude.
In Slack, Claude joins as a team member with access to the channels and tools you choose. Tag Claude in and delegate tasks to it while you focus on other work.
Introducing Claude Tag, a new way for teams to work with Claude.
In Slack, Claude joins as a team member with access to the channels and tools you choose. Tag Claude in and delegate tasks to it while you focus on other work.
@samhbarton@Calyxapp I’d be interested.
I’ve built the same thing out in obsidian with my own plugin and context management system.
Curious to see yours and share notes.
@owengretzinger So does this only work with symphony and codex or does it apply to codex as well? Would you be able to replicate this with a different project management tool like ClickUp or a configured obsidian?
@vixsheikh@nbaschez I’m not a dev, so I don’t know GitHub, but this problem has already been solved before with law firms and consultancies.
The system exists, it’s just framing it in a tool that’s needed.
More thoughts in a video I shared with a dev:
https://t.co/v3sz2zKLeJ
Mr beast reveals he made his first 250 employees read ‘The Goal’ so they understand when he says Bottleneck
“I made my first 250 employees read it it helps get everyone on the same page of when I say bottleneck I use the word bottleneck quite a bit especially when filming”
“if I tell you you’re the bottleneck to the production to me that’s a very very serious sentence”
@aronprins https://t.co/z3lfz1bNpJ
AI integration for companies based on Work Architecture (https://t.co/tBklrXQsKA).
Goal is 10 companies solving real problems with defined systems by the end of Q3
I'm writing more and more everyday.
It's always been one of the highest leverage activities for me, but even more so with my increased adoption of AI.
I spend way less time clicking buttons now that AI can handle execution.
I write to reflect and to cultivate judgment.
Introducing Claude Design by Anthropic Labs: make prototypes, slides, and one-pagers by talking to Claude.
Powered by Claude Opus 4.7, our most capable vision model. Available in research preview on the Pro, Max, Team, and Enterprise plans, rolling out throughout the day.
Anyone in knowledge work runs into this problem when they try to collaborate with a team of people that’s using Claude Code/Agents.
I’m working on a workspace architecture that has three spaces for the AI Native knowledge team.
Someone will solve this, probably Anthropic. But for now we are solving it for our team with Obsidian + GitHub + Google Drive.
The 3 workspaces:
The Desk
The Shelf
The Table
People will work at their Desk (Obsidian) with Claude Code.
This is the most current and honest place for context.
Teams will reference stable context from the Shelf (GitHub) with Claude Code.
This is where verified and managed context lives: skills, decision logs, project scopes, client details, etc.
When you work on something for a client, your agent grabs context from the Shelf and pairs it with the context at your Desk to be current with your persona context and aligned with the team’s context.
Clients will access, review, and share deliverables at the Table (Google Drive).
Ready to read and share with people they way they are used to accessing deliverables.
It’s no different than how large law firms or consulting firms have worked for decades. They have extensive knowledge bases with shared stable context and best practices so they don’t duplicate work or drift from precedence. They have entire teams dedicated to maintaining this context.
But the small companies never had the resources or bandwidth to maintain a thorough knowledge base. They kept this context in a Google Drive, Notion, Slack, email threads, and in reality, most of it is in their heads.
But with AI, we have agents that can serve as the knowledge management workers small firms could never afford.
Would love to discuss this further with anyone trying to solve this problem or experiencing it themselves.
Someone is going to build a worldclass “Brain” for enterprises & make a stupid amount of money.
Why? As @da_fant said, “coding w ai is solved bc all context is in the git repo. knowledge work is difficult bc context is spread out. an ai system that creates a git repo w all context for a knowledge worker will be able to 100% automate the work.”
When companies talk about being data ready for AI, this is what they’re implicitly saying.
Engineering has been prepared for this moment for a long time because of the deterministic nature of code, the centralization/versioning of data (read: GitHub), and AI tools that are largely build by engineers for engineers.
But for the rest of white collar work, there’s a TON of catching up to do to properly harness the power of the technology.
The big challenge here, and why no one has truly cracked the code for "an ai system that creates a git repo w all context for a knowledge worker" is because unlike code, most knowledge is 1) distributed, 2) unstructured, and 3) unverifiable.
It's distributed: transcripts live in Granola. Documents in Notion. Customer Data in Hubspot. ERP. Emails. Slack messages. Random spreadsheets. SOP docs. Etc. Etc.
Building an ingestion engine that connects to all of your disparate data sources and auto-updates based on the shelf-life of the data is the first, and frankly, easiest step of the process.
Next, it's unstructured: let's say I want to create a proposal for a potential client. To nail the proposal, I want it to pull important information from a variety of sources. The specific asks & background from our initial sales call. Previous proposals to anchor ourselves to a proven format. And completed sprint boards from Linear, so the pricing & timeline in the document is grounded in truth.
Whether it's a thoughtful filesystem (a la Obsidian) or an OpenClaw-esque memory structure, the brain needs to be great at self-organizing in a thoughtful schema. This is very hard, especially if you want to build a generalizable brain that can be shaped to an array of different enterprises.
And finally, most knowledge is unverifiable: writing a function, running a unit test, and seeing if the code works is easy. It works or it doesn't. Using AI to accelerate your content creation process is highly subjective. What is a good/bad idea? Is the content in your voice or not? Does it feel like slop or novel? Answering these questions are both difficult and non-verifiable.
That same system described above doesn't just have to be great at organizing & forming coherent relationships, but it also has to be great at self-improving based on feedback from the user. Memory systems (like those introduced by OpenClaw) are great to a point, but as you scale the corpus of data within your company's brain, things like compaction and cleaning become wildly important to avoid the needle in the haystack problem.
Someone is going to figure out how to solve this problem, and when they do, not only will they make a shit ton of money, but they'll be robinhood for knowledge workers, enabling non-engineers to enjoy the sort of leverage that only technical folks have felt for the last few years.
We built our launch video in Claude Code using HyperFrames.
Now it's yours.
Open source, agent-native framework. HTML to MP4.
$ npx skills add heygen-com/hyperframes
RT + Comment "HyperFrames" to get the full source code of this launch video (must follow)
We built our launch video in Claude Code using HyperFrames.
Now it's yours.
Open source, agent-native framework. HTML to MP4.
$ npx skills add heygen-com/hyperframes
RT + Comment "HyperFrames" to get the full source code of this launch video (must follow)