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The API features an MCP and a CLI and ultimately opens the door to more members from our community building tools and apps on top of Buffer. Opening up Buffer has a huge potential impact for our customers and community members, and we couldn’t be more excited to see what you build.

Buffer’s API is Open for Building

Opportunity here to build shared tools for civil society orgs using Buffer.

It is the procurement model. Companies that signed up for a productivity tool are discovering they signed up for a metered utility, and the meter runs when nobody is looking. The fix may be straightforward: capped budgets per engineer, tiered access for high-leverage roles, agent runtime quotas.

Many of the larger buyers are already there. But the implication is that the era of “give every employee a Claude Code seat” is closing, and what replaces it will look more like AWS billing than like Office licences.

Microsoft’s quiet Claude Code retreat and the real cost of enterprise AI

Important for nonprofits (a) to budget; (b) to develop sector resources.

That is why, if we want this technology to go well, it is enormously important that there be people outside those incentives—people who care about things going well and insist on safety, who are paying close attention, who are willing to say hard things, who are willing to be our earnest, thoughtful, critics. It is through dialogue and mutual effort, through the push and pull, that humanity will achieve great things.

Anthropic co-founder Chris Olah’s remarks on Pope Leo XIV.

Sounds like a civil society call to action to me.

Former officials and industry leaders fear the Cybersecurity and Infrastructure Security Agency no longer has the capacity to help utilities, banks and other critical infrastructure operators prepare for a coming wave of AI-fueled cyberattacks.

CISA takes backseat in White House AI cyber response

I just read AI Agent Traps which has given me a more expansive reference for a set of cyber risks.

Now, it’s true that some workers don’t have to be forced to use AI. Workers who enjoy a high degree of autonomy (that is to say, workers who are positioned to ignore workplace coercion) can adopt AI in ways that they feel suited to, just as those early web users and Visicalc smugglers did. They can fulfill the maxim that labor-driven automation improves quality, while resisting capital’s insistence that automation be used to increase throughput at quality’s expense.

They can act as centaurs (workers assisted by technology), not as reverse-centaurs (workers who are recruited to serve as peripherals for machines). As with all technology questions, what the technology does is nowhere near as important as who the tech does it for and who the tech does it to…

[Pluralistic: The AI bubble isn’t like the internet bubble (26 May 2026)

On making my own tools: a Readwise-to-PDF Archiver

I’ve been spending time lately refining my knowledge management system. I want to be sure I’m archiving permanent PDFs of the material that is really key to the projects I’m working on—not just the stuff I save to Readwise, but the articles I actually take the time to annotate and think through.

To make that easier, I used Gemini CLI to help me build a little archiver utility. I’ve shared it on GitHub here: Readwise_PDFArchiver_Public.

This is the third workflow utility I’ve created for myself now. Each time I do this, it gets a bit easier. I’m learning how to ask for things more efficiently, I know to clarify the architecture at the beginning, and I’m getting better at working through the technical hurdles that come up.

It’s interesting to look at these three small utilities—two of them don’t use AI at all in the final code; I just used AI to write them. The other one does use it for some analysis. What AI has really opened up for me is the option to use Python for these routine tasks that just make the maintenance of my digital systems work better.

It’s about building a bespoke workflow. The trick is keeping it only on the things that are just mine, like my PKMS. Versus workflows that are shared or nested in a broader collaboration. All of it has me thinking about how I improve my own computing environment and how we have a solid set of organizational Standard Operating Procedures so that we can be orchestrated in our team-wide or organization-wide efforts.

But what if the key to serving your users best depends in large part upon training a machine learning algorithm? What if that ML algorithm needs a massive training dataset? In an age when machine learning is in its ascendancy, this is increasingly a critical design objective.

Seeing Like an Algorithm | Remains of the Day

This an older article — and long! — but if you do any kind of modeling to better understand the preferences of your users, it is well worth the read.

[Switching vendors] also applies to other layers of the tech stack (database, etc.) to various extents as well as to some other types of software, e.g. it’s trivial to export your bookmarks from one bookmark manager to another if they both have APIs or import/export capabilities — or, with a bit more effort, you can write your own.

