Sure (e/acc)

The future is agentic!
Supercharging Legal Workflows @Lawlyfy AI ⚖️🇰🇪

[Sure (e/acc) @SureCapital]

It was awesome facilitating this session at @moringaschool on recent advancements in Generative AI and their impact on the future of work in the tech sector.
My key message was: "AI Won't Replace Humans — But Humans With AI Will Replace Humans Without AI."
#AI #GenerativeAI

[Moringa School @moringaschool]

Our AI event today with Antony Sure has been instrumental in helping us understand how best we can use and adopt AI to future proof our tech careers.

#moringaschool #ai

[rari @0xwhrrari]

Anthropic Research Lead:

"99% of our engineers run swarms of 300+ self-improving agents"

"Close the loop, give the model a way to verify its own output"

In a 20-minute session, an Anthropic Team member breaks down how to build agents that improve themselves

The real setup is Claude running through loops, plan mode, and dynamic workflows

Better than most $300 agent courses

Bookmark and watch the talk

Then read the article below

[Sakana AI @SakanaAILabs]

Introducing Sakana Fugu: A full multi-agent orchestration system accessible via a single model API.

Our ‘Fugu Ultra’ model matches the performance of Fable and Mythos, delivering frontier capability without the risk of export controls.

Try it: https://t.co/hhO6qTawgb 🐡

[Sure (e/acc) @SureCapital]

@paolo_scales 60

[Sure (e/acc) @SureCapital]

@dimitarangg Intent

[Raytar @Raytar]

Met a guy making $1.6 million a year.

Three days ago he was at a Meta conference. Told me he saw the best AI talk of his life.

Boris Cherny was on stage. Showed how the Anthropic team actually uses Claude day to day.

Boris deleted his IDE eight months ago. Now he codes from his phone.

I watched it last night. Had to pause it twice.

Not because it was hard. Because I realized I've been using Claude like a toy.

He sent me the recording. It was never published.

Posting it below.

[a16z @a16z]

Narrative violations abound:

- Demand for software engineers is rising
- Software devs are rising as a share of new jobs
- AI exposed industries are seeing above-trend wage growth
- Open PM jobs haven't been higher since 2022

More from a16z's David George on the "AI job apocalypse" myth: https://t.co/7sbadmEElG

[Peter Pang @intuitiveml]

https://t.co/SFQ9zsi1nQ

[Harrison Chase @hwchase17]

https://t.co/aZnKZ1SVXB

[Nav Toor @heynavtoor]

https://t.co/mFvSsFIzlm

[The Boring Marketer @boringmarketer]

learning direct response copy is the only “hack” you need to grow on social

[Sure (e/acc) @SureCapital]

Running GLM 5.2 in my Claude Code harness currently and I have to say, it seems the harness is the moat for Anthropic.

For most tasks, there’s barely any noticeable difference it outputs compared to Claude.

[Sure (e/acc) @SureCapital]

@AlfieJCarter Agents

[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

[Sure (e/acc) @SureCapital]

@brillaas Landing page

[Rahul @sairahul1]

Andrej Karpathy: "90% of Claude's mistakes come from missing context, not a weak model."

41% mistake rate without a CLAUDE.md. 11% with the 4-rule baseline. 3% with the 12-rule version below

here are the 12 rules senior engineers settled on:

1. think before coding: state assumptions, don't guess. the model can't read your mind, stop hoping it will

2. simplicity first: minimum code, no speculative abstractions. the moment you let Claude add "for future flexibility," you've added 200 lines you'll delete next quarter

3. surgical changes: touch only what you must. don't let it improve adjacent code, that's how PRs blow up

4. goal-driven execution: define success criteria upfront, loop until verified. without them Claude either loops forever or stops too early

5. use the model only for judgment calls: classification, drafting, summarization, extraction. NOT routing, retries, status-code handling, deterministic transforms. if code can answer, code answers

6. token budgets are not advisory: per-task 4000, per-session 30000. by message 40 of a long debug, Claude is re-suggesting fixes you rejected at message 5

7. surface conflicts, don't average them: two patterns in the codebase? pick one. Claude blending them is how errors get swallowed twice

8. read before you write: read exports, callers, shared utilities. Claude will happily add a duplicate function next to an identical one it never read

9. tests verify intent, not just behavior: a test that can't fail when business logic changes is wrong. all 12 of Claude's tests can pass while the function returns a constant

10. checkpoint every significant step: Claude finished steps 5 and 6 on top of a broken state from step 4. nobody noticed for an hour

11. match the codebase conventions: class components? don't fork to hooks silently. testing patterns assumed componentDidMount, hooks broke them without surfacing

12. fail loud: "completed successfully" with 14% of records silently skipped is the worst class of bug. surface uncertainty, don't hide it

what actually compounds instead of the next framework:

- the CLAUDE.md file as institutional memory across sessions
- eval-driven changes, not vibe-driven
- checkpoints over speed
- explicit conflicts over silent blending
- discipline over framework, every time
- one repo, one rules file, no exceptions

you don't need a better AI

you need better context engineering

complete playbook below ↓

[Ihtesham Ali @ihtesham2005]

Does money buy happiness? A Princeton Nobel laureate said no above $75,000. A Penn researcher with 1.7 million data points said yes. The day they sat down together to settle the fight, the answer they reached should change how you think about your own life.

The Nobel laureate is Daniel Kahneman. The Penn researcher is Matthew Killingsworth.

The fight between them lasted 13 years, and the way it ended is one of the cleanest examples in modern science of two smart people being wrong in opposite directions about the same question.

In 2010 Kahneman and his Princeton colleague Angus Deaton published a paper that became one of the most quoted findings in the history of social science.

They analyzed 450,000 responses to the Gallup-Healthways Well-Being Index and concluded that emotional well-being rose steadily with income up to about $75,000 a year, and then flattened out completely. Above that line, the extra money was not buying any more daily happiness.

The headline traveled around the world. Every news outlet ran the number.

A CEO in Seattle famously cut his own salary to raise his employees to that exact threshold. The 75,000 dollar figure became cultural shorthand for the idea that the rich are not actually any happier than the rest of us once basic needs are met.

For 11 years almost nobody seriously challenged it. Kahneman had a Nobel Prize in Economics, the sample size was massive, and the conclusion was emotionally satisfying in a way that made everyone feel a little better about not being wealthy.

Then in 2021 a 33 year old researcher at the University of Pennsylvania published a paper that quietly destroyed the entire finding. His name is Matthew Killingsworth.

He had spent the previous decade building a smartphone app called Track Your Happiness that pinged users at random moments during their day and asked them a simple question.

How do you feel right now, on a scale from very bad to very good. The app was designed to catch happiness in the act, not to ask people to recall it later.

By 2021 he had collected over 1.7 million real-time happiness reports from 33,000 adults. When he plotted income against in-the-moment well-being, there was no plateau anywhere.

The line just kept rising. People earning $200,000 were happier on average than people earning $100,000. People earning $400,000 were happier than people earning $200,000. The curve flattened slightly but never stopped climbing.

The famous $75,000 ceiling that the world had been quoting for 11 years simply did not exist in his data.

Now there were two Nobel-quality findings sitting in direct contradiction with each other. One of them had to be wrong, and neither researcher was willing to walk away.

What happened next is the part of the story almost nobody knows.

Kahneman called Killingsworth and proposed something rare in academic science. He called it an adversarial collaboration. The two of them, joined by Penn psychologist Barbara Mellers as a neutral referee, would sit down together and reanalyze the raw data from both studies, line by line, until they figured out which one of them was wrong.

The paper they co-authored was published in March 2023 in the Proceedings of the National Academy of Sciences. And the answer they reached was not what either of them had expected.

Both of them had been right at the same time. They had been measuring two different populations without realizing it.

When the team broke Killingsworth's 1.7 million data points apart by baseline happiness, the picture clarified completely. For the happiest 70 percent of people, more money kept buying more happiness all the way up to $500,000 a year, with no sign of slowing down.

For people in the middle, the same pattern held. But for the bottom 20 percent of the sample, the ones who were already unhappy before the question of money even came up, the curve flattened almost exactly where Kahneman's original paper had said it would. Above roughly $100,000 a year, adjusted for inflation, more money did nothing for them.

This is the finding that changes how the question should be asked.

If you are not already unhappy, money keeps buying happiness for a much longer stretch than Kahneman's original paper suggested. The runway is wider than the world has been telling itself for a decade.

If you are already unhappy, money does almost nothing past a certain point. There is a ceiling, but the ceiling is not about income. It is about the underlying state of the person collecting it.

The deeper insight in Killingsworth's original research, the one almost nobody talks about, is the part that should sit with you longer than the income numbers. The Track Your Happiness app had been telling him for years that the single biggest predictor of in-the-moment well-being is not money at all. It is whether your mind is on the thing you are doing.

His most cited paper, written with Daniel Gilbert at Harvard, is titled A Wandering Mind Is an Unhappy Mind. The data from the app showed that people are mentally absent from what they are doing 47 percent of the time, and that mental absence is one of the strongest predictors of unhappiness in the entire dataset. More predictive than income. More predictive than the activity itself. More predictive than almost any demographic variable you could measure.

Which means the unhappy 20 percent that Kahneman's plateau actually described were probably not unhappy because they did not have enough money. They were unhappy for reasons that more money could not reach.

The reason the curve flattened for them at $100,000 a year is the same reason it would have flattened at $300,000 or $700,000. The thing they were missing was not buyable.

The most uncomfortable line in the entire 2023 paper is the one that nobody on the internet quotes. The authors note that the relationship between income and happiness, while real, is much weaker than the relationship between attention and happiness. A person earning $40,000 who is fully present in their own life will, on average, report higher in-the-moment well-being than a person earning $400,000 whose mind is somewhere else.

The fight about money was the wrong fight the entire time.

The two researchers spent 13 years arguing over whether the dollar ceiling was at $75,000 or $500,000, and the data from Killingsworth's own app was sitting there the whole time saying the ceiling was not about dollars at all. The ceiling is whether you can hold your attention on the life you actually have.

You can run the experiment yourself the next time you catch your mind drifting. Stop. Put your phone down. Look at the room you are in, the person across from you, the food in front of you, the work you are actually doing. That is the part the apps cannot sell you and the salary cannot buy you.

The data has been clear for over a decade. The plateau is not in your bank account. It is in your attention.

[Sure (e/acc) @SureCapital]

@yanndine Mcp

[Corey Haines @coreyhainesco]

Marketing Skills v2.4.2 is out.

🆕 /ai-seo now covers Google's Open Knowledge Format (OKF) — a v0.1 markdown spec for representing your site as an agent-readable bundle. Google announced it June 12.

The honest version: Google built OKF for data teams to share metadata (BigQuery, APIs, metrics). Using it to make your site agent-readable is a clever secondary use. No crawlers target OKF bundles yet — this is a register-early bet, not a traffic play. The skill says so plainly, and tells you when to skip it.

What the update adds:
→ A 104-line OKF reference — concept, minimal examples, and the frontmatter spec (type required; title / description / resource / tags / timestamp recommended)
→ Where it fits in the agent-readable stack — alongside sitemap.xml, robots.txt, llms.txt, and schema markup
→ How to implement — the free OKF Generator, a pending WordPress plugin, or by hand
→ When to skip it entirely
→ New triggers: llms.txt, OKF, Open Knowledge Format, knowledge bundle, agent-readable site

44 skills. Free, open source.

npx skills add coreyhaines31/marketingskills

[Sure (e/acc) @SureCapital]

@AlfieJCarter MCP

[Ren @ren_stocks]

Satya Nadella just posted something that validates the entire AI buildout thesis from the very top of the stack.

The model is commoditizing. The durable value is the learning loop a company builds on top of the model.

He splits it into two assets:

Human capital -- the knowledge, judgment, relationships, and pattern recognition of your people.

Token capital -- the AI capability the firm builds and owns.

He says the real opportunity is building a learning loop where human capital and token capital compound together.

If the model layer is commoditizing then the durable returns are not in the model makers. They are in the infrastructure that powers every company building its own loop. Compute. Memory. Interconnect. Power.

The full stack underneath the application layer.

The model wars will have winners and losers. The infrastructure underneath gets bought either way.

Bullish the AI buildout.

Every layer. If you want to understand them in detail, check out my Substack.

https://t.co/Wna5UzCOVT