Darragh Kennedy

Director of Engineering @Zendesk - views are my own

[Neil Mehta @neilmhta]

NEW investigation for @WSJ:

- Polymarket is paying scores of offshore clippers to quietly promote its international exchange in the U.S. (though it’s banned from letting Americans trade on the platform)
- Polymarket made dummy websites mirroring its real site, then paid creators to use the fake site and pretend to win thousands.
- Creators altered headlines and used outdated footage to imply they won bets—even when they often lost
- Polymarket paid Adin Ross multiple millions to promote the site

All that and more in my latest story with @ByKLong @ceostroff @brenna__smith

[John Cleese @JohnCleese]

Eureka!! Hope you all are enjoying the World Cup

[Midjourney @midjourney]

A technical dive inside our new "Midjourney Scanner"

[Google Cloud Tech @GoogleCloudTech]

Introducing the Open Knowledge Format (OKF), an open specification that formalizes the LLM-wiki pattern into a portable, interoperable format.

AI is only as smart as the context we give it. As we build more advanced, agentic AI systems, they need accurate metadata and context to be useful. But in most organizations, that context is locked inside fragmented data catalogs, isolated wikis, scattered code comments, or the minds of senior engineers. Every time a new AI agent is built, teams are forced to solve the exact same context-assembly problem from scratch.

To solve this, we've announced OKF, a vendor-neutral, open specification that formalizes the "LLM-wiki pattern" into a portable, interoperable format. It provides a standardized way to represent the enterprise knowledge that modern AI systems rely on.

— Just markdown: readable in any editor, renderable on GitHub, indexable by any search tool
— Just files: shippable as a tarball, hostable in any git repo, mountable on any filesystem
— Just YAML frontmatter: for the small set of structured fields that need to be queryable: type, title, description, resource, tags, and timestamp

We’ve also shipped reference implementations to help you hit the ground running, including an enrichment agent for BigQuery, a static HTML visualizer, and live sample bundles on @github → https://t.co/ilhAMCrcTc

➕ Knowledge Catalog can now natively ingest OKF!

Stop reinventing data models and building bespoke integrations for every new AI tool. Here's more about how OKF works → https://t.co/FR4kJRsgEH

[Darragh Kennedy @darraghke]

@LiebermanAustin Useful only if you wanted to be punched in the face.

[Scott Bryan @scottygb]

BBC: “What was your screen time?”

Student: “Nine hours.”

BBC: “You’re gong to have a lot more time to fill. What will you do?”

Student: “Stare at a wall.”

[Football Away Days @FBAwayDays]

A spine tingling rendition of Flower of Scotland being belted out by The Tartan Army, before their first World Cup Finals game in 28 years.

A moment that many have waited a lifetime for. 🥹🏴󠁧󠁢󠁳󠁣󠁴󠁿❤️

[Yunyu Lin (yun/yu) @yunyu_l]

Marco Rubio finding out he has to be the CEO of Anthropic after it gets nationalized

[Jeff Dean @JeffDean]

People replace their phones every ~4 yrs. This means there are hundreds of millions of old phones discarded each year that are still perfectly usable as computing devices. @Google in collabration with @UCSD is exploring how to turn these old phones into cloud-computing “phone clusters”. Putting phones back in service in this way can directly reduce the environmental footprint of computing by avoiding the need for further raw material extraction, and taking advantage of the embodied carbon already incurred from manufacturing these devices, and modern phones actually are already quite powerful computers. Read more in the blog below ⬇️

[Charity Majors @mipsytipsy]

this statement is true, but it all comes down to:

"What does quality mean, in this particular circumstance?"

talking about "fast" and "slow" is toddler-level reasoning, and it irks me when the AI debate gets so wrapped around that axle.

[Peter Berezin @PeterBerezinBCA]

AI spending is massively skewed toward a small share of power users.

[Anne Applebaum @anneapplebaum]

Amazing: KPMG wrote a report describing the successful use of AI by businesses. But the case studies turned out to be AI hallucinations.
https://t.co/s3LE8vedNi

[TechCrunch @TechCrunch]

Meta’s months-old AI unit is a soul-crushing gulag, say the engineers stuck inside it https://t.co/3QDYqVYwSr

[Darragh Kennedy @darraghke]

@Seasoner19 @Potstirrer111 They specifically call out holiday periods as putting it in breach.

[Aaron Levie @levie]

At Box, we just surveyed 1,640 IT leaders across the US, Japan, and Europe about agentic AI adoption. Many standout findings, but a big one was that the companies that adopted AI the most are planning to grow headcount the most.

