Adrian

[No context Hazz ⛩ @HazzimIO]

No comments.

[Midjourney @midjourney]

A technical dive inside our new "Midjourney Scanner"

[Electra @Electrarythm]

Brainwash yourself

[vivek @itsreallyvivek]

https://t.co/ZhOgyq7Vgn

[FIFA World Cup @FIFAWorldCup]

Germany take top spot 📊

#FIFAWorldCup

[Andrew Curran @AndrewCurran_]

https://t.co/yUVogvdruU

[@levelsio @levelsio]

Europe has gotten very poor fast while America has only gotten richer in the last few years (especially after inflation)

EU GDP per capita for 2026 is projected to be $51,030/year

US GDP per capita for 2026 is projected to be $94,430/year!

US is almost double as rich as Europe now, and that widening gap has been relatively recent (since 2008)

And if you're in Europe you really are starting to feel it now, the services are very poor, but for American prices

[Niko McCarty. @NikoMcCarty]

The Magnetobiology Episode:

A company in San Francisco, called @NonfictionBio, is building proteins (like antibodies and enzymes) that can be controlled using small magnets.

In this episode, co-founder Maria Ingaramo and scientific advisor Andrew York explain how they engineered a protein, MagLOV, that responds strongly to magnetic fields, why most prior attempts have failed to replicate, and how the mechanism of magnetically-controlled proteins actually works. They also get into the “dream” use cases, like cancer drugs that activate only at the tumor, which might have a lower toxicity inside the body. This podcast is made possible by @AsteraInstitute.

I'm happy with how this episode came out. I think my interviewing skills are improving, and I'm getting better at building up context throughout the episode. Enjoy!

Search for "The New Biology" on YouTube, Spotify, and Apple Podcasts.

Timestamps:
00:00 - Opening
00:54 — Introduction
01:35 — The dream
05:38 — Why magnets vs. light or ultrasound
10:05 — The physics
17:48 — On the name "magnetogenetics"
21:25 — Birds and cryptochromes
27:09 — Why is the field filled with so much junk?
29:51 — Adam Cohen's molecule
33:24 — Markus Meister’s debunking
38:06 — The experiment
46:22 — Finding the LOV domain
54:11 — Singlets, triplets, and cysteine
56:54 — What the magnet is actually doing
1:05:13 — The conformational-change red herring
1:12:46 — The Quantum Biology Institute
1:19:31 — Founding Nonfiction Labs
1:24:38 — How to convince skeptical investors
1:29:39 — What a magnetogenetic medicine might look like
1:38:50 — First clinical indications
1:45:12 — The regulatory path
1:48:01 — What the field needs
1:54:30 — Appendix: Whiteboard lecture

[Satya Nadella @satyanadella]

https://t.co/vLmiBKTtX3

[zerohedge @zerohedge]

LLM model matrix

[Christopher Potts @ChrisGPotts]

Does a token buy you more or less now than it did a few months ago? We built a consumer price index (CPI) for AI coding output from Anthropic's Opus 4.6 model in SWE-chat, Feb 5–Apr 15, 2026. What we find looks like tokenflation:

[Hiten Shah @hnshah]

Shopify has figured out what makes AI work inside of companies. This is exactly what I've been doing at my companies since OpenClaw came out. Get a bot in Slack for team usage in public channels and it'll feel like the future.

[Amazon @amazon]

TODAY: Amazon is opening its entire logistics network—freight, distribution, fulfillment, and parcel shipping capabilities—to every business, of all types and sizes. 📦

Amazon has built one of the most reliable and efficient supply chains on Earth. Now, Amazon Supply Chain Services gives all businesses access to the same infrastructure that moves, stores, and ships goods for hundreds of thousands of Amazon sellers.

Healthcare, automotive, manufacturing, retail, and more. Businesses across industries can now tap into Amazon's logistics network. Learn more here. ⬇️

[Claude @claudeai]

With the Autodesk Fusion connector, designers and engineers can create and modify 3D models through conversation.

[Ara Kharazian @arakharazian]

Ramp data is now available via API

We built Ramp AI Index and Ramp Rate to better inform business decisionmaking. In the process, they became economic indicators in their own right.

So, press, researchers, investors, policymakers, anyone trying to vibecode a bloomberg terminal >> it's free

[Max Levchin @mlevchin]

If there is one thing you take from this pod, it’s this: socialism corrupts more profoundly than simple words can express, and as the grift gets tougher, the socialists inevitably move from propaganda to oppression as the primary motivating approach. Don’t let it happen here!