What If Lock-In Doesn’t Matter So Much Anymore?

Another for the “how do we bring more portability and agency to the nonprofit tech stack” file. Those are also the things that bring negotiating power.

This is a literal revolution but one against the participatory web, against us: The goal is to take away the web and guide people into Google’s abstraction on top of it. An abstraction they control and moderate. It’s about monopolizing access to information. A true Metaverse unbound by open standards and your ability to build your own corner of the web according to your needs and desires. Which – given how strong Google’s influence is on web standards – will change the shape of the standards for the technological landscape we are building the web on.

On Google declaring war on the Web

Let’s be frank: we haven’t been paying attention to context for a long time. That’s what hurts long simmering news stories, fuels mis/disinformation, dampens civic engagement.

So the question is how do we use these tools, how do we reshape them, what are we advocating for?

A demand co-op is a cooperative that pools and directs the spending power of its members. Demand determines what gets built, who survives, and where wealth flows. Most communities already have enormous spending power, but because that demand is unorganized, the value created from it is captured by outside businesses and investors. A demand co-op coordinates that spending so economic activity can build communal businesses, assets, and long-term ownership instead of constant leakage.

What is a demand co-op

This reminds me of open source bounties. I’ve been thinking a lot about to aggregate the small nonprofits served by TechSoup turn philanthropy into negotiating power. Group buying, mutualism, are all a part of the answer. I will add this to the list.

The choice reflects a broader Google strategy: Stay at the frontier, but also prioritize models cheap and fast enough to deploy across products used by billions, rather than chasing benchmark supremacy alone.

How Google plans to win the AI war

What impact does this have on the environmental impact?

If they wanted an AI essay, they would have asked ChatGPT themselves. They asked you because they wanted your human judgment.

no slop grenade

Please.

It’s the night clerks that have the most customer interaction–in fact, they’re almost certainly the highest leveraged, most insightful marketing cohort in your organization.

They have information, and if we give them agency, they could transform the customer experience.

The night clerk | Seth’s Blog

This makes me think about adding an interview agent to the AI swarm project I posted about yesterday. To make it easier for our frontline staff to influence the organization. And give them more superpowers for talking to nonprofits.

Building with an AI swarm

I learned about AI swarms over the weekend. It’s a group of specialized AI agents that are coordinating on a centralized task. Here’s an example that provides a good idea of what is possible: A Practical Experiment in Building an AI Agent Swarm.

The job I set my swarm to was something that we’ve been trying to get over the line at work but the conceptual hurdle of building within our system was too much. It’s not an unusual problem for an organization that has been working for years, diligently doing work in a specific domain, and building up – in equal measure – rich context and knowledge along side legacy tech that abstracted that into a set of technologies that become rigid overtime.

So a basic description of what I wanted: a way to get our context into a verbose knowledge base. That is, a knowledge base written for agents, for reforming and republishing, for integrating into other systems – rather than a knowledge base written for humans. I also needed a way to organize this knowledge base so using it is as efficient as possible, reduces the likelihood of hallucinations, and can be reviewed regularly for errors by both agents and by humans. Oh, and I also wanted to display a set of information as a filterable web page.

My swarm included:

• a planner agent, this functioned to help orchestrate and make sure I was keeping everything documented along the way
• a discovery agent, who searches the web to find new leads and information relative to my project
• domain agents, who each have expertise in different functional areas
• a validator agent, who checks links and schema to make sure things are correct and work across the project

These agents are each represented as simple markdown files. That is, files I can read, review, and edit. That means they stayed in my domain area and I can easily alter them without spending tokens. Just with fingers on a keyboard. Just like I’m writing this post.

The agents went out and found leads. Then the domain experts researched the leads assigned to them and wrote markdown files describing the results of their research. These markdown files became the heart of the system. Specific portions powered the web page. The rest became the information that I could use for a variety of other projects.

I learned a lot in this process:

1. How to organize the agents so that I didn’t have too many but did have enough to be able to set off specialized tasks.
2. How to organize the markdown files. I’m using skills files as the model for this. It will let me build out more assets for each of the areas, things that can go beyond what’s in the markdown files when needed.
3. Just how far I could get with the tools available to me: I was using Antigravity and then Gemini CLI when I ran out of Antigravity tokens.
4. How to use change logs and other related assets so that I can review the work and, based on the reviews, improve my agents and process generally.
5. How to think about a road map in this agent swarm world. Not too different, actually, from thinking about building out a staffing plan.

I also learned what I don’t know:

1. How to choose between markdown and JSON. You could make an argument that my knowledge base pages could be in JSON rather than markdown. I realize that I’m not exactly sure how to play out the pros and cons.
2. How to think about adding agents over time. Do I want case study agents that build content to augment the verbose knowledge base? Do I want other associated assets? Do the domain agents do those?
3. How do I build for scale? I could make a rapid prototype but if I though 100 people were going to hit that page, 1,000, 10,000 what changes and what do I need to do?

The next step isn’t technical; it’s human. I need to share this prototype in a way that builds excitement for augmented productivity, rather than triggering the natural anxiety of displacement. In an agent-powered world, the staffing plan changes. The core leadership task remains the same: building alignment, fostering confidence, and ensuring the team has the agency to drive the new tools forward, rather than being driven by them.

This should be front of mind for every CTO, CFO, and head of operations reading this. Because when the pricing corrects, and it will, the companies that treated AI as a permanently cheap utility are going to wake up to bills that make their current SaaS spend look quaint.

Every AI Subscription Is a Ticking Time Bomb for Enterprise

This exactly what we need to worry about in the nonprofit sector. How do we use AI in a way that accelerates our work and impact and proactively manages the expenses.

AI vendors are clearly the “new new gatekeepers”. Like the previous ones, they will dominate how we learn about the world even while some of us turn to open source and liberatory alternatives. But they may not dominate how we connect and share our experiences of the world, and that’s the core of the opportunity: how do we design pro-social frameworks and spaces that sit alongside an agentic information ecosystem?

Notable links: May 15, 2026

And how do help people know the difference?

This year’s Key Recommendations in this report convey fundamental best-practice expectations for social media platform safety, privacy, and expression. As we have done for the past six years, GLAAD continues to implore social media companies to meet these basic best practices: improve content moderation; provide meaningful transparency; respect data privacy; demonstrate commitments to workforce diversity and civil discourse; and strengthen and enforce policies that protect LGBTQ people from hate, harassment, and disinformation — while also not suppressing LGBTQ content and creators.

Executive Summary – 2026 Social Media Safety Index | GLAAD

Read this with yesterday’s share on content moderation as infrastructure in mind.

Papers that are more difficult to read might be worth it if AI increased the amount of good science being produced. But this doesn’t seem to be the case. Organization Science is desk-rejecting (e.g., rejecting a paper before even sending it to peer reviewers) nearly 70% of manuscripts that made heavy use of AI. This number drops to 44% for papers written without AI.

Easy Is Overrated

Can we do this kind of analysis on grant submissions?

Without uncertainty tolerance, we risk getting stuck at what statisticians call a local maximum. Like a mountain climber standing at the top of a hill, unaware of a taller peak just out of sight, our discomfort with uncertainty can keep us wedded to a business strategy, a job, or a relationship that is safe, but not optimal. Uncertainty tolerance allows us to persist through ambiguity—and it has never been more relevant.

5 Questions to Help You Navigate Uncertainty

I refer to this kind of uncertainty as navigating with a compass, not a map. I will read How To Not Know — this post is by the author — to see if it adds to the toolkit.

Moderation is infrastructure. It must be built like roads and power grids–with deliberate choke points, fail‑safe valves, and capacity limits. In road engineering, it is the ‘design speed’ of the road that users drive, not the posted speed limit; the tighter the curves, the shorter the sightline, the more measured the pace. The same is true of platforms–architecture sets behavior more effectively than policy.

What We Will Refuse to Build Again | dangerousmeta!

How do we architect for thoughtfulness and meaningful engagement?