Obviously lots of ways you can read that data and variables mixed in, but it’s actually quite intuitive that the companies that become most productive want to (and are able to) reinvest back into the business to keep getting the gains going.

The narrative of jobs being wiped out assumes that companies will take a fixed approach to what they want to be able for work on. What’s happening in practice is it’s causing companies to want to light up more engineering projects, sell to more customers, automate more processes to give time back, and more. That all leads to more work to be done by people.

[Trung Phan @TrungTPhan]

LinkedIn, X, Instagram, Reddit

[Mitchell Hashimoto @mitchellh]

Fable is a good model. As with all new models, it is simultaneously excellent and entirely unremarkable (relative to other models). It is slow and expensive, and the "loops are all you need" discourse they are pushing is obvious in the context of someone using Fable-class models

What I've found so far is that for broad scope design (code architecture) tasks, Fable is unremarkable. Or, not better enough to justify its cost and speed.

But in highly targeted goal-oriented loops, it is another beast entirely. It is very slow but produces very good results.

I let it churn on optimizing a SwiftUI-layout resolver in Go I wrote and it was able to bring it down to an order of magnitude I could not reach myself (micro => nanosecond scale). But it took 2 hours and $40 to do it and I had to claw back some changes it overfit to Apple Silicon. Still, very worth it.

In comparison, for "implement this feature/change" iterative work, I ran head-to-head Fable vs GPT5.5 vs. GLM-5.1. They all produced equally acceptable final results, but GPT5/GLM did it in a couple minutes and Fable was churning away for 40 minutes. And GLM cost me less than a dollar, GPT5.5 ~$1.50, and Fable cost $9.

You can see that in this context, interactively working with an agent is nonsense. Its too slow. You need to write loops to keep the agent working and you probably want to highly parallelize the work being done. As with all things, I think a balance makes sense...

My sense is that I'd reserve Fable for targeted, surgical analysis and work. Not for daily driving everyday tasks.

I'm going to keep spending a shitload of money (relatively) and maining Fable for the rest of the week to continue to judge, will report if anything changes. I'll continue to head-to-head as well.

[MTS @MTSlive]

SITUATION DETECTED: For the first time in internet history, agentic traffic has surpassed human traffic online, per Cloudflare Radar.

[Rohan Paul @rohanpaul_ai]

🗞️ Google DeepMind's paper has some great advice on how we should actually give tasks to AI.

It is not just about telling an AI to do something and hoping for the best. Instead, this framework looks at delegation as a string of choices where you figure out if you should even hand the task over, how to explain it, and how to check the work afterward.

Current systems rely on rigid rules that break when things fail unexpectedly. The researchers suggest building a dynamic market where agents bid on tasks using smart contracts.

This requires strict monitoring and cryptographic proofs to guarantee correct work without leaking private data.

Instead of trusting a simple rating, agents will use verifiable digital certificates to prove their exact skills.

- Keeping things flexible when things change

This new system is built to be adaptive rather than stuck in its ways. It treats the handoff as a live process where authority and responsibility can shift around in real time. If the situation changes or something breaks, the framework helps manage that failure so the whole project does not go off the rails. It works for both humans giving tasks to AI and for when AI needs to handle things on its own.

- Finding the right amount of trust
One of the coolest parts is how it handles trust. They made formal trust models that look at how hard a task is and how well the AI has done in the past. This stops people from "over-delegating," which is when you give an AI something it is not ready for. It also stops "under-delegating," which happens when you do all the work yourself even though the AI could have handled it easily.

- Double checking the work

You cannot just take an AI's word for it, so this framework has specific ways to validate the output. It sets up rules for when to accept an answer based on how confident the AI is. It also has backup plans ready to go if the AI fails. This is super important for real world jobs where trusting a machine blindly could cause a bunch of errors to pile up.

- When AI agents hire other AI agents

The framework also covers what happens when 1 AI agent hands a task to another AI agent. The system tracks who is actually accountable and makes sure the right authority is passed down the line so nothing gets lost in the network.

- Making sure the work actually fits
It is a step by step approach to make sure the AI's contribution actually makes sense for the bigger goal. By treating this as a structured process, they are making it much safer for companies to use AI in their daily operations without worrying about constant mistakes.

----

arxiv. org/abs/2602.11865

"Intelligent AI Delegation"

[OpenAI @OpenAI]

OpenAI frontier models and Codex are now generally available on AWS, giving enterprises a new way to build on Amazon Bedrock with OpenAI through the security, compliance, and governance workflows they already use.

This is also the beginning of a broader expansion of OpenAI capabilities on AWS, including future availability for cybersecurity capabilities like Daybreak.

https://t.co/vMws0YU6Q3