[Eric Glyman @eglyman]

99% of Ramp uses ai daily. but we noticed most people were stuck — not because the models weren't good enough, but because the setup was too painful and unintuitive for most. terminal configs, mcp servers, everyone figuring it out alone.

so we built Glass. every employee gets a fully configured ai workspace on day one — integrations connected via sso, a marketplace of 350+ reusable skills built by colleagues, persistent memory, scheduled automations. when one person on a team figures out a better workflow, everyone on that team gets it and gets more productive.

the companies that make every employee effective with ai will compound advantages their competitors can't match. most are waiting for vendors to solve this. we decided to own it.

[Greg Brockman @gdb]

The world is transitioning to a compute-powered economy.

The field of software engineering is currently undergoing a renaissance, with AI having dramatically sped up software engineering even over just the past six months. AI is now on track to bring this same transformation to every other kind of work that people do with a computer.

Using a computer has always been about contorting yourself to the machine. You take a goal and break it down into smaller goals. You translate intent into instructions. We are moving into a world where you no longer have to micromanage the computer. More and more, it adapts to what you want. Rather doing work with a computer, the computer does work for you. The rate, scale, and sophistication of problem solving it will do for you will be bound by the amount of compute you have access to.

Friction is starting to disappear. You can try ideas faster. You can build things you would not have attempted before. Small teams can do what used to require much larger ones, and larger ones may be capable of unprecedented feats. More and more, people can turn intent into software, spreadsheets, presentations, workflows, science, and companies.

People are spending less energy managing the tool and more energy focusing on what they are actually trying to create. That shift brings a kind of joy back into work that many people haven’t felt in a long time. Everyone can just build things with these tools.

This is disruptive. Institutions will change, and the paths and jobs that people assumed were stable may not hold. We don’t know exactly how it will play out and we need to take mitigating downsides very seriously, as well as figuring out how to support each other as a society and world through this time. But there is something very freeing about this moment. For the first time, far more people can become who they want to become, with fewer barriers between an idea and a reality. OpenAI’s mission implies making sure that, as the tools do more, humans are the ones who set their intent and that the benefits are broadly distributed, rather than empowering just one or a small set of people.

We're already seeing this in practice with ChatGPT and Codex. Nearly a billion people are using these systems every week in their personal and work lives. Token usage is growing quickly on many use-cases, as the surface of ways people are getting value from these models keeps expanding.

Ten years ago, when we started OpenAI, we thought this moment might be possible. It’s happening on the earlier side, and happening in a much more interesting and empowering way for everyone than we’d anticipated (for example, we are seeing an emerging wave of entrepreneurship that we hadn’t previously been anticipating). And at the same time, we are still so early, and there is so much for everyone to define about how these systems get deployed and used in the world.

The next phase will be defined by systems that can do more — reason better, use tools better, plan over longer horizons, and take more useful actions on your behalf. And there are horizons beyond, as AI starts to accelerate science and technology development, which have the potential to truly lift up quality of life for everyone. All of this is starting to happen, in small ways and large, today, and everyone can participate. I feel this shift in my own work every day, and see a roadmap to much more useful and beneficial systems. These systems can truly benefit all of humanity.

[Aaron Levie @levie]

The more enterprises I talk to about AI agent transformation, the more it’s clear that there is going to be a new type of role in most enterprises going forward. The job is to be the agent deployer and manager in teams. Here’s the rough JD:

This person will need to figure out what are the highest leverage set of workflows on a team are (either existing or new ones) where agents can actually drive significantly more value for the team and company.

In general, it’s going to be in areas where if you threw compute (in the form of agents) at a task you could either execute it 100X faster or do it 100X more times than before. Examples would be processing orders of magnitude more leads to hand them off to reps with extra customer signal, automating a contracting review and intake process, streamlining a client onboarding process to reduce as many straps as possible, setting up knowledge bases than the whole company taps into, and so on.

This person’s job is to figure out what the future state workflow needs to look like to drive this new form of automation, and how to connect up the various existing or new systems in such a way that this can be fulfilled. The gnarly part of the work is mapping structured and unstructured data flows, figuring out the ideal workflow, getting the agent the context it needs to do the work properly, figuring out where the human interfaces with the agent and at what steps, manages evals and reviews after any major model or data change, and runs and manages the agents on an ongoing basis tracking KPIs, and so on.

The person must be good at mapping the process and understanding where the value could be unlocked and be relatively technical, and has full autonomy to connect up business systems and drive automation. This means they’re comfortable with skills, MCP, CLIs, and so on, and the company believes it’s safe for them to do so. But also great operationally and at business.

It may be an existing person repositioned, or a totally net new person in the company. There will likely need to be one or more of these people on every team, so it’s not a centralized role per se. It may rile up into IT or an AI team, or live in the function and just have checkpoints with a central function.

This would also be a fantastic job for next gen hires who are leaning into AI, and are technical, to be able to go into. And for anyone concerned about engineers in the future, this will be an obvious area for these skills as